Graduate Theses

Hemorheological Considerations in the Development of Microfluidic Blood Oxygenation Devices

2026-04-07T03:05:43Z

Hemorheological Considerations in the Development of Microfluidic Blood Oxygenation Devices Pincot, André M. Novel supersaturation oxygenation technology promises a leap forward in the enhancement of ECMO capabilities and deployment of a more efficient, versatile, and portable form of extracorporeal oxygenation technology. The showcased membrane bubble generation supersaturation technique offers superior oxygenation performance to conventional ECMO allowing for reductions in blood flow rate, thus promising to reduce the shear-based thrombosis that limits current oxygenation technology in medium to long-term treatment of severely aff licted patients. The membrane supersaturation concept promises to address that gap in reliable, extended treatment by greatly reducing shear to delay and prevent thrombus formation in the device and associated extracorporeal life support (ECLS) circuit. The bubbles produced by the membrane generator are small in size, sufficient to completely diffuse and fully oxygenate a larger volume of blood when combined with an additional deoxygenated blood flow. Further, the technique’s high oxygen flux will offer new options for reducing size footprint and ruggedization for austere conditions given further investment and development. This will necessitate the creation of custom membrane solutions and further optimization of device channel geometries via simulation using advanced blood models such as the tensorial enhanced structural stress thixotropic-viscoelastic (t-ESSTV) constitutive model developed and discussed in this work. A characteristic feature of human blood rheology is a distinctive stress hysteresis during a ramp up in the shear rate from zero, followed by a ramp back to zero. This is a result of the fact that human blood has a longer characteristic time of shear-induced rouleaux breakdown compared to the shear aggregation of the rouleaux. We demonstrate this telltale phenomenon of human blood rheology using a triangle ramp protocol to control time-dependent changes in the shear rate. The unique hysteresis data are then used along with steady state data to fit parameters of a recently published thixo-elasto-viscoplastic rheological model, the tESSTV model. These best-fit parameter values from the hysteresis ramps are then used to predict step-up/down in shear rate, small amplitude oscillatory shear, uni-directional large amplitude oscillatory shear, and large amplitude oscillatory shear flow. Additionally, correlations between the calculated fitting parameters and physiological data are analyzed to inform the interpretation of model behavior in physical terms. The goodness of fit of the triangle ramp protocol and rheological hysteresis data are then evaluated alongside recently developed techniques to assess thixotropy via computation of hysteresis loop area. The results indicate the efficacy of the t-ESSTV model in potentially predicting the complex characteristics of blood rheology in useful ways for future use in modeling circulating flows under a variety of mechanical and biological loading conditions and predicting understanding rheological effects on resulting pathologies.

Conceptualizing Machine Learning for Dynamic Information Retrieval of Electronic Health Record Notes

2026-04-07T03:05:42Z

Conceptualizing Machine Learning for Dynamic Information Retrieval of Electronic Health Record Notes Jiang, Sharon The large amount of time clinicians spend sifting through patient notes and documenting in electronic health records (EHRs) is a leading cause of clinician burnout. By proactively and dynamically retrieving relevant notes during the documentation process, we can reduce the effort required to find relevant patient history. In this work, we conceptualize the use of EHR audit logs for machine learning as a source of supervision of note relevance in a specific clinical context, at a particular point in time. Our evaluation focuses on the dynamic retrieval in the emergency department, a high acuity setting with unique patterns of information retrieval and note writing. However, our framework is general and can be applied to other clinical settings and with other data modalities (e.g., labs, medications, imaging). We apply our framework to the oncology setting to demonstrate its utility to other clinical workflows. We show that our methods can achieve an AUC of 0.963 in the ED and 0.937 in oncology when predicting which notes will be read in an individual note writing session. We additionally conduct user studies with several clinicians and find that our framework can help clinicians retrieve relevant information more efficiently.

Microneedles for Easier Fish Skin Penetration and Longer Attachment

2026-04-07T03:05:52Z

Microneedles for Easier Fish Skin Penetration and Longer Attachment Raad, Jad Aquaculture is the farming of aquatic animals for commercial purposes. This growing industry supplies around 50% of the world’s seafood and has reduced overfishing. However, it has also facilitated the spread of diseases between fish by growing them in close quarters, which results in poor growth and higher mortality levels. Injection vaccination is the most common way to combat this issue, but it is labor-intensive and stress-intensive on the fish. As an alternative to this method, the Marelli lab proposed using impermeable silk microneedle patches to encapsulate the medication and deliver it through diffusion to rainbow trout fry. When a microneedle patch was tested on a 7 g fry, it had difficulty penetrating the skin and only stayed attached for 10 min after injection. Consequently, it caused significant stress to the fish upon insertion and fell short of the 4 hrs required for complete payload diffusion into the animal. This work aimed to reduce the force necessary for the needle to pierce fish skin and augment the force needed to dislodge it, allowing for easier piercing and longer animal attachment time. Thus, the study intended to decrease the patch’s insertion force and increase its retraction force. The initial needles were cone-shaped and had an angle of 21º. To assess the effects of needle tip angle and overall shape on the forces, the new needles’ tip angle varied between 15º, 20º, and 25º, and a cylindrical base was added to them and varied between 0%, 33%, and 66% of the total needle height. The insertion and retraction forces of microneedle patches were quantified and revealed that sharper needles and needles with cylindrical bases amounting to 66 % of the total needle height reduced the insertion force. In contrast, the retraction force was independent of both factors. The 25º 66%, 15º 33%, and 15º 0% needles displayed the lowest insertion forces and were tested on zebrafish to quantify how long they could stay attached. Preliminary tests on the live animals demonstrated that the new needles stayed attached to the fish for up to 8 hrs. This improved upon the initial Marelli lab design, which remained attached for 30 min at most. Overall, pursuing live fish testing would allow for selecting the best-performing design and further developing it as a viable alternative to current vaccination methods.

An Invertebrate-inspired Approach to Design and Manufacturing in Soft Robotics

2026-04-07T03:05:54Z

An Invertebrate-inspired Approach to Design and Manufacturing in Soft Robotics Arase, Cathleen Soft robotics has many potential applications including deep sea biological sampling, fruit picking, physical therapy, assistive devices, surgery and other grasping tasks; however, within that realm many soft actuators lack the ability to output high force. In order to attempt to overcome this challenge, many soft roboticists are interested in variable stiffness actuators, but soft-rigid hybrid robots may also be helpful in solving this challenge. In fact, many invertebrates are able to undergo large deformations and have the ability to change their stiffness. Many of these invertebrates integrate components such as spicules or ossicles, which are small bones, making the invertebrates essentially a soft-rigid hybrid system. Taking inspiration from these invertebrates, soft rigid hybrid systems can be designed to increase the capabilities of soft actuators. Within the field of soft robotics, there are many practical problems to be overcome in the development of soft-rigid hybrid hybrid machines, including design, manufacturability, and delamination between soft and rigid components. This thesis focuses on work towards addressing these problems. The work explores invertebrates and invertebrate-inspired soft-rigid hybrid robots as a framework for understanding constraints in soft robotic systems. It then proceeds to explore manufacturing techniques for creating cast soft-rigid hybrid robots. Following this, it explores a novel method for decreasing the delamination forces between rigid overmolded components and soft walls of actuators, and finally it concludes with steps towards creating a soft actuator that incorporates those components as well as a comparison to a rigid example using a linkage mechanism for grasping.

Development of a High-Throughput Cryoprotection Screening Platform for Cell Therapies

2026-04-07T03:05:52Z

Development of a High-Throughput Cryoprotection Screening Platform for Cell Therapies Dey Barsukova, Anita Type 1 Diabetes is a devastating disease in which the immune system attacks insulin-producing beta cells in the pancreas, disrupting the normal blood glucose regulation mechanism and resulting in damage to major organ systems. An emerging therapy for Type 1 includes transplanting stem cell-derived beta cell aggregates into patients, restoring normal regulation of blood glucose and eliminating the need for insulin injections. Reliable cryopreservation methods are required to meet global demand for these aggregates, but current protocols result in low cell viability post thaw and require complex post processing to remove the toxic cryopreservation agent (CPA) formulation before implantation. In this work, a high-throughput screening method is developed to identify a novel non-toxic CPA formulation that would enable the scale-up of this new Type 1 Diabetes treatment. The development and validation of workflow steps are presented, in addition to data from pilot experiments that execute all workflow steps.

Applications of Light Reflectance Sensing in the Gastrointestinal Tract with Ingestible Devices for Disease Diagnosis

2026-04-07T03:05:46Z

Applications of Light Reflectance Sensing in the Gastrointestinal Tract with Ingestible Devices for Disease Diagnosis Chen, Hao (Jack) Disease diagnosis in the gastrointestinal tract can be challenging, often requiring difficult endoscopic procedures or expensive imaging techniques. Pill-sized ingestible sensors represent an alternative method for disease diagnosis in the gastrointestinal tract that is minimally-invasive and cost-effective, thus promoting patient adherence and preventative screening of diseases. In this thesis, I investigate the design of ingestible sensors that emit light and measure light reflectance in the gastrointestinal tract for three applications: the detection of gastric mucosal contact, the diagnosis of upper gastrointestinal bleeding, and the diagnosis of small intestinal ischemia. To enable these applications, I develop arrays of LEDs and photodiodes that monitor the changes in reflectivity of the tissue and changes in color of the tissue. The sensor arrays are fabricated and assembled in ingestible form factors and validated in ex vivo and in vivo experiments with swine. The results demonstrate that the sensing of light reflectance enables accurate differentiation of gastric mucosa versus gastric lumen for the detection of mucosal contact, accurate detection of gastric bleeding even in the presence of red drinks or gastric fluid, and accurate detection of small intestinal ischemia even in the presence of bile and chyme. For the application to diagnose small intestinal ischemia, I present initial mechanical and electrical designs of an ingestible capsule system that activates in the small intestines via the dissolution of a pH-sensitive polymer, then performs duty cycling to enable ischemia detection during the entire small intestinal transit time. I aim to continue the development and validation of these ingestible sensors with the vision of providing minimally-invasive devices to enable cost-effective screening and monitoring of gastrointestinal diseases and conditions.

A Framework for Detection and Observation of Radiation Chemistry Species on an MR-LINAC

2026-04-07T03:05:38Z

A Framework for Detection and Observation of Radiation Chemistry Species on an MR-LINAC Warner, Noah Stanley Radiation therapy, used in over half of cancer treatments, aims to target tumors while preserving healthy tissue. Existing techniques lack the ability to measure tissue damage during therapy, causing potential over- or under-irradiation, leading to severe side effects. Radiation inflicts DNA damage via direct and indirect mechanisms, the extents of each are inconsistent between patients, causing differences in response to radiation. Magnetic resonance-linear accelerators (MR-linacs) are promising to evaluate indirect DNA damage by measuring radiation chemistry species (RCS) produced during irradiation. In this work, MRI methods were developed to observe free radical production, radiation chemistry was modeled for select RCS scavengers and verified experimentally. These methods were then employed to measure MRI signal changes for complex combinations of RCS scavengers and radiosensitizing nanoparticles. Radiation chemistry experimental T1 changes were used to fit the relaxivity of the superoxide free radical and this value was assumed for all subsequent calculations. MRI T1 changes due to free radical production by radiation are presented in solutions consisting of water, 10 mM coumarin, 20 μM mito-TEMPO, 5 mM glutathione, a 20 μM mito-TEMPO and 5 mM glutathione mixture, 10 μM gold nanoparticles and 60 μM phosphate buffered saline. Radiation chemistry simulations completed for water and 10 mM coumarin show good agreement with their respective experimental T1 changes. Largest T1 changes and largest rates of production of superoxide were found in the 20 μM mito-TEMPO and 5 mM glutathione mixture, while smallest T1 changes and smallest rates of production of superoxide were found in the 20 μM mito-TEMPO solution. The main conclusions of this work show that a framework to detect T1 changes due to the production of free radical species during imaging and irradiation on a MR-linac has been developed, with the predominant source of T1 change over time due to free radicals attributed to the production of superoxide.

Single-cell dissection of mature conventional dendritic cells in the tumor microenvironment in metastatic melanoma

2026-04-07T03:05:40Z

Single-cell dissection of mature conventional dendritic cells in the tumor microenvironment in metastatic melanoma Wang, Cassia B. Although immunotherapy has revolutionized cancer treatment, the response rate of metastatic melanoma to immune checkpoint inhibitors (ICI) remains at less than 50%. One of the determinants of response might be explained by the underlying molecular mechanisms in the tumor microenvironment (TME), which is the composition of tumor cells and its surrounding environment of other cell types which play various roles in facilitating or inhibiting the progression of cancer. It was in our interest to specifically investigate the immunological factors driving observed clinical outcomes. Using single-cell technologies, mature conventional dendritic cells (mDCs) were identified in a cohort of metastatic melanoma samples and were present at a higher proportion in a subset of ICI anti-PD1-treated patients with better progression free survival (PFS). Elaborating on this finding, we generalized the characterization of mDCs in metastatic melanoma by using methods to determine mDCs’ association with other subtypes found in the TME, reveal the molecular features of mDCs compared to other conventional dendritic cells (cDCs), and find differentiating factors among samples with different mDC proportions. Through computational analysis of single-cell transcriptomes and epigenomes in metastatic melanoma, we aim to uncover critical immunological features and interactions within the TME, with potential for enhancing melanoma outcomes.

Revisiting MHD Generators with HTS Magnets

2026-04-07T03:05:49Z

Revisiting MHD Generators with HTS Magnets Clingerman, Matthew Hikaru Magneto-hydrodynamic (MHD) power generators can convert thermal and kinetic energy to electrical energy without any moving mechanical parts. They have the promise of competing against typical turbo-generators in a power plant. The advent of high temperature superconducting (HTS) magnets can give MHD generators the edge over other generators as the efficiency increases with the magnetic field strength. A robust mathematical model is derived to account for the plasma physics, fluid dynamics, and magneto-hydrodynamics involved with directing and harnessing the flow of an ionized gas. The resulting analytical model is computationally solved and then analyzed. It is clear that HTS magnets greatly benefit MHD generators. For a coal-fired power plant, the enthalpy ratios between the input and output of the generator surpass 50%. In other words, over half of the thermal energy produced by the power plant is converted to electricity by the MHD generator. The remaining fraction of energy is directed to a bottoming cycle for additional energy conversion. In the end, modest estimates put the overall efficiency of this system over 65%, compared to the current most advanced coal power plants of less than 45% efficiency.

Oxide coarsening and agglomeration during melt-based additive manufacturing of dispersion-strengthened alloys

2026-04-07T03:05:50Z

Oxide coarsening and agglomeration during melt-based additive manufacturing of dispersion-strengthened alloys Hou, Wenyuan (Roger) Dispersion-strengthened alloys densified with laser powder bed fusion, a melt-based additive manufacturing technique, have coarser dispersoids, lower dispersoid number densities, and greater tendency to form slag compared to conventional wrought dispersion-strengthened alloys. These differences degrade creep and fatigue resistance, and mitigating their extent is critical to printing high-performance components for demanding high-temperature structural applications. In this work, experiments and modeling were used to assess how printing parameters, alloy chemistry, and powder feedstock collectively affect dispersoid evolution and slag formation. Laser powder bed fusion parameter studies were used to assess the effects in Ni-20Cr-Y₂O₃ feedstock produced via resonant acoustic mixing then consolidated with systematic variations in laser parameters (power, speed), Y₂O₃ concentration, and Al content. Dispersoid structure was subsequently characterized using small angle neutron scattering. The finest dispersion achieved among fully dense (>99.5 rel. density) specimens has mean dispersoid diameter 21 nm and number density 230 μm-3. Dispersoid diameter was shown to decrease with the following adjustments: decreasing laser power, increasing scan speed, decreasing Y₂O₃ concentration, and keeping Al content below 0.3 wt%. Model predictions for dispersoid diameter were consistent with experimental values, and several key factors which influence the evolution of dispersoids were identified: convection-influenced thermal excursion, Y₂O₃ solubility, reaction with Al, nucleation, and diffusion-driven growth. The model also considers oxide dissolution over multiple melt cycles to establish bounds for slag-free printing of ODS alloys, showing a tradeoff between build rate and the quality of the oxide feedstock.

Impact of lesion preparation-induced calcium fractures in vascular intervention for atherosclerotic disease: in silico assessment

2026-04-07T03:05:48Z

Impact of lesion preparation-induced calcium fractures in vascular intervention for atherosclerotic disease: in silico assessment Sogbadji, Jonas Atherosclerosis is the most common form of obstructive vascular disease and is the predominant cause of mortality world-wide. Endovascular interventions like balloon angioplasty and stent implantation have dominated as therapies with tremendous impact and yet are least effective in most severe disease – especially those with heavily calcified lesions. Intravascular lithotripsy (IVL) has been proposed to “prepare” lesions and optimize endovascular intervention with the idea of removing and/or modifying lesions resistive stiffness so as to make balloon or stent placement more effective. Despite clinical enthusiasm, there remains a lack of understanding as to how this occurs, and which lesions would be most amenable to and most affected by IVL. The range and extent of lesions are substantial presenting a formidable challenge in managing their modification. This complexity hampers the extrapolation of findings from both clinical and preclinical models. In silico models offer a means by which to examine diverse lesion morphologies and a range of lesion modifications to address these deficiencies, and in particular to understand if there is a correlation between calcium morphology alteration and improvement of stenting outcomes. We build a computational platform to connect stenting outcomes to IVL induced calcium modification. Three models were inspired by clinical optical coherence tomography image analyses and a stenting procedure was simulated for a number of variations within each model. Results show that expansion of stents and treated arteries rose with the volume of tissue affected and excised. For one particular model, stent expansion reached a local maximum. 3 In silico models provide a valuable perspective for considering complex vascular interventions – not only in simulating effects that are challenging to recapitulate in preclinical models but in helping develop a tool that can predict susceptible candidate lesions and help determine the ideal extent of lesion modification to optimize overall effect.

Certifiable Cooperative Localization for Underwater Navigation

2026-03-28T03:03:41Z

Certifiable Cooperative Localization for Underwater Navigation Morrison, John P. Accurate underwater positioning remains one of the most significant obstacles to autonomous underwater vehicle (AUV) operations. Satellite-based navigation signals are unavailable underwater, so AUVs must dead-reckon using inertial sensors, coupled with velocity or heading references. Due to random noise and variable biases in inertial sensor measurements, the AUV’s position uncertainty grows steadily over the course of the mission, but can be reduced through range measurements to fixed or mobile references. The associated range-aided simultaneous localization and mapping (SLAM) problem is particularly challenging to solve with existing optimization methods. Individual range measurements provide limited geometric constrains on vehicle position and are subject to non-linear errors due to multi-path propagation. Attempts to optimize typical range-aided SLAM cost functions often return solutions which represent local, rather than global minima, resulting in unpredictable vehicle behavior when used for closed-loop navigation. This thesis applies a recently developed certifiable optimization algorithm, Certifiably Correct Range-aided SLAM (CORA), to the problem of cooperative localization between AUVs. CORA leverages aspects of the range-aided SLAM problem structure to find solutions which can be certified to be globally optimal. This method is integrated into a novel cooperative localization scheme, in which each vehicle maintains a locally held, periodically updated copy of the centralized, multi-agent factor graph. The cooperative localization framework presented here leverages acoustic modems for both range measurement and the sharing of sub-graphs through inter-vehicle communication. This approach was validated through extensive field trials using two modular, low-cost Spurdog AUVs were equipped with WHOI Micromodem2 payloads. Results from single and multi-vehicle deployments demonstrated that CORA substantially outperforms existing solvers when faced with poor landmark initialization and reduced observability as a result of real-world communication failures. The results presented here demonstrate the added value of coupling certifiable estimation with cooperative localization for multi-AUV localization problems, particularly in challenging, GPS-denied environments.

Characterizing and Mitigating Small-Diameter Tool Wear in Nickel-Based Superalloy Machining

2026-03-17T03:06:39Z

Characterizing and Mitigating Small-Diameter Tool Wear in Nickel-Based Superalloy Machining Brush, Alexander Sparry This thesis investigates tool failure in the micromachining of single-crystalline René N4 turbine blades coated with ceramic thermal barrier layers. The work done in this thesis was promoted through a partnership between Massachusetts Institute of Technology (MIT) and GE Vernova. This thesis is complemented by the thesis written by Luke Placzek. Together these works offer a comprehensive case study in process analysis and manufacturing optimization. This thesis begins with groundwork to document tool failure mechanisms and frequencies through photographic analysis. This was done alongside a study of historical data to analyze tool breakage frequency in the context of the turbine blade. Based on these insights, an Analysis of Variance (ANOVA) test followed by a Tukey’s Honestly Significant Difference (HSD) test identified statistically significant differences in tool breakage rates across machines and rows. A detailed study of tool wear progression was conducted to better understand how small-diameter endmills wear when machining the nickel-based superalloy René N4. Utilizing all these findings, an updated tool path was created to optimize tool life This work lays the foundation for an improved machining strategy to reduce tool breakage in manufacturing turbine blades. Estimations show that the refined CAM strategies may reduce tool breakage by roughly 33 percent. Preliminary models estimate the implementation of the suggested improvements will save GE Vernova 2.5 million dollars per year.

A cross-industry analysis using Q-Methodology for streamlining engineering workflow

2026-03-17T03:06:48Z

A cross-industry analysis using Q-Methodology for streamlining engineering workflow Gupta, Harshit Non-value added times arising from disconnected systems, legacy architecture, repeated iterations, product version mismatch, and manual processes remains one of the most persistent inefficiencies in modern design and manufacturing organizations which can be resolved by leveraging digital technology. Through this thesis, a framework has been laid out to understand and summarize the gaps among the various departments of an organization from the standpoint of information flow across the complete manufacturing workflow. The goal is to find gaps and pain points in the adoption of the ’Digital Thread’ with the objective of becoming software-driven enterprises. The objective is to identify opportunities to automate and optimize processes, and how information can be streamlined across departments. As a snapshot, the project investigates how digital transformation can bridge the gap between design and manufacturing, with a focus on concurrent engineering in high-mix, low-volume production, and high-volume, low-mix production environments. The research uses Q-methodology to understand how the perception of use of digital tools vary across industries and organizations, especially among vertically integrated and supplier dependent enterprises. Evaluation is done across different roles in an organization, ranging from executives and strategy teams to engineers, metrology specialists, and shop floor managers perceive current workflows, bottlenecks, and opportunities for improvement. The analysis reveals differences and similarities in interests and opinions to map the landscape of the current and growing needs across different industries and product portfolio. The results of the thesis can be used by participating teams to re-design workflow, communication and process plans and add flexibility through automation to the existing process. The thesis conclusion will also help PTC to understand the capabilities that their softwares are missing out on that can be integrated in their future iterations to help serve their customers better for faster and better product development. The shift towards software-driven manufacturing is the need of the hour with increasing stress on re-industrialization and the Thesis contributes to the current evolving discussion. The Thesis ends with a discussion on potential avenues for exploration gathered from participants through qualitative interviews that can be used as a roadmap to get a sense of future directions of the dynamic industry.

User-Responsive Solutions for Cognitive Load Reduction in CAD Platforms

2026-03-17T03:06:37Z

User-Responsive Solutions for Cognitive Load Reduction in CAD Platforms Bai, Jane Amid the evolution of cloud-based Computer-Aided Design (CAD) platforms, traditional educational approaches fail to address the diversity of cognitive obstacles that users across expertise levels and learning behaviors fac. This thesis investigates whether behavior-adaptive CAD tools can reduce friction as hypothesized by Cognitive Load Theory (CLT) while enhancing skill development in modern engineering environments.A two-phase mixed-methods approach was employed that combined large-scale behavioral persona identification with controlled user testing. TF-IDF and PCA on the results of an MIT-wide survey identified four distinct behavioral archetypes corresponding to unique tool usage patterns and learning preferences independent of technical proficiency. A/B testing of three behavior-adaptive custom tools which addressed workflow optimization, parametric knowledge retention, and contextual-aware passive modelling guidance was done with novice and advanced users. Command logging captured behavioral features and analysis discovered significant cognitive load reduction, improved workflow efficiency, and higher-retained skill development. NLP of post-session survey responses revealed deeper conceptual engagement. From these results, a three-stage model progressing from friction reduction through behavioral analytics to continuous personalization optimization was developed to inform business applications. The findings demonstrate that effective CAD education requires addressing individual behavioral patterns rather than traditionally uniform skill-based approaches. Behavior-adaptive tools enhance learning pathways and workflows by preserving user agency over creative and parametric decisions during modelling while reducing cognitive friction. Keywords: Computer-Aided Design (CAD), Cognitive Load Theory (CLT), Behavioral Analytics, Behavior-Adaptive Learning, Engineering Education

Smart Manufacturing of Desktop Fiber Extrusion Devices (FrED): Design Optimization and Digital Factory Implementation

2026-03-17T03:06:49Z

Smart Manufacturing of Desktop Fiber Extrusion Devices (FrED): Design Optimization and Digital Factory Implementation Ng, Yong This thesis presents the design and implementation of FrED Factory, a lab-scale, digitally integrated smart manufacturing environment developed to support scalable production and experiential learning in advanced manufacturing. Built around the Fiber Extrusion Device (FrED), a desktop analog to an industrial optical fiber draw tower. The project addresses both physical manufacturability and digital system coordination, aiming to simulate real-world Industry 4.0 practices in an educational setting. To ensure repeatable and efficient production, key design components were optimized through tolerance analysis of laser cut acrylic frames. Standard Operating Procedures (SOPs) were developed to guide consistent execution of processes including 3D printing, laser cutting, procurement, and assembly. A structured Bill of Materials (BOM) was implemented to manage subassemblies and support real-time inventory tracking. On the digital front, the FrED Factory leverages Tulip, a no code Manufacturing Execution System (MES), to deploy dynamic work instructions, manage work orders, and monitor shopfloor performance. Tulip’s EdgeMC hardware was used to integrate Internet of Things (IoT) devices for machine status tracking. MQTT protocols were applied to capture 3D printer activity via OctoPrint, and current sensors were deployed to automatically log Quality Control (QC) station usage. The result is a modular, scalable, and data-rich smart factory environment that enables students to gain hands-on experience with modern manufacturing systems. For educators, the FrED Factory provides a tangible platform for teaching digital manufacturing, while industry professionals can view it as a blueprint for applying lean, connected workflows in small-scale, high-mix production environments.

Integrated Materials for Ion Transport Management in Anion Exchange Membrane Electrolyzers

2026-03-17T03:06:47Z

Integrated Materials for Ion Transport Management in Anion Exchange Membrane Electrolyzers Aamer, Zara Electrochemical CO₂ separation systems leveraging anion exchange membranes (AEMs) offer significant energetic advantages over traditional bipolar membrane electrodialysis (BPMED), but suffer from hydroxide crossover, which reduces current efficiency (CE) and system performance. This work explores the transport dynamics of carbonate and hydroxide ions in AEM systems and introduces a hybrid PES-AEM bilayer membrane architecture to mitigate hydroxide crossover while preserving sufficient CO₂ recovery. We demonstrate that the bilayer system achieves a reduced relative transport factor (R = 1.4) and enables up to 3.8x improvement in CE compared to conventional AEM systems at realistic capture conditions. Further analysis reveals that transport properties in the least conductive domain of a multi-membrane system dominate overall behavior, allowing non-selective, low-conductivity materials such as porous PES to reduce hydroxide crossover effects. This study outlines key membrane material parameters influencing relative ionic transport and highlights the potential of hybrid architectures to unlock energy-efficient CO₂ electrochemical regeneration for direct air capture (DAC) integration.

Dimensioning Defects with Monocular Vision in Automated Optical Inspection

2026-03-17T03:06:45Z

Dimensioning Defects with Monocular Vision in Automated Optical Inspection Boyd, Logan Automated optical inspection (AOI) systems are common tools for quality control in industrial manufacturing. AOI systems use robotic systems to load components, take images, and detect defects, often also characterizing the defects by size or class. Among various approaches to this machine vision, monocular systems are popular because they are cheap and simple to integrate while offering intuitive visualization. However, monocular vision alone lacks depth resolution and struggles to accurately dimension defects on 3D surfaces, especially if the imaged component’s pose is ambiguous. This paper presents a transparent, open-sourced, end-to-end image processing pipeline for dimensioning surface defects on industrial components using RGB images. The pipeline estimates component pose through a 2D-3D correspondence, segments defects with machine learning or image comparison techniques, then projects the component’s CAD mesh into the image to calculate the lengths of segmented defect instances. The pipeline was developed on a 3D-printed test object and demonstrated with each of three segmentation methods, yielding defect dimensions with average error between 0.6-1.2mm.

Advancing Manufacturing Readiness Reviews and Fiber Extrusion Processes: A Two-Sided Approach to Product Maturity in Optics and Sensing

2026-03-17T03:06:13Z

Advancing Manufacturing Readiness Reviews and Fiber Extrusion Processes: A Two-Sided Approach to Product Maturity in Optics and Sensing Groll, Matthew This thesis engages with two important facets of the manufacturing discipline. The first half reflects on the ongoing efforts of the Charles Stark Draper Laboratory towards growing capabilities in production with an emphasis on standardization for responsible organizational scaling. Specific work is presented towards advancing the Manufacturing Readiness Review (MRR) process, informed by staff interviews, in the form of recommended approaches and template materials for technology leads to employ during future manufacturing review cycles. The second half covers more hands-on, active product and process development work for MIT’s Fiber Extrusion Device (FrED). Findings relate towards both improving the production process of the device as well as capabilities and observations of the extruded fiber. Inventory management recommendations are detailed for different production scenarios, and successful extrusion of acrylic, novel to the current studied capabilities of the FrED, is demonstrated. Observations on the resulting fiber’s optical properties are characterized along with a repeatable approach for doing so. While distinct, together these topics provide holistic insights into moving from concept to production.

Scaled Deployments of Seismic Penetrators to Measure Stability of Antarctic Ice Shelves

2026-03-17T03:06:41Z

Scaled Deployments of Seismic Penetrators to Measure Stability of Antarctic Ice Shelves Steen, Parker Ice shelves play a critical role in regulating the flow of Antarctic ice sheets and thereby global sea level rise. Recent ice shelf collapses are poorly understood due to a lack of seismic measurements of an ice shelf’s response to extreme environmental forces, such as ocean tides and tsunamis. Instrumenting ice shelves is a challenge due to transportation limitations, unpredictable weather, and dangerous crevassing. Air-dropped seismic penetrators have been developed in the Seismogeodetic Ice Penetrator (SGIP) project to alleviate manual installation pain points and access remote locations. The design of two SGIPs dropped into the Ross Ice Shelf in 2025 is reconsidered to determine how the design must and could evolve to be able to deploy seismic sensors at a scale necessary to achieve science goals. The power budget for a remotely dropped penetrator that transmits all recorded data is determined. Power architectures with solar panels or a wind turbine are optimized to minimize the total height of a penetrator powered by primary batteries by 23% with Iridium and 29% with Starlink. A Barrowman aerodynamic model is evaluated against empirical results. The model is calibrated and used to consider penetrator drops from fixed-wing aircraft, with results suggesting that horizontal belly drops are optimal but that vertical aft or side drops are possible. A unit cost curve is found for scaled production volumes. Finally, scaled deployments with LC-130H and Basler aircraft are considered to optimize the aircraft cost of seismic data, finding both aircraft to be viable, but the LC-130H more cost effective.

Reachability Prediction and Optimal Path Planning for Autonomous Ocean Vehicles

2026-03-17T03:06:17Z

Reachability Prediction and Optimal Path Planning for Autonomous Ocean Vehicles Mule, Ellen M. For intelligent ocean exploration and sustainable ocean utilization, the need for smart autonomous underwater vehicles (AUVs), surface craft, and small aircraft is rapidly increasing. Creating time-optimal navigation routes for these vehicles has wide-ranging applications, including ocean data collection, transportation and distribution of goods, naval operations, search and rescue, detecting marine pollution, ocean cleanup, conservation, and solar-wind-wave energy harvesting. In this thesis, we employ the Massachusetts Institute of Technology – Multidisciplinary Simulation, Estimation, and Assimilation Systems (MIT-MSEAS) time-optimal and hazard-time-optimal path planning theory and schemes based on exact Hamilton–Jacobi partial differential equations (PDEs) and Level Set methods. We apply this methodology to ocean gliders and floats during several real-time sea experiments—the Mini-Adaptive Sampling Test Run (MASTR) and Grand Adaptive Sampling Experiment (GRASE) in the Gulf of Mexico, and the New England Seamounts Acoustic (NESMA) experiment in the North Atlantic. Using the MIT-MSEAS multi-resolution ocean modeling and data assimilation system to provide deterministic and probabilistic ocean current forecasts, we compute time-reachable sets as well as time-optimal paths for a variety of ocean vehicle missions. The governing differential equations for reachability analysis and time-optimal path planning were numerically integrated in real time, forced by our large-ensemble ocean forecasts. We illustrated deterministic and probabilistic forward reachability analyses, glider recovery planning, time-optimal routing for gliders in distress, and planning of future glider and float deployments. Results show that the actual paths of gliders were contained within our reachable set forecasts and in accord with the dynamic reachability fronts. These forecasts were successfully employed for glider recovery and informed strategic decisions for future missions. Additionally, we demonstrated the ability to incorporate risk such as severe weather or vessel traffic into hazard-time-optimal path planning for simulated collaborative air-sea drone missions. Overall, the integration of data-driven multi-resolution ocean modeling with exact reachability theory and numerical schemes enables principled, operationally relevant path planning for diverse ocean missions.

Analytical Model for Orbital Motion Under J₂

2026-03-17T03:06:13Z

Analytical Model for Orbital Motion Under J₂ Nedungadi Martinod, Marco Antonio As the number of operational satellites and debris objects in Earth orbit continues to accelerate, the ability to predict orbital trajectories with both accuracy and efficiency has become an indispensable capability. Numerical integration of the full Cartesian equations of motion offers generality but at high computational cost, while traditional analytical theories are efficient but often restricted by singularities in the classical orbital element set. Analytical formulations expressed in nonsingular elements can combine efficiency with global validity, and provide physical insight into the structure of orbital perturbations. This thesis develops a globally valid analytical model for orbital motion under the Earth's second zonal harmonic (J₂) in the modified equinoctial element (MEE) framework. The MEE set eliminates the singularities present in circular and equatorial orbits, allowing uniform treatment across all regimes. Two principal contributions are made. First, explicit first-order mean equations of motion are derived using a generalized averaging method applied to the J₂ disturbing function. The resulting system reduces to two planar rotations of the eccentricity and inclination vectors with constant rates, together with a secular drift in the true longitude. These equations reproduce Brouwer's classical secular results when mapped back to Keplerian elements, while retaining the nonsingular advantages of the MEE formulation. Second, closed-form mean--osculating transformations are obtained, enabling consistent recovery of short-period variations from the mean solution. These transformations allow a dual representation: efficient mean propagation combined with reconstruction of instantaneous orbital states. The analytical model is validated against high-fidelity Cartesian propagation across a set of representative orbit classes, including LEO, GEO, GTO, and Molniya orbits. In all cases, the mean element evolution predicted by the MEE-based theory shows close agreement with numerical integration. Over week-long propagation intervals, relative position errors remain small, while computational cost is substantially reduced compared to Cowell integration. These results establish the MEE-based analytical framework as both theoretically rigorous and practically effective, providing a foundation for accurate, efficient, and globally valid orbit prediction.

Characterization of Transonic Fan Response to Inlet Distortion

2026-03-17T03:06:04Z

Characterization of Transonic Fan Response to Inlet Distortion Levy, Benjamin Adam This thesis seeks to characterize transonic fan response to three-dimensional inlet flow distortion, which is a challenge of business jet propulsor-airframe integration. The specific context is ensuring fan operability in crosswind while retaining high cruise efficiency. A body force approach is used with a pre-processing workflow that simplifies the inputs of the body force model. This enables rapid assessment of changes to the fan work distribution, a step towards achieving potential benefits of fan-inlet co-optimization. The workflow is used to explore the sensitivities of fan response to an applied non-uniformity, to fan work distribution, and to bulk swirl. Incidence, as a metric for evaluating distortion, is found to offer an improved assessment of fan operability trends compared to metrics that only depend on the stagnation pressure distribution. Such metrics are not found to capture sensitivities of fan response to increasing circumferential extent of the stagnation pressure defect. Sensitivity of the local response of the fan in the low stagnation pressure region to the radial work distribution are dominated by effects seen in 2D distortions: steeper local pressure ratio characteristics increase the attenuation of the stagnation pressure non-uniformity. However, such designs generate more severe stagnation pressure non-uniformities downstream of the rotor at other spanwise positions due to radial variations in the distortion pattern and rotor pressure rise. The effect of bulk swirl on the characteristic slope produces coupling of stagnation pressure and swirl, where combined counter-swirl and stagnation pressure distortion is found to produce more severe fan operability penalties than the superposition of each separate effect. The characterization of inlet distortion response contributed by this thesis is a necessary step in optimizing the propulsor inlet design with constraints on off-design operability.

From Sketch to CAD Code: Multimodal AI for Controllable Design Generation

2026-03-17T03:06:38Z

From Sketch to CAD Code: Multimodal AI for Controllable Design Generation Man, King Yiu Brandon Generative artificial intelligence (AI) has demonstrated transformative potential in creative and technical fields, yet its application to engineering design remains underdeveloped. Unlike domains where AI outputs can be directly consumed, engineering design demands integration across heterogeneous tools, multimodal data, and highly structured workflows. This thesis develops and evaluates AI-driven approaches for enabling copilot-style systems that assist engineers throughout the early stages of design, where decisions have the greatest impact on cost and performance. We identify and address three central challenges: the need for user control over abstract generative processes, the scarcity of high-quality engineering datasets, and the complexity of integrating AI into diverse design toolchains. Our first contribution is Sketch2Prototype, a multi-stage framework that transforms conceptual sketches into text, images, and manufacturable 3D meshes. Evaluated on a dataset of 1,087 sketches, the system produces more diverse and manufacturable prototypes than direct sketch-to-3D methods, while enabling iterative refinement through a controllable intermediate text stage. Our second contribution is VideoCAD, a synthetic dataset of over 41,000 annotated CAD modeling videos—up to twenty times longer in action horizon than prior UI agent datasets—capturing pixel-precise, long-horizon interactions in a professional CAD environment. We benchmark state-of-the-art behavior cloning models and large language models on VideoCAD, and introduce VideoCADFORMER, a transformer-based architecture that achieves superior performance on long-horizon CAD action prediction. Finally, we present VisionCAD, a fine-tuned Large Language Model (LLM) that constructs CAD Generation code from point cloud and image data, trained with a dataset of over two million image, point cloud, and CADQuery triplets. Together, these contributions demonstrate that generative AI, multimodal learning, and large-scale dataset generation can be combined to accelerate design exploration, improve manufacturability, and integrate seamlessly into engineering workflows. By addressing both the data and workflow bottlenecks, this work lays the foundation for AI copilots that enhance productivity, creativity, and precision in engineering design.

FrED and the FrED Factory: The MIT Approach to Designing a Smart Learning Factory

2026-03-17T03:06:07Z

FrED and the FrED Factory: The MIT Approach to Designing a Smart Learning Factory Bradley, Russel It’s hard to learn manufacturing without being in a factory. Existing manufacturing education approaches—including educational kits, machine shops, and learning factories—often fail to capture the natural variability and the flow of products, processes, and people inherent in volume production, which drive the dynamics of real manufacturing systems. This paper/thesis talks about the design and development of the learning factory at MIT, also known as the FrED Factory. FrED Factory is a fully operational factory within campus that produces and delivers manufacturing education kits, Fiber Extrusion Device (FrED), while simultaneously delivering education. The combination of a learning factory producing learning products creates a unique ecosystem of manufacturing education. The FrED and the FrED Factory ecosystem have impacted learners with authentic learning experiences. Project-based learning experiences are delivered through groups of students working to develop FrED and the FrED Factory. The products of this development, the learning device and learning factory, amplify impact by serving as platforms for manufacturing education. The FrED and FrED Factory initiative has impacted learners from K-12, undergraduate, graduate, and professional education at MIT and beyond.

Characterizing CMOS Devices for Use in Future X-Ray Astrophysics Instruments

2026-03-17T03:06:05Z

Characterizing CMOS Devices for Use in Future X-Ray Astrophysics Instruments Lupo, Jonathan Complementary Metal-Oxide-Semiconductor (CMOS) detectors and Charge-Coupled Devices (CCDs) are the two primary imaging technologies used in optical and X-ray detection. Both rely on pixel arrays that convert incoming photons into electrical charge but differ in readout architecture: CCDs shift charge across the array to a common output node, while CMOS devices incorporate amplifiers and readout circuitry at each pixel. CCDs have long been favored in astronomy for their high sensitivity, low noise, and deep depletion regions that enhance detection of higher-energy X-rays. However, they suffer from slow readout, high power demands, and susceptibility to radiation-induced charge transfer losses. CMOS detectors, in contrast, offer fast readout, low power consumption, and increased resilience in radiation environments, while enabling on-chip processing and high time resolution. These advantages make CMOS increasingly attractive for astrophysical applications, particularly in capturing faint, transient, or rapidly varying X-ray phenomena. This work evaluates the potential of two modified commercial CMOS detectors from Sony’s uEye SE series, the IMX226 and IMX662, for low- to intermediate-energy X-ray astrophysics. To enhance sensitivity, the optical windows were removed and, for the IMX226, the microlens array was eliminated to reduce absorption at low energies. The detectors were characterized at the MIT Kavli Institute X-ray Detector Lab, with performance evaluated in terms of X-ray response, readout noise, pixel-to-pixel gain variation, linearity, dark current, and contributions to overall energy resolution. Detector testing used X-ray emission lines from Polonium-250 and Iron-55 at 277 eV (C), 677 eV (F), 5.9 keV (MnKa), and 6.4 keV (MnKb). Measurements were performed in a vacuum chamber to minimize absorption, with optical linearity tested separately on an optical assembly setup using an integrating sphere. Both detectors showed strong potential as low-cost X-ray sensors, with energy resolutions approaching theoretical limits across key emission lines. Readout noise was low (2.28 e⁻ for IMX226, 3.54 e⁻ for IMX662), gain variation was minimal when measured (≤0.32%), and linearity remained stable with errors below 0.6% across high- and low-energy regimes. Dark current was negligible for the IMX662 and modest for the IMX226 (0.57 e⁻/pixel/sec). While readout noise and gain variation explain much of the measured energy resolution, additional unaccounted noise was observed, indicating that further optimization is required.

Vision-Language Models for Engineering Design: From Technical Documentation Benchmarking to CAD Generation

2026-03-17T03:05:57Z

Vision-Language Models for Engineering Design: From Technical Documentation Benchmarking to CAD Generation Doris, Annie Clare Engineering product development is slowed by two bottlenecks: interpreting technical requirements and producing accurate, editable computer-aided design (CAD) models. This thesis evaluates and advances vision-language models (VLMs) – large-scale foundation models that process both text and images – to support engineers in these time-consuming tasks. While benchmarks exist for evaluating VLM performance in areas such as medical imaging, optical character recognition, and robotics, benchmarks for engineering design tasks remain scarce. We develop DesignQA, which remedies this problem, by combining visual data, textual design requirements, CAD images, and engineering drawings in a benchmark. It enables us to rigorously quantify the VLMs’ abilities to understand and apply engineering requirements in technical documentation. Developed with a focus on real-world engineering challenges, DesignQA uniquely combines visual data – including textual design requirements, CAD images, and engineering drawings – derived from the Formula SAE student competition. The benchmark features automatic evaluation metrics and is divided into segments – Rule Comprehension, Rule Compliance, and Rule Extraction – based on tasks that engineers perform when designing according to requirements. We evaluate state-of-the-art models (at the time of writing) like GPT-4o, GPT-4, Claude-Opus, Gemini-1.0, and LLaVA-1.5 against the benchmark. Our study uncovers the existing gaps in VLMs’ abilities to interpret complex engineering documentation, including the inability to reliably retrieve relevant rules from the Formula SAE documentation and challenges in analyzing engineering drawings. These findings underscore the need for VLMs that can better handle the multifaceted questions characteristic of design according to technical documentation. After establishing an engineering-design-specific benchmark, we investigate whether additional training can improve VLM performance on engineering tasks. In particular, we address CAD generation from images, a problem motivated by scenarios such as sketch-toCAD workflows, recovery of lost files, or cases where only an image is available due to privacy concerns. While recent developments in AI-driven CAD generation show promise, existing models are limited by incomplete representations of CAD operations, an inability to generalize to real-world images, and low output accuracy. We develop CAD-Coder, an open-source VLM fine-tuned to generate CadQuery code directly from images, trained on GenCAD-Code (163,671 image–code pairs). On a 100-sample test subset, CAD-Coder outperforms strong VLM baselines (e.g., GPT-4.5, Qwen2.5-VL-72B), achieving a 100% valid-syntax rate and the highest 3D-solid similarity. It also shows early generalization, producing CAD code from real photographs and executing operations (e.g., filleting) not seen during fine-tuning. The performance and adaptability of CAD-Coder highlight the potential of VLMs fine-tuned on design-specific tasks to streamline workflows for engineers. We conclude with directions for design-specific VLMs, including synthetic-data pipelines to improve dataset coverage and reinforcement-learning strategies that exploit objective geometric rewards. Together, DesignQA and CAD-Coder indicate a practical path toward VLM assistants that accelerate requirement-aware engineering design and image-to-CAD workflows. All code, data, and trained models are released publicly to support reproducibility and future research.

AI-Augmented CAD Onboarding: A Personalized Approach to Reducing Learning Friction

2026-03-17T03:06:47Z

AI-Augmented CAD Onboarding: A Personalized Approach to Reducing Learning Friction Aiouche, Nada Autodesk Fusion is a leading cloud‑based CAD platform, yet new users often face steep learning curves due to scattered resources, inconsistent guidance, and a lack of personalization. This thesis addresses these challenges and proposes an adaptive AI assistant, embedded within Fusion, as a potential solution to streamline onboarding, reduce search time, surface hidden tools, and deliver guidance tailored to the user’s learning style. By centralizing learning support within the design environment, the proposed system aims to reduce cognitive load and keep users focused on productive work rather than on searching for help. Based on surveys, interviews, and controlled user testing comparing tasks with and without simulated AI support, the study suggests that personalized, context‑aware assistance can improve task flow, reduce frustration, and provide particular benefits for beginners. Findings indicate that such a solution not only accelerates skill acquisition but also supports long‑term engagement by making the early stages of learning more intuitive and less discouraging. Finally, this thesis outlines practical next steps Autodesk can take to develop, integrate, and validate such a system to realize its full potential in accelerating adoption, improving retention, and enhancing the overall user experience.

Design and Implementation of a Low-Cost Bioreactor System for Synechocystis sp. PCC 6803: Integrated Cultivation, Lysis, and Filtration for Sustainable Glucose Harvesting

2026-03-17T03:06:43Z

Design and Implementation of a Low-Cost Bioreactor System for Synechocystis sp. PCC 6803: Integrated Cultivation, Lysis, and Filtration for Sustainable Glucose Harvesting Baho, Ingie This thesis describes the design, modeling, and fabrication of a three-part bioreactor and biomass processing system designed to cultivate Synechocystis sp. PCC 6803 and extract its intracellular glucose. The resulting glucose can support sustainable biomanufacturing for diverse downstream applications, including serving as a feedstock for K. rhaeticus to produce cellulose, as a precursor for biofuel production, or as an ingredient in food supplements. The system incorporates a photobioreactor, a lysis module for acid and ultrasound-based cell disruption, and a pressure-driven filtration setup. The photobioreactor was equipped with a pH, dissolved oxygen, and temperature probe; and optical density was continuously monitored using a custom-built module. The lysis unit contained an ultrasound, a pH, and temperature probe in addition to pumps connected to acid and base chambers. The filtration unit was connected to a compressed air tank and designed with a pressure control valve, safety valve, and syringe filter. Glucose concentration was quantified offline using high-performance liquid chromatography (HPLC). Various light regimes were tested, and under an incident light intensity of approximately 400 µmol m⁻² s⁻¹ at a color temperature of 6500 K, cultures were shown to reach a biomass productivity of 90 mg L⁻¹ day⁻¹, with a specific growth rate of 0.166 day⁻¹ and glucose concentrations up to 5.08 mg L⁻¹. Innovative culture strategies were explored at a small scale, including the cultivation of Synechocystis sp. PCC 6803 in spent K. rhaeticus media to promote economic and sustainable media recycling. When supplemented with additional nutrients, the spent media supported Synechocystis growth up to an OD680 of 0.5. To further characterize the photobioreactor and expected growth based on environmental parameters, both mathematical and machine learning models were built. While the mathematical models were not experimentally validated, the machine learning model model achieved a strong predictive accuracy with a mean absolute error and variance of 0.0009±0.0003 over a 10-fold cross-validation. The system demonstrates up to 65% reduction in cost compared to commercial alternatives.

Design of Passive, Air-Based Squeeze Film Damping for Kinematic Couplings

2026-03-17T03:06:09Z

Design of Passive, Air-Based Squeeze Film Damping for Kinematic Couplings Gazdus, Hannah In precision machine design, kinematic couplings are a common choice for aligning and fixturing parts due to their high repeatability. Their centering ability, along with their high stiffness from hertzian contact, enables kinematic couplings to minimize errors. Although kinematic couplings are applied in dynamic situations such as machining, they are currently designed using only static methods with little regard to vibration-induced error. Machine designers thus do not fully understand how kinematic couplings will behave in situ and do not take advantage of easily applicable damping methods to minimize vibration-induced error. This thesis provides a framework for dynamically modeling kinematic couplings with air-based squeeze film damping. This method of damping takes advantage of the inherent air layer between the top and bottom plates of a kinematic coupling; being so simple to leverage, this work advocates for the inclusion of such damping in every kinematic coupling. This work demonstrates that squeeze film damping can increase a coupling’s damping over 100X, significantly raising dynamic stiffness and reducing vibration-induced error. This work’s design principles will allow for more rigorous and thorough development of kinematic couplings, which is especially necessary for applications where vibration-induced errors must be minimized.

3D-Printed Tangential Flow Filtration and High-Throughput Microfluidic Electroporation for Scalable Microbial Processing

2026-03-17T03:06:44Z

3D-Printed Tangential Flow Filtration and High-Throughput Microfluidic Electroporation for Scalable Microbial Processing Cui, Yuhe Bacterial transformation via electroporation is fundamental to modern biotechnology applications including therapeutic protein production, biomaterial synthesis, and agricultural enhancement. However, conventional electroporation workflows face critical bottlenecks that limit their scalability and industrial applicability; mainly inefficient electrocompetent cell preparation and low-throughput transformation processes. This thesis presents two complementary 3D-printed technologies that independently address these limitations for scalable microbial processing. First, a novel spiral channel tangential flow filtration (TFF) system was developed that replaces conventional centrifugation-based methods for preparing electrocompetent cells. The spiral geometry enhances mixing dynamics and enables continuous washing of bacterial cultures, dramatically reducing preparation time while improving cell recovery compared to traditional centrifugation and membrane filtration approaches that suffer from time constraints, labor intensity, and membrane fouling. Second, a 3D-printed microfluidic electroporation platform featuring geometry-optimized electric field distribution was designed. Building upon established M-TUBE principles, the bilaterally converged channel architecture creates localized field enhancement at reduced applied voltages, enabling high-efficiency transformation of larger cell volumes. This design overcomes the throughput limitations of conventional cuvette-based systems that require manual handling and process only small volumes. Both technologies leverage additive manufacturing to create cost-effective alternatives to traditional protocols. Computational fluid dynamics simulations and experimental validation demonstrate significant improvements in processing time, transformation efficiency, and throughput compared to conventional methods. These complementary technologies demonstrate the potential for future integration into a complete workflow for scalable microbial transformation, with promising implications for broader implementation in industrial biotechnology, synthetic biology, and large-scale research applications.

Integrating Digital Threads Across Engineering Organizations: A Q-methodology Analysis of Challenges with a Novel Factor Selection Approach

2026-03-17T03:06:15Z

Integrating Digital Threads Across Engineering Organizations: A Q-methodology Analysis of Challenges with a Novel Factor Selection Approach Kong, Kanglin This research investigates the integration of digital tools across design, manufacturing, and quality management functions through a cross-industry analysis informed by Q-methodology. Despite considerable investments in digital transformation, many manufacturing organizations face persistent gaps between design intent and production execution, exacerbated by fragmented digital threads, limited adoption of Model-Based Definition, and the continued reliance on manual, error-prone workflows. Through qualitative interviews and quantitative Q-sort analyses conducted among participants across diverse industries, this study identifies key patterns of pain points and solutions perceived differently by stakeholder groups. It reveals insights into how variations in industry characteristics influence digital maturity, particularly regarding the adoption and integration of Product Lifecycle Management, Model-Based Enterprise, and Design for Manufacturability practices. Findings underscore the critical role of enhancing digital thread connectivity, ensuring early integration of manufacturability feedback, embedding automated cost analytics, and facilitating supplier readiness for full MBD adoption. Furthermore, the research highlights the necessity of strategic organizational change management alongside technological advancements. This work provides a nuanced understanding of organizational perceptions and identifies tangible pathways toward a cohesive, software-driven approach for bridging gaps among engineering functions, thereby informing future strategies for manufacturers and software vendors alike.

Human-in-the-Loop Task Directed Exploration and Planning in Unknown Environments

2026-03-17T03:06:08Z

Human-in-the-Loop Task Directed Exploration and Planning in Unknown Environments Jois, Aneesh Ramesh For robots to perform everyday tasks autonomously, like humans, they should be able to perceive, explore and act in novel environments while pursuing high level goals. This capability is known as task-directed exploration, and is essential in domains ranging from household assistance robots to disaster response. However, existing approaches each fall short in fulfilling the task directed exploration problem. Classical symbolic planners require brittle, hand crafted domain models and assume complete knowledge of the environment. POMDP based formulations provide a principled approach to planning under uncertainty but are computationally intractable in large, open world settings. Foundation models such as large language models (LLMs) and vision language models (VLMs) offer strong commonsense knowledge and pattern recognition capabilities but lack the structured spatial grounding and adaptivity required for embodied execution. This thesis presents a unified framework that closes this gap by tightly integrating foundation models with a real time semantic mapping and planning stack. The system consists of four components: (i) a dual layer perception module that combines a deterministic 3D scene graph with a frontier based probabilistic belief field, using vision language models for object labeling and large language models for room classification; (ii) a symbolic task planner that converts natural language instructions into high level activity plans; (iii) an exploration executive that selects informative waypoints, monitors task progress, and dynamically triggers replanning and human queries; and (iv) a unified value of information (VoI) metric that governs both autonomous exploration and selective human interaction, enabling the robot to reason about uncertainty and task utility in a principled way. Demonstrated in realistic simulated environments, the proposed framework allows agents to ground natural language goals in their surroundings, explore efficiently, reason over partial knowledge, and adapt plans as new information is acquired, while involving the user only when doing so meaningfully improves performance.

Model-Based Design of an Indirectly Irradiated Thermochemical Hydrogen Production Reactor Capable of Radiant Heat Recovery

2026-03-17T03:05:53Z

Model-Based Design of an Indirectly Irradiated Thermochemical Hydrogen Production Reactor Capable of Radiant Heat Recovery Scott, Peter Renewable/green hydrogen is of great interest as an alternative fuel for decarbonizing sectors such as shipping, aviation, chemicals, and heavy industry. The high cost of green hydrogen through electrolysis, an off-the-shelf mature technology, has led researchers to explore alternative water-splitting methods including thermochemistry, which can also be used for cosplitting of H2O and CO2 to produce syngas that can be converted to liquid fuels. Moreover, the process can operate on stored high temperature heat, making 24/7 operation possible. This thesis focuses specifically on the two-step thermochemical redox cycle using non-stoichiometric metal oxides. While the process has been demonstrated at the lab and pilot scales, efficiencies have so far been limited by the large temperature swing between the reduction and oxidation conditions, resulting in high sensible heat losses. In our previous work, we have introduced the Reactor Train System (RTS), a concept that features multiple and identical individually sealed, indirectly irradiated, metal oxide-containing reactors which move between a hot reduction zone and a cooler oxidation zone, engaging in counterflow radiative heat recovery in between. Prior modeling of the RTS, which revealed promise for high efficiency and heat recovery effectiveness, used either zero- or one-dimensional models of the RTS reactors and assumed a basic reactor design that featured a sapphire window for radiative heat transfer between the source and the redox material. A detailed conceptual design and higher-fidelity modeling of the RTS reactors is the focus of this thesis. This thesis is a comprehensive documentation of the model-based iterative design process of a novel thermochemical hydrogen reactor with highly unique and challenging functional requirements, from initial concept to early prototyping. The primary engineering challenge is that the structural pressure vessel also acts as the heat transfer interface, and must serve both purposes while undergoing extreme thermal cycling. The original windowed reactor concept is first investigated using a radiative heat transfer model, with findings of unfavorable heat losses and concerns regarding practicality guiding us towards a reactor design using a fully ceramic vessel acting also as a heat transfer interface. A more advanced thermomechanical model was then used to select a geometry which we call the Multi-Tubular Radiative Recovery Reactor (MiTR3 ) instead of one larger ceramic vessel, and to study the design parameters of the MiTR3 such as tube wall thickness with critical insight into the stress and failure probability of the ceramic tubes. Besides its mechanical strength and favorable thermal properties, this design is scalable and adaptable to different operating conditions and redox materials. Moreover, it utilizes easy to assemble off-the-shelf components. We then further augmented our modeling capabilities with multidimensional, time-dependent thermo-fluid and chemical reaction physics, incorporating both reduction and oxidation kinetics into the conservation equations for full-cycle simulations using ceria as the metal oxide. This enabled further study of the impact of important parameters, especially operational parameters such as redox material loading and form factor, gas flow rates, etc., and a deeper understanding of realistic system level efficiencies and productivities that take into consideration the impact of auxiliary components such as vacuum pumping and gas separation technologies on both. Finally, our ongoing experimental work with a benchtop-scale, single-tube reactor prototype aimed at derisking components and validating modeling results is presented, alongside plans for future prototyping efforts.

Towards Probabilistic Dynamically-Orthogonal Primitive Equation Forecasts for the Gulf of Mexico

2026-03-17T03:06:36Z

Towards Probabilistic Dynamically-Orthogonal Primitive Equation Forecasts for the Gulf of Mexico Rodriguez, Victor Alonso Forecasting circulation in the Gulf of Mexico requires an explicit treatment of uncertainty associated with the Loop Current and its eddies, whose geometry and timing can fluctuate irregularly and lead to chaotic deterministic forecasts. Building on the dynamically orthogonal (DO) methodology for evolving low-rank stochastic representations and on efficient DO numerical schemes for geophysical fluid flows, this thesis develops and assesses massive probabilistic Primitive Equation (PE) hindcasts for the Gulf using the Dynamically Orthogonal Primitive Equations (DO–PE) framework as implemented for realistic ocean dynamics in previous MIT-MSEAS studies. The workflow extracts a time-dependent stochastic subspace from a balanced MIT MSEAS PE ensemble via singular-value decomposition, represents the initial nonGaussian coefficient cloud with Gaussian mixture models, and subsequently evolves the DO–PE mean, modes, and coefficients under dynamics, numerics, and forcings consistent with the MIT MSEAS PE modeling system. A 12-day hindcast simulation experiment spanning 28 May–8 June 2015 quantifies skill and convergence across truncations, with weak-type tests (means, standard deviations, kernel-density marginals) and strong-type tests against matched full-order realizations started from identical initial states. Consistent patterns emerge. Uncertainty concentrates along the Loop Current jet, the Yucatán inflow, and eddy peripheries. For weak convergence, as the retained dynamic modes increase from 15 to 60, standard-deviation maps sharpen and expand coherently along these dynamically active features, and the statistics indicate convergence with the normalized RMSEs for both mean and standard deviation fields decreasing in a largely monotonic fashion. At depth and for sea-surface height, late-time mean-error behavior can become mildly non-monotonic, indicating sensitivity to mode allocation among variables. In strong-convergence experiments, DO–PE reconstructions initialized at coefficient quantiles closely track the corresponding full-order trajectories: pathwise misfits remain modest, organize along shear zones, and their RMSE time series lie below persistence and within the envelopes implied by the weak-type spread, reinforcing that truncation primarily filters small-scale content while preserving trajectory-level evolution over the 10–12-day window. Together, these results demonstrate a practical, reproducible pipeline for massive probabilistic forecasting in the Gulf of Mexico that respects PE dynamics while quantifying and localizing forecast uncertainty in flow-dependent ways (details, configuration, and figures in Chapters 3–4). This thesis also introduces dynamic web pages for the interactive visualization of DO–PE output, facilitating the inspection of mean fields, modes, and standard deviations over time in Chapter 5.

Optimization and Control of Sorption-Based Atmospheric Water Harvesting Devices

2026-03-17T03:06:12Z

Optimization and Control of Sorption-Based Atmospheric Water Harvesting Devices Čas, Jan Luka Water scarcity is a global challenge with only one-third of the world’s population having consistent access to clean drinking water. Atmospheric water harvesting is a promising approach owing to the significant amount of water, i.e., 13000 trillion liters, present in the atmosphere. While significant recent research has focused on developing innovative sorbent materials, components and system designs, there is limited understanding of how to optimize device performance through active control. Key operating parameter selection, specifically, desorption temperature and cycle length, has relied on experimental trial and error. In this thesis, model predictive control (MPC) was used to dynamically optimize power input and cycle time in atmospheric water harvesting devices, for the first time. Real time optimization using a custom defined cost function was achieved based on a simplified heat and mass transfer model. The model allowed for the cost function to be based on water output and therefore eliminated the need for setpoint definition a priori. Through a modular, customizable software and hardware stack, the device demonstrated reliability and maintainability while preserving user interaction. MPC was evaluated against five distinct sorbent isotherm types, using three distinct operating modes: maximizing water production, maximizing operational profit and increasing thermal efficiency. All modes outperformed a constant temperature setpoint by dynamically determining the appropriate end time of the cycle, which depending on the material varied up to 10,000 s. Furthermore, the controller was able to increase thermal efficiency up to 3 percentage points compared to the reference by dynamically tapering power input to match water production. Experimental validation was performed with a device built by the Device Research Laboratory. The results showed excellent agreement between measured water output and real-time prediction, which provides a viable strategy for future controller deployment. This work paves a way for more sophisticated device operation through real-time optimization of power input and cycle length and highlights a modular software and hardware design to realize high performance atmospheric water harvesting devices.

A Heuristic-Based Framework for Cost-Effective Product Design Enabled by Manufacturing Automation: Application to Large-Scale Sheet Metal Structures

2026-03-17T03:06:10Z

A Heuristic-Based Framework for Cost-Effective Product Design Enabled by Manufacturing Automation: Application to Large-Scale Sheet Metal Structures Flores Medina, Enrique Sheet metal is prominent as a raw material for fabrication due to its flexible nature. Through cutting, bending, and joining, it can take a plethora of shapes, explaining its vast adoption in the construction, automotive, and aerospace industries. Furthermore, with automation, the labor and human error associated with its manufacturing can be mitigated. Nonetheless, the versatility of sheet metal can fade under the non-trivial dimensional and thickness constraints of some automated processes, particularly bending. This research, conducted in the context of a large-scale sheet metal manufacturer offering high customization, aims to maximize sheet metal’s automation capabilities while retaining its flexibility. To achieve this, two approaches are used: 1) the adoption of rollformed steel profiles with automated tube laser cutting as an additional manufacturing value stream, and 2) the development of a design automation tool that, upon receiving the dimensions and structural load conditions of a rectangular prism (called sub-module), generates a low-cost, automation compliant design. Findings show that optimal modules generally use medium to low-gauge channels as connected structural members, and thin-gauge sheet metal panels as slabs and shear walls, minimizing material use: the main cost component. Generated designs show cost reductions of up to 32% when compared to legacy counterparts. For the most produced product, this translates to yearly cost savings that range from $1.7 to $5.2 million.

Design and Evaluation of a Bionic Knee with Myoneural Control

2026-03-17T03:06:00Z

Design and Evaluation of a Bionic Knee with Myoneural Control McCullough, John A. Building bionic limbs requires the convergence of surgical innovation and robotic engineering: surgical constructs must reliably extract and amplify intent signals from the body, while robotic systems must accurately interpret these signals to deliver precise, responsive assistance and meaningful feedback. Individuals with above-knee amputation often experience reduced mobility and diminished agency when using conventional prosthetic devices. These limitations can impede human-prosthesis embodiment, the integration of the prosthesis into the user’s body schema. This thesis advances the goal of seamless human-machine integration by presenting the design and evaluation of a powered knee prosthesis. The hardware, software, and embedded systems of a prior prototype were upgraded to create a modular, field-deployable research platform. The resulting system incorporates a control framework that enables volitional actuation of the knee joint via electromyographic signals recorded from surgically reconstructed agonist-antagonist muscle pairs. To evaluate the system, one participant with an above-knee amputation completed a series of experimental tasks using both their prescribed microprocessor-controlled prosthesis and the bionic knee. Neural control performance was assessed through blindfolded free-space tasks, while functional capability was evaluated during sit-to-stand transitions, squatting, level-ground walking, and stair ascent. The bionic prosthesis, weighing 2.6 kg, comparable to commercially available powered knees, demonstrated robust, real-time control across all tasks. Volitional neural inputs enabled intuitive and responsive joint actuation, resulting in superior performance and perceived embodiment relative to the passive device. During sit-to-stand and squatting tasks, ground reaction force data revealed increased weight-bearing on the prosthetic side, reflecting enhanced user confidence. Gait analysis showed improved temporal symmetry during walking with the bionic knee, indicating more balanced interlimb coordination. Embodiment scores were consistently higher across all measured domains, with the participant describing the prosthesis as “feeling like my leg” and “helping me.” These findings underscore the potential of neurally integrated prosthetic systems to restore volitional control, improve functional performance, and promote a more embodied user experience.

Planning for in-Space Robotic Assembly of Modular CubeSats

2026-03-17T03:06:02Z

Planning for in-Space Robotic Assembly of Modular CubeSats Freitag, Leila As the space industry continues to grow, developments such as the proliferation of small satellites have lowered the barrier to entry to space, making it faster and easier to launch payloads into orbit. However, the need for rapid deployment in space remains, particularly for rapid replacement of satellites that are nodes in larger constellations or supporting time-sensitive missions such as natural disaster monitoring. On-orbit assembly provides a solution to meet this demand. This thesis describes the development of Orbital Locker, a robotic system designed to enable the autonomous in-space assembly and deployment of modular satellites. The concept of operations involves a free-flying satellite that acts as a storage ``locker'', carrying modular CubeSat components and assembling and deploying them on request. Orbital Locker is an initial small-scale demonstration that is intended to be scaled up, consisting of a Cartesian gantry robot, and CubeSat modules dimensioned such that three modules stack to form a 1U CubeSat. The focus of this thesis is the software architecture of the system including module identification and assembly planning, and assembly testing in microgravity. Module identification makes use of fiducial markers to localize modules within the Locker, tracking the inventory of parts available. The assembly planner uses a graph-based method to optimize the steps required to assemble a desired satellite. It first generates a graph representation of possible assembly states and then uses a graph search algorithm to find the optimal sequence. Results from microgravity testing of the autonomous assembly on a ZeroG flight are presented, where a 1U CubeSat form factor was assembled in 72 seconds. Throughout this work, emphasis is placed on the extensibility of the system to support future scaled-up systems containing a larger inventory of modules.

Human Factors Observations in Flightcrew Response to System Failure Events in Transport Category Aircraft from 2000 to 2024

2026-03-17T03:05:56Z

Human Factors Observations in Flightcrew Response to System Failure Events in Transport Category Aircraft from 2000 to 2024 Perez Gago, Cecilia Understanding the effects of changes in aircraft technology on pilot response to system failure is crucial in the context of recent aviation safety events. This thesis makes human factors observations on pilot response to system malfunction in transport category aircraft through an analysis of final investigation reports produced by investigative authorities worldwide from 2000-2024. In the collected reports, system failure events in aircraft of newer generations correlated with higher percentages of appropriate response. Pilot response appropriateness was found to vary between systems, with particularly low appropriate response to failure of instruments and navigation, fuel, and autoflight systems (in decreasing order). When comparing the findings from the 2000-2024 data collection to those from a 1990-2000 study, pilot appropriate response was found to have increased for failures of the hydraulic and electrical systems. Pilot response to instruments and navigation, and autoflight failures was found to be low in both studies. Crew Alerting System (CAS) messages as initial stimuli for failure awareness were found to support increased levels of appropriate response percentages for failure of the electrical and hydraulic systems. CAS messages did not lead to a substantial improvement in appropriate response to failure of instruments and navigation, fuel, or the autoflight system. Finally, Endsley’s Situation Awareness theory was used as a framework to derive observations in the formulation of pilot responses to system failure across cases. CAS messages and system synoptic displays were observed to contribute to appropriate pilot perception, comprehension, and projection of failure of simple systems. Significant underlying complexity in the function of the autoflight and instruments and navigation systems, and the increased use of sensing, correlated with difficulty in comprehension and projection of system behavior following multiple failure events in 2000-2024 reports. Additionally, examples of failures across systems which displayed delayed or subtle stimuli, and unexpected system dependencies, were observed to lead to difficulties in flightcrew achievement of Level 2 and Level 3 Situation Awareness. Changes in aircraft technology were deemed to have had a varying effect on pilot situation awareness during failure of different airplane systems. Improvements in pilot response were observed in relatively simple systems, and gaps were identified given increased vulnerabilities in failure of systems with high functional complexity.

Integrative Spatial Technologies for Mapping Axonal Vulnerability in Alzheimer’s Disease

2026-03-17T03:06:06Z

Integrative Spatial Technologies for Mapping Axonal Vulnerability in Alzheimer’s Disease Leible, Daniel Alzheimer’s Disease (AD) is the most common neurodegenerative disorder and is histopathologically defined by the accumulation of extracellular amyloid β (Aβ) plaques and intracellular neurofibrillary tau tangles. Pathology progression in AD follows a highly stereotyped, hierarchical pattern, implying a circuit-specific neuronal vulnerability to the underlying pathophysiological processes. Understanding the molecular and subcellular mechanisms driving this selective vulnerability has the potential to enable targeted, circuit-specific therapeutic approaches for early intervention in the detrimental spread of disease. This thesis systematically reviews the current mechanistic understanding of selective vulnerability and early disease development in AD and explores how emerging integrative spatial technologies can address remaining open questions. First, molecular and subcellular processes underlying axonal Aβ and tau accumulation are examined, with a focus on cytoskeletal dynamics and axonal transport deficits. Second, intrinsic structural and metabolic risk factors shared by vulnerable axons are outlined, offering a potential explanation for the early regional onset of pathology. Since AD pathology appears to spread from these initial sites along synaptic connections, mechanisms of transsynaptic propagation of vulnerability are discussed next. Finally, the thesis compares integrative spatial technologies used to map disease progression and proposes neuronal barcoding as a promising strategy to overcome existing limitations.

SLAM for Structured Environments Using Mechanically Scanned Imaging Sonar

2026-03-17T03:05:58Z

SLAM for Structured Environments Using Mechanically Scanned Imaging Sonar Motz, Andrew J. As the modern utilization of the maritime environment only grows, uncrewed systems present the future of safety, efficiency, and capability. For submerged operations, Autonomous Underwater Vehicles (AUVs) enable scientists, industry, and militaries to access remote, inhospitable locations and execute a variety of tasks beyond the capabilities of human occupied or operated systems. Much of this autonomy relies on the vehicle having a detailed understanding of its own position. Inertial Navigation Systems provide an estimate of the distance traveled by combining numerous sensors, but are subject to unbounded error accumulation over long distances. Traditional methods of correcting for this error found in terrestrial robotics are largely unavailable in the undersea domain due to the absorption and scattering effects of electromagnetic signals in water. Acoustic communications and imaging such as Sound Navigation and Ranging (SONAR) is the most reliable and trusted method for AUVs. This thesis presents a novel method for performing Simultaneous Localization and Mapping (SLAM) through acoustic means utilizing a Mechanically Scanned Imaging Sonar (MSIS). MSIS utilize a single beam sonar mechanically rotated around the vehicle to scan a full 360◦ area. Compared with other sonar systems of similar capabilities, they require less size, weight, and power, and are available at a lower price point. The primary contribution of this thesis is a SLAM processing pipeline from MSIS to global position estimate. The pipeline extracts information from the MSIS data regarding the vehicle’s relative location compared to observed landmarks and then probabilistically matches the observed data to a best estimate vehicle position. The system is compatible with either an a priori map or a constantly updated SLAM global map. Individual beams from the MSIS are fused together into a submap. Contrast-based image processing identifies features of interest in the submap and appropriate features are then classified as observed landmarks. A probabilistic coarse-to-fine voting scheme identifies the most likely pose of the vehicle using the global map. When performing SLAM without an a priori map, observed landmarks are then evaluated and either added to the global map or used to update the position of known landmarks. While prior works have established MSIS SLAM by focusing on a single return per sonar beam, this thesis utilizes submaps to extract numerous features from a series of consecutive beams, allowing for more detailed and comprehensive feature mapping. Experimental validation was performed using an ISS360 sonar mounted on a REMUS-100 AUV with the processing pipeline running via Robot Operating System on the vehicle backseat computer. The vehicle was assisted by divers traversing underneath the WHOI Iselin pier and performed both localization and SLAM using the submerged pier pilings. The system performed real-time localization, successfully bounding previously unbounded localization drift to an average of 3.4m, resulting in over a 90% reduction in absolute error after approximately one hour of submerged operations. The SLAM results mirrored the a priori accuracy demonstrating similar error bounds validating the system performance.

Computational study of static replication for barrier options

2026-03-11T03:04:39Z

Computational study of static replication for barrier options Sun, Hai Po. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 1997; Includes bibliographical references (leaves 75-76).

Modeling, control and experimentation of a two dimensional linear motor

2026-03-11T03:04:33Z

Modeling, control and experimentation of a two dimensional linear motor Castañeda Vega, José Israel. Thesis: M.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1997; Includes bibliographical references (leaf 118).

A study of anode dimensions in mercury-vapour thermionic rectifiers

2026-03-11T03:04:42Z

A study of anode dimensions in mercury-vapour thermionic rectifiers Fussell, Lewis. Thesis: M.S., Massachusetts Institute of Technology, Department of Electrical Engineering, 1932; Includes bibliographical references (leaf 50).

Boiling and spreading rates of instantaneous liquid methane spills on water

2026-03-11T03:04:37Z

Boiling and spreading rates of instantaneous liquid methane spills on water Chatlos, David Joseph. Thesis: M.S., Massachusetts Institute of Technology, Department of Chemical Engineering, 1982; Supervised by Robert C. Reid.; Includes bibliographical references (leaves 86-88).

Application of Fourier transform spectroscopy to the absolute determination of the chemical shift of protons.

2026-03-11T03:04:45Z

Application of Fourier transform spectroscopy to the absolute determination of the chemical shift of protons. Wright, Francine Elaine. Thesis: M.S., Massachusetts Institute of Technology, Department of Physics, 1975; Vita.; Bibliography: leaves 65-66.

Geology of Deception Gulch and the Verde Central mine

2026-02-20T03:04:15Z

Geology of Deception Gulch and the Verde Central mine Benedict, P. C. (Platt Carrico), 1900-1969. Thesis: M.S., Massachusetts Institute of Technology, Department of Geology and Geophysics, 1923

Nonlinear elastic analysis of reinforced concrete structures by the finite element method

2026-02-20T03:04:07Z

Nonlinear elastic analysis of reinforced concrete structures by the finite element method Tulga, Said Şahin. Thesis: M.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1979; Includes bibliographical references.

Subcontractor bidding strategy

2026-02-20T03:04:10Z

Subcontractor bidding strategy Gilbane, Thomas Freeman. Thesis: M.S., Massachusetts Institute of Technology, Department of Civil Engineering, 1975; Bibliography: leaves 104-105.

Evaluating Large Language Models as Circuit Design Assistants

2026-02-13T03:49:28Z

Evaluating Large Language Models as Circuit Design Assistants Cox, Matthew J. Large language models (LLMs) have exploded in capability in recent years. Previous attempts at AI systems for circuit design have had limited proficiency and been restricted in problem scope. LLMs, with their breadth of knowledge and reasoning ability, are a promising technology for a much more general-purpose circuit design assistant. We developed a dataset of electrical engineering problems and solutions with which to test an LLM-based system, since no such publicly available dataset exists to our knowledge; unmodified GPT-4 was able to solve 42% of the problems. We did a preliminary comparison of several knowledge bases to use for RAG knowledge injection, finding that a small, curated set of resources performed better than a larger, less-focused set of resources, though there were confounding factors which may have skewed the result. While this work is a start, significant future work is needed to continue developing an LLM-based circuit design assistant.

Increasing Program Code Coverage Using Fuzzing and Targeted Branch Exploration

2026-02-13T03:49:33Z

Increasing Program Code Coverage Using Fuzzing and Targeted Branch Exploration Nguyen, Gary Code coverage is a longstanding metric for evaluating how thoroughly a program has been tested. Achieving high coverage remains a priority goal for quality assurance and software stability. Exhaustive enumeration of possible input paths to every code region is desirable in theory but computationally infeasible in practice, especially in large-scale codebases. Fuzzing is a widely used technique for input generation and is effective at exploring smaller programs but often struggles with more complex conditional logic and nested modules. Concolic execution, which exhaustively explores paths using constraint solving, can work effectively with complex conditional logic but suffers from path explosion. Targeted branch exploration is a similar approach for input generation but sidesteps the path explosion problem by focusing more on specific constraint paths of interest. In this thesis, I introduce a hybrid system that combines fuzzing and targeted branch exploration with the goal of improving code coverage by leveraging the complementary strengths of each. The system uses fuzzing to quickly generate a broad input corpus and follows up with targeted branch exploration to explore paths that fuzzing struggles to reach. Findings from experiments on two C projects of different complexities show that the system did not outperform the individual techniques in terms of raw coverage, revealing limitations of the approach and opportunities for future improvement.

Machine-Learned Representations of Basis Sets and Their Application in Quantum Computational Chemistry

2026-02-13T03:49:30Z

Machine-Learned Representations of Basis Sets and Their Application in Quantum Computational Chemistry He, Wenhao Quantum simulations of electronic structure promise to deliver significant speedups over classical methods, but remain limited by the number of qubits on near-term devices. A key strategy to reduce quantum resource requirements is to truncate the molecular Hilbert space via compact and efficient basis sets. However, most optimized basis sets either rely on predefined heuristics or require expensive classical computations, such as CASSCF orbital optimization or ℓ1-norm minimization of the Hamiltonian. In this work, we introduce a general machine learning framework for fast basis set prediction in quantum computational chemistry. Our method employs an equivariant graph neural network that outputs a Hermitian matrix encoding optimized molecular orbitals. The eigenvectors of this matrix define a transferable and efficient basis set, trained on orbitals obtained via CASSCF and Hamiltonian ℓ1 norm optimization. We evaluate our model on hydrogen chains and demonstrate that the predicted bases achieve energy accuracy and Hamiltonian sparsity comparable to orbital-optimized methods, while reducing classical preprocessing time. In addition, the predicted orbitals can be directly used as high-quality initial guesses for CASSCF calculations, further accelerating their convergence.

SmellNet: A Large-scale Dataset for Real-world Smell Recognition

2026-02-13T03:49:31Z

SmellNet: A Large-scale Dataset for Real-world Smell Recognition Feng, Dewei The ability of AI to sense and identify various substances based on their smell alone can have profound impacts on allergen detection (e.g., smelling gluten or peanuts in a cake), monitoring the manufacturing process, and sensing hormones that indicate emotional states, stress levels, and diseases. Despite these broad impacts, there are virtually no large-scale benchmarks, and therefore little progress, for training and evaluating AI systems’ ability to smell in the real world. In this paper, we use portable gas and chemical sensors to create SmellNet, the first large-scale database that digitizes a diverse range of smells in the natural world. SmellNet contains about 180,000 time steps of 50 substances (spanning nuts, spices, herbs, fruits, and vegetables) with 50 hours of data. Using SmellNet, we trained AI models for real-time classification of substances based on their smell alone. Our best methods leverage sequence models, contrastive learning to integrate high-resolution Gas Chromatography–Mass Spectrometry molecular data, and a new temporal difference method that identifies sharp changes in sensor readings. Our best models achieve up to 65.35% accuracy on pre-recorded data, and generalize to real-world conditions with 10.71% accuracy on nuts and 25.38% on spices in the challenging 50-way online classification task. Despite these promising results, SmellNet highlights many technical challenges in building AI for smell, including richer feature learning, on-edge smell models, and robustness to environmental changes.

Towards Zero-Shot Pretrained Models for Efficient Black-Box Optimization

2026-02-13T03:49:16Z

Towards Zero-Shot Pretrained Models for Efficient Black-Box Optimization Meindl, Jamison Chivvis Global optimization of expensive, derivative-free black-box functions requires extreme sample efficiency. While Bayesian optimization (BO) is the current state-of-the-art, its performance hinges on surrogate and acquisition function hyperparameters that are often hand-tuned and fail to generalize across problem landscapes. We present ZeroShotOpt, the first general-purpose, pretrained model for continuous black-box optimization tasks ranging from 2 D to 20 D. Our approach leverages offline reinforcement learning on large-scale optimization trajectories collected from 12 BO variants. To scale pretraining, we generate millions of synthetic Gaussian process-based functions with diverse landscapes, enabling the model to learn transferable optimization policies. As a result, ZeroShotOpt achieves robust zero-shot generalization on a wide array of unseen synthetic and real-world benchmarks, matching or surpassing the sample efficiency of leading global optimizers, including BO, while also offering a reusable foundation for future extensions.

Temperature Characterization of Colloidal Quantum Dot Light Emitting Diodes

2026-02-13T03:49:26Z

Temperature Characterization of Colloidal Quantum Dot Light Emitting Diodes Nguyen, Thienan D. Colloidal quantum dot light emitting diodes reveal to be promising candidates for the next generation of display technologies. Their brighter emissions, greater color purity, and higher efficiency make them highly desirable in consumer electronics. As such, research into the performance and stability of these novel LEDs are crucial for their operation in displays. These investigations are ongoing, with focused efforts on improving the operating stability through different quantum dot materials and passivation methods. However, less attention is paid in confidently understanding the fundamental relationships between current, voltage, and luminance by which these devices operate. These electrical characteristics reveal insights into the operation of these devices and the behavior of charge carriers. Additionally, temperature-dependent electrical measurements can showcase different behavior at different temperatures and deviations from the expected performance at set temperatures. Temperature dependent processes are revealed and from such, a better understanding of how the device operates is gained. In this thesis, an investigation into the temperature-dependent electrical characteristics of quantum dot light emitting diodes was conducted by measuring the current-voltage-luminance, JVL, relationships at various cryogenic temperatures. These temperatures ranged from 78K, liquid nitrogen boiling point, to 293K, room temperature. This investigation revealed the temperature dependent nature and origin of turn-on voltage, current, EQE, EQE roll-off, and hysteresis.

Uncertainty-Aware Knowledge Graph Retrieval Methods and Their Use in LLM Question-Answering

2026-02-13T03:49:13Z

Uncertainty-Aware Knowledge Graph Retrieval Methods and Their Use in LLM Question-Answering Rich, Benjamin R. Knowledge Graph Question Answering (KGQA) encompasses a set of techniques aimed at generating accurate, interpretable responses to natural language queries posed over structured, graph-based datasets. Recent approaches to KGQA involve reducing the knowledge graph (KG) to a relevant subgraph, which is then encoded in natural language as a series of triples (subject, predicate, object) and passed to a large language model (LLM) for interpretation and answer generation. These methods have shown state-of-the-art accuracy. However, this paradigm is undermined by a critical vulnerability: the retrieval of irrelevant or erroneous facts can amplify LLM hallucinations and degrade system trustworthiness, while the reasoning process remains opaque. This thesis addresses this challenge by extending an existing stateof-the-art KGQA architecture with uncertainty-aware subgraph retrieval methods. To achieve this, we modify the retrieval component to learn the epistemic uncertainty of each candidate triple’s relevance to a given query. We implement these modifications using Bayesian methods and learn a well-calibrated approximation of the posterior distribution over triple relevance. By explicitly modeling this uncertainty, the retriever model is shown to provide a fine-grained confidence score for each piece of evidence. We expose these metrics downstream to the LLM during reasoning and evaluate whether LLMs can reason over uncertainty-related metrics to improve KGQA. We find that LLMs cannot reason effectively over uncertainties in most cases, but that agentic workflows that provide selective access to uncertainty metrics may enhance performance. We evaluate our approach against established benchmarks using HIT-rate and set-comparison accuracy metrics. Additionally, we introduce reasoning-path and statistical trust metrics derived from calibrated uncertainty scores. Our analysis reveals a significant positive correlation between path-based uncertainty metrics and the veracity of the Large Language Model’s (LLM) answers. These findings establish a robust foundation for developing uncertainty-grounded trust mechanisms in LLM-agnostic KGQA systems. As a proof of concept, a lightweight classifier trained exclusively on the LLM’s inputs and outputs demonstrates substantial predictive power in identifying correct responses. Finally, we briefly explore using uncertainty to identify out-of-distribution (OOD) queries.

Applied Compiler Optimizations for Proving Code

2026-02-13T03:49:29Z

Applied Compiler Optimizations for Proving Code Ruiz, Ricardo The recent popularity of massively distributed, trustless systems has created a demand for cryptographic proofs: systems to prove that a piece of data is a valid output for a given program. These systems exist, but face very high runtimes for the generation of proofs. Significant effort has been invested in optimizing the prover systems, but relatively less has been focused on optimizing the code that gets read as an input. This paper proposes a new approach to optimizing prover systems by modifying the compiler to produce proof-ready code. It proposes a benchmarking framework for comparing the relative proof costs of RISC-V instructions; the resulting analyis find that shift instructions do not offer heavy savings over multiplication. The finding suggests that strength reduction, a fundamental optimization in modern compilers, can sabotage end-to-end performance. The paper proposes methods for applying this knowledge to better optimize code, leaving the door open for future researchers to continue to make code proofs more performant and accessible.

Reconstructing Cross-Species Ancestral Adeno-Associated Viruses for Enhanced Gene Therapy Delivery

2026-02-13T03:49:32Z

Reconstructing Cross-Species Ancestral Adeno-Associated Viruses for Enhanced Gene Therapy Delivery Xie, Yuxin Adeno-associated viruses (AAV) are one of the most promising vectors for gene therapy because of their established safety, low immunogenicity, and capability to achieve sustained gene expression. However, many naturally occurring AAV variants have limitations in their potency, particularly in penetrating biological barriers like the blood-brain barrier (BBB). Additionally, their broad and nonspecific tropism can translate into suboptimal cross-species transduction efficiency and potential toxicity, complicating the clinical transition from animal model to humans. These challenges impede the use of naturally occurring AAVs for therapeutic gene delivery in many neurological disorders-such as autism spectrum disorders (ASD), Parkinson’s disease (PD), Huntington’s disease (HD)—as well as other systemic conditions like cystic fibrosis (CF). To overcome these barriers, we developed a computational framework based on ancestral sequence reconstruction (ASR) to engineer synthetic ancestral AAV capsids with the goal of enhanced targeting specificity and potency. We first validated this computational framework by replicating the previously engineered Anc80L65 capsid. Then, with 75 naturally occurring functional AAV sequences and additional experimentally screened variants exhibiting brain-targeting potency, we built an evolutionary framework. We applied multiple computational methods such as enhanced multiple sequence alignment, maximum-likelihood-based phylogenetic tree inference, and ancestral sequence reconstruction with Bayesian inference. With this methodology, we predicted several novel ancestral AAV capsid sequences at critical evolutionary nodes, particularly those representing functional transitions with potential improved blood-brain barrier penetration and CNS tropism. Our computational framework thus streamlines and accelerates the process of designing ancestral AAV variants with targeted gene therapy applications.

Generating Unprecedented Extreme Scenarios with Limited Data

2026-02-13T03:49:19Z

Generating Unprecedented Extreme Scenarios with Limited Data Chang, Kai Quantifying and predicting rare and extreme events persists as a crucial yet challenging task in understanding complex dynamical systems, ubiquitous in science and engineering. Many practical challenges arise from the infrequency and severity of these events, including the considerable variance of simple sampling methods and the substantial computational cost of high-fidelity numerical simulations. Numerous data-driven methods have recently been developed to tackle these challenges. However, a typical assumption for the success of these methods is the occurrence of multiple extreme events, either within the training dataset or during the sampling process. This leads to accurate models in regions of quiescent events but with high epistemic uncertainty in regions associated with extremes. To overcome this limitation, we introduce the framework of Extreme Event Aware (e2a or eta) or η-learning which does not assume the existence of extreme events in the available data. η-learning reduces the uncertainty even in ‘unchartered’ extreme event regions, by enforcing the extreme event statistics of a few observables during training, which can be available or assumed through qualitative arguments or other forms of analysis. This type of statistical regularization results in models that fit the observed data, but also enforces consistency with the prescribed statistics of some observables, enabling the generation of unprecedented extreme events even when the training data lack extremes therein. Theoretical results based on optimal transport offer a rigorous justification and highlight the optimality of the introduced method. Additionally, extensive numerical experiments illustrate the favorable properties of the ηlearning framework on several prototype problems and real-world precipitation downscaling problems.

A*-Decoding: Token-Efficient Inference Scaling

2026-02-13T03:49:18Z

A*-Decoding: Token-Efficient Inference Scaling Chatziveroglou, Ioannis Inference-time scaling has emerged as a powerful alternative to parameter scaling for improving language model performance on complex reasoning tasks. While existing methods have shown strong performance gains under fixed compute budgets, there has been little focus on optimally utilizing that budget during inference. In this work, we introduce A*-decoding, a search-based inference-time strategy that builds on the A* search algorithm to optimally utilize a fixed compute budget by prioritizing high-quality reasoning paths during generation. We frame language model decoding as a structured search in a state space of partial solutions, applying the A* transition model to identify promising continuations guided by an external process supervision signal. In our experiments, A*-decoding reaches the performance levels of strong inference scaling baselines like best-of-N and particle filtering while using up to 3x fewer tokens and 30% fewer PRM passes under equivalent compute budgets. On the MATH500 and AIME 2024 benchmarks, A*-decoding enables Llama-3.2-1B-Instruct to match the performance of the 70x larger Llama-3.1-70B-Instruct, and allows Qwen3-1.7B to reach o1-like reasoning accuracy. These results highlight the power of structured search in decoding, offering an alternative to brute-force sampling or scale-driven gains. Our work demonstrates how thoughtful inference-time strategies can enhance reasoning in SLMs, pointing toward future advances in more efficient and scalable language model deployment.

U-Net Network Enhancements to Facilitate Rapid Electron Microscopy Imaging for Connectomics

2026-02-13T03:49:27Z

U-Net Network Enhancements to Facilitate Rapid Electron Microscopy Imaging for Connectomics Varma, Vikram Imaging the structural and functional connections between cells in the brain allows neuroscientists to understand the brain by studying neuronal wiring diagrams. To automatically segment and classify images that are used in the construction of these neuronal wiring diagrams, or connectomes today, machine learning segmentation techniques require an image scanned with an electron microscope at either a slow dwell time or with small pixel sizes. However, a scalable and more rapid implementation of connectome construction has not yet been realized because of the significant cost of multi-beam electron microscopes and the relatively slow time in which connectomes can be constructed using a single-beam electron microscope. Segmented connectomes include sections that can be segmented properly with a fast scanned image as well as sections that require slow scanning for proper segmentation. Due to this fact, a potential way to enhance the time in which connectomes can be produced and segmented is to first scan samples at fast resolution and perform segmentation using a convolutional neural network, identify those areas of interest that require more detailed imaging through a learning-based error detection network, and then rescan only those identified high interest areas to produce a fused image for segmentation. The proposed thesis will analyze various machine learning methods for segmentation using the U-Net network and review proposed enhancements to the U-Net network that can better utilize electron microscopy images for construction of segmented connectomes. The successful use of fused electron microscopy images will potentially enable higher speed and lower cost electron microscopy imaging for connectomics.

Low-Temperature Germanium Waveguides for Mid-Infrared Sensing Applications

2026-02-13T03:49:21Z

Low-Temperature Germanium Waveguides for Mid-Infrared Sensing Applications Zhang, Erin Wei Waveguide integrated devices that operate in the mid-infrared (mid-IR) wavelength range (2.5-12 µm) are used for sensing the fundamental absorption bands in a variety of molecules. Germanium (Ge) is commonly used for photodetection in the nearinfrared (near-IR) wavelength range of 1.2-1.6 µm due to its strong absorption from a 0.8 eV direct band gap. At longer wavelengths in the mid-IR range, Ge exhibits transparency that makes it a desirable waveguide material for sensing applications. Its epitaxial growth compatibility with silicon (Si) substrates makes Ge-on-Si an effective platform for mid-IR waveguides. For back-end-of-line (BEOL) integration of waveguides in sensing applications, the thermal budget limits the temperature to below 450°C. In this work, we investigated the use of h-line exposure as a commercially viable, low-cost option for patterning low temperature (LT) Ge-on-Si waveguides using direct write lithography. Waveguide dimensions for optimal confinement in single-mode transverse electric (TE) polarization at wavelengths of 3 µm and 10.4- 11.3 µm were modeled and the direct lithography process was refined. Through dose testing and adjustments to the raster direction and pixel resolution, it was found that direct write lithography lacked the resolution required for low-loss waveguides. Scanning electron microscopy (SEM) revealed inconsistent waveguide widths and sidewall roughness, and e-beam lithography was identified as the preferred lithography process. For future integration of LT-Ge in a foundry process design kit (PDK), a universal thickness of 1.7 µm was found to support single-mode waveguide operation from 3-11.3 µm wavelength.

Assessing Log-Based Coordination Systems for Managed Cloud Environments

2026-02-13T03:49:14Z

Assessing Log-Based Coordination Systems for Managed Cloud Environments Jimenez, Gabriel The distributed systems landscape is undergoing a significant shift toward managed cloud environments, reducing the prevalence of self-hosted coordination services such as ZooKeeper. While ZooKeeper remains a proven and feature-rich solution for coordination tasks, its deployment in cloud environments can introduce component redundancy. This is because the underlying cloud platform already provides internal mechanisms to ensure coordination guarantees. This thesis investigates the design and evaluates the performance of a log-based coordination service library tailored for managed cloud environments. The proposed library removes the ensemble management overhead inherent in ZooKeeper by delegating durability and consistency responsibilities to the cloud provider’s data layer. This architectural simplification enables a modular design, allowing for tailored implementations that exploit the strengths and mitigate the limitations of a system's specified data layer. The library demonstrated feature parity with ZooKeeper for a targeted subset of coordination features, including leader election, membership tracking, and ephemeral state management. The same is noted for migration from an existing ZooKeeper-based application to this work's library, requiring minimal design changes while preserving coordination guarantees. While the results show that this design does not yet match mature coordination services in raw performance, they highlight potential avenues for further research, particularly in optimizing log-based coordination systems for the unique characteristics of cloud-managed data layers. Given the industry’s steady movement toward cloud-native infrastructure, these findings provide a foundation for future exploration into lightweight, platform-integrated coordination solutions.

Proof-of-Work Based Mitigation of Real Time Video DDoS Attacks

2026-02-13T03:49:20Z

Proof-of-Work Based Mitigation of Real Time Video DDoS Attacks Echezona, Chukwuemekalum As the Internet continues to grow in size and complexity, Distributed Denial of Service (DDoS) attacks grow in size and complexity alongside it. One particularly common form of DDoS attack is the TCP SYN flood, which exploits the TCP handshake process to exhaust server resources. This thesis investigates the use of a novel proof-of-work (PoW) based mitigation method to respond to such attacks, specifically in the context of WebRTC video conferencing applications. PoW aims to shift the computational burden from the server to the client, by utilizing a hard to solve puzzle that is easily verifiable. Guided by the same evaluation framework used by the original contributors, we conducted controlled experiments using SPHERE, a national research testbed, and the open-source Jitsi Meet video conference application to simulate DDoS attacks and measure their impact on video quality metrics such as upload/download bitrate and video framerate. Our experiments involved multiple scenarios with and without active attacks and PoW mitigation activate. Results demonstrate that PoW imposes minimal overhead on legitimate clients while maintaining high efficacy when faced with the threat of a SYN Flood attack, regardless of whether the attackers do the proof-of-work before sending traffic. These findings highlight PoW as a promising low overhead mitigation method for WebRTC conference systems under the threat of DDoS attacks.

Optimizing Priority-Based Search for Lifelong Multi-Agent Path Finding

2026-02-13T03:49:13Z

Optimizing Priority-Based Search for Lifelong Multi-Agent Path Finding Huang, Natalie The lifelong Multi-Agent Path Finding (MAPF) problem requires planning collision-free trajectories for agents operating continuously in dynamic environments. Traditional solvers such as Priority-Based Search (PBS) use fixed branching heuristics, which can be inefficient in high-congestion scenarios. This work explores how learning-based methods can improve PBS decision-making. We develop supervised learning (SL) policies trained from high-quality beam search trajectories and reinforcement learning (RL) policies learned directly through simulation, enabling adaptive branching strategies. Evaluations on warehouse-style and Kiva-style maps with varying agent densities show that learned policies can significantly boost throughput in congested warehouse layouts, while identifying scenarios where classical heuristics remain competitive. Our findings provide guidance on solver selection based on environment layout and congestion characteristics.

Learning to Interpret Language Model Diffs

2026-02-13T03:49:23Z

Learning to Interpret Language Model Diffs Goel, Avichal Finetuning-induced changes to a model’s weights (a “model diff”) are semantically meaningful but often difficult to interpret. This makes us wonder: can we describe the content of an unknown model diff using natural language? We introduce diff interpretation training, a method that teaches a model describe its own finetuning-induced modifications. Our approach uses synthetic model diffs to train a lightweight adapter, which in turn can be applied to a compatible finetuned model to make it self-describing. Using two simple task settings, we demonstrate that our method can successfully decode model diffs into accurate natural language descriptions.

Approximate L² Error Control by Solution Post-Processing for Finite Element Solutions of PDEs with Higher-Order Adaptive Methods

2026-02-13T03:49:17Z

Approximate L² Error Control by Solution Post-Processing for Finite Element Solutions of PDEs with Higher-Order Adaptive Methods Botto Tornielli, Marcos Julian With the substantial computing resources available today, computational fluid dynamics simulations allow scientists and engineers to simulate physical problems very accurately. However, achieving this accuracy requires a sufficiently refined computational mesh, which is a primary driver for the high cost of complex simulations. Mesh adaptation methods provide an automated way to determine the regions where a mesh needs the most refinement and generate a new mesh that efficiently targets these regions. In this thesis, we build on previous work in a posteriori error estimation and mesh adaptation for finite element methods to propose a new mesh adaptation method based on L² error control by solution post-processing. A key feature of our method is its natural extension to higher-order discretizations while providing a problem-independent adaptation methodology. Problem-independent adaptation methods do not depend on specific information about the partial differential equation (PDE) problem being solved, and can therefore be applied to a wide range of problems without modification. We present numerical results applying the approximate L² error control method to a two-dimensional advection-diffusion problem with anisotropic features. These results demonstrate the proposed method’s ability to generate well-adapted anisotropic meshes for solutions with polynomial orders 1, 2, and 3. We also apply the approximate L² error control method to a more complex two-dimensional Reynolds-Averaged Navier-Stokes problem with turbulent flow over a flat plate. We compare the convergence of the drag coefficient and the characteristics of adapted meshes obtained with the proposed method and with an output-based adaptation approach. As expected, the approximate L² error control method is not as effective as the output-based approach in reaching a converged drag coefficient value, but it nevertheless demonstrates the ability to effectively control the approximate L² error in the Mach field.

Advancing Ubiquitous Tactile Sensing through Comprehensive Tooling for Resistive Matrix-Based Sensors

2026-02-13T03:49:25Z

Advancing Ubiquitous Tactile Sensing through Comprehensive Tooling for Resistive Matrix-Based Sensors Murphy, Devin Resistive matrix-based tactile sensors offer a scalable and intuitive approach to capturing human-environment interactions, yet deploying them in real-world systems remains challenging because they must remain portable, adaptive, and long-lasting. This thesis presents the WiReSens Toolkit, an open-source hardware and software platform for developing resistive tactile sensing systems that meet the demands of real world applications. The toolkit features adaptive hardware for interfacing with resistive sensors and a web-based GUI that mediates access to otherwise complex functionality, including 1) multi-device programming and wireless visualization across three distinct communication protocols 2) autocalibration methods for adaptive sensitivity and 3) intermittent data transmission for low-power operation. As a use case for the toolkit, the thesis then introduces a method for the automatic design and fabrication of custom tactile sensing gloves using flexible printed circuit boards (FPCBs), enabling rapid, scalable production. Together, these contributions lower barriers to adoption and support broader exploration of tactile sensing in HCI, robotics, and ubiquitous computing.

Quantization Methods for Matrix Multiplication and Efficient Transformers

2026-02-13T03:49:20Z

Quantization Methods for Matrix Multiplication and Efficient Transformers Savkin, Semyon We study quantization in Machine Learning. First, we introduce NestQuant — a technique for quantization of matrix products and post-training quantization of LLMs. Beyond reducing the memory footprint, quantization accelerates inference, as the primary bottleneck during autoregressive generation is often the memory bandwidth. NestQuant leverages two nested lattices to construct an efficient vector codebook for quantization, along with practical encoding and decoding algorithms. The approach is grounded in recent theoretical work that characterizes the optimal rate–distortion trade-off for matrix products. Empirically, on Llama-3-8B, it reduces the perplexity gap between full-precision and quantized models by more than 55% relative to the current state-of-the-art technique (SpinQuant). Second, we investigate data-domain quantization for RF signals. We propose a tokenized transformer for source separation that discretizes RF waveforms into learned tokens and operates directly on the resulting sequences, outperforming strong convolutional baselines. Together, these contributions connect information-theoretic limits with deployable systems: structured vector quantizers accelerate LLM inference and enable competitive discrete representations for RF tasks.

Improving Data-Driven Contact Localization and Force Estimation for Barometric Tactile Sensors

2026-02-13T03:49:15Z

Improving Data-Driven Contact Localization and Force Estimation for Barometric Tactile Sensors Chun, Ethan Barometric tactile sensors offer a cheap, robust, and customizable means for robots to perceive the world. Central to their operation are models that extract useful information from the sensors’ raw pressure readings. In this work, I focus on improving data-driven methods for single-point contact localization and force estimation using a previously presented three-quarter sphere barometric tactile sensor. To allow modeling of time-dependent effects in the sensor material, I introduce a multi-threaded data collection system that captures ground truth contact and sensor data at exactly 100 Hz. I construct both feed-forward and recurrent networks using this data, finding that a recurrent network achieves a 15% lower mean absolute error for angular contact localization on the sphere compared to prior methods. The recurrent architecture’s computational efficiency ensures that the architecture can still run within the constraints of the sensors’ microcontroller. Despite this improvement, I find that more expressive models such as LSTMs tend to overfit on the collected data and physical phenomena observed during deployment were not well represented by the training metrics. To better understand the extent that these data-driven methods alone can improve sensor performance, I shift focus away from the modeling and analyze the physical sensor instead. I find that viscous effects in the sensor can render the prediction task unlearnable without historical data and that thermal effects introduce a train-test distribution shift. Finally, I discuss design criteria for a theoretical future barometric tactile sensor that may mitigate the effects found during my modeling and analysis.

Probabilistic Programming over Heterogeneous Language and Hardware Targets

2026-02-13T03:49:09Z

Probabilistic Programming over Heterogeneous Language and Hardware Targets Rojas Collins, Elias G. Modern probabilistic programming applications, from large-scale Bayesian inference to real-time decision making, require both the expressiveness of CPU-oriented languages such as Gen.jl and the massive parallelism of GPU-backed array languages such as GenJAX, yet existing platforms force users to trade modeling flexibility for performance. This thesis introduces GenUflect, a metalanguage that embeds multiple Gen-compatible dialects inside a single program, allowing each sub-component to run on the most appropriate language and hardware target while preserving Gen’s programmable-inference interface. GenUflect extends Gen’s dynamic-modeling language with the @union, @vmap, @amortize, @amortize≤, and @runtime_union combinators; these macros compile at build-time (or justin-time) to autonomous generative functions written in the target dialect, link them through a lightweight FFI layer, and manage cross-device data via zero-copy MirrorArrays and lazily materialized traces. The resulting programs remain sound by construction because each foreign subtrace is itself a valid Gen generative function. Empirical studies demonstrate that this hybrid approach yields large practical gains. On a split linear-vs-sinusoidal regression task, GenUflect matches pure GenJAX throughput while running higher-order control logic on the CPU, and is up to two orders of magnitude faster than a pure Gen implementation for datasets of 105 points. In a collapsed-Gibbs sampler for a Dirichlet-process mixture model, GenUflect’s elastic allocation (@amortize≤) lets vectorized GPU kernels adapt to a growing number of clusters; the same inference that takes over an hour in Gen executes in seconds with GenUflect. A probabilistic inverse-graphics pipeline further showcases how heterogeneous submodels can cooperate seamlessly within unified inference code. By coupling language interoperability with automated data movement and compile-time code generation, GenUflect bridges the gap between flexibility and speed, enabling scalable, expressive probabilistic programs that natively exploit both CPUs and accelerators.

Under-Coverage of Double Machine Learning Due to Implementation Choices

2026-02-13T03:49:08Z

Under-Coverage of Double Machine Learning Due to Implementation Choices Siegmann, Charlotte B. Double ML estimators can estimate coefficients of interest with far fewer functional form assumptions than linear econometric methods. However, DML requires researchers to make a range of implementation choices, including the selection of the function class, the random seed, and hyperparameter configurations. While asymptotic theory suggests these choices should not affect final estimates, we show that for 10 economic analyses (8 of them published and peer-reviewed), implementation choices affect the results. In half of the datasets, different implementation choices even change the interpretation of findings between negative, null, or positive effects. We link these results to a framework for empirically assessing the performance of machine-learning-based estimators, focusing on precision, coverage, and susceptibility to manipulation. This is meant to complement asymptotic theory. We demonstrate that the coverage of DML confidence intervals is too low—placing an upper bound of 48% on the expected coverage of conventional 95% confidence intervals for published DML economics papers. We show that in the status quo, the susceptibility of DML to manipulation by researchers is high, but propose ways to mitigate this susceptibility.

Optimizing Irreversible Perturbations of the Unadjusted Langevin Algorithm

2026-02-13T03:49:05Z

Optimizing Irreversible Perturbations of the Unadjusted Langevin Algorithm Zhu, Qianyu Julie A central task in Bayesian inference and scientific computing is to compute expectations with respect to probability distributions that are only known up to a normalizing constant. Markov chain Monte Carlo (MCMC) methods, and in particular Langevin dynamics, provide a powerful framework for this task by constructing stochastic processes that converge to the target distribution. However, practical implementations face two challenges: slow mixing when the target distribution is anisotropic or multimodal, and persistent discretization bias introduced by numerical schemes. This thesis investigates irreversible perturbations of overdamped Langevin dynamics, aiming to accelerate mixing while controlling discretization error. Irreversible perturbations introduce skew-symmetric drift terms that preserve the target distribution while inducing rotational flow, thereby enhancing exploration. Although prior work has established their benefits in continuous-time settings, the impact of discretization and the design of optimal perturbations for discrete-time algorithms remain open problems. We develop a framework for optimizing constant (position-independent) irreversible perturbations in the Unadjusted Langevin Algorithm (ULA). Our approach balances two competing objectives: maximizing the spectral gap of the continuous dynamics to accelerate convergence, and minimizing discretization error that drives estimation bias. Motivated by this, we introduce new criteria that jointly evaluate bias and efficiency, and we show how these criteria identify perturbations that improve performance beyond existing constructions. Theoretical analysis is complemented by numerical experiments on Gaussian and nonGaussian targets. These experiments demonstrate that appropriately designed irreversible perturbations can reduce mean-squared error without sacrificing stability, while poorly chosen perturbations can degrade performance. The results highlight the importance of geometry-aware design and motivate systematic optimization strategies for irreversible perturbations. Overall, this work extends the theoretical and practical understanding of irreversible Langevin dynamics, bridging the gap between continuous-time spectral analysis and discrete-time numerical performance. It provides principled tools for constructing efficient MCMC samplers, with potential applications in high-dimensional Bayesian inference and modern machine learning.

Single Camera Motion Compensated Viewpoint Shift

2026-02-13T03:49:03Z

Single Camera Motion Compensated Viewpoint Shift Snowdon, Adam Eye contact is a necessary tool for human connection and in most video conferencing situations, eye contact is not possible. Standard laptop and webcam configurations position the camera at the top of the screen, meaning that when the user looks at other people’s faces in the center of the screen, the camera captures the user looking downward, creating the impression of poor eye contact for remote participants. Solutions involving 3D modeling of the face to synthesize a gaze-corrected view have been explored and exist but are too computationally costly for most personal computers. To address this computational challenge, we draw inspiration from 2D frame interpolation techniques to synthesize a virtual camera view that repositions the user’s apparent gaze toward the camera. Our method uses a single camera located at the top of the user’s screen and requires only a brief setup period. Assuming there is only one user, our approach creates a virtual camera view that transforms the user’s viewpoint from the screen center to the camera position, enabling more realistic eye contact in video conference calls.

A Framework for 3D Mouse Brain Reconstruction: RNA-based Stitching of Adjacent Tissue Slices and Co-Registration of Multimodal Imaging Data

2026-02-13T03:48:56Z

A Framework for 3D Mouse Brain Reconstruction: RNA-based Stitching of Adjacent Tissue Slices and Co-Registration of Multimodal Imaging Data Pan, Jessica N. Mapping the brain’s complex neural networks requires tracing the long-distance pathways of individual axons, a task that demands a comprehensive 3D reconstruction of the brain. Recently, spatially resolved transcriptomics (SRT) methods enable the study of gene expression and biomolecule distribution in each neuron in its spatial context, opening the door to more thoroughly investigating cell-cell interactions between neurons. However, SRT methods are limited to slices of tissue; therefore, computational alignment is essential to reconstruct a cohesive 3D volume while correcting for both batch effects and inherent sample variability. This thesis presents a novel framework that addresses these challenges through three primary contributions. First, a memory-efficient, non-referenced-based algorithm was developed to align the superficial surfaces of adjacent, high-resolution tissue slices. Second, these surface transformations were interpolated through the tissue slices on a proof-of-concept dataset of three adjacent slices. Third, methods for co-transforming fluorescent protein imaging data were explored to fully resolve the cell boundaries between neurons. These three methods are necessary steps towards creating a fully-resolved, multimodal 3D model of the brain.

Optimizing Non-Convex Objectives to Plan More Optimal Motion for Manipulators

2026-02-13T03:49:11Z

Optimizing Non-Convex Objectives to Plan More Optimal Motion for Manipulators Garg, Shruti Non-convex optimization is essential to tackle increasingly complex and practical problems in kinematic motion planning. Although introducing non-convexity often sacrifices guarantees of feasibility and optimality–making solutions more susceptible to local minima or failure to converge–many robotic systems and tasks are non-convex by nature, necessitating at least somewhat non-convex formulations. In this thesis, we aim to mostly constrain non-convexity to the objective. This optimization structure helps preserve certain feasibility guarantees in theory and usability in practice while enhancing optimality of solutions, even if global optimality is not achieved. In the first chapter, we demonstrate the effectiveness of non-convex objectives in scenarios where motion planning involves a non-convex parameterization of the configuration space. We keep constraints strictly convex, with the non-convexity quarantined to the objective. This structure guarantees a feasible solution given a feasible initial guess. We primarily use our method to post-process Graphs of Convex Sets solutions in three domains: constrained bimanual motion, motion with guaranteed non-collision, and planning in SO(3). In each case, the non-convex objective compensates for distortion introduced by the parameterization, resulting in more efficient and natural motion. In the second chapter, we propose teleoperation scheme with full-body motion planning for non-holonomic mobile manipulators. Our key contribution is a Differential Inverse Kinematics (DiffIK) formulation that crafts non-convex objectives to avoid singularities and joint limits leading to more robust feasible motion. Unlike before, the constraints are not strictly convex, so the optimization has no guarantees of feasibility. However, we mitigate the non-convexity in the constraints as much as we can by linearizing around the robot’s current position and approximating the highly non-convex non-holonomic constraint. We explore multiple formulations for singularity avoidance and empirically demonstrate that integrating these objectives into DiffIK improves motion quality for teleoperation for the RBY-1 robot.

CableSplat: Optimized 3D Gaussian Splatting for 1D Deformable Pose Estimation

2026-02-13T03:49:02Z

CableSplat: Optimized 3D Gaussian Splatting for 1D Deformable Pose Estimation Pai, Sameer A key challenge in the robotic manipulation of deformable objects is the lack of accurate and efficient systems for estimating their pose in real-time, especially in the presence of occlusion. In this thesis we propose CableSplat, a novel non-parametric method leveraging 3D Gaussian Splatting to estimate the pose of a linear deformable object given RGB images of the object from multiple viewpoints. To facilitate the evaluation of the performance of this method, we develop both simulated and real-world pipelines to collect calibrated and segmented recordings of cables undergoing various manipulations and transformations. We find that our method is consistently able to estimate cable pose to within an average error of ∼2.5mm across simulated tasks. Furthermore, performance on a scene reconstruction metric drops only slightly between simulated and real-world data, suggesting high-fidelity state estimation even in the real world. CableSplat is therefore a promising candidate for the extension of existing manipulation systems to deformables.

scPhen: Single-Cell Phenotype Predictor for Alzheimer’s Disease

2026-02-13T03:49:10Z

scPhen: Single-Cell Phenotype Predictor for Alzheimer’s Disease Guo, Sophie J. Advances in artificial intelligence (AI) and generative AI for representation learning have transformed our ability to model complex biological systems. Single-cell RNA sequencing (scRNA-seq) provides unprecedented resolution into cellular heterogeneity, offering a powerful substrate for modeling disease circuitry. However, predicting patient-level phenotypes from scRNA-seq remains challenging due to limited sample sizes, variable cell counts, and the computational burden of modeling long-context dependencies. We present scPhen, a flexible, parametric deep-learning framework for phenotype prediction from single-cell transcriptomic data, applied here to Alzheimer’s disease (AD) as a paradigm of complex, heterogeneous pathology. scPhen consists of a cell embedding module and a patient embedding module, designed to capture both fine-grained molecular patterns and higher-order cell–cell relationships. The framework supports multiple architectural backbones, including Transformers, Graph Neural Networks (GNNs), and state-space models such as Mamba, Mamba2, and BiMamba2, allowing exploration of tunable components for optimized performance. Across classification and regression tasks, state-space models, and in particular BiMamba2, demonstrated superior predictive accuracy and computational efficiency compared to Transformer-based and hybrid approaches. We further integrated attention-based multiple instance learning to enable variable cell counts per patient and to prioritize phenotype-informative cellular subsets. Interpretability analyses using Integrated Gradients and cell-level attention scores revealed gene programs and cell populations associated with AD progression, highlighting known neuroinflammatory signatures and suggesting novel molecular targets. By unifying cutting-edge sequence modeling architectures with scalable single-cell analysis, scPhen provides a generalizable, high-resolution approach to phenotype prediction. While demonstrated here in AD, this framework is readily extensible to other complex diseases and multi-modal cellular datasets, bridging computational innovation and biological discovery.

Predicting Task Functional Localizers Using Naturalistic fMRI

2026-02-13T03:49:09Z

Predicting Task Functional Localizers Using Naturalistic fMRI Wilke, Jordan Functional magnetic resonance imaging (fMRI) data collected during naturalistic stimuli has shown promise for predicting individual traits, biomarkers of disease and functional brain localizations, potentially offering advantages over traditional resting-state approaches. This study investigated the use of interpretable deep learning models to predict demographics and functional task localizer activations from fMRI time-series data collected while participants viewed naturalistic stimuli. Using the data of 143 subjects from the Human Connectome Project, I analyzed 7T fMRI scans from participants watching movies to predict sex, age, and functional localizer activations across multiple cognitive tasks. I employed state-of-the-art machine learning architectures, including DICE and Glacier models, specifically chosen for their interpretable design features that build directed connectivity matrices and produce weighted temporal attention maps. These models aimed to capture dynamic brain activity patterns while maintaining the ability to understand which temporal features drive predictions. The results successfully reproduced previous findings for sex classification but showed poor performance for age prediction, with correlations ranging from -0.175 to 0.243. For functional localizer predictions, models initially appeared to achieve high performance with some specific contrasts having correlations around 0.9 and Dice scores generally above 0.6. However, detailed analysis revealed that these models were primarily predicting group averages rather than learning meaningful inter-subject variability, as evidenced by chance-level subject identification accuracy. This finding contrasts with previous works that demonstrated successful prediction of individual differences in functional localizations. The failure to capture inter-subject variability represents a significant limitation, as individual differences in functional regions of interest are crucial for applications such as pre-surgical mapping and disease prediction. My findings suggest that predicting from raw fMRI time-series may require different approaches than those used here, with preprocessed functional connectivity matrices showing promising results, and highlight the importance of sufficient training data to separate signal from noise when learning directly from naturalistic stimuli. Despite these challenges, this work establishes important methodological foundations and identifies key limitations that must be addressed in future research combining naturalistic stimuli with machine learning for fMRI prediction tasks. The findings emphasize the need for models that can capture individual functional differences while maintaining the interpretability necessary for understanding how naturalistic stimuli drive brain-based predictions.

Probabilistic Programming with Low-Level, High-Performance GPU Programmable Inference

2026-02-13T03:49:04Z

Probabilistic Programming with Low-Level, High-Performance GPU Programmable Inference Chung, Karen GPU-compatible probabilistic programming languages (PPLs) have enabled high-performance, data-parallel programmable inference. However, these systems face fundamental trade-offs between expressiveness and performance, as their GPU code generation is automated and black-boxed, limiting optimization opportunities and imposing restrictions on program expressivity. This thesis introduces GenCUDA, a probabilistic programming system that addresses this limitation by embedding the CUDA GPU programming language directly into a C++/CUDA frontend, enabling GPU programmable inference with fine-grained control over runtime and memory profiles. GenCUDA extends the Gen probabilistic programming architecture by providing a dynamic modeling language (DML) that allows users to write performance-critical sections of generative functions as CUDA kernels while maintaining automatic trace management and the generative function interface (GFI). The system supports both sequential and parallel execution contexts through specialized effect handlers that seamlessly compose CPU and GPU code paths. Key technical contributions include: (1) a high-performance GPU distributions library achieving 10-100× speedups over TensorFlow-Probability, (2) memory-efficient trace management via template-optimized parallel effect handlers, and (3) vectorized generative functions that enable massive parallelization of inference algorithms. We demonstrate GenCUDA’s capabilities through comprehensive benchmarks on inference algorithms applied to diverse models including factor graphs, mixture models, and Hidden Markov Models. Results show significant performance improvements over JAX-based implementations: up to 3× speedup for importance sampling on a hierarchical model, 5.7× speedup for parallel Gibbs sampling on factor graphs, and memory efficiency improvements for large-scale mixture models supporting up to 6× as many clusters compared to existing frameworks’ limits. The system maintains the composability and expressiveness of probabilistic programming while unlocking GPU performance optimization techniques such as kernel fusion and memory hierarchy exploitation that are inaccessible to higher-level frameworks. GenCUDA demonstrates that embedding low-level GPU programming within automated probabilistic inference workflows can achieve both performance gains and algorithmic expressivity without sacrificing the modularity of probabilistic programming paradigms.

Simplifying Equivariant GPU Kernels through Tile-based Programming

2026-02-13T03:49:00Z

Simplifying Equivariant GPU Kernels through Tile-based Programming Kotak, Mit E(3)-equivariant neural networks have demonstrated success across a wide range of 3D modeling tasks. Until recently, they were bottlenecked due to their high memory and wall-time requirements. In this thesis we first provide an overview of recent GPU kernel efforts by both academia and industry that address this issue. These approaches tradeoff performance for engineering complexity, while still being algorithmically bottlenecked at 10 % GPU utilization. We instead trade off engineering complexity for performance. This not only lowers the barrier to GPU programming but also builds an abstraction layer to reason about future algorithmic innovations that can improve GPU utilization. Our kernel 𝐵3, based on the tiling- optimizations in just 100 lines of PyTorch-like code. We explore the performance-simplicity tradeoff with two case studies and demonstrate the practicality of our kernel workflow through downstream integration with a production model. We hope this work serves as inspiration to broaden and deepen existing equivariant kernel efforts.

From coarse fate choice to precise pattern: post-mitotic progenitor targeting

2026-02-13T03:49:07Z

From coarse fate choice to precise pattern: post-mitotic progenitor targeting Nie, Mel F. Planarians possess remarkable regenerative abilities, driven by pluripotent stem cells called neoblasts. While neoblasts are known to give rise to progenitor cells that form various tissues, whether and the extent to which these progenitors migrate across the animal remains unclear. Irradiation experiments eliminate all neoblasts outside shielded areas, allowing for the visualization of cell migration from the remaining neoblasts, but irradiated animals may not reflect homeostatic progenitor migration patterns. To address this, 5-ethynyl-2’-deoxyuridine (EdU) labeling and plug transplant techniques were used to trace progenitor movement in non-irradiated planarians. Using whole-mount fluorescence in situ hybridization (FISH) and the quantification of EdU-labeled cells, this study demonstrates that progenitor cells are capable of migrating long distances and exhibit a pronounced anterior bias in their movement and integration.

Development of Ensemble Strategies for Generalization in Deepfake Image Detection

2026-02-13T03:49:01Z

Development of Ensemble Strategies for Generalization in Deepfake Image Detection Wagh, Rohan M. The growing accessibility of generative models has enabled the rapid proliferation of deepfake content, posing significant challenges in image-based biometric security and media authenticity. In this thesis, six diverse facial deepfake image datasets are assembled, and four modern detection models are evaluated in a cross-domain scenario. We observe that individual models fail to generalize to images generated by techniques outside the scope of their training data. This often hinders the applicability of a single model in real-world deepfake detection. This thesis proposes ensemble strategies as a means of addressing this lack of generalization. We find that the ensemble models outperform individual models in classifying deepfake images, particularly in terms of accuracy and recall. An exhaustive evaluation of combinations of models shows that ensembles of similar models provide limited benefit, whereas ensembles of complementary models lead to significant improvements in classification performance. Ensembling models based specifically on accuracy and recall metrics also produces models that lower the rate of more harmful false negative predictions. This work highlights the value of ensemble models in improving generalization across diverse image families and provides a framework for building robustness in real-world deepfake detection systems.

MEKIN numerical modeling and simulation of the SPERT-III E-core transient tests

2026-02-03T04:58:28Z

MEKIN numerical modeling and simulation of the SPERT-III E-core transient tests Tan, Lip-Bu. Thesis: M.S., Massachusetts Institute of Technology, Department of Nuclear Engineering, 1980; Includes bibliographical references.

Evaluation of recirculating well technology with a cost comparison to pump and treat technology for containment of the CS-10 contaminant plume at the Massachusetts Military Reservation

2026-02-03T04:58:24Z

Evaluation of recirculating well technology with a cost comparison to pump and treat technology for containment of the CS-10 contaminant plume at the Massachusetts Military Reservation Smith, Mathew D. (Mathew Darin) Thesis: M. Eng., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 1997; Includes bibliographical references (leaves 43-45).

The crystallization of sucrose

2026-02-03T04:58:17Z

The crystallization of sucrose Brown, Ernest K. Thesis: M.S., Massachusetts Institute of Technology, Department of Chemical Engineering, 1929; Includes bibliographical references (leaf 81).

Systems of Visualization for Musical Futures

2026-01-30T03:24:59Z

Systems of Visualization for Musical Futures Naseck, Perry This thesis investigates how large-scale visual systems can communicate the presence, agency, and foresight of improvising musical agents–human and AI–during live performance. We propose a framework for manifesting AI collaborators on stage through five principles: musical transparency, live improvisational reactivity, demonstrated virtuosity, communication for collaboration, and visual fit. Two public performances operationalize these ideas: an addressable-light sculpture that renders harmonic space, and a stage-sized kinetic sculpture built from novel, low-cost Generic Pan Tilt fixtures that visualize the AI’s planned “musical futures.” The latter combines a real-time, MIDI-conditioned, Transformer-based hand-motion model with deterministic, pattern-based mappings that signal states such as resting and regeneration. Audience surveys indicate that viewers perceived links between musical turns and kinetic gestures while requesting clearer explanatory cues. We document the open-source hardware, firmware, and control protocols of the Generic Pan Tilt platform and reflect on design tradeoffs for accessibility, reliability, and expressivity. Finally, we outline a real-time analysis toolchain–motif detection, parallelism, and continuous energy/tension estimators–that emits OSC triggers for lighting, media, kinetic, and spatial-audio systems, enabling reactive shows beyond timecode. Together, these systems advance performable visualizations of human-improvised and AI-driven musical futures.

Design Rules for LLM-Generated Code: A RealWorld Case Study

2026-01-30T03:24:56Z

Design Rules for LLM-Generated Code: A RealWorld Case Study Lawrence, Jennifer M. This thesis conducts a case study exploring the interaction between software design, extensibility, and LLM code generation. The central problem we investigate is whether LLMs violate software design principles in ways that introduce bugs and ultimately hinder extensibility. We examine several repositories belonging to the RealWorld collection, a project that demonstrates combinations of frameworks, database, and programming languages for building full stack web apps modeled on an existing social media application. We create a concept-based implementation of the RealWorld API. Concept Design defines software systems in terms of the abstract purposes and relationships of self-contained units of functionality. It enforces stringent design standards and aims to aid humans better understand complex software behavior. To test code extensibility, we develop three phases of new functionality to be added to the RealWorld API. Each phase is intended to mimic real-world software development, adding functionality that is commonly found in social media platforms while increasing nuance and complexity. The code for these extensions is generated by an AI agent, then reviewed by a human coder who classifies and fixes any bugs. In this study, we examine how LLMs interact with software paradigms like Concept Design, the kinds of design violations they produce, and whether these violations correlate with bugs that impede extensibility.

Cognify: An On-Device, AI-powered Learning Assistant

2026-01-30T03:24:55Z

Cognify: An On-Device, AI-powered Learning Assistant Huang, Siyong Large Language Models (LLMs) have proven highly effective for a wide range of natural language processing tasks, but their size and compute requirements often restrict their use to powerful cloud-based infrastructures. In recent years, significant progress has been made in shrinking LLMs while maintaining performance levels comparable to much larger models. We are approaching the point where the capabilities of massive, multi-billion parameter models can be realistically replicated on consumer-grade devices. This thesis builds upon that foundation by developing an AI-powered note-taking application that runs entirely offline, using only the compute resources available on a personal laptop. The application is designed to listen to lectures alongside the student and provide support in real-time—through transcription, notes generation, and enabling context-aware search. Achieving this level of interactivity locally introduces challenges in reducing end-to-end latency, which this project addresses through both model-level optimizations and the design of efficient prompting and inference algorithms. A demo of the app can be found on Youtube.

Performance Analysis of the Apple AMX Matrix Accelerator

2026-01-30T03:24:55Z

Performance Analysis of the Apple AMX Matrix Accelerator Zhou, Jonathan Apple Silicon integrates a dedicated Apple Matrix Coprocessor (AMX) that executes outer-product style computations with high throughput, but its public programming model remains largely hidden behind the Accelerate framework. This thesis turns AMX into a more predictable and practical target by combining (i) empirical throughput characterization, (ii) a case study on AMX specific matrix multiplication (GEMM) design, and (iii) an interpretable rule-based latency model that predicts cycle counts for short AMX instruction sequences. First, microbenchmarks quantify AMX load/store and compute limits across matrix and vector modes and data types. We analyze throughput in both GFLOPS and AMX instructions per cycle, and also observe output register based throughput limitations. Second, we develop an in-place GEMM that uses masked outer products and strategically overlapping tiles to avoid scratch buffers used by Accelerate, outperforming Accelerate while preserving simplicity. Third, we introduce a compact latency model that decomposes cycles into per-instruction BaseTime, symmetric SwitchLatency for instruction changes, and instruction FullLatency (data dependency) terms. Fitted with non-negative coordinate descent on length-2 loops and validated on length-3 sequences via a lightweight loop simulation, the model obtains reasonably high accuracy while remaining helpful for those trying to understand the architecture.

Optimizing Large Language Models from a Data SystemsPerspective

2026-01-30T03:24:54Z

Optimizing Large Language Models from a Data SystemsPerspective Chen, Peter Baile Strong retrieval and reasoning capabilities are essential for large language models (LLMs) to effectively handle a broad spectrum of downstream tasks, such as open-domain question answering and solving math or science problems. While current LLM-based frameworks achieve strong performance on complex retrieval and reasoning tasks, they do so at a high computational cost. Additionally, they often lack structured, systematic problem-solving strategies, leading to unexpected failures. In particular, these models typically operate in an iterative, online, and isolated fashion—failing to exploit relationships across data sources, opportunities for offline computation, and the benefits of reusability—resulting in less-than-optimal outcomes. In contrast, traditional data management systems are engineered for both efficiency and accuracy, with careful coordination across all stages of the query pipeline. Inspired by these principles, this work proposes novel approaches to improve LLMbased retrieval and reasoning by incorporating optimization techniques from data systems. Our evaluation across a range of knowledge- and reasoning-intensive datasets demonstrates significant gains in both accuracy and computational efficiency.

Fabrication of Superconducting Reflectionless Filters for Quantum Microwave Circuits

2026-01-30T03:24:51Z

Fabrication of Superconducting Reflectionless Filters for Quantum Microwave Circuits Bui, Eric The performance and scalability of superconducting quantum circuits depends critically on the microwave environment. Minimizing signal reflections and suppressing thermal noise are essential for achieving high-fidelity readout and preserving qubit coherence. A significant challenge arises from the use of conventional cryogenic components such as isolators and circulators, which exhibit nonideal out-of-band reflection characteristics. Reflections degrade impedance matching and limit the performance of broadband quantum limited amplifiers. Superconducting implementations of reflectionless microwave filters offer a promising solution to mitigate these issues. The focus of this work is the fabrication and cryogenic characterization of reflectionless filters compatible with superconducting qubit fabrication flows. Devices were implemented on high resistivity silicon substrates using aluminum ground planes, integrated nichrome resistors, and crossovers formed with SiO2 interlayer dielectric. Cryogenic measurements at 20 mK demonstrate high return loss, confirming the viability of these filters for co-fabrication with traveling-wave parametric amplifiers (TWPAs) and circuit quantum electrodynamics (cQED) architectures. The filters exhibit low insertion loss in the passband to maintain quantum measurement efficiency and provide broadband reflection suppression across frequencies relevant to superconducting qubits, offering a scalable way to manage microwave noise in superconducting quantum processors.

CONDOR: Clinical Ontology-aware Networked Data Organization and Retrieval

2026-01-30T03:24:23Z

CONDOR: Clinical Ontology-aware Networked Data Organization and Retrieval Dongo Aguirre, Gyalpo Melchisedeck Until now, state-of-the-art research into AI-driven clinical workflows has been confined to proprietary, closed-source systems from vendors like Epic and Oracle, or private experiments like Stanford’s ChatEHR, creating a critical barrier to academic innovation. This thesis introduces CONDOR, the first fully open-source and replicable research environment designed to simulate an agentic, conversational AI interacting with a high-fidelity Electronic Health Record (EHR). By integrating an open-source, FHIR-native EHR (Medplum) with a complex, realistic public clinical dataset (MIMIC-IV FHIR), CONDOR provides a foundational testbed that has been previously unavailable to the research community. The framework’s primary contribution is a novel alignment and evaluation methodology that adapts the principles of SelfCite to the clinical domain. We propose a ‘ClinicalConfidence‘ score to quantify the trustworthiness of generated statements and programmatically generate a high-quality preference dataset for alignment using Simple Preference Optimization (SimPO). We compare a standard vector-based Retrieval-Augmented Generation (RAG) baseline against a more advanced GraphRAG architecture that leverages a two-tiered knowledge graph of patient data and medical ontologies. Our results demonstrate that the full CONDOR system, combining GraphRAG with SimPO alignment, significantly improves citation quality and verifiability, establishing a new open-source benchmark for the development of safe and reliable clinical AI.

Multi-Stage LLM Reasoning for Automated Detection and Classification of High-Impact Misinformation

2026-01-30T03:24:50Z

Multi-Stage LLM Reasoning for Automated Detection and Classification of High-Impact Misinformation Nair, Anushka Manchanda As of 2025, social platforms have become a primary news source, magnifying the reach of misleading content [1]. Exposure to misinformation has been linked to shifts in public attitudes and behavior, including vaccine uptake [2] and voting behaviors [3]. However, current misinformation detection approaches can often focus on a narrow definition of misinformation: factual claims that can be clearly judged as true or false. However, recent research suggests the problem lies elsewhere: overt falsehoods (“vaccines contain microchips”) can carry little harm, while technically accurate but decontextualized narratives can be more influential. Allen et al. (2024) [4] found that factually accurate ”vaccine-skeptical” content had a much greater impact on vaccine hesitancy than misinformation flagged by fact-checkers. These narratives can work by omitting information, misleading framing, or cherry-picked evidence, forms of manipulation that can elude traditional fact-checking. Though professional fact-checkers are often able to recognize these tactics and the broader context of information, they cannot keep pace with the volume of online content. This thesis designs a Large Language Model (LLM) based pipeline meant to partner with, rather than replace, human fact checkers. The system decomposes content into its explicit and implicit claims, rhetorical tactics, and the “missing context” questions it raises; retrieves evidence from fact-check databases and reliable sources; and synthesizes grounded explanations while assigning calibrated harm scores to guide triage. Evaluated on fact-checked tweets, the pipeline matched expert judgments in 92.6% of cases where experts agreed, and flagged for review posts where experts disagreed, a gray zone requiring human judgment. The system’s explanations ranked higher than crowdsourced Community Notes in helpfulness, clarity, and trustworthiness when assessed by an LLM, and harm evaluations aligned with human reviewers in 87.5% of cases, enabling prioritization of content with greatest potential impact. Despite constraints of sample size and processing latency, the results demonstrate the feasibility of a human–AI workflow that treats disagreement as a signal and directs scarce attention towards high-impact misinformation that current automated systems can miss.

Spatial Emission and Polarization Control in Integrated Photonics for Optical-Trapping and Trapped-Ion Systems

2026-01-30T03:24:49Z

Spatial Emission and Polarization Control in Integrated Photonics for Optical-Trapping and Trapped-Ion Systems Sneh, Tal Recent advances in silicon photonics have yielded impressive results in fields including biophotonic optical tweezers and trapped-ion quantum systems. However, the majority of these demonstrations, while offering advantages in size, cost, and dense integration, lag behind their bulk-optic counterparts, limited by a lack of critical advanced functionality such as spatial control of light in the near field or polarization control at visible wavelengths. This thesis addresses this gap by designing and experimentally demonstrating the first, to the best of our knowledge, cell experiments using single-beam integrated optical tweezers, chip-based 3D printers, and integrated polarization rotators and splitters at blue wavelengths. First, we demonstrate optical trapping and tweezing of microspheres using a nearfield-focusing integrated optical phased array, at a standoff distance over two orders of magnitude larger than prior integrated demonstrations. We then use this system to perform the first cell experiments using single-beam integrated optical tweezers. Second, we use a tunable integrated optical phased array operating at red wavelengths to print designs in a visible-light-curing resin, demonstrating the first chip-based 3D printer. Third, we design and experimentally demonstrate the first integrated polarization rotators and splitters operating at blue wavelengths, enabling polarization control on chip for sophisticated integrated manipulation of trapped-ion and neutral-atom quantum systems. Finally, we develop key polarization-diverse integrated-photonics devices and utilize them to implement a variety of integrated-photonics-based polarization-gradient-cooling systems, culminating in the first demonstration of polarization-gradient cooling of a trapped ion by an integrated-photonics-based system.

ALPACA: An Algorithmic Pipeline for Automated Contour Annotation of Carnatic Music: A Dynamic Programming Framework for Pitch Segmentation and Note Transcription

2026-01-30T03:24:45Z

ALPACA: An Algorithmic Pipeline for Automated Contour Annotation of Carnatic Music: A Dynamic Programming Framework for Pitch Segmentation and Note Transcription Parthasarathi, Sruthi In recent years, a wide range of computational techniques have been developed to extract information from recorded performances of Western music. However, these methods often achieve limited success when applied to non-Western musical traditions. Carnatic music, in particular, poses unique challenges due to the absence of a standardized notation system and the lack of a consistent mapping between frequency bands and note categories. This project introduces a dynamic programming–based transcription framework, incorporating novel methods for label estimation, contour segmentation, and related subtasks, and establishes the foundations for end-to-end automatic transcription of this art form.

Modeling Diverse Treatment Policies from Observational Health Data

2026-01-30T03:24:53Z

Modeling Diverse Treatment Policies from Observational Health Data Ejilemele, Abe Learning policies for real world tasks often requires modeling human behavior, especially in domains like healthcare and driving. In these settings, skills are learned from expert human demonstrations, but such data are typically multimodal, violating the common single expert assumption. We study sequential clinical treatment decision making in the offline imitation learning setting, where environment interaction is prohibited, reflecting the challenges of experimentation in safety critical domains. Existing methods for multi expert offline imitation learning often restrict the latent space, underspecify its structure, or omit objective terms that prevent latent collapse and encourage behavior discovery. We propose a fully offline approach that addresses these shortcomings and improves learning from multi expert demonstrations through modifications to the formulation of the latent approximate posterior and the model architecture. We suggest that our method is more robust to real world settings where the true number of demonstrators may not be known. We also incorporate an occupancy matching term into our objective that injects awareness of the rollout distribution over trajectories into our behavior cloning objective. We evaluate our method against baselines on both simulated multi expert demonstrations from an extended S-CVSim and real world demonstrations from MIMIC. Our approach achieves consistently higher next step action prediction and behavior discovery performance. While ground truth expert policies are unavailable for MIMIC, visual analysis shows our method uncovers clinically meaningful variations in expert strategies, reflecting treatment population diversity.

Towards a Modular Superconducting Quantum Processor using Chiral Waveguide Quantum Electrodynamics

2026-01-30T03:24:46Z

Towards a Modular Superconducting Quantum Processor using Chiral Waveguide Quantum Electrodynamics Yankelevich, Beatriz As the field of superconducting quantum computing advances, networking qubits within a single system becomes essential for building modular processors. Modularity allows the system to circumvent scalability constraints and enable architectures and computational schemes that exploit non-local connectivity to enhance processing capabilities. This work proposes non-local entanglement generation methods based on the theory of chiral quantum waveguide dynamics, which is the quantum-optical framework that describes systems of atoms coupled non-reciprocally to a continuum of modes. We leverage these effects to design a chiral communication module composed of multiple superconducting qubits, capable of both directional single photon routing and the realization of chiral, driven-dissipative entanglement protocols.

Fine-tuning Boltz for Antibody-Antigen Binding Prediction

2026-01-30T03:24:32Z

Fine-tuning Boltz for Antibody-Antigen Binding Prediction Kim, Ji Won Accurate prediction of antibody-antigen binding is a central challenge in computational immunology. Its direct implication for therapeutic antibody design and vaccine development has made it one of the most rapidly growing fields. Recent advances in protein language models and structure prediction have provided new tools for modeling, yet these approaches often fall short in capturing the fine-grained features that drive binding specificity in antibody and antigens. This thesis evaluates multiple strategies for improving predictive performance. First, we investigate a custom multiple sequence alignment (MSA) experiment. Standard Boltz-2 training relies on MSAs from broad protein databases, which capture global diversity but under-represent lineage-specific constraints. To address this, we constructed antibody-specific MSAs to test whether restricting the search space to antibody repertoires improves model learning. Unfortunately, gains in downstream binding prediction were limited, suggesting that further work needs to be done in training models for specific databases in the first place. Our second line of investigation focused on fine-tuning Boltz-2, a generative structural foundation model, using curated antibody–antigen data. By leveraging Boltz-2’s internal sequence embeddings, we trained a predictive model for binding affinity. This approach yielded stronger ROC performance compared to baseline models, achieving a validation AUROC of 0.645, demonstrating the advantages of structural generative priors for antibody–antigen binding prediction.

Deterministic Circuit Range Avoidance is (Likely) Intractable

2026-01-30T03:24:47Z

Deterministic Circuit Range Avoidance is (Likely) Intractable Ilango, Rahul Circuit Range Avoidance (denoted Avoid) is a computational problem where, given a Boolean circuit with more output bits than input bits, one must output a string outside of the range of the circuit. A simple counting argument implies that such a string must always exist and also guarantees that outputting a uniformly random string is correct with good probability. A natural question is whether this can be derandomized: does there exist an efficient deterministic algorithm for Avoid? We give the first evidence that deterministically solving Avoid is intractable. We show that there is no polynomial-time algorithm for Avoid under plausible assumptions in complexity theory and cryptography. Specifically, our assumptions are that NP ≠ coNP and that subexponentially-secure indistinguishability obfuscation exists.

An Empirical Evaluation of LLMs for the Assessment of Subjective Qualities

2026-01-30T03:24:42Z

An Empirical Evaluation of LLMs for the Assessment of Subjective Qualities Ranade, Esha Large Language Models (LLMs) have achieved remarkable success in natural language processing tasks and are increasingly being used for language generation. Significant advancements in this field have unlocked capabilities that enable their adoption in sophisticated roles, including acting as evaluators or "judges" of text for various attributes such as factuality, relevance, fluency, and reasoning quality. However, their understanding and ability to assess subjective attributes, such as the level of formality in a piece of writing, and produce content matching these subjective attributes remains unclear and underexplored. This research develops a methodology to study how LLMs evaluate subjective attributes. It has three primary contributions: (i) a reproducible user study to generate human-annotated labels for different attributes, (ii) an analysis of the extent to which different LLMs provide subjective labels aligned with human annotators, and (iii) an analysis of the extent to which LLMs generate content aligned with specified intended subjective labels, relative to humans. The user study and the analyses have been conducted both with and without a reference scale. The scale itself, the survey design, and the evaluation questions have all undergone multiple rounds of iteration informed by study tester feedback to improve clarity, consistency, and reliability for the final study. Comparisons between human-generated ratings and LLM-generated ratings for both human-generated content and LLM-generated content reveal the extent to which LLMs align with human judgment, providing insights into their capabilities and limitations. While humans typically do better in their roles, LLMs are able to attain reliably high levels of success in producing and judging text, despite tending to err on the more-formal side. Both groups’ performance increases significantly with the aid of a formalized reference scale. Across the suite of models tested, OpenAI’s GPT family leads overall performance, with Anthropic’s Claude and Meta’s LLaMA series showing notable strengths in specific formality ranges. Although this work focuses on the formality attribute of text, the methodology developed can be used to evaluate other subjective qualities of text, such as conciseness, usefulness, or persuasiveness. Ultimately, these findings may guide future efforts to fine-tune LLMs to produce text that more precisely matches the desired stylistic or ethical standards.

Accelerating Burst Parallelism of SigmaOS processes with CRIU

2026-01-30T03:24:29Z

Accelerating Burst Parallelism of SigmaOS processes with CRIU Tang, Frederick σOS is a multi-tenant cloud operating system designed to integrate the agility of serverless environments with the interactivity of microservices. A goal of achieving this integration is the ability to start new instances of server processes quickly. However, σOS only handles σcontainer initialization, and does not assist with runtime and app initialization costs. One approach to overcome this challenge is to checkpoint processes using Checkpoint/Restore in Userspace (CRIU). CRIU is a linux toolset which can start new server instances by restoring them from a saved checkpointed state, avoiding the full cost of reinitialization and setup. This thesis introduces σCRIU, which adapts CRIU for burst-parallel spawning of microservices in σOS. σCRIU implements a number of optimizations: compressing checkpointed proc metadata to reduce network communication costs, implementing demand-paging using a lazy page service, and caching kernel metadatadata to reduce CRIU’s restore operation latency. These optimizations allow σCRIU to start new microservices on remote machines quickly while still making use of CRIU’s existing proven checkpoint and restore technology.

Visually Accurate Database-Enabled Reconstructions of Scenes (VADERS)

2026-01-30T03:24:39Z

Visually Accurate Database-Enabled Reconstructions of Scenes (VADERS) Gosalia, Mehek This work introduces a novel pipeline for scene reconstruction that jointly prioritizes semantic accuracy and visual fidelity, addressing a gap in current approaches. Prior pipelines often emphasize either semantic analysis or photorealistic rendering, but rarely both. This method combines scene analysis, segmentation, and retexturing to yield reconstructions that preserve structural semantics, while convincingly reflecting the visual qualities of the original image. The motivation lies in the limitations of existing systems. Existing databaseassisted approaches depend on proprietary datasets that restrict stylistic diversity or using in-the-wild assets. This constrains expressiveness and often produces results that are visually misaligned. Conversely, pipelines optimized for visual realism neglect semantic correctness, generating outputs that may appear plausible but lack categorical or structural grounding. Our framework addresses this by first enforcing semantic accuracy via selecting database assets, then editing those assets to be stylistically faithful to the reference, producing reconstructions that are both interpretable and expressive. We begin with database-assisted scene analysis, using an open-source asset database containing chairs, lamps, sofas, tables, and benches. Input images are depth-mapped, segmented, and parsed into object masks, which are matched to database assets based on semantic labels and visual correspondence. Each asset is broken into semantic segments and rescaled per-component using vision-language model predictions to match the reference object better. Finally the asset is retextured based on the image mask of the reference object in the input image. Evaluation on six diverse scenes—both photographs and artworks—shows the pipeline produces semantically grounded, visually accurate reconstructions under non-research conditions. Future work will focus on expanding the asset database, reducing reliance on proprietary texturing, and releasing an open-source implementation to broaden accessibility.

Designing Planar Silicon Solar Cells for Singlet Fission Sensitization

2026-01-30T03:24:48Z

Designing Planar Silicon Solar Cells for Singlet Fission Sensitization Wang, Janet Z. Singlet fission (SF)-sensitized silicon (Si) solar cells offer a path towards surpassing the Shockley-Queisser efficiency limit for single-junction solar cells. However, realizing efficient charge transfer from the SF material to Si remains a significant challenge that requires careful interface engineering. Prior work showed that Si microwire cells sensitized with tetracene (Tc) and a zinc phthalocyanine (ZnPc) donor layer can boost photocurrent and external quantum efficiency (EQE). Planar devices are simpler to fabricate than microwire devices and reproduce the planar geometry of optical test samples to connect studies of the interface to device performance. This thesis integrates modeling and experimental approaches to guide the design of planar SF-sensitized Si solar cells. We developed a fabrication process for planar cells comparing varied oxide passivation layer growth conditions and surface treatments, Si(100) versus Si(111) orientation, and junctions formed by diffusion doping versus ion implantation. Complementary surface photovoltage (SPV) measurements on matching optical stacks show evidence of an illumination-induced transient positive charge density at the Tc/ZnPc/oxide/Si interface, consistent with increased field effect passivation. We find that SPV responses on AlOx/n-Si are dominated by substrate band bending; consequently, SiOx is the preferred passivation to suppress the background and isolate the SPV signals driven by the organics. A drift–diffusion model shows that the diffusion doping (exponential) emitters reduce surface recombination rates compared to ion implantation (Gaussian) emitters. We also show that a positive fixed charge density at the surface enhances short wavelength EQE, with the effect strongest for Gaussian emitters. Together, these results provide practical design rules for planar SF-sensitized Si cells and the study of charge transfer at organic-Si interfaces.

Microsecond Time Synchronization for Computing Fiber Networks

2026-01-30T03:24:43Z

Microsecond Time Synchronization for Computing Fiber Networks Li, Jenny Y. We present a microsecond-accurate time synchronization method and time localization system for a sensor network of spatially-separated, low-power Bluetooth nodes, with the goal of integrating this system into thermally-drawn computing fibers. Each node consists of an nRF54L15 SoC paired with an ICS-43434 digital I2S microphone, enabling synchronized audio data collection. Our design leverages Bluetooth LE connection events to synchronize local clocks with sub-10 µs accuracy across a multi-peripheral topology; we trigger precise, CPU-independent hardware events to timestamp audio samples. We demonstrate that timestamped I2S data stored in external SPI flash can be correlated across devices to extract TDoA measurements for localizing sound sources. Cross-correlation techniques allow us to estimate direction and position, with localization errors reduced from 4.17 m to 0.39 m through clock synchronization. This prototype provides a roadmap for embedding synchronized sensing and computation within fibers and smart textiles, with implications for on-body audio perception and distributed sensing in flexible electronics.

From String to Structure: Graph Threading for Physical Assembly

2026-01-30T03:24:37Z

From String to Structure: Graph Threading for Physical Assembly Lin, Rebecca Y. E. Many artistic and engineering applications—from beadwork to deployable structures—create intricate, and sometimes dynamic, designs by threading cord through tubular components. We model the underlying design challenge—threading tubes so that they achieve a target connectivity when the string is pulled taut—as graph threading. In this formulation, tubes and their junctions correspond to edges and vertices of a graph, and the goal is to find a closed walk that induces a connected graph at every vertex while avoiding U-turns. We study two optimization objectives motivated by fabrication and deployment: minimizing length to reduce material cost and assembly time, and minimizing turn to reduce frictional resistance during deployment. For the length metric, we present a polynomial-time algorithm via reduction to minimum-weight perfect matching, prove tight worst-case bounds on optimal threadings, and identify special cases with faster algorithms. For the turn metric, we characterize the complexity landscape, proving NP-hardness for graphs of maximum degree 4, tractability for degree 3, and giving exact and approximation algorithms for restricted variants, including rectangular grid graphs. Finally, we turn from theory to fabrication, proposing multi-configuration threading—a new approach for achieving multiple predetermined configurations within a single system. As in earlier chapters, framing the problem in graph-theoretical terms provides access to powerful problem-solving techniques, guiding both algorithmic analysis and physical design.

A Data Attribution-Based Approach to Model Diagnosis in LC-MS/MS Structure Prediction

2026-01-30T03:24:40Z

A Data Attribution-Based Approach to Model Diagnosis in LC-MS/MS Structure Prediction Khoo, Ling Min Serena Elucidating the structure of small molecules from complex mixtures using liquid chromatography tandem mass spectrometry (LC-MS/MS) is a challenging task with far-reaching implications in many areas such as drug discovery, environmental science and metabolism research. Yet, despite its importance and significant efforts to develop machine learning (ML) models for the task of elucidating the molecular structures of unknown compounds from LC-MS/MS spectra, the performance of these ML-based models remains limited. As a result, the performance of current ML-based models has been reported as insufficient for practical applications, thereby warranting a deeper investigation into their limitations to advance ML-based molecular structure elucidation from LC-MS/MS and enable their utility in real-world settings. Here, we leverage data attribution methods to systematically identify and validate hypotheses about the sources of generalization challenges that hinder current model performance. Our goal is to automatically uncover insights into the failure modes of existing ML models for LC-MS/MS, thereby laying the foundation for developing more robust and accurate models.

Modeling Dynamic Objects in Scenes with Generative Particle Systems

2026-01-30T03:24:31Z

Modeling Dynamic Objects in Scenes with Generative Particle Systems Li, Eric Humans readily interpret the motion of deformable and rigid bodies, even when encountering unfamiliar objects with minimal shape or texture cues. In such cases, motion serves as a critical signal for recognition and understanding. Inspired by this ability, we propose a generative model that represents 3D matter as small Gaussians (“particles”) drawn from clusters capturing groups of coherently moving matter. We develop an e!cient inference algorithm based on parallelized block Gibbs sampling to recover stable particle motion and rigid groupings. Our model provides a tractable, object-centric generalization of as-rigidas-possible (ARAP) regularizers used in motion tracking. To assess alignment with human perceptual judgments, we test our approach on random dot kinematograms—sparse motion displays in which dot trajectories convey latent object structure, often used to probe visual understanding of motion and grouping. In this setting, our approach captures human-like responses, including graded patterns of uncertainty across ambiguous conditions. Applied to naturalistic RGB videos, it infers dense particle representations that track object motion and deformation over time. These results demonstrate that our model enables persistent latent scene structure suitable for object-level reasoning.

The Arm Qubit for Faster, Higher Fidelity Readout and Gates

2026-01-30T03:24:44Z

The Arm Qubit for Faster, Higher Fidelity Readout and Gates Kline, Jeremy B. Currently, superconducting qubit processors are bottlenecked by errors during two-qubit gates, readout, and idle time. All three error contributions could be reduced if we improved the speed of operations (without introducing additional leakage errors) compared to the qubit lifetime. Readout and two-qubit gates are multimode interactions and therefore are limited by the coupling strength between the modes. In this thesis, we introduce a two-mode superconducting qubit which uses one mode to facilitate strong coupling to other modes of the quantum processor and one mode to store data with high coherence. Simulations show that this architecture could enable order-of-magnitude reductions in error during readout and two-qubit gates.

Clustering Algorithms for Component Placement in Printed Circuit Boards

2026-01-30T03:24:41Z

Clustering Algorithms for Component Placement in Printed Circuit Boards Petrusenko, Vlada In 2024, approximately 12 billion printed circuit boards (PCBs) were manufactured globally [1], with the trend increasing gradually, and the majority of PCB layouts still being completed manually. The manual design process amounts to millions of hours of tedium that can be eased with automation. One of the biggest challenges is that the complex Printed Circuit Board designs typically have hundreds, sometimes thousands of components and even more net connections between them. This makes both manual and automated placement very time-consuming. As a way to improve placement performance, in this thesis, we constructed a custom weighted undirected graph representation of components and nets for any board that would encode physical and electrical constraints. Additionally, we integrated the Louvain and Leiden clustering algorithms for component clustering in PCB placement. We also showed comparative metrics with the spectral clustering algorithm applied to unweighted graph representations, which is the prior state of this project, but it has no knowledge of electrical and physical constraints associated with PCB designs and would thus produce results that require more manual correction. This new clustering approach was able to generate more optimal clustering and reduced average runtime by 51.05%, decreased estimated length of routing by 7.72%, and improved component association score by 12.8%.

Seeing the Forest Through the Trees: Knowledge Retrieval for Streamlining Particle Physics Analysis

2026-01-21T04:07:55Z

Seeing the Forest Through the Trees: Knowledge Retrieval for Streamlining Particle Physics Analysis McGreivy, James C. Generative Large Language Models (LLMs) are a promising approach to structuring knowledge contained within otherwise unmanageable corpora of research literature produced by large-scale and long-running scientific collaborations. Within experimental particle physics, such structured knowledge bases could expedite methodological and editorial review. Complementarily, within the broader scientific community, generative LLM systems grounded in published work could make for reliable companions allowing non-experts to analyze openaccess data. Techniques such as Retrieval Augmented Generation (RAG) rely on semantically matching localized text chunks, but struggle to maintain coherent context when relevant information spans multiple segments, leading to a fragmented representation devoid of global cross-document information. In this work I utilize the hierarchical organization of experimental physics articles to build a tree representation of the corpus, and present the SciTreeRAG system which leverages this structure with the aim of constructing contexts more focused and contextually rich than a standard RAG. Additionally, I develop methods for using LLMs to transform the unstructured corpus into a structured knowledge graph representation. I then implement SciGraphRAG, a retrieval system that leverages this knowledge graph to access global cross-document relationships eluding standard RAG, with the goal of encapsulating domain-specific connections and expertise. I demonstrate proof-of-concept implementations of both systems using the corpus of the LHCb experiment at CERN.

Development of Low-Cost In Situ Gas Sensors for Oceanographic Applications

2026-01-21T04:07:47Z

Development of Low-Cost In Situ Gas Sensors for Oceanographic Applications Gower, Elizabeth Ann Anthropogenic activity has increased atmospheric carbon dioxide (CO₂) levels, disrupting the global carbon cycle and driving widespread environmental change. The ocean acts as a major sink. Accurate and scalable in situ monitoring of oceanic carbon chemistry is vital for understanding the impacts of climate change and informing marine carbon dioxide removal (mCDR) strategies. Many existing in situ instruments for marine applications are constrained by their size, cost, power requirements, or reliance on consumable reagents. Developing low-cost, compact, low-power, and accurate in situ sensors would significantly enhance the spatiotemporal resolution of oceanographic data and enable widespread monitoring of dissolved gases throughout the ocean. This, in turn, would deepen our understanding of how, where, and when changes are occurring within the marine carbon cycle. Two key variables essential for studying this cycle are the partial pressure of carbon dioxide (pCO₂) and dissolved inorganic carbon (DIC). This thesis presents the development of two sensors, one for in situ pCO₂ measurement and another for novel DIC quantification, both designed to be affordable, reliable, and scalable tools for advancing our understanding of ocean chemistry and the global carbon system. First, the development, calibration, and open-ocean deployment of a miniaturized Dissolved Multi-Gas Sensor (DMGS) that measures pCO₂ and partial pressure of oxygen (pO₂) is presented. The sensor was integrated into a custom-built surface drifter designed to entangle with Sargassum mats and send data autonomously. The drifter utilized commercial off-theshelf (COTS) components and cost roughly $1000 to build. After lab testing, a drifter was deployed in the Great Atlantic Sargassum Belt (GASB) and collected data for 22-days. In addition to gas data, the drifter tracked temperature, light intensity, humidity, pressure, and location sending measurements via an Iridium satellite. The resulting data captured dynamic changes in localized gas concentrations, temperature, and light levels that highlighted photosynthetic and respiratory activity within Sargassum patches. These drifters demonstrate the value of in situ data to investigate marine biogeochemical processes that contribute to the marine carbon cycle, especially in areas with high biologic activity. Next, this thesis presents the iterative development of a novel DIC sensor with potential for future in situ applications. Initial prototypes tested the feasibility of using a COTS CO2 sensor in both static and flow-through configurations, however sensor saturation issues prompted a shift to a pressure-based detection method. Multiple test setups were evaluated for pressure stability and sensor sensitivity, culminating in a bottle-based flow system that demonstrated the potential for reagent-minimized, pressure-based DIC quantification. With the final setup, a COTS pressure sensor that sat behind a gas permeable membrane was found to repeatably and accurately quantify DIC from acidified seawater. This approach of quantifying DIC via pressure change is novel in the field of gas sensing and maintains a low-cost, accessible design. Together, the sensors developed in this thesis expand the toolkit for marine carbon monitoring and provide a foundation for affordable, distributed sensing networks. These technologies enable higher-resolution insights into ocean biogeochemistry and support critical monitoring, reporting, and verification (MRV) frameworks needed to evaluate the effectiveness of mCDR techniques. Continued refinement of these low-cost platforms could play a key role in understanding and mitigating anthropogenic impacts on marine systems.

Applying Reference Class Forecasting to Multifamily Investments: Identifying and Capturing Operational Alpha through the Outside View

2026-01-21T04:07:55Z

Applying Reference Class Forecasting to Multifamily Investments: Identifying and Capturing Operational Alpha through the Outside View Firouzian, Fardean This thesis applies Reference Class Forecasting (RCF) to multifamily real estate underwriting as a means of countering optimism bias, strategic misrepresentation, and other distortions embedded in the traditional “inside view.” Adapted from its proven application in infrastructure and corporate capital budgeting, RCF anchors projections in the actual performance distributions of comparable assets rather than in deal-specific narratives. The research centers on the development of the “Comp Warehouse,” a structured repository of property-level financials organized by market, asset class, vintage, and unit scale. By benchmarking assumptions against statistically valid reference classes, the approach enforces empirical discipline and highlights opportunities for “operational alpha”—the marginal increase in net operating income (NOI) achieved when underperforming assets converge on median peer performance. A South Florida case study demonstrates the method’s utility in an acquisition context. Analysis of 48 assets across Melbourne, Miami, Fort Lauderdale, and West Palm Beach shows that while rent levels cluster tightly around market medians, operating expenses vary widely, producing large dispersion in realized NOI. Applying the framework to a 191-unit Class A property in Fort Lauderdale illustrates how RCF can ground underwriting assumptions by distinguishing between defensible revenue-driven growth strategies and less plausible expense-reduction projections proposed in a bidding scenario. Recognizing constraints of both scale and frequency, this thesis also explores artificial intelligence as a tool for automating the ingestion and standardization of operating statements and rent rolls. Properly deployed in a human-in-the-loop framework, AI can reduce data friction, expand sample sizes, and sharpen forecasting precision. The contribution of this thesis is twofold: it demonstrates the feasibility of applying RCF to the multifamily sector—an asset class whose relative standardization, liquidity, and data availability make it especially conducive to outside-view benchmarking—and it situates the methodology within a technology-native architecture designed to scale empirical discipline, enhance underwriting rigor, and systematically capture operational alpha.

Concretely-Efficient Multi-Key Homomorphic Secret Sharing and Applications

2026-01-21T04:07:53Z

Concretely-Efficient Multi-Key Homomorphic Secret Sharing and Applications He, Kaiwen Homomorphic secret sharing (HSS) is a powerful cryptographic primitive that enables efficient, low-communication secure computation without the use of fully homomorphic encryption. Public-key HSS is a well-known variant that supports inputs from multiple parties, but all parties must agree on a joint public key before any party can encode their inputs, requiring extra rounds of communication in applications. Recently, Couteau et al. (EUROCRYPT 2025) constructed multi-key HSS (MKHSS)—a new primitive which allows parties to encode their inputs under independent keys—under the DCR assumption. MKHSS assumes only a reusable common reference string, without the need for prior interactions between parties or a public-key infrastructure. In this paper, we construct and implement the first concretely-efficient MKHSS scheme under the same assumptions used by Couteau et al. Using an algorithmic insight that reduces the largest modulus in Couteau et al. from N⁴ to N², our optimized implementation can homomorphically multiply inputs in 5.0 milliseconds—while an implementation of Couteau et al. requires 224.6 milliseconds—thereby achieving a 45× speedup. A powerful application of MKHSS is to realize attribute-based non-interactive key exchange (ANIKE), which generalizes password-based key exchange (PAKE) to arbitrary attribute policies. ANIKE is currently only known from MKHSS. We use our implementation to evaluate the first concretely-efficient ANIKE schemes for a range of practically useful policies. Using our implementation, two parties can perform a geolocation-based key exchange in 1.65 seconds and a fuzzy PAKE on an 8-word passphrase in 7.59 seconds for realistic parameters, on a single core. Compared to using Couteau et al., which requires 62.5 and 253 seconds, we achieve 38× and 33× speedups, respectively.

Reciprocity and Normality in the Scattering Matrix of Disordered Media

2026-01-21T04:07:46Z

Reciprocity and Normality in the Scattering Matrix of Disordered Media Bharadwaj, Shreyas K. The scattering matrix formalism provides a practical characterization of wave transport in linear, source-free systems by relating a set of operationally defined input and output spatial channels. The matrix is structured as a block operator, with diagonal blocks encoding same-side reflection matrices (RMs) and off-diagonal blocks encoding transmission matrices (TMs) in opposing propagation directions. Under Helmholtz reciprocity, symmetry relations are imposed: RMs are symmetric, and forward and reverse TMs are mathematical transposes of each other. These relations were employed as constraints to correct system-induced aberrations in measured scattering matrices of complex optical media via a matrix-based gradient descent procedure. Resulting phase corrections corresponded closely with classical aberration modes without heuristic parameterizations, suggesting that these modes naturally arise to restore reciprocity-induced symmetry. Vectorial TMs were measured for single- and double-pass propagation through step-index MMFs and scattering samples, with corrected phase terms showing agreement across sample types. Furthermore, matrix normality was introduced as a descriptor of stable modal transport. Normal matrices admit unitary diagonalization, reflecting orthogonal eigenchannels and spectrally coherent propagation. Near-normal behavior was observed in fiber TMs, while RMs of scattering slabs remained strongly non-normal, as quantified by a normalized Henrici departure. Sufficient conditions for normality were identified in terms of the system Green’s function and its bi-compression onto the measurement basis. A complementary dispersion experiment investigated two regimes: nearly-normal MMFs, where the Wigner–Smith time-delay operator was jointly diagonalizable and supported accurate first-order spectral models; and mechanically compressed fibers, where loss of normality produced noncommuting operators and collapse of model fidelity. These results suggest that normality captures well-behaved modal transport, underpinning the validity of parametric models and other operator-based analyses of disordered media. Together, reciprocity and normality impose complementary constraints on wave transport: reciprocity governs global symmetry, while normality captures internal coherence of modal propagation. Relevance is noted for matrix-based imaging, inverse scattering theory, and non-Hermitian wave physics, where symmetry and modal stability remain central.

Mesh Differentiable Rendering for Real-World Scenes

2026-01-21T04:07:54Z

Mesh Differentiable Rendering for Real-World Scenes Charatan, David Differentiable rendering has established itself as an effective tool for 3D reconstruction and novel view synthesis. Most state-of-the-art differentiable rendering methods use purpose-built renderers to optimize specialized, nonstandard 3D representations. However, most downstream applications of differentiable rendering rely on 3D meshes, which are near-universally supported due to their suitability for a wide range of rendering, simulation, and 3D modeling workflows. While prior methods have explored using 3D meshes directly within gradient-based optimization, they have been limited to object-centric scenes and cannot reconstruct real-world, unbounded scenes. This work addresses this shortcoming via a differentiable rendering formulation that combines an off-the-shelf, non-differentiable triangle rasterizer with a 3D representation that consists of nested mesh shells. During every forward pass, these shells are extracted from an underlying signed distance field. Then, the shells are independently rasterized and the resulting images are alpha-composited using opacities derived from the shells' per-vertex signed distance values. Notably, the shells' vertex positions are updated only via the underlying signed distance field, not via backpropagation through the rasterizer itself. This makes our method compatible with off-the-shelf, non-differentiable triangle rasterizers. To the best of our knowledge, our method is the first differentiable mesh rendering method that scales to unbounded, real-world 3D scenes, where it produces high-quality novel view synthesis results whose quality approaches the quality of state-of-the-art, non-mesh-based methods. Our method's performance is also competitive with state-of-the-art surface rendering methods on object-centric scenes. Ultimately, our method suggests that it may be possible to solve the differentiable rendering problem using tools from the conventional graphics toolbox rather than relying on specialized renderers.

Task-Aware Spatial and Temporal Aggregation for Capacity Expansion Planning

2026-01-21T04:07:49Z

Task-Aware Spatial and Temporal Aggregation for Capacity Expansion Planning Duguey, Gabriel As we plan tomorrow’s electricity system, we face fundamental questions: where should new power plants go, which technologies deserve investment, and how much transmission is enough? These decisions are the domain of Capacity Expansion Planning (CEP), a class of optimization models that guide long-term infrastructure investments in power systems. To be realistic, CEP models must capture fine-grained spatial and temporal variations because demand varies by city and climate, while wind and solar output depend on weather patterns that shift hour by hour and location by location. But representing the system with thousands of time steps and hundreds of nodes makes the optimization problem computationally too large to solve. This thesis addresses the core question: how can spatial and temporal aggregation in CEP models be designed to preserve planning-relevant patterns that drive investment decisions? Existing approaches often treat aggregation as a neutral preprocessing step, relying on heuristics like political boundaries or geographic proximity. In contrast, we propose a task-aware pipeline that treats aggregation as an integral modeling decision, explicitly aligned with planning objectives. The approach builds a composite similarity metric that blends diverse planning-relevant signals, including, but not limited to, duration curves, ramping behavior, and spatial correlation, and uses k-medoids clustering to define spatial zones. Temporal aggregation is then applied to daily system-wide profiles, selecting representative days that maintain cross-zonal interactions. The result is a reduced spatio-temporal dataset fed into a CEP model. The resulting investment decisions are re-evaluated at full resolution to evaluate their feasibility and real cost. Experiments on a New England case study show the pipeline consistently outperforms common baselines like political boundaries, geographic proximity, or capacity factor statistics. Among 50 feature weightings, the best design reduces system cost by 13% compared to heuristics. Correlation-based features drive the best results, while raw amplitude and geographic location often degrade performance when used alone.

Earth as Equity Partner: A Revolutionary Approach to Ecological Conservation through Housing Development

2026-01-21T04:07:45Z

Earth as Equity Partner: A Revolutionary Approach to Ecological Conservation through Housing Development McDonough, Kate Duddington Farm is a 312-acre site north of Baltimore, Maryland. A stream restoration project was completed at the location nearly a decade ago in concert with the State of Maryland, the Manor Conservancy, Ecotone, and landowners Harry and Tara McDonough. The project was conducted with some success, however due to a lack of State oversight and long-term management provisions, the ecology has since declined. The following proposal outlines a new model for long-term land restoration and conservation, whereby land conservation and restoration are financed not solely through short term grants and fragile easements, but through the thoughtful use of modest real estate interventions. A small cluster of homes is developed on one portion of the site. The act increases the value of the land, generates equity, and establishes a permanent conservation fund. The design protects habitat and invites people into a deeper relationship with the natural world. The plan offers scalability in taking the land value capture and applying it to future land conservation projects, compounding returns and projecting a model to preserve hundreds of thousands of acres of critical land across the United States. This model highlights Indigenous ecological knowledge (TEK) and traditional practices of engaging with the land, highlighting a deeper understanding of how humans and nature can coexist in mutually healthy ways. The model is designed at a time when watersheds, national parks, and old-growth forests are faced with the greatest threat to global ecology. Duddington Farm is used as a retrospective case, but the broader goal is to create a regenerative framework for conservation-based development across critical watershed regions.

Automated Finite Elements

2026-01-21T04:07:51Z

Automated Finite Elements Collin, Teodoro Fields Finite element methods (FEMs) are a powerful and ubiquitous tool for solving engineering problems. Experimenting with different finite elements can improve the quality and efficiency of solutions. Furthermore, in some cases, the wrong (but nonetheless most common) choice of finite element will produce solutions which converge to the wrong answer regardless of mesh resolution. However, in practice, the choice of finite element is not explored due to the complexity of re-deriving and re-implementing finite element methods. Trying a new finite element is challenging because practitioners must manually deduce formulas to use these elements and they must implement these formulas within the context of a potentially complex system. We address this problem by introducing ElementForge, a finite element system that is parametric over the literate mathematical specification of a finite element in a domain-specific language (DSL). The ElementForge compiler reasons about tensor spaces, tensors, and tensor bases from first principles to derive implementations of finite elements. The ElementForge compiler is able to automatically derive implementations of finite elements previously only derived by hand. Further, ElementForge minimally couples several key mathematical concepts, mainly tensor fields, mesh topologies, sparse tensors, and assembled finite element operators, to produce a complete finite element system that is parametric over the choice of element. Consequently, the elements derived by the compiler can be applied parametrically to new meshes, PDEs, and boundary conditions. We evaluate our system by implementing several simulations with different finite elements, demonstrating that our system can explore tradeoffs in generality, accuracy, speed, and representational complexity. For example, we are able to implement the Morley, Bell, Argyris, and Hermite like elements with less than 50 lines of code and use them all in a single simulation.

LumiModeling: A Gaussian Splatting-Based Tool for Recreating Dynamic Material and Lighting Interaction in Architecture

2026-01-21T04:07:43Z

LumiModeling: A Gaussian Splatting-Based Tool for Recreating Dynamic Material and Lighting Interaction in Architecture Cao, Biru This thesis presents LumiModeling, a real-time visualization tool based on Gaussian Splatting (GS) that simulates the dynamic interplay between materiality and lighting in architectural environments. While conventional design workflows rely on geometric modeling and photorealistic rendering, they often abstract complex material behaviors and fall short in capturing light-material interactions. In contrast, GS enables the reconstruction of high-fidelity 3D models from 2D image sets, representing viewdependent effects such as reflection, transparency, and surface roughness. A comparative analysis using real-world data from the MIT Stata Center and the Met Warehouse demonstrates GS’s advantages over mesh-based photogrammetry, particularly in rendering reflective and transparent materials. This work extends existing GS capabilities by implementing a relightable pipeline based on the existing model Relightable3DGaussian (Gao et al., 2023), in which each Gaussian point is augmented with physical parameters, including BRDF, surface normals, and incident lighting. The Stata Center dataset is used to test the relighting of GS. A user study involving architecture professionals reveals that perceptual focus shifts from geometry to materiality and lighting as visual realism increases. The findings highlight the potential of relightable GS in architectural visualization and anticipate its integration into future design workflows.

Urban Data Memory: Using Generative AI to Structure and Visualize Zoning Data for Urban Planning Evaluation

2026-01-21T04:07:52Z

Urban Data Memory: Using Generative AI to Structure and Visualize Zoning Data for Urban Planning Evaluation Kupershmidt, Adi Urban planners face significant challenges in systematically and quantitatively evaluating past planning practices, stemming, among other reasons, from the scarcity of accessible structured data. The period from a plan’s initiation to implementation can span generations; recorded data from the planning processes are often deemed obsolete for addressing present concerns by the time of post-occupancy evaluation. This research examines whether generative AI can help bridge this gap and under what conditions - highlighting both challenges and opportunities - by introducing a system that responsively transforms qualitative zoning data into structured, queryable formats to support the quantitative analysis of planning practices. A database of ~150 approved semi-structured urban plans under Tel Aviv municipality’s local jurisdiction supports this project's case study. The system relies on proprietary LLMs (ChatGPT, Claude), streamlining a natural language query input through 3 agentic tasks: (1) RAG (Retrieval Augmented Generation) based querying, generating free-text answers from all plans, (2) structuring the answers to a valid JSON, and (3) visualizing structured data. Key findings indicate an 85.45% precision of the system, as evaluated through an end-to-end assessment of 11 representative queries, each validated against 40 manually labeled plans. The tool provides actionable insights, enabling queries such as trends in sheltered bicycle parking approvals or the status of affordable housing planning over the past decade. This research underlines the significance of flexibly structuring non- and semi-structured data for urban science. It addresses the growing gap between static legacy data collection and real-time policymaking, democratizing access to planning information and fostering informed decision-making practices. Integrating cutting-edge AI-driven tools contributes to the current discourse on AI applications for city management and planning by providing a replicable model for more cities and planning datasets to build upon and improve.

Behavioral Responses to Congestion Pricing in New York City: Mode Shift, Preference Change, and Effect Persistence

2026-01-21T04:07:41Z

Behavioral Responses to Congestion Pricing in New York City: Mode Shift, Preference Change, and Effect Persistence Shen, ChenAn This thesis examines the behavioral impacts of New York City’s congestion pricing policy on weekday peak-hour travel into the pricing zone. Using a two-stage Bayesian Multinomial Logit framework applied to monthly aggregate mobility data, the study disentangles underlying preference shifts from observed mode share changes in response to the toll. Stage 1 estimates population-level travel sensitivities to cost and time, while Stage 2 uses a hierarchical structure to capture heterogeneity across demographic segments defined by income, age, and gender. The analysis spans January–June 2025 and compares results to the same months in 2024 as a counterfactual scenario without pricing. Findings show that while the policy generated a sustained mode shift away from private automobiles toward public transit, preference adaptation varied by demographic group and evolved over time. Some cohorts reinforced the intended policy effects through reduced transit travel time sensitivity, while others exhibited partial reversal as cost sensitivity shifted. These dynamic patterns underscore the importance of evaluating both immediate and evolving behavioral responses when designing congestion pricing strategies and highlight the value of aggregate behavioral modeling for timely, data-driven policy assessment.

Asset Limits, Savings Behavior, and Welfare: Evidence from the SIPP and a Life-Cycle Model

2026-01-21T04:07:33Z

Asset Limits, Savings Behavior, and Welfare: Evidence from the SIPP and a Life-Cycle Model Gamble IV, James Monroe This paper examines how asset limits in means-tested welfare programs shape household saving behavior. I exploit cross-state variation in Temporary Assistance for Needy Families (TANF) asset limits by linking these limits to individual-level data from the Survey of Income and Program Participation (SIPP) and estimating ordinary least squares (OLS) regressions with state and year fixed effects. I find that a $1 increase in the liquid asset limit corresponds to a $0.75 decrease in non-housing wealth among single mothers without a high school diploma. This suggests that less stringent asset tests reduce incentives to save, consistent with models in which more generous public insurance lowers the need for precautionary saving. To interpret these findings, I develop a dynamic life-cycle model of saving under income and medical expense risk, calibrated to key moments from the Hubbard, Skinner, and Zeldes framework. The model embeds Medicaid-style transfer rules and a guaranteed consumption floor. Simulations indicate that a $7,000 consumption floor can reduce median assets by up to 20% among low-education households, reflecting a decrease in self-insurance as public support increases. I then extend the model to include Achieving a Better Life Experience (ABLE) accounts, which are tax-advantaged savings vehicles for individuals with disabilities exempt from means testing. Simulations indicate that ABLE eligibility increases early-life consumption by approximately $10,000 and reduces retirement savings, with account holders shifting more spending into their working years. Together, these results yield a direct mapping from policy levers, including asset-limit generosity, earnings disregards, childcare subsidies, and ABLE exemption rules, to predicted shifts in median household assets. This offers policymakers a practical tool to balance public insurance and private precautionary savings.

Strain-resolved transcriptomics: exploring functional heterogeneity of the gut microbiota in health and disease

2026-01-21T04:08:09Z

Strain-resolved transcriptomics: exploring functional heterogeneity of the gut microbiota in health and disease Burgos Robles, Emanuel Felipe The gut microbiome plays a critical role in inflammatory bowel diseases (IBDs), yet current analyses treat bacterial species as functionally uniform, ignoring extensive strain-level diversity that may drive disease mechanisms. Here, we developed a strain-resolved metatranscriptomics framework to investigate how transcriptional activity varies across bacterial lineages and relates to IBD pathogenesis. Using paired metagenomics and metatranscriptomics data from 1,067 fecal samples (103 IBD and 335 non-IBD patients), we first constructed phylogenetic trees for over 250 bacterial species using the single nucleotide variants within essential housekeeping genes, enabling the identification of bacterial strains. Next, we devised a statistical approach to assign mRNA reads to these strains, leveraging the natural genetic variation that is present across them. My analysis revealed that closely related bacterial strains exhibit dramatically different transcriptional programs, with some strains enriched in IBD patients showing upregulation of genes involved in stress response, sugar metabolism pathways, and antimicrobial resistance. Notably, we identified transcriptionally active but genomically low-abundance taxa, highlighting the importance of measuring the transcriptional activities of strains beyond species composition. Lineage-aware differential expression analysis uncovered strain-specific adaptations to inflammatory environments. This strain-resolved approach provides a powerful framework for understanding microbial functional heterogeneity and identifying specific bacterial lineages that could potentially contribute to disease pathogenesis, potentially guiding more targeted microbiome-based therapeutic interventions.

Large Language Models and Quantifying the Regulatory Expenses of Affordable Housing: A Thorough Examination Utilizing Generative Assessment

2026-01-21T04:08:08Z

Large Language Models and Quantifying the Regulatory Expenses of Affordable Housing: A Thorough Examination Utilizing Generative Assessment Xu, Bangjie This thesis presents an innovative methodology using Large Language Model-based methods to extract and quantify housing regulations from municipal zoning codes, making possible the most comprehensive examination of regulatory costs at the municipal level across California to date. A multi-staged extraction framework is devised that delivers 85-95% accuracy in the identification and standardization of complex regulatory requirements from legal documents. Applying this methodology to over twenty California cities over the period 2015-2025, it is estimated that regulatory constraints raise the cost of developing a housing unit by roughly between 5% to 10% (or $50,000 and $100,000+) per housing unit, with the most acute constraints in the state’s coastal metros. This method is used to find that factors such as regulation costs limit housing supply elasticity from 1.24 in low-regulation jurisdictions to 0.08 in high-regulation areas. The LLM-based framework allows us to conduct analyses at an unprecedented scale and granularity and to reveal, for example, that the relaxation of regulation by streamlining policies like the Los Angeles Transit Oriented Communities program boosts housing production in eligible zoned areas by 43%. This study makes significant contributions to the restructuring of California’s housing regulation system in response to the affordability crisis, and its methodology presents a replicable tool for regulatory analysis in other policy domains.

Reimagining Public Land: Municipal Land Use to Ease Housing Crisis in Boston

2026-01-21T04:08:06Z

Reimagining Public Land: Municipal Land Use to Ease Housing Crisis in Boston Murphy, Ryan Boston is in the midst of a severe housing crisis, driven by decades of underproduction, rising construction costs, restrictive zoning, and an inelastic real estate market that has resulted in persistent affordability challenges. This thesis explores the untapped potential of city-owned land as a powerful tool to increase housing supply and affordability in Boston. Using Boston’s 2022 Citywide Land Audit and detailed development assumptions, the analysis estimates that between 19,000 and 31,000 new housing units could be constructed across city-controlled parcels, including between 3,200 and 6,100 affordable units under the current Inclusionary Development Policy. The research draws on case studies from peer cities such as Chicago and Atlanta where municipal land has been successfully leveraged through transparent disposition processes, fast-tracked entitlements, and flexible affordability models. It argues for a policy shift in Boston toward a more streamlined, market-aware, and scalable land release strategy that prioritizes speed, cross-subsidization, and financial feasibility. Key recommendations include expanding the Welcome Home, Boston program to include mixed-income and rental housing, implementing predictable RFP cycles, offering tax abatements, and expediting the entitlement process.

Modular Zipping for Transformable and Dynamic Systems

2026-01-21T04:08:01Z

Modular Zipping for Transformable and Dynamic Systems Hagemann, Niklas There is a need for products, machines and environments that can change shape, transform and evolve according to their use. This thesis proposes the design of a simple, modular actuator based on reversible folding and interlocking (zipping) of flexible 3D printed strips. The proposed zipper design allows for continuous control states between a compact and fully deployed state. The modular actuators can be integrated into a variety of systems to enable compact, shape- and stiffness-changing structures, robots and other devices. Designs are presented for single- and double-zipper modules using the same basic zipper design. The modules can be used as modular components of compact robotic systems with the ability to expand and contract according to their environment, or used as adjustable structural components to create deployable, shape-and stiffness-changing objects. The zipper design points the way towards simplified mono-material components that embed transformation and reversibility into everyday devices, products and spaces, and enabling objects that are as easy to transform, reconfigure and reverse as they are to manufacture.

Embodied Representation of Time in Virtual Reality

2026-01-21T04:08:03Z

Embodied Representation of Time in Virtual Reality Kim, Suwan Recent advancements in 3D graphics and AI-assisted generative techniques have accelerated the creation of realistic scenes for immersive technologies, including virtual reality, yet most systems continue to encode time as a linear parameter, relying on timeline-based playback. Mesh-based representations are typically constrained by fixed topologies and rely on predefined animations, which limit their capacity to encode temporal change as a spatial or perceptual phenomenon. In reality, human experience of time is embodied and dynamic, perceived through interaction and memory. Existing digital systems fail to capture this dimension, reducing time to a passive parameter. This thesis proposes a framework for representing time as an embodied and spatial dimension within virtual reality by embedding it directly into the geometry and interaction logic of point cloud data. The system consists of three parts: (1) processing 2D images into layered volumetric point clouds to enable structural fluidity and temporally responsive spatial form; (2) enabling perceptual and spatial modulation in response to user distance and contact, with color influencing the character of change and opacity shaping its perceptual reveal at both global and local scales; and (3) enabling real-time visualization of modulated point cloud through a custom pipeline optimized for mobile virtual reality. By embedding temporal dynamics directly into geometry and interaction logic, this thesis contributes a novel representational approach to spatiotemporal modeling in immersive systems. By doing so, we create new opportunities for architectural visualization, interactive simulations, game design, and reimagining how we perceive and construct digital spaces.

Patent Visibility and the Diffusion of Trapped Knowledge: Evidence from US Grants

2026-01-21T04:08:04Z

Patent Visibility and the Diffusion of Trapped Knowledge: Evidence from US Grants Yao, Randol H. Valuable knowledge developed in one part of the world may remain “trapped" locally due to frictions in how knowledge is recognized and shared globally. This paper examines how granting US patents to foreign-origin inventions—by elevating their visibility and credibility— untraps the knowledge and facilitates global diffusion. Using examiner leniency as an instrument, complemented by a difference-in-differences design, I find that US grants of home country patents significantly increase both the likelihood and intensity of forward citations, including marked increases from third countries. A novel measure of “trappedness” reveals that knowledge from historically more trapped countries and sectors sees larger diffusion benefits after the US grants. These findings highlight the central role of the US as a platform of global knowledge recognition and diffusion, particularly in turning overlooked ideas into globally relevant innovations.

Characterization of the East China Sea Continental Shelf Circulation Northeast of Taiwan Surrounding Mien-Hua Canyon

2026-01-21T04:07:59Z

Characterization of the East China Sea Continental Shelf Circulation Northeast of Taiwan Surrounding Mien-Hua Canyon Rafferty, Lieutenant Commander Keefe Submarine canyons have a proven and direct influence on continental shelf circulation and flow dynamics, especially in relation to western boundary currents. There are two key circulation features northeast of Taiwan on the East China Sea continental shelf: (1) the cold dome, a cyclonic feature that appears primarily in summer and is associated with upwelling, and (2) Kuroshio intrusions onto the continental shelf in the vicinity of Mien-Hua Canyon. This paper is a descriptive physical oceanography study with a focus on characterizing the circulation patterns northeast of Taiwan surrounding Mien-Hua Canyon, closely correlating these patterns with the migration of the Kuroshio and its variability and intrusions onto the southern East China Sea continental shelf, leading to the formation of the cold dome. The Institute of Oceanography at the National Taiwan University and WHOI executed a joint international field survey at Mien-Hua Canyon aiming to improve the understanding of canyon flow dynamics between the East China Sea continental shelf northeast of Taiwan and the Kuroshio as the North Pacific Gyre westward boundary current. This joint oceanographic expedition expands on previous joint US/Taiwan physical oceanographic and ocean acoustic studies in the China Seas dating back to ASIAEX in the South China Sea during 2000-2001 and QPE in the East China Sea during 2008-2009. The strengthening and weakening of Kuroshio transport and intensity northeast of Taiwan is closely correlated to the timescales of mesoscale westward propagating eddies arriving to the East Taiwan Channel. When a canyon has a Rossby number ~1 or Rossby radius equivalent to the width of the canyon in a region of left-bounded flow, induced cyclonic flow will experience an upwelling regime within the canyon system with dominant upwelling located at the downstream canyon rim vertically constrained by Rossby Height. Observational analysis of canyon bottom-moored ADCPs and vertical temperature arrays supports previous theory on submarine canyon dynamics on a continental shelf. Satellite sea surface temperature and absolute dynamic topography observations render the formation of a cold dome northeast of Taiwan coincident with this joint oceanographic survey.

From Wallets to Wages: Consumer Income, Job Design, and Pay Disparities

2026-01-21T04:08:01Z

From Wallets to Wages: Consumer Income, Job Design, and Pay Disparities Roh, Soohyun Pay differences between organizations are a key source of wage inequality. I propose a novel account of these differences by starting from the consumers that these businesses serve. Firms that serve high-income consumers specialize jobs into higher-paying and higher-skilled positions focused on quality, while those that serve lower-income consumers emphasize cost minimization by requiring workers to perform a wider range of general tasks. Matching consumer foot traffic data and establishment-level wage records, I find that establishments serving higher-income consumers pay their workers more. This effect holds comparing among establishments in the same neighborhoods and industries. Longitudinally, establishments increase wages when they shift toward higher-income customers. Analysis of online job postings further reveals that jobs at higher-income-serving firms involve a narrower set of tasks that command higher market value. These findings show how consumer markets shape firms’ internal job design and contribute to pay inequality across organizations.

The Fate of Federal Buildings: Real Estate Disposition and the Future of Washington, D.C.

2026-01-21T04:07:56Z

The Fate of Federal Buildings: Real Estate Disposition and the Future of Washington, D.C. Mulcahy, Robby L. The United States federal government is the largest property owner in the country, with more than 370 million square feet of real estate under its control. Much of this portfolio is outdated, underutilized, and located in the urban cores of American cities. Nowhere is this more evident—or more consequential—than in Washington, D.C., where the federal government controls approximately 27% of the office market. As federal agencies adopt hybrid work models, and as the operational needs of government evolve, the existing real estate footprint has become increasingly inefficient, expensive, and misaligned with civic and market realities. This thesis investigates the opportunity to rethink federal land ownership and management as a catalyst for urban regeneration, civic stewardship, and housing production. Using the James V. Forrestal Building as a focal case study, the research examines the historical, policy, and spatial dynamics that have led to the current moment of reckoning. Located on Independence Avenue SW, straddling 10th Street between the National Mall and the Wharf, Forrestal is emblematic of the postwar federal design ethos: monumental, inward-facing, and hostile to street life. Once a symbol of bureaucratic permanence, the building now stands as a physical and symbolic barrier to urban connectivity and civic vitality. The case of Forrestal is used to explore broader questions: How can the federal government dispose of surplus property more effectively? What policy tools exist—or are needed—to unlock value and enable redevelopment? And what role should cities play in shaping the outcomes of federal land disposition? The thesis employs a mixed-methods approach that includes policy analysis, stakeholder interviews, precedent case studies, and spatial analysis of Southwest D.C. The work identifies a range of obstacles to effective disposition, including Title V of the McKinney-Vento Homeless Assistance Act, opaque OMB budget scoring rules, jurisdictional fragmentation, and the absence of a coordinating authority across federal agencies. It also identifies key lessons from successful projects such as The Yards, Walter Reed, and the Volpe Center, where thoughtful structuring and strong federal-local partnerships enabled transformative redevelopment of surplus land. The thesis concludes with ten detailed recommendations for reform, including reauthorization of the Federal Assets Sale and Transfer Act (FASTA), modernization of Title V and OMB scoring, the creation of Federal Redevelopment Zones, and the prioritization of housing, civic infrastructure, and design quality in disposition strategy. It argues that the federal government must shift from a passive landlord to an active steward of public land—one that collaborates with cities, integrates public benefit, and reflects democratic values through the built environment. In this moment of shifting federal needs, declining office demand, and urban transformation, the question is not whether federal real estate reform is needed—it is whether we will seize the opportunity. The fate of buildings like Forrestal will shape not only the skyline of Washington, D.C., but also the federal government’s legacy in America’s cities for generations to come.

Analyst Incentives

2026-01-21T04:08:00Z

Analyst Incentives Green, Brice Analyst forecasts have been shown to reflect substantial behavioral biases and predict a number of macroeconomic phenomena. While we typically treat reported forecasts as statistical expectations, under uncertainty the reported point estimate will be sensitive to the payoff structure facing the forecaster. Using data on careers from LinkedIn, I describe the incentive structures faced by analysts, shedding light the extent to which pay and career success are tied to performance. Further, I extend a causal estimator to identify credible counterfactual forecasts and provide tentative causal evidence of the relationship between forecast errors and promotions.

Maverick Neuroscientist: What Does a Life in Science Look Like Outside the Ivory Tower? : Dr. Eugenio Vargas-Pena is a renowned psychiatrist in Paraguay, who conducts neuroscience research without university ties, funding, or peer review. ls his embodiment of the gentleman scientist an alternate path for those who want to break away from institutionalized science?

2026-01-21T04:07:57Z

Maverick Neuroscientist: What Does a Life in Science Look Like Outside the Ivory Tower? : Dr. Eugenio Vargas-Pena is a renowned psychiatrist in Paraguay, who conducts neuroscience research without university ties, funding, or peer review. ls his embodiment of the gentleman scientist an alternate path for those who want to break away from institutionalized science? Chomik-Morales, Jessica This longterm narrative investigates the life and work of Dr. Eugenio Vargas-Peña, a neuropsychiatrist in Asunción, Paraguay who built a fully functional lab in his countryside home. Vargas-Peña conducts brain research independently, guided by decades of self-study, clinical practice, and an unwavering belief in the value of curiosity-driven inquiry. The piece interweaves historical context, character study, and personal narrative, using the author's own background in neuroscience and science communication to frame an inquiry into legitimacy, recognition, and alternative pathways in science. It asks: What defines a scientist today? Who gets to decide which ideas are taken seriously? And what are the consequences-creative or catastrophic-of working outside institutional boundaries? Through the lens of one man's eccentric yet earnest intellectual journey, this thesis invites broader reflection on the pressures shaping contemporary research and the enduring romance of unorthodox scholarship.

Accelerating RTL Simulation Through Fine-grained Task Dataflow and Selective Execution

2026-01-13T04:08:30Z

Accelerating RTL Simulation Through Fine-grained Task Dataflow and Selective Execution Elsabbagh, Fares Fast simulation of digital circuits is crucial to build modern chips. Current processors and SoCs integrate hundreds of complex components, including cores, accelerators, and memory hierarchies. Simulating these systems is necessary to verify correctness and explore the design space. Simulation can happen at different levels of abstraction. In this work we focus on Register-Transfer-Level (RTL) simulation. While RTL simulators are frequently used in development due to their quick compilation times, their runtime performance is slow. This is because as the designs are scaled up, multicore communication and scheduling overheads limit performance and scalability. We present ASH, a parallel architecture tailored to RTL simulation workloads. ASH consists of a tightly codesigned hardware architecture and compiler for RTL simulation. ASH exploits two key opportunities. First, it performs dataflow execution of small tasks to leverage the fine-grained parallelism in simulation workloads. Second, it performs selective event-driven execution to run only the fraction of the design exercised each cycle, skipping ineffectual tasks. ASH hardware provides a novel combination of dataflow and speculative execution, and ASH’s compiler features several novel techniques to automatically leverage this hardware. We evaluate ASH in simulation using large Verilog designs that represent different types of architectures. With 256 simple cores, ASH is gmean 1,485× faster than 1-core Verilator, and it is 32× faster than Verilator on a server CPU with 32 complex cores while using 3× less area.

Task Scheduling Techniques to Accelerate RTL Simulation

2026-01-13T04:08:25Z

Task Scheduling Techniques to Accelerate RTL Simulation Sheikhha, Shabnam Fast simulation of digital circuits is crucial to build modern chips. Slow simulation lengthens chip design time and makes bugs more frequent. While simulation can happen at different levels of abstraction, Register-Transfer-Level (RTL) simulation is the usual bottleneck in chip design, as it is needed for ongoing debugging and evaluation. Current simulators scale poorly across CPU cores, because they are unable to exploit the fine-grained parallelism inherent in simulation workloads. We present ASH, a parallel architecture tailored to simulation workloads. ASH consists of a tightly codesigned hardware architecture and compiler for RTL simulation. ASH exploits two key opportunities. First, it performs dataflow execution of small tasks to leverage the fine-grained parallelism in simulation workloads. Dataflow execution exposes abundant parallelism, as each task can run as soon as its inputs are available. Second, it performs selective event-driven execution to run only the fraction of the design exercised each cycle, skipping ineffectual tasks. Selective execution introduces dynamic data dependences since skipped tasks do not communicate data. ASH employs speculative execution to handle these dependencies. ASH’s hardware provides a novel combination of dataflow and speculative execution, and ASH’s compiler features several novel techniques to automatically leverage this hardware. The key compiler techniques include a novel partitioning for minimizing data communication while maintaining load balance, and a strategic coarsening mechanism to reduce the overheads of fine-grained tasks. We evaluate ASH in simulation using large Verilog designs. With 256 simple cores, ASH is gmean 1,485× faster than 1-core Verilator, and it is 32× faster than Verilator on a server CPU with 32 complex cores while using 3× less area.

Scheduling Strategies for Bus Operator Retention: A Mixed-Methods Evaluation of Bus Operator Preferences and 4-Day Workweek Feasibility

2026-01-13T04:08:29Z

Scheduling Strategies for Bus Operator Retention: A Mixed-Methods Evaluation of Bus Operator Preferences and 4-Day Workweek Feasibility Baum, Amelia Rose Public transit agencies face significant and growing challenges related to workforce shortages, absenteeism, and employee retention, which threaten service reliability. Reports found that 90% of U.S. transit agencies are experiencing a workforce shortage, with 84% claiming that the shortage affects their ability to provide scheduled service. Industry-wide, operator absence is a significant contributor to missed work at transit agencies nationwide and has, in many cases, delayed the full reinstatement of service at transit agencies following the COVID-19 pandemic. The quality of bus operators' work is significantly impacted by inflexible crew scheduling constraints. However, most studies focus on pay, benefits, and infrastructure, neglecting the importance of scheduling. This thesis aims to fill this gap by examining the potential for crew scheduling improvements to enhance the quality of life for bus operators through a three-part case study at the Chicago Transit Authority. Part 1 analyzes the historical work preferences of CTA bus operators, providing actionable insights for scheduling improvements. Part 2 presents a high-fidelity proof of concept in HASTUS, using block schedules (10-hour-a-day runs that are intended to be run by an operator 4 days a week) and rostering to reduce negative work traits, increase consecutive and weekend days off for most operators, while maintaining schedules for the top 20% of senior operators. Part 3 evaluates the new 10-hour, 4-day-per-week packaged schedules via an LLM-based paired alternatives survey of operators at one CTA garage, measuring the desirability of the proof of concept and collecting qualitative feedback. Overall, the new schedules substantially improve the quality of work for operators by guaranteeing at least one weekend day off, at least two consecutive days off, and increasing day-to-day schedule consistency and overnight rest time, while maintaining constant vehicle requirements and total pay hours. The survey results show that 72% of operators at the 74th Street garage support the new schedule paradigm, demonstrating strong support for their potential adoption and encouraging future exploration of a block schedule hybrid rostering paradigm at the CTA and other transit agencies.

Sampling Methods for Fast and Versatile GNN Training

2026-01-13T04:08:28Z

Sampling Methods for Fast and Versatile GNN Training Alkhatib, Obada Graph neural networks (GNNs) have become a commonly used class of machine learning models that achieve state-of-the-art performance in various applications. A prevalent and effective approach for applying GNNs on large datasets involves mini-batch training with sampled neighborhoods. Numerous sampling algorithms have emerged, some tailored for specific GNN applications. In this thesis, I explore ways to improve the efficiency and expressivity of existing and emerging sampling schemes. First, I explore system solutions to facilitate the development of fast implementations of different sampling methods. I introduce FlexSample, a system for efficiently incorporating custom sampling algorithms into GNN training. FlexSample leverages the types of performance optimizations found in SALIENT, a state-of-the-art system for fast training of GNNs with node-wise sampling. In experiments with 4 GNN models which use layer-wise and subgraph sampling, FlexSample achieves up to 1.3× speed-up for end-to-end training over PyTorch Geometric with the same sampling code. Furthermore, FlexSample extends SALIENT with highly-optimized C++ implementations of FastGCN and LADIES layer-wise sampling, which achieve 2×–5× speed-up over their respective Python implementations. Second, I introduce a novel framework for learning neighbor sampling distributions as part of GNN training. Key components of this framework, which I name PertinenceSample, are: (i) a differentiable approximation of node-wise sampling for GNNs; and (ii) a parametrization of node sampling distributions as node- or edge-wise weights of attention-like GNN layers. I present an initial exploration of the potential of PertinenceSample for improving node classification accuracy in the presence of noisy edges. Specifically, in two synthetic experiments where roughly half of a node’s neighbors may have similar features but different labels, I demonstrate that extending a GraphSAGE model with a 2-layer perceptron for learning the PertinenceSample weights can improve classification accuracy from 50%–75% to (nearly) 100%.

Optimizing Graph Neural Network Training on Large Graphs in A Distributed Setting

2026-01-13T04:08:27Z

Optimizing Graph Neural Network Training on Large Graphs in A Distributed Setting Murzynowski, Philip Graph neural networks (GNNs) are an important class of methods for leveraging the information present in graph structures to perform various learning tasks. Distributed GNNs can improve the performance of GNN execution by dividing computation among multiple machines and scale to large graphs by partitioning graph features and the graph structure. Although distributed GNNs are able to achieve self-relative speedup, they are often slower than well-optimized code running on a single machine. For example, evaluation of the prevalent Distributed DGL system on graphs in the Open Graph Benchmark shows Distributed DGL can achieve speedup of over 2× when moving from one to four nodes, but execution of Distributed DGL on 4 nodes is 2× slower than a well-optimized GNN system, such as the SALIENT system, on a single machine. In my thesis, I argue that it is possible for a distributed GNN system to be both fast and scalable. Specifically, I show that it is possible to match the performance of well-optimized, non-distributed codes for GNN training and also achieve good scalability when running in the distributed setting. I present a system called Distributed SALIENT and motivate its design through profiling and identifying bottlenecks that arise in the distributed setting. Key components of Distributed SALIENT include the use of well-optimized code for local computations, pipelining of inter-machine communication, and a careful trade-off between data partitioning and partial replication. I evaluate Distributed SALIENT on the Open Graph Benchmark (OGB) and show that Distributed SALIENT achieves good speedup compared to SALIENT’s well-optimized single-node code while only using replication factors of roughly 5%. In fact, in experiments with training a 3-layer GraphSAGE model on the large OGB papers100M data set, Distributed SALIENT on 8 nodes is 8.6x faster than SALIENT on 1 node.

When Your Home Becomes a Panda Park: The Opportunity and Upheaval of China's Giant Panda National Park

2026-01-13T04:08:22Z

When Your Home Becomes a Panda Park: The Opportunity and Upheaval of China's Giant Panda National Park Zhao, Celina In December 2016, China launched the Giant Panda National Park (GPNP). A massive ecological initiative aimed at safeguarding its beloved national symbol and international icon of conservation, the park marked an unequivocal win for giant pandas. But for the 100,000 people already living in and around the borders, the outcome was not as clear. The GPNP seeks to establish a harmonious balance between biodiversity protection and human development. But the vast amount of land covered by the park means not all places are equally primed to achieve that goal. A handful of communities have been designated as exclusive entrance communities, with lavish funding to become the face of the national park. In others, a persistent question simmers: Are pandas more important than people? Central to this story is how individuals are adapting to and reimagining their futures. Rather than a binary of winners and losers, the GPNP has sparked a wide range of human responses -showing that the path to a sustainable future between people and pandas is far from black and white.

Reviewing I.S. : how to handle legacy systems?

2026-01-07T03:23:47Z

Reviewing I.S. : how to handle legacy systems? Orlando, Ricardo, 1966- Thesis: S.M.M.O.T., Massachusetts Institute of Technology, Sloan School of Management, Management of Technology Program, 1999; Includes bibliographical references (leaves 100-106).

The effects of changing economic conditions on energy costs in stainless-steel clad pressurized water reactors

2026-01-07T03:23:33Z

The effects of changing economic conditions on energy costs in stainless-steel clad pressurized water reactors Trapp, Donald L. Thesis: M.S., Massachusetts Institute of Technology, Department of Nuclear Engineering, 1962; Appendix contains numerous pamphlets.; Includes bibliographical references (leaves 135-136).

The design of a control system for the terminal phase of a satellite rendezvous

2026-01-07T03:23:50Z

The design of a control system for the terminal phase of a satellite rendezvous Hollister, Walter M., 1930- Thesis: M.S., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 1959; Includes bibliographical references (leaf 47).

Noise analysis of circuit models representing maser operation.

2026-01-07T03:23:54Z

Noise analysis of circuit models representing maser operation. Hempstead, Robert Douglas. Thesis: M.S., Massachusetts Institute of Technology, Department of Electrical Engineering, 1965; Bibliography: leaves 106-108.

Improving railroad terminal control systems : a case study of Southern Railway's Brosnan yard

2026-01-07T03:23:43Z

Improving railroad terminal control systems : a case study of Southern Railway's Brosnan yard Ferguson, William Lloyd. Thesis: M.S., Massachusetts Institute of Technology, Department of Civil Engineering, 1979; Bibliography: leaves 194-195.

Atmospheric Impacts of Hydrogen as an Aviation Fuel

2025-12-17T03:06:42Z

Atmospheric Impacts of Hydrogen as an Aviation Fuel Gibney, Evan M. Hydrogen is being investigated as a promising zero-carbon aviation fuel, offering the potential to eliminate direct CO₂ emissions while being produced with low lifecycle greenhouse gas emissions. Despite these benefits, there are additional indirect climate and air quality costs associated with direct hydrogen emissions which are often overlooked. We quantify the perturbation in the atmospheric composition associated with the introduction of hydrogen-fueled aircraft, broadening the current understanding of the non-CO₂ effects of these fleets. We use the GEOS-Chem High Performance (GCHP) global chemistry-transport model to conduct a spatially discretized, multi-year impact assessment of the atmospheric impacts of hydrogen-fueled aviation. We implement a flux surface boundary condition for hydrogen to provide an improved representation of the soil sink, relative to the default fixed boundary condition. This results in a net surface exchange of-16.7 Tg H₂ per year. Two hydrogen scenarios are evaluated using the updated GCHP implementation, which are representative of a high and low mitigation scenario for direct hydrogen emission rates. For the two scenarios, respectively, we observe increases in the mean atmospheric methane mixing ratio of 3.34 ppbv and 10.7 ppbv, corresponding to an increased methane lifetime of between 0.24% and 0.77%, respectively. The increased methane lifetime as well as in-situ oxidation of stratospheric hydrogen results in an increased stratospheric water vapor burden of 0.42 Tg and 2.3 Tg (or 0.052% and 0.28%) for the high and low mitigation scenarios, respectively. Additionally, we show the perturbation to tropospheric ozone levels to be between-0.047% and +0.30%, where the decreased ozone results from the removal of NOₓ emissions associated with fuel cells and low hydrogen emission rates. This analysis provides the foundation for understanding the implications of potential future hydrogen-based aviation fleets on climate and air quality.

A Changing Climate Beneath Our Feet: How plant and microbial life in tropical soils are shifting and what that could mean for the future of our warming planet

2025-12-17T03:06:34Z

A Changing Climate Beneath Our Feet: How plant and microbial life in tropical soils are shifting and what that could mean for the future of our warming planet Ocharoenchai, Nanticha Discussions about climate change and carbon sequestration have largely revolved around plant structures we can easily see, like leaves that absorb CO₂ for photosynthesis and woody trunks that store carbon as biomass. Carbon credits that companies and consumers buy to compensate for emissions they’ve produced are primarily calculated based on these parts, as are models that predict climate change impacts. But researchers are now beginning to understand that what we see aboveground is only part of the equation. The other part lies beneath our feet in an intricate, expansive, covert realm where plant roots, microbial communities and soil dynamics interact. These belowground systems are crucial for cycling carbon through the Earth and regulating the climate, but relatively little is known about them compared to aboveground systems. This is especially true in tropical regions, where one-third of the world’s terrestrial carbon storage lies. However, these systems are evolving quickly with climate change, contradicting what models have previously projected. With so many global decisions based on such models, these uncertainties hold planetary significance for our future. A group of scientists is climbing an uphill battle, racing against time to understand this understudied field.

Engineering Winter

2025-12-17T03:06:43Z

Engineering Winter White, Mackenzie As winters warm and snowfall becomes less reliable, ski resorts worldwide increasingly depend on artificial snow to stay open. Snowmaking, once a stopgap, has become the backbone of entire seasons in a sprawling choreography of pumps and pressurized mist designed to hold trails together. At resorts like Vermont’s Bromley Mountain, snowmakers work through the night, drawing millions of gallons from limited reservoirs and operating within narrowing windows of cold air. What emerges is a portrait of winter in transition: less predictable, more expensive, increasingly manufactured. The efforts to preserve winter recreation carry growing costs in energy, water, and equitable access. Many smaller, independent ski areas struggle to meet the demands of climate adaptation, while larger resorts expand their operations, widening the divide in who can afford to sustain operations. In the American West, where rivers depend heavily on snowpack melt, the spread of snowmaking ties winter recreation to a water system already under immense strain. As artificial snow becomes the norm, winter is increasingly a season bought, built, and rationed, raising the question of whether attempts to keep the season alive are accelerating the changes that threaten to erase it.

IP Networks Over Heterogeneous Embedded Serial Links

2025-12-11T03:08:19Z

IP Networks Over Heterogeneous Embedded Serial Links Perry, Nathan The Internet Protocol (IP) provides a number of key benefits to networked devices: it serves as a "narrow waist" enabling functional modularity by decoupling lower-layer devices from application behavior, it provides a notion of transitive connectivity and a number of standardized methods to achieve it, and most importantly, it is ubiquitous, enabling almost all networked applications to mutually communicate. Many embedded microcontrollers cannot take advantage of the benefits of IP because they lack the dedicated networking hardware which is as a practical matter required to interact with nontrivial networks. I observe that multihop point-to-point IP networks can in principle be constructed over the communication media that microcontrollers commonly do have, such as UARTs, I2C, SPI, and CAN bus, but software support is lacking to make this networking approach accessible. Therefore, this thesis develops and evaluates interstice, a platform-independent, open-source software library designed to enable the flexible implementation of modular packet forwarders in userspace. It can be used to interconnect devices and their IP stacks across a variety of conventional and unconventional links. Interstice exposes a reprogrammable, dynamically-updatable packet-forwarding strategy, enabling forwarder nodes in principle to act as hubs, bridges, full routers, or implement firewalls or NAT, as application requirements and platform constraints permit. This approach enables benefits for modular, networked systems of microcontrollers which need to talk to the outside world: using IP enables internal microcontrollers to communicate with external devices such as PCs and smartphones without the need for application gateways. Further, to the extent that such networks are runtime-reconfigurable, features of IP such as address assignment, dynamic routing, and link-agnosticity can be incredibly beneficial. Interstice is evaluated here primarily against networks of various types of serial links (UART, I2c, CAN) speaking PPP, selected to demonstrate utility of the approach to connect embedded devices lacking dedicated networking peripherals, and further that link drivers can be specialized to take advantage of the specific characteristics of each link. The approach is showcased in application scenarios including a networked milling machine, and is analyzed for a number of performance metrics.

BioLIG: Designing Biologically Derived Electronics and Their Speculative Lives

2025-12-11T03:08:22Z

BioLIG: Designing Biologically Derived Electronics and Their Speculative Lives Li, Yuqing Lucy Imagination is the origin of reality. Cultivating new infrastructural and ecological imaginaries is crucial to addressing the climate crisis. Where is the space to prototype new social and technological relations? Transient electronics is an emerging field in advanced materials focused on making electronics that don’t last. Devices are designed to be transient for biomedical, environmental monitoring, or energy storage applications. It is a fascinating and unconventional direction that advances the area of biocompatibility, redefining waste and time-programmable decay {Making electronics that, 2022}. However, in a manufacturing system that fundamentally favors the inert and invariant, transient properties can be precisely the qualities that make adaptation most challenging, often failing at the very stage of imagination. Taking inspiration from transient electronics, this thesis consists of a set of novel biomaterials, a workflow, and three fictional stories to enrich our imagination and instill agency amidst entangled humanitarian, ecological, and technological crises. BioLIG is a material for prototyping accessible and compostable electronics. It uses laser-induced graphene as an organic, bio-derived conductor and affordable biomaterials as the substrate. Three sheets and two inks make up a toolkit to create biocomposites with different properties, colors, and textures specifically designed for prototyping sensors and circuits with transient behaviours. Through a series of characterisations, BioLIG is evaluated and demonstrates that with one material, its electrical performance is on par with synthetic substrates. However, the goal is not to create a replacement material but to prototype new social and technological relations to transient materials. Through a questionnaire, I collected stories, ideas, and questions from makers, designers, and artists for BioLIG and used those as the basis for imagination. In a speculative house, on three floors, three stories unfold of a hoarder, a city forester, and a family living in a time with a leap in our relationship to fabrication, to electronics, and to decay.

Advancing Biosecurity in the Age of AI: Integrating Novel Detection, Suppression, and Evaluation Approaches

2025-12-11T03:08:21Z

Advancing Biosecurity in the Age of AI: Integrating Novel Detection, Suppression, and Evaluation Approaches Justen, Lennart J. Civilization confronts a growing challenge: advancing transformative biological science while safeguarding against catastrophic misuse, a tension amplified by the rapid convergence between biology and artificial intelligence. The COVID-19 pandemic starkly revealed our vulnerabilities to self-replicating, exponential biological phenomena, yet current defenses remain dangerously inadequate—often blind to novel pathogens until too late and lacking barriers against rapid airborne transmission. This thesis argues that robust biosecurity enables, rather than hinders, progress, and advances three key defensive capabilities. First, it evaluates blood metagenomics for pathogen-agnostic surveillance, reanalyzing public datasets to quantify viral signatures and guide the implementation of much-needed early-warning systems sensitive to novel pathogens. Second, it advances far-UVC, a type of ultraviolet between 200-235 nm, for continuous indoor air disinfection, critically assessing its safety profile through an international expert review and establishing research priorities essential for deploying this vital physical defense against airborne threats. Third, it develops rigorous methodologies for evaluating AI's rapidly evolving biological capabilities, benchmarking frontier models across diverse tasks to track progress, reveal limitations in current assessments, and guide responsible innovation in this powerful dual-use technology. Collectively, these contributions help accelerate technologies to mitigate biological risks, thereby helping secure the conditions for continued, beneficial advancement of biology in the age of AI.

From Dialogue to Decision: An LLM-Powered Framework for Analyzing Collective Idea Evolution and Voting Dynamics in Deliberative Assemblies

2025-12-11T03:08:17Z

From Dialogue to Decision: An LLM-Powered Framework for Analyzing Collective Idea Evolution and Voting Dynamics in Deliberative Assemblies Poole-Dayan, Elinor Deliberative assemblies—representative samples of citizens engaged in collective decision-making through facilitated learning and deliberation—are increasingly recognized as powerful tools for revitalizing democratic governance. Yet, core aspects of how deliberation shapes which ideas advance, how perspectives evolve, and why certain recommendations succeed remain opaque and underexamined. This thesis addresses these gaps by investigating: (1) How might we trace the evolution and distillation of ideas into concrete recommendations within deliberative assemblies? and (2) How does the deliberative process shape delegate perspectives and influence voting dynamics over the course of the assembly? To answer these questions, I develop LLM-based methodologies for empirically analyzing transcripts from a tech-enhanced student deliberative assembly. The first framework identifies and visualizes the space of expressed suggestions, revealing that seemingly large gaps between ideas and final recommendations often reflect productive deliberative filtering—while also surfacing overlooked viable ideas. A second analysis integrates post-assembly survey data with transcript-grounded voting patterns to uncover the primary drivers of vote change: edits to recommendations, evolving opinions, and strategic shifts in response to updated priorities. Building on this, I introduce a framework for reconstructing each delegate’s evolving stance across the assembly, linking shifts in perspective to specific deliberative moments and justifications. Together, these methods contribute novel empirical insight into deliberative processes and demonstrate how LLMs can surface high-resolution dynamics otherwise invisible in traditional assembly outputs. The findings lay groundwork for new tools that support facilitators and delegates during live assemblies, improve transparency for decision-makers, and elevate ideas that may otherwise be missed. Looking ahead, this work opens pathways for comparative research across assemblies and highlights the potential for human-centered AI to meaningfully enhance deliberative democratic practice. As societies seek new modes of participatory governance amid growing polarization and institutional mistrust, tools that strengthen deliberation without compromising its core human character are urgently needed.

Delibrary: From Discussion to Outcomes and Back(casted) Again, a Visualization Tool for Deliberative Assemblies

2025-12-11T03:08:14Z

Delibrary: From Discussion to Outcomes and Back(casted) Again, a Visualization Tool for Deliberative Assemblies Wong, Wing Cheung Michael With trust in traditional democratic institutions waning, it is increasingly important to examine how potential new institutions could be created and bolstered, with particular emphasis on restoring trust and empowering the public. One potential solution, the citizen's or deliberative assembly, can serve to bridge the governance and legitimacy gap between real-world policy decision-making processes and citizen-driven impact by leveraging random sortition and a well-designed deliberation process. In this thesis, I explore how AI-driven sensemaking via GPT4o-mini--a Large-Language Model (LLM)--synthesized with custom-built visualization tools, can potentially reveal the dynamics within citizen deliberative assemblies where representative, randomly selected citizens navigate public interest issues through facilitated deliberation--and how such tools can serve to amplify transparency within both the assembly process itself and the issues they explore. Through building three different prototype visualization frameworks and the development of an AI-powered topic identification process called backcasting, I analyze novel datasets from two tech-enhanced assemblies; fully recorded discussions from both an on-the-ground citizens' assembly in Deschutes County, Oregon, as well as an MIT student assembly on sustainability. In backcasting, assembly outcomes are linked to transcriptions of assembly discussions via LLM tagging, uncovering what, when, who, and where participants deliberate about topics that eventually become proposals/recommendations/outcomes. Furthermore, I analyze the sentiment with which an assembly delegate presented their view on a certain recommendation (agreement, disagreement, etc.) in addition to the supporting reasoning patterns this delegate used to express their view, if any (e.g. whether they draw from personal experience, reference outside expertise, etc.). To evaluate the final prototype tool, I interview subject matter and assembly experts, assembly organizers/facilitators, as well as assembly delegate members to assess the potential and drawbacks of this visualization tool and AI sensemaking backbone. Positive feedback obtained from these user studies include the clear potential for research, narrative building, and facilitation improvement, in addition to greater perceived transparency into the workings of an assembly process. Further work is still needed, however, to address significant lingering issues, such as adjusting presentation to better serve specific use cases and to reduce complexity and confusion, the most referenced drawback of Delibrary. Overall, my thesis aims to \textbf{build transparent insights into the human-led structures of assemblies, enabling relevant stakeholders--from delegates, policy makers, to the general public--to achieve a better understanding of the assembly process and engender legitimacy perception by illustrating that delegates drawn from all walks of life do have meaningful voice in an impactful process}. By helping to promote this understanding and perception of legitimacy of an effective and respectful deliberation process, I strive to ultimately help scaffold healthier democratic decision-making.

Interactive Storybooks for Early AI Literacy

2025-12-04T03:09:39Z

Interactive Storybooks for Early AI Literacy Pu, Isabella As artificial intelligence (AI) becomes increasingly present in children's everyday environments, there is an urgent need for developmentally appropriate tools that help young learners understand and shape these technologies. To be effective, these tools must not only successfully convey complex concepts but also engage children in ways that are meaningful, accessible, and fun. This thesis introduces the Interactive Storybooks for Early AI Literacy, a series of ten interactive storybooks for children ages 6–9 that combine narrative, mini-games, and scaffolded creative AI interactions to teach core AI and robotics concepts. The storybooks follow an overarching narrative featuring a friendly robot, Doodlebot, who must learn creative tasks with the child's help, framing the child as an AI designer and introducing them to the concept of training AI models through the narrative. The storybooks additionally contain interactive games and activities which help keep kids excited and engaged, while providing structured opportunities to experiment with and explore AI creation tools. First, a pilot study was conducted at a community summer camp with four Interactive Storybooks. Children expressed joy and pride in their AI creations, used the characters as emotional anchors for learning, and began to successfully articulate key AI concepts. Four engagement archetypes emerged: the Reader, the Gamer, the Showcaser, and the Social Connector, each representing a distinct way children interacted with the storybooks. However, despite behavioral signs of engagement, many children described the narrative portions as boring and claimed to prefer games. To explore this tension, a home deployment study compared two versions of the system: a "Books" condition with the full narrative and a "Games" condition with only instructional text. Both conditions included the same mini-games and AI interactions. While children in both groups reported similar levels of enjoyment, those in the Books condition showed significantly higher learning gains, greater increases in perceived knowledge and confidence, and stronger connections to the characters. Children in the Books condition also more frequently referenced the narrative when describing AI concepts and demonstrated more creative and iterative behavior during and after gameplay. Overall, these findings suggest that combining storytelling, gameplay, and creative AI interactions is an effective and engaging approach to teaching AI and robotics to young children. Narrative context appears to support concept recall, deepen emotional investment, and promote thoughtful experimentation, even with complex concepts for this age group, like AI and robotics. Based on insights from both studies, this thesis concludes with six design recommendations for creating developmentally appropriate, emotionally resonant AI education tools for early learners using narrative and play.

Volume Mount Devices

2025-12-04T03:09:35Z

Volume Mount Devices Han, Alan As Moore's Law ends and AI demands increasingly tax our climate and resources, the limitations of two-dimensional electronics integration have become critical bottlenecks. Surface-mount devices (SMDs) remain entrenched in industry practice despite being insufficient for today's computing challenges and sustainability needs. This thesis introduces the volume mount device (VMD), a three-dimensional electronics packaging standard that bypasses the traditional die-to-server stack while offering a scalable, reversible framework inspired by natural ecosystems' circularity. The VMD approach embeds both electrical function and mechanical structure into modular elements that assemble freely in 3D space. Rather than building circuits on planar PCBs, this system constructs functional circuits by linking components into a self-constraining lattice architecture. My current implementation leverages existing supply chains by incorporating SMD components on small tile PCBs, while establishing a pathway toward eventually replacing SMDs at the IC packaging level. I developed a hybrid assembly system combining 3D printing and pick-and-place automation to build multi-layered electronic assemblies efficiently. Where prior work achieved only tens of parts at hundreds of components per hour (CPH), my system demonstrates automated assembly of hundreds of integrated elements at approximately 1000 CPH. I evaluate various geometric configurations, assess performance overhead compared to conventional approaches, and develop cost-effective, self-aligning connector interfaces for reliable joints—creating a foundation for electronics systems that can be assembled, disassembled, and reassembled as needed while improving resilience against supply chain disruptions.

Controlling for the Ionospheric and Baseline-Offset Uncertainties in the CHIME/FRB Outriggers VLBI Network for Milliarcsecond Precision

2025-12-04T03:09:33Z

Controlling for the Ionospheric and Baseline-Offset Uncertainties in the CHIME/FRB Outriggers VLBI Network for Milliarcsecond Precision Willis, Jacob Fast radio bursts (FRBs) are a novel form of radio transients discovered in 2007. These bright, extragalactic radio signals have an inferred all-sky rate of hundreds of detections per day. The properties of FRBs hold valuable clues about the extreme physical processes driving them while also holding information about the astrophysical plasmas they traverse on their journey to Earth. The Canadian Hydrogen Intensity Mapping Experiment (CHIME)/FRB project has led the field with the hundreds of FRB detections the collaboration has published to date. However, these detections typically have localization regions so large that we cannot identify a single host galaxy, never mind its local environment. To improve upon this, CHIME/FRB has been transformed into a very long baseline interferometry (VLBI) array, drastically increasing the angular resolution of CHIME/FRB from arcminute to sub-arcsecond precision. In this work, I present my contributions to commissioning the CHIME/FRB VLBI Outrigger station located at the Green Bank Observatory (GBO) in West Virginia. This includes measuring and validating GBO's exact position to enable the localization of FRBs to sub-arcsecond precision. For VLBI networks spanning thousands of kilometers, the difference in the local ionospheric environments is significant and leads to errors in the CHIME/FRB Outrigger localizations. I present a thin shell model of the ionosphere to parameterize the local ionospheric environment for each VLBI station. This model may be used to interpolate the error induced by the ionosphere in FRB observations.

Using Z-hadron correlations to probe the medium response in PbPb and pp collisions at √ˢNN = 5.02 TeV

2025-12-04T03:09:22Z

Using Z-hadron correlations to probe the medium response in PbPb and pp collisions at √ˢNN = 5.02 TeV Chou, Pin-Chun The first measurement of Z-hadron two-particle correlation function are reported in PbPb collisions at √ˢNN = 5.02 TeV, using the PbPb collision data taken in 2018. The integrated luminosity of the PbPb data is 1.67 ±0.03 nb⁻¹ which made the analysis possible for the first time. Collision data with at at least one Z boson with 40

Materializing Light: Real-time, Handheld Fabrication of Programmable Structural Color

2025-12-04T03:09:31Z

Materializing Light: Real-time, Handheld Fabrication of Programmable Structural Color Myers, Paris G. Structural color is nature’s programmable color palette. While pigments and dyes absorb light to produce color, structural color uses nanoscale, light-reflecting structures to appear iridescently colored. We present MorphoChrome, an optical device for real-time, handheld, programmable structural color fabrication. Analogous to painting with light, MorphoChrome creates multicolor, structurally colored designs by exposing a commercially available holographic photopolymer film to user-controlled wavelengths. Within the device, red, green, and blue laser diodes go through an optical prism, combining light and producing mixed color outputs on the film. Additionally, we introduce a resin-based process to adhere and integrate the structurally-colored film with flexible and rigid objects and diverse making processes. In this thesis, we focus on the device optical design and fabrication, color-mixing, color output UI controller, device aperture tips, and holographic photo-polymer film adherence process. We evaluate the available color space and color resolution, and demonstrate creative fabrication applications.

Next Week Tonight: Simulating Counterfactual Narratives of the future using Agentic Knowledge Graphs

2025-12-04T03:09:32Z

Next Week Tonight: Simulating Counterfactual Narratives of the future using Agentic Knowledge Graphs Agarwal, Gauri Understanding the ripple effects of events—both real and speculative—is essential for navigating complex futures. Large Language Models (LLMs) have emerged as powerful tools that offer a user-friendly and narrative experience for question answering and reasoning across large corpuses of unstructured data [15, 96]. While LLMs can respond to complex ‘what-if’ questions, they typically provide single, unverifiable answers. Even with retrievalaugmented generation (RAG) that grounds LLM responses on external sources, the opacity of reasoning pathways undermines trust in model outputs [97]. Next Week Tonight builds on the narrative and reasoning capability of LLMs further by enhancing the exploration of what-if futures and making it more transparent and evidencebased. NWT exposes the underlying knowledge graph, allowing users to inspect inference pathways directly. This also enables the generation of multiple, diverse scenarios from a single condition—each following different but explainable causal chains. In testing 15 counterfactual prompts that span diverse news topics, NWT produced scenario narratives that were rated as significantly more causally coherent, transparent, and easier to audit than standard chat completions. Beyond technical performance, NWT reinvents scenario planning as an interactive narrative experience - encouraging curiosity, critical thinking, and deeper engagement with the complexities of future events. By surfacing not only what could happen but why and how, NWT aims to empower analysts, policymakers, and the public to navigate uncertainty with greater clarity and confidence. Github: https://github.com/viral-medialab/next-week-tonight

Inferring Clonal Dynamics in Blood using Single-Cell Measurements

2025-12-04T03:09:25Z

Inferring Clonal Dynamics in Blood using Single-Cell Measurements Perry, Andrea N. In this work, we uniquely tag hematopoietic (blood) stem cells with genetic barcodes and follow their progeny over time to ask whether clonally related cells in myeloproliferative neoplasms (MPNs) favor particular blood cell fates. Myeloproliferative neoplasms are clonal disorders driven most frequently by the JAK2-V617F mutation, which arises in a single hematopoietic stem cell (HSC) and ultimately dominates the normal process of blood cell production. Although all patients carry the same driver mutation, they still branch into three distinct disease forms—essential thrombocythemia (ET), polycythemia vera (PV), or primary myelofibrosis (PMF)—and the reason for this variation remains unknown. One compelling hypothesis is that the JAK2-V617F mutation may arise in HSC subsets with intrinsic biases toward platlet-producing cells (as in ET) or red blood cell precursors (PV). To investigate this question, we analyzed bone-marrow cKit⁺ cells from mice engineered for inducible MPN disease and CRISPR array repair lineage tracing (CARLIN), using single-cell RNA sequencing. Our gene expression analysis shows that the mutation keeps key signaling and stress-response genes switched on and boosts growth-promoting enzymes, collectively pushing blood production toward the myeloid line. At the resolution of individual CARLIN clones (i.e. cells grouped by a shared progenitor), however, we observe no robust mutation-induced lineage bias—an outcome attributable to limited clone recovery and inter-mouse variability. Crucially, this work establishes a scalable analysis pipeline for future, higher-yield CARLIN experiments. Enhancing lineage-tracing sensitivity, barcode diversity, and biological replication will be essential to test whether these interferon-/stress-response and kinase programs manifest as subtle, clone-level fate biases in JAK2-driven MPN.

SERenaDE: Hardware Acceleration of Cloud Serialization Frameworks

2025-11-26T03:06:36Z

SERenaDE: Hardware Acceleration of Cloud Serialization Frameworks Zarkos, Christos V. Serialization frameworks are a fundamental operation of datacenters, as they enable language- and platform-neutral communication and storage. However, software serialization faces major performance bottlenecks, resulting in a significant fraction of cloud cycles dedicated to this process. Prior work has proposed specialized hardware accelerators to address these overheads. While these proposals achieve considerable speedups, they are expensive in terms of verification, fabrication, and deployment, and often hardcode too many details about the (de)serialization framework in hardware. We propose SERenaDE, a serialization framework designed to integrate general-purpose accelerators currently deployed in datacenters in order to accelerate and offload serialization to hardware. Specifically, we repurpose the Intel In-Memory Analytics Accelerator (IAA), an accelerator engine offering fast compression, to enable fast and transparent to the user serialization and deserialization, completely removing software serialization from the execution pipeline. We evaluate our system on latest-generation production machines, both with synthetic microbenchmarks, and open-source representative fleet-wide benchmarks. Our results show comparable performance in terms of per-request latency across all types of messages, while significantly improving throughput - especially at the tail -, maintaining thread scalability and achieving high compression ratios alongside substantial speedups for larger messages. Under 95th latency percentile latency constraints SERenaDE improves serialization and deserialization throughput by 13% and 30% respectively, while achieving from 0.2x to 6.94x smaller serialized message sizes for messages of a total memory layout larger than 4KB.

Physics-Optimized Design of 3D Shapes with Part-Based Control

2025-11-26T03:06:39Z

Physics-Optimized Design of 3D Shapes with Part-Based Control Zhan, Sean We introduce PhysiOPart, a computational approach for rapid generative design of 3D objects optimized for physical integrity. PhysiOPart enables users to edit and combine object parts to explore a vast design space. To model continuous surfaces of arbitrary resolution without topology restrictions, we parametrize parts with neural implicit representations. However, when parts are assembled to form an object, the resulting geometry is not guaranteed to be functional. Existing generative modeling approaches use task-specific neural predictors to approximate physical behaviors with limited accuracy. We propose an end-to-end differentiable physics simulation pipeline that performs linear static analysis to optimize for user-specified objectives, leveraging learned geometry priors. Our part-based formulation with finite element method is highly customizable, allowing for user-defined per-part materials, loads, and boundary conditions. The optimized designs exhibit improved physical behavior and can be fabricated.

Fast Assembly of Curved Structures from Flat Configuration

2025-11-26T03:06:43Z

Fast Assembly of Curved Structures from Flat Configuration Zaman, Akib Imagine deploying an emergency shelter that transitions seamlessly from a flat configuration to a lifted structure, or a folded robot that is sent through a tunnel and subsequently activated to expand into a larger form at the endpoint, with a single, collective pull of strings. This scenario raises two critical questions: (i) how to decompose the structure into a flat state that encodes the 3D geometry, and (ii) where to place strings through the unit modules to achieve complete actuation. Although these questions have been explored individually, comprehensive solutions remain scarce. To address this challenge, this thesis presents a computational approach for designing freeform structures that can be rapidly assembled from initially flat configurations by a single string pull. Target structures are decomposed into rigid, spatially varied quad tiles optimized to approximate a user-provided surface, forming a flat mechanical linkage. A two-step algorithm is then applied to determine a physically realizable string path that controls only a subset of tiles, enabling smooth actuation from flat to assembled configuration. First, the minimal subset of tiles required for string control is computed by considering both the structure’s geometry and inter-tile interactions. Second, a valid string path is identified through these tiles that minimizes friction, thereby transforming the flat linkage into the target 3D form upon tightening a single string. The resulting designs can be manufactured in flat form using computational fabrication techniques: such as 3D printing, CNC milling, or molding, thereby simplifying both production and transportation. Validation is provided through a series of physical prototypes and application case studies, ranging from medical devices and space shelters to large-scale architectural installations.

Graph-based Vector Search Algorithms for Retrieval-Augmented AI Systems

2025-11-26T03:06:31Z

Graph-based Vector Search Algorithms for Retrieval-Augmented AI Systems Zhang, Ziyu The recent advancement of large language models (LLMs) and large multimodal models (LMMs) greatly enhances the capabilities of AI systems such as recommendation systems and coding assistants, making them more practical for real-world deployment. However, these models cannot directly interact with large volumes of data in a knowledge corpus during inference/task time due to inherent architectural limits and cost concerns. Encoding data into vector embeddings and leveraging approximate nearest neighbor search (ANNS) have thus become an important data processing primitive in AI systems following the introduction of retrievel-augmented generation (RAG). However, the complexity of tasks these AI systems aim to solve introduces challenges for existing ANNS algorithms. I developed methods to expand existing ANNS algorithms to address two such challenges: freshness and heterogeneity in the data. Graph-based ANNS algorithms have been proven to have superb cost versus approximation quality trade-off yet follow a simple intuition of best-first search. I focus on adapting graph-based ANNS algorithms to two settings featuring emerging challenges. (1) Data is updated constantly. Existing algorithms are inefficient under deletions and not robust against different orderings in the workload. I propose methods addressing these problems and developed an algorithm supporting updates effectively and efficiently based on Vamana, a state-of-the-art graph-based ANNS algorithm. (2) Data is heterogeneous in format, modality, and how they relate to a query, making the similarity difficult to capture by the canonical ANNS definition. I explore ways to model the similarity between heterogeneous sources and using graph-based ANNS approaches to perform semantic search in this setting. I test this approach under an end-to-end multimodal question-answering system developed in-house.

Characterization of pGaN-gate power HEMTs

2025-11-26T03:06:35Z

Characterization of pGaN-gate power HEMTs Yu, Yue This thesis presents a comprehensive study of p-GaN gate GaN High Electron Mobility Transistors (HEMTs) with a focus on understanding how fabrication process variations and gate structural designs impact key electrical performance metrics. Five industry-fabricated wafers, each processed with distinct etch depths, contact strategies, and p-GaN surface configurations, were characterized using a combination of DC and pulsed I–V measurements. Full-transistor modules were evaluated alongside specialized test structures to enable both system-level and localized analysis. DC measurements using the Keysight B1505A system revealed that more aggressive gate contact schemes improved ON-resistance and transconductance, but often at the cost of increased gate leakage and reduced threshold control. Pulsed-IV characterization with the Auriga AU4750 system uncovered dynamic Ron degradation behavior and charge trapping effects, especially under high drain bias conditions. Extracted time constants demonstrated process-dependent trends, with wafers retaining more of the p-GaN surface exhibiting slower charge detrapping and more severe transient effects. Specialized test structures provided additional insights into gate lateral conduction, sheet resistance, and contact asymmetry, reinforcing the connection between device layout, processing, and observed variability. These findings highlight critical trade-offs in the design and fabrication of p-GaN gate GaN HEMTs and offer design-aware strategies for optimizing performance and reliability.

Crystallization of Glauber's salt

2025-11-25T06:32:25Z

Crystallization of Glauber's salt Coberly, C. Wheeler. Thesis: M.S., Massachusetts Institute of Technology, Department of Chemical Engineering, 1936; Includes bibliographical references (leaf 39).

Analysis of angular scintillation of radar echoes

2025-11-25T06:32:44Z

Analysis of angular scintillation of radar echoes Graham, James William. Thesis: M.S., Massachusetts Institute of Technology, Department of Electrical Engineering, 1952

The design and construction of an ultra-high vacuum field-ion microscope.

2025-11-25T06:32:36Z

The design and construction of an ultra-high vacuum field-ion microscope. Olson, Gregory Bruce. Thesis: M.S., Massachusetts Institute of Technology, Department of Metallurgy and Materials Science, 1970; Bibliography: leaf 35.

Shipleasing as a prospective method of l/t financing for international shipowners.

2025-11-25T06:32:28Z

Shipleasing as a prospective method of l/t financing for international shipowners. Angelicoussis, John Anthony. Thesis: M.S., Massachusetts Institute of Technology, Sloan School of Management, 1974; Includes bibliographical references.

The effects of rapid thermal annealing on gallium arsenide grown by MOCVD on silicon substrates

2025-11-25T06:32:40Z

The effects of rapid thermal annealing on gallium arsenide grown by MOCVD on silicon substrates Lehman, LeNore Louise. Thesis: M.S., Massachusetts Institute of Technology, Department of Materials Science and Engineering, 1988; Includes bibliographical references.

Acoustic-phonetic and lexical constraints in word recognition: lexical access using partial information

2025-11-25T06:32:20Z

Acoustic-phonetic and lexical constraints in word recognition: lexical access using partial information Huttenlocher, Daniel P. Thesis: M.S., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 1984; Bibliography: leaves 73-77.

An investigation of the merits of a four-element coplanar vacuum tube when used as a modulator at carrier telephone frequencies

2025-11-25T06:32:33Z

An investigation of the merits of a four-element coplanar vacuum tube when used as a modulator at carrier telephone frequencies Perkins, Edwin H. Thesis: M.S., Massachusetts Institute of Technology, Department of Electrical Engineering, 1930; Includes bibliographical references (leaf 115).

A precision method for the determination of dew points of complex gaseous systems

2025-11-25T06:32:38Z

A precision method for the determination of dew points of complex gaseous systems Cox, John Tatum. Thesis: M.S., Massachusetts Institute of Technology, Department of Chemical Engineering, 1936; Includes bibliographical references (leaf 43).

AbsInt-AI: Language Models for Abstract Interpretation

2025-11-18T06:27:57Z

AbsInt-AI: Language Models for Abstract Interpretation Wang, Michael Static program analysis is a foundational technique in software engineering for reasoning about program behavior. Traditional static analysis algorithms model programs as logical systems with well-defined semantics, enabling strong guarantees such as never missing a bug. However, traditional analyses almost always rely on uniform, hard-coded heap abstractions. While more adaptive abstractions are possible in theory, they are rarely implemented in practice due to their complexity and fragility. This limits their precision and flexibility, especially in dynamic languages like JavaScript, where heap structures are heterogeneous and difficult to analyze statically. In this work, we introduce AbsInt-AI, a language-model-guided static analysis framework based on abstract interpretation with adaptive, per-object heap abstractions for JavaScript. This enables the analysis to leverage high-level cues, such as naming conventions and access patterns, without requiring brittle, hand-engineered heuristics. Importantly, the LM agent operates within a bounded interface and never directly manipulates program state, preserving the soundness guarantees of abstract interpretation. ABSINT-AI reduces false positives by up to 34% for bug detection compared to traditional static analysis while maintaining soundness. Our ablations show that the LM’s interactions with the analysis environment are crucial, outperforming non-agentic direct LM predictions by 25%.

Optimizing Video Streaming at Scale Across Devices, Networks, and Temporal Drift

2025-11-18T06:27:48Z

Optimizing Video Streaming at Scale Across Devices, Networks, and Temporal Drift Sharma, Harsha Video-streaming platforms tune dozens of playback parameters across thousands of client devices. Our measurements from Prime Video show that device-specific tuning can enhance stream quality. Yet traditional blackbox optimization methods like Bayesian optimization become prohibitively expensive due to the large configuration space and the constant emergence of new device types. We introduce AZEEM, a scalable recommendation system leveraging few-shot prediction to rapidly identify promising configurations for new devices. The key insight behind AZEEM is that devices exhibit performance similarities that enable predictions from limited observations. Trained on offline data of device-playback configuration interactions, AZEEM efficiently narrows down the search space to a small set of configurations likely to contain optimal or near-optimal candidates. Additionally, AZEEM addresses temporal distribution shift—where the best-performing configurations change over time—by recommending a small, robust set of candidates rather than a single configuration. Evaluations using largescale real-world datasets show that AZEEM reduces exploration cost by 5.8 − 13.6× and improves stream quality compared to state-of-the-art Bayesian optimization and multi-armed bandit approaches, enabling effective device-specific optimization at scale. The material in this thesis is primarily sourced from the paper "Predict, Prune, Play: Efficient Video Playback Optimization Under Device Diversity and Drift" authored by Harsha Sharma, Pouya Hamadanian, Arash Nasr-Esfahany, Zahaib Akhtar, Mohammad Alizadeh, which is currently under submission.

Oreo: Protecting ASLR Against Microarchitectural Attacks

2025-11-18T06:27:35Z

Oreo: Protecting ASLR Against Microarchitectural Attacks Song, Shixin Address Space Layout Randomization (ASLR) is one of the most prominently deployed mitigations against memory corruption attacks. ASLR randomly shuffles program virtual addresses to prevent attackers from knowing the location of program contents in memory. Microarchitectural side channels have been shown to defeat ASLR through various hardware mechanisms. We systematically analyze existing microarchitectural attacks and identify multiple leakage paths. Given the vast attack surface exposed by ASLR, it is challenging to effectively prevent leaking the ASLR secret against microarchitectural attacks. Motivated by this, we present Oreo, a software-hardware co-design mitigation that strengthens ASLR against these attacks. Oreo uses a new memory mapping interface to remove secret randomized bits in virtual addresses before translating them to their corresponding physical addresses. This extra step hides randomized virtual addresses from microarchitecture structures, preventing side channels from leaking ASLR secrets. Oreo is transparent to user programs and incurs low overhead. We prototyped and evaluated our design on Linux using the hardware simulator gem5.

On Counting Substructures with Graph Neural Networks

2025-11-18T06:27:54Z

On Counting Substructures with Graph Neural Networks Tahmasebi, Behrooz To achieve a graph representation, most Graph Neural Networks (GNNs) follow two steps: first, each graph is decomposed into a number of subgraphs (which we call the recursion step), and then the collection of subgraphs is encoded by several iterative pooling steps. While recently proposed higher-order networks show a remarkable increase in the expressive power through a single recursion on larger neighborhoods followed by iterative pooling, the power of deeper recursion in GNNs without any iterative pooling is still not fully understood. To make it concrete, we consider a pure recursion-based GNN which we call Recursive Neighborhood Pooling GNN (RNPGNN). The expressive power of an RNP-GNN and its computational cost quantifies the power of (pure) recursion for a graph representation network. We quantify the power by means of counting substructures, which is one main limitation of the Message Passing graph Neural Networks (MPNNs), and show how RNP-GNN can exploit the sparsity of the underlying graph to achieve low-cost powerful representations. We also compare the recent lower bounds on the time complexity and show how recursion-based networks are near optimal.

Design and Control of a Multi-Fingered Soft-Rigid Hybrid Robotic Hand

2025-11-18T06:27:44Z

Design and Control of a Multi-Fingered Soft-Rigid Hybrid Robotic Hand Norton, Wil J. In robot hands, compliance improves the quality of grasps and allows for robustness in contact with the environment, which is why soft robot hands, which are inherently compliant, generate such interest despite being complex to control and model. In prior work, our lab developed a soft-rigid hybrid architecture for a robot finger, with the intention of making a compliant finger that is as easy to control as a rigid robot. This thesis details the work done to take this architecture and develop it into a five-fingered dexterous gripper capable of highly compliant grasping — over several iterations, we create an integrated tendon-driven hand that is robust, maintainable, and inexpensive. We develop a precise controller for the soft-rigid hybrid finger, and extend it for both position and task space control of the hand — additionally we implement variable stiffness control within the controller without the need for additional hardware, via adjusting gain values in the control loop. We test the ability of the hand to complete the full set of human grasping postures, and demonstrate that the soft-rigid architecture enables a high degree of generalization, able to complete 28 of the 33 identified human grasp postures. Additionally, tests illustrate the hand’s advantages in completing traditionally difficult manipulation tasks such as picking up thin deformable objects (such as a dollar bill or folding cloth) as well as in interfacing with soft or delicate target objects. We adapt a teleoperation system to map the movements of the robot gripper to a glove worn by a human operator, and evaluate the usability of the hand as a teleoperation target for completing several tasks — we illustrate promising results that the compliance of the hand compensates for operator error and allows for fast completion of tasks requiring environmental or object contact, traditionally difficult tasks for existing rigid robots. Finally, we discuss the use of the teleoperation system to record demonstrations which we then use to train an imitation learning model, utilizing an implementation of denoising diffusion probabilistic models, to complete grasping tasks. We show that our soft-rigid fingers allow a dexterous hand to be trained to perform autonomous grasping with a relatively small set of expert demonstrations, and that the compliance of the physical structure allows for variance in the environment and object position to be compensated for by the physical properties of the hand.

Development of Multi-Modality Imaging Cart for Barrett’s Esophagus

2025-11-18T06:27:28Z

Development of Multi-Modality Imaging Cart for Barrett’s Esophagus Qu, Ashley Barrett’s Esophagus (BE) is a key precursor to esophageal adenocarcinoma (EAC), but current screening and risk assessment methods are ineffective and costly. Many BE cases remain undiagnosed due to asymptomatic patients, and existing risk algorithms rely on patient data rather than biomarkers. This work aims to start building a risk progression model by using a multi-modal imaging system combining autofluorescence spectroscopy, optical coherence tomography, and diffuse reflectance spectroscopy to perform label-free optical biopsies on ex-vivo tissue. These images will be co-registered and validated with histological biomarkers for BE. The ultimate goal is to develop a non-invasive endoscopic capsule and algorithm to better assess BE progression and enhance early detection of EAC.

Complexity of Basis-Restricted Local Hamiltonians

2025-11-18T06:27:51Z

Complexity of Basis-Restricted Local Hamiltonians Ma, Henry A major goal of quantum complexity theory is to understand which computational problems can be solved with access to certain quantum resources. The subfield of Hamiltonian complexity specifically considers computational problems that ask about properties of local Hamiltonians, which are of critical importance in quantum complexity because they can be viewed as quantum generalizations of classical constraint satisfaction problems. In this work, we study the complexity of certain restricted variants of the Quantum-k-Sat problem, a quantum analog of the NP-complete k-Sat problem. We introduce new variants of Quantum-k-Sat which place a basis restriction on the input Hamiltonian H = Σᵢ hᵢ . Each variant is defined by a fixed collection of bases B₁, . . . , Bᵣ of n-qubit space. We require that each Hamiltonian term hi must be diagonal in one of these bases. Our results resolve the complexity of certaim basis-restricted variants of Quantum-k-Sat. First we show the Quantum-6-Sat problem with Hamiltonian terms restricted to be diagonal in an X/Z mixed basis is QMA₁-complete. Second, we combine basis restriction with the restriction of commutativity, and show the following easiness result, which applies generally to higher-level quantum systems (qudits) and bases Q and R (which are real-valued and satisfy an overlap condition): The commmuting Quantum-Sat problem on qudits, where Hamiltonian terms are either diagonal in the Q basis, the R basis, or a single mixed Q/R basis, is in NP.

Future of Personalized, Aligned Language Models

2025-11-18T06:27:43Z

Future of Personalized, Aligned Language Models Han, Seungwook Aligning Large Language Models (LLMs) to cater to different human preferences, learning new skills, and unlearning harmful behavior is an important problem. Search-based methods, such as Best-of-N or Monte-Carlo Tree Search, are effective, but impractical for LLM adaptation due to their high inference cost. On the other hand, using Reinforcement Learning (RL) for adaptation is computationally efficient, but performs worse due to the optimization challenges in co-training the value function and the policy. We present a new framework for reward optimization, Value Augmented Sampling (VAS), that can maximize different reward functions using data sampled from only the initial, frozen LLM. VAS solves for the optimal reward-maximizing policy without co-training the policy and the value function, making the optimization stable, outperforming established baselines, such as PPO and DPO, on standard benchmarks, and achieving comparable results to Best-of-128 with lower inference cost. Unlike existing RL methods that require changing the weights of the LLM, VAS does not require access to the weights of the pre-trained LLM. Thus, it can even adapt LLMs (e.g., ChatGPT), which are available only as APIs. In addition, our algorithm unlocks the new capability of composing several rewards and controlling the extent of each one during deployment time. By bringing together stability, flexibility, and efficiency, we explore the future of aligned, personalized language models that can be adapted seamlessly to meet a wide spectrum of human preferences.

Post-Carbon Seoul: Low-Carbon Interventions for a High-Carbon Housing Stock

2025-11-18T06:27:33Z

Post-Carbon Seoul: Low-Carbon Interventions for a High-Carbon Housing Stock Ji, Yewon Seoul, South Korea, exhibits an exceptionally rapid residential demolition-reconstruction cycle of approximately 30 - 40 years, resulting in one of the world’s shortest apartment building lifespans. This entrenched status quo, fueled by post-war policies, real estate speculation, and finance models treating housing primarily as a short-term asset, contrasts sharply with other developed nations. This research critiques South Korea’s model of rapid demolition for its significant, often overlooked, environmental impacts and social costs. To evaluate alternatives, the methodology comprises three key stages: A) a comparative analysis of the financial frameworks and sustainability outcomes characterizing Western residential longevity versus the unique Korean housing model; B) the formulation of a novel alternative practice focused on adaptive reuse and retrofitting, specifically tailored to integrate within South Korea’s economic system and cultural context; and C) the practical demonstration and assessment of this practice through a design case study, incorporating strategies like phased interventions and low-carbon materials such as mass timber. The analysis reveals that this alternative extends building lifespan and achieves substantial carbon reductions by preserving the embodied carbon within existing structures. It offers long-term financial benefits, presenting a viable economic pathway aligning key stakeholder interests through enduring value over speculative gains.

Scaling Automatic Question Generation to Large Documents: A Concept-Driven Approach

2025-11-18T06:27:53Z

Scaling Automatic Question Generation to Large Documents: A Concept-Driven Approach Noorbakhsh, Kimia Assessing and enhancing human learning through question-answering is vital, especially when dealing with large documents, yet automating this process remains challenging. While large language models (LLMs) excel at summarization and answering queries, their ability to generate meaningful questions from lengthy texts remains underexplored. We propose Savaal, a scalable question-generation system with three objectives: (i) scalability, enabling question-generation from hundreds of pages of text (ii) depth of understanding, producing questions beyond factual recall to test conceptual reasoning, and (iii) domainindependence, automatically generating questions across diverse knowledge areas. Instead of providing an LLM with large documents as context, Savaal improves results with a threestage processing pipeline. Our evaluation with 76 human experts on 71 papers and PhD dissertations shows that Savaal generates questions that better test depth of understanding by 6.5× for dissertations and 1.5× for papers compared to a direct-prompting LLM baseline. Notably, as document length increases, Savaal’s advantages in higher question quality and lower cost become more pronounced.

Ab initio modeling of superconducting nanowire single-photon detectors

2025-11-18T06:27:39Z

Ab initio modeling of superconducting nanowire single-photon detectors Simon, Alejandro Single-photon detectors are widely used in modern communication, sensing, and computing technology. Among these detectors, superconducting nanowire single-photon detectors (SNSPDs) possess the highest detection efficiencies, the shortest timing jitter, and the lowest dark count rates. However, for several applications, including those in the biological, astronomical, and quantum computation fields, there remains a desire to push the capabilities of modern detectors even further. To realize these improvements, it is necessary to develop an understanding of the physical mechanisms underpinning single-photon detection in these devices. However, current models are phenomenological, requiring experimental data for input, or can only recover qualitative agreement, severely limiting their predictive ability. In this thesis, we begin by describing the existing theoretical frameworks used to model superconducting materials and devices, both in equilibrium and nonequilibrium. We then illustrate an example of a phenomenological approach to modeling superconducting devices by developing an electrothermal model for the superconducting nanowire cryotron and demonstrating its efficacy in predicting the DC behavior and power dissipation of the device. Finally, we expand upon the current state-of-the-art SNSPD theory by utilizing recent advances in density functional theory to develop an ab initio model for the photon detection mechanism of SNSPDs. We then validate the predictions of our model with experimental data from the literature. The resulting model requires no experimental input, provides quantitative predictions of SNSPD performance, and can be extended to describe other superconducting devices, thus enabling the possibility of conducting a systematic search of materials for enhanced device performance.

Trapping and Laser Cooling an Ensemble of Ytterbium-171 Atoms for use in an Atomic Clock

2025-11-18T06:27:27Z

Trapping and Laser Cooling an Ensemble of Ytterbium-171 Atoms for use in an Atomic Clock Velez, Gustavo A. Optical lattice clocks require careful preparation of atomic ensembles in order to ensure homogeneous interactions with the clock laser. We demonstrate loading and laser cooling of an ensemble of ytterbium-171 atoms in a 2D optical dipole trap created by an optical cavity. Our loading method ensures that all atoms are located in the intersection of 2 perpendicular dipole traps as verified through absorption imaging. Raman sideband cooling was used to cool the atomic ensemble from 15.7 uK to 6.3 uK as measured through optical sideband spectroscopy on the 578 nm clock transition. Together, these steps improved the transfer of atoms during a Rabi oscillation from the ground to the clock state from approximately 45 percent excitation fraction to 80 percent excitation fraction. The final atomic ensemble preparation is now sufficient for running an atomic clock.

Improving and Analyzing Model Merging Methods for Adaptation

2025-11-18T06:27:02Z

Improving and Analyzing Model Merging Methods for Adaptation Pari, Jyothish In this work, we explore the limitations of combining models by averaging intermediate features, referred to as model merging, and propose a new direction for achieving collective model intelligence through what we call compatible specialization. Current methods for model merging, such as parameter and feature averaging, struggle to effectively combine specialized models due to representational divergence during fine-tuning. As models specialize to their individual domains, their internal feature representations become increasingly incompatible, leading to poor performance when attempting to merge them for new tasks. We analyze this phenomenon using centered kernel alignment (CKA) and show that as models specialize, the similarity in their feature space structure diminishes, hindering their capacity for collective use. To address these challenges, we investigate routing-based merging strategies, which offer more flexible methods for combining specialized models by dynamically routing across different layers. This allows us to improve on existing methods by combining features from multiple layers rather than relying on fixed, layer-wise combinations. However, we find that these approaches still face limitations when layers within models are representationally incompatible. Our findings highlight the importance of designing new approaches for model merging that operate on well-defined input and output spaces, similar to how humans communicate through language rather than intermediate neural activations.

Evaluating Differences in GPT-4 Treatment by Gender in Healthcare Applications

2025-11-18T06:27:26Z

Evaluating Differences in GPT-4 Treatment by Gender in Healthcare Applications Pan, Eileen LLMs already permeate medical settings, supporting patient messaging, medical scribing, and chatbots. While prior work has examined bias in medical LLMs, few studies focus on realistic use cases or analyze the source of the bias. To assess whether medical LLMs exhibit differential performance by gender, we audit their responses and investigate whether the disparities stem from implicit or explicit gender cues. We conduct a large-scale human evaluation of GPT-4 responses to medical questions, including counterfactual gender pairs for each question. Our findings reveal differential treatment based on the original patient gender. Specifically, responses for women more often recommend supportive resources, while those for men advise emergency care. Additionally, LLMs tend to downplay medical urgency for female patients and escalate it for male patients. Given rising interest in “LLM-as-a-judge” approaches, we also evaluate whether LLMs can serve as a proxy for human annotators in identifying disparities. We find that LLM-generated annotations diverge from human assessments in heterogeneous ways, particularly regarding error detection and relative urgency.

Highly Integrated Graphene-Based Chemical Sensing Platform for Structural Monitoring Applications

2025-11-18T06:27:20Z

Highly Integrated Graphene-Based Chemical Sensing Platform for Structural Monitoring Applications López Ángeles, Christian Emmanuel Two-dimensional materials, such as graphene, hold promise for sensing applications. Graphene's remarkable surface-to-volume ratio, when employed as a transducer, enables the sensor channel to be readily modulated in response to chemical changes in proximity to its surface, effectively converting chemical signals into the electrical domain. However, their utilization has been constrained due to variations in device-to-device performance arising from synthesis and fabrication processes. To address this challenge, we employ Graphene Field Effect Transistors (GFETs) in developing a robust and multiplexed chemical sensing platform. This platform comprises a silicon chip with multiple arrays of sensing units distributed on its surface. This chip is coupled with custom-designed high-speed readout electronics for structural monitoring applications. For example, in harsh environmental conditions, structures constructed from reinforced concrete may experience degradation due to corrosion, a chemical process initiated by carbonation from atmospheric CO₂ and significant fluctuations in temperature and humidity. Under normal conditions, concrete maintains a pH level within the alkaline range of 13 to 14. However, when subjected to carbonation, its pH decreases to values between 8 and 9. Our platform excels in real-time pH monitoring. By conducting I-V sweep measurements in the sensor channel, we have established a correlation between [H⁺] concentration and the device transfer characteristics, i.e. gate-source voltage (𝑉_𝐺𝑆) at graphene's Dirac point with an accuracy of roughly 97%. Additionally, we evaluate changes in graphene channel resistance induced by pH variations. This system and correlation allow for the prompt detection of any deviations induced by corrosion within a concrete environment.

Towards More Interpretable AI With Sparse Autoencoders

2025-11-18T06:26:52Z

Towards More Interpretable AI With Sparse Autoencoders Engels, Joshua While large language models demonstrate remarkable capabilities across diverse domains, the specific representations and algorithms they learn remain largely unknown. The quest to understand these mechanisms holds dual significance: scientifically, it represents a fundamental inquiry into the principles underlying intelligence, while practically–and with growing urgency– it is vital for mitigating risks from these very same increasingly powerful systems. The initial section of this thesis tackles this challenge of interpreting internal language model representations (features) by employing sparse autoencoders (SAEs). An SAE decomposes neural network hidden states into a potentially more interpretable basis. In Chapter 2, we introduce an unsupervised, SAE-based methodology that successfully identifies inherently multi-dimensional features. Notably, we establish that language models causally represent concepts such as days of the week and months of the year using circular structures. This work provided the first definitive evidence of causal, multi-dimensional features, thereby refuting the one-dimensional linear representation hypothesis. Chapter 3 further assesses whether SAEs identify “true” atomic language model features. We compare the generalization performance and data efficiency of linear probes trained on SAE latents against those trained on the original hidden state basis. The negative outcomes of these experiments suggest limitations in SAEs for capturing the true ontology of language models. Motivated by the aforementioned limitations, the second part of this thesis investigates sparse autoencoders themselves, exploring potential improvements and characterizing their failure modes. Chapter 4 examines the portion of activations not reconstructed by SAEs, which we term “Dark Matter.” We find that a significant fraction of this dark matter is linearly predictable, and furthermore, that specific tokens poorly reconstructed by SAEs remain largely consistent across SAE sizes and sparsities. This suggests that SAEs may systematically fail to capture certain input subspaces, which we hypothesize to contain inherently dense features. Subsequently, Chapter 5 investigates a method to enhance SAE utility: freezing the learned SAE parameters and finetuning the surrounding language model components to minimize KL divergence with the original model’s output distribution. This technique results in a 30% to 55% decrease in the cross-entropy loss gap incurred by inserting the SAE into the model.

Transmission Line Dynamics Modeling For Power Electronics-Enabled Control in the Electric Power Systems

2025-11-18T06:27:23Z

Transmission Line Dynamics Modeling For Power Electronics-Enabled Control in the Electric Power Systems Lawson, Riley E. In the analysis and operation of electric power systems, understanding the rates at which dynamic phenomena evolve is critical. Classically, power systems operate on multiple time scales, with slower mechanical dynamics from synchronous machines, faster electromechanical controls and protection, and very fast electrical dynamics from transmission networks. This time scale separation results in system modeling techniques which neglect certain component dynamics. However, in systems with significant penetration of power electronic devices and under fast time scale phenomena, the rates at which dynamics evolve become less separated, necessitating the modeling of all system dynamics. In large-scale systems, this becomes computationally challenging due to the high dimensionality of the interconnected system model. This work investigates the role transmission line dynamics play at very fast time scales in power systems. Theoretical results are presented to analyze which transmission line dynamics contribute significantly to power system dynamics, allowing for the intelligent incorporation of transmission line dynamics into computationally tractable models. For the first time, the use of control co-design techniques are demonstrated algorithmically to design fast power electronics-enabled control to stabilize unstable dynamics in electric power systems. This technique allows the design of controls, in an iterative way, to create stable interconnected systems. Finally, transmission line modeling impacts on the design of protection on fast time scales is analyzed. This work presents techniques to protect from short circuits in response to load disconnections, and introduces DC circuit breaker configurations to cause current commutation. In the modern day, power systems operators possess the technology to implement fast control of dynamics, however, due to insufficient information on how to model and prepare for them, system operators instead rely on using conventional, overly conservative control schemes. This work aims to bridge this gap by presenting methodologies to incorporate these dynamics into next-generation system models, and how to design control and protection to mitigate the risks these fast dynamics pose.

Foundation Models for Protein Phenotype Prediction

2025-11-18T06:27:09Z

Foundation Models for Protein Phenotype Prediction Calef, Robert Understanding the roles of human proteins remains a major challenge, with approximately 20% of human proteins lacking known functions and more than 40% missing context-specific functional insights. Even well-annotated proteins are often poorly characterized in diverse biological contexts, disease states, and perturbations. We present ProCyon, a foundation model for modeling, generating, and predicting protein phenotypes across five interrelated knowledge domains: molecular functions, therapeutic mechanisms, disease associations, functional protein domains, and molecular interactions. To support this, we created ProCyon-Instruct, a dataset of 33 million protein phenotype instructions, representing a comprehensive resource for multiscale protein phenotypes. By co-training a large language model with multimodal molecular encoders, ProCyon integrates phenotypic and protein data. A novel architecture and instruction tuning strategy allow ProCyon to process arbitrarily interleaved proteinand-phenotype inputs, achieve zero-shot task transfer, and generate free-form text phenotypes interleaved with retrieved protein sequence, structure, and drug modalities in a single unified model.

Functionalization of CNFET arrays for chemical sensing

2025-11-18T06:26:43Z

Functionalization of CNFET arrays for chemical sensing Song, Jaekang Practical deployment of gas sensors for general-purpose applications requires integrated chips that operate at room temperature. However, real-world implementation has been limited by challenges such as the integration of highly sensitive and selective sensors, as well as insufficient statistical validation. In this work, we present an integrated gas sensor array comprising 2048 carbon nanotube field-effect transistors (CNFETs), functionalized with conductive metal-organic frameworks (cMOFs) and metal nanoparticles. Our functionalization approach enhances sensor responses by up to two orders of magnitude and enables on-chip pattern generation. Furthermore, the large number of redundant sensors allows for statistically significant measurements. The improved sensitivity is attributed to increased Schottky barrier modulation. We also demonstrate the chip’s capability to classify bacteria and yeast based on the gas mixtures emitted from cultures grown on agar plates. This work highlights the potential of integrated gas sensors as a practical, rapid, and cost-effective approach for general gas sensing applications, including biomedical applications.

High-efficiency, low-loss Floquet Josephson Traveling Wave Parametric Amplifier

2025-11-18T06:26:56Z

High-efficiency, low-loss Floquet Josephson Traveling Wave Parametric Amplifier Wang, Jennifer Advancing error-corrected quantum computing and fundamental science necessitates quantum-limited amplifiers with near-ideal quantum efficiency and multiplexing capability. However, existing solutions achieve one at the expense of the other; for example, Josephson traveling wave parametric amplifiers (JTWPAs) are highgain, broadband, and chip-based quantum amplifiers that conventionally incur a bandwidth-noise tradeoff. When operated at 20-dB gain and instantaneous bandwidths of a few GHz, JTWPAs typically reach near-quantum limited intrinsic efficiencies of 70% - 85% relative to that of an ideal phase-preserving quantum amplifier. This is due to information leakage to the sidebands of the JTWPA, which can be recovered by adiabatically transforming the input modes to Floquet modes of the system within the device. In this thesis, we experimentally demonstrate the first Floquet-mode travelingwave parametric amplifier (Floquet TWPA). Fabricated in a superconducting qubit process, this Floquet TWPA achieves minimal dissipation, quantum-limited noise performance, and broadband operation. Our device exhibits > 20-dB amplification over a 3-GHz instantaneous bandwidth, <0.5 -dB average in-band insertion loss, and the highest-reported intrinsic quantum efficiency for a TWPA of 92.1±7.6%, relative to an ideal phase-preserving amplifier. When measuring a superconducting qubit, our Floquet TWPA enables a system measurement efficiency of 65.1 ± 5.8%, the highest-reported in a superconducting qubit readout experiment utilizing phase-preserving amplifiers to the best of our knowledge. Finally, we discuss the noise limitations of our current experimental setup, as well as impedance matching strategies that will enable us to push towards ideal JTWPA performance. These general-purpose Floquet TWPAs are suitable for fast, high-fidelity multiplexed readout in large-scale quantum systems and future monolithic integration with quantum processors.

Towards Scalable Robot Learning without Physical Robots

2025-11-18T06:26:42Z

Towards Scalable Robot Learning without Physical Robots Park, Younghyo The development of generalist robots—capable of performing a wide range of tasks in diverse environments—requires large-scale datasets of robot interactions. Unlike language or vision domains, where data can be passively collected at scale, robotic data collection remains costly, labor-intensive, and constrained by physical hardware. This thesis explores two complementary directions to overcome this challenge. First, we examine the limitations of training robots from scratch using reinforcement learning (RL). While RL has achieved promising results in simulation, its scalability is hindered by a largely overlooked bottleneck: environment shaping. Designing suitable rewards, action and observation spaces, and task dynamics typically requires extensive human intervention. We formalize environment shaping as a critical optimization problem and introduce tools and benchmarks to study and eventually automate this process, a necessary step toward general-purpose RL. Second, we introduce an alternative paradigm for robot data collection that does not rely on real-world robots. Using the Apple Vision Pro, we develop DART, an augmented reality (AR) teleoperation platform that streams human hand motions to cloud-hosted robot simulations. This setup enables scalable, low-latency collection of high-quality robot demonstrations without the overhead of physical setup or maintenance. Our user studies show that DART more than doubles data collection throughput while reducing operator fatigue, and policies trained in simulation using this data successfully transfer to the real world. Together, these contributions address two key bottlenecks in robot learning: the human effort required for RL environment design, and the dependence on physical robots for data. They lay the groundwork for scalable, accessible approaches to training generalist robot models.

A Reconfigurable, Distributed-Memory Accelerator for Sparse Applications

2025-11-18T06:27:16Z

A Reconfigurable, Distributed-Memory Accelerator for Sparse Applications Golden, Courtney K. Iterative sparse matrix computations lie at the heart of many scientific computing and graph analytics algorithms. On conventional systems, their irregular memory accesses and low arithmetic intensity create challenging memory bandwidth bottlenecks. To overcome such bottlenecks, distributed-SRAM architectures use tiled arrays of high-bandwidth local storage to achieve very high aggregate memory bandwidth. However, current distributedSRAM architectures suffer from either poor programmability due to over-specialization or poor compute performance due to inefficient general-purpose hardware. This thesis proposes Quartz, a new architecture that uses short dataflow tasks and reconfigurable compute in a distributed-SRAM system to deliver both high performance and high programmability. Unlike traditional sparse CGRAs or on-die reconfigurable engines, Quartz allows reconfigurable compute to be highly utilized and scaled by (1) providing high memory bandwidth to each processing element and (2) introducing a task-level dataflow execution model that fits this new setting. Our execution model dynamically reconfigures tile hardware based on inter-tile messages to execute tasks on local data with fine-grained data partitioning across tiles. To make execution efficient, we explore novel data partitioning techniques that use graph and hypergraph partitioning to minimize network traffic and balance load. This is especially challenging for computations where one operand’s sparsity pattern (i.e., distribution of nonzeros) exhibits dynamic behavior across iterations, and we are the first to provide techniques to address this case. To ensure programmability, we show how a wide range of computations (expressed in an extended version of tensor algebra’s Einsum notation) and flexible data distributions can be systematically captured in small tasks for execution on Quartz. We evaluate Quartz in simulation, using an 8-chiplet design with 2,048 tiles and 824 MB of SRAM per chiplet, running six different iterative sparse applications from scientific computing and graph analytics. Quartz’s architecture, data partitioning techniques, and programming model together achieve gmean 26.2× speedup over the prior state-of-the-art programmable distributed-SRAM architecture.

Dipole Contact Engineering for Field-Effect Transistors Based on Two-Dimensional Materials

2025-11-18T06:27:03Z

Dipole Contact Engineering for Field-Effect Transistors Based on Two-Dimensional Materials Gupta, Ayush Sagar In the next several years and decades, the expanded use of artificial intelligence and edge computing will demand more powerful and energy-efficient electronics. Two-dimensional (2D) semiconductors, and in particular transition metal dichalcogenides (TMDs) such as molybdenum disulfide (MoS₂), are promising candidates for future field-effect transistors. TMDs can enable aggressive lateral and vertical device scaling, and they can add computing power density and new memory and sensing capabilities via 3D integration. However, several key challenges remain before 2D-channel transistors become commercially viable, including large contact resistances at the source and drain due to the van der Waals surface of 2D materials and the Fermi level pinning effect. A variety of methods have been explored to make ohmic contacts to MoS₂, the most promising of which so far is to use semimetals such as Bi and Sb, however these materials suffer from thermal instability. This thesis addresses these challenges by (1) exploring the ultimate limit of contact metal workfunction scaling to better understand the metal-MoS₂ interface, and (2) introducing a new method of reducing contact resistance to 2D materials by inserting dipole layers at the contact interface. Initial work on ultralow-workfunction (ULWF) metal deposition on MoS₂ and subsequent device fabrication is presented, though further study is required to mitigate effects from deposition equipment and the reactive nature of these metals. In parallel, the Janus TMD MoSSe is explored as an example system for dipole contacts, with extensive material characterization of the Janus TMD MoSSe being performed, and the effect of a dipole layer on the contact properties of FETs being established. Together, these results are a significant step towards solving one of the major hurdles for the commercial introduction of 2D-channel transistors.

Specialization of Vision Representations with Personalized Synthetic Data

2025-11-18T06:26:48Z

Specialization of Vision Representations with Personalized Synthetic Data Chae, Nayoung (Julia) Modern vision models excel at general purpose downstream tasks. It is unclear, however, how they may be used for personalized vision tasks, which are both fine-grained and data-scarce. Recent works have successfully applied synthetic data to general-purpose representation learning, while advances in Text-to-Image (T2I) diffusion models have enabled the generation of personalized images from just a few real examples. Here, we explore a potential connection between these ideas, and formalize the challenge of using personalized synthetic data to learn personalized representations, which encode knowledge about an object of interest and may be flexibly applied to any downstream task relating to the target object. We introduce an evaluation suite for this challenge, including reformulations of two existing datasets and a novel dataset explicitly constructed for this purpose, and propose a contrastive learning approach that makes creative use of image generators. We show that our method improves personalized representation learning for diverse downstream tasks, from recognition to segmentation, and analyze characteristics of image generation approaches that are key to this gain.

Optimizing Microservice Design Parameters

2025-11-18T06:27:22Z

Optimizing Microservice Design Parameters Chen, Qihang Production-level cloud services are increasingly deployed as microservices. An important question is given application logic, how to design an effective microservice architecture. Existing studies have underscored the importance of microservice cohesiveness and coupling, using these metrics to drive automatic design optimizations. However, they have not accounted for the potential impact that such design changes may have on overall system performance, which is confirmed by our case study. In this work, we present a system that can automatically identify microservice designs that are well-balanced across performance, coupling, and cohesiveness to meet cloud provider’s requirements. the system uses a multi-round dynamic programming approach, selectively identifies promising design candidates, generates the corresponding microservice code, measures and compares the results to ultimately determine the optimal design. The designs produced by our system typically achieve over 20% throughput improvement under the same QoS with less than a 10% increase in average LCOM, and often outperform the original benchmark architectures across all evaluated metrics.

City in the River: Regeneration of the Santa Catarina River as an Intermittent Urban River

2025-11-18T06:27:13Z

City in the River: Regeneration of the Santa Catarina River as an Intermittent Urban River Martínez Chapa, Daniela Full of dichotomies, the Santa Catarina River is both dry and wet, present but forgotten, central yet disconnected, valued yet feared. How should an intermittent river in a dense urban context be regenerated? This thesis reimagines its ecological, hydrological, and public potential. Set in Monterrey, Mexico, this research addresses the urgent need to rethink water management in the face of the intensifying climate crisis through different urban systems and regeneration strategies within the river basin. Focusing on the Santa Catarina River, long dismissed as a plot, void, or threat, this work proposes how an intermittent river might be re-understood not as an absence of activities or function but as a space of seasonal abundance, ecological possibility, and urban interaction. Historically engineered for control, the river has been used as a flood channel, markets, sports complexes, transportation corridors, and more. However, rarely has it been seen, treated, or protected as a river. Through the development of a pilot zone, this research suggests a replicable framework of regenerative strategies to slow down, retain, and absorb water flows, supporting both dry and wet season dynamics. These include restoring riparian ecologies, reintroducing soft edges, enabling groundwater recharge, and designing permeable, public, and accessible urban interventions that reconnect the city with the riverbed. This thesis is not a fixed proposal but a living toolkit, an adaptable model to be tested, expanded, and reimagined in the pilot as time and nature take over. At stake is not only the river’s future but also the city’s capacity to shift from resistance to relation, becoming one with it, becoming a city in the river.

Banjiha Stories (2025)

2025-11-18T06:27:00Z

Banjiha Stories (2025) Park, Habin Banjiha are everywhere in Seoul. You don’t always see them—tucked below eye level, half-hidden underground—but they’re there. First built as military bunkers after the Korean War, later turned into last-resort housing, banjiha have become symbols of urban failure—spaces of neglect, flooding disasters, a problem to be erased. Both media portrayals and policy responses have advocated for their disappearance. But does removal truly protect the people who call these spaces home? This thesis moves beyond the idea that banjiha are simply failures of the city. Through three homes —three lives, it traces how these spaces are shaped, not only by policies and architecture but by the people who inhabit them. A home vulnerable to flooding, where protections exist—but not with the greatest risk. A place worn by time, held together by quiet repairs. A financial foothold in a city where affordable housing is disappearing. A space of temporary sacrifice. A shelter to return to, again and again. This is not just a story of risk or resilience, neglect or demolition. It is a story of how people live; how they adapt, negotiate, and make do in spaces that were never designed with them in mind. Rather than asking how to erase banjiha, this thesis asks: What can we learn by noticing them? What would it mean to shift the conversation—from removal to recognition, from assumption to understanding? To see these homes is to recognize not just their constraints, but the small interventions that could reshape them: a door that opens both ways so no one is trapped, policies that hold upstairs owners accountable for leaks, materials layered to prevent mold rather than mask it. Not grand reinventions, but deliberate shifts—openings for a different way forward. But before deciding what must change, we must first learn to see.

Probabilistic Inference for Inference Time Scaling of Language Models

2025-11-18T06:26:45Z

Probabilistic Inference for Inference Time Scaling of Language Models Puri, Isha Large language models (LLMs) have achieved significant performance gains via scaling up model sizes and/or data. However, recent evidence suggests diminishing returns from such approaches, motivating a pivot to scaling test-time compute. Existing deterministic inference-time scaling methods, usually with reward models, cast the task as a search problem, but suffer from a key limitation: early pruning. Due to inherently imperfect reward models, promising trajectories may be discarded prematurely, leading to suboptimal performance. We propose a novel inference-time scaling approach by adapting particle-based Monte Carlo methods. Our method maintains a diverse set of candidates and robustly balances exploration and exploitation. Our empirical evaluation demonstrates that our particle filtering methods have a 4–16x better scaling rate over deterministic search counterparts on both various challenging mathematical and more general reasoning tasks. Using our approach, we show that Qwen2.5-Math-1.5B-Instruct surpasses GPT-4o accuracy in only 4 rollouts, while Qwen2.5-Math-7B-Instruct scales to o1 level accuracy in only 32 rollouts. Our work not only presents an effective method to inference-time scaling, but also connects rich literature in probabilistic inference with inference-time scaling of LLMs to develop more robust algorithms in future work. Code, videos, and further information available at probabilistic-inference-scaling.github.io/

Toward Systematic Integration of Inverter-Based Resources in Electricity Markets

2025-11-18T06:26:34Z

Toward Systematic Integration of Inverter-Based Resources in Electricity Markets Pierre, Jordina This thesis introduces a multi-layer control architecture for inverter-based resources (IBRs), separating fast local feedback control from slower self-dispatch and system-level market coordination. Existing integration methods for IBRs limit their control flexibility and completely restrict their market participation potential. Two common practices include treatment of IBRs as negative loads and setting a fixed power factor during grid commissioning. Modeling IBRs as negative loads excludes them from dispatch coordination in electricity markets, significantly limiting incentive for contribution to grid reliability and flexibility. Likewise, a fixed power factor prevents the IBR from providing voltage support through reactive power absorption/injection. With a fixed power factor, constant real and reactive power limits are imposed on the inverter, even during voltage transients, ignoring the fact that an inverter’s available capacity can vary significantly due to internal current constraints and the power provided by the renewable energy source. To address the need for reactive power adjustment in IBRs and pave the way for their active participation in electricity markets , this work presents a coordinated control approach that enables IBRs to transition into active, self-dispatching participants. This thesis proposes a first layer hybrid PLL plus Q-V droop based controller in the first layer which governs millisecond-scale autonomous behavior, including low-voltage ride-through and real-time power adjustment based on voltage deviations at the point of common coupling and irradiance fluctuations from the renewable energy source, in this case solar. Given implementation from the first layer and predicted irradiance, Layer 2, which will be implemented in future work, uses a model predictive controller to provide bid functions for both real and reactive power while keeping voltage at the Point of Common Coupling within its limits. Finally, the third layer performs centralized market clearing through a security-constrained optimization by the system operator. By advocating for self-dispatched, constraint aware control, this thesis challenges the prevailing passive modeling paradigm and offers a structured, physics-informed alternative. It demonstrates how IBRs can evolve into reliable, market-integrated assets, enabling smarter renewable integration and a more resilient, cost-effective and decarbonized grid.

Approximations to worst-case data dropping: unmasking failure modes

2025-11-18T06:28:40Z

Approximations to worst-case data dropping: unmasking failure modes Huang, Jenny Yijian A data analyst might worry about generalization if dropping a very small fraction of data points from a study could change its substantive conclusions. Checking this non-robustness directly poses a combinatorial optimization problem and is intractable even for simple models and moderate data sizes. Recently various authors have proposed a diverse set of approximations to detect this non-robustness. In the present work, we show that, even in a setting as simple as ordinary least squares (OLS) linear regression, many of these approximations can fail to detect (true) non-robustness in realistic data arrangements. We focus on OLS in the present work due its widespread use and since some approximations work only for OLS. Of the approximations that do not fail our tests, we find not only that a simple recursive greedy algorithm is the most conceptually straightforward but also that it can be orders of magnitude faster to run than the others.

Highly Scaled p-GaN-gate HEMTs for Low Voltage Power Electronics

2025-11-18T06:28:06Z

Highly Scaled p-GaN-gate HEMTs for Low Voltage Power Electronics Darmawi-Iskandar, Patrick Rising global energy demands, driven by the advent of artificial intelligence (AI), cloud computing, and Internet of Things (IoT) devices, underscore the need for more efficient power electronics. In particular, power switches based on wide bandgap semiconductors such as gallium nitride (GaN) have emerged as promising alternatives to traditional silicon devices for low-voltage (10-100 V) applications. This work investigates the design, fabrication, and scaling of p-GaN-gate highelectron-mobility transistors (HEMTs). A p-GaN-gate epitaxial structure was developed with considerations for short channel effects. A self-aligned, gate-first process employing tungsten metallization was implemented to enable gate lengths as small as 100 nm. Device scaling was studied systematically, revealing the importance of gate aspect ratio and gate-to-drain spacing in managing short channel effects and maintaining breakdown voltage. Electrical characterization showed strong device performance, although contact resistance accounted for a substantial portion of total on-resistance. To address this, a modified fabrication approach incorporating regrown contacts was introduced, resulting in reduced contact resistance and improved overall device characteristics. The combined results highlight practical strategies for enhancing the performance and scalability of p-GaN-gate HEMTs for next-generation low-voltage power electronics.

Modelling Diarists: Diary-writing and Moral Anxieties in China, 1918–62

2025-11-18T06:28:04Z

Modelling Diarists: Diary-writing and Moral Anxieties in China, 1918–62 Li, Tien Yi This thesis is a history of diary-writing in China from 1918 through 1961. Diaries are an increasingly popular but still inadequately understood primary source for historians of modern China. Previous scholars have suggested that, in the twentieth century, diary-writing became increasingly popular due to Japanese and Soviet influences, the increasing availability of manufactured blank diaries, and ruling governments that used diary-writing as a way of enforcing ideological conformity. This thesis traces an alternative history, starting from the popularization of published diaries in Shanghai in the long 1920s; to diaries’ emergence as a recognizable genre that could discoursed be theorized; to the moment the genre gained its reputation as a kind of self-expression par excellence; to its widespread inclusion into school curricula; to loosely connected attempts on the part of educators to delimit a normative way of diarywriting that, ironically, increasingly regimented self-expression. In doing so, this thesis contributes to the existing historiography by offering three correctives: I argue that 1) the initial proliferation of diaries was economically––not ideologically––motivated, 2) the popularization of diary-writing was not a concerted effort orchestrated by China’s political leaders but at best a loosely connected effort led by a middling class of educators, textbook writers, and intellectuals, and 3) diary-writing was not only regimented by communist ideology in the Maoist era but shifting moral principles and anxieties throughout the twentieth century. All in all, this thesis demonstrates the value of diaries for studying moral knowledge, epistemologies, and anxieties at the grassroots in midcentury China.

Parallel Batch-Dynamic Graph Algorithms: Coreness Decomposition and Spanners

2025-11-18T06:27:54Z

Parallel Batch-Dynamic Graph Algorithms: Coreness Decomposition and Spanners Koo, Jaehyun This thesis contributes to the burgeoning field of batch-dynamic parallel algorithms by presenting parallel batch-dynamic graph algorithms for coreness decomposition and spanners, as well as a number of other related problems. The first class of problems we consider involves approximating coreness decomposition and several closely related concepts, such as (subgraph) density estimation, arboricity estimation, and low out-degree orientations. These are extremely useful structures for organizing graphs based on their density. Our algorithms process any batch of edge insertions and deletions in polylogarithmic depth while using work that is linear in the batch size (up to logarithmic factors), in the worst case. The second class of problems we consider concerns graph spanners. Over the past two to three decades, graph sparsifications that approximately preserve key graph properties have become essential tools in algorithm design. In particular, spanners—reducing the number of edges while approximately preserving pairwise distances—have been widely studied. We present the first such algorithms for computing and maintaining spanners. These algorithms achieve near-optimal amortized runtime—processing each batch in polylogarithmic depth with work nearly linear in the batch size for any number of processors.

Bridging the Sim-to-Real Gap for Athletic Loco-Manipulation

2025-11-18T06:27:05Z

Bridging the Sim-to-Real Gap for Athletic Loco-Manipulation Fey, Nolan Achieving athletic loco-manipulation on robots requires moving beyond traditional tracking rewards—which simply guide the robot along a reference trajectory—to task rewards that drive truly dynamic, goal-oriented behaviors. Commands such as “throw the ball as far as you can” or “lift the weight as quickly as possible” compel the robot to exhibit the agility and power inherent in athletic performance. However, training solely with task rewards introduces two major challenges: these rewards are prone to exploitation (reward hacking), and the exploration process can lack sufficient direction. To address these issues, we propose a two-stage training pipeline. First, we introduce the Unsupervised Actuator Net (UAN), which leverages real-world data to bridge the sim-to-real gap for complex actuation mechanisms without requiring access to torque sensing. UAN mitigates reward hacking by ensuring that the learned behaviors remain robust and transferable. Second, we use a pre-training and fine-tuning strategy that leverages reference trajectories as initial hints to guide exploration. With these innovations, our robot athlete learns to lift, throw, and drag with remarkable fidelity from simulation to reality.

A Wound Designates a Subject

2025-11-18T06:28:39Z

A Wound Designates a Subject Lum, Luca E. What haunts when haunting itself has been foreclosed? This thesis develops “ghostlessness” as a conceptual and aesthetic framework across my work in moving image, drawing, and writing. Ghostlessness refers to conditions that suppress haunting where it would otherwise emerge or be felt. Drawing from theoretical elaborations of hauntology, where the present is understood as structured by both suppressed pasts and unrealized futures, ghostlessness names the absence—or foreclosure—of that temporal disruption. It marks a contemporary condition in which systems oriented toward predictive governance and managed futurity preemptively neutralize rupture, sealing wounds before they can fester, reroute, or become sites of transformation. Through the works gathered here, I explore how ghostlessness functions not simply as absence but as affective and infrastructural suppression—rendering the spectral illegible, unaddressable, or unreal. Against this, my practice seeks to recapture the value of haunting in death-ridden, crisis-laden times where its presence is more prevalent than ever – hence its management, erasure, and suppression: ghostlessness.

Stylizing 3D Models With Generative AI for Fabrication

2025-11-18T06:28:05Z

Stylizing 3D Models With Generative AI for Fabrication Tejedor, Leandra This thesis presents two novel approaches for modifying 3D models using generative AI for stylization while ensuring the resulting models preserve the properties required for fabrication. The first method, Style2Fab, separates functional and stylistic sections of 3D models to enable targeted modifications that preserve the model's intended functionality. By distinguishing between these sections, Style2Fab allows for alterations that maintain the model's functional purpose while providing flexibility in its aesthetic design. This approach ensures that the modified models retain their original functionality after stylistic changes. The second method, MechStyle, incorporates finite element analysis (FEA) into the generative modeling pipeline to maintain the structural integrity of the modified models. By analyzing changes in stress values during a simulated drop test at various stages of the stylization process, MechStyle restricts changes to those that preserve the model's structural viability. This ensures that the resulting models are both stylistically accurate to the user's desired results and structurally sound for 3D printing.

The Limits of Recovering Planted Subgraphs

2025-11-18T06:28:02Z

The Limits of Recovering Planted Subgraphs Rajaraman, Amit Given an arbitrary subgraph H = Hₙ and p = pₙ ∈ (0, 1), the planted subgraph model is defined as follows. A statistician observes the union of the “signal,” which is a random “planted” copy H* of H, together with random noise in the form of an instance of an Erdős–Rényi graph ´ G(n, p). Their goal is to then recover the planted H* from the observed graph. Our focus in this work is to understand the minimum mean squared error (MMSE), defined in terms of recovering the edges of H*, as a function of p and H, for large n. A recent paper [MNS⁺23] characterizes the graphs for which the limiting (as n grows) MMSE curve undergoes a sharp phase transition from 0 to 1 as p increases, a behavior known as the all-or-nothing phenomenon, up to a mild density assumption on H. However, their techniques fail to describe the MMSE curves for graphs that do not display such a sharp phase transition. In this paper, we provide a formula for the limiting MMSE curve for any graph H = Hₙ, up to the same mild density assumption. This curve is expressed in terms of a variational formula over pairs of subgraphs of H, and is inspired by the celebrated subgraph expectation thresholds from probabilistic combinatorics [KK07]. Furthermore, we give a polynomial-time description of the optimizers of this variational problem. This allows one to efficiently approximately compute the MMSE curve for any dense graph H when n is large. The proof relies on a novel graph decomposition of H as well as a new minimax theorem which may be of independent interest. Our results generalize to the setting of minimax rates of recovering arbitrary monotone boolean properties planted in random noise, where the statistician observes the union of a planted minimal element A ⊆ [N] of a monotone property and a random Ber(p)^⊗N vector. In this setting, we provide a variational formula inspired by the so-called “fractional” expectation threshold [Tal10], again describing the MMSE curve (in this case up to a multiplicative constant) for large n.

Efficient Routing in the CityMesh Decentralized Fallback Wireless Network

2025-11-18T06:28:01Z

Efficient Routing in the CityMesh Decentralized Fallback Wireless Network Liu, Ziqian As modern communication systems increasingly rely on centralized network infrastructure, they become more vulnerable to disruptions caused by disasters, failures, or cyberattacks. To address this risk, CityMesh proposes a decentralized fallback wireless network that leverages existing Wi-Fi devices, such as access points (APs), in buildings to maintain essential connectivity during outages. However, achieving scalable and reliable message delivery in such a network, without introducing excessive overhead, poses significant challenges. This thesis presents a new routing protocol for CityMesh, designed to operate efficiently at city scale. We first identify the limitations of traditional shortest-path source routing in CityMesh’s context, including the use of unreliable links and overhead from redundant transmissions. To address these issues, we introduce a safer path selection metric that prioritizes link reliability, a waypoint-based routing compression scheme, and a conduit mechanism to increase robustness to local failures. Our protocol further supports compact routing tables through a grid-based addressing scheme, enabling constant-size packet headers and scalable routing decisions. Additionally, we propose a suppression strategy to reduce unnecessary transmissions both between and within buildings. Finally, we extend our approach to reconnect disconnected network segments by formulating a relay placement strategy based on map data and geometric heuristics. Additionally, to reconnect fragmented network segments, we develop a practical relay placement algorithm by leveraging on the convex hull optimization and re-using global map knowledge, which ensures fast relay point computation in feasible locations such as roads and bridges. Simulations across 20 global cities show that our routing protocol achieves up to 2× higher packet delivery rates and reduces transmission overhead by up to 28× compared to GPSR under high packet loss and realistic localization error. The routing table footprint sampled across 4 randomly selected cities shows on average under 2 KB memory usage per device. Our fast relay placement algorithm also demonstrates only a small number of relays are needed to achieve full network connectivity for most of the cities, which validates CityMesh’s core premise that existing urban Wi-Fi infrastructure is sufficient to support a robust, scalable decentralized fallback network with minimal augmentation.

GPU-accelerated Inference for Discrete Probabilistic Programs

2025-11-18T06:28:00Z

GPU-accelerated Inference for Discrete Probabilistic Programs Ghavami, Matin This thesis presents a comprehensive approach to GPU-accelerated inference for discrete probabilistic programs. We make two key contributions : (1) a factor graph IR implemented in JAX that supports variable elimination and Gibbs sampling, and (2) a modeling DSL with a compiler that lowers programs to the factor graph IR. Our system enables significant performance optimizations through static analysis of the factor graph structure. Variable elimination is optimized by reduction to tensor contraction with optimized contraction paths, while Gibbs sampling is automatically parallelized through graph coloring techniques. Empirical evaluations on standard benchmarks demonstrate orders of magnitude performance improvements over existing systems, with the parallelized Gibbs sampler showing speed-ups of up to 144x on Bayesian networks and even greater improvements for models with regular graph topologies such as Ising models and hidden Markov models.

The Vernaculars of Our Networks: From The Cloud to a Plurality of Grassroots Digital Infrastructures

2025-11-18T06:27:59Z

The Vernaculars of Our Networks: From The Cloud to a Plurality of Grassroots Digital Infrastructures Hernandez-Cornejo, Mark A. This thesis is concerned with DIY "off-the-cloud" networks as socio-technical models that can reinscribe a community's organizational processes, identity, and culture. It questions how these networks can break away from corporate and extractive services of "the cloud" in order to achieve digital sovereignty as well as resist the hegemonic understanding of Western universal technology. Rather than grafting an outside network onto a community, how might the nodes of a network emerge from the cultural ontologies and local knowledge systems, creating a "vernacular cloud," with political, epistemic, and ontological implications? The social practice of what I call 'net/work' involves the facilitation of local digital territories that create a grassroots politics of "organic internets." In Chapter One, recent attempts to break from monopolized services like Google and Facebook are examined, providing insight into why these networks are formed and how they “de-link” from “the cloud.” Drawing from Walter Mignolo's understanding of "de-linking," the thesis argues that this process is a political project that is also epistemologically and economically non-western. Chapter Two examines the notion of 'community' in community networks through the lens of grassroots organizing such as mutual aid, delving into the care and maintenance required for system administration. Chapter Two builds on Geri Augusto's understanding of "re/trans" as a project that has developed new assemblages of knowledge and integrated them into different landscapes. It examines community networks from the Global South, where network nodes have the potential to be cosmo-ontological. Chapter Three provides examples of the principles outlined in Chapters One and Two from my work in pursuit of technical autonomy within an organization.

Sensing Buildings: Environmental Impact of Sensor Technologies and Data Infrastructure in Buildings

2025-11-18T06:27:56Z

Sensing Buildings: Environmental Impact of Sensor Technologies and Data Infrastructure in Buildings Lesina-Debiasi, Simon Building operations and the construction sector are one of the largest contributors to global carbon emissions and energy consumption. While novel construction materials and insulation offer lower embodied carbon solutions, improved heating and cooling devices offer cost and energy effective building services. Above all, “smart” devices promise remote control, oversight, and optimization of building operations. With the rising implementation of AI solutions to every sector, it is important to see the digital devices as an interface to the material machinery they are connected to. The way through which we are introduced to these systems as solutions to environmental problems leaves out the operational and infrastructural costs of the devices. Making material design decisions that are conscious of the mining operations that source the rare earth minerals, to the pumping of oil for polymer coatings, to the chemical baths that separate it from the ore, all the way to the hard drives in server rigs that are cooled with water and driven by electricity, the cloud is nothing but materiality and resources. When evaluating buildings operations and construction techniques for sustainability considerations and environmental impact, connected services such as data networks and optimizations that rely on large server infrastructures and cloud computing are not part of the scope. This thesis reveals the missing components of energy evaluations in “smart” devices within the walls, floors, windows, doors, and roofs of our building, to create a framework through which building efficiency and sustainability can be reconsidered. Through historic research, literature reviews, and experiments, this work shines some light on the environmental impact of data infrastructure to which our buildings are connected. The work presented in this thesis does not claim to be comprehensive nor to solve the problem of optimizing buildings for energy efficiency. Instead, the goal is to build upon existing and established research on data infrastructure, smart technology, climate research etc. showing that, while the efforts currently taken might be improving the efficiency in a building on-site, considerations that are impacting the energy consumption off site need to be taken into consideration.

Decarbonization strategies for North American urban landscapes: Evaluating pavements and vegetation across design typologies

2025-11-18T06:27:50Z

Decarbonization strategies for North American urban landscapes: Evaluating pavements and vegetation across design typologies Ramirez Cuebas, Adriana Urban landscapes are increasingly recognized as critical to climate mitigation, yet remain underrepresented in carbon accounting frameworks relative to buildings and infrastructure. This thesis advances landscape carbon assessment by introducing a typology-based Life Cycle Assessment (LCA) framework for landscape architecture. The framework integrates anthropogenic emissions and natural carbon dynamics while addressing uncertainty. It proceeds through three layers of analysis: 1) developing landscape system and project categories for carbon footprint benchmarking, 2) benchmarking the performance of the proposed landscape systems and urban typologies; and 3) assessing the mitigation potential of decarbonization strategies across systems and project types. Concrete pavers on reinforced concrete slabs and asphalt pavements (78 to 104 kgCO₂e/m²) are the most carbon intensive in the production-to-construction stage. Turfgrass and shrubs show wide variability, functioning as sources or sinks depending on species mix, maintenance, and flux magnitudes, underscoring the need for species-specific, ecologically dynamic modeling (-21 to 42 kgCO₂e/m² and -35 to 258 kgCO₂e/m²). Canopy systems act as consistent carbon sinks (-611 to -388 kgCO₂e/m² over 50 years) despite significant emissions from transportation and structural soil. Landscape systems were used to benchmark four urban typologies—streetscapes, plazas, courtyards, and urban parks. Their 50-year carbon footprints range from –80 to 21 kgCO₂e/m² in urban parks, –13 to 63 in courtyards, 22 to 79 in plazas, and 3 to 80 in streetscapes. Applying decarbonization strategies makes all typologies achieve net carbon sink status at the high bound. Urban parks achieve neutrality immediately post-construction, courtyards in 13 years, plazas in 26 years, and streetscapes by year 33. At higher emission estimates, urban parks and courtyards deepen carbon sink performance, plazas cross into net sink territory, and streetscapes approach neutrality. The detailed findings highlight the influence of planting density, maintenance regimes, and land cover composition. By structuring assessment around land covers and urban typologies, this thesis delivers a transferable carbon accounting framework aligned with design practice, offering actionable insights for embedding climate accountability into landscape architecture and public policy.

Simulation and Design of Quantum Processors for Low‑Overhead Quantum Error Correction

2025-11-18T06:27:47Z

Simulation and Design of Quantum Processors for Low‑Overhead Quantum Error Correction Pahl, David This thesis investigates the simulation and design of the hardware architecture required for large‑scale quantum error correction (QEC). Specifically, we design microwave circuits for fast and high‑fidelity readout and devise a long‑range coupler (LRC) that spans five qubit lattice sites, suitable for low‑overhead quantum low‑density parity‑check (qLDPC) codes [1]. We present a prototypical nine‑qubit qLDPC code incorporating two long‑ range couplers and optimized readout circuits, achieving state‑of‑the‑art readout fidelities of up to 99.63% in 56 ns and demonstrating strong, well‑targeted couplings mediated by the LRC. Our simulations employ an efficient microwave abstraction based on ABCD transfer matrices, modeling complete qubit devices as networks of circuit elements. We use this formalism to develop a closed‑loop optimization algorithm that determines optimal readout parameters in seconds. The ABCD framework also accurately captures the multi‑mode behavior of the LRC, offering a valuable tool for developing large‑scale, low‑ overhead QEC devices.

Cost-Based Optimization for Semantic Operator Systems

2025-11-18T06:27:25Z

Cost-Based Optimization for Semantic Operator Systems Russo, Matthew D. Recently, AI developers have turned to modular AI systems in order to achieve state-ofthe-art performance on challenging benchmarks and industry problems. New programming frameworks have enabled developers to build these systems by composing them out of semantic operators—i.e., LLM-powered maps, filters, joins, aggregations, etc.—inspired by relational operators from data management systems. While these systems of semantic operators can achieve strong performance on benchmarks, they can be difficult to optimize. For example, an optimizer may need to determine which model, prompting strategy, and retrieval mechanism to use for each operator. Existing optimizers are limited in the number of optimizations they can apply, and most (if not all) cannot optimize system quality, cost, or latency subject to constraint(s) on the other dimensions. In this thesis, we build an extensible, cost-based optimizer called Abacus, which searches for the best implementation of a semantic operator system given a (possibly constrained) optimization objective. The optimizer estimates operator performance by leveraging a minimal set of training examples and, if available, prior beliefs about operator performance. We evaluate the optimizer on a range of workloads including biomedical multi-label classification (BioDEX), information extraction from legal contracts (CUAD), and multi-modal question answering (MMQA). We demonstrate that systems optimized by our work achieve 18.7%-39.2% better quality and up to 23.6x lower cost and 4.2x lower latency than the next best system.

Efficient Learning and Computation of Linear Correlated Equilibrium in General Convex Games

2025-11-18T06:27:36Z

Efficient Learning and Computation of Linear Correlated Equilibrium in General Convex Games Pipis, Charilaos We propose efficient no-regret learning dynamics and ellipsoid-based methods for computing linear correlated equilibria—a relaxation of correlated equilibria and a strengthening of coarse correlated equilibria—in general convex games. These are games where the number of pure strategies is potentially exponential in the natural representation of the game, such as extensive-form games. Our work identifies linear correlated equilibria as the tightest known notion of equilibrium that is computable in polynomial time and is efficiently learnable for general convex games. Our results are enabled by a generalization of the seminal framework of Gordon et al. [2008] for Φ-regret minimization, providing extensions to this framework that can be used even when the set of deviations Φ is intractable to separate/optimize over. Our polynomial-time algorithms are similarly enabled by extending the Ellipsoid-Against-Hope approach of Papadimitriou and Roughgarden [2008] and its generalization to games of non-polynomial type proposed by Farina and Pipis [2024a]. We provide an extension to these approaches when we do not have access to the separation oracles required by these works for the dual player. This work will appear in STOC 2025, [Daskalakis et al., 2025].

Explaining Black-Box Classifiers by Implicitly Learning Decision Trees

2025-11-18T06:27:01Z

Explaining Black-Box Classifiers by Implicitly Learning Decision Trees Lange, Jane We present algorithms for finding two types of objects that explain the classification of a black-box model f : {±1}^d → {±1} on an instance x ∈ {±1}^d . The first is a certificate: a small set of x’s features that in conjunction essentially determines f(x). The second is a counterfactual: a nearest instance x′ for which f(x′) ≠ f(x). We obtain both algorithms via a connection to the problem of implicitly learning decision trees. The implicit nature of this learning task allows for efficient algorithms even when the complexity of f necessitates an intractably large surrogate decision tree. We solve the implicit learning task by bringing together techniques from learning theory, local computation algorithms, and complexity theory. Our approach of “explaining by implicit learning” shares elements of two previously disparate methods for post-hoc explanations, global and local explanations, and we make the case that it enjoys advantages of both. Our certification algorithm runs in time poly(d, C(f)) and outputs a certificate of size poly(C(f)), where C(f) is the “average certificate complexity" of f. Our counterfactual algorithm runs in time S(f)^[O(∆f (x))] ·log d, where S(f) is the sensitivity of f (a discrete analogue of the Lipschitz constant) and ∆f (x) is the distance from x to its nearest counterfactual. We further prove a lower bound of S(f)^[Ω(∆f (x))] + Ω(log d) for finding counterfactuals, thereby showing that the guarantees of our algorithm are essentially optimal.

Analog On-chip Training and Inference with Non-volatile Memory Devices

2025-11-18T06:27:27Z

Analog On-chip Training and Inference with Non-volatile Memory Devices Lee, Jungsoo As the demand for computation in neural networks continues to rise, conventional computing resources are increasingly constrained by their limited energy efficiency. One promising solution to this challenge is analog in-memory computing (AIMC), which enables efficient matrix-vector multiplications by encoding synaptic weights into the conductance of nonvolatile memory devices. These devices are structured into crossbar arrays. To explore the potential of non-volatile memory devices in AIMC, investigations involve simulating crossbar array operations using IBM’s AIHWKIT. With this tool, I investigate the implementation of various analog computing algorithms, including TikiTaka. AIMC is evaluated for simple MNIST classification tasks and more complex deep learning models, Long Short-Term Memory (LSTM) networks. I demonstrate that devices can be categorized based on their asymmetry and non-linear weight modulation behavior. Performance improvements through the Tikitaka algorithm are observed only when the device provides a sufficient converge-dragging force; otherwise, the algorithm may even degrade performance. I also investigate how pulse-to-pulse noise and device-to-device variability affect system performance, as well as how different peripheral circuit configurations influence the overall behavior. Finally, I propose an Analog Low-Rank Adapter (Analog LoRA) by applying analog computing to the fine-tuning of large language models. I explore the necessary conditions for Analog LoRA to achieve performance comparable to its digital counterpart. Based on these findings, I present design guidelines for effectively applying analog computing to various machine learning tasks on edge devices.

CMOS-Compatible Wafer-Scale Synthesis and Rapid Characterization of Two-Dimensional Transition Metal Dichalcogenides

2025-11-18T06:26:54Z

CMOS-Compatible Wafer-Scale Synthesis and Rapid Characterization of Two-Dimensional Transition Metal Dichalcogenides Jiao, Yixuan Two-dimensional transition metal dichalcogenides (TMDs) such as monolayer MoS₂ offer great promise for next generation nanoelectronics due to their atomic thickness, tunable bandgaps, and excellent electrostatic control. However, industrial semiconductor manufacturing demands CMOS-compatible, wafer-scale growth and conventional CVD methods often exceed thermal budgets and introduce contaminants, while achieving uniform, defect-free monolayers remain difficult. This thesis presents in-depth discussion on low-temperature MOCVD system design and optimization methodology for uniform monolayer TMD synthesis. We investigate the effect of alkali halide promoters (e.g. NaCl) and novel alkali-free promoters (e.g. NH4Cl and crystal violet) on synthesis of monolayer MoS₂. By optimizing the NaCl-promoted route, we achieve coalesced monolayer MoS₂ films with enlarged grain domains and demonstrate field-effect transistors with improved mobility. In parallel, we develop a CMOS-compatible crystal violet seeding method that avoids alkali metal contaminants and yields uniform monolayer coverage. To support process development, a rapid characterization pipeline was introduced: optical/SEM imaging combined with machine learning to quickly map thickness, grain size, and infer electronic quality across the wafer. These contributions collectively advance the integration of 2D TMD materials into CMOS fabrication, enabling monolithic 3D integration in future electronics.

Automating the Search for Artificial Life with Foundation Models

2025-11-18T06:27:21Z

Automating the Search for Artificial Life with Foundation Models Kumar, Akarsh With the recent Nobel Prize awarded for radical advances in protein discovery, foundation models (FMs) for exploring large combinatorial spaces promise to revolutionize many scientific fields. Artificial Life (ALife) has not yet integrated FMs, thus presenting a major opportunity for the field to alleviate the historical burden of relying chiefly on manual design and trial-anderror to discover the configurations of lifelike simulations. This paper presents, for the first time, a successful realization of this opportunity using vision-language FMs. The proposed approach, called Automated Search for Artificial Life (ASAL), (1) finds simulations that produce target phenomena, (2) discovers simulations that generate temporally open-ended novelty, and (3) illuminates an entire space of interestingly diverse simulations. Because of the generality of FMs, ASAL works effectively across a diverse range of ALife substrates including Boids, Particle Life, Game of Life, Lenia, and Neural Cellular Automata. A major result highlighting the potential of this technique is the discovery of previously unseen Lenia and Boids lifeforms, as well as cellular automata that are open-ended like Conway’s Game of Life. Additionally, the use of FMs allows for the quantification of previously qualitative phenomena in a human-aligned way. This new paradigm promises to accelerate ALife research beyond what is possible through human ingenuity alone.

Uncertainty-aware Joint Physical Tracking and Prediction

2025-11-18T06:26:46Z

Uncertainty-aware Joint Physical Tracking and Prediction Dasgupta, Arijit Humans possess a remarkable capacity to track and predict the motion of objects even when visual information is temporarily absent. This thesis investigates how missing sensory evidence—such as during occlusion—alters current and future beliefs about object motion, and introduces an uncertainty-aware framework to model this process. A behavioral experiment was conducted in which participants continuously predicted the future destination of a ball moving in 2.5D environments with occlusion. Results demonstrate that participants dynamically updated their predictions throughout occlusion, exhibiting adaptive belief revision and physically grounded reasoning. To model this behavior, a structured Bayesian modeling and inference approach for joint tracking and prediction was developed that integrates perception, state estimation, and future prediction in a unified process. The approach, implemented via a Sequential Monte Carlo algorithm embedded within a GPU-accelerated and parallel probabilistic programming system, maintains time-varying beliefs over both present and future object states, conditioned on observed images. These belief states are explicitly represented in symbolic form, enabling interpretable, frame-by-frame introspection of uncertainty and prediction over time. When compared against human responses, the model closely matched the temporal evolution of time-aligned decisions and outperformed plausible alternative hypotheses that failed to reason during occlusion. These findings affirm that the absence of changing visual evidence does not engender a void in physical reasoning, but is evidence in itself—processed and revised through structured, probabilistic inference. By integrating probabilistic programming with human behavioral data through structured Bayesian modeling and inference, this thesis advances a computational account of intuitive physical reasoning and provides a foundation for building interpretable, uncertainty-aware AI systems that mirror human-like physical intelligence.

Toward an Age-Ready Suburbia

2025-11-18T06:27:19Z

Toward an Age-Ready Suburbia Du, Minghao; Zhuang, Kaicheng As America’s population ages, suburban neighborhoods face urgent challenges. Originally designed for young, car-dependent families, the suburban landscape today often presents barriers to aging in place, including poor walkability, inaccessible housing, and limited access to essential services and care. This thesis investigates these challenges and proposes a strategy for reimagining suburban environments through demographic analysis, spatial mapping, persona-driven research, architectural prototyping, and community planning. It traces the historical evolution of suburbia, critically evaluates existing senior housing typologies, and advances new frameworks for retrofitting residential neighborhoods to better support aging populations. Focusing on Sacramento, California, the research identifies high-priority areas where aging, affordability challenges, and mobility barriers intersect. Grounded by a pilot care home project, the study demonstrates how modest interventions, such as retrofitting single-family homes into small-scale residential care environments, can enhance both livability and care access. The first phase of the pilot project has been constructed, offering a demonstration of the proposed model’s feasibility. A phased development and financial strategy are also outlined to ensure broader applicability. While rooted in Sacramento, the thesis offers a framework relevant to many suburban contexts across the United States, particularly naturally occurring retirement communities (NORCs) where older adults are aging in place. Rather than creating isolated senior enclaves, the work promotes a distributed, community-integrated model that strengthens neighborhood resilience and supports intergenerational living. By combining design innovation with policy awareness and development feasibility, the thesis presents a scalable and adaptable approach to reshaping suburbs for an aging society.

Calibration and Control of Superconducting Qubits for Low‑Overhead Quantum Error Correction

2025-11-18T06:27:41Z

Calibration and Control of Superconducting Qubits for Low‑Overhead Quantum Error Correction Pahl, Lukas The ability to coherently and reliably manipulate quantum information marks a fundamental technological leap—realizable through a universal, fault‑tolerant quantum computer. Achieving this goal requires progress across all layers of the quantum computing stack, from physical qubits to theoretical algorithms. In this work, we address multiple layers of this stack. We develop a software architecture for scalable device calibration using modular calibration graphs. We introduce real‑time frequency stabilization techniques, demonstrating improved single‑qubit gate fidelities and progress toward multiqubit feedback. Finally, we explore how quantum error correction overhead can be reduced using low‑density parity‑check codes. We present logical protocols for a non‑local nine‑qubit code, which significantly outperforms comparable surface code implementations in both qubit efficiency and computational capability. These results represent practical steps toward overcoming key challenges in fault‑tolerant quantum computing.

ModelDiff: A Framework for Comparing Learning Algorithms

2025-11-18T06:27:15Z

ModelDiff: A Framework for Comparing Learning Algorithms Shah, Harshay We study the problem of (learning) algorithm comparison, where the goal is to find differences between models trained with two different learning algorithms. We begin by formalizing this goal as one of finding distinguishing feature transformations, i.e., input transformations that change the predictions of models trained with one learning algorithm but not the other. We then present ModelDiff, a method that leverages the datamodels framework (Ilyas et al., 2022) to compare learning algorithms based on how they use their training data. We demonstrate ModelDiff through three case studies, comparing models trained with/without data augmentation, with/without pre-training, and with different SGD hyperparameters. Our code is available at https://github.com/MadryLab/modeldiff.

Sanctuary for Who?

2025-11-06T03:07:47Z

Sanctuary for Who? Salazar, Juan Philadelphia, often recognized as the poorest major city in the United States, became a Sanctuary City in 2014. The designation committed the region to policies limiting cooperation with federal law enforcement in the persecution of undocumented communities. Policies have ranged from refusing to detain individuals without judicial warrants to prohibiting Immigration and Customs Enforcement (ICE) from accessing municipal databases or facilities for detention purposes. At the community level, the notion of the Sanctuary City sought to promote organizing against unlawful persecution of residents. Over the past eleven years, however, the framework of protection it promised has faltered under mounting federal pressure. The Sanctuary City's symbolic authority and limited scope have failed to shield residents from persecution or restrict ICE's intensifying operations within the area. In 2019, Juntos, the city's foremost immigrant advocacy organization, criticized Philadelphia's Sanctuary status as inadequate. Cited the ongoing persecution of its communities and the declining quality of life for all residents, the organization urged the city to abandon the term "Sanctuary." They instead petition the city to focus instead on meaningfully protecting all residents of Philadelphia, stating, "Let us instead work together to build the kind of city we all want to live in." Junto's critique forms the basis of this thesis, using it as an invitation to reimagine the Sanctuary City as a shift from a policy framework toward a general ethic and design sensibility. This thesis proposes that Philadelphia's crux, like all cities, lies in its ability to sustain communities' pursuit of a dignified life. As a primary agent in the formation of cities, the architect must then make this struggle their own and deploy the tools of their discipline to protect life and inspire dignity. By framing Philadelphia as a city shaped by deindustrialization, disinvestment, and policing, the thesis explores how architecture can respond to these forces by reviving the city's industrial character and establishing new boundaries able to safeguard community rights. Integrating legal, spatial, and semantic insights from federal authorities' rules of engagement will provide novel typologies and programs for the city that address its systemic inequities while fostering environments where life and dignity can flourish. By inscribing meaningful boundaries, and re-equipping the city to make for itself, the thesis suggests architecture becomes a tool for collective protection and urban regeneration.

Structural analysis at scale: Computational modeling of embodied carbon in complex floor layouts

2025-11-06T03:07:03Z

Structural analysis at scale: Computational modeling of embodied carbon in complex floor layouts Hirt, Natasha K. To meet the needs of growing populations, rates of new construction are increasing at a record pace worldwide. The built environment, already one of the single largest contributors to global CO₂e emissions, will become a significant environmental challenge in the coming decades. To mitigate the anticipated environmental impact of future construction, we need to rethink how we build. One strategy, which is the subject of this work, is improving the material efficiency of flexural systems like floors. Floors are among the most materially wasteful structural components in buildings, and while decades of research have explored optimal floor system design, the complexity of proposed solutions has limited their practical implementation. Furthermore, the industrial tools available to structural designers do not lend themselves to flexible experimentation or large-scale analysis. As a result, most flexural systems today rely on approximations and rules of thumb rather than mathematically optimal designs, data-driven decision making, or iterative design processes. This thesis bridges the gap between practical engineering, material efficiency, and design freedom. It presents novel, code-compliant tools for the computational analysis and optimization of flat slabs supported by a network, or grillage, of beams, using a model system of reinforced concrete supported by steel W-sections. The method is used to perform a large-scale analysis of 24,192 unique combinations of beam topologies and assembly design decisions. The results of this analysis find improvements in structural embodied carbon of up to 53.4% over the business-as-usual design case, and also yield generalizable takeaways about the key factors influencing material efficiency in floor slabs. One of the advantages of the method is its flexibility in taking on a range of complex design challenges. These are presented as extensions to the method, and include designing with a constrained inventory for a series of real-world case studies, and automatically deriving novel structural geometries from dense ground structures. The method and results shown in this thesis expand the range of analysis tools that engineers have access to, enabling a wide range of creative designs and explicitly linking design decisions to environmental impact.

Inscrutability: An Epistemological Experiment

2025-11-06T03:07:52Z

Inscrutability: An Epistemological Experiment Huang, Brian Hudson Through four different projects, this thesis explores the idea of dimensions of representation, a concept introduced by 20th century French philosopher Michel Foucault in his book The Order of Things. Foucault argues that the Classical episteme, which Foucault defines as the discourse surrounding knowledge-making that lasted from the 17th century to the 19th century, was determined by the idea of dimensions of representations. Dimensions of representations states that during the Classical episteme, knowledge was formulated by representations of the external world, such as through systems of classification, ordering, and relations, rather than through resemblance. The first project, Holes in the Sieve (2023) will address the problematics of classification through a infamous case in the history of paleoanthropology: the Piltdown Man. The second project, Contrapposto in Space (2024) addresses how representation has been instrumentalized in technoscience through space research. Finally, the last two projects, the Poem Box (2024) and Micropoetry (2025) posit a way forward at the limits of representation by engaging with semiotic theory. By engaging with language games, poetry opens up the possibility to deny the position of being knowable, allowing one to disappear into inscrutability.

Koalisi Lahan–Gambut: Assembling Peat–Land Futures in Kalimantan

2025-11-06T03:07:28Z

Koalisi Lahan–Gambut: Assembling Peat–Land Futures in Kalimantan El Haq, Haidar Throughout Indonesia’s colonial and postcolonial histories, the peatlands of Kalimantan have been not only politically contested spaces but also sites of ontological struggle. From transmigrasi programs to Suharto’s Mega-Rice Project and most notably today’s carbon offset regimes, peat has been transformed into a paradoxical ecology: degraded yet investible, conserved yet profitable. These transformations enclose land, force communities to choose between extraction or restoration, criminalize fire, and abandon regenerative forms of cultivation. These are histories of ontological occupation institutionalized: the marginalization of both peat’s inhabitants and the soil itself as world-making agents, shaped by speculative regimes of governance, rooted in planetary imaginaries of climate salvation and fantasies of productivity. This thesis proposes Koalisi Lahan–Gambut (Peat–Land Coalition), a speculative parainstitution that explores how coalitional spatial practices might reclaim inhabitation in peat ecologies. Situated in a Ngaju village within the buffer zone of one of the world’s largest carbon offset territories—between deep peat and riverine edges, between restoration enclosures and plantation areas—the coalition works through the murkiness of peat, the heterogeneity of its inhabitants, and the crowded terrain of overlapping institutional claims. It foregrounds the frictions between gambut (peat) and lahan (land). Structured across three inquiries, the document presents a Living Glossary that assembles field terms and relational epistemologies drawn from Kalimantan’s peatlands; a genealogy of Governance, Carbon Fix, and Buffer Zone that traces the historical and institutional processes that rendered peatlands governable; and Landing in the Buffer Zone, which turns to the coalition’s situated experiments in becoming-with, inhabiting, and reclaiming the space between peat and land.

An Experimental Study on the Effects of Three-Piece Oil Control Ring Design and Liner Finish on Lubricating Oil Consumption in a Hydrogen-Fueled Single-Cylinder Reciprocating Engine

2025-11-06T03:07:40Z

An Experimental Study on the Effects of Three-Piece Oil Control Ring Design and Liner Finish on Lubricating Oil Consumption in a Hydrogen-Fueled Single-Cylinder Reciprocating Engine Tamburro, Alexandra Reducing lubricating oil consumption (LOC) in reciprocating engines is an increasingly important objective in the pursuit of lower greenhouse gas emissions, longer maintenance intervals, and compliance with tightening environmental regulations. In 2022, the U.S. transportation sector alone was responsible for 29% of national greenhouse gas emissions, 87% of which originated from systems powered by reciprocating engines [1]. While significant progress has been made in fuel efficiency, oil consumption remains as a key contributor to carbon emissions. This research investigates the impact of design parameters in three-piece oil control rings (TPOCRs) and liner surface finish on oil consumption behavior. Utilizing a hydrogen-fueled engine—where the only source of CO₂ emissions is from consumed lubricating oil—this study develops a high-fidelity, FTIR-based method for direct LOC measurement. A derivation of oil consumption based on air and fuel mass flow rates and measured CO₂ emissions is presented, alongside a sensitivity analysis which identified FTIR measurement uncertainty and ambient CO₂ variation as dominant error sources. All experiments were conducted at 2000 RPM under medium load (4 bar IMEP). The experimental results showed that under the tested condition, 1) increasing liner roughness increases the LOC and 2) changing the orientation of any rails with asymmetrical profile to favor up-scraping results in an elevation of LOC. Analyses applying liner vaporization and TPOCR models showed that the changes in liner oil film thickness brought by the TPOCR changes have negligible effect on the LOC from the oil evaporation. Increases in upper-rail up-scraping ability and the oil accumulation inside the TPOCR groove can both elevate the LOC although further investigation is needed to understand the oil transport paths leading to the LOC. This work provides a foundation for future optimization of TPOCR design by highlighting key ring-liner interactions and oil transport mechanisms. Further study of asymmetric geometries and surface characteristics will provide further insights for reducing oil consumption in engine platforms.

Design of Future Energy Infrastructure: Understanding trade-offs between Renewable Capacity, Storage and Transmission Networks for Low-Carbon Landscape

2025-11-06T03:07:15Z

Design of Future Energy Infrastructure: Understanding trade-offs between Renewable Capacity, Storage and Transmission Networks for Low-Carbon Landscape Bhupathi, Hari Raghavendran In 2021, the United States committed to achieving net-zero greenhouse gas emissions by 2050, requiring a fundamental transformation of its energy infrastructure. This thesis develops a nationwide optimization model to minimize capital expenditures and understand the trade-off between renewable capacity, storage, and transmission networks. The results show that the least-cost configuration, achieved when nuclear and battery capital costs fall by 50%, requires approximately $3.25 trillion in new investment - a 37% reduction relative to the baseline scenario. Comparative scenario analysis reveals a marked shift toward centralized storage when nuclear costs decline, which improves reliability and reduces contingency requirements - mirroring inventory pooling dynamics in supply chains. Concurrently, wind capacity additions fall sharply, with each 10% reduction in nuclear cost halving the predicted wind capacity addition. Transmission infrastructure evolves accordingly: 765 kV lines decline as nuclear becomes more decentralized, while 230 kV lines expand modestly to manage increased intermittency. By quantifying trade-offs across technologies and identifying system tipping points, this work offers a framework for policymakers and long-horizon investors.

Barometer-Based Tactile Sensing: Characterization, Processing, and Applications for Dynamic Manipulation

2025-11-06T03:06:30Z

Barometer-Based Tactile Sensing: Characterization, Processing, and Applications for Dynamic Manipulation Shah, Sharmi Reliable tactile feedback is essential for robotic systems to interact effectively with their environments, especially in dynamic manipulation tasks where detecting contact onset, direction, and force is critical for control and planning. This thesis advances the development of barometer-based tactile sensors for low-force interactions, building upon prior work from the Biomimetic Robotics Lab. Previous work demonstrated that neural networks could infer contact location and three-axis contact force from barometers embedded within an elastomer. However, these models did not account for the viscoelastic behavior of the elastomer, which degrades sensor repeatability and bandwidth. To address these limitations, this thesis introduces a recurrent neural network (RNN) architecture that captures viscoelastic transients in the sensor response. The proposed methods are evaluated on two sensor geometries: a spherical sensor and a slimmer ellipsoid variant. An automated data collection pipeline is developed to generate temporally-continuous, uniformly sampled datasets across the sensor surface. RNN models trained on this data show that temporal modeling improves force prediction accuracy across both designs. To improve angle prediction accuracy, a binning strategy is used to enforce a uniform prior over contact orientations. The resulting "Binned RNN" neural networks are small-scale and demonstrate high sensitivity, enabling responsive tactile feedback. The utility of these tactile sensors is demonstrated by integrating the sensors onto a dexterous two-finger gripper and performing light grasping and estimation of object reorientation using solely tactile measurements. This work shows that accounting for viscoelastic effects through informed sampling and temporal modeling enhances the practical performance of elastomer-based tactile sensors in robotic systems.

Can diffusion models capture extreme event statistics?

2025-11-06T03:07:37Z

Can diffusion models capture extreme event statistics? Stamatelopoulos, Stamatios For many important problems it is essential to be able to accurately quantify the statistics of extremes for specific quantities of interest, such as extreme atmospheric weather events or ocean-related quantities. While there are many classical approaches to perform such modeling tasks, recent interest has been increasing in the usage of generative models trained on available data. Despite the sporadic success of such methods, it is not clear for what systems or datasets a system-agnostic generative AI tool is capable of generating previously ‘unseen’ extreme events in a manner that accurately extrapolates the tails for the observable of interest. Here, we propose an apriori criterion, which based on the geometry of the training dataset, it can predict whether a generative AI tool will be able to extrapolate the tails, i.e. generate previously unseen extreme events. The idea is to quantify whether existing extreme events lie in the interior of the dataset or its boundary. In the former case it is shown that generative AI tools can work in an ‘interpolation’ mode and generate new extreme events. On the other hand, if the topology of the dataset is such that extremes live in the boundary of the domain then the generative AI algorithm needs to operate in an extrapolation mode which does not lead to accurate results. We illustrate our findings on a specific class of Diffusion Models (DMs) called Denoising Diffusion Probabilistic Models (DDPMs) and we test on three datasets, a simple on-hyperball dataset following a Weibull distribution for the radii of the data points of dimensionality 2 • 10³, a dataset sampled from the so-called Majda-McLaughlin-Tabak Wave Model (MMT), of dimensionality 8.1 • 10³ and a dataset consisting of Lagrangian turbulence trajectories, of dimensionality 2 • 10³.

Face Me, I face you: Towards an Indigenous Economy of Glass in Southern Nigerian Dwellings

2025-11-06T03:06:53Z

Face Me, I face you: Towards an Indigenous Economy of Glass in Southern Nigerian Dwellings Ajienka, Soala Lolia This thesis proposes the weaving together of two lost traditions - the practice of primary glassmaking in southern Nigeria and the U-shaped bungalow typology of multi-family housing - as a means to address both the qualitative and quantitative housing deficits in Port Harcourt and to support the broader requisites of macroeconomic productivity in Nigeria. The thesis frames the argument that the materiality and application of glass can reconnect the inhabitation and construction of Face Me, I Face You (FMIFY) housing to Nigerian history, culture, and identity. By charting a blueprint for localized material production and engaging questions of affordability, cost structure, and financing, this work positions design as a technical solution and an act of cultural authorship. As an architect, builder, and member of the community, I advocate for a new practice in which the bond between local craftsmanship and housing development is re-established - through material choices, construction systems, economic benchmarking and spatial design strategies. This body of work braids together three interconnected narratives: First, it traces the historical evolution of the U shaped bungalow typology, revealing its roots as a colonial adaptation of the rural compound house, the economic conditions that have led to its physical obsolescence yet sustained market relevance and examining how its cultural significance was gradually diluted through climate-insensitive design and the introduction of imported materials. Second, this body of work rediscovers Nigeria’s precolonial glassmaking traditions, with a focus on artisanal production methods that offer environmental efficiency, energy intelligence, and deep cultural resonance - qualities in stark contrast to the high-energy, standardized imported glass that dominates today’s housing. Third, it integrates these two recoveries through built interventions: redesigning roof structures to support artisanal glass rondels, optimizing daylighting, ventilation, and thermal comfort, and reorganizing courtyards to revive their role as culturally vibrant, socially essential spaces. By leveraging indigenous glassmaking practices and small-batch production models, this thesis advocates for the creation of a circular economy, generating local employment, reducing embodied energy, and restoring cultural resilience - while delivering environmentally sensitive and economically viable housing solutions that demonstrate comparable return on costs for their owners. Foregrounding opacity as a design value, the project seeks to balance communal life with cultural and spatial notions of privacy, challenging the hegemony of imported transparency. Through the strategic curation of apertures, the careful modulation of light and shadow, and the integration of locally crafted glass rondels, the thesis re-envisions the Face Me I Face You typology. Ultimately, this work positions artisanal glass not only as a building material, but as a medium for recalibrating housing production in southern Nigeria toward systemic resilience and self-determination.

DexWrist: A Robotic Wrist for Constrained and Dynamic Manipulation

2025-11-06T03:06:17Z

DexWrist: A Robotic Wrist for Constrained and Dynamic Manipulation Ulloa, Gabriella E. DexWrist is a compliant robotic wrist designed to advance robotic manipulation in highly-constrained environments, enable dynamic tasks, and speed up data collection. DexWrist is designed to be close to the functional capabilities of the human wrist and achieves mechanical compliance and a greater workspace as compared to existing robotic wrist designs. The DexWrist can supercharge policy learning by (i) enabling faster teleoperation and therefore making data collection more scalable; (ii) completing tasks in fewer steps which reduces trajectory lengths and therefore can ease policy learning; (iii) DexWrist is designed to be torque transparent with easily simulateable kinematics for simulated data collection; and most importantly (iv) expands the workspace of manipulation for approaching highly cluttered scenes and tasks. More details about the wrist can be found at: https://sites.google.com/view/dexwrist/home.

Guiding Labor: Sensable Instructions through Digital Jigs

2025-11-06T03:07:18Z

Guiding Labor: Sensable Instructions through Digital Jigs Griffin, Danny Contemporary architects find themselves at a juncture, navigating the transition from traditional modes of instruction to an asymmetrical integration of digital technologies. Drawings remain central to architectural practice, yet a widening gap persists between tools for making drawings and tools for interpreting them. Since Alberti’s division between intellectual and productive labor, architectural instructions have been generated in remote offices and executed on distant construction sites. Digital tools have expanded the information density of drawings, yet the process of interpretation remains predominantly analog. Graphical conventions, though precise, are abstract, and so paper instructions alone lack spatial meaning. Builders ultimately rely on the aid of analog locating techniques to translate these abstractions into actions. Tools as simple as strings and squares have long been present on construction sites, enabling this translation. Over time, the shape and function of such devices has evolved in response to different pressures of location, from the Gothic template which left room for the builder to improvise, to the industrial jig that constrained movement to ensure replicability. The limitations of analog locating became clear when the plumb bob, long trusted to mark which direction was vertical, proved inadequate for navigating trajectories of flying objects. The solution was to embed physical devices with memory, marking a transition from tools which measure where they are to those that know where they are going. This shift from stateless to stateful devices gradually entered construction sites, and though we might distrust the devices that make possible the steering of missiles, this paradigm shift offers a productive challenge to the field of architecture. If simplifying complex construction is worthwhile, then communication pathways which more faithfully transfer information from digital model to physical destination must be explored. Central to this transformation are the tools which anchor instructions on site: interfaces already mediating between architect and builder, which must now evolve to interpret digital signals from afar. Digital jigs will be the conduits of paperless instruction on physical sites, enabling what this thesis terms sensable instructions: instructions receivable by both machines and humans.

Natural Interaction: 3D Modeling in Wearable VR Using a Gesture and Speech Interface

2025-11-06T03:06:33Z

Natural Interaction: 3D Modeling in Wearable VR Using a Gesture and Speech Interface Bei, Yining Designers often rely on keyboard and mouse for 3D modeling, a method that can feel unintuitive or restrictive—especially in collaborative or spatially immersive settings. This thesis explores how multimodal interaction, specifically the combination of hand gestures and voice commands, can support more natural, efficient, and accessible 3D modeling in virtual reality (VR). Built on a custom Unity-based system integrating Meta Quest hand tracking and Wit.ai voice recognition, the study investigates how these two input modes—gesture and speech—can be used together to manipulate and modify 3D geometry in real time. The research proceeds in three phases: (1) a formative study analyzing how users intuitively deploy gestures, revealing common preferences, task breakdown strategies, and limitations in gesture inputs; (2) system design and implementation of both gesture-only and gesture + speech interfaces for navigation and object manipulation (e.g., translation, scaling, duplication); and (3) a comparative user study evaluating gesture-only, gesture + speech, and keyboard + mouse workflows in terms of learning curve, task efficiency, and user satisfaction. Results show that gesture + speech enables smoother transitions across modeling subtasks and allows users to offload certain parameters (e.g., numeric values, distances) to voice while using gestures for spatial control. Participants reported higher engagement and lower cognitive load compared to keyboard-based workflows, especially in tasks involving spatial scale and collaboration. This thesis demonstrates the feasibility and design potential of multimodal interaction for immersive modeling workflows and offers insights for future XR design tools that seek to blend precision with embodied interaction.

Understanding the Limits of Coupled Condensation and Desorption in Sorption-Based Atmospheric Water Harvesting (SAWH) Devices

2025-11-06T03:06:56Z

Understanding the Limits of Coupled Condensation and Desorption in Sorption-Based Atmospheric Water Harvesting (SAWH) Devices Stamler, Natasha Lia Access to clean water is a serious challenge around the world, with almost 2/3 of the global population experiencing water scarcity at some point during the year, especially in dry regions. One solution to this problem is sorbent-based atmospheric water harvesting (SAWH) due to its ability to produce drinking water in a range of environments, including at low humidity. SAWH device operation is composed of adsorption and desorption phases. During adsorption, moist air flows into the device and is adsorbed onto the sorbent bed. This is followed by the desorption phase during which the sorbent is heated to desorb the water as vapor, which is then transported to a colder condenser surface on which it is condensed as liquid water. Finally, the condensed water can be collected outside the device. However, current state-of-the-art SAWH devices are inefficient, with less than 70% of their adsorbed water being collected. This means the adsorbed water is either not condensed or condensed but not collected. This work discusses the impact of the coupling between desorption and condensation on the efficiency of SAWH devices. In general, SAWH systems can suffer from three scenarios of inefficient desorption-condensation: flux-limited, when the desorption rate in the device is insufficient to fully utilize the condenser’s condensation capacity; transport-limited, when the time scale of the vapor transport from the sorbent bed to the condenser is slow compared to the desorption operation time; and condenser-limited, when the condenser has a poor thermal design compared to the vapor flux. We developed a system-level model of a SAWH device to inform design strategies to mitigate these three bottlenecks and optimize device performance. Additionally, we quantified hydrocarbons, common airborne contaminants, as a mechanism for slowing water collection. Experimental findings are used to develop a model for the impact of airborne hydrocarbon adsorption on surface wettability and water retention for six metals commonly used as condenser materials. The findings from these models can inform design recommendations for SAWH devices as well as various other industrial applications in which water condenses on metal surfaces such as refrigeration and power generation. Future work will focus on continued experimental validation of the models.

Impact of Vimentin Intermediate Filaments on 3D Multicellular Collective Behavior

2025-11-06T03:06:31Z

Impact of Vimentin Intermediate Filaments on 3D Multicellular Collective Behavior Rodriguez, Camille Dyani Vimentin, a type III intermediate filament, is an understudied component of the cytoskeletal system. However, in recent studies we can see its structural and mechanical properties aid in a cell's survival and migration. It forms a hyperelastic network and works synergistically with actin and microtubule to protect against large deformations. Despite vimentin intermediate filaments critical role in many biological processes, there are limited studies on its role in collective migration in 3D in vitro. To elucidate vimentin’s role in a collective cell cluster, single MCF-7 cells are embedded in a Matrigel-Alginate gel, which then grow into multicellular systems. The MCF-7 cells utilized are vimentin null, chemically inducible to form vimentin networks that interact with the other components of the cytoskeleton. These MCF-7 allow for controlled expression of mature vimentin intermediate filament (VIFs) which then form networks. We study these multicellular clusters over the course of 14 days. We demonstrate that there are key differences in morphology and mechanics, with the presence of vimentin. Our results suggests VIFs create more irregular cell clusters with more visible dynamic interplay with the environment. Uninduced (no VIFs) clusters were overall less dynamic and exhibited spherical morphology and minimal protrusions. Cluster with mature VIFs tended to form more elongated multicellular clusters with increased number of projections into the surrounding gel. In these induced (with VIFs) clusters these projections are shown to be constantly protruding and retracting along with the nuclei continually reorganizing. Our results show that these projections are accompanied with increased protrusive and contractile gel displacements. These results indicate that vimentin network generate an dynamic and functional morphology, along with mechanically perturbing their environment in the early stages of cell cluster collective behavior.

Planning for Dynamic Nonprehensile Object Transport

2025-11-06T03:06:20Z

Planning for Dynamic Nonprehensile Object Transport Wang, Eric K. Generalized planning methods for dynamic manipulation struggle to efficiently solve kinodynamic constraints. Gradient-based methods suffer from initialization sensitivity, local optimum convergence, and lack of feasibility guarantees, while sampling-based methods can require large computation times if there exist challenging boundary conditions. Iterative Time Optimal Path Parameterization, or iTOPP, guarantees a feasible local minimum for a dynamic grasping problem by iteratively decreasing transit time for a trajectory initially generated to satisfy kinodynamic contact constraints. We demonstrate solutions that can handle initial or final goal states defined as quasistatically infeasible, in which purely quasistatic motions cannot generate a warm start trajectory. We also design an indirect adaptive controller that can track a desired dynamic grasping trajectory assuming unknown object mass and location parameters.

Fundamental Behavior of Nanoporous Networks in the Out-of-Autoclave Manufacturing of Carbon Fiber Reinforced Polymer Composites

2025-11-06T03:08:28Z

Fundamental Behavior of Nanoporous Networks in the Out-of-Autoclave Manufacturing of Carbon Fiber Reinforced Polymer Composites Webb, Alisa Nicole Throughout the aerospace industry, carbon fiber reinforced polymer (CFRP) laminated composites are used extensively in spacecraft and aircraft vehicles due to their high specific strength and stiffness and other properties. Processing these advanced structural CFRP composites, especially in prepreg form, is often completed via autoclaves where elevated temperatures and pressures of typically 180 ◦C (350 ◦F) and 0.7 MPa (7 bars), respectively, are applied to cure the polymer matrix and compress the constituent laminae together. However, autoclaves are energy intensive, expensive, and impose geometrical constraints on components due to thermal gradients within the chamber. Thus, there exists a need to find alternative manufacturing techniques. Throughout this thesis, an alternative method to autoclave processing is presented using vacuum-bag only (VBO) techniques with nanoporous networks (NPNs) in the interlaminar regions in autoclave-required epoxy prepreg CFRP composites. Nanoporous materials are defined as materials containing pores in the mid nanometer to low micrometer range. Once placed in the interlaminar region of the laminate, voids are reduced by the induced capillary pressures of the NPNs, and trapped gas evacuates through the NPN. By utilizing capillary flow porometry, capillary pressure and through-thickness permeability are quantified for various NPNs, along with other porous materials. Capillary pressure and permeability exhibit an inversely proportional relationship for all tested materials with CNT-based and polymer aerogel NPNs providing capillary pressures higher than an autoclave pressure of 0.7 MPa. Accordingly, an Ashby-type plot is presented as an aid for NPN selection for composites manufacturing. Previous studies of unidirectional glass fiber reinforced polymer (GFRP) composites and unidirectional CFRP composites show success with NPN-enabled VBO-manufacturing using aligned carbon nanotubes (A-CNTs) and electrospun polymer nanofiber (EPN) mats. However, success with woven prepreg has not been consistently achieved before this thesis. Autoclave woven epoxy CFRP laminates of IM7/8552 are manufactured using EPN and polymer aerogel NPNs with a VBO procedure. Once manufactured, these laminates were characterized for quality through void content analysis. 0.11 void vol% was achieved which is well within the 1 vol% of void requirement for aerospace-grade composite components. To aid the in the understanding of NPNs, in situ experiments utilizing microcomputed tomography are developed to investigate the (presumed Newtonian) flow of resin throughout the NPN as a function of temperature, which varies throughout a typical manufacturer recommended cure cycle (MRCC), along with the void evolution throughout the cure cycle. Based on this new in situ understanding, a manufacturing process modification is devised to produce void-free woven laminates at the 152.4 mm laminate scale. Through manufacturing, material characterization, and designed in situ experiments, this thesis demonstrates the use of NPNs for VBO-manufacturing of low-void content aerospace-grade CFRP composites to replace autoclaves for energy and cost savings.

Mediators: Participatory Collective Intelligence for Multi-Stakeholder Urban Decision-Making

2025-11-06T03:08:26Z

Mediators: Participatory Collective Intelligence for Multi-Stakeholder Urban Decision-Making Gao, Jin Cities are dynamic and evolving organisms shaped through the check-and-balance of interest exchange. As cities gain complexity and more stakeholders become involved in decision-making, reaching consensus becomes the core challenge and the essence of the urbanism process. This thesis introduces a computational framework for AI-augmented collective decision-making in urban settings. Based on real-world case studies, the core decision-making process is abstracted as a multiplayer board game modeling the check-and-balance dynamics among stakeholders with differing values. Players are encouraged to balance short-term interests and long-term resilience, and evaluate the risks and benefits of collaboration. The system is implemented as a physical interactive play-table with digital interfaces, enabling two use cases: simulating potential outcomes via AI self-play, and human–agent co-play via human-inthe-loop interactions. Technically, the framework integrates multi-agent reinforcement learning (MARL) for agent strategy training, multi-agent large language model (LLM) discussions to enable natural language negotiation, and retrieval-augmented generation (RAG) to ground decisions in contextual knowledge. Together, these components form a full-stack pipeline for simulating collective decision-making enriched by human participation. This research offers a novel participatory tool for planners, policymakers, architects, and the public to examine how differing values shape development trajectories. It also demonstrates an integrated approach to collective intelligence, combining numerical optimization, language-based reasoning, and human participation, to explore how AI–AI and AI–human collaboration can emerge within complex multi-stakeholder environments.

From Scar To Scaffold: The Afterlife of the Oil Pipeline for a Decarbonizing World

2025-11-06T03:08:24Z

From Scar To Scaffold: The Afterlife of the Oil Pipeline for a Decarbonizing World Apostolopoulou, Katerina With over 86,000 kilometers of crude oil pipelines—and more than 2.13 million kilometers of total oil and gas pipelines in the United States as of 2024—many segments are already corroded and aging, deeply embedded within urban and ecological systems that are increasingly endangered. As the global energy transition accelerates, this thesis investigates the future of these infrastructures, reconsidering the vast network of decommissioned and declining legacy pipelines not as obsolete relics, but as latent spatial assets for ecological repair, climate resilience, and socio-environmental justice. Moving beyond narratives of extraction and decay, the project repositions pipelines as linear territories of opportunity—capable of being retrofitted into new civic, ecological, and infrastructural frameworks. Central to the project is the transformation of the pipeline’s linear, extractive logic into a circular and connective one: a loop that is both finite and infinite, territorial and experiential. Focusing on a strategically selected loop of crude oil pipelines spanning 14 states, the thesis constructs a cartographic and architectural framework to reimagine these lines as sites of ecological repair, social infrastructure, and alternative energy distribution—where design, much like a biological scaffold, acts as a catalyst for regeneration along landscapes shaped by extraction. Through spatial analysis, typological classification, and mapping, five territorial conditions are defined along the pipeline loop, each offering distinct opportunities for intervention. These are tested through speculative design prototypes that transform the pipeline through operations of repurpose, renewable energy distribution, or ecological remediation. The interventions reframe invasive infrastructures into public and environmental assets—generating new spaces for inhabitation, production, and collective memory. Ultimately, the thesis proposes a post-carbon design paradigm rooted in ecological reciprocity, collective agency, and infrastructural care—revealing hidden energy landscapes and inscribing them with new values: resilience, equity, and repair.

Control and Aerodynamic Design of a Solar Road Vehicle with Articulated Surfaces

2025-11-06T03:08:18Z

Control and Aerodynamic Design of a Solar Road Vehicle with Articulated Surfaces Salmon, Jason The automobile industry is critical to modern society. Simultaneously, the constant release of toxic emissions such as greenhouse gases into the atmosphere is detrimental to health and the environment. Vehicles which exploit cleaner energy sources would be preferable to reduce the horrific scale of human-initiated damage such as climate change. However, solar road vehicles—though designed and fabricated by some—have not reached a sufficient level to be production-worthy. The low efficiency of solar cells and the high energy demands of the average land vehicle are irreconcilable for most manufacturers using industry methods and design precedent. Therefore, this work centres around the design and control of a solar road vehicle which fundamentally breaks from the mould of the typical road vehicle design—a vehicle which employs extensive articulated surfaces (dubbed "solar wings") which can be angled to directly face the sun, thereby maximising solar irradiation. A solar tracker using Bayesian inference achieving promising results in both convergence and accuracy is presented. Additionally, a systematic method for optimizing a solar road vehicle with solar wings is developed and documented.

Design and Commercialization Strategy of a Gantry-Based Automation Platform for High-Throughput Raman Spectroscopy

2025-11-06T03:07:39Z

Design and Commercialization Strategy of a Gantry-Based Automation Platform for High-Throughput Raman Spectroscopy Romero, Catalina Raman spectroscopy is a powerful optical technique that enables rapid, label-free molecular analysis. This offers significant potential to be used across pharmaceutical development, microbiome research, and food diagnostics. However, the utility of Raman spectroscopy in high-throughput applications has been limited by the lack of cost-effective, modular automation platforms capable of handling large volumes of samples with precision and repeatability. Conventional Raman workflows are constrained by manual sample handling, slow throughput, and high user variability, limiting their applicability in high-volume testing environments. To address these challenges, this thesis presents the development and initial validation of a custom two-axis (XY) gantry and a robotic well plate stacker automation platform designed to streamline the sample handling workflow in Raman spectroscopy systems, facilitating high-throughput, precise, and reproducible positioning of microplate samples under a Raman microscope. This thesis also provides a commercialization framework for the system as a standalone automation product, targeting pharmaceutical high-throughput screening, microbiome analysis, and food safety testing. The platform serves the unmet needs in these industries, where labor-intensive and inconsistent sample positioning limits scalability. The commercialization analysis includes an evaluation of market sizing, competitive benchmarking, pricing models, and go-to-market strategies. The modular platform has the potential to enable broader adoption of Raman-based analysis tools by reducing labor intensity and improving repeatability in sample positioning workflows. This work lays the foundation for the future integration of optical feedback and automated analysis, with the goal of transforming how Raman-based diagnostics are conducted at scale.

Intelligence Through Interaction: Leveraging Physical Interactions In Simple Robots To Produce Complex Behaviors

2025-11-06T03:08:15Z

Intelligence Through Interaction: Leveraging Physical Interactions In Simple Robots To Produce Complex Behaviors Spino III, Pascal This thesis investigates how intelligent robot behavior can emerge from physical interactions rather than sensing, computation, and actuation in the traditional sense. Two robotic systems are presented to explore this concept in different domains. The first is a swarm of simple rolling robots whose collective morphology is shaped by distributed control laws and magnetic interactions, enabling decentralized construction-like behaviors such as bridge formation. The second is a soft underwater robot inspired by anguilliform swimming, which achieves efficient locomotion through a single actuator that leverages fluid–structure interactions in a compliant silicone tail. Useful behavior arises in both systems from the physical design and the dynamics of environmental interaction, rather than from algorithmic or computational complexity. These results demonstrate that physical intelligence can serve as a powerful design principle for building adaptive, robust, and minimal robotic systems.

Hidden Monuments

2025-11-06T03:08:12Z

Hidden Monuments Lee, Sesil Jeju Island’s burial culture is embedded in the island’s distinct landscape, where sandam burial mounds are not isolated monuments but quietly coexist with fields, ranches, and forests. These sites are living records of intangible heritage—ancestral beliefs, Beolcho rituals, and vernacular stone-stacking practices—manifested not through formalized memory, but through their modest yet persistent presence in the landscape. Today, however, these spaces are under threat: policies favoring cremation, rapid urbanization, and shifting land values render them increasingly invisible or obsolete. In the past few decades, two-thirds of sandam have been displaced, and with fewer than six out of over 100,000 burial sites designated as cultural heritage, traditional models of conservation are inadequate—unable to engage with the dispersed, landscape-bound nature of these burial grounds. This project reimagines Jeju’s burial mounds not as relics to be preserved, but as spatial anchors for cultural and communal expressions. Through a series of small-scale architectural interventions—gates, stages, passages, and shelters—deployed along paths tracing sandam clusters, the work explores how memory can be practiced rather than displayed. By offering ways to engage with the buried, the forgotten, and the living simultaneously, the project expands the idea of heritage: not as a static record, but as a participatory and evolving relationship between people, land, and memory.

Development of Mechanical and Electrical Interfaces for Rapid Swap Battery Systems

2025-11-06T03:07:52Z

Development of Mechanical and Electrical Interfaces for Rapid Swap Battery Systems Wucherer, Abigail In the drive towards a globally decarbonized energy economy, rapid swap battery packs provide a potential means to improve electric vehicle adoption in high utilization industrial vehicles where lengthy charge times are a barrier to electrification. High voltage, high current battery connectors are a critical component for coupling the pack to the electric vehicle, distributing power from the battery to the drivetrain. Most state-of-the-art connections require precision alignment of contact surfaces, and bolted preload or retention mechanisms, hindering the implementation of rapid swap battery systems. The need for robust, high life cycle, high-power contacts motivates a new approach to connector design. The integration of electrical connectors with the battery mount’s structural loop creates a new design space where preload, geometry, and contact resistance may be optimized. This co-design approach enables mechanical and electrical functional requirements to be considered in conjunction to ensure reliable fulfillment in both areas while reducing the time for battery pack swaps. This work introduces two distinct approaches for aligning the pack to the vehicle, locking the battery in place, and engaging electrical contact with geometry unique to the system design. These approaches offer higher reliability, mechanical and electrical longevity, and automatic alignment capabilities during loading of the battery pack. Across both designs, the contact resistance is the primary metric for evaluating the electrical performance, and the contact pressure is used to evaluate the risk of mechanical wear. The first approach integrates a quasi-kinematic coupling-based connector with integrated electrical contacts, allowing for repeatable and accurate positioning of the battery pack to the vehicle. A slotted ball and socket design approach is considered to accommodate for angular misalignment and establish repeatable contact area through elastic averaging. The second approach proposes a planar contact to further reduce the contact pressure and increase contact longevity without the need for expensive and rare hardened coatings. This system relies on a rail and flat system for guiding the battery pack into a locked position and engaging the planar contacts.

Weaving Borders, Mapping Place: Afghan War Rugs of the Soviet-Afghan War (1979-1989)

2025-11-06T03:08:16Z

Weaving Borders, Mapping Place: Afghan War Rugs of the Soviet-Afghan War (1979-1989) Hakemy, Arezo Early Afghan war rugs delineate place through their pictorial design, embedding spatial memory into the tactile surface of the woven field. Emerging in the wake of the Soviet invasion in the late 1970s, these rugs integrate modern war iconography of tanks, helicopters, and maps into a medium historically tied to regional identity, spiritual practice, and craft. While earlier scholarship has often read these rugs as commodities of war tourism, this thesis moves beyond this interpretation to foreground the rug as a placemaking device, one that asserts territory and agency through mapping techniques. Afghan war rugs frame and define space on a land that has largely been considered placeless, at times porous and seemingly unknown. Through their borders, these rugs resist the geopolitical narratives that have long reduced Afghanistan to a war-torn frontier. The border serves as a framing device, structuring the rug’s design while simultaneously asserting territorial presence. Whether following a prescribed cartoon or improvising patterns, the weaver actively engages in “border-ing,” exercising cartographic agency by embedding personal, traditional, and political motifs into the rug. This research interrogates how early Afghan war rugs engage in spatial representation against the backdrop of the Soviet-Afghan war from 1979-1989. From historical colonial mapping projects to Soviet and American cartographic investigations, Afghanistan’s borders have long been sites of surveillance, resource extraction, and imperial ambition. Yet, in contrast to these external mapping practices, the war rug’s design is a resistant act of placemaking. Examining the rug as both artifact and map, this study explores how Afghan weavers reclaim their landscapes through rug making, embedding memory and materiality into woven form.

Social Sensory Somatic Scores for Species, Spaces, Soils, and Structures of Steep Slopes

2025-11-06T03:07:36Z

Social Sensory Somatic Scores for Species, Spaces, Soils, and Structures of Steep Slopes Bondarenko, Lina Modern knowledge systems have physically and conceptually “flattened” the world, erasing the ecological, political, and sensory complexities inherent to sloped terrain. By attending closely to the slope—as both a material condition and a generative metaphor—this thesis foregrounds movement as a form of resistance to regimes of exploitation, abstraction, and estrangement that have historically transformed land into data and place into property. Weaving together interdisciplinary methodologies from performance studies, landscape architecture theory, feminist geography, ecological theology, environmental history, sensory ethnography, and media studies, SSSSSSSSSS dances an inclined methodological structure, oscillating deliberately between critical systemic analysis and situated sensory experience. Ch1. sets the stage among steep slopes and introduces the discipline to movement as pedagogy, enacting the urgency for new methodologies into schemes of the project’s medium and the book’s format. Ch.2 is a feminist investigation of the ways modern infrastructures and spaces have been designed to reinforce land abstraction and commodification in the name of improvement-- severing embodied relationality, contributing to societal apathy toward ecological and social crises. Imperial post-enlightenment statecraft, the suppression of wildness, and the standardization of level form have flattened our upright movements to enact a state of senslessness. Contradicting Ch.2’s straight critique, Ch.3 attempts to reweave the sinuous nuance of symbiogenesis between soils and species, revealing that humans are but one among many sloped organisms moving, and inclining, and co-evolving as the lithosphere; we have been slorgs all along. Slorgs belong to divine mythologies of terrain’s elevations and have reciprocated in admiration, mimicking topographic spatial functions and adorning the summits with artistic interventions--some inadvertently contributing to the damaging regimes of Ch.2. Interwoven through both chapters, outliers resisting those forces of governance and exploitation are often those displaced by them-- those moving in ways the system polices and erases from comprehension-- refugees, queers, witches, tricksters, artists, herbalists, and healers. The intended medium of SSSSSSSSSS coalesces in Ch.4: inviting the general public to participatory happenings with hills, composing scores, coaxing their inner slorgs to slither askew, sloping themselves as moving loci for sympoietic becoming. Multi-species attune to a social, sensed, somatic experience, co-composing spatial relations among local steep soils. Slorgs challenge the abstractions of dominant epistemologies in the temporal, situated act of trusting their own proprioception in collective balance, affirming the multidimensional value of embodied, ecological geo-choreography. Social Sensory Somatic Scores for Soils, Structures, Spaces, and Species of Steep Slopes are presented through photographs in Ch.4 and in moving image, available as supplemental material.

Cooling Machines: Exploring the Heat Mitigation Effect of Urban Trees with Computer Vision

2025-11-06T03:07:43Z

Cooling Machines: Exploring the Heat Mitigation Effect of Urban Trees with Computer Vision Klimenko, Nikita As the impacts of climate change on cities become more pronounced, urban authorities are under pressure to prepare existing streetscapes for increased levels of heat stress. While many aspects of existing urban morphology have an impact on heat exposure (e.g. sky view factor, glazing levels, facade materials), they cannot be rapidly changed at large across existing urban infrastructures. Urban authorities across the world increasingly turn to planting trees as a way of cooling urban streetscapes. Urban vegetation is indeed known to have a cooling effect, primarily due to trees providing shade and preventing urban materials from heating up, as well as due to their ability to maintain their own internal temperature due to evapotranspiration. Even though the positive impacts of urban trees on thermal comfort are long known and well-studied, little work is dedicated to how these impacts vary across trees of different species and morphology. This is due to both the complexity of studying vegetation life cycles at sufficient scale, as well as due to the dispersed nature of the issue across disciplines of biology, urban climate, design, and data science. Nevertheless, this specific knowledge is vital to urban planners for deciding which trees have the most cooling effect in specific parts of the city. This thesis embraces the notion of trees as ‘cooling machines’ and dissects the diverse morphological and contextual factors that affect the role of individual trees on local urban heatscape. Leveraging a set of computer vision methodologies, including species recognition, context-aware segmentation, and photogrammetry, the thesis examines a large dataset of thermal imagery of urban trees collected in Los Angeles and Dubai to describe the impact of individual tree species, height and form, as well as spatial context on the cooling effect. Building on this approach, the thesis proposes a prototyping framework for architects to cure urban heatscapes via targeted curation of tree planting schemes, tying the visual and thermal aspects of urban greenery. This approach will allow cities to leverage the power of urban vegetation in the most efficient way, and tame urban heat in a scalable and globally affordable manner.

Co-Authoring Beyond the Human: Disordering Architectural Processes through Play and Multi-Agent Co-Existence

2025-11-06T03:07:50Z

Co-Authoring Beyond the Human: Disordering Architectural Processes through Play and Multi-Agent Co-Existence Dundar Arifoglu, Nasibe Nur This thesis reconsiders architectural authorship and the extended processes through which the built environment is shaped, using a series of playful, participatory interventions to expose the human-centric assumptions embedded in spatial decision-making. Presented as a collection of games and booklets, the work invites participants to engage with a wide spectrum of architectural processes—from site understanding and planning to permitting, construction, and post-occupancy—through the perspectives of multiple agents entangled in shared environments. These agents include beings, materials, living organisms, legal frameworks, and other forces typically excluded from spatial authorship, challenging conventional boundaries and expanding the discourse around the entangled forces and relations that shape the spaces we inhabit. A series of playful explorations opens space for friction, misalignment, and shared authorship. Each booklet engages a distinct stage of the architectural process through participatory formats that make visible the biases, exclusions, and regulatory fictions often treated as neutral. By gamifying these systems, the work reveals how architectural decision-making tends to privilege hierarchy, human control, and speed—often at the expense of multispecies co-existence. This thesis positions play as a critical lens: a way to rehearse alternative futures, to listen differently, to embody other perspectives, and to surface the black-box logics embedded in architectural norms. It invites readers and players to participate in unbuilding these assumptions. And the games evolve—with each use, each misreading, each encounter, and each agent who joins the conversation.

The Objectiles Guide to Time Travel: Re-Envisioning Building Materials as Narrative-Collecting Object-Projectiles on a Trajectory Through Space-Time

2025-11-06T03:06:37Z

The Objectiles Guide to Time Travel: Re-Envisioning Building Materials as Narrative-Collecting Object-Projectiles on a Trajectory Through Space-Time Chaussabel, Celia Quynh-Mai As the architectural discipline grapples with its role in resource depletion, carbon emissions, and waste generation, there is a growing urgency to stop sourcing new materials and to reuse materials from existing buildings instead. One challenge to integrating reused materials into current building practices is technical: inventorying, deconstructing, reconditioning, and designing with reused materials is slower and more labor-intensive than with new ones. But another challenge is cultural: the materials that make up architecture are currently perceived as unmoving and single-use, with little consideration for their trajectories from raw resource to landfill. This thesis is focused on developing an aesthetic sensibility and design methodology that helps us re-envision materials as objects on a trajectory instead: Objectiles, or object-projectiles. Objectiles are objects on an adventure across space-time to collect as many uses as possible. Rather than remaining associated with one primary use, Objectiles are impressionable, bearing ambiguous traces of all the uses they encounter as they re-circulate. Through the aesthetic qualities that hint at their many uses, Objectiles invite us to time travel - to imagine the potential past and future narratives that may precede or follow their present physical state. Embedding the aesthetics of Objectiles into architecture can lead to the development of a new collective consciousness of the materials that surround us. They can make us aware that all the objects around us have trajectories that extend beyond their present state, and lead to an alternative material culture of greater care in how we use, re-circulate, and dispose of all objects.

Problem-Independent Regrets on Expectation-Dependent Multi-Armed Bandits

2025-11-06T03:07:16Z

Problem-Independent Regrets on Expectation-Dependent Multi-Armed Bandits Ai, Rui The independence axiom (IA) proposed by Von Neumann and Morgenstern [50] is the cornerstone of the expected utility theory. However, some empirical experiments show that the IA is often violated in the real world. We propose a new kind of multi-armed bandit problem where the expectation of outcomes may influence the agent’s utility which we call expectation-dependent multi-armed bandits and rationalize the choice of agents in Machina’s paradox lacking the IA. We design provably efficient algorithms with low minimax regrets and show their consistency of time horizon T with corresponding regret lower bounds, revealing statistical optimality. Furthermore, as we first consider bandits whose corresponding utility depends on both reality and expectation, it provides a bridge between machine learning and economic behavior theory, shedding light on how to interpret some counterintuitive economic scenarios, like bounded rationality explored by Zhang et al. [54].

Differentially Private Synthetic Data Generation for Relational Databases

2025-11-06T03:06:32Z

Differentially Private Synthetic Data Generation for Relational Databases Alimohammadi, Kaveh Existing differentially private (DP) synthetic data generation mechanisms typically assume a single-source table. In practice, data is often distributed across multiple tables with relationships across tables. This study presents the first-of-its-kind algorithm that can be combined with \emph{any} existing DP mechanisms to generate synthetic relational databases. The algorithm iteratively refines the relationship between individual synthetic tables to minimize their approximation errors in terms of low-order marginal distributions while maintaining referential integrity; consequently eliminates the need to flatten a relational database into a master table (saving space), operates efficiently (saving time), and scales effectively to high-dimensional data. We provide both DP and theoretical utility guarantees for our algorithm. Through numerical experiments on real-world datasets, we demonstrate the effectiveness of our method in preserving fidelity to the original data.

VLEO-Bench: A Framework to Evaluate Vision-Language Models for Earth Observation Applications

2025-11-06T03:07:20Z

VLEO-Bench: A Framework to Evaluate Vision-Language Models for Earth Observation Applications Zhang, Chenhui Large Vision-Language Models (VLMs) have demonstrated impressive performance on complex tasks involving visual input with natural language instructions. However, it remains unclear to what extent capabilities on natural images transfer to Earth observation (EO) data, which are predominantly satellite and aerial images less common in VLM training data. In this work, we propose VLEO-Bench, a comprehensive evaluation framework to quantify the progress of VLMs toward being useful tools for EO data by assessing their abilities on scene understanding, localization and counting, and change detection tasks. Motivated by real-world applications, our framework includes scenarios like urban monitoring, disaster relief, land use, and conservation. We discover that, although state-of-the-art VLMs like GPT-4V possess extensive world knowledge that leads to strong performance on open-ended tasks like location understanding and image captioning, their poor spatial reasoning limits usefulness on object localization and counting tasks.

She Swims in Silence: Spatial Narrative, Women's labor in Contemporary Art

2025-11-06T03:06:21Z

She Swims in Silence: Spatial Narrative, Women's labor in Contemporary Art Feng, Haozhen This thesis investigates the collective lives of Chinese women sent to Xinjiang in state-led migration after 1949 and the erasure of their gendered narratives. Drawing on a unique family history and archival evidence, the thesis reveals how the personal identities of these female “Aid to Xinjiang” participants were stripped away and subsumed under the grand socialist nation-building myth. Through practice-based artistic research, the project attempts to restore their lost voices and unacknowledged suffering and labor, framing the exhibition as a form of praxis. By analyzing the exhibition alongside case studies and critical analysis, the thesis, inspired by Bernard Stiegler’s theory of the “history of representational forms” and interwoven with ideas from philosophers like Judith Butler and Nicholas Mirzoeff, interrogates the gendered silences in official history and highlights the tension between state mythologies and personal memories. In doing so, the exhibition as an interdisciplinary form of research not only restores agency to a silenced group of women, but also demonstrates how artistic practice can serve as an alternative historiography to challenge dominant narratives and recover marginalized voices.

Evaluation of Universal Docking Solutions for Autonomous Underwater Vehicles

2025-11-06T03:06:38Z

Evaluation of Universal Docking Solutions for Autonomous Underwater Vehicles Pryal, Erik Jeffrey Due to their energy-constrained nature, Autonomous Underwater Vehicles (AUVs) need effective docking and charging stations to extend their mission durations. However, diverse AUV designs challenge the universal compatibility of docking stations. This study provides a framework for understanding what makes a docking station universal and offers two potential solutions: the Tapered Funnel Docking Station and the Magnetic Hub Docking Station. The Tapered Funnel features a conical entry that progressively narrows to accommodate various AUV diameters. The Magnetic Hub passively secures the AUV using magnetic forces and an external appendage guided into position by a square duct. MATLAB simulations evaluate these two charging station designs for compatibility with AUVs, alignment capabilities, and docking efficacy under realistic conditions. Both designs are tested through Monte Carlo simulations to address varying AUV approach conditions, showcasing their potential as universally feasible solutions. Future exploration into material durability, sensor integration, and power transfer efficiency will refine these designs for field applicability. This research lays the groundwork for universal docking standards and proposes adaptable solutions to alleviate operational limitations in underwater missions.

Dowel-laminated timber from waste lumber offcuts: Towards structural component circularity

2025-11-06T03:06:20Z

Dowel-laminated timber from waste lumber offcuts: Towards structural component circularity Blowes, Rachel In the context of the global climate crisis, there is a need to develop low embodied carbon building systems. Moreover, construction and demolition generate substantial amounts of waste. The use of salvaged materials for structural applications presents the opportunity to divert this waste while reducing the embodied carbon of new structural components. This thesis proposes a typology for dowel-laminated timber (DLT) slabs built up from waste lumber offcuts. A mechanical model for a segmented DLT system composed of geometrically heterogeneous offcuts is developed. Prototypes of this mass timber system are fabricated and tested to observe their failure behavior and to evaluate the mechanical model. A computational workflow is introduced which employs algorithmic methods for inventory assignment and structural optimization to design slabs which meet deflection requirements under loading. These approaches are undertaken to evaluate whether DLT systems can leverage the irregularity of salvaged lumber dimensions to produce structurally efficient forms.

Allopoietics in Real Time: Unfolding Among Art, Publics, Space, and Time

2025-11-06T03:06:58Z

Allopoietics in Real Time: Unfolding Among Art, Publics, Space, and Time Aubry, Vinzenz This thesis proposes a conceptual lens for understanding contemporary generative arts by introducing the terms Allopoietics and Liquid Media. Building on generative and participatory art, it focuses on the real-time processes among artworks, publics, spaces, and time through which meaning dynamically emerges. Drawing on the author’s artistic works—Conjunktion, Looking at the Sun, and Public Eyes—as well as critical engagement with hermeneutics, process philosophy, and media theory, this thesis explores how agency is distributed across these processes, offering a means to reconsider all elements as equally generative. Allopoietics, derived from cybernetics, describes the generative capacity of systems to produce outcomes beyond the sum of their actants, emphasizing collective unfolding over isolated creation. Liquid Media expands the notion of interfacing beyond traditional media to include publics, space, and time, conceptualizing these as mutable and entangled actants. These concepts outline an Aesthetics of Real Time that evaluates the dynamic relations among increasingly immediate systems. By proposing these new terms, the thesis invites a shift in perspective from object to process: viewing artworks not as stable materializations but as parts of real-time systems of collective meaning-making. While emerging from an artistic practice, this conceptual framework resonates with insights from contemporary sociology and cultural studies, where notions of fluidity, distributed agency, and relationality increasingly shape our understanding of complex systems and realities.

Developing a Functional in Vitro Model of the Neuromuscular Interface

2025-11-06T03:06:55Z

Developing a Functional in Vitro Model of the Neuromuscular Interface Schwendeman, Laura A. The neuromuscular system is responsible for the coordination of movement throughout the body, and while research has revealed many of the mechanisms involved in the function of the neuromuscular system, there are still many gaps in our understanding of how all of the components of the system work and how they are affected by environmental factors and disease. This work focuses on developing methods and an in vitro model for studying a subsystem of the neuromuscular system known as the neuromuscular junction (NMJ), which is the connection between skeletal muscle and motor neurons and is relevant in many neuromuscular degenerative diseases. This work identifies that current in vitro NMJ models are cohesively lacking the ability to support long-term, functionally contractile muscle tissue while providing compartmentalization and clear optical access for live imaging of muscle and motor neuron co-cultures. This work therefore presents STAMP, a microgroove patterning method for creating aligned, more physiologically relevant, functional, and optically accessible skeletal muscle tissue cultures on top of fibrin hydrogels. Through investigating a series of different sizing parameters, STAMP is shown to effectively align mouse and human skeletal muscle monolayers in vitro and influence the direction of muscle contraction under electrical and optogenetic stimulation while preserving skeletal muscle tissue integrity and viability. The STAMP approach provides a way to mold hydrogels and the morphology of muscle tissue and will be beneficial for addressing the need for compliant and optically clear substrates in modeling the neuromuscular junction.

Turning and Turbulence: A Comparative Study of Agility and Fluid Mechanics in Men’s and Women’s Soccer

2025-11-06T03:06:22Z

Turning and Turbulence: A Comparative Study of Agility and Fluid Mechanics in Men’s and Women’s Soccer Sonner, Jessica E. Female soccer players demonstrate high levels of agility but remain underrepresented in research and experience anterior cruciate ligament (ACL) tears two to eight times more frequently than their male counterparts [1]. These injuries are often associated with high-torsion movements at the knee, such as quick change-of-direction maneuvers in soccer [2]. To examine gender-based differences in agility, this study introduces an in-game metric based on change-of-direction speeds, derived from center-ofmass tracking data from the 2022 Men’s and 2023 Women’s FIFA World Cups. Results show that across positions, ball proximity, and game segments, female athletes tend to change direction both faster and more frequently than male athletes—supporting current injury hypotheses and informing gender-specific cleat design considerations. Beyond individual movement, this study also examines collective team behavior through a fluid mechanics lens. No significant gender differences were found in power spectral densities or second-order structure functions, suggesting symmetry in the underlying coordination dynamics. A direct cascade was observed in the 0–15m range, indicating a consistent transfer of energy across spatial scales. Team dispersion and the Area-Dominant Spread Index correlated with structure function slopes, bridging spatial metrics with turbulence-based models of group behavior.

Leaky Vessels

2025-11-06T03:07:13Z

Leaky Vessels Cong, Frank (Haotian) This thesis serves as a written synthesis of my art practice. It starts with Louis Pasteur’s swan neck flask, Robert Boyle’s air pump, the theater of proof, and cabinets of natural historians to discuss the intentional gesture of containment, exclusion, and controlled permeability in scientific containers and the knowledge production paradigm behind them. I argue that these containers possess another intrinsic gesture – to leak – that opens space for social and cultural dimensions to engage. I propose “leaky vessels” as an analytical tool and a methodology that foregrounds the tension between intentional and unintentional in order to attend to the issues of care, belief, and labor that arise within this dynamic. Chapter 2 develops the concept of “leaky” in three aspects – aesthetic intervention, historical residue, institutional sabotage – by analyzing art practices by Eve Andrée Laramée, Oron Catts and Ionat Zurr, Candice Lin, Maria Thereza Alves, Critical Art Ensemble, and Claire Pentecost. Each case demonstrates how alternative approaches to apparatuses can expose and unsettle the systems of control that govern knowledge authority, allowing seepage, contamination, and embodied histories to return to spaces designed to exclude them. Chapters 3 and 4 turn inward to examine my own art practice, Guardian and The Guarded (2024), RapidRise (2024), and Sweat Dough (2025). In Chapter 3, I discuss the experience of entering the biomaker space at MIT and cultivating animal cells in a pendant, interrogating how care, proximity, and cosmology might challenge the lab’s sterile and utilitarian logic. Chapter 4 discusses the other two projects that operate outside the lab, where I investigate how bodily entanglement with dough fermentation can leak into the broader context of food cultures, labor histories, and symbolic inheritance. Together, these chapters propose a practice that embraces contamination and relationality. Those that leak in and leak out are precisely where new layers of meaning reside.

Dynamics of torpedo depth control

2025-11-05T05:14:46Z

Dynamics of torpedo depth control Carleton, John Thomas. Thesis: M.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1992; Includes bibliographical references (leaf 72).

An investigation of the engineering aspects of a wind tunnel magnetic suspension system

2025-11-05T05:14:10Z

An investigation of the engineering aspects of a wind tunnel magnetic suspension system Chrisinger, John Edvil. Thesis: M.S., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 1959; Includes bibliographical references (leaf 62).

Design and Evaluation of Skill-Based Imitation Learning Policies for Robotic Manipulation

2025-10-30T03:24:17Z

Design and Evaluation of Skill-Based Imitation Learning Policies for Robotic Manipulation Palleiko, Andrew Imitation learning is a popular approach for obtaining intelligent robotic policies by learning from human demonstrations. Within this field, there is significant interest in the development of multi-task architectures that can efficiently learn diverse sets of tasks. Skill-based imitation learning methods, which abstract action sequences into ``skill'' representations for planning, offer structural advantages for handling the challenges of multi-task imitation learning that make them an attractive option for this problem. This work presents a novel skill-based imitation learning architecture formulation, with a causal transformer VAE skill-abstraction network paired with an autoregressive transformer planning policy. We find that our skill-abstraction network shows promise in identifying meaningful skills, but that the chosen planning architecture is poorly suited for predicting these skills due to multimodality in the resulting latent space. This is followed by a set of evaluations applied to an existing skill-based method with comparisons to a non-skill-based network on a multi-task dataset. We systematically investigate the performance impacts of six different policy and dataset conditions: data quantity, task variety, retry behavior, control precision, goal representations, and zero-shot transfer. Our experiments reveal limited increases in skill-based policy performance with more demonstrations or task variety, but improvements across architectures through exposure to demonstration retry behavior. Overall, the skill-based architecture demonstrates superior robustness to goal representation variations and low-level process noise than the non-skill-based policy, while neither architecture achieves meaningful zero-shot generalization to novel task combinations. These findings provide insights into the current state of IL methods, with the additional goal of establishing a framework for the evaluation of future multi-task IL architectures.

Towards Multimodal Streaming Perception: A Real-Time Perception Scheduling Framework Based on Relevance

2025-10-30T03:24:17Z

Towards Multimodal Streaming Perception: A Real-Time Perception Scheduling Framework Based on Relevance Huang, Dingcheng In modern human-robot collaboration (HRC) applications, multiple perception modules jointly extract visual, auditory, and contextual cues to achieve comprehensive scene understanding, enabling the robot to provide appropriate assistance to human agents intelligently. While executing multiple perception modules on a frame-by-frame basis enhances perception quality and information gains in offline settings, it inevitably accumulates latency, leading to a substantial decline in system performance in streaming perception scenarios. Recent work in scene understanding, termed Relevance, has established a solid foundation for developing efficient methodologies in HRC. However, modern perception pipelines still face challenges related to information redundancy and suboptimal allocation of computational resources. Drawing inspiration from the relevance concept and the inherent sparsity of information in HRC events, we propose a novel lightweight perception scheduling framework that efficiently leverages output from previous frames to estimate and schedule necessary perception modules in real-time. Our experimental results demonstrate that the proposed perception scheduling framework effectively reduces computational latency by up to 27.52% compared to conventional parallel perception pipelines, while also achieving a 72.73% improvement in MMPose accuracy and comparable YOLO accuracy. Additionally, the framework demonstrates high keyframe accuracy, achieving rates of up to 98% in dynamic scenes. The results validate the framework’s capability to enhance real-time perception efficiency without significantly compromising accuracy. Additionally, the framework shows potential as a scalable and systematic solution for multi-modal streaming perception systems in human-robot collaboration.

Fluid Sealing Challenges in Solid Oxide Electrolysis Cells and Rapid Swap Battery Systems

2025-10-30T03:24:08Z

Fluid Sealing Challenges in Solid Oxide Electrolysis Cells and Rapid Swap Battery Systems Lindberg, Ian G. This thesis explores the design and development of several mechanical elements relevant to two technologies Important to a global transition to green energy, hydrogen and electric vehicles. The portion of the thesis relating to hydrogen focuses on preloading mechanisms and high temperature seals, two design spaces crucial to the implementation of solid oxide hydrogen generation. Due to the high operating temperatures (600°C - 800°C), seal materials commonly used in other applications are inadequate and glass or vermiculite based seals must be used. The delicateness of these seals makes them a common failure point, and consistent application of a preloading force is key to mitigating this. The concept of a variable-bypass piston is proposed as a preloading mechanism suitable for the high temperatures present inside solid oxide electrolyzer systems, and the development of seal geometries as well as flow characterization of porous steel wool seals to enable parametric design is documented. As an alternative to current sealing methods, initial development of a composite seal utilizing materials and manufacturing methods originating in the semiconductor industry was also conducted. The final section of the thesis proposes the concept and covers initial testing of fluid transfer through a kinematic coupling, a topic of potential interest for implementing liquid pack cooling in a system of rapidly swappable batteries for electric vehicles.

Enhancing the Performance of Skeletal Muscle Powered Biohybrid Robots

2025-10-30T03:24:27Z

Enhancing the Performance of Skeletal Muscle Powered Biohybrid Robots Bawa, Maheera Skeletal muscle powers all voluntary motion in many living creatures, enabling behaviors such as walking, jumping, swimming, and flying. The field of biohybrid robotics aims to use biological actuators, such as skeletal muscle, to power adaptable robots that respond to their environment. Previous work in this field has focused on deploying 3D skeletal muscle tissues to power robotic function. In natural systems, muscles can also be organized in 2D formats to power a range of movements such as fish-like swimming and peristaltic pumping. However, long-lasting 2D cultures of skeletal muscle have been precluded by force-generating cells delaminating from their underlying substrate. Building on previous work from our lab demonstrating a method to culture contractile skeletal muscle in 2D formats, this work aims to enhance the performance of these systems by tuning substrate stiffness and topography. We show that optimizing system parameters prolongs actuator lifetime and enhances force by 100x.

Development and Implementation of a Smart Factory for Educational Fiber Extrusion Device Production

2025-10-30T03:24:20Z

Development and Implementation of a Smart Factory for Educational Fiber Extrusion Device Production Fillon, Marie This thesis presents the development and production of FrED (Fiber Extrusion Device), an educational manufacturing system designed to bridge the gap between theoretical instruction and hands-on practice in process control, computer vision, and smart manufacturing. Building on an existing prototype, this work focused on transitioning FrED from a proof-of-concept into a production-ready system by designing scalable workflows, improving hardware and software integration, and developing tools to ensure traceability and repeatability across builds. A major contribution of this thesis was the enhancement and implementation of a smart factory environment capable of supporting batch production. This included designing and deploying applications using Tulip Interfaces to manage inventory, guide subassembly processes, and monitor production metrics in real time. A modular SKU system and structured bin labeling framework were introduced to reduce errors, maintain version control, and support future growth. Station-specific apps were developed and refined to ensure consistent assembly and simplify onboarding across a rotating team of users. In parallel, this thesis contributed to the evaluation and refinement of a vision-based diameter measurement system using a low-cost USB camera. The system was analyzed under various operating conditions and its limitations under motion and variable lighting were quantified. Multiple image processing strategies were explored and robustness metrics were developed to inform future improvements. To ensure pedagogical relevance, the system was tested in user-facing workshops and public demo sessions. Feedback informed updates to both the assembly process and instructional content. By the end of the development cycle, the system supported the successful production of 35 complete FrED units, establishing a replicable model for small-scale manufacturing. This thesis demonstrates how modular digital infrastructure can enable scalable hardware deployment. It also highlights the practical challenges of transitioning from prototype to production and proposes tools and methods that can support broader adoption of smart manufacturing principles in learning environments.

Analyzing Vibration in Omni-Wheels: A Design of Experiments Approach to Optimizing Omni-Wheel Selection

2025-10-30T03:24:06Z

Analyzing Vibration in Omni-Wheels: A Design of Experiments Approach to Optimizing Omni-Wheel Selection Sanghai, Rohan S. Omni-wheels, known for enabling holonomic motion in robotic systems, often introduce vibration due to their complex geometry and multiple contact points. Unlike caster wheels with established testing standards, omni-wheels lack comprehensive characterization methods. While parallel studies by Ilkbahar [1] and Donnellan [2] explore their rolling resistance and static load capacity, a systematic analysis of vibration characteristics remains absent from the literature. This thesis presents an investigation of the vibration behavior of various omniwheel designs using a Design of Experiments (DOE) approach. A full factorial experimental design was developed, considering factors such as wheel type, rotational speed, applied load, and wheel orientation angle. Individual regression models were developed for each of six wheel types, treating operational parameters as continuous variables. Vibration levels were measured using root mean square (RMS) acceleration, derived from Fast Fourier Transform (FFT) and Power Spectral Density (PSD) analyses of accelerometer data. Results show that rotational speed consistently increased vibration across all wheel designs, while lateral motion (90° angle) consistently reduced vibration compared to forward motion. The effect of applied load varied significantly between wheel designs, with some wheels showing reduced vibration under load while others remained unaffected. Wheels DZ(1) and Vex(5) demonstrated the lowest average vibration levels, though post-test inspection revealed trade-offs with durability, including roller deformation and material degradation. Interaction effects, particularly between angle and speed, were statistically significant for all wheel types, indicating that the benefits of lateral motion are enhanced at higher speeds. This research provides a framework for optimizing omni-wheel selection to minimize vibration by developing wheel-specific predictive models that quantify sensitivities and interaction effects across various designs and conditions, improving system performance and stability. The findings highlight that wheel selection must consider not only vibration performance but also trade-offs with durability and rolling resistance, establishing vibration characteristics as a critical consideration alongside other performance metrics when selecting omni-wheels.

Designing and Optimizing Magnetohydrodynamic Induction Marine Energy Harvester

2025-10-30T03:24:09Z

Designing and Optimizing Magnetohydrodynamic Induction Marine Energy Harvester Scali, William T. Magnetohydrodynamic (MHD) power generation presents a promising approach for harvesting energy from marine environments, offering a sustainable alternative for powering naval assets and coastal infrastructure. While energy harvesting technologies are widely used in terrestrial and aerial applications, their implementation in marine environments remains limited. This thesis explores the feasibility of an MHD Inductive Marine Energy Harvester, optimizing its design for undersea naval applications to enhance energy efficiency and reduce carbon emissions with minimized construction costs. A theoretical 2D model was developed based on Maxwell’s equations and Fourier analysis to characterize the physics governing MHD power generation in seawater. This model was extended to multiple concentric gaps on one device, refining predictions of power output under varying flow regimes. Numerical simulations using MATLAB enabled the evaluation of key parameters, including fluid conductivity, magnetic field strength, and shroud design, to optimize energy conversion efficiency. Furthermore, geographical and coastal tide analyses were conducted to determine optimal deployment locations, maximizing power extraction from natural marine currents. Economic viability was assessed through a cost-benefit analysis, comparing the energy yield per unit cost of the harvester against existing renewable energy technologies and other maritime power sources. Results indicate that under specific conditions, MHD generators can effectively supplement energy demands, reducing reliance on conventional fuel or other electrical power sources. The findings of this research contribute to the advancement of marine renewable energy technologies, demonstrating the potential of MHD induction-based harvesting as a scalable solution for sustainable power.

Solar-Powered Critical Cooling: A Theoretical Feasibility Study for Human Thermal Regulation

2025-10-30T03:24:01Z

Solar-Powered Critical Cooling: A Theoretical Feasibility Study for Human Thermal Regulation Hall, Jeff Over the last 50 years, the leading global environmental hazard has not been hurricanes, lightning, tornadoes, floods, or earthquakes, but extreme heat events. With climate models projecting an increase in the frequency, intensity, and duration of heatwaves in the coming decades this threat to life is expected to only increase. Air conditioning has been demonstrated to reduce mortality during heatwaves yet uses an order of magnitude more energy than necessary to keep a human cool. Using principles of similitude to extrapolate the capability of existing vapor compression equipment, an objective function to maintain energy balance in a human exposed to extreme heat is developed across a design space. The function shows that in a standard forced convection air conditioning system, there no opportunity to provide emergency cooling of a human due to the slow mass flow rate needed to cool air in a single stream. As such, status-quo attempts to cool humans with general-purpose air conditioning will always be an inefficient use of energy. By focusing on keeping people cool, not spaces, we propose three paths forward for critical human cooling that appropriately match the energy needs of humans: radiative cooling, liquid cooling devices, and low-mass flow air conditioning.

Behavioral Methods for Next-Generation Shipboard Power System Simulation: Letting SPARCS Fly

2025-10-30T03:24:12Z

Behavioral Methods for Next-Generation Shipboard Power System Simulation: Letting SPARCS Fly Almquist, Ethan T. Design requirements on modern naval platforms are increasing the complexity and criticality of onboard electric plants. They form the backbone of warship operational capability and are at the heart of maritime decarbonization. Tasks such as assessing the ship's capacity in a damaged state, optimizing the mission profile of a fleet of vehicles, and evaluating broad design spaces in an efficient manner are increasingly difficult as electric network complexity increases. Traditional modeling techniques are either too computationally expensive, or lack the fidelity necessary to produce meaningful insights into the electric network's operation. Behavioral modeling bridges this gap, but is underdeveloped to support the system architectures of tomorrow's ships. This work details the advancement of behavioral modeling of electrical systems to incorporate hybrid AC/DC and ring bus architectures, the development of parallelization techniques, and SPARCS: a software package offering Shipboard Parallelized Analytics with a Rapid Configuration Simulator.

Multimodal Non-Contact Sensing of Neonatal Vital Signs Using Radar and Video

2025-10-30T03:24:15Z

Multimodal Non-Contact Sensing of Neonatal Vital Signs Using Radar and Video Chityat, Inbar Preterm neonates represent a vulnerable population which traditional contact-based monitoring devices are not optimized for their small size and complicated physiology. Adhesive sensors and wires can cause infections, discomfort, and impair the delivery of clinical care. Therefore, these most fragile patients could significantly benefit from remote health monitoring. This thesis establishes the foundation for a multimodal device designed for noncontact monitoring of neonates in the Neonatal Intensive Care Unit (NICU) that integrates a video camera and a radar. The device is used to estimate vital signs such as respiratory rate (RR), using both unimodal (solely video or radar) and multimodal fusion approaches that combine data from both sensors. Preliminary testing was conducted on neonatal simulator mannequins, followed by a clinical study at Tufts Medical Center NICU which collected data from 16 neonates so far (with the goal of reaching 20). The collected data was processed, labeled, and organized using image processing techniques and manual review, and then analyzed using a Video Vision Transformer (ViViT) architecture, incorporating early, intermediate, and late fusion strategies. Initial analysis was conducted on the mannequin data and the first neonatal subject. The results show that for estimating RR in neonates, the early fusion approach outperformed the unimodal methods. In movement detection, compared to human labeling, the fusion techniques achieved high accuracy and precision. To conclude, this study demonstrates that multimodal analysis has the potential to outperform unimodal approaches by improving accuracy against gold standard monitoring, particularly in challenging real-life conditions, including motion artifacts and poor lighting. This work represents a step toward more robust, non-invasive monitoring solutions for neonatal care, with implications for broader applications in remote health monitoring.

Incubators for Species which Exhibit Temperature-Dependent Sex Determination: Application to Hawksbill Sea Turtles in Rising Ambient Temperatures

2025-10-30T03:24:24Z

Incubators for Species which Exhibit Temperature-Dependent Sex Determination: Application to Hawksbill Sea Turtles in Rising Ambient Temperatures Finlason, Katana R. As global ambient temperatures continue to rise, with the highest recorded annual averages since 1850 being within the last ten years, problems emerge for species exhibiting temperature-dependent sex determination. This is the process by which the sex of an animal’s embryo is determined based on the temperature of environment in which it is incubated, which can result in skewed sex ratios within a population like in the case of the critically endangered Hawksbill Sea Turtles (Eretmochelys imbricata). Reportedly, 85-95% of Hawksbills sampled in the wild are currently female [3]. This sex-imbalance can negatively impact the species’ ability to procreate, leading to the potential for extinction. Currently, no viable, long-term solutions exist to effectively and safely cool sea turtle eggs while still keeping them within their natural habitat. This research proposes the creation of sea turtle egg incubators designed to achieve a temperature range that will produce a higher percentage of male hatchlings to help rectify this imbalance in habitats heavily affected by climate change. These incubators are designed to be affordable, easy to build and, most importantly, safe for the sea turtle eggs. Three-month-long temperature trials for the incubator were conducted in Jamaica with conservationist community partners at Oracabessa Bay Sea Turtle Project. Results showed that this incubator is not only easy to manufacture and use, but that it successfully regulates the temperature range in favor of more male hatchlings, while also increasing the emergence rate of the hatchlings from 70% in natural nests to over 80%. During one of the hottest months in Jamaica, the incubator, deployed without water changes, doubled the predicted percentage of males produced by natural nests. When provided with cool water changes the incubator quintupled this value. Throughout the months of August to October, the incubator achieved a temperature range that is predicted to produce 85-99% male hatchlings, thus counteracting the feminization phenomenon occurring in nature.

Minimal Constraint and Precision Placement in Cyclic Testing of High Life-Cycle Products

2025-10-30T03:24:42Z

Minimal Constraint and Precision Placement in Cyclic Testing of High Life-Cycle Products Edington, David J. In the electrification of heavy industry, rapid swappable batteries provide an effective means to minimize vehicle downtime and the cost of operation. However, to allow this technology to take hold, further development of electrical contacts that can both pass high amperage and undergo a high cycle life needs to occur. The development of these electrical contacts is a highly experimental process, and thus establishing a method and test equipment to determine the physical and electrical characteristics of these contacts over their lifetime will allow for the accelerated development of these products. This body of work serves as a design guide to establish a physical testing mechanism to assess contact resistance degradation and physical wear over the lifespan of an electric connector. Data will then be collected on initial contact prototypes to characterize their performance. With this data, designs may be iterated and improved upon in pursuit of creating a universal standard for battery swap technology on electric vehicles in heavy industry.

Distinct roles for energy storage and transmission infrastructure in a renewables-based electric power system

2025-10-30T03:24:28Z

Distinct roles for energy storage and transmission infrastructure in a renewables-based electric power system Kim, Beomjun Due to the intermittency of renewable resources, achieving a high coverage of renewable generation at low cost is one of the main hurdles to realizing zero-carbon electricity generation. In this study, we analyze the roles of energy storage systems (ESS) and transmission infrastructure in the cost-optimal deployment of a renewable electricity grid in the United States. We find that storage and transmission serve distinctly different functions: transmission is useful for addressing hours-long resource lows, but only plays a supplementary role in mitigating long-duration resource lows. Conversely, storage can handle both short-duration and long-duration resource lows. These different functions are driven in part by the large spatial footprints of the most extreme long duration resource lows. Furthermore, the total cost of renewable energy in the system and the cost-determining technological components in the system are dependent on the renewables penetration toward total demand—known as the energy availability factor (EAF). When the EAF is sufficiently low, the cost of a cost-optimized system is driven solely by generation costs. For low to intermediate EAF, both generation and transmission costs are dominant factors. At high EAF, generation and storage costs become the dominant factors.

Wedged Vortex Generator Applications for Marine Vessels

2025-10-30T03:24:25Z

Wedged Vortex Generator Applications for Marine Vessels Kimmeth, Jack This thesis investigates the effectiveness of vortex generators (VGs) in reducing viscous drag in hydrodynamic applications. Initial experimental and computational fluid dynamics analyses identified wedge-shaped VGs as the optimal design for flow manipulation. Comparative testing of three wedge shaped VG sizes at 1.3 m/s revealed the most effective configuration, which was subsequently evaluated across speeds ranging from 1.0 m/s to 1.6 m/s. The results showed a viscous drag reduction of 7.9% at 1.4 m/s. These findings were extrapolated to a full-scale bulk carrier using appropriate geometric and dynamic scaling factors. Total resistance was partitioned using Holtrop-Mennen approximations, allowing the drag reduction to be realistically applied to operational conditions on a trans-Pacific route. Material and installation cost estimates were also developed. Finally, implications for propulsion efficiency, flow-induced vibrations, and cavitation are discussed, with recommendations for future self-propelled model testing to further explore these effects.

Prosody in Kichwa

2025-10-30T03:24:10Z

Prosody in Kichwa Chango Masaquiza, Soledad This thesis investigates the prosodic system of Salasaka Kichwa, focusing on the interaction between pitch, morphosyntactic structure, and word order in both elicited and spontaneous speech. Based on data from ten native speakers of the Salasaka community, the study analyzes approximately 150 utterances using Praat and ToBI-style prosodic annotation. The findings reveal a consistent alignment between the nuclear pitch accent and the leftmost constituent of the verb phrase in neutral declarative sentences, supporting the hypothesis that Salasaka Kichwa exhibits a head-final syntactic structure. This default prosodic alignment is disrupted by the presence of focus-sensitive or interrogative morphemes such as -mi and -chu, which reliably attract the pitch peak regardless of their position in the clause. In ditransitive constructions, pitch prominence consistently targets the dative-marked argument. Accusative-marked objects also receive prominence, but only when modified; in such cases, it is typically the modifying adjective or contrastive element that bears the highest pitch. Overall, the study demonstrates that prosodic prominence in Salasaka Kichwa is not governed by syntactic structure alone. Instead, it emerges from a layered interaction between morphology, information structure, and pragmatic marking offering new insights into how prosody encodes grammatical and communicative functions in underdescribed head-final languages.

Model Predictive Control Approaches for Dynamic Table Tennis Swinging

2025-10-30T03:24:26Z

Model Predictive Control Approaches for Dynamic Table Tennis Swinging Nguyen, David H. This thesis presents three model predictive control (MPC) formulations for robotic table tennis swinging, addressing the challenge of generating precise, real-time paddle trajectories for dynamic ball interactions. We explore key differences in optimization structure, solver strategy, and real-time implementation, evaluating each approach through hardware experiments that measure strike condition tracking and hit success. The final controller integrates the full task of a table tennis possession by planning the return ball trajectory through the contact dynamics, and generating a swing to achieve it. This controller improves the hit rate of the system from 88.3% to 97.6% and significantly enhances strike condition accuracy and smoothness enabling control over the landing location and spin of the ball.

Modeling the Impact of Helicopter Vibrations on the Musculoskeletal Health of US Army Blackhawk Helicopter Pilots

2025-10-30T03:24:02Z

Modeling the Impact of Helicopter Vibrations on the Musculoskeletal Health of US Army Blackhawk Helicopter Pilots Johnston, Julie E. The UH-60, used for troop transport, MEDVAC, and mission control, has evolved over the last 45 years from the Alpha Model to the Lima and Mike models that are currently utilized. Previous studies investigated the impact of Whole-Body Vibrations (WBV) on aviators and the resulting musculoskeletal injury, but none have investigated the efficacy of the Mike model’s Active Vibration Control System (AVCS) on reducing the impact of helicopter vibrations on musculoskeletal health. Computational analyses of a biomechanical model using OpenSim and motion capture at varying levels of vibration was conducted. This quantifies the response of the spine and the surrounding muscles when vibratory loads are applied while positioned to manipulate the flight controls. A musculoskeletal model was developed to represent the aviator in the seated posture required to effectively manipulate the flight controls. To develop the model, the team recorded motion capture data with a pilot in a pilot test for concept validation. This data was then processed and input in the OpenSim inverse kinematics tool to determine joint angle and to demonstrate the muscle-tendon length of several muscles in the back. Unlike the initial predictions, the muscles in the right side of the back were not consistently longer than those of the left side. A survey was also developed that builds upon previous efforts, seeking to understand the aviator’s perspective on musculoskeletal injury and prevention, with a focus on the back. Aviators are asked to describe the cause of their injury, methods of injury prevention, and recovery techniques encompassing numerous subpopulations of flight experience: Lima-majority, Mike-only, Mike-majority, and an even mixture of L/M. The data attempts to characterize the impact of the AVCS on aviator spine health. The AVCS should decrease the rate of injury by reducing the vibratory loads experienced by the aviator. This survey is unique to previous questionnaires as it focuses on the user’s perspective of differences between the two models, and the injury or pain felt by each service member. While it was expected to see a trend of reduced injury occurrence amongst the Mike-only aviators versus those with Lima-majority flight hours, this was not the case. Injury prevalence was consistent across most populations, indicating the potential inefficacy of the AVCS. Analysis of open-ended responses, particularly from the hybrid group, provide some context for the perceived impacts of using the AVCS. Some population demographics were not represented in this survey due to the nature of the unit being surveyed, which may impact the validity of some results. By quantifying the perceived efficacy of the AVCS as it relates to chronic musculoskeletal injury using a survey of pilot experience factors (flight hours, airframes, operating theatres, etc.), and by representing the maladaptive posture of the pilots with a computational simulation based on experimental pilot data; a full picture is developed of the risk of issue related to the near and long-term health of US Army Aviators. The aim is to expand the overall understanding of how vibration is impacting the musculoskeletal health of aviators and their perceived impact on lifelong health from the profession. The ultimate goal is to aid in the design of additional countermeasures to improve aviator spine health and to serve as a platform for optimization of systems like AVCS.

Hydrodynamic Behavior of Pop-Up Satellite Archival Tags (PSAT) Subject to Vortices

2025-10-30T03:24:13Z

Hydrodynamic Behavior of Pop-Up Satellite Archival Tags (PSAT) Subject to Vortices Hoo, Stephanie Pop-up Satellite Archival Tags (PSATs) are a combination of satellite and archival tags used by marine biologists to collect large scale movement and behavioral data of large pelagic life for up to two years [1]. However, current commercial PSATs have an unusually high failure rate when tagged on tuna and cost upwards of $4000, making it both difficult and expensive to collect data [14]. Upon investigation, the top two failure modes of tuna-affixed PSATs have been identified as drag from movement/tissue healing and pressure cycling [14]. Current commercial PSAT manufacturers do not account for the vortices shed by fish when testing their designs— a large oversight that could account for their high failure rate [15]. The work herein determined the effects of vortex shedding on PSAT hydrodynamic behavior, used these results to inform the design of novel PSAT body shapes, and conducted a head-to-head comparison of these designs with existing commercial PSATs.

Combating Corrosion and Monitoring Microgrids on Coast Guard Patrol Boats

2025-10-30T03:24:00Z

Combating Corrosion and Monitoring Microgrids on Coast Guard Patrol Boats Buchanan, Maxwell Calvin Marine corrosion presents a persistent threat to the reliable operation of U.S. Coast Guard Fast Response Cutters (FRCs). This thesis investigates hybrid cathodic protection strategies combining impressed current cathodic protection (ICCP) systems and sacrificial zinc anodes to combat corrosion on such vessels. Observing over 550 cumulative months of ICCP system data across 46 FRCs, this thesis identifies operational trends, failure modes, and unique regional behaviors. To validate observed patterns and explore failure scenarios, the study implements finite element modeling using COMSOL Multiphysics. These simulations replicate normal operation, reference electrode failure, propeller passivation, localized zinc loss, and hull coating failure for both a generic 35m hull and the FRC hull. These models emphasize how system behavior responds to material variations, temperature, and system health, offering a diagnostic framework for optimizing ICCP configurations. Field and laboratory experiments further ground the computational findings. These include shipboard hull potential surveys and analysis of zinc anode wastage across multiple cutters. Controlled experiments on nickel aluminum bronze (NAB) passivation using miniaturized ICCP test systems are explored for further study. Initial results show variation in zinc consumption and corrosion behavior depending on ICCP setpoints, with higher protection levels (-1050 mV) often correlating with reduced zinc depletion. The thesis also explores energy diagnostics onboard FRCs via non-intrusive load monitoring (NILM). A case study on the USCGC WILLIAM CHADWICK describes monitoring auxiliary machinery loads through NILM signatures and suggests expansion to critical panels and DC systems. By integrating fleet data, physical experimentation, and simulation, this thesis advances future efforts in patrol boat corrosion monitoring, ICCP optimization, and resilient microgrid management.

Incorporating High-Resolution Tactile Perception for Performative and Generalized Robotic Manipulation Through Compliance Estimation and Hardware Design

2025-10-30T03:24:22Z

Incorporating High-Resolution Tactile Perception for Performative and Generalized Robotic Manipulation Through Compliance Estimation and Hardware Design Burgess, Michael In robotics, replicating the natural proficiency with which humans perform manipulation tasks has proven challenging. Modern control schemes are predominantly learning-based and thus depend heavily on data collected via teleoperated demonstrations. Humans rely on our tactile perception to perform contact-rich and dynamic manipulation tasks. By more seamlessly incorporating high-resolution tactile sensing and haptic feedback into teleoperation interfaces, we can work to create stronger demonstration data to support the development of more effective learned control policies. In this thesis, we present two contributions toward this goal. First, we develop an algorithm to estimate the compliance of grasped objects in real-time from tactile images to provide haptic feedback to remote users. This algorithm combines both analytical and learning-based approaches to better generalize across both object shapes and materials. Second, we create a 1-DoF robotic gripper design with integrated tactile sensing. Inspired by the principle of self-similarity, this gripper is designed to better conform to complex object geometries than traditional designs and more securely grasp objects of many shapes and sizes. Together, these contributions can be utilized to create robust, tactile-aware teleoperation platforms. These platforms would facilitate more effective data collection and thereby promote the development of more performative autonomous action in generalized robotic manipulation scenarios.

The Net Climate Impact of AI: Balancing Current Costs with Future Climate Benefits

2025-10-30T03:23:54Z

The Net Climate Impact of AI: Balancing Current Costs with Future Climate Benefits Turliuk, Jennifer What is the net impact of artificial intelligence on climate change? Existing studies focus on AI's footprint, but few analyze AI's trade-offs. This paper develops a framework to quantify both the Greenhouse Gas (GHG) emissions and the climate change costs and benefits of AI systems, addressing the time value of carbon and the installed base of existing AI infrastructure. We examine the energy demands of AI, which are growing rapidly and threatening companies' net-zero commitments, while also analyzing AI's potential to enable emissions reductions through applications such as optimized energy systems, demand response, grid management, and electrification acceleration. This research introduces the Net Climate Impact Score (NCIS) of AI, a novel equation to calculate the net climate impact of AI technologies that considers both immediate emissions and potential future benefits, and provides a methodology for assessing AI projects holistically. We demonstrate that while current AI applications are predominantly emissions-intensive, strategic deployment focused on energy system transformation could potentially deliver net climate benefits within specific time frames and applications. However, improvements in energy efficiency and emissions reductions resulting from AI are, absent climate policy, likely to generate both direct and indirect rebound effects that could undermine the emissions reductions and reduce the climate benefits of AI. The research concludes with policy and industry recommendations that propose technological pathways that could maximize AI's positive impact while minimizing its environmental footprint.

Wide Range Switched Mode RF Power Amplifiers and their applications

2025-10-30T03:24:16Z

Wide Range Switched Mode RF Power Amplifiers and their applications Pressel, Adam Jay Switched-mode power amplifiers (SMPAs) are desired that can work across a wide range of power levels and load impedances with fast response speed while maintaining high efficiency. Such designs would be valuable for many applications including plasma generation and wireless power transfer. We introduce a new wide-range SMPA architecture that provides direct output voltage modulation, enabling it to modulate output power and compensate for resistive load variations. Dynamic frequency modulation is leveraged to address reactive load variations. The new architecture enables all the semiconductor switches to maintain zero-voltage switching across all operating conditions. Experimental results shows that the wide-range half bridge power amplifier was able to deliver a wide power range of 25 W - 95 W power range across each individual resistive load in the range of 5 Ω - 20 Ω with up to j15 Ω reactance. The maximum dc-ac efficiency is 86 with 20 Ω load and 110.5 W load power.

Tension-Leg Platform for Offshore Diffusor-AugmentedHydrokinetic Turbine

2025-10-30T03:24:05Z

Tension-Leg Platform for Offshore Diffusor-AugmentedHydrokinetic Turbine Mannier, Robert B. Harnessing marine energy offers significant potential for advancing clean and sustainable power generation. This thesis focuses on the design and optimization of a diffuser-augmented hydrokinetic turbine, supported by a tension-leg platform, to harness ocean and tidal currents for renewable energy production. By incorporating diffuser technology, the turbine’s efficiency is enhanced, increasing the coefficient of power and enabling effective energy capture even in environments with lower current speeds. The research involves 2D and 2D axisymmetric modeling of the diffuser and turbine using Actuator Disk Theory (ADT), with tools such as Rhino and Star CCM+. Mounted on a floating tension-leg platform anchored to the seabed, the turbine is designed to exceed the Betz limit, maximizing power output and advancing offshore energy harvesting capabilities. This thesis is solely focused on the design and optimization of the hydrokinetic turbine, providing an in-depth analysis of diffuser performance. The findings contribute to the development of marine renewable energy technologies, promoting sustainable and efficient power generation from ocean and tidal currents.

On the Application of an Output-based Adaptive, Higher-order Finite Element Method to Sonic Boom Propagation

2025-10-30T03:23:48Z

On the Application of an Output-based Adaptive, Higher-order Finite Element Method to Sonic Boom Propagation Trono Figueras, Renato The reduction of sonic boom loudness to within acceptable limits is a crucial factor for the viability of supersonic aircraft. This thesis presents a computational framework for simulating sonic boom propagation using an output-based adaptive, higher-order finite element method. The research employs the Variational Multiscale with Discontinuous Subscales (VMSD) method, integrating Continuous Galerkin (CG) and Discontinuous Galerkin (DG) features, referred to as VMSD-BR2. This approach leverages static condensation to manage computational cost while utilizing DG stabilization techniques for enhanced stability and adjoint consistency. A key component of this work is the application of the dual weighted residual (DWR) method for output error estimation, which in turns drives the mesh optimization process. The method’s efficacy is validated using smooth solutions for the viscous Burgers equation and the adjoint PDE for a volume output functional. Additionally, artificial viscosity is incorporated via a shock sensor PDE approach to handle shock presence, with necessary corrections applied to the DWR error estimate. The VMSD-BR2 method is applied then to a real-world scenario solving the augmented Burgers equation, which models the propagation of sonic booms. The results include the pressure perturbation field, adapted meshes, ground-level B-SEL filtered pressure, and perceived loudness at ground, demonstrating the method’s practical application.

C. elegans as a Platform for Multimodal Neural Data Integration

2025-10-30T03:24:08Z

C. elegans as a Platform for Multimodal Neural Data Integration Simeon, Quilee Systems neuroscience has traditionally been fragmented into investigations at discrete levels of organization, creating methodological and conceptual gaps that hinder unified understanding of neural function. This thesis examines the nematode Caenorhabditis elegans as a platform for integrating diverse neural data modalities, offering a pathway to bridge these gaps. The hermaphrodite C. elegans, with its completely mapped connectome, optical transparency, genetic tractability, and stereotyped nervous system of only 302 neurons, presents an opportunity for comprehensive measurements across multiple dimensions of neural function. The review is organized around three fundamental neural data modalities accessible in C. elegans: (1) molecular genetic profiles, (2) network connectivity, and (3) neural activity dynamics. Historically studied in isolation, these complementary data types are increasingly being bridged through technological and computational innovations. We examine experimental advances enabling whole-nervous-system measurements of these modalities, as well as data standardization efforts and computational frameworks for cross-modal integration. While understanding the relationship between neural activity and behavior remains a fundamental goal of systems neuroscience, this thesis focuses on neural data acquisition and integration rather than behavioral analysis, which has been extensively covered elsewhere.1 We conclude with some original proposals to overcome current limitations in multimodal data acquisition and synthesis, and suggest future directions toward a holistic understanding of how molecular components, network connectivity, and cellular physiology collectively give rise to neural function in C. elegans. These integrative approaches establish a roadmap that may eventually scale to more complex nervous systems and advance our understanding of neural computation across species.

A Nickel Short: Rethinking Element Scarcity in Pursuit of a Fusion-Powered World

2025-10-30T03:24:04Z

A Nickel Short: Rethinking Element Scarcity in Pursuit of a Fusion-Powered World Sutcliffe, Douglas Fusion energy presents a promising solution for current global decarbonization goals. This thesis presents an adaptable model for evaluating mineral sufficiency in the global deployment of fusion power. Using the ARC Magnetic Confinement (MC) Deuterium-Tritium (D-T) fusion concept as a framework, this research integrates mineral usage estimates from the International Energy Agency (IEA) with MIT Energy Initiative’s (MITEI) energy production forecasts by generation technology. Using MITEI’s $2,800/kW cost scenario for fusion power generation, the model situates the demand for fusion-critical minerals within the broader context of growing mineral needs driven by the clean energy transition, and offers specific, quantitative insights into mineral sufficiency risks. The study finds that beryllium will face significant shortages solely due to fusion demand, with resource exhaustion projected to occur within 40 years. When accounting for additional demands from Electric Vehicles (EVs), battery storage, and transmission infrastructure, chromium and nickel are projected to exhaust economically extractable reserves within 21 to 35 years at current prices. The research further reveals that for nine of the thirty elements evaluated, over 50% of production is concentrated in a single country, and for half of the minerals China is the largest producer, introducing geopolitical risks. Notably, at just 13 kg per reactor, the demand for Rare Earth Elements (REEs) is not exposed to a significant risk, even without the top producing country. The research also surfaces current reactor designs and strategies which could help mitigate each identified risk.

As global ambient temperatures continue to rise, with the highest recorded annual averages since 1850 being within the last ten years, problems emerge for species exhibiting temperature-dependent sex determination. This is the process by which the sex of an animal’s embryo is determined based on the temperature of environment in which it is incubated, which can result in skewed sex ratios within a population like in the case of the critically endangered Hawksbill Sea Turtles (Eretmochelys imbricata). Reportedly, 85-95% of Hawksbills sampled in the wild are currently female [3]. This sex-imbalance can negatively impact the species’ ability to procreate, leading to the potential for extinction. Currently, no viable, long-term solutions exist to effectively and safely cool sea turtle eggs while still keeping them within their natural habitat. This research proposes the creation of sea turtle egg incubators designed to achieve a temperature range that will produce a higher percentage of male hatchlings to help rectify this imbalance in habitats heavily affected by climate change. These incubators are designed to be affordable, easy to build and, most importantly, safe for the sea turtle eggs. Three-month-long temperature trials for the incubator were conducted in Jamaica with conservationist community partners at Oracabessa Bay Sea Turtle Project. Results showed that this incubator is not only easy to manufacture and use, but that it successfully regulates the temperature range in favor of more male hatchlings, while also increasing the emergence rate of the hatchlings from 70% in natural nests to over 80%. During one of the hottest months in Jamaica, the incubator, deployed without water changes, doubled the predicted percentage of males produced by natural nests. When provided with cool water changes the incubator quintupled this value. Throughout the months of August to October, the incubator achieved a temperature range that is predicted to produce 85-99% male hatchlings, thus counteracting the feminization phenomenon occurring in nature.

2025-10-30T03:23:53Z

As global ambient temperatures continue to rise, with the highest recorded annual averages since 1850 being within the last ten years, problems emerge for species exhibiting temperature-dependent sex determination. This is the process by which the sex of an animal’s embryo is determined based on the temperature of environment in which it is incubated, which can result in skewed sex ratios within a population like in the case of the critically endangered Hawksbill Sea Turtles (Eretmochelys imbricata). Reportedly, 85-95% of Hawksbills sampled in the wild are currently female [3]. This sex-imbalance can negatively impact the species’ ability to procreate, leading to the potential for extinction. Currently, no viable, long-term solutions exist to effectively and safely cool sea turtle eggs while still keeping them within their natural habitat. This research proposes the creation of sea turtle egg incubators designed to achieve a temperature range that will produce a higher percentage of male hatchlings to help rectify this imbalance in habitats heavily affected by climate change. These incubators are designed to be affordable, easy to build and, most importantly, safe for the sea turtle eggs. Three-month-long temperature trials for the incubator were conducted in Jamaica with conservationist community partners at Oracabessa Bay Sea Turtle Project. Results showed that this incubator is not only easy to manufacture and use, but that it successfully regulates the temperature range in favor of more male hatchlings, while also increasing the emergence rate of the hatchlings from 70% in natural nests to over 80%. During one of the hottest months in Jamaica, the incubator, deployed without water changes, doubled the predicted percentage of males produced by natural nests. When provided with cool water changes the incubator quintupled this value. Throughout the months of August to October, the incubator achieved a temperature range that is predicted to produce 85-99% male hatchlings, thus counteracting the feminization phenomenon occurring in nature. Espinal, Michael A. Foams, widely used in packaging, insulation, protective gear, and medical implants, are versatile materials but mechanically inefficient due to their bending-dominated microstructure, leading to an exponential loss of stiffness and strength at low relative densities. Architected materials address this limitation through engineered microstructures that achieve near-linear scaling of properties with relative density. However, truss- and plate-based designs suffer from stress concentrations, while shell-based architectures, though more mechanically efficient, remain highly sensitive to defects and are challenging to fabricate at scale via additive manufacturing. Spinodal architected materials, derived from scalable spinodal decomposition processes, offer a promising alternative with aperiodic, double-curvature microstructures that enhance mechanical efficiency at low relative densities. Nevertheless, their behavior beyond the elastic regime remains largely unexplored. This thesis investigates the nonlinear mechanics of spinodal architected materials by combining a comprehensive experimental dataset with computational modeling. A total of 107 unique morphologies were fabricated and subjected to uniaxial compression along three principal directions, resulting in a dataset of 321 stress-strain curves. Morphologies were generated via simulated spinodal decomposition, allowing controlled variation of anisotropy. Explicit finite element simulations, validated against experimental data, revealed that plastic energy dissipation dominates the large-strain mechanical response. To quantitatively link local morphology to global mechanical behavior, we introduce the Normal Participation Factor (NPF) — a scalar geometric parameter that captures the alignment between surface normals and the loading direction. We demonstrate that the NPF is a material-agnostic proxy for equivalent plastic strain and is linearly correlated with the total energy dissipated during deformation. Combining insights from both experiments and simulations, we establish the NPF as a first-order predictive tool for mechanical behavior under large strains, enabling structure-property predictions without reliance on costly simulations or extensive experimental testing. Altogether, this work lays the foundation for developing finite-strain structure-property relationships in spinodal architected materials, advancing their potential for real-world applications.

Mapping Informality: An Approach to Classifying Sidewalk Informal Practices and Elements Through Street View Imagery

2025-10-30T03:23:49Z

Mapping Informality: An Approach to Classifying Sidewalk Informal Practices and Elements Through Street View Imagery Co, Dominic Lim By 2050, the United Nations estimates that 68 percent of the world’s population will live in cities, with 90 percent of that growth concentrated in rapidly urbanizing informal communities across Africa, Latin America, and Asia. In these contexts, informality, defined as unregulated commerce, adaptive reuse of space, incremental construction, and self-organized infrastructure, shapes the everyday choreography Jane Jacobs called the “sidewalk ballet.” Yet because governments rarely collect census-grade data on such activity, informality remains poorly documented and weakly understood. This thesis introduces a transferable computational framework to formalize informality by transforming street imagery into an auditable taxonomy of informal street-level elements, activities, and practices. The framework is tested in two contrasting districts, i.e. District 1 and District 5 of Ho Chi Minh City, where sidewalks are highly contested by vendors, pedestrians, and regulators. The contribution of this thesis is two-fold. First, this thesis contributes a three-stage pipeline for classifying sidewalk informality. Using Seesaw (Moll et al., 2022), a CLIP-based feedback loop retrieves and soft-labels candidate scenes. This is followed by manual verification and fine-tuning a lightweight ResNet on binary categories (e.g. stationary vs mobile vendors, etc.). Compared to the zero-shot model Qwen-VL-Max, the fine-tuned ResNet delivered more balanced performance (precision/recall: 0.62– 0.78) and better handled nuanced, context-sensitive distinctions. In contrast, Qwen-VL-Max favored recall and object salience but struggled with subtle or spatial cues like mobile vs. stationary setups. Second, this thesis also developed a taxonomy and annotated dataset of informality which was used to reveal spatial inequities in sidewalk use. By converting curbside complexity into structured, updateable categories, the framework enables planners to recognize the adaptive value of informal practices, target genuine hazards, and design interventions for more equitable urban planning.

Nuclear Microreactor-Powered Container Ships for Maritime Decarbonization

2025-10-30T03:23:57Z

Nuclear Microreactor-Powered Container Ships for Maritime Decarbonization Dickerman, Matthew F. The maritime shipping industry, responsible for approximately 3% of global greenhouse gas emissions, faces growing pressure to achieve net-zero emissions by 2050 under the International Maritime Organization (IMO) framework. Alternative fuels such as liquefied natural gas, ammonia, and methanol present challenges related to energy density, infrastructure, safety, and cost. Nuclear microreactors offer high energy density, zero operational emissions, and multi-year endurance, but require coordinated regulatory development and stakeholder engagement for commercial adoption. This thesis evaluates the feasibility of integrating microreactors into container ship designs employing electric propulsion and standardized intermodal logistics. Holos-Quad microreactors are selected based on their modular architecture, transportability, and compatibility with marine operations. Detailed ship concepts are developed for Feeder, Panamax, and New-Panamax classes, accompanied by a phased fleet development strategy. Economic modeling compares the lifecycle costs of conventional and microreactor-powered ships, incorporating capital expenditures, operating costs, financing assumptions, and carbon pricing. Fleet-level analysis indicates that microreactor-powered ships can achieve comparable or improved profitability while eliminating nearly 44 million metric tons of CO2e emissions across a ten-ship fleet. Sensitivity analyses confirm the robustness of these results across a wide range of future scenarios. By integrating stakeholder analysis, technical feasibility assessments, and economic modeling, this research establishes a commercially viable framework for zero-emission nuclear-powered shipping, offering a scalable pathway toward sustainable maritime operations.

A magnetic levitation testbed for development of real-time control frameworks applied in fusion

2025-10-30T03:23:41Z

A magnetic levitation testbed for development of real-time control frameworks applied in fusion Lee, Yehoon This thesis presents the development of a magnetic levitation device as a hardware-in-theloop platform to be used for research in Control and Data Acquisition frameworks applied to fusion experiments. Specifically, the testbed is aimed to demonstrate distributed, modular control using a plasma control system framework being developed at the Plasma Science and Fusion Center at MIT. This framework integrates a real-time control framework, MARTe2, and a data management framework, MDSplus, to provide platform flexibility and robust data management for rapid prototyping of control systems. Both frameworks are widely used individually in fusion experiments worldwide. The magnetic levitation setup is centered around a single electromagnet coil which levitates a permanent disk magnet from above. Implemented with the integrated MARTe2/MDSplus framework, the controller, actuator, and sensors are distributed over the network. With the magnetic levitation testbed, this thesis achieves three objectives: 1. formulation of a physicsbased model of the system, 2. development of a controller in a modular, networked framework, and 3. training and implementation of learning-based methods within the framework. First, a state-space model for single-axis magnetic levitation is formulated based on theory and refined with magnetic field measurements. A feedback controller is then developed and implemented with MATLAB Simulink. Afterwards, a vision-based observer is developed to estimate position and tilt of the levitated magnet. Pose-image datasets are auto-labeled using fiducial markers and are used to train a convolutional neural network. Finally, the trained network will be applied in system identification of the final controlled system. Through the process of system development, this thesis proposes that the integrated MARTe2/MDSplus framework is robust in performing real-time control of a networked system, and its structural modularity is advantageous for developing and testing learning-based models.

A Fast Assay of Bacteria Cell Permeability for Genetic Transformation

2025-10-30T03:23:51Z

A Fast Assay of Bacteria Cell Permeability for Genetic Transformation Nieves, Charmaine Bacterial cell genetic engineering is fundamental for research aiming to learn more about bacterial species for a broad range of applications. One method of intracellular delivery of foreign DNA during the genetic engineering process is the use of electroporation to create pores along the bacterial cell membrane. Current methods for assessing pore formation do not directly measure cell permeabilization or enable same-day assessment. In this thesis, a novel fast-screening protocol combining SYTOX green, microfluidics, and fluorescence imaging is evaluated for its capability to assess multiple conditions for cell permeabilization within a single day. By imaging bulk suspensions of post-electroporated cells stained with intracellularly delivered SYTOX, multiple electroporation conditions can be rapidly screened for cell permeabilization. This fast-screening protocol utilizes standard microbiology equipment and low-cost microfluidic imaging chambers, lowering the barrier to adoption and significantly reducing experimental time compared to conventional protocols involving foreign DNA delivery. Importantly, by decoupling permeabilization assessment from foreign DNA uptake, this method isolates the effect of membrane permeabilization from confounding factors such as restriction-modification systems. As a result, it provides a more accurate qualitative and quantitative assessment of bacterial membrane disruption. This approach enables same-day evaluation of electroporation conditions regardless of bacterial growth rate, potentially accelerating the optimization process for intracellular delivery in gene editing applications.

Probabilistic Human Arm Motion Prediction via Structured Multitask Variational Gaussian Processes

2025-10-30T03:23:40Z

Probabilistic Human Arm Motion Prediction via Structured Multitask Variational Gaussian Processes Chong, Jinger S. Accurate human motion prediction with uncertainty estimation is essential for safe and efficient human-robot collaboration, where robots must anticipate and react to human movements in real-time. Existing methods either rely on sophisticated techniques that demand extensive training data and sacrifice interpretability, or use simpler approaches like conventional Gaussian Processes (GPs) that fall short in performance. To address this gap, we propose a novel structured multitask variational GP framework that explicitly incorporates joint dependencies to reflect human kinematics. We further enhance this framework by integrating angular velocity constraints, which improve the physical plausibility of predictions. The addition of constraints alone yields up to a 66% reduction in mean angle error (MAE) and an 84% improvement in the likelihood of predicting ground truth (NLL), outperforming standard GP baselines across a wide range of motion types and prediction horizons. Among model variants, our structured GP with constraints offers the best tradeoff—achieving MAE within 1.1–2.6% and NLL within 0.001–0.012 of the best-performing model, while maintaining significantly lower overconfidence rates (OCR), particularly at short horizons where the independent GP model OCR reaches nearly 45%. These results underscore the importance of incorporating structure and context in human motion prediction, demonstrating that even simpler probabilistic models like GPs can achieve substantial performance gains when augmented with such information.

Permanent Magnet Synchronous Motors: Nonlinear Dynamic Modeling, Hardware Characterization, and High-Bandwidth Torque Control for Applications in Dynamic Robotics

2025-10-30T03:23:45Z

Permanent Magnet Synchronous Motors: Nonlinear Dynamic Modeling, Hardware Characterization, and High-Bandwidth Torque Control for Applications in Dynamic Robotics Roy, Ronak The high-level control algorithms that are responsible for achieving dynamic locomotion in legged robots depend on accurate torque production for matching real-life performance with simulated performance. To achieve accurate torque production, actuators must run high-bandwidth, low-level torque control. Developing high performance low-level controllers requires accurate actuator models. This thesis covers the physical model of a Permanent Magnet Synchronous Motors (PMSM), a very common type of actuator in dynamic robotics. This thesis details the derivation of the PMSM linear model, how to adapt the model dependent on the physical construction of a real motor, and the implementation of FieldOriented Control (FOC) to achieve torque control. This thesis also describes a novel design of a high-precision dynamometer, which allows a motor to be coupled with an impedance and a torque sensor in order to accurately characterize the torque production characteristics of the motor. Using this dynamometer and other experimental setups, this thesis validates the model and determines parameters for multiple different actuators. Finally, this thesis proposes an augmented PMSM model that considers the nonlinear saturation behavior of the motor, validating the principle with hardware experiments, and demonstrates a nonlinear torque model and gain-scheduled current controller that improve torque tracking performance.

Development of an Apparatus and Testing Strategy for Characterizing Rolling Resistance of Omnidirectional Wheels

2025-10-30T03:23:38Z

Development of an Apparatus and Testing Strategy for Characterizing Rolling Resistance of Omnidirectional Wheels Ilkbahar, Kayra B. Omnidirectional wheels (omni wheels) are a type of wheel technology similar to caster wheels but capable of simultaneous longitudinal and lateral motion, making them suitable for holonomic motion applications. In recent years, their popularity has grown substantially in areas such as educational robotics, autonomous vehicles, and industrial automation. Despite their similarity to caster wheels in both function and application, omni wheels are a much less mature technology and few agreed-upon standards exist for their design and testing. This thesis covers the design of a test procedure and its requisite test apparatus to characterize the rolling resistance of omni wheels across various test conditions, and focuses specifically on the mechanical and electrical design of an apparatus which can measure the rolling resistance coefficient of omni wheels while modulating their load weight, travel angle, and travel speed.

A Structural Approach to Measuring Time-varying Risk Aversion

2025-10-22T03:34:36Z

A Structural Approach to Measuring Time-varying Risk Aversion von Turkovich, Nick Non-homothetic preferences have the potential to rationalize important asset pricing facts including time-varying risk premia and business cycle movements in asset prices (e.g., Campbell and Cochrane (1999)). This paper offers a structural approach to measuring time-varying risk aversion. Motivated by the literature on consumption commitments (e.g., Flavin and Nakagawa (2008), Chetty and Szeidl (2016), Chetty, Sandor, and Szeidl (2017)), I develop a model in which investors have nonseparable preferences over housing and nonhousing consumption, and investors must consume a minimum amount of housing each period. Non-housing consumption is assumed to be flexibly chosen. The key insight is that the intratemporal optimality condition between the two goods reveals information about the surplus consumption ratio, a key variable driving risk aversion. A cointegrating relationship between relative quantities and prices allow us to identify the elasticity of intratemporal substitution and measure surplus housing consumption. Using aggregate U.S. consumption data from 1959 to the present, the measured surplus consumption ratio demonstrates clear business cycle fluctuations, rising during expansions and falling during recessions. Consistent with the theory, this measure also predicts future excess returns.

Decarbonization of Gas Heating in Massachusetts: An Evaluation of Current Trends and Opportunities

2025-10-22T03:34:36Z

Decarbonization of Gas Heating in Massachusetts: An Evaluation of Current Trends and Opportunities Epstein, Andrew The commonwealth of Massachusetts has ambitious decarbonization goals enshrined in law and has been establishing the regulations to achieve them. Through its Department of Public Utilities regulatory rulings, the state has required local gas and electric utilities to pursue decarbonization not only by reducing the emissions of their electric supply but also by actively supporting gas load reduction. The residential heating sector dominates this effort, with programs like MassSave incentivizing customer adoption and now MA DPU 20-80-B requiring gas utilities to demonstrate that they have sufficiently evaluated the possibility of non-pipeline alternatives, including but not limited to electrifying customers instead of reinvesting in the gas system for all future gas investments. This paper looks at a single Massachusetts utility, National Grid, and evaluates where its customers are switching to electric heat and which mechanisms are driving current adoption. It further evaluates where geographically National Grid could invest in electrification instead of replacing gas investments under the new 20-80-B order. In doing so it establishes a model for cost benefit calculations related to prospective NPA projects. This paper then examines the degree to which ongoing electrification efforts are aligned with one another. Finally, this paper explores concerns that the process of electrification might be regressive, leaving behind those who cannot afford to electrify their systems and leaving them to pay ever-increasing prices as the full gas system is paid for through rates from a decreasing population of consumers. In evaluation of such concerns, it determines the geographic correlation between ongoing decarbonization efforts and communities already facing housing burden.

Streamlining Diagnostics of Electrical-Connection-Related Errors in General Assembly Using Augmented Reality Wearables

2025-10-22T03:34:53Z

Streamlining Diagnostics of Electrical-Connection-Related Errors in General Assembly Using Augmented Reality Wearables Salata, Elizabeth Electrical connection errors arise frequently during manufacturing. It is optimal to repair these errors during General Assembly Trim Line stations when the wiring harnesses are still exposed and easily accessible. However, the time required to locate the cause of the errors often exceeds Trim station cycle times, so most repairs are delayed until after General Assembly. Due to the implications of shutting down the line, this results in significantly higher repair times, scrap costs, and resources. To overcome these challenges, there is clear evidence supporting the use of Augmented Reality (AR) tools to innovate and streamline manufacturing processes. This master's thesis identified deficiencies in the current standard operating procedure for addressing errors and used a human-centered design approach to develop a novel error diagnostic process using an AR overlay technique to pin point on the vehicle where the problem lies. This thesis also conducted an experiment to assess the performance, success rate, and perceived cognitive load of the two processes. The data collected from the experiment provided sufficient evidence that the diagnostic process developed for this thesis reduces the elapsed time to locate the connection error by 75% with a statistically significant reduction in overall perceived cognitive load. The likelihood of widespread adoption of the AR overlay process was assessed from an estimate of further AR hardware development, safety considerations in automotive manufacturing environments, and the level of enthusiasm of all stakeholders who were consulted for this research project.

A Techno-Economic Assessment of Hybrid Renewable Energy and Battery Storage Systems for Data Centers

2025-10-22T03:34:32Z

A Techno-Economic Assessment of Hybrid Renewable Energy and Battery Storage Systems for Data Centers Sirgo, Alex As the demand for data centers continues to grow, so does their energy consumption, making it increasingly important to develop sustainable and cost-effective strategies for powering them with carbon-free electricity. This thesis explores a techno-economic modeling framework that evaluates combinations of solar, wind, and battery energy storage systems to assess their ability to meet a data center’s electricity demand with on-site renewable generation. The model fills a gap in current literature by focusing on real-time energy matching using co-located infrastructure, rather than traditional off-site procurement methods like power purchase agreements and renewable energy credits. Using real-world weather and price data, the simulation calculates hourly generation, storage behavior, and grid interactions across a 20-year period. A financial model then calculates the levelized cost of energy (LCOE) for each system configuration. Results show that wind energy generally provides the lowest-cost renewable supply option, while hybrid solar and wind configurations improve renewable penetration. Battery storage plays a key role in shifting excess generation to periods of undersupply, but its economic viability depends on system sizing. Across different system configurations, renewable penetration ranged from 31.3% to 97.8%, while LCOE varied from $27.5/MWh to over $100/MWh, illustrating the trade-offs between cost and grid independence. By providing a structured analysis of the trade-offs between renewable penetration and cost, this research offers insight into how data centers and other energy-intensive facilities can design dedicated carbon-free energy systems. The findings underscore the importance of balancing resource diversity and storage investment to achieve decarbonization goals while maintaining economic viability.

Diagnostics in Additive Manufacturing Using Image-Based Machine Learning

2025-10-22T03:34:45Z

Diagnostics in Additive Manufacturing Using Image-Based Machine Learning Varma, Arun Alejandro Additive Manufacturing (AM) is a vital capability in the aerospace industry. Blue Origin manufactures a substantial share of engine parts via metal AM. To meet growing customer demand, the company must dramatically increase engine throughput and, thus, 3D prints. Blue Origin has identified non-destructive testing (NDT) – particularly, Computed Tomography (CT) scanning – as an unsustainable bottleneck to expanding AM capacity. Not only is this process expensive, but, critically, there are not enough aerospace-grade CT machines in the world to support projected throughput. Without process change, meeting customer demand will soon become impossible. Yet, these scans provide important quality control, and any reduction in NDT must be accompanied by assurances of engine part integrity. This thesis introduces a diagnostic system that safely alleviates the bottleneck, and further yields insights that end-stage NDT alone cannot provide. The proposal is a machine learning system that evaluates the manufacturing process itself, examining layer-by-layer photographs captured during printing. It is predicated on two hypotheses: (1) These images, considered together, provide a synthetic 3D illustration of the build process; and (2) Machines can be taught to assess these process signatures dependably. The resulting system provides rich diagnostics. It achieves near-perfect anomaly recognition – 100% when using conservative defect thresholds. Operationally, the system can (at minimum) safely enable a 37-54% reduction in NDT, translating to millions of dollars in annual cost savings. In practice, this reduction will likely be higher. The system further enables early process intervention and a more data-driven approach to manufacturing intelligence. This work turns what began as an unsustainable bottleneck into an opportunity for enhanced quality control, process intelligence, and long-term manufacturing resilience.

Substitution among Social Media Platforms: Evidence from App Tracking Panel Data

2025-10-22T03:34:30Z

Substitution among Social Media Platforms: Evidence from App Tracking Panel Data Lagutina, Rina This thesis explores a novel approach to competitive intelligence in the social media ecosystem by leveraging external mobile panel data to study substitution dynamics. It focuses on contextspecific behavioral patterns to identify which platforms compete for user attention in given situations. Using mobile app session data from April 2023 for approximately 5,000 users, the analysis segments usage into three behavioral contexts – morning, evening, and at-home sessions – and characterizes user-app interactions through descriptive statistics. K-means clustering is applied to identify archetypes of usage behavior across these contexts, revealing distinct patterns such as quick-check habits, deep content consumption, and intensive texting. By comparing app usage profiles across contexts, the study uncovers shifts in how and when platforms are used, highlighting subtle substitution dynamics. To validate the findings, the study analyzes app usage during service outages, testing if potential substitutes see increased engagement when a competing platform is unavailable. These insights offer a richer, contextaware framework for product managers to uncover indirect competition and tailor platform strategies to specific user behaviors. Limitations include reliance on behavioral data without content-level detail, mobile-only focus, and demographic skew in the panel.

Harnessing Generative AI in Developing Economies: A Systems Framework for Policy Design in Bangladesh

2025-10-22T03:34:46Z

Harnessing Generative AI in Developing Economies: A Systems Framework for Policy Design in Bangladesh Bari, Md Mustabeen Ul This thesis develops a systems-based policy framework for Generative Artificial Intelligence (GenAI) implementation in developing economies, with specific application to Bangladesh. While GenAI's potential productivity and labor market impacts are well-studied in developed economies, limited research addresses the challenges faced by developing countries positioned primarily as technology consumers rather than producers. The research employs causal loop diagramming to map interactions between five critical policy domains: human capital development, digital infrastructure, data sovereignty, sectoral stimulus, and governance. The resulting framework identifies four primary reinforcing mechanisms that can accelerate adoption and three balancing mechanisms related to labor displacement. To validate the framework, the research analyzes contrasting implementation approaches from India and Egypt, demonstrating the importance of cross-domain synergies in effective policy design. Applied to Bangladesh, the framework yields a dual-entry strategy focusing on healthcare and education sectors as initial implementation domains, leveraging the country's strategic advantages while addressing resource constraints through a consortia-based implementation model that creates institutional resilience. The thesis contributes both a reusable conceptual toolkit for analyzing GenAI policy in resource-constrained settings and an initial context-anchored roadmap for Bangladesh. Future research should refine the framework through longitudinal case studies while developing more detailed, stakeholder-engaged implementation plans for Bangladesh that include concrete budget allocations, institutional responsibilities, and measurable outcomes.

The Value of Digitizing Manufacturing Environments

2025-10-22T03:34:40Z

The Value of Digitizing Manufacturing Environments Briggi, Conor S. There is significant variability and dispute around the value of digitally transformed manufacturing environments and no single methodology is broadly accepted. The variability stems from time-dependencies, implementation effectiveness, and the dynamic environments digital solutions are deployed in. However, an accurate accounting of this value is essential to company strategic planning. The research outlines how to approach this variability, cost parameters to consider, primary sources of value generation, and best practices for implementing Smart Factories. A tool that addresses these issues was successfully developed and deployed at Stanley Black & Decker, helping the company to assess performance of the digitization efforts and tailor the delivered solution to optimize manufacturing performance. Results from this tool showed a positive expected return on investment and are provided to contextualize efforts in similar areas.

Multimodal Generative AI Chatbot for Root Cause Diagnosis in Predictive Maintenance

2025-10-22T03:34:54Z

Multimodal Generative AI Chatbot for Root Cause Diagnosis in Predictive Maintenance Lorente Anon, Carla Predictive maintenance plays a critical role in industrial operations by enabling organizations to detect potential equipment failures before they occur. However, while sensor data can identify anomalies such as excessive vibration or temperature fluctuations, technicians often struggle to efficiently diagnose and resolve the root causes of these alarms. This research presents a generative AI-powered chatbot designed to enhance the root cause diagnosis process in predictive maintenance by leveraging multimodal retrieval-augmented generation (RAG) and advanced AI-driven troubleshooting capabilities. The chatbot integrates multiple functionalities to support maintenance teams in resolving alarms quickly and accurately. Its time series analysis module processes real-time sensor data, identifying abnormal patterns and guiding users through a structured troubleshooting workflow. The retrieval-augmented generation (RAG) engine allows the chatbot to retrieve and synthesize relevant troubleshooting information from technical manuals, historical maintenance records, and structured knowledge bases, ensuring that technicians receive precise, grounded outputs. Additionally, the chatbot supports multimodal interactions, enabling users to upload images, audio, and video for more comprehensive diagnostics. By analyzing uploaded images of damaged components, transcribing spoken maintenance reports, and processing video footage of equipment malfunctions, the chatbot enhances problem identification and resolution. Another key feature of the chatbot is its interactive guided conversation system, which enables multi-turn dialogues that refine diagnostics dynamically based on technician input. Instead of providing static troubleshooting steps, the chatbot continuously adapts its responses to ensure that users receive the most relevant recommendations as the diagnostic process unfolds. To maintain safety and reliability, the system incorporates AI guardrails, filtering inappropriate or irrelevant inputs while ensuring that generated responses align with best practices for industrial maintenance. An evaluation framework is proposed to assess the chatbot’s effectiveness, focusing on retrieval accuracy, response relevance, and diagnostic efficiency. Initial results demonstrate approximately 30% reduction in diagnostic time, highlighting the chatbot’s potential to improve maintenance workflows, reduce downtime, and enhance technician productivity. This research underscores the transformative role of multimodal generative AI in predictive maintenance and lays the foundation for broader industrial applications. As a result of this work, a patent has been filed to protect the novel architecture and methods developed. Future work could focus on expanding retrieval capabilities to include video, integrating intelligent task automation for dynamic work order generation, and refining alarm prioritization using adaptive risk-based assessments.

Intangible Investments and the Accrual-Cash Flow Relationship

2025-10-22T03:34:34Z

Intangible Investments and the Accrual-Cash Flow Relationship Soares, Fabio This paper investigates whether the weakening negative relationship between accruals and operating cash flows can be attributed to the immediate expensing of intangible investments under current accounting standards. Building on the framework proposed by Green et al. (2022), I examine how the mechanical capitalization of intangible investments affects the accrual-cash flow relationship across firms with varying R&D intensities. I show that the capitalization impacts the relationship in unexpected ways, indicating that the proposed rationale cannot fully explain the observed trend. I further exploit differences in accounting treatments under IFRS and US GAAP to test whether increased capitalization of intangible investments through development costs strengthens the relationship. I find that the relationship is significantly more negative under IFRS than US GAAP, independently of R&D expenditure, suggesting that increased capitalization alone does not explain the differences. Additionally, the positive trend observed for high R&D firms in both standards highlights that increased capitalization is insufficient to reverse the weakening trend. These results challenge the view that current accounting practices are the primary cause of the weakening accrual-cash flow relationship and underscore the need for further exploration of alternative explanations.

Winning Over Gen Z: The Evolving Strategies of Sports Leagues and Media in Response to Changing Youth Habits

2025-10-22T03:34:35Z

Winning Over Gen Z: The Evolving Strategies of Sports Leagues and Media in Response to Changing Youth Habits Zeng, Arnaud This thesis examines how sports leagues and media companies are evolving to better connect with Generation Z, a generation whose changing expectations and habits – on-demand and socially driven – are reshaping the landscape of sports consumption. With fewer Gen Z fans watching full games on traditional mediums, the industry is being pushed to rethink its approach, adapting not just how content is delivered, but also what kind of content is created. Through a combination of expert interviews and industry data, this paper looks at the rise of short-form content, the importance of digital-first platforms, and the growing influence of storytelling through influencers or behind the scenes. It also explores how new competition formats are exploiting what it now means to be a fan. The goal is to understand how the sports ecosystem is adjusting to remain relevant to its youngest audience.

Metal Additive Manufacturing Capabilities for Footwear Prototyping and Product Creation

2025-10-22T03:34:43Z

Metal Additive Manufacturing Capabilities for Footwear Prototyping and Product Creation Xi, Tiffany In the footwear industry, the speed in which footwear designs reach the market impacts the ability for a company to accurately meet the demands of its customers as the probability of consumer preferences changing increases with time. This research investigates the impact of incorporating metal additive manufacturing capabilities into the product creation process of a major athletic footwear company. The study aims to determine whether and under which applications metal additive manufacturing can increase the speed at which footwear designs reach the market, while maintaining or improving the desired product quality. A case study approach was employed, focusing on the development of rubber outsole molds using metal additive manufacturing technology. The study compared two process flows that excluded and included metal additive manufacturing. The case study evaluated these processes based on the speed of the development process and the quality of the produced footwear samples. The footwear sample quality was measured against production-equivalent samples obtained from the company’s manufacturing partner. The results demonstrated that incorporating metal additive manufacturing capabilities led to a reduction in the time required for mold design and fabrication. This speed advantage was primarily attributed to the ability to directly fabricate detailed textures into the mold, eliminating the need for outsourced etching processes. The visual quality of samples produced did not fully match those created by the company's manufacturing partners but were sufficient for initial sample development. Importantly, the traction properties were comparable to those of the manufacturing partner's samples, indicating that the functional quality of the samples is adequate for product development purposes. This research provides valuable insights into the potential of metal additive manufacturing in accelerating footwear product development. Future work recommendations include exploring advanced modeling and design software and examining the impact of machine parameters on build quality. The findings of this study have implications for both the footwear industry and other sectors considering the integration of metal additive manufacturing technologies into their product development processes.

Evaluating Impact Investing through a Systems Thinking Lens: Hallmarks of a Transformational Approach

2025-10-22T03:34:33Z

Evaluating Impact Investing through a Systems Thinking Lens: Hallmarks of a Transformational Approach Zhang, Yu (Sherry) As impact investing increasingly aspires to drive systemic change, the question of how to evaluate such efforts remains underexplored. Traditional evaluation approaches often grounded in linear causality and program-level outputs, and struggled to capture the complexity, interdependence, and emergent nature of systemic transformation. This thesis investigates how systemic investing can be evaluated by integrating systems thinking, evaluation theory, and investing practice. It develops a conceptual framework of thirteen hallmarks that characterize systemic investing evaluation across dimensions such as time horizons, stakeholder engagement, cross-sector collaboration, and capital dynamics. Drawing on 46 real-world cases, the research identifies 112 indicators to make these hallmarks observable and assessable in practice. To support practical application, the thesis also introduces an AI-assisted scoring tool that automates the evaluation of narrative content using the framework. Together, these contributions aim to support more reflective, adaptive, and system-aware evaluation practices in the emerging field of systemic investing.

Multi-Objective Optimization of Container Load Plans for Modulating Inventory Flow

2025-10-22T03:34:38Z

Multi-Objective Optimization of Container Load Plans for Modulating Inventory Flow Sen, Shweta Conventional strategies for container load planning (CLP) predominantly emphasize maximizing container utilization, which can result in suboptimally-timed inventory arrival, increased inventory holding costs, and downstream operational inefficiencies. Using a real-world case study from a global footwear and apparel retailer, this research formulates a novel multi-objective mixed-integer linear programming (MOMILP) model that jointly considers container utilization, transportation and storage costs, and timing accuracy of inventory delivery. The proposed model utilizes a branch-and-bound algorithm to evaluate numerous load configurations, assessing the impact of different load rules and weighting parameters on transportation performance metrics and inventory flow. Results highlight the cruciality of prioritizing delivery precision in transportation management decisions, demonstrating that solely maximizing volume utilization can adversely affect overall cost efficiency when downstream inventory storage and operational requirements are considered. This work also provides a process map of load planning activities and identifies targeted operational improvements, such as consolidation bypass and purchase order (PO) partitioning, that can enhance inventory flow smoothness, reduce transportation costs, and support more responsive logistics networks. Collectively, this work extends existing CLP methodologies by incorporating delivery timing and inventory storage considerations into load planning decisions, offering practical enhancements for logistics optimization.

Principles and Practices of Gap-Closing Investing

2025-10-22T03:34:32Z

Principles and Practices of Gap-Closing Investing Kapor, Mitchell This thesis examines the principles and practices of gap-closing investing, a distinctive model of early-stage venture capital investing that seeks to close gaps in access, opportunity, and outcomes for low-income communities and communities of color. Developed by Dr. Freada Kapor Klein and Mitchell Kapor through Kapor Capital, gap-closing investing integrates social impact objectives with a performance-driven investment strategy. The thesis combines historical analysis of socially responsible investing and impact investing with case studies of venturebacked startups to situate gap-closing investing within a broader tradition of values-based finance. It traces the ethical roots of impact investing to religious traditions, the emergence of socially responsible investing funds in the 1970s, and the formalization of impact investing terminology in the late 2000s. Gap-closing investing is distinguished by a developmental approach to startup growth, a redefinition of founder selection criteria emphasizing “distance traveled” over pedigree, and a focus on mitigating structural barriers through capital allocation. The thesis critically compares gap-closing investing to Corporate Social Responsibility (CSR) and Environmental, Social, and Governance (ESG) frameworks, arguing that gap-closing uniquely centers systemic impact as a core investment goal rather than a secondary consideration. The findings challenge the perception that impact investing is inherently concessionary, using performance data from Kapor Capital’s portfolio to demonstrate that intentional, equity-focused investing can produce both superior financial returns and measurable social outcomes. Gap-closing investing is presented as both a pragmatic investment strategy and a model for using venture capital to drive systemic change toward a more inclusive economy.

Predictive Model for Battery State of Health

2025-10-22T03:34:44Z

Predictive Model for Battery State of Health Garza Lozano, Catalina As battery energy storage systems (BESS) become critical components of grid infrastructure, accurately assessing their State of Health (SoH) is essential for optimizing performance, reducing costs, and ensuring contractual compliance. This thesis investigates the development of accurate, real-time SoH estimation models for utility-scale battery storage sites operated by NextEra Energy. Current SoH measurements—derived from annual capacity tests and Battery Management System (BMS) data—are often inaccurate or infrequent, leading to either over- or under-augmentation and resulting in financial inefficiencies. To address this gap, four state estimation models were developed and evaluated: an Unscented Kalman Filter (UKF), a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN), a multitask RNN, and a Delayed Reinforcement Learning (DRL) model. Each model uses operational data—such as voltage, current, temperature, and State of Charge (SoC)—to estimate degradation patterns and predict SoH at the rack, lineup, and site levels. Their outputs were compared against ground-truth capacity test results from a large-scale battery storage site. The DRL model demonstrated the highest accuracy, achieving a deviation of only 1.6 months compared to capacity test data, significantly outperforming existing BMS readings and the other three models. These findings underscore the value of advanced machine learning techniques in enabling proactive maintenance, optimized augmentation scheduling, and cost-efficient storage site management. This research offers a scalable framework for real-time SoH estimation across large fleets of battery storage assets and contributes to the broader goal of improving grid reliability through smarter energy storage management.

Data-Driven Key Performance Indicator Modeling for Robotic Mobile Fulfillment Systems

2025-10-22T03:34:39Z

Data-Driven Key Performance Indicator Modeling for Robotic Mobile Fulfillment Systems Sowards, Steffan This work presents a study on the development and application of data-driven operational efficiency and throughput Key Performance Indicator (KPI) modeling for Robotic Mobile Fulfillment Systems (RMFS). Through rigorous analysis of extensive operational data from an operating RMFS, we demonstrate the efficacy of machine learning approaches in predicting and optimizing the performance of complex warehouse automation systems. The research employs advanced techniques, including gradient boosted bagged tree ensembles and AutoML, to capture complex input interactions and provide parallel predictions across multiple KPIs. Our models achieve a mean R² value of 0.7838 across all templates and KPIs, with particularly strong performance in our top performing metric across templates (mean R² of 0.9660). The study introduces a novel framework for feature engineering and selection, emphasizing actionable inputs while excluding intermediate variables to enhance model interpretability and practical utility. We validate our approach against novel operating conditions, demonstrating the models’ ability to generalize to unseen scenarios. Interpretability techniques, including SHAP analysis and permutation feature importance, provide valuable insights into system behavior and key performance drivers. This research establishes a generalizable framework for leveraging data-driven modeling in predicting and optimizing brownfield warehouse automation system behavior. The developed approach offers significant potential for enhancing operational decision-making, system design, and strategic planning in the rapidly evolving field of e-commerce fulfillment.

Systems Approach to Component Code Optimization for Wound Closure Portfolio

2025-10-22T03:34:29Z

Systems Approach to Component Code Optimization for Wound Closure Portfolio Dubelier, Madeline Product portfolio management involves strategically analyzing, optimizing, and expanding a company’s offerings to maximize value and align with business goals. While companies often focus on portfolio expansion to meet evolving customer needs and gain market share, product deletion is frequently overlooked, leading to code proliferation and undermining operational efficiency. Effective variety management often requires input from stakeholders across the supply chain, yet few published methods take this approach. This work presents a systematic supply chain management approach to portfolio optimization using a case study from Johnson & Johnson MedTech. The case study is on pledgets, key components in non-absorbable suture systems. Recent pledget product quality issues exposed the need for a systematic approach to reducing component variety and operational efficiency. A current-state analysis addressed multiple dimensions of complexity. The evaluation combined qualitative and quantitative data and led to a five-stage optimization strategy. The proposed future state portfolio reduces component variety by 60%, guided by three constraints: continue to meet customer needs, protect competitiveness, and reduce manufacturing complexity. This method provides a replicable model for rationalizing legacy portfolios in the medical device industry.

An Optimization-Based Approach to Efficient Clearance Inventory Allocation

2025-10-22T03:34:31Z

An Optimization-Based Approach to Efficient Clearance Inventory Allocation Perez Munoz, Karla Mayra Allocating clearance inventory effectively remains a critical challenge in retail environments characterized by short decision cycles, fluctuating demand, and operational constraints. Decisions made during the clearance period are particularly impactful, as they determine the final opportunity to recover value from unsold products before they lose relevance or perishability. This thesis presents a mathematical optimization model designed to support the redistribution of discounted articles across a network of stores, with the objective of maximizing revenue while satisfying constraints related to stock availability, store capacity, and observed demand at the article-size level.Developed in collaboration with a leading global fashion retail company, the model was built to align with existing business processes and balances analytical rigor with simplicity in implementation. The model incorporates business-defined parameters and is tested using real operational data from selected distribution centers. It demonstrates significant improvements over the current practice of single-item allocation and addresses the computational challenges posed by the high dimensionality of real-world retail problems. By implementing efficient iterative procedures and demand-scaling mechanisms, the model ensures tractability while capturing the complexity of the business environment.

Gas Network Preparations for Networked Geothermal

2025-10-22T03:34:23Z

Gas Network Preparations for Networked Geothermal Serbent, M. Patrick As Massachusetts pursues its goal of achieving net-zero carbon emissions by 2050, the transition from natural gas to sustainable thermal energy solutions presents both opportunities and challenges for its 1.6 million natural gas customers. This thesis investigates the potential of networked geothermal systems as a viable alternative to traditional natural gas infrastructure, with a focus on leveraging existing gas network replacement programs, such as the Gas System Enhancement Plan (GSEP), to facilitate this shift. A four-phase methodology —encompassing site selection, model development, cost analysis, and business case formulation— evaluates the feasibility of integrating high-density polyethylene (HDPE) piping into leak-prone pipe replacement efforts as a preparatory step for future geothermal or hydrogen applications. Findings suggest that HDPE offers potential material and inventory cost advantages over medium-density polyethylene (MDPE), with added flexibility for low-carbon conversions, though significant upfront costs and regulatory uncertainties remain barriers. An example site already scheduled for main replacement work showed a 6% total increase in cost for the project based on the change in pipe from MDPE to HDPE. This work underscores the potential of aligning infrastructure modernization with climate goals, offering a framework for utilities like National Grid to navigate the energy transition in cold, densely populated regions.

Machine Learning and Biosecurity in the Age of Pandemics: Advancing Biological Research and Safeguarding Synthetic Biology

2025-10-22T03:34:27Z

Machine Learning and Biosecurity in the Age of Pandemics: Advancing Biological Research and Safeguarding Synthetic Biology Siddiqui, Sameed Muneeb This thesis explores the dual imperatives of enhancing biosecurity and accelerating outbreak response. The research addresses two key areas. First, the thesis analyzes the implications of a national nucleic acid synthesis screening framework on outbreak response agility. A first-hand perspective is provided, identifying potential bottlenecks stemming from lagging customer verification and sequence screening approaches. Concrete solutions, such as pre-verification of first responders, priority processing channels, pre-approval of standard countermeasure sequences, and optimized computational screening, are proposed to mitigate these challenges and ensure rapid response capabilities without compromising biosecurity. Second, a machine learning architecture for biological sequence modeling, “Lyra” is presented. Lyra is grounded in the biological principle of epistasis and leverages state space models (SSMs) combined with projected gated convolutions to efficiently capture both local and long-range sequence interactions. We demonstrate new mathematical theory to connect SSMs with the approximation of polynomial functions - key to predicting epistatic effects. This subquadratic architecture achieves state-of-the-art performance on diverse biological tasks, including protein fitness landscape prediction, RNA function prediction, and CRISPR guide design, while utilizing substantially fewer parameters and computational resources than existing foundation models like transformers. The thesis concludes by highlighting the synergistic potential of advanced machine learning and thoughtful policy to significantly improve pandemic preparedness.

In-or-Out: Creators’ Odyssey for Success

2025-10-22T03:34:25Z

In-or-Out: Creators’ Odyssey for Success Li, Zelin The creator economy is flourishing, driven by shifts in advertising budgets and a surge in the supply of content creators. This has introduced a new challenge for firms: identifying which early-stage creators will grow to become stars. By identifying future stars, firms can choose who to invest their scarce resources in. They may also be able to purchase effective influence at a (proportionately) lower price than what they will pay once a creator becomes a star. Past research has shown that predicting which content will become viral is challenging. Instead, we focus on using content to predict which early-stage creators will grow their follower bases. We measure both the positioning of a creator’s early content and how the creator adjusts this positioning. We find that the initial position is not predictive of future success. However, subsequent adjustments in position are predictive, particularly if the creator’s initial follower base has grown consistently, rather than over a short period of rapid (viral) growth. Our insights inform the construction of predictive models that outperform baseline models in out-of-sample predictions of which creators will grow their followers the fastest.

Strategic Cooperation in Water Management: A Game-Theoretic Approach to Sustainable Infrastructure in Chilean Mining

2025-10-22T03:34:28Z

Strategic Cooperation in Water Management: A Game-Theoretic Approach to Sustainable Infrastructure in Chilean Mining Moscoso Restovic, Rodrigo Y. Through a game-theoretic methodology this thesis examines collaborative approaches to managing water infrastructure within Chilean mining operations. The research examines cooperative stakeholder interactions to tackle water scarcity and growing demand in Chile's mining industry among mining firms, local residents and regulatory bodies. It utilizes game theory with a focus on cooperative games and bargaining models to develop a structured analytical framework for analyzing stakeholder dynamics including their incentives and cooperative opportunities. The thesis centers on creating a mathematical model that shows stakeholders as rational entities who seek to maximize their benefits while facing resource constraints and regulatory limitations. The implementation of cooperative game theory allows for detailed examination of coalition building processes along with resource sharing agreements and benefit allocation practices which helps to define stable cooperative possibilities. The primary findings show that mining companies achieve greater efficiency gains through water infrastructure collaboration than through separate individual investments. This thesis presents quantitative evidence that partnerships among mining projects generate significant financial savings and lead to better resource usage and positive environmental and social results. Sensitivity analyses identify that cooperative stability depends on several critical factors, including the asymmetries existing in the different mining projects, the sequence in which investment decisions are made, and the transfer price for water selling for those projects that prefer free rides. The final part of the thesis presents concrete suggestions for policymakers and industry leaders to develop cooperative frameworks through specific policy mechanisms and incentive systems that support long-term collaboration. The study advances existing academic knowledge by utilizing detailed game-theoretic approaches to address practical problems in sustainable mining practices. The findings reveal that strategic partnerships serve as fundamental tools for managing resources which can effectively tackle the urgent water scarcity challenges Chile faces.

Driving Manufacturing Best Practices Using Multimodal AI

2025-10-22T03:34:26Z

Driving Manufacturing Best Practices Using Multimodal AI Zachary, Mark Multimodal artificial intelligence offers promising solutions for enhancing operational excellence in contract manufacturing, where small job shops typically operate with limited standardization and high process variability. This research develops a part similarity tool that integrates geometric, material, and scale information to improve quoting accuracy and engineering efficiency in high-mix, low-volume production environments. After examining the fragmented manufacturing landscape and reviewing current AI applications in manufacturing, the study introduces an approach based on Variational Autoencoders for encoding 3D geometry alongside material properties and dimensional scale information. The technical implementation addresses challenges of multimodal fusion, missing data handling, and computational efficiency, while a qualitative ablation study demonstrates how this comprehensive approach outperforms single-modal methods in manufacturing relevance. Engineers benefit from improved insights for manufacturing planning, while estimators achieve more consistent cost predictions using the multimodal system. Reinforcement learning with human feedback provides a mechanism for continuous refinement, creating a framework that bridges geometric similarity with manufacturing context and reduces subjectivity in critical business processes. The research contributes both theoretical insights into multimodal learning and practical implementation strategies for standardizing operations in contract manufacturing environments.

Made in Mexico: How Chinese Firms Navigate Nearshoring Amid Global Trade Disruptions

2025-10-22T03:34:24Z

Made in Mexico: How Chinese Firms Navigate Nearshoring Amid Global Trade Disruptions Zeng, Bob This research explores the surge of Chinese manufacturing investments in Mexico as a strategic adaptation to recent global trade disruptions, specifically the U.S.–China trade tensions and the COVID-19 pandemic. By analyzing Chinese firms' motivations and strategies, the study highlights how they leverage Mexico’s strategic geographic proximity, favorable trade conditions under the USMCA, competitive labor market, and established industrial infrastructure to secure continued access to the North American market while minimizing tariff impacts and supply chain risks. Sector-specific analyses of the automotive, electronics, and renewable energy industries reveal distinct operational, regulatory, and cultural challenges encountered by these companies during their transition to Mexican production facilities. In addressing these challenges, Chinese firms have adopted strategies such as supply chain localization, rigorous adherence to North American regulatory frameworks, and effective cross-cultural management practices. Furthermore, the analysis situates this trend within the broader geopolitical context, emphasizing the role of evolving U.S. trade policies and proactive Mexican industrial initiatives in shaping the nearshoring landscape. The findings suggest that while Chinese investment in Mexico presents significant opportunities for industrial upgrading and enhanced bilateral cooperation, the longevity and effectiveness of these ventures depend on firms' strategic flexibility, deeper integration into local economies, and adept management of complex geopolitical and regulatory environments. By evaluating these elements, the research provides valuable insights into the drivers behind the increased Chinese presence in Mexico and the broader implications for global trade patterns, supply chain resilience, and regional economic integration.

Bridging the Gap: Strategies and Roles of Chinese Fintech Entrepreneurs and Startups in Sub-Sahara African Markets

2025-10-22T03:34:22Z

Bridging the Gap: Strategies and Roles of Chinese Fintech Entrepreneurs and Startups in Sub-Sahara African Markets Zhu, Yuan This thesis examines the strategies and operational practices of Chinese fintech entrepreneurs in sub-Saharan African markets, with a focus on how they navigate regulatory fragmentation, localize business models, and build trust in low-infrastructure environments. Drawing on fieldwork and semi-structured interviews with founders, executives, and product leads from fifteen China-linked fintech firms across Nigeria, Kenya, and Francophone Africa, the study investigates how these actors engage with underdeveloped financial systems while adapting knowledge and models from China’s digital finance ecosystem. The research identifies several distinct approaches to market entry and adaptation, including platform integration, compliance-focused positioning, and informal ecosystem engagement. Findings suggest that these ventures do not simply export Chinese models but instead reconfigure them in response to local constraints in regulation, consumer trust, and institutional capacity. By analyzing firm-level strategies in diverse regulatory and market settings, this study contributes to broader discussions on transnational entrepreneurship, financial infrastructure development, and the evolving role of private actors in advancing digital inclusion across emerging economies.

A Data-Driven Work Center Assignment and Pricing Strategy for a Job Shop

2025-10-22T03:34:22Z

A Data-Driven Work Center Assignment and Pricing Strategy for a Job Shop Carson, Alix Job shops with semi-autonomous work centers must understand their capacity utilization and financial state to maximize efficiency and profitability. Machine monitoring software allows managers to see the state of machines at any time and capture real-time capacity utilization. Job shops are positioned to maximize these work centers and must connect their manufacturing and operations strategy to the real-time shop data to maximize efficiency. This research is a case study in how a job shop can create a right-to-win strategy targeting jobs that are compatible and profitable for semiautonomous machines. ADDMAN Precision Baltimore (APBAL), a precision machine shop in the aerospace and defense industry, is facing labor constraints and underutilized work centers. This research aims to develop a structured quoting strategy and strategic pricing model to optimize job allocation between APBAL’s two semi-autonomous machining centers: the Makino Machining Complex 2 (MMC) and the Fanuc Robodrill. By integrating qualitative observations, historical job data, and machine utilization metrics, this study identifies inefficiencies in current job assignment practices. Key findings indicate that aligning work center assignments with projected profitability and capacity utilization can improve overall efficiency. A decision-making framework and pricing matrix are proposed to enhance job quoting accuracy, optimize machine usage, and increase APBAL’s competitiveness in securing high-volume contracts. The results offer a scalable framework for APBAL and its parent company, ADDMAN Engineering, to deploy across other machining facilities, ultimately improving operational performance and financial outcomes.

A Technoeconomic Model for Maritime Applications of Green Power Technologies

2025-10-22T03:34:19Z

A Technoeconomic Model for Maritime Applications of Green Power Technologies Tuana, Daniel I. S. Growing societal and regulatory pressures are causing industries around the world to consider greener alternatives to conventional fossil fuel power technologies. As a result, power solution suppliers like CAT are facing strategic uncertainties: if, where, and when their core product markets will be disrupted by the novel adoption of alternative technologies. With the intention of helping to inform CAT’s future product and service strategy in conjunction with previous research related to powering mines and data centers, this thesis outlines the development of a code to estimate and compare the total cost of ownership of battery, hydrogen fuel cell, and nuclear power technologies to incumbent fossil fuel-driven systems in a variety of maritime scenarios including serving shoreside port electricity demand and on-water power demand across a diverse set of vessel segments. The code leverages first principles, empirical models, and researched assumptions to model the performance and costs of power systems in response to stochastically generated and deterministic power demand profiles over the useful lifetimes of the assets. For vessel applications, the code also estimates the volumes and masses of the alternative systems as a basis to judge their practicality. Hypothetical power systems for four archetypal ports and six vessel segments (across a range of power nodes) were studied to identify potential opportunities in and adjacent to the marine markets CAT currently serves. The outcomes of the study align with conventional intuition regarding the application of the technologies considered. Under certain conditions, the results support the technoeconomic case for the implementation of battery technology on short-haul vessels whose operations are predictable and would not be disrupted by shortened refueling/recharging intervals. Similarly, the results show that adoption of small modular nuclear reactors at ports and on large vessels with consistently large baseload power demand can provide economic advantages over incumbent fossil fuel technologies. The results of the simulations are sensitive to several technology-agnostic parameters including discount rates, fuel and electricity prices, demand growth rates, and other macro-economic conditions. In future, with ample case-specific data, the code developed for this thesis may provide convincing justification for the adoption of an alternative technology to serve the power demand of an individual port or vessel.

Discrete Event Simulation as a Predictor for Factory Traffic Management

2025-10-22T03:34:21Z

Discrete Event Simulation as a Predictor for Factory Traffic Management Ramirez Echavarria, Esteban Manufacturing environments increasingly rely on automation and data-driven decision-making to optimize efficiency and production rates. This study explores the application of Discrete Event Simulation (DES) to model material flow and predict AGV (Automated Guided Vehicle), crane, and cart movements within a factory. The goal is to develop a digital twin that enables real-time decision-making, optimizes scheduling, and minimizes bottlenecks. To achieve this, we utilize SimPy, an open-source Python-based DES library, in conjunction with a custom-built API and React.js front-end interface. The study evaluates available DES software options and justifies the selection of SimPy based on flexibility, integration capabilities, and its suitability for modeling custom business rules. The solution is structured into modular components handling path planning, transporters, flows, stations, hot-cold starts, and utilities, ensuring adaptability to future improvements. A validation framework was established, utilizing historical data comparison and real-time validation to assess the simulation’s predictive accuracy. Over a 40-day testing period, the simulation achieved 89.6% accuracy and a sensitivity, or true positive rate (TPR), of 80.2%. The simulation provides a reliable first-pass scheduling tool that can be further refined with improved data collection. The findings indicate that while full automation of AGV deployment is not yet feasible, this study lays the foundation for future integration with the factory’s Vehicle Management System (VMS). Business implications include the potential for automated scheduling, enhanced material flow visibility, and optimization of capacity planning. Future work should focus on improving data accuracy, integrating live factory data streams, and refining algorithms for predictive scheduling.

From Strategy to Execution: An Optimization Approach to New Product Placement in the Apparel Industry

2025-10-22T03:34:17Z

From Strategy to Execution: An Optimization Approach to New Product Placement in the Apparel Industry Netteberg, Sofie F. This thesis presents the development and implementation of a new product placement optimization model for a large global apparel and footwear company’s supply chain, aimed at maximizing network-wide profits while aligning with long-term strategic goals amidst demand volatility. The model leverages a mixed-integer linear programming approach, integrating probabilistic demand simulations to optimize the placement of new products within the company’s existing network of third-party partner company factories. Key elements of the model, including decision variables, price and cost coefficients, an objective function, and constraints that reflect operational realities and strategic priorities, are discussed in detail. Through analysis and results validation, this research demonstrates how data-driven optimization can improve network profitability and adherence to companies’ long-term strategic supply chain objectives and develop networks that are more profitable. The thesis then includes an exploration of historic demand variability at the host company, followed by a recommendation to integrate probabilistic forecasting in network planning to generate production networks more robust to volatility in consumer product demand. The findings contribute to advancing data-driven decision-making in supply chain management and offer actionable insights for future product placement strategies.

Policy Approaches and Entrepreneurial Responses in Strategic Industries: Comparing Innovation Ecosystems in China and the United States

2025-10-22T03:34:18Z

Policy Approaches and Entrepreneurial Responses in Strategic Industries: Comparing Innovation Ecosystems in China and the United States Ni, Mengmeng This thesis investigates how government policy approaches shape regional entrepreneurial ecosystems and influence entrepreneurial strategy in strategic industries across China and the United States. Through comparative analysis of four region-industry pairs—Shanghai's semiconductor sector, Shenzhen's drone technology sector, Boston's biotechnology cluster, and New York's fintech ecosystem—the study examines the dynamic interplay between institutional design and entrepreneurial behavior. Drawing on Porter's Cluster Theory, Mazzucato's Entrepreneurial State concept, and the MIT REAP framework, the research develops a novel policy categorization encompassing four innovation governance tools: Cluster and Crisis Response Tools, Innovation Ecosystem Tools, Market-Shaping Tools, and Institutional Restructuring Tools. A qualitative case study methodology is employed, with in-depth firm-level analyses of Biren Technology in Shanghai and Moderna in Boston illustrating how entrepreneurs strategically respond to distinct institutional environments. The findings reveal four distinct models of innovation governance: Shanghai’s state-directed coordination, Shenzhen’s regulatory experimentation, Boston’s market-based orchestration, and New York’s regulation-centered oversight. Across contexts, entrepreneurs emerge as interpretive agents who actively leverage, adapt to, and at times reshape institutional conditions. This thesis contributes to the literature by offering comparative insights into the co-evolution of public policy and entrepreneurial strategy. It also provides practical implications for policymakers designing innovation ecosystems and for entrepreneurs navigating increasingly complex regulatory and technological landscapes.

Developing a Data-Driven Approach to Reducing Excess Inventory in a Multi-Echelon Supply Chain

2025-10-22T03:34:15Z

Developing a Data-Driven Approach to Reducing Excess Inventory in a Multi-Echelon Supply Chain Gosen Cappellin, Carlos Daniel The medical technology company MedTechCo, specifically its Spine division, has deployed millions of implants in hospitals to meet demand. When inventory deployment and allocation are not managed appropriately to ensure that products are in the right place at the right time, excess inventory arises. Currently, MedTechCo Spine holds large amounts of excess inventory that are not utilized effectively. The objective of this research is to leverage a data-driven approach to define and reduce implant excess inventory at scale for MedTechCo’s Spine business unit in the United States. The research strategy used in this thesis begins with a root cause analysis to understand the causes of excess inventory. A robust data model was then developed to determine appropriate inventory levels by SKU, map all excess field inventory, and prioritize the most valuable excess SKUs. This data model was used to automate the company’s ERP system to repurpose excess inventory, limit unnecessary inventory deployments to the field, and eliminate redundant backorders. Finally, an impact analysis was performed to measure the potential excess inventory reduction in both dollar value and units. Time constraints limited the implementation of the recommendations during the research period. However, MedTechCo Spine agreed to incorporate the proposed recommendations into its ERP system and operational processes in mid-2025. These recommendations will help reduce implant excess field inventory, unlocking tied-up capital, creating flexibility in the supply chain to meet demand changes, and enabling additional investment in innovation.

AI and ML in Real Estate Underwriting: Transforming Financial Decision-Making and Operational Efficiency

2025-10-22T03:34:16Z

AI and ML in Real Estate Underwriting: Transforming Financial Decision-Making and Operational Efficiency Jaklis, Cyril Real estate is the world's largest untapped market, at $650 trillion (Statista, 2023), yet technological innovation, particularly in financial underwriting, is underrepresented. Excel spreadsheets, broker-driven data collection, and expensive public database subscriptions are still used by most institutional players and family offices. These outdated approaches result in inefficiencies and higher operational expenses. Firms are now waiting for more innovative tools to improve their workflows and predict their Net Operating Income (NOI). Development and maintenance costs are often underestimated due to optimistic estimates and unplanned material or labor cost price escalations. This paper examines how to increase the accuracy of underwriting by examining the full underwriting process, identifying operational inefficiencies, and analyzing how new technologies like Artificial Intelligence (AI) and Machine Learning (ML) are currently being utilized to better value properties and reduce error margins. The analysis covers the entire underwriting process, from data sourcing, collection, structuring, and analysis. It also reviews the platforms and software tools utilized to connect these phases, from initial appraisal to investment memo and investment committee (IC) decision-making. The objective is to understand practical constraints, recognize opportunities for optimization, and explore where investors can strategically position themselves to leverage these technologies while also providing a forward-looking outlook on the changing function of AI/ML in the sector over the next decade.

Investigation Into Sources of Volatility in Sortation Center Processes to Improve Productivity and On-Time Delivery

2025-10-22T03:34:14Z

Investigation Into Sources of Volatility in Sortation Center Processes to Improve Productivity and On-Time Delivery Fenstermacher, Andrew D. Target Corporation has expanded its Last Mile Delivery (TLMD) capabilities through an omni-channel, "stores-as-hubs" strategy, using stores as fulfillment centers for online orders. The Target Sortation Centers was developed to receive packages from stores in the region, sort, route and dispatch these packages each day to accomplish faster delivery for online orders. Designed to never hold inventory, the goal is to have every package received delivered that same day. This presents new operational challenges common for brick-and-mortar retailers that develop an omni-channel strategy. This thesis investigates core processes in Sortation Centers to identify sources of volatility and propose improvements that enhance productivity and on-time delivery while minimizing labor costs and incomplete volume. Many of the current processes in Target’s Sortation Centers are manual and unstandardized. Moreover, improving operations and piloting changes is challenging, especially during peak seasons. To address these challenges, this study employs discrete event simulation (DES) using SimPy, informed by current operational data and in-person observations, to model and analyze current processes. Key findings reveal that pre-sorting TLMD volume from other national carrier volume at the stores prior to linehaul pick up for same day packages decrease the overall completion times for the day’s volume by 5.8% and lowers incomplete volume probability by up to 85% under excess volume scenarios. These process changes enhance site resilience to demand volatility without significant capital investment. The research underscores the value of DES for testing process improvements virtually and highlights the need for network-level optimization across Target’s omnichannel supply chain. Recommendations include piloting floor loading and pre-sorting in select markets, alongside future exploration of performance standards, automation, and standardized processes to further mitigate volatility impacts.

Computer Vision for Cell Line Development

2025-10-22T03:34:08Z

Computer Vision for Cell Line Development Albright, Jackson A. Anomalies in Cell Line Development prove to have significant impact on material and opportunity cost when screening for the Master Cell Bank that is used for all clinical drug development. Cell Line Development scientists spend hundreds of hours collectively identifying anomalies in fluorescent and brightfield imagery to ensure only high-performing cell clones are downselected for testing. The use of computer vision models alleviates this burden on scientists and better standardizes the selection process. Three techniques were tested for classifying anomalous and nominal fluorescent images: an autoencoder, an edge CNN and an RGB SVM. Examining performance through composite metrics such as F1 Score and MCC, the autoencoder (0.8744 and 0.8619, respectively) outperformed the edge CNN (0.8488 and 0.8257) and RGB SVM (0.8343 and 0.8252) for fluorescent anomaly classification. The high performance of the autoencoder came from training solely on anomalous images and using a percentile-based threshold to classify images on their reconstruction error. Data robustness proved to be an issue, with certain test datasets having worse performance due to inherent variability of images within both nominal and anomalous classes. Gathering and labeling more datasets for training and testing will allow models to learn from this variability and provide higher confidence in model performance for real-time screening applications. Adjusting the structure of the traditional autoencoder to that of a variational autoencoder will also help with learning the variability of images within classes, and improve performance on previously unseen data. Overall, the current iteration of the models proves to be beneficial for anomaly detection in Cell Line Development and demonstrates that some modifications to data sourcing and model architecture could see even better performance. These same techniques could be applied to similar biopharmaceutical applications provided care is taken to properly source clean and labeled image data and construct appropriate model architectures for the images' inherent features.

Sensor simulation for autonomous vehicles: Diffusion based image and depth generation for driving scenes

2025-10-22T03:34:12Z

Sensor simulation for autonomous vehicles: Diffusion based image and depth generation for driving scenes Bieske, Linn Background: Autonomous vehicle (AV) testing requires extensive real-world data collection, which is costly and time-consuming. Existing simulation techniques struggle to generate high-fidelity sensor data, particularly for multimodal signals like RGB camera images, LiDAR depth maps or LiDAR point clouds. Recent advances in generative AI, specifically diffusion models, offer a solution for improving synthetic driving scene simulations. Objective: This thesis enhances diffusion-based generative models to: 1) Encode LiDAR depth data into a stable diffusion model’s latent space, 2) Generate simultaneously, consistently and with high fidelity eight RGB camera images, 2D LiDAR depth maps and 3D LiDAR point clouds for a full 360-degrees range, and 3) Evaluate the realism and consistency of the generated sensor data. Methods: A multimodal, multi-view latent stable diffusion model was trained to generate complete 360’ synthetic driving scenes and simulate camera and LiDAR sensor signals for autonomous vehicles. The generated scenes were evaluated for sensor alignment, realism, and depth accuracy. Results: The diffusion model produced realistic, spatially consistent camera and LiDAR sensor data, reducing reliance on real-world validation miles and lowering AV testing costs. To further improve the quality of the multimodal driving scene generation it is recommended to retrain the VAE on LiDAR data. Conclusion: This work advances AV simulation by extending stable diffusion models to multimodal sensor data. Future improvements should focus on real-time generation and expanding to additional sensor types or hardware setups for enhanced simulation fidelity.

Predictive Modelling of Customer Membership Purchases to Minimize Marketing Costs

2025-10-22T03:34:09Z

Predictive Modelling of Customer Membership Purchases to Minimize Marketing Costs Liu, Ying This thesis develops and evaluates a series of predictive models to improve the efficiency of marketing resource allocation in the context of an outbound campaign for a premium membership product. The central objective is to identify customers most likely to respond positively to a membership offer, thereby minimizing outreach costs and maximizing return on investment. The study leverages a dataset from a large retail superstore that includes customer demographics, transactional behavior, and campaign response history. Data preprocessing involved the creation of engineered features such as age and tenure groupings and the transformation of categorical variables into factor types suitable for classification algorithms. Three modeling approaches were applied: classification with logistic regression, classification and regression trees (CART), and random forest. Logistic regression yielded strong predictive performance with an AUC of 0.851 and identified several statistically significant predictors, including spending on wine and meat products, recent purchase behavior, and tenure length. However, its primary limitation lies in its inability to accommodate cost asymmetries, as it lacks the capacity to incorporate a loss matrix which assigns different penalty to false positives and false negatives. The CART model addressed this limitation by introducing a customized loss matrix that reflects the asymmetric cost structure of marketing misclassifications—assigning a higher penalty to false negatives than to false positives. While this cost-sensitive structure aligned better with business objectives, the CART model achieved a moderate AUC of 0.767, reflecting limited classification accuracy and robustness. To overcome these limitations, a Random Forest model was implemented, combining the strengths of ensemble learning with cost-sensitive training. It achieved the highest AUC of 0.864 and allowed for the integration of a loss matrix during training. Feature importance analysis revealed that variables such as number of days since the last purchase, the amount spent on meat products, and a customer's enrollment length with the company were among the most influential predictors of customer response. The model not only improved classification performance but also supported strategic targeting through interpretable outputs. An economic evaluation demonstrated the practical value of the predictive model. Under a loss matrix where the cost of a false positive was set to $2 and a false negative to $10, the Random Forest model reduced total campaign costs by approximately 30% compared to a non-targeted approach. This cost savings translates into a meaningful economic impact, particularly when applied to large-scale campaigns. Overall, the findings support the use of Random Forest with a cost-sensitive design as a superior modeling framework in marketing applications. By aligning machine learning with real-world cost structures, this approach offers both statistical rigor and economic relevance for data-driven decision-making in customer acquisition strategies.

Data, Analytics, and Optimization for Production Planning

2025-10-22T03:34:04Z

Data, Analytics, and Optimization for Production Planning Malinowski, Maxwell X. This thesis serves as a case study for the implementation of data analytics and optimization within a high mix low volume electronics production environment in the Aerospace and Defense industry. This case study demonstrates the benefits of data analysis for defining and quantifying operational bottlenecks and explores the implementation of an optimization model to better allocate resources for production planning. Results demonstrate the insights derived from using data and analytics in this environment, and further discussion explores what contributes to an effective implementation of an optimization model in a production setting.

Debt Complexity and Equity Behavior

2025-10-22T03:33:42Z

Debt Complexity and Equity Behavior Li, Jack I examine how the complexity of firm debt affects the incorporation of news into equity prices. As residual claimants to firm cash flows, equity investors must be able to value all outstanding debt contracts, suggesting that complex debt can interfere with their ability to process news effectively. Using a model in which debt complexity causes a subset of investors to initially underweight news precision, I derive three predictions for the equity behavior of debt-complex firms around news events: (1) they exhibit greater post-announcement drift, (2) they show elevated trading volume both on announcement day and in the post-announcement period, and (3) their return volatility decreases on announcement day but increases during the post-announcement period. These predictions are supported by empirical evidence in the context of earnings announcements, suggesting that debt complexity introduces meaningful frictions in how news is incorporated into equity markets.

Mapping Wildness: Simulating Post-Extraction Wildland Regeneration

2025-10-22T03:34:07Z

Mapping Wildness: Simulating Post-Extraction Wildland Regeneration Griggs, Crystal Ling This thesis introduces a novel approach to wildlife habitat classification for ecological regeneration. It is focused by the extreme environmental degradation of mountaintop removal (MTR) in the Appalachian Mountains, a violent coal extraction process that has significantly altered the landscape of this ecologically sensitive region. By integrating remote sensing and Geographic Information Systems (GIS) with machine learning, this research aims to develop a method that transcends traditional human egocentric landscape assessments, advocating for a model that foregrounds the habitats and needs of critically endangered species by simulating landscape regeneration and assessing topographical alterations in terms of how design decisions impact wildlife. Central to this study is the concept of Umwelt, the subjective experiences of nonhuman species, including how their spatial perception and spectrum are used to discern details within their environment. Umwelt broadens traditional spatial understanding by emphasizing that each species experiences the world through its sensory filters, which shape its interactions within their habitat. This understanding guides the research’s approach to approximating the Umwelt of the Cerulean Warbler (Setophaga cerulea), a surrogate species in this work, which has faced steep declines due to habitat loss in Appalachia. Through the development of a habitat suitability model that utilizes advanced computational tools and multispectral imagery, the thesis endeavors to offer a new perspective on environmental planning and conservation efforts - a computational approach to near-approximations of Umwelt. The methodological framework seeks not only to classify post-extraction landscapes for their potential in supporting wildlife but also to inform design and land use decisions that are sensitive to the temporal and complex processes of natural habitat regeneration. By challenging the prevailing paradigms of landscape restoration, which often lack consideration for the intricacies of wildland dynamics such as the multitudes of species interactions and interdependencies, this research proposes a new methodology that empowers wildlife to guide the ecological recovery process. The findings underscore the potential of applied GIS and machine learning in environmental advocacy, setting a precedent for future research and practice aimed at the regeneration of ecosystems that considers the ecological realities of all species involved.

Hydrogen Adoption Dynamics: A Flexible Modeling Framework for U.S. Industrial Applications

2025-10-22T03:33:23Z

Hydrogen Adoption Dynamics: A Flexible Modeling Framework for U.S. Industrial Applications Ray, Jennifer As climate change concerns drive the need for decarbonization, hydrogen stands as a potential tool to help reduce emissions across the United States industrial and energy sectors. This thesis develops a flexible modeling framework for hydrogen adoption across multiple industrial applications, designed specifically to support strategic investment decision-making in an evolving market. The tool analyzes six major industries – steel, chemicals, energy storage, biofuels, vehicles, and natural gas– through two metrics: potential hydrogen consumption and threshold prices for economic viability. The framework applies scenario analysis to examine how government policy and technological advancement influence potential market trajectories. Analysis reveals significant sensitivity to input assumptions. Even small variations in the assumed initial hydrogen production cost can result in significantly different adoption timelines. In scenarios where initial hydrogen production costs are $5/kg, widespread adoption requires maximum policy support and technological progress. However, reducing the initial cost by just $1 to $4/kg makes broader adoption feasible with less reliance on government intervention. Light-duty fuel cell electric vehicle penetration rate and steel industry growth rate emerge as the most sensitive parameters affecting overall hydrogen demand, followed by biofuel blending rate and hydrogen injection percentage into natural gas infrastructure. The vehicles industry is identified as a first mover in widespread hydrogen adoption, followed by steelmaking and methanol production. Hydrogen adoption for natural gas blending, methanol for export, and methanol-to-gasoline applications occur later due to their lower threshold price for economic viability. Under optimal conditions with strong government support and significant technological advancements, total hydrogen demand could reach 48.8 million metric tons by 2050, approximately a sevenfold increase from scenarios with minimal support. The tool’s value lies not in projecting a definitive, single-point forecast, but in providing a flexible framework that helps stakeholders navigate market uncertainties as the decarbonization landscape evolves. Future research should integrate supply-side dynamics, infrastructure requirements, and geographic variability to enhance projection accuracy.

Towards Green Aluminum

2025-10-22T03:34:01Z

Towards Green Aluminum Schurr, Kevin Aluminum is an important metal to facilitate the energy transition. Its high strength to weight ratio and easy recyclability make it a useful material in many industries from automobiles to food packaging. However, the aluminum smelting process accounts for 2% of all global greenhouse gas emissions due to both the high amount of power needed to facilitate the electrolysis reaction and to the consumption of carbon anodes in the process. As regulatory changes in Europe raise the monetary cost of emitting carbon, smelters are investigating new technologies to integrate into their operations to cut Scope 1 and 2 emissions. Two such technologies are carbon capture systems to abate process emissions and small modular nuclear reactors to reduce emissions incurred during electric power generation. This work explores the technical and economic feasibility of leveraging these systems at Aluminum of Europe, a primary aluminum smelter subject to these changing European regulations. Results suggest that while these technologies have not been specifically adapted for aluminum production yet, they can play an important role in reducing the overall emissions from the smelting process under specific economic conditions. However, the analysis indicates that, at present, significant subsidies are required for such projects to be financially viable.

Fully Connected Digital Ecosystems within Hospitals – AI/ML Solutions for Improved Patient Care

2025-10-22T03:33:54Z

Fully Connected Digital Ecosystems within Hospitals – AI/ML Solutions for Improved Patient Care Dugan, Andrew D. Cardiogenic shock (CS) in the context of acute myocardial infarction (AMI) remains a significant challenge in critical care, with high mortality rates despite the availability of advanced mechanical circulatory support (MCS) devices like the Impella pump. However, adoption of these devices in clinical practice remains limited. This thesis explores two complementary strategies to address these challenges: developing machine learning (ML) models to predict shock severity and assessing the feasibility of integrating hospital Electronic Medical Record (EMR) data into Abiomed’s digital ecosystem to support standardized shock care. In the first phase, ML models were trained on multiple clinical datasets to predict Society for Cardiovascular Angiography and Interventions (SCAI) shock stages based on patient data. While these models demonstrated strong predictive performance, feature analysis revealed that SCAI stages often reflect physician treatment decisions rather than purely patient physiology. This raises concerns about their utility as real-time clinical decision tools and suggests that ML applications may be better suited to prompting early data collection and intervention before severe shock develops. The second phase evaluated the feasibility of EMR integration to support the broader adoption of standardized shock protocols. After considering regulatory, operational, and technical factors, third- party data aggregation emerged as the most practical path forward. Integrating EMR data could improve outcome tracking, support protocol adoption, and strengthen partnerships between Abiomed and hospitals, creating a foundation for more consistent and proactive shock management. Together, these findings highlight the need for predictive tools that guide early clinical action and infrastructure that supports seamless data integration. By advancing both, Abiomed can expand its role in cardiogenic shock care, improve patient outcomes, and lead the evolution of data-driven, standardized treatment strategies.

Strategic Recommendations for Legacy Automakers in the Evolving Automotive Landscape

2025-10-22T03:34:04Z

Strategic Recommendations for Legacy Automakers in the Evolving Automotive Landscape Tike, Gauri The automotive industry is undergoing a transformative shift with technological advancements in many areas such as Electric cars, Autonomous vehicles, Software Defined Vehicles, and decarbonization of mobility. Alternate means of transportation are also becoming available and sometimes even the cost is even lower than owning a car. The best way to get from point A to point B might not be the car in some of the cities. It might involve heterogenous modes of public transportation, using a bike, using ride hailing service or using a car for different portions of the route. Despite the concerns about the environment, we are still seeing an increase in global car ownership trends. These changing times pose challenges to legacy automakers. While they are experts in traditional car manufacturing, modern cars not only require traditional mechanical and electrical skills but also need deep expertise in developing software for these cars. With the growing EV adoption, we are seeing Chinese EV automakers are capturing market share quickly. What is the future of mobility with all these developments? What do traditional automakers need to do in this era to remain successful? In this report we will examine key trends in mobility: Global electric vehicles (EVs) adoption, software-defined vehicles (SDVs), autonomous vehicles (AVs), environmental implications. Based on this research we will propose strategic recommendations for traditional automakers in order to continue their success over the next decade and beyond.

Navigating Fintech Innovations: Strategic Insights from the United States and India

2025-10-22T03:34:13Z

Navigating Fintech Innovations: Strategic Insights from the United States and India Shanbhag, Rishabh Ganesh This thesis examines how fintech ventures are reshaping financial services through new technologies and strategic choices tailored to different markets. It first looks at key innovations: digital payments, digital wealth management, and open banking, and how they have transformed everyday financial activities. The research then compares how fintech companies operate in the US and India by analyzing how market conditions, government initiatives, regulations, and consumer behaviors shape adoption. Finally, through case studies of Robinhood (US), Revolut (Global), and Paytm (India), the thesis examines how fintech firms navigate the choice between competing with traditional players and collaborating with them to scale under different market scenarios. Together, these insights aim to help entrepreneurs, investors and policymakers understand how strategy and technology come together in the fintech industry.

Forming the Future: A Digital Approach to Simulating Thermoplastic Manufacturing

2025-10-22T03:34:00Z

Forming the Future: A Digital Approach to Simulating Thermoplastic Manufacturing Harkavy, Rachael This thesis develops a digital framework for simulating and validating thermoplastic composite manufacturing processes, focusing on reducing the time associated with new product development. Using Finite Element Analysis (FEA) software (SimSof) and high-precision 3D scanning tools (ScanSof), the research introduces a geometric similarity metric to quantify deviations between simulated and real-world parts. By aligning simulations with production data, the study aims to replace costly physical trials with reliable digital models, accelerating customer onboarding and improving manufacturing efficiency. Key contributions include establishing a systematic pipeline for integrating simulation tools into Oribi Composites’ workflow, defining critical parameters such as laminate width, material card accuracy, and mesh size, and validating their impact on simulation accuracy. Results demonstrate that accurate material modeling and parameter selection significantly enhance digital twin accuracy, while mesh size has minimal influence, allowing for computational cost savings. The research also highlights challenges in replicating real-world conditions digitally, including inconsistent material cards, and limited control over pressure profiles. Despite these limitations, the study proves that simulations can reliably predict manufacturable designs within customer tolerances, reducing reliance on physical iterations.

Toward a Sustainable and Scalable Ecosystem: Breaking the Cycle of Intergenerational Poverty for Single Mothers in Japan with Private Sector Engagement

2025-10-22T03:34:11Z

Toward a Sustainable and Scalable Ecosystem: Breaking the Cycle of Intergenerational Poverty for Single Mothers in Japan with Private Sector Engagement Imaeda, Hiroko Despite Japan’s reputation as an economically advanced nation, it faces one of the highest relative poverty rates among OECD countries, with nearly half of all single-mother households living below the poverty line. This thesis examines why poverty among single mothers persists despite a formal support ecosystem and proposes a systemic redesign grounded in life-stage-aligned, user-centered principles. Drawing on historical-institutional analysis, organizational theory, fieldwork interviews, and auto-ethnographic insights, the study identifies deeply embedded barriers that reinforce fragmented, crisis-oriented support systems misaligned with real-life trajectories. In response, it introduces the "Single Mother Journey" framework, reframing single mothers not as a static category but as a dynamic population with distinct, evolving needs. Through this lens, the thesis exposes critical gaps in preventive support, labor market misalignment, and information accessibility. Building on these findings, it proposes a future-ready support ecosystem, positioning corporations, local municipalities, NPOs, and education institutions as collaborative actors. It presents mumtec, a conceptual digital platform designed to consolidate fragmented services, personalize interventions by life stage, predict crisis points, and generate adaptive policy feedback. The thesis moves beyond surface-level critique by connecting institutional analysis with practical system design to offer a scalable framework for inclusive innovation. Listening to the silent voices of single mothers navigating precarity is an ethical imperative and a strategic necessity for sustainable, resilient societies.

Optimizing automotive production scheduling to reduce finished vehicle inventory

2025-10-22T03:33:55Z

Optimizing automotive production scheduling to reduce finished vehicle inventory Johnson, Christopher This thesis addresses inefficiencies in automotive finished vehicle inventory management arising from misalignment between production scheduling and outbound logistics. Traditional production planning prioritizes manufacturing efficiency, causing significant inventory accumulation as vehicles await completion of full shipment loads. This research proposes an Integrated Production and Outbound Distribution Scheduling approach, introducing an optimization step within existing production scheduling workflows to align production sequences for expedited load formation. Back-testing on two automotive assembly lines over 82 weeks reveals a mean inventory reduction potential of 63–65%, with variability influenced by production volumes and vehicle configurations. A proof-of-concept implementation confirms the practical feasibility of optimized schedules, reducing inventory holding times by 33% without disrupting manufacturing operations. Computational performance analysis demonstrates good scalability for instances with fewer than 600 vehicles, though larger scenarios still yield meaningful inventory reductions. This work highlights substantial opportunities for automotive original equipment manufacturers to enhance efficiency by integrating outbound logistics into production scheduling.

Transforming Unstructured Data into Actionable Insights: A Use Case of Generative AI in Operational Technology Problem Management

2025-10-22T03:34:05Z

Transforming Unstructured Data into Actionable Insights: A Use Case of Generative AI in Operational Technology Problem Management Gallardo Moncayo, Gabriel A. The increasing availability and reduced cost of Generative AI applications for the general public have motivated organizations across all industries to implement AI-based solutions in their daily operations. Still, they struggle to determine the capabilities and limitations of this technology when implementing it in their specific context. This thesis addresses these challenges through a practical case study: deploying a text-based Generative AI system (using Large Language Models - LLMs) for automated downtime event characterization within a global industrial operational technology (OT) setting by transforming unstructured problem management reports into structured, actionable business insights. The developed software system contains a data pre-processing stage, followed by four LLM-based tasks (LLM-extraction, LLM-autoclassification, multi-aspect multi-level LLM-classification, and LLM-accuracy). We wrap everything in a well-structured and easy-to-understand evaluation framework that ensures the system’s output is format-reliable, accurate, and consistent. Through simple prompt engineering techniques and continuous failure modes analysis, we achieve high accuracy (>89%) and consistency (>79%) for downtime events characterization at 1% of the current cost. In the end, we prove that it is possible to implement an AI-based solution within current operational processes while properly communicating its capabilities and limitations and adapting its usage to the most added value purpose.

Optimizing Raw Wire Inventory Management: A Data-Driven Approach to Demand Forecasting and Supply Chain Decision Support

2025-10-22T03:33:44Z

Optimizing Raw Wire Inventory Management: A Data-Driven Approach to Demand Forecasting and Supply Chain Decision Support Gebner, Adam R. This thesis investigates methods to improve demand forecasting and inventory management for raw wire. Challenges such as supply chain disruptions from the COVID-19 pandemic, operational variability, and loss of expertise exposed vulnerabilities in the existing manufacturing system, leading to shortages and inefficiencies. By leveraging extensive production data, this research develops and evaluates tools to mitigate these issues while aiming for a 100% service rate. The project leveraged extensive production data to predict future wire requirements, optimize inventory, and achieve a 100% service rate. Key contributions include: 1. A data-driven demand simulation model, reducing forecast error and surpassing baseline methods 2. Quantification of waste distributions and variability in wire consumption 3. An inventory simulation framework for policy evaluation and shortage mitigation 4. Clustering analysis to classify demand patterns and identify key wire categories 5. A decision support tool supporting real-time visibility into inventory levels and risks The models and tools developed through this project provide enhanced capabilities to predict future wire requirements and manage inventory more effectively through continued development. Though the initial results indicate potential business value, areas for future work include incorporating additional data sources, exploring advanced machine learning techniques, and conducting longer-term pilot studies to quantify business impact. This project demonstrates the value of leveraging data analytics and simulation modeling to enhance supply chain decision-making in complex manufacturing environments.

Economies of Space: Developing a Lean Manufacturing Framework for Work Center Floorspace Reduction

2025-10-22T03:34:02Z

Economies of Space: Developing a Lean Manufacturing Framework for Work Center Floorspace Reduction Gerbino, Jacob This thesis aims to develop a lean manufacturing framework with the goal of optimizing the use of floorspace in Boeing's Interiors Responsibility Center South Carolina (IRCSC). The primary goal is to eliminate wasted floorspace while increasing production capacity and efficiency. The motivation behind this project stems from the need to address the fully allocated production floorspace at IRCSC and the pressing requirement to add new product lines without expanding the facility's physical footprint. Additionally, the project seeks to prepare IRCSC for possible increases in production rates for the 787 Dreamliner Program, necessitating a redesign of work centers to support higher output levels while enhancing efficiency and reducing costs. The project employs the DMAIC (Define, Measure, Analyze, Improve, Control) methodology and lean tools such as spaghetti diagramming and value stream mapping to treat "Misused Space" as an additional form of waste, alongside the traditional forms of lean waste. The framework was applied to a sample interior product work center to test its effectiveness. The study involved mapping the current layout, observing technician travel, conducting time studies, and analyzing value stream maps. The methodology facilitated the creation of a new floorplan and scheduling system that consolidates cure times and balances workloads between work cells. Discrete event simulation was used to validate the proposed changes, ensuring they would achieve the desired improvements. The results of the study revealed inefficiencies in the current layout and scheduling practices of the work center. The proposed changes demonstrated a potential 25% reduction in floorspace and a 55% decrease in product throughput time. The new scheduling and work allocation strategy reduced product throughput time from nine days to four, and the new layout reduced worker travel distances by as much as 50% in some work cells. The lean manufacturing principles and scheduling optimizations discussed in this thesis should be applied to other work centers within IRCSC. Future research should explore advanced methodologies and tools to handle the complexities of more interconnected work centers.

The Impact of AI Integration in Healthcare: Exploring Regulatory, Cultural, and Strategic Barriers

2025-10-22T03:33:38Z

The Impact of AI Integration in Healthcare: Exploring Regulatory, Cultural, and Strategic Barriers Venkatanarayanan, Sriya This thesis investigates the barriers and enablers to predictive AI adoption in healthcare through a thematic synthesis of 13 academic articles and real-world case studies published over the last five years. Barriers were categorized into three domains: regulatory, cultural, and strategic. These included challenges such as fragmented regulation, clinician skepticism, data quality limitations, and poor alignment with clinical workflows. Cross-cutting patterns, stakeholder tensions, and recurring meta-themes revealed that these barriers are deeply interconnected. Drawing from over 200 individual findings, an actionable visual framework was developed to guide responsible and sustainable predictive AI integration. The proposed model, consisting of an internal “Pyramid” of enablers and an external “Circular Loop” of ecosystem conditions, provides a practical structure for aligning governance, engagement, and workflow with ongoing commitments to equity, collaboration, safety, and transparency.

Generative AI in Private Equity for Accumulative Advantage

2025-10-22T03:33:51Z

Generative AI in Private Equity for Accumulative Advantage Mahajan, Bonny This research explores the use of Generative AI (Gen AI) for achieving accumulative gains across various business and technical functions within commercial enterprises under private equity firms. While based on applied experiments in a private equity-owned, resource constrained portfolio company, many of the findings presented here may apply in other types of organizations.Through this study, we conduct case studies across key departments such as customer service, purchasing, engineering, employee management, and marketing. For each use case, we delve into the utilization of custom-built or publicly available Gen AI-based tools, aiming to understand the unique considerations and challenges that may arise when implementing Gen AI solutions in industries like manufacturing, which have traditionally been underserved by the tech sector.Through this research, we identify the critical role of humans in the loop, emphasizing the importance of UI/UX design, domain expertise, and local culture in the successful adoption and acceptance of Gen AI tools designed to enhance workforce efficiency in portfolio companies. This study also aims to illustrate how investing in Gen AI technologies is ultimately an investment in a company’s most valuable resource—its employees. By equipping employees with innovative tools, the organization not only improves productivity and job satisfaction but also fosters a culture of continuous improvement and adaptability. This research highlights the transformative potential of Gen AI in reshaping traditional business processes and driving sustainable growth in different functions of organizations.

Standard Work for High Mix Low Volume Manufacturing

2025-10-22T03:33:37Z

Standard Work for High Mix Low Volume Manufacturing McNulty, Will This thesis examines the challenges of developing standard work at scale in a high-mix low-volume (HMLV) manufacturing environment. The research is conducted at Re:Build Composite Resources, a thermoset composites (TSC) manufacturer. In the context of the company, impending growth demands more skilled laminators and the manual, complex nature of TSC lamination exposes the need for improved and documented standard procedures. By documenting existing processes through operator shadowing, time studies, and quality data analysis a “best-known” standard was created for the production steps of a subset of parts. Two pilot parts—one focused on cutting scrap rates, the other on boosting throughput—demonstrated how standard work instructions and a standard work schedule designed for one-piece flow significantly reduced errors and production variability. The thesis also explores the effectiveness and limitations of using computer vision as a tool to automate work instruction and time study data set generation. Beyond the immediate improvements in quality, efficiency, and new operator onboarding, the project’s scalable framework lays out a roadmap for broader adoption of standard work in fast-growing HMLV operations. By focusing first on parts that yield the most significant gains — either due to high volume or high unit cost — organizations can maximize returns on continuous-improvement efforts while not overburdening their engineering staff with excess analysis and documentation.

Evaluating The Feasibility of Electrified Process Heating for Drug Substance Manufacturing

2025-10-22T03:34:03Z

Evaluating The Feasibility of Electrified Process Heating for Drug Substance Manufacturing Bhirgoo, Priya Darshini The pharmaceutical industry relies on high-temperature fluids such as pure steam to support critical operations including equipment cleaning and sterilization and on hot Water-For-Injection (WFI) as a key ingredient for drug substance manufacturing. These high-temperature process-driven heat demands are fulfilled through fossil fuel-based heating which contributes significantly to Scope 1 carbon emissions. Recognizing the link between environmental stressors and human health, Amgen has committed to achieving carbon neutrality by 2027. This thesis explores the feasibility and implications of transitioning from fossil fuel-based process heating to a fully electric system at one of Amgen’s drug substance manufacturing sites. Amgen’s existing fossil fuel-based steam system was analyzed through site visits, engineering reviews, and stakeholder engagements to quantify capital and operating costs, energy usage, and carbon emissions. A fully electric alternative was designed by researching commercial technologies and collaborating with suppliers as well as internal stakeholders. The analysis found that while the capital investment required for electrification is comparable to that of traditional steam systems, the operating costs for an electric system are significantly higher, driven by the higher price of electricity relative to natural gas. From a sustainability perspective, electrification eliminates on-site Scope 1 carbon emissions but shifts emissions to Scope 2, making the environmental benefit dependent on the carbon intensity of the local electricity grid. As grids transition to renewable energy sources, the potential for long-term emissions reductions strengthens. Future work should focus on evaluating the costs of necessary electrical infrastructure upgrades and identifying regions with lower-carbon, lower-cost electricity grids where electrified systems could be more readily implemented.

Partnerships as Retention Levers: A Study of Credit Card–Entertainment Collaborations

2025-10-22T03:33:58Z

Partnerships as Retention Levers: A Study of Credit Card–Entertainment Collaborations Tchelikidi, Cloe In mature, competitive sectors such as financial services and media and entertainment, customer loyalty is increasingly difficult to sustain. This thesis explores the emergence of cross-industry partnerships, specifically between credit card issuers and digital entertainment platforms, as a strategic response to rising churn and declining differentiation. Through a case study of the American Express Digital Entertainment Credit, the research examines how lifestyle-aligned benefits can foster deeper behavioral engagement, reduce switching, and enhance customer lifetime value. The thesis situates these partnerships within the broader evolution of loyalty strategies, marked by hyper-personalization, subscription fatigue, and platform convergence. Findings suggest that flexible, recurring rewards embedded in consumers’ daily routines offer a path to durable retention, especially among younger, digital-native cohorts. The study concludes that such partnerships are not peripheral marketing tools but increasingly core to competitive strategy in commoditized markets.

Exploring the Dynamics of Regulatory Compliance, Cost Management, and Competition in the Pharmaceutical Industry

2025-10-22T03:33:59Z

Exploring the Dynamics of Regulatory Compliance, Cost Management, and Competition in the Pharmaceutical Industry Wu, Lanchen This paper explores how financial pressures, regulatory enforcement, and market dynamics interact to shape pharmaceutical manufacturing quality and drug supply stability. Using a causal loop diagram (CLD), it examines how cost-cutting behavior affects control and validation capabilities, interacts with regulatory agency oversight, and contributes to recurring drug shortages. The analysis highlights how competition drive companies to operate at or near the minimum regulatory requirements, gradually eroding quality systems. Because of the nature of medical products, the quality of a drug cannot be directly assessed by individual users, distributors, or payers, making it necessary for government agencies like the FDA to rely on internal manufacturing data to ensure all drugs meet a minimum standard of quality. Regulatory oversight serves as a safeguard rather than a tool for guiding business decisions. However, its effectiveness is constrained by the frequency of inspections, the capacity of auditors, and limited resources—especially when government budgets are stretched and other priorities take precedence. The paper also discusses how manufacturers may avoid detection by strategically presenting information during inspections, making it harder for auditors to spot issues and allowing weakened controls to persist. Over time, these dynamics reinforce one another, creating a self-sustaining cycle in which cost pressures lead to a minimal compliance, quality issues, and regulatory responses that increase costs further. As the number of manufacturers shrinks due to market consolidation, supply disruptions become more severe when failures occur. Regulatory discretion—intended to avoid immediate shortages—can unintentionally reduce incentives for long-term quality investment, further weakening the system’s resilience. To address these issues, the paper proposes structural changes, including financial accountability for payers during shortages, tighter regulatory focus on process reliability, and linking regulatory flexibility to quality improvement obligations. These approaches aim to create balancing mechanisms that reduce cost-driven deterioration of quality and promote a more stable pharmaceutical supply chain.

Ant Group’s Transformative Impact on China’s Financial Industry

2025-10-22T03:33:30Z

Ant Group’s Transformative Impact on China’s Financial Industry Pan, Kathryn Ant Group, China’s leading digital finance company, has fundamentally transformed the nation’s financial industry through groundbreaking innovations in digital payments, micro-lending, wealth management, and investment advisory. This paper explores the company’s role in reshaping China’s financial ecosystem, analyzing its impact on traditional banking institutions, regulatory policies, and consumer behavior. Utilizing analytical frameworks such as Porter’s Five Forces, PEST analysis, and SWOT analysis, this study provides a comprehensive assessment of the external and internal factors influencing Ant Group’s development and competitive positioning. This research highlights Ant Group’s key financial innovations, including its online transaction platform, offline payment services, online credit solutions, digital fund distribution channels, and AI-driven investment advisory. By leveraging advanced technologies such as artificial intelligence, blockchain, and big data analytics, Ant Group has enhanced service efficiency, expanded accessibility, and strengthened risk management capabilities. These innovations have significantly advanced financial inclusion, extending financial services to previously underserved populations. However, Ant Group’s rapid growth has also intensified regulatory scrutiny, prompting major restructuring efforts and adjustments to its business model. This paper employs three major analytical frameworks: PEST analysis, Porter’s Five Forces, and SWOT analysis. The PEST analysis examines the political, economic, social, and technological factors shaping Ant Group’s trajectory, highlighting the impact of evolving government policies and macroeconomic conditions on its operations. Meanwhile, Porter’s Five Forces framework assesses the competitive dynamics within China’s financial sector, identifying key market pressures such as rising competitions and regulatory constraints. Finally, the SWOT analysis evaluates Ant Group’s internal strengths and weaknesses, as well as external opportunities and threats, offering a comprehensive perspective on the company’s strategic positioning. Drawing from these analyses, the paper offers strategic recommendations to ensure Ant Group’s sustained growth and resilience in an increasingly complex financial environment. These recommendations include strengthening regulatory compliance, fostering strategic alliances with both domestic and international partners, and further leveraging technological advancements to expand its service offerings. Additionally, the study explores potential global expansion strategies, considering how Ant Group can adapt its innovative financial solutions to international markets while navigating diverse regulatory landscapes. By examining Ant Group’s evolution and the broader implications of its digital finance model, this study contributes to a deeper understanding of fintech’s disruptive power in China’s financial sector. The findings provide valuable insights for industry leaders, policymakers, and scholars interested in the intersection of financial technology, regulation, and strategic business management. As digital finance continues to evolve, Ant Group’s trajectory serves as a critical case study in balancing innovation, regulation, and market competition within a rapidly shifting financial landscape.

Process Optimization and Proactive Quality Control to Increase Investment Casting Throughput

2025-10-22T03:33:50Z

Process Optimization and Proactive Quality Control to Increase Investment Casting Throughput Sircar, Julia Sarita Blue Origin is an aerospace company with ambitious throughput goals in response to increased commercial space exploration. Pressure to increase throughput is especially apparent within its BE-4 engine business, as the engines support Blue Origin and its customers. Blue Castings is one of the primary in-house manufacturing plants that supports BE-4 production; the plant manufactures rocket engine components through a process called investment casting. Investment casting, by nature, is a complex process involving long rework times, high incidence of defects, and significant process variability. These characteristics contribute to the discrepancies between Blue Origin’s target BE-4 production rate, the production rate feasible at Blue Castings, and its actual delivery rate. This thesis explores how defect management and prevention techniques can improve throughput at Blue Castings and reduce the number of Blue Origin’s schedule delays attributable to Blue Castings. The research began with a baseline investigation and analysis of Blue Castings’ actual and best-case throughput rates compared to its goal. Two gaps were identified: 1) a gap between actual and feasible throughput, and 2) a gap between feasible and target throughput. The analyses highlight the need for better process and quality management to close both gaps. Through a mixed-method approach, the researcher explored and piloted process and data improvements to understand their impact on throughput. This included qualitative and quantitative data collection through on-site interviews, random sampling of defect data, and queries from the manufacturing execution system. With this data, the researcher investigated how machine learning can predict rework severity and support defect prevention. A case study on a selected part number demonstrated the potential to improve throughput by reducing unnecessary rework. By aligning stock-on surface criteria to downstream machining requirements, average rework loops were reduced from thrice the industry benchmark to below the benchmark. This increased capacity at the rework work center and improved the overall delivery of this part. The research also demonstrated how a cross-functional collaboration to formalize producibility lessons reduces the creation of defects, promotes systematic knowledge-sharing, and accelerates improvements similar to the stock-on surface case study. In parallel, this research evaluated how Blue Castings could improve defect documentation and tracking without causing significant additional effort for operators. The researcher’s findings highlight the limitations of handwritten weld maps and inconsistent data capture practices on effectively preventing defects. Digitization of defect tracking is recommended to enable consistent defect data collection and improved root cause and trend analyses. As data quality improves, applying classification ML models for predictive analytics can scale throughput. This work provides recommendations for Blue Castings to implement mechanisms that reduce rework, improve producibility, and increase throughput to align with Blue Origin’s goals.

Searching with Intuition: Exploring (the bounds of) LLM-guided Search with Unknown Objectives

2025-10-22T03:33:57Z

Searching with Intuition: Exploring (the bounds of) LLM-guided Search with Unknown Objectives Kaashoek, Justin H. Large language models (LLMs) can perform a wide range of search and optimization tasks over discrete spaces. This work seeks to explore the limits of LLM-guided search. We construct a set of text optimization tasks with different levels of "intuitiveness'' and evaluate whether LLMs can effectively optimize objectives. We show that the LLM's performance depends not only on its intuition for the objective, but also on the alignment between the objective and its priors. We also find that the LLM can successfully optimize an objective even without an explicit description of the objective. Our results largely focus on greedy search strategies; we develop a theoretical characterization of conditions under which greedy search is optimal, meaning the LLM's failures result from a fundamental inability to take gradient-like steps, not suboptimal search.

Development of a Throttleable Attitude Control Scheme for Electrospray Propulsion Systems

2025-10-22T03:33:53Z

Development of a Throttleable Attitude Control Scheme for Electrospray Propulsion Systems Harjono, Hanna-Lee Electrospray thrusters have emerged as highly promising propulsion options for small satellites due to their compact size, low weight, and power requirements. These thrusters offer precise, efficient, and scalable attitude control, making them ideal for missions requiring fine adjustments and advanced capabilities such as formation flying and docking maneuvers. However, to fully exploit the potential of electrospray thrusters, control strategies specific to them must be developed. In this work, a parameterized, PID gain-scheduled attitude controller that leverages the unique throttleability of electrospray thrusters is developed and validated. The developed controller is adaptable across operating conditions, as well as electrospray thrust coefficient values. Extensive modeling efforts are undertaken to incorporate the throttleability and operational constraints of electrospray thrusters, ensuring accurate performance predictions. The control system is simulated under various operating conditions to assess and verify its functionality and robustness against disturbance torques. Validation experiments in a magnetic levitation CubeSat testbed are proposed.

Simulation Modeling of Drug Substance Tech Transfer Timelines at Amgen

2025-10-22T03:33:20Z

Simulation Modeling of Drug Substance Tech Transfer Timelines at Amgen Goel, Viraat Yogi Technology transfer (TT), or the process by which a product's manufacturing is moved and scaled, is a complex business process with countless deliverables and stakeholders. This is especially true in biomanufacturing, where drug commercialization timelines are measured in years, manufacturing facilities are specially designed, and regulations must be stringently met. This systems-level complexity can create inefficiencies in the TT process, lengthening timelines and wasting resources. In this project, we use simulation modeling techniques to digitally model Amgen's Commercial Tech Transfer (CTT) process for biologic drugs. We use virtual experimentation to identify key bottlenecks in the TT workflow, quantify how workstream alterations impact project timelines, and identify process changes likely to shorten timelines. We also extend this analysis to Amgen's New Product Introduction (NPI) process, identifying how coordination between upstream and downstream processes may accelerate NPI timelines. Finally, we link this project to the ongoing development of TT data visualization dashboards.

Safety Stock Modeling for a Medical Devices Supply Chain

2025-10-22T03:33:52Z

Safety Stock Modeling for a Medical Devices Supply Chain Chong, Julie This thesis examines the current inventory management practices at a leading manufacturer of medical devices, and identifies areas for significant improvement. The analysis reveals inefficiencies in safety stock management, with finished goods inventories being excessively high and raw material stocks being underestimated. The study applies single-echelon and multi-echelon inventory modeling to demonstrate potential cost savings through optimized safety stock levels. Additionally, it highlights the importance of reevaluating high service level targets and improving forecasting accuracy to reduce reliance on costly countermeasures. The thesis also emphasizes the need for effective management of component lead times and enhanced data visibility. Recommendations include transitioning to data-driven safety stock calculations, adopting multi-echelon inventory optimization, reassessing service level targets, enhancing forecasting accuracy, and improving component lead time management. By implementing these strategies, the company can enhance operational efficiency, reduce costs, and build greater resilience in its supply chain.

Expanding home broadband coverage through existing Low Earth Orbit megaconstellations

2025-10-22T03:33:46Z

Expanding home broadband coverage through existing Low Earth Orbit megaconstellations Gonzalez Martinez, Gretel Expanding broadband access to underserved areas continues to be a significant challenge for Internet Service Providers (ISPs). While its services perform well in high-density regions, they face scalability limitations in sparsely populated areas where infrastructure costs must be spread across a smaller customer base. This study explores the potential of Low Earth Orbit (LEO) satellite megaconstellations as a scalable solution for extending broadband coverage in the United States. By analyzing the technical capabilities, deployment timelines, and economic feasibility of partnering with LEO satellite providers, this research offers a strategic framework for integrating satellite broadband into ISPs service portfolio. A customer demand model identifies approximately 17 million unserved households within the addressable market of one of the largest U.S. telecommunications companies. The business case assessment evaluates broadband profitability by optimizing customer base size relative to proximity to existing infrastructure. While fiber optics remains the most profitable solution in high-density areas and fixed wireless access effectively utilizes excess 5G capacity, both require substantial infrastructure investment, limiting their feasibility for rural broadband expansion. In contrast, a satellite broadband partnership emerges as the most cost-effective solution for at least 1 million households, surpassing the profitability of currently existing offerings. With minimal capital investment, satellite technology enables rapid customer acquisition and scalable nationwide expansion. The analysis highlights that wholesale agreements play a critical role in profitability and the need to secure a minimum revenue share of 16.5% to reach the break-even point. Performance modeling and curve approximation techniques estimate that if Kuiper meets Federal Communications Commission (FCC) deployment milestones, it could serve 8.5 million customers by 2026, with full nationwide coverage projected by 2029. Under a 200x oversubscription model, Kuiper’s total subscriber capacity could scale to 32.8 million, demonstrating its ability to complement current broadband o!erings. While LEO broadband networks can achieve capacities in the tens of Tbps, they remain far below fiber networks, which operate in the thousands of Tbps. Rather than competing directly, satellite broadband is positioned as a complementary solution, addressing connectivity gaps in rural and underserved regions. To capitalize on these findings, this study recommends leveraging existing LEO megaconstellations to expand broadband coverage nationwide. A phased rollout should begin with a beta program in California, the state with the highest number of unserved households, to validate network performance and optimize deployment for broader expansion. Partnering with an existing LEO megaconstellation could e!ectively bridge the digital divide in rural areas, expand service offerings, and enable a stronger position in the growing satellite broadband market.

Economic Determinants of Increased Use of Performance-Vesting Provisions in CEO Incentives

2025-10-22T03:33:38Z

Economic Determinants of Increased Use of Performance-Vesting Provisions in CEO Incentives Kim, Jason Gwanhee This study examines the determinants of firms adopting performance-vesting long-term incentive (PLI) awards, a rapidly growing form of executive compensation. Using data provided by Equilar on Russell 3000 firms, I investigate how a firm's contracting environment and inter-firm networks influence the adoption and design of PLI awards. I find that stock liquidity and analyst coverage significantly increase the likelihood of adoption by enhancing the informativeness of performance measures. The findings suggest that firms adopt PLI awards to better align managerial incentives with shareholder interests, focusing on the measures that are both reliable and strategically aligned. I also show that board interlocks, particularly those involving compensation committee members, and shared compensation consultants play a significant role in facilitating the diffusion of PLI awards across firms.

Business Value of Enterprise Digital Architecture

2025-10-22T03:33:56Z

Business Value of Enterprise Digital Architecture Venkata Aditya, Saraswatula (Adi SV) Digital technologies are fundamentally reshaping markets and organizations globally. This thesis is exploratory research that seeks to explain how large multi-regional and global enterprises determine, prioritize, measure, and manage business value outcomes of digital investments over time. I examine the value construct of digital initiatives in firms from different industries by interviewing various stakeholders. Insights surfaced from this primary research are analyzed in conjunction with the concepts from current literature. Qualitative findings are proposed, and a list of value metrics is presented that can serve as a future reference for firms. A causal loop diagram is proposed to visualize firm capabilities and value dynamics.

Optimizing Inventory Rebalancing: Strategies for Managing Excess Inventory in a Dynamic Supply Chain

2025-10-22T03:33:45Z

Optimizing Inventory Rebalancing: Strategies for Managing Excess Inventory in a Dynamic Supply Chain Oludipe, Lanre The increasing demand for faster consumer delivery has led retailers to establish smaller regional distribution centers alongside traditional main distribution centers (MDCs). However, the limited capacity of some of these regional centers heightens the need for precise inventory forecasting and deployment to minimize excess inventory, particularly when few viable outlets exist for excess inventory. This research examines strategies to mitigate excess inventory at regional centers through inventory rebalancing, the integration of additional outlets, and modifications to existing inventory policies. A Monte Carlo simulation was conducted to compare the performance of the current system with a modified system incorporating these enhancements. The results showed that the modified system improved capacity utilization and reduced inventory deployment from the MDC without affecting margin. These improvements can enable more agile operations at smaller regional centers, reduce inventory buildup, and reduce the pressure of precise inventory deployment.

Electric Vehicle Fleet Charging: A Simulation-Based Comparison of Charging Strategies and Cost Implications

2025-10-22T03:33:36Z

Electric Vehicle Fleet Charging: A Simulation-Based Comparison of Charging Strategies and Cost Implications Knapp, Rachael The global shift to electric vehicles (EVs) is progressing rapidly, driven by the need to reduce greenhouse gas (GHG) emissions and global reliance on fossil fuels. However, fleet electrification presents unique challenges, particularly in regard to rolling out the necessary charging infrastructure and operational efficiency. This study examines how various depot-based fleet charging strategies impact up-front capital and long-term operational expenditures. The operational feasibility of each method is evaluated through the use of a discrete event simulation. The study incorporates fleet data to assess the time required to charge the fleet, the number of chargers needed, and the number of associates needed to operate manual strategies. The analyzed charging methods include dedicated level 2 charging, vehicle swapping, level 2 cable swapping, level 3 cable swapping, sequential and simultaneous charging. Key findings indicate that while a 1:1 vehicle-to-charger ratio ensures charging reliability within the designated time, it incurs the highest capital costs. Alternative strategies, such as cable swapping and simultaneous charging, significantly reduce costs while successfully charging the fleet within the charging window.

An Integrated Optimization Model for Large-Scale EV Fleet Deployment: Balancing Emissions Reduction and Operational Costs

2025-10-22T03:33:33Z

An Integrated Optimization Model for Large-Scale EV Fleet Deployment: Balancing Emissions Reduction and Operational Costs Kasliwal, Mohit This thesis presents an integrated optimization framework designed for the large-scale deployment of electric vehicles (EVs) within commercial fleets, specifically focusing on balancing emissions reduction and operational cost efficiencies. Utilizing Verizon’s extensive fleet of over 10,000 light-duty vehicles across 1,000 sites as a case study, the research addresses the challenges and complexities in effective site selections for such a large and dispersed fleet. The research involved developing and testing several optimization models under varying scenarios, including scenarios prioritizing maximum operational savings, maximum emissions reduction, and a hybrid model employing an internal cost of carbon (ICC) to balance both operational and environmental objectives. The model essentially develops a ranking system for sites – suggesting which sites to electrify in which year and order, with how many EV conversions (from existing ICE vehicles) at each site. The results highlight the importance of tailoring EV deployment strategies to site-specific conditions, such as unique vehicle usage patterns, grid emissions profiles, regional operational costs, and available incentives. Particularly, smaller sites were found to offer greater relative benefits in terms of both cost savings and emissions reductions per unit of capital invested due to their high average mileage, making them strategic priorities for early electrification. Operational feasibility was also thoroughly examined, recommending practical constraints such as limiting the number of sites electrified annually to ensure project manageability and effectiveness. Sensitivity analyses addressed critical uncertainties such as battery degradation over the vehicle lifespan and the impact of extreme weather on EV performance. These analyses underscore the necessity of conservative battery range buffers ("safe ranges"). Robust load management strategies can be deployed to significantly reduce demand charges and optimize charging schedules based on time-of-use rates where available. Recommendations from the study advocate for implementing a hybrid optimization strategy incorporating an ICC based on corporate goals, continuous adaptive management informed by ongoing data collection, and strategic infrastructure investments to future-proof EV deployments. Policy alignment is also critical to enhance economic viability via incentives and ensure regulatory compliance. Finally, the thesis proposes future research directions, including investigation of advanced load management and integration with renewable energy sources, exploring bi-directional charging to add revenue streams, incorporating marginal operating emissions rate (MOER) data to further reduce grid emissions and exploring the resilience of EV fleets to power outages. These initiatives aim to further enhance strategic decision-making and ensure the long-term sustainability and efficiency of fleet electrification programs.

Breaking the Chain: Building Resilience in the Insurance Value Chain

2025-10-22T03:33:45Z

Breaking the Chain: Building Resilience in the Insurance Value Chain Chuah, Chung Jin This thesis examines how strategic transformation approaches reshape the resilience of the Property & Casualty (P&C) insurance industry in the light of ongoing technological disruption, climate change, and regulatory pressures. Through empirical analysis of 9 insurers, the study reveals that while all transformation types improve performance, phased 'test-refine-execute' strategies achieve superior outcomes by combining operational focus with strategic agility. The research identifies four implementation levers: (i) digital modernization, (ii) phased transformation execution, (iii) resource-allocation agility, and (iv) aligned leadership - which together explain why some transformations succeed where others fail."

Domain Adaptation of VLM for Soccer Video Understanding

2025-10-22T03:33:35Z

Domain Adaptation of VLM for Soccer Video Understanding Jiang, Tiancheng(Tony) Vision Language Models (VLMs) have demonstrated strong performance in multi-modal tasks by effectively aligning visual and textual representations. However, most video under- standing VLM research has been domain-agnostic, leaving the understanding of their transfer learning capability to specialized domains underexplored. In this work, we address this by exploring the adaptability of open-source VLMs to specific domains, and focusing on soccer as an initial case study. Our approach uses large-scale soccer datasets and LLM to create instruction-following data, and use them to iteratively fine-tune the general-domain VLM in a curriculum learning fashion (first teaching the model key soccer concepts to then question answering tasks). The final adapted model, trained using a curated dataset of 20k video clips, exhibits significant improvement in soccer-specific tasks compared to the base model, with a 37.5% relative improvement for the visual question-answering task and an accuracy improvement from 11.8% to 63.5% for the downstream soccer action classification task.

Minimizing Cost of Intra-Yard Finished Vehicle Logistics Through Automation and Optimization

2025-10-22T03:33:22Z

Minimizing Cost of Intra-Yard Finished Vehicle Logistics Through Automation and Optimization Garber, Jeremy This thesis analyzes and validates autonomous Finished Vehicle Logistics (FVLa) operations, at the plant of an automative Original Equipment Manufacturer (OEM), through the development of a Vehicle-Plug-In (VPI) system with Level 4 autonomous driving capabilities. The research combines process flow analysis with FlexSim simulation modeling to optimize operational parameters and assess safety performance. Results demonstrate FVLa operational feasibility with a recommended VPI inventory of 750 units and 6-hour replenishment cycle. The study's key contributions include a validated operational model using Economic Order Quantity calculations and a safety framework utilizing Bayesian Networks, establishing foundations for the planned 2028 implementation while maintaining required throughput rates and safety standards.

Decarbonized Cement Manufacturing via Advanced Production Technologies

2025-10-22T03:33:39Z

Decarbonized Cement Manufacturing via Advanced Production Technologies Norwalk, Michael Cement production is the second-largest source of industrial carbon dioxide emissions world-wide. Due to the chemical reactions inherent in its production and the temperatures required to drive those reactions, cement is considered a “hard-to-decarbonize” industry. In this study, three emerging technologies to reduce the carbon intensity of industrial processes, namely, direct high-temperature electric process heat, electric process heat utilizing thermal storage, and liquid amine-based carbon capture are assessed in the context of a greenfield cement production facility relative to a new-build conventional cement plant fueled with natural gas. Cement plants utilizing this set of technologies were modeled in five U.S. geographies to determine the relative economic returns. The economics were assessed, inclusive of available economic incentives, both for the scenario in which the cement produced is sold in the U.S. market and for the scenario in which the cement produced is exported to the European Union (E.U.) market to assess potential benefits from the E.U. carbon pricing system. The analysis indicates that at current technology prices, the economic returns of the assessed technologies, while in some cases profitable, continue to lag those of conventional production technology for the domestic U.S. market. As costs come down as technology is deployed, the economics of carbon capture solutions have the potential to be competitive with conventional technology. The E.U. carbon emissions penalties are effective in altering the economics in such a way that implementing carbon capture systems becomes the most attractive economic option, demonstrating the power of carbon emissions markets. With increased technology deployment as well as the adoption of targeted incentives in the U.S. market, the adoption of low carbon cement production technologies can be accelerated.

Single-Polarity Ion Electrospray Propulsion

2025-10-22T03:33:43Z

Single-Polarity Ion Electrospray Propulsion Shaik, Saba Zareen Electrospray thrusters are highly efficient spacecraft propulsion devices that accelerate ions sourced from ionic liquid propellants to produce thrust. Typically, electrosprays are fired in a dual-polarity configuration in which the polarity of the ion beam is periodically reversed. This strategy is difficult to implement and imposes limitations on system size and performance. We instead propose a single-polarity design where negative ions are emitted continuously from the thruster, enabling extreme miniaturization, faster startup, better emission stability, and simpler power processing. This thesis investigates two challenges associated with the single-polarity design. First, system lifetime is of principal importance for electrospray propulsion systems in general and must be verified for a single-polarity implementation. Long-duration electrospray tests are performed, demonstrating that single polarity thrusters achieve comparable lifetimes and performance to state of the art systems with high mass utilization and minimal hardware degradation. An additional challenge is propellant electrochemistry, triggered when positive counterions accumulate in the ionic liquid. A suite of experiments is conducted to identify and characterize electrochemical processes, including electrical double-layer potential evolution and gas-phase product formation, in electrospray thrusters over long firing durations.

Comparative Analysis of Semiconductor Investment Environments in the U.S. and China

2025-10-22T03:33:31Z

Comparative Analysis of Semiconductor Investment Environments in the U.S. and China Zhang, Hanxue Semiconductors are fundamental to Artificial Intelligence(AI) and central to global technological competition. Against this backdrop, this thesis compares semiconductor primary investment environments in the United States and China, examining their implications for industry development and innovation. The study employs a mixed-methods approach, combining expert interviews, data analysis, and natural language processing (NLP). It draws on primary market investment, M&A deals and government grants data to examine capital structures, investment stages, sectoral focus, and exit efficiency. Furthermore, it analyzes nearly 3,000 semiconductor industry reports(2020-2025) to identify evolving strategic priorities and thematic trends shaping these environments. Findings reveal that China’s state-led, vertically integrated model prioritizes upstream capacity building and supply chain autonomy, supported by government guidance funds, private capital, and policy-driven mechanisms. However, there remains a significant gap in leading-edge chips, necessitating precise investments and patient capital to bridge this divide. While the U.S. ecosystem, shaped by major technology firms and federal support, focuses on design innovation and cutting-edge technologies. However, structural constraints such as limited exit pathways, fragmented fabrication capacity, and insufficient industrial policies may hinder the U.S. in nurturing innovation-driven small and medium-sized enterprises (SMEs) in the semiconductor industry. This thesis highlights the structural divergence between the U.S. and China’s semiconductor ecosystems by examining policy, primary market capital, and investment dynamics. It offers policymakers and investors a strategic overview of how these forces shape innovation and resilience, while identifying emerging investment priorities and future development paths.

Forecasting Automotive Production Volume Using Regression and Time Series Modelling

2025-10-22T03:33:22Z

Forecasting Automotive Production Volume Using Regression and Time Series Modelling Gong, Yutao Accurate forecasting of automotive production volumes is a critical capability for suppliers navigating an increasingly volatile industry. Overly optimistic forecasts, particularly from Original Equipment Manufacturers (OEMs), lead to misallocated capacity and lost opportunities across the supply chain. This thesis investigates whether advanced statistical models can improve upon benchmark industry forecasts and provide automotive suppliers with more reliable, practical tools for demand planning. Several forecasting methodologies are evaluated, including ARIMA, standard linear regression, Lasso regression, Theta model, and a hybrid Boosted Theta model. Models are tested across North America, Europe, and Greater China using 2000-2024 vehicle production and macroeconomic data. Results show that Theta model outperforms industry forecasts across both 1-year and 5-year horizons in North America and Europe. Its simplicity, low data requirements, and robustness to market volatility make it suitable for industrial use. The model was successfully implemented at Commonwealth Rolled Products, an aluminum rolling mill in Kentucky, portfolio company of American Industrial Partners (AIP), where it was adopted for 2025 planning and drove a shift towards data-centric forecasting practices. This research presents a real-world example of applying academic techniques to solving actual business problems, serving as a valuable reference for suppliers seeking to improve forecast accuracy and operational planning in the evolving automotive landscape.

The role of university venture funds in supporting early-stage Japanese startups

2025-10-22T03:33:40Z

The role of university venture funds in supporting early-stage Japanese startups Brillaud, Nami This thesis explores how university venture funds in Japan are uniquely positioned to turn the country’s innovation capacity into entrepreneurial capacity by supporting early-stage startups. While Japan consistently ranks high in research output, much of this potential is not being translated into successful entrepreneurship. Risk capital is scarce compared to other ecosystems, particularly for deep tech, and support systems for early-stage startups are still limited. University venture funds – which inherently connect universities, entrepreneurs, and risk capital – are well positioned to bridge this gap. Yet despite their growing relevance, their evolving role in supporting Japanese early-stage startups is understudied. This study compares university venture funds with different profiles – ranging from leading and longstanding funds like UTEC, to public-private venture funds established through government initiatives, to recent funds with diversified structures – analyzing how they are structured, how they invest, and what results they have seen so far. It then builds on startup examples and interviews with university venture funds to identify how these funds can better support early-stage startups through improved fund operations, stronger pre-seed support, as well as a strategic approach to growth and exits. Ultimately, this thesis advocates for actionable solutions informed by global practices but adapted to Japan’s unique startup ecosystem.

Analyzing Procurement Data for Cost Saving Application

2025-10-22T03:33:41Z

Analyzing Procurement Data for Cost Saving Application Pan, Haoting In an increasingly data-driven business environment, procurement analytics plays a critical role in optimizing costs and improving supply chain efficiency. This thesis examines the development and implementation of the Lifecycle Cost Management (LCM) tool at Caterpillar Inc., a global leader in heavy equipment manufacturing. Given Caterpillar's decentralized procurement structure, managing cost-saving initiatives across its 150 facilities (Caterpillar | Caterpillar Frequently Asked Questions (FAQs), n.d.) and 28,000 suppliers (Caterpillar | Caterpillar at a Glance, n.d.) poses a significant challenge. The LCM tool leverages machine learning models to identify overpriced purchase orders (POs) and generate actionable cost-saving opportunities. This study explores the methodology used to enhance LCM's predictive capabilities, including data sourcing and cleaning, feature engineering, model selection, and validation. Various regression models, clustering techniques, and machine learning algorithms, such as Random Forest and XGBoost, are tested to identify cost outliers. A validation process is implemented to ensure that flagged outliers are cost-saving opportunities appropriate for execution. Beyond technical development, the thesis addresses the processes of digital tool adoption within Caterpillar’s procurement teams. A change management approach is employed, incorporating buyer interviews, stakeholder engagement, and iterative user experience (UX) improvements. Through case studies, the study highlights the machine learning model performance and tangible financial impact of LCM. The LCM tool has identified more than $100M data-driven potential savings, and hopes to realize 20% of the savings. Because Caterpillar’s procurement contracts are often long-term, these savings can be considered perpetual. Findings indicate that while machine learning models effectively identify cost outliers, their success is contingent on robust data governance, stakeholder buy-in, and integration into procurement workflows. The study underscores the importance of data management, organizational alignment, and continuous refinement of digital procurement tools. Future work recommendations are enhancing data infrastructure, integrating AI-driven contract management and analysis, and refining cost estimation methodologies. The insights gained contribute to the broader application of procurement analytics and digital transformation in manufacturing enterprises.

Impact Evaluation and Prioritization Framework for Manufacturing Inspection Technology Investment

2025-10-22T03:33:48Z

Impact Evaluation and Prioritization Framework for Manufacturing Inspection Technology Investment DiDio, Isabella Advancements in visual inspection technologies and machine learning algorithms present Johnson & Johnson Vision with an opportunity to enhance quality control for Acuvue contact lenses, addressing inefficiencies such as unnecessary scrap, customer complaints, and lead time variability. With over 5 billion lenses produced annually across 100 manufacturing lines, the proposed inspection implementation of advanced camera optics and machine learning aims to improve defect detection accuracy, minimize manual inspection, and reduce customer complaints. An impact evaluation and prioritization framework was developed to strategically implement these upgrades across 100 manufacturing lines, integrating historical data analysis, financial modeling, and engineering risk assessments. Key findings highlight that complaint reduction, scrap savings, and labor cost reductions are the primary drivers of cost savings, with inventory savings offering incremental benefits over time. In conclusion, this research demonstrates the process of integrating advanced technologies into manufacturing processes. By aligning engineering solutions with strategic business objectives, the findings provide actionable insights for managing large-scale technological upgrades across global networks.

The Technical Discourse of Miyake Design Studio: Episode in the Interpretation of Cloth, 1995-2007

2025-10-22T03:33:29Z

The Technical Discourse of Miyake Design Studio: Episode in the Interpretation of Cloth, 1995-2007 Tan, Yi-Ern Samuel In the late 1980s, Miyake Design Studio began to register patents concerning the Studio’s development of novel techniques to process pleated clothing. Their first patent, filed in 1989, was registered in designer Issey Miyake’s name, detailing the use of an industrial machine to pleat an entire garment after sewing, reversing the order of the conventional approach to creating pleated garments. In the years that followed, this entry into what I term “technical discourse” would proliferate with the Studio’s establishing of the PLEATS PLEASE brand specializing in pleated garments, and the A-POC (“a piece of cloth”) project with designer and textile engineer Fujiwara Dai. Each of these projects produced numerous patents, including a period between 1997 and 2008 I call the “Miyake Patent Explosion” when the Studio filed twenty patents with the Japan Patent Office and its international counterparts. In contrast to aesthetic discourses proposing the value of a work on its artistic merits and intellectual content, technical discourse points to the profusion of texts produced and circulated by the Studio—in this thesis, patents and legal claims—to uphold the utility of their products and their protection as intellectual property. By engaging with technical discourse, Miyake Design Studio were not only creating legal safeguards around the ideas it considered proprietary. Rather, their extensive production of technical discourse positioned Miyake as a figure who exceeded the boundaries of fashion, approaching its adjacent categories of unhyphenated design, architecture, and art within whose circles his objects circulate as currency. Exploring these texts as they are deployed in the defense of intellectual property, I argue that technical discourse can be treated as a form of historical archive that allows us to historicize claims to technological inheritance that bear upon the discussion of Miyake’s work. Specifically, I look to patents as a citational practice, or as Alain Pottage and Brad Sherman write, a “chain of reference” through which patent lawyers and engineers make deliberate connections between one technology and another to acknowledge, distinguish, and legitimize. Examining three episodes where technical discourse opens the way for historical narrative—a lawsuit over imitation goods, a case of mistaken identity in design criticism, and a moment of technological dissolution—I argue that we cannot divorce Miyake and his work from the technical complex that surrounds the Studio’s production of objects. Turning to these technical discourses that exist in the public record, I suspend the promise of monographic history that peers into the mind of the individual and probe instead the possibilities of seeing agencies beyond those attributed to the authorial figure of Miyake— his corporate apparatus, his allies, his admirers, his critics, his opponents, the receptive public.

Investigation of consolidation and plastic resistance on clays

2025-10-30T15:50:03Z

Investigation of consolidation and plastic resistance on clays Marsal, Raúl J. Thesis: M.S., Massachusetts Institute of Technology, Department of Civil and Sanitary Engineering, 1944; Vita. Appendix contains numerous pamphlets.

An optical instrument for the synthesis of sound

2025-10-30T15:50:01Z

An optical instrument for the synthesis of sound Brown, Sherwood Fiske. Thesis: M.S., Massachusetts Institute of Technology, Department of Physics, 1930

A spiritual and cultural synagogue center for modern American Jewry : an architectural and sociological study

2025-10-10T03:04:41Z

A spiritual and cultural synagogue center for modern American Jewry : an architectural and sociological study Goody, Marvin E. Thesis: M. Arch., Massachusetts Institute of Technology, Department of Architecture, 1951; "A thesis submitted in partial fulfillment of the requirements for the degree of Master in Architecture, Massachusetts Institute of Technology, August 22, 1951."; Includes bibliographical references (leaves 93-95).

An Operational Value Stream Analysis for Developmental Excellence

2025-10-07T04:14:42Z

An Operational Value Stream Analysis for Developmental Excellence Shaw, Eric T. The aerospace and defense industry faces increasing challenges in new product development, where financial constraints and risk aversion hinder innovation. Using a multidisciplinary approach that integrates contract theory, computational fluid dynamics (CFD), and machine learning, this research explores the impacts of engineering requirements, financial alignment among stakeholders, and improved efficiencies in predictive modeling techniques for two separate air vehicle programs: A and B. A Monte Carlo analysis using SEER-H estimation software quantifies the financial and schedule impacts of engineering requirements, revealing a 10–30% cost increase due to volatility in air vehicle development design parameters. Moreover, a game-theoretic contract negotiation simulation illustrates the importance and opportunity of financial incentive alignment among key stakeholders. Additionally, predictive analytics leveraging machine learning models better capture the relevant flow mechanics, improving the circumferential distortion estimations in nacelle aerodynamics by over 10% compared to traditional heuristics. Finally, a CFD-based actuator disk source modeling approach demonstrates a 60% reduction in steady-state distortion at some portions of the flight envelope, due to the impact of the fan upstream influence on inlet flow distortion suggesting increased operational capability for the air vehicle program B. This research provides actionable recommendations to enhance the operational value stream of new air vehicle program development, emphasizing the need for pre-RFP requirements validation, advanced machine learning applications for predictive engineering, and refined CFD modeling to identify technical risks earlier in the design process.

A Computational Framework for Simulating Entanglement-Based Drone Countermeasures with Flexible Filaments Immersed in Viscous Flow

2025-10-07T04:15:01Z

A Computational Framework for Simulating Entanglement-Based Drone Countermeasures with Flexible Filaments Immersed in Viscous Flow Sonandres, Jake T. In this work, we present a computational framework for modeling the coupled dynamic interactions of highly flexible slender filaments immersed in a viscous flow and their entanglement with themselves and moving structures. This work is motivated by a novel drone countermeasure that entangles propellers with flexible filament clouds, inducing a loss of thrust and control authority in the drone. However, the framework is relevant to a wider range of applications, including actin filaments in cell biology, carbon nanotubes in composite materials, and rope-like structures in industrial settings. Each filament is modeled with the three-dimensional geometrically exact Kirchhoff-Love torsion-free finite element beam formulation. The fluid flow resulting from filament aerodynamic interaction is described through a Boundary Integral (BI) formulation of the incompressible Stokes equations based on the Stokeslet discretization. The heavy computational load of the resulting dense system is addressed through the use of fast GPU-based dense linear solvers. The BI formulation is coupled to the filament solid mechanics by enforcing momentum balance at the dynamically evolving filament-fluid interface. Additionally, the solid contact interactions between filaments are modeled with a point-to-point frictional contact algorithm that applies discrete contact and frictional forces at the closest point between the beam elements. We address the difficulties associated with contact between elements represented with third-order Hermitian polynomial shape functions and the strategies adopted to overcome these challenges. To capture propeller fouling for drone countermeasures, we incorporate a propeller and motor model whose thrust and torque responses are affected by contact interactions during entanglement. We verify our framework against simple analytical solutions and demonstrate its capabilities with numerical examples that attempt to capture large-scale filament entanglement behavior. In particular, we apply our methodology to demonstrate the process by which filament entanglement can restrict motion and reduce the efficacy of propellers. The results show that the framework can be used to understand the connection between filament entanglement, key system properties, and the resulting thrust generated by the propeller.

Global sustainable aviation fuel production potential from current agricultural production: a holistic data analytics and systems analysis approach

2025-10-07T04:14:52Z

Global sustainable aviation fuel production potential from current agricultural production: a holistic data analytics and systems analysis approach Martin, Estelle Claude Aline Aviation contributes significantly to global greenhouse gas emissions, driven primarily by its dependency on fossil-based jet fuel. Sustainable Aviation Fuel (SAF) offers a short-term option to mitigate these emissions. However, its current scalability remains limited, constrained by access to sustainable biomass. Realizing SAF’s potential in the near term, using the agricultural and industrial systems already in place requires a detailed understanding of biomass availability, resource competition, and the scalability of SAF production. This thesis presents a comprehensive system analysis framework and a data-driven methodology for evaluating SAF production potential based on current agricultural output, without assuming land expansion or major yield improvements and preserving food utilization. It evaluates the SAF production potential from increasing biomass availability by redirecting biomass currently used for some non-food purposes, and by utilizing processing and agricultural residue. In-depth analysis of four high-potential case studies, one for each main biomass family (starchy, sugary, oily, and fats and greases), was used to construct a detailed model of the supply chain. This structure was then applied globally across all countries and relevant feedstocks to estimate SAF production potential and associated system requirements. Findings from the case studies show that these four high-potential opportunities could collectively meet only up to 13.1% of global jet fuel demand in 2023, assuming 100% neat SAF. The global analysis estimates that the SAF production potential from the considered streams of increased biomass availability could meet up to about two-thirds of global jet fuel demand, with 28.7% derived from agricultural residues, 25.9% from redirected main products, and 12.5% from processing residues. These contributions hence remain insufficient to fully displace fossil jet fuel. This work provides an estimate of what could be achieved using the existing agricultural and industrial systems, what resource would be required, and how it compares to global resource availability.

Causal inference for complex systems and applications to turbulent flows

2025-10-07T04:14:27Z

Causal inference for complex systems and applications to turbulent flows Sánchez, Álvaro Martínez Causality lies at the heart of scientific inquiry, serving as the fundamental basis for understanding interactions among variables in physical systems. Despite its central role, current methods for causal inference face significant challenges due to nonlinear dependencies, stochastic interactions, self-causation, collider effects, and influences from exogenous factors, among others. While existing methods can effectively address some of these challenges, no single approach has successfully integrated all these aspects. Here, we address these challenges with SURD: Synergistic-Unique-Redundant Decomposition of causality (Nat. Commun., vol. 15, 2024, p. 9296). SURD quantifies causality as the increments of redundant, unique, and synergistic information gained about future events from past observations. The formulation is non-intrusive and applicable to both computational and experimental investigations, even when samples are scarce. We benchmark SURD in scenarios that pose significant challenges for causal inference and demonstrate that it offers a more reliable quantification of causality compared to previous methods. We further illustrate the applicability of our approach in two turbulent-flow scenarios: the energy transfer across scales in the turbulent energy cascade and the interaction between motions across scales in a turbulent boundary layer. Our results show that, without accounting for redundant and synergistic effects, traditional approaches to causal inference may lead to incomplete or misleading conclusions.

Systems Theoretic Process Analysis of Sociotechnical Systems

2025-10-07T04:14:57Z

Systems Theoretic Process Analysis of Sociotechnical Systems Harrington, Polly The safety and success of complex modern systems, such as hospitals, aircraft, or software, depend on their ability to integrate people and technical components. For example, doctors must be able to use their computerized surgical tools to treat their patients successfully, airplane pilots must be able to operate the required controls for takeoff and landing, and regulators must be able to interpret the data they receive to make critical decisions. However, designing systems that facilitate safe interactions between humans and technology is not a simple task. System designers must consider not only the constraints of the technical components but also human requirements throughout the entire system. However, accidents in modern systems continue to prove that more work is needed to identify and prevent unsafe interactions between humans and technology Systems Theoretic Process Analysis (STPA) is a hazard analysis methodology based on systems theory that has been used to improve system safety in various industries, including healthcare, aviation, nuclear power, and automotive design. However, if hazard analysts using STPA lack significant expertise in human factors engineering (HFE), they may be unable to thoroughly and rigorously identify critical unsafe interactions. This thesis presents a process for utilizing HFE to improve the results of STPA analyses on sociotechnical systems. In particular, the process focuses on the thorough identification of causal scenarios in sociotechnical systems by incorporating relevant human factors concepts. The process allows analysts without significant training in HFE to improve their ability to identify useful scenarios for humans in their system. The effectiveness of the improved process is demonstrated using a healthcare case study on over-the-counter clinical laboratory tests in the United States. By establishing a process for non-HFE experts to use when conducting STPA analyses, more systems can be developed that enhance human performance rather than increase conflict between humans and the engineered system.

Development of Algorithms for Quantitative Analysis of Long Electrical Arcs in Crossflows

2025-10-07T04:14:46Z

Development of Algorithms for Quantitative Analysis of Long Electrical Arcs in Crossflows Lin, Fayleon A single lightning strike can deliver a steady current of hundreds of amps during its attachment to an aircraft. Therefore, it is imperative to have an adequate lightning protection system in the aircraft to minimize the probability of catastrophic accidents. Current guidelines for lightning protection systems are based on prior service experience and historical data, which might become insufficient with future generation aircraft. These often adopt novel and unconventional aircraft designs, often deviating significantly from current designs. Therefore, efforts are underway to update these guidelines with novel methods such as designs aided by numerical simulation that can accurately model the behavior of lightning attachment and the subsequent swept-stroke phase. To aid in the development of these numerical methods, ample data of not only the electrical arcs but also their interactions with the surrounding flow are necessary for validation. However, most studies on long electrical arcs lack a detailed investigation of the coupling between the electrical arcs and the surrounding flow field. For that purpose, teams from the Massachusetts Institute of Technology (MIT), ONERA, and Universitat Politècnica de Catalunya (UPC) conducted an extensive experimental campaign in April 2024 that investigates this coupling in detail for the first time. Data gathered from this experiment include electrical properties of the arc, high-speed video of the arc column, and the velocity field of the surrounding flow. Approximately 200 cases were conducted with various geometrical and electrical configurations. To meaningfully analyze all the data, a set of algorithms was developed to automatically process, analyze, and visualize these data. Detailed analysis of the root and column behavior was performed; electrical properties were verified to be consistent with literature values; and coupling between the velocities of the arc column and the flow field was determined by simultaneous visualization of both data forms.

Performance and Analysis of a Deployable Diffractive Optical Element for Small Satellite Missions

2026-01-13T19:42:33Z

Performance and Analysis of a Deployable Diffractive Optical Element for Small Satellite Missions Bahlous-Boldi, Adam A. As space missions push toward smaller, lighter, and more deployable instrumentation, diffractive optical elements (DOEs) offer a compelling alternative to traditional optics. Their ability to focus light through engineered phase profiles rather than curved surfaces allows for large-aperture, flat optics that are far lighter and easier to package for launch. However, this benefit comes with trade-offs: DOEs are sensitive to wavelength mismatch, manufacturing errors, and environmental deformations—especially thermal gradients and membrane tensioning in space. This thesis develops a comprehensive framework for understanding and simulating the performance of DOEs under realistic operating conditions. Beginning from first principles, the work contrasts geometric and wave-optical models for Fresnel zone plates and multilevel diffractive lenses, leading to quantitative predictions of diffraction efficiency and PSF quality under non-idealities. A key contribution is the analytical and numerical analysis of how uniform thickness errors, wavelength mismatches, and thermal expansions degrade optical performance, both in efficiency and wavefront fidelity. To evaluate these effects in detail, a flexible simulation tool was developed in MATLAB, enabling both Fourier and integral-based propagation through arbitrarily deformed DOEs. These models are applied to a conceptual space-based LIDAR system—SPECIES—that uses a deployable DOE optic to demonstrate the feasibility and limitations of this approach. The results show that DOEs can tolerate some global deformations - for example, a 1 mm deformation results in a 38% performance loss in an F3 LiDAR system with a 1 mm detector diameter. However, they remain highly sensitive to fine-scale shape errors, posing significant challenges for high-precision applications like fiber coupling or imaging. The findings provide new insight into the tolerances, benefits, and trade-offs of DOEbased systems in space, and lay the groundwork for future missions seeking to leverage lightweight diffractive optics for remote sensing and optical communication.

Partial Gravity Load Simulation Using Mechanical Off-Loading and Lower Body Negative Pressure

2025-10-07T04:14:54Z

Partial Gravity Load Simulation Using Mechanical Off-Loading and Lower Body Negative Pressure Davalos, Daniela L. Prolonged exposure to reduced gravity environments can lead to significant deconditioning of the cardiovascular, musculoskeletal, and ocular systems. These effects increase the risk of orthostatic intolerance, bone loss, and conditions such as Spaceflight Associated Neuro-ocular Syndrome (SANS). As spaceflight missions grow longer and more frequent, especially with increased extravehicular activity (EVA) on the Moon or Mars, it is critical to develop effective countermeasures and Earth-based analogs to simulate these gravitational environments and evaluate physiological impacts. This thesis addresses these challenges through two complementary approaches. First, it presents the design and development of the MIT Moonwalker IV, a passive mechanical offloading system that simulates partial gravity by applying vertical support via a spring-cable mechanism. In a treadmill-based pilot study, one participant showed at least a 50% reduction in metabolic demand while running under simulated Martian gravity. These findings validate the Moonwalker IV as a metabolic analog for EVA task simulation. Second, this thesis evaluates a collapsible lower body negative pressure (LBNP) suit as a wearable countermeasure for micro and partial gravity environments. By applying negative pressure to the lower body, the suit helps restore the mechanical loading and hydrostatic fluid gradients typically provided by Earth’s gravity. The suit was tested in both simulated reduced gravity via a head-down/head-up tilt paradigm and and true reduced gravity via parabolic flight. Each condition was evaluated both with and without –20 mmHg of LBNP. Results demonstrated that the collapsible LBNP suit produced cardiovascular responses comparable to those observed in traditional rigid LBNP chambers. It also induced lower body fluid shifts as measured by segmental leg bioimpedance, reduced intraocular pressure, and generated ground reaction forces similar to standing in 1G. These findings support the complementary use of Earth-based analog systems to simulate partial gravity and wearable devices to simulate Earth gravity in reduced gravity environments. They offer valuable tools for preparing astronauts and preserving physiological health during long-duration space missions.

Unforgettable Generalization in Language Models

2025-10-07T04:14:36Z

Unforgettable Generalization in Language Models Zhang, Eric When language models (LMs) are trained to forget (or “unlearn”) a skill, how precisely does their behavior change? We study the behavior of transformer LMs in which tasks have been forgotten via fine-tuning on randomized labels. Such LMs learn to generate near-random predictions for individual examples in the “training” set used for forgetting. Across tasks, however, LMs exhibit extreme variability in whether LM predictions change on examples outside the training set. In some tasks (like entailment classification), forgetting generalizes robustly, and causes models to produce uninformative predictions on new task instances; in other tasks (like physical commonsense reasoning and scientific question answering) forgetting affects only the training examples, and models continue to perform the “forgotten” task accurately even for examples very similar to those that appeared in the training set. Dataset difficulty is not predictive of whether a behavior can be forgotten; instead, generalization in forgetting is (weakly) predicted by the confidence of LMs’ initial task predictions and the variability of LM representations of training data, with low confidence and low variability both associated with greater generalization. Perhaps most surprisingly, random-label forgetting appears to be somewhat insensitive to the contents of the training set: for example, models trained on science questions with random labels continue to answer other science questions accurately, but begin to produce random labels on entailment classification tasks. Finally, we show that even generalizable forgetting is shallow: linear probes trained on LMs’ representations can still perform tasks reliably after forgetting. Our results highlight the difficulty and unpredictability of performing targeted skill removal from models via fine-tuning.

Guessing Random Additive Noise Decoding in Coded Multiple-Input Multiple-Output Systems

2025-10-07T04:14:49Z

Guessing Random Additive Noise Decoding in Coded Multiple-Input Multiple-Output Systems Wu, Benjamin Multiple-Input Multiple-Output (MIMO) wireless communication systems incorporate forward error correction (FEC) to achieve high reliability under fading and interference. In this thesis, we explore the emerging FEC paradigm of Guessing Random Additive Noise Decoding (GRAND) in a point-to-point MIMO system. Treating GRAND as an FEC decoder disjoint from the MIMO detector, we compare the soft-decision Ordered Reliability Bits GRAND (ORBGRAND) to CRC-Assisted Successive Cancellation List (CA-SCL) decoding of the CRC-Assisted Polar (CA-Polar) [105, 128] code found in the 5G New Radio standard. For this code, we find that ORBGRAND outperforms CA-SCL (list size 16) by 1 dB E_b/N₀ at block error rate of 10⁻³, under 16-QAM and Linear Minimum Mean Square Error detection, with two transmit antennas and four receive antennas. We also show that ORBGRAND, when paired with other moderate redundancy linear codes, can yield substantial savings in the range of 0.5 − 2 dB in E_b/N₀ over CA-SCL decoding (list size 16) of CA-Polar codes with the same code parameters, for a block error rate of 10⁻³. We provide extensive benchmarks comparing ORBGRAND to CA-SCL and other soft-decision GRAND variants. We also integrate a GRAND decoder producing soft output into a MIMO iterative detection and decoding (IDD) receiver. Specifically, we apply an established technique which utilizes soft-output GRAND as the component decoder for the block turbo decoding of product codes. This block turbo decoder is evaluated as a soft output decoder within a MIMO IDD receiver. We demonstrate competitive or superior performance relative to Belief Propagation (BP) decoding of 5G Low-Density Parity Check (LDPC) codes. This approach also marks a use of GRAND for low-rate, high-redundancy FEC in a MIMO system. With GRAND in MIMO still being an emerging area of research, this work is an exploratory evaluation of GRAND for FEC in MIMO, and highlights GRAND’s potential as a versatile and performant decoder in different MIMO receiver architectures.

Improving Accuracy Predictions of Companion Classifiers for LLM Routing

2025-10-07T04:14:19Z

Improving Accuracy Predictions of Companion Classifiers for LLM Routing Wu, Jessica L. The increasing versatility of Large Language Models (LLMs) calls for developing effective routing systems to match tasks with the most suitable models, balancing accuracy and computational cost. This research introduces a novel meta-cascade routing framework that combines meta-routing, where a predictive model selects the appropriate LLM for a task, and cascading, where models are queried in sequence to optimize cost and performance. A critical component of this framework is the companion classifier, defined as a fine-tuned model trained to predict whether a particular LLM will generate an accurate response. We investigate whether incorporating features such as model responses into these classifiers can improve routing accuracy. Our preliminary experiments, using the Routerbench dataset, focus on training companion models that provide more stable and accurate routing decisions.

Formal Verification of Relational Algebra Transformations in Fiat2 Using Coq

2025-10-07T04:14:31Z

Formal Verification of Relational Algebra Transformations in Fiat2 Using Coq Teshome, Christian Data-intensive applications often involve operations over structured datasets, such as filtering, joining, and projecting records. Relational database systems generally use query planners to optimize high-level SQL queries into efficient execution plans. While these systems apply well-established query transformations, they typically assume the correctness of these transformations rather than formally proving them. The absence of formal guarantees can be a significant limitation for systems with strict correctness requirements. This thesis contributes to Fiat2, a Python-like high-level programming language for data-intensive workloads that integrates formal verification via the Coq proof assistant. We focus on proving the correctness of several rewrite-based query optimizations commonly used in database engines. Specifically, we formalize and prove the correctness of algebraic rewrites involving combinations of filters, joins, and projections, as well as join-reordering rewrites. All rewrites are proven in Coq to preserve the semantics of the original program under list semantics, meaning that the output lists are fully equivalent (or permutations, in the case of join reordering). These verified rewrites serve as a foundation for future optimization in Fiat2, enabling significant optimizations while preserving the semantics of the original queries with correctness guarantees. The results demonstrate the feasibility of integrating formally verified query optimizations into a practical high-level programming language.

Converting PyTorch Models to StreamIt Pipelines

2025-10-07T04:14:17Z

Converting PyTorch Models to StreamIt Pipelines Rajvee, Muhender Raj With the rise of large language models, there have been efforts to optimize machine learning inference to support a large volume of queries. Currently, the two main ways to do this are running optimized kernels for computing the forward inference pass and distributing computation across multiple GPUs or different cores in a GPU. Machine learning libraries such as PyTorch produce dynamic computation graphs in order to represent the forward pass of the model. PyTorch allows conversion of these dynamic graphs into static ones through just-in-time (JIT) compilation. These graphs can then be optimized further by the compiler. We propose an alternate way of optimizing these dynamic graphs. We convert the dynamic computation graph of PyTorch to pipelines in StreamIt, a domain specific language (DSL) for streaming applications, and use the multi-stage compilation property of BuildIt to compile this pipeline in stages to inference code. We found that, while the inference latencies of models compiled in this way are slightly higher, they are still comparable to those of PyTorch models and are open to future optimizations.

An Interactive Visual Paradigm for Knowledge Graph Question-Answering

2025-10-07T04:14:00Z

An Interactive Visual Paradigm for Knowledge Graph Question-Answering Ramkumar, Vayd Sai In an era of information overload, verifying data reliability and provenance is critical, yet knowledge graphs (KGs) often remain complex for non-expert users. This thesis introduces TRACE, a Reasoning and Answer-path Comprehension Engine, a visualization tool enhancing transparency in KG question answering (KGQA). By abstracting intricate KGs into intuitive meta-nodes, TRACE simplifies exploration of large, multi-topic datasets. Its interactive interface allows users to navigate semantic communities and trace reasoning paths, fostering trust through clear answer derivation. Unlike cluttered traditional graph visualizations, TRACE’s meta-node approach provides a scalable, user-friendly solution, concealing technical complexities while enabling robust query validation. Large language models support natural language query parsing and community summarization, making KGs accessible to diverse audiences. TRACE positions itself as a vital widget for information platforms, empowering users to counter misinformation confidently. A user study and pipeline evaluation confirmed TRACE’s intuitive interface excels for complex queries, though multi-hop paths pose challenges, while processing tests demonstrated its scalable paradigm for large datasets. By prioritizing transparency and usability, TRACE redefines KGs as reliable tools for knowledge discovery, laying a foundation for future systems to deliver trustworthy, accessible information in a digital landscape fraught with uncertainty.

Spectral Analysis of Local Atomic Environments

2025-10-07T04:14:37Z

Spectral Analysis of Local Atomic Environments Phung, Tuong The representation of local environments is a cornerstone challenge in computational materials science, with profound implications for property prediction and materials discovery. This thesis presents a comprehensive investigation of spectral descriptors constructed from spherical harmonic expansions to represent the geometries of local atomic environments. Systematic computational experiments evaluate the robustness of these descriptors to geometric perturbations and their capacity to differentiate structurally similar configurations. The findings reveal a clear performance hierarchy, with higher-order descriptors offering increased geometric expressivity and reconstruction accuracy in resolving challenging structural cases. This research further examines methods for inverting spectral representations back to atomic coordinates, demonstrating that directly optimizing three-dimensional positions through gradient-based techniques yields markedly better reconstruction accuracy than approaches operating in Fourier space. Dimensionality reduction via latent space embeddings is also explored, showing that essential geometric features can be preserved in significantly compressed representations. Through methodical analysis of descriptor limitations, performance boundaries, and sensitivity to hyperparameters, this work establishes practical benchmarks and implementation guidelines for spectral descriptors. These contributions strengthen the foundation for reliable machine learning models in computational materials science, advancing both the accuracy and efficiency of atomic-scale modeling for materials design and discovery.

Design and Optimization of Shipping Container for Package-Less Units

2025-10-07T04:14:22Z

Design and Optimization of Shipping Container for Package-Less Units Minja, Baraka Package-less shipping aims to deliver units without company X’s added packaging. This requires the fulfillment systems and processes to have gentler handling. Part of this change involves the design and implementation of a container that will carry units from a distribution center to a delivery facility. This thesis presents the container analysis that was completed to determine what the optimal container features and container type are for package-less shipping. Collapsible bags provide the best solution for package-less shipping in comparison to nestable and collapsible totes. Since ergonomic weight is the limiting constraint, the lower weight of the collapsible bag will allow for 1 or 2 more units per container. In addition, it benefits from 1) lower process cost for returning to dock (3.7% cost reduction as compared to a nestable tote) 2) better ergonomics (collapsible tote has undesirable pinch points) and 3) improved cycle time (estimated 2s to open/collapse compared to 4s for collapsible tote). Additional considerations that require more analysis relate to units per container and relocation. Based on company X’s past orders and unit types for the package-less shipping process, it is estimated that ~210 units per container (17.08 cu. Ft.) is the max achievable for NA before it reaches the ergonomic weight cap. However, company X is expecting the package-less shipping distribution center process to be constrained to ~105-133 units. Analysis of container relocation from delivery facilities to distribution centers indicates it is worthwhile investigating alternative relocation strategies in lieu of dedicated 53-foot container trailers to achieve lower relocation costs. The collapsible bag is the best option assuming it has at least an expected lifetime of 2 years, which is when its NPV exceeds that of the two alternatives. These results are sensitive to assumptions made, and it is necessary to fine tune this analysis when the end-to-end package-less shipping process has been fully mapped out.

Transformation Tolerance of Facial Recognition Technology and Informative Evaluation Metrics

2025-10-07T04:14:08Z

Transformation Tolerance of Facial Recognition Technology and Informative Evaluation Metrics Nakamura, Haley Marie Over the last decade, machine learning based facial recognition (FR) systems have continued to increase in popularity while spreading to unique deployment settings. Despite the large variance among FR input distributions, popular facial recognition benchmarks continue to characterize system performance using one aggregate score over a single dataset. In many cases, the limitations of this score are unclear to downstream users: assuming benchmark accuracy is high, how is it expected to change for an image sampled from a distinct distribution? Which transformations can the model handle robustly, and which cause failure? Meanwhile, there is a large body of human facial perception research that aims to understand the underlying mechanisms of human recognition. This field offers methodological inspiration for more informative evaluation techniques, including the characterization of recognition performance as a function of a quantifiable input transformation. This work performs such an analysis. The performance scores of five state-of-the-art FR models are characterized as a function of Gaussian blur strength, intersecting with color variation. The performance-blur relationship is modeled as an s-curve, creating a highly interpretable format for discussion. Blur strength was consistently statistically significant to performance, but color variation did not significantly impact any model. Results are then compared to prior human recognition experiments. The best models outperform humans in low-blur regimes while humans outperform all models in high-blur regimes. These results motivate the need for modern benchmarks that capture a range of input distributions. The analysis presented can lead to a deeper understanding of FR systems, and provide a clearer interpretation of how model performance changes under quantified distribution shifts.

Design Transfer as a Lever for Accelerated Medical Device Innovation: A Case-Based Mapping Approach

2025-10-07T04:13:55Z

Design Transfer as a Lever for Accelerated Medical Device Innovation: A Case-Based Mapping Approach Magzoub, Amna Ahmed Eltayeb In highly regulated industries such as medical devices, accelerating New Product Development (NPD) without compromising quality or compliance is a persistent challenge. This thesis investigates the design transfer process, a critical, yet under- examined phase of NPD, as a strategic lever to reduce time-to-market. The project uses swimlane flowcharts and Design Structure Matrices (DSM) to map real-world processes, identify breakpoints, and classify rework (both planned and unplanned) in four case studies from Stryker Corporation. Key patterns emerged across case types: insufficient early-stage validation, misaligned cross-functional communication, and inadequate integration with suppliers were recurrent drivers of inefficiency. Compara- tive analysis revealed that concurrent engineering practices and knowledge sharing significantly reduce unplanned rework cycles and improve development speed. The study proposes actionable recommendations for optimizing design transfer including: leveraging corporate know-how through intentional knowledge transfer meetings dur- ing the process benchmarking process, increased risk-taking during the development process by embracing concurrent engineering approaches, and investing in early-stage co-development by adopting regular collaboration activities with suppliers. These findings can inform broader process improvements in the development of medical devices, and serve as a blueprint for other complex, cross-functional environments.

The Effect of the Solar Cycle on Satellite Orbital Lifetime

2025-10-07T04:15:13Z

The Effect of the Solar Cycle on Satellite Orbital Lifetime Lisy, Celvi A. The lifetime of a satellite in Low Earth Orbit (LEO) is affected by the 11-year solar cycle. At a fixed altitude, increasing solar activity increases atmospheric density which leads to an increase in drag, and a decrease in mission lifetime without using propulsion to recover altitude. Satellites may have longer orbital lifetimes if more of their mission is operational during a solar minimum due to lower solar activity and lower atmospheric drag. Satellites with larger area-to-mass ratios generally have shorter orbital lifetimes than satellites with small area-to-mass ratios. Missions that get delayed and have more of their operations during solar maximum than planned originally may have too short of a mission lifetime or, conversely, may be at risk of increasing their orbital lifetime past regulatory limits (five years for satellites in LEO according to the FCC) if they launch closer to solar minimum. For example, a satellite with an area-to-mass ratio of 0.014 m2/kg – such as a 1U CubeSat – and a one-year mission that is launched in 2021 without onboard propulsion, would have an orbital lifetime of 1.051 years. However, if that mission were delayed a year, a common occurrence in the industry, it would no longer be able to achieve its mission as its orbital lifetime with a deployment in 2022 is 0.44 years. Conversely, if the same 1U CubeSat is launched during solar max in January 2025, it would have an orbital lifetime of 2.2 years, and would re-enter in February of 2027. However, if that mission were delayed a year, the satellite would launch in January 2026 and instead be in orbit for 6.4 years before re-entering. They could be fined for violating the FCC deorbit limit of five years. This thesis quantifies the effect of launch or processing delays on satellite orbital lifetime based on their orbit altitude and vehicle parameters such as mass, cross sectional area, altitude, and bus size. In general, it is found that four-year and six-year delays have the greatest effect on a satellite’s orbital lifetime because the satellite will be deorbiting almost half a solar cycle (5.5 years) from its intended deployment year. However, two-year delays can still affect satellite operators, as they can increase the orbital lifetime, even by up to 1.5 years for low area-to-mass ratio satellites in 400 km orbits and almost five years for satellites in orbits higher than 500 km. Two-year delays can also decrease the orbital lifetime of a satellite by up to 1.7 years for low area-to-mass ratio satellites in 400 km orbits and almost two years at altitudes higher than 500 km.

Electrical Diagnostics for Nanosecond Pulsed Discharge Reactors

2025-11-24T15:39:35Z

Electrical Diagnostics for Nanosecond Pulsed Discharge Reactors Rao, Sankarsh R. This thesis provides an introduction to transmission line theory (telegrapher’s equations) as the mathematical background needed to correctly perform and interpret electrical measurements in nanosecond pulsed discharge reactors. The mathematical framework is implemented in a numerical tool called VI-View, which is made available to the community to aid with the interpretation of electrical measurements and help explain discrepancies between different experimental arrangements and probe configurations. A brief manual on how to use the tool is provided, followed by a series of six case studies relevant to experimental setups/situations encountered in practice. The analysis of these case studies summarizes best practices when performing electrical and energy measurements in nanosecond pulsed discharge reactors. Case Studies 1 and 2 cover in-situ and remote measurements for reactors using one voltage and one current probe. Case Study 3 covers how two current probes, one on the high-voltage end and one on the low-voltage end, can achieve the same energy measurements as Case Studies 1 and 2. Case Studies 4 and 5 show how cables with varying lengths and dissimilar properties — as can sometimes be encountered in practice — affect the electrical signals. Case Study 6 shows how a variable resistance — a step drop from 50MΩ to 10Ω — within a load can be a first approximation to a plasma reactor with a discharge. Finally, an outlook on how these case studies connect to real, experimental waveforms is presented along with the limitations of the tool.

Stochastic Methods for Setting Effective Aviation NOₓ Policies

2025-10-07T04:15:10Z

Stochastic Methods for Setting Effective Aviation NOₓ Policies Reider, Sarah Nitrogen Oxides (NOₓ) from aviation emissions are well known to have detrimental effects on air quality and the climate. Presently, they are regulated to preserve local air quality around airports. As part of the regulation process, aircraft engines are placed on a test stand with NOₓ levels measured at different thrust settings meant to mimic the aircraft’s emissions during landing and take-off. These are then constrained as a function of the engine’s overall pressure ratio (OPR) and rated thrust, with the allowed NOₓ emissions increasing with OPR. Despite increases in the stringency of this regulation, recent research suggests this regulation is insufficient for protecting surface air quality degradation from NOₓ emissions at cruise. Moreover, at high OPRs, NOₓ emissions increase substantially for relatively small reductions in fuel burn. In light of this, a new metric representative of cruise emissions is being investigated. This work considers effective methods to define this new regulation given a wide range of uncertainties in the tradeoff between NOₓ and CO₂ emissions at high OPRs. First, an estimate for the combined climate and air quality cost of NOₓ from aviation cruise emissions is estimated as ∼$95,000/tonne using a 2019 flight inventory. Then, cruise limits are proposed informed by the combined impact of NOₓ and CO₂ at cruise and with a similar slope to the current LTO standard. Finally, a Monte Carlo simulation is run, sampling NOₓ and CO₂ social costs for a series of hypothetical aircraft designed using the open-source Transportation Aircraft System OPTimization (TASOPT) model. This work takes a worst-case scenario approach, where the only response engine manufacturers can make to stricter standards is to reduce OPR and sacrifice fuel efficiency. Each aircraft’s emissions are evaluated during cruise to determine the probability of increasing environmental harm under different policy scenarios given these uncertainties. The combined cost of NOₓ and CO₂ are compared to the baseline engines that meet current regulations for each scenario. Results show defining a cruise metric informed by the weighted combined cost of CO₂ and NOₓ could reduce total environmental cost at cruise by 15 – 43% while carrying a 6 – 7.4% risk of increasing total environmental cost for wide-body aircraft engines in the most stringent scenario. Less stringent scenarios showed similar risks of increasing harm for smaller potential environmental savings. In all cases, the risks associated with the proposed limits are driven by low-likelihood extremes in the uncertainty distributions of NOₓ and CO₂, further suggesting the benefit of an environmentally conscious standard.

Domain-Independent Mode Estimation for Human-Robot Collaboration

2025-10-07T04:15:08Z

Domain-Independent Mode Estimation for Human-Robot Collaboration Gomez, Annabel Reyna To collaborate safely and intelligently with humans, robots must infer high-level semantic sates, such as intentions or interaction modes, from uncertain sensor input. While dynamic, probabilistic mode estimation is commonly used in fault diagnosis, this thesis extends the problem to activity recognition, where the goal is to estimate qualitative, symbolic human-object interaction states in real time. Robust human activity recognition is essential for collaborative and assistive robotics, particularly in dynamic or safety-critical environments. The core solution presented in this thesis is a mode-estimator and its efficient implementation using the A* with bounding conflicts (A*BC) algorithm. This performs best-first enumeration over symbolic activity states while integrating recursive Bayesian filtering to maintain belief under noisy observations. Unlike low-level trajectory tracking or deep-learned classifiers, qualitative spatial filtering operates at the right level of abstraction to recognize symbolic actions. It can also generalize across domains with minimal retraining and support efficient, probabilistically grounded reasoning about uncertainty in both perception and symbolic mode transitions. The proposed system fuses RGB-D perception, object segmentation, qualitative spatial reasoning (QSR), and probabilistic inference into a real-time pipeline capable of tracking and inferring symbolic human-object interaction states. Evaluated in a human-robot rehabilitation setting, this domain-independent system successfully infers latent human and object activity states from noise RGB-D data. It resolves ambiguity using Vision-Language Model (VLM)-guided semantic arbitration and demonstrates robustness and adaptability in unstructured environments. This work establishes qualitative spatial filtering with A*BC as a generalizable and efficient solution for semantic activity recognition, laying the foundation for future perception-driven collaborative systems.

Distributed Energy Dynamics Control for Stable Power Electronic-Enabled Electric Power Systems

2025-10-07T04:15:06Z

Distributed Energy Dynamics Control for Stable Power Electronic-Enabled Electric Power Systems Gada, Hiya Akhil The increasing penetration of renewable and inverter-based resources is transforming modern power systems into fast, nonlinear, and heterogeneous networks. These converterdominated systems operate on timescales much faster than traditional synchronous machines, making conventional modeling and control approaches, rooted in quasi-static phasor analysis and centralized architectures, inadequate for ensuring stability and scalability. This thesis adopts an energy space modeling approach grounded in first principles of energy conservation and system interconnection. It extends the previously introduced second-order energy dynamics model by relaxing the assumption that energy in tangent space can be treated as an independent disturbance. The resulting contribution is a third-order model that treats stored energy in tangent space as a dynamic state, enabling more expressive and accurate modeling of fast-timescale system behavior. Leveraging this extended energy space model, the thesis develops a multilayered distributed control architecture in which the nonlinear physical dynamics of each component are lifted to the higher-level linear energy space, capturing internal energy dynamics and real/reactive power flows, and integrated with the lower-level physical dynamics with well-defined mappings. Distributed controllers are designed in this energy space using only local states and minimal neighbor interaction, assuming a system-level coordination mechanism provides consistent references. Two control strategies, energy-based feedback linearizing control and sliding mode control, are developed and shown to achieve asymptotic convergence to reference outputs. The framework is validated on two systems: an inverter-controlled RLC circuit and a synchronous generator under load. Finally, the energy space framework is extended to structurally model inter-area oscillations (IAOs). An inter-area variable is defined as the difference between power incident on a tie-line from Area I and power reflected into tie-line from Area II. Simulations on a 3-bus, 2-area system confirm consistency with eigenmode analysis and show how tie-line strength and generator inertia affect IAO dynamics. A novel resonance phenomenon is also identified: instability arising from interaction between a system’s natural IAO frequency and time-varying disturbances from intermittent DERs. This previously unmodeled behavior is captured explicitly within the energy dynamics framework and may help explain recent blackout events in the Iberian Peninsula.

Pushing the Limits of Active Data Selection with Gradient Matching

2026-01-23T15:40:45Z

Pushing the Limits of Active Data Selection with Gradient Matching Zhang, Chris As modern machine learning systems grow in scale, the inefficiencies of training on large, noisy, and imbalanced datasets have become increasingly pronounced—particularly in computer vision, where real-world data often contain labeling errors, occlusions, and redundancy. While large models can partially compensate by training exhaustively on massive datasets, this indiscriminate approach is computationally expensive and often inefficient. Active data selection offers a more efficient alternative by prioritizing examples that contribute most to model improvement. However, existing selection strategies (such as Rho Loss) still fall short of the optimal achievable performance. In this work, we propose the Gradient Informed Selection Technique (GIST), an active data selection method that prioritizes examples based on their gradient alignment with a small, fixed holdout set. At each training step, GIST computes perexample gradients and selects those that are most aligned with the holdout gradient, thereby guiding model updates toward better generalization. We evaluate GIST on noisy (Clothing1M) and clean (ImageNet) datasets and show that it consistently outperforms baselines across a range of selection ratios—that is, the proportion of a batch of data that the model selects to update weights on. To address the computational overhead of gradient-based selection, we introduce efficient variants using restricted-layer gradients, low-rank approximations, and gradient quantization. We also analyze GIST’s selection behavior, showing that it implicitly balances classes and repeatedly selects high-utility examples—two factors that enhance both robustness and learning efficiency. Our findings suggest that a more effective data curriculum is both discoverable and practical, and that GIST is a step toward achieving it.

The Phase Transition for Recovering a Random Hypergraph from its Edge Data

2025-10-07T04:14:48Z

The Phase Transition for Recovering a Random Hypergraph from its Edge Data Yao, Andrew The weighted projection of a hypergraph is the weighted undirected graph with the same vertex set and edge weight equal to the number of hyperedges that contain the edge; the projection is the unweighted graph with the same vertex set and edge set consisting of edges with weight at least one. For d ≥ 3, after observing the unweighted and weighted projection of a random d-uniform hypergraph that is sampled using a generalization of the Erdős–Rényi random model, we study the recovery of a fraction of the hyperedges and the entire hypergraph. For both cases, we show that there is a sharp phase transition in the feasibility of recovery based on the density of the hypergraph, with recovery possible only when the hypergraph is sufficiently sparse. Particularly, we resolve numerous conjectures from [5]. Furthermore, we display an efficient algorithm that is optimal for both exact and partial recovery. We also analyze the phase transition for exact recovery by exhibiting a regime of probabilities that is below the exact recovery threshold by a polylogarithmic factor for which exact recovery is possible.

Empowering Mobile-Only App Generation — Offline AI Code Generation with App Inventor

2025-10-07T04:14:16Z

Empowering Mobile-Only App Generation — Offline AI Code Generation with App Inventor Yuan, Joyce As digital tools become more accessible, creating software is becoming a powerful way for anyone to make real-world impact. Computational action—the idea that learners can build computing artifacts with authentic relevance to their lives and communities—reframes computing as a tool for empowerment. Low-code platforms like MIT App Inventor support this vision by fostering digital agency through purposeful creation. Recent advances in large language models (LLMs) expand these possibilities further by enabling code generation from natural language, offering a timely opportunity to lower the barrier to app creation. MIT App Inventor has long championed accessibility, allowing even young learners in underserved regions to build meaningful mobile apps. Its natural language tool, Aptly, enables users to describe app ideas and generate functional code. However, Aptly’s reliance on cloud-based LLMs limits access for users without stable internet—often those who could benefit most. This thesis addresses that challenge by enabling AI-powered app creation to run entirely offline on mobile devices. We fine-tune and quantize LLaMA 3B using QLoRA and deploy it on iOS with MLC LLM, enabling on-device inference without internet. We also introduce a custom evaluation framework tailored to Aptly’s grammar, combining a Tree-sitter parser and a modified CodeBLEU metric to assess both semantic and syntactic quality. Using curated evaluation datasets, we benchmark out-of-box and fine-tuned models across prompting strategies. In our evaluations, fine-tuned GPT-4.1 achieved the highest normalized CodeBLEU score (0.36 ± 0.12) and parsed over 81% of completions, outperforming its baseline by more than 5%. QLoRA-finetuned LLaMA improved parseability by 11.7% over its base model, showing progress in adapting smaller models to the Aptly domain, though semantic fidelity remains a challenge. Our results show that offline natural language–to–app generation is feasible, and that smaller models can be adapted to the Aptly domain. By lowering the technical and infrastructural barriers to app creation, this work lays the foundation to empower AI-assisted programming that is accessible, offline, and on the phone.

AutoDiff: A Scalable Framework for Automated Model Comparison

2025-10-07T04:15:09Z

AutoDiff: A Scalable Framework for Automated Model Comparison Woo, Andrew Kyoungwan Post-training adaptations such as supervised fine-tuning, quantization, and reinforcement learning can cause large language models (LLMs) with identical architectures to exhibit divergent behaviors. However, the mechanisms driving these behavioral shifts remain largely opaque, limiting the reliability and interpretability of adapted models. AutoDiff is a scalable, automated framework for tracing model divergence on a per-neuron basis. It exhaustively profiles every feed-forward (MLP) unit across a pair of models, identifies the neurons with the largest activation gaps, and links these differences to downstream behavioral changes. The pipeline identifies exemplars that maximize between-model activation divergence and clusters the highest-gap neurons into an interpretable, queryable difference report. Proof-ofconcept experiments on GPT-2 small validate AutoDiff’s ability to rediscover synthetic perturbations without manual supervision. A larger case study on Llama3.1–8B contrasts the base model with several adapted variants, surfacing neurons whose behavioral shifts align with observed topic-level gains and losses. By uncovering these mechanistic divergences, AutoDiff transforms black-box model updates into actionable insights, enabling safer deployment, principled debugging, and interpretable model evaluation.

Schrödinger’s Carbon: Until Measured, Operational Emissions Remain Uncertain

2025-10-07T04:15:07Z

Schrödinger’s Carbon: Until Measured, Operational Emissions Remain Uncertain Xia, Julia Rapidly improving generative artificial intelligence has led to significant investments in datacenter infrastructure, driving power demand, and raising environmental concerns. This has led to a growing body of research towards modeling embodied and operational carbon of datacenter servers across a variety of paradigms. However, most existing models take in deterministic inputs and output a singular average value that does not capture the inherent variability in estimating embodied and operational carbon emissions. Further, these average outputs obscure the impact of interacting factors, such as those related to deployment or software characteristics; each of which has its own underlying uncertainty distribution. This means in most cases, these averages do not accurately represent a particular server’s context. This thesis explicitly parameterizes and quantifies the full probabilistic distribution of operational carbon in AI inference tasks. It explores several factors of variability— deployment, spatiotemporal, and computational profile— and quantifies their impact on the overall carbon footprint through statistical and sensitivity analysis. While this work focuses on operational carbon, uncertainty propagation and understanding of variability should be used across a datacenter server’s entire life cycle. When this methodology is used alongside the existing uncertainty-aware embodied carbon measurements, it enables a holistic assessment from cradle to grave. This facilitates informed decision-making in server replacement, workload scheduling, hardware procurement, capacity planning, and more scenarios.

Ideator Explorer: Enhancing AI-Assisted Ideation through Interactive Visualization

2025-10-07T04:15:02Z

Ideator Explorer: Enhancing AI-Assisted Ideation through Interactive Visualization Wen, Haoran Current AI-assisted ideation systems, often based on linear chat interfaces, struggle to help users effectively manage the complexity of creative exploration, hindering both divergent thinking across multiple paths and the convergent synthesis of ideas. This thesis introduces and evaluates Ideator Explorer, a human-AI ideation system built upon an interactive graph visualization interface designed to overcome these limitations. The core of the system is its spatial, tree-like representation of branching idea sequences. Formative user studies indicate that this visualization approach is preferred over chat interfaces for its organizational benefits and its effectiveness in helping users track parallel lines of thought during exploration. The spatial layout inherently supports both the exploration of diverse idea branches (divergence) and the identification of potential connections (convergence). This research focuses on the design and evaluation of this interactive graph interface, examining how its specific visualization and interaction techniques impact the user’s ability to navigate, organize, and develop ideas within complex ideation processes. The primary contribution is a novel, visually driven interface paradigm for human-AI collaboration that enhances the management and exploration of the creative solution space.

Type Checker for Annotated Assembly Programs

2025-10-07T04:14:57Z

Type Checker for Annotated Assembly Programs Zanders, Julian The rise of speculative-execution attacks, such as Spectre, has presented a security challenge to developers. Speculation on secret data can expose it, but running without speculation is suboptimal for runtime. To fix this, researchers have been evaluating “smart” speculation schemes, which determine when to speculate and when not to in order to balance runtime with security. Our lab proposes Octal, a solution that utilizes software and hardware in tandem. Data values are marked as secret or public using type inference, and the veracity of inference is checked using a type checker. Then, hardware can separate the secret and public values. My contributions were to the type checker, as well as some scripting to evaluate results.

Real-Time Non-Line-of-Sight Imaging Using Single-Photon LiDAR

2025-10-07T04:15:00Z

Real-Time Non-Line-of-Sight Imaging Using Single-Photon LiDAR Tsao, Nicholas Robust real-time imaging systems have allowed for many advances in robotics and autonomous navigation, though limited visibility in many real-world settings remains a significant challenge. Non-Line-of-Sight (NLOS) sensing allows for imaging systems to “see around corners", expanding their range of perception, providing access information for realtime decision-making. A promising approach to NLOS sensing is through single-photon LiDAR, which is commonly used for range-finding in many imaging systems. In addition to range-finding, single-photon LiDAR systems can provide a deeply rich data source in the form of photon count histograms after reflecting off scene geometry, capturing detailed information from multiple bounces. NLOS imaging can be achieved by parsing third-bounce light from such single-photon LiDAR sensors, which can be used for a variety of detection and localization tasks, and recent work has demonstrated capabilities in a wide range of applications. This work aims to further develop the NLOS imaging system by demonstrating a fully functional NLOS system using low-cost, consumer-grade SPAD hardware for real-time NLOS imaging, detection, and localization. We lay the ground work for NLOS imaging systems by developing infrastructure for NLOS processing in real-time, and we examine the potential for NLOS systems to operate on cheap hardware using data-driven approaches. Our work implements and demonstrates full end-to-end capacity for these NLOS imaging systems in a number of applications including person detection and localization, facilitating future research in this field and paving the way for NLOS integration into consumer devices.

Automated Finetuning via Sparse Autoencoders

2025-10-07T04:14:51Z

Automated Finetuning via Sparse Autoencoders Sivakumar, Ragulan Currently, the field of interpretability is traditionally confined to diagnostics. However, this thesis presents a novel method using interpretability in sparse autoencoders to achieve better performance in small models via instruction finetuning. Specifically, we present UnderstandTune, an autonomous method for assembling high-quality instruction finetuning datasets with minimal human intervention, requiring only concise task descriptions rather than evaluation dataset distributions. Our empirical evaluations show that UnderstandTune consistently outperforms uninformed finetuning baselines across multiple benchmarks. Complementing this, Lalon introduces a mixture-of-informed-experts (MoIE) architecture that routes queries to specialized models independently finetuned via UnderstandTune. This modular approach achieves competitive performance against larger monolithic models in specialized domains, while utilizing fewer parameters, training examples, and computational resources. The framework’s modularity enables independent optimization of components from sparse autoencoders to MoIE routing mechanisms. This research demonstrates how interpretability can be used to enhance performance through intelligent data curation and suggests a new paradigm where interpretability and efficiency reinforce each other toward more capable, resource-efficient AI systems.

Generative Machine Learning Models for RNA Structure Prediction and Design

2025-10-07T04:14:44Z

Generative Machine Learning Models for RNA Structure Prediction and Design Rubin, Dana Ribonucleic acid (RNA) is a fundamental molecule in biology, central to the regulation and execution of life’s most essential processes. Its diverse roles range from encoding genetic information to catalyzing biochemical reactions. Beyond its modern biological functions, RNA is also believed to have played a pivotal role in the origins of life which underscores the evolutionary significance of RNA. Unlocking the full potential of RNA research and design requires a deep understanding of the intricate relationship between RNA’s three-dimensional structure and sequence. Predicting RNA 3D structures remains a challenging problem due to the complexity of its folding landscape and the limited availability of high-resolution structural data. Inspired by recent advances in deep learning for protein folding and design, this thesis explores novel geometric and generative architectures for modeling RNA. We first present a systematic study on RNA structure prediction using equivariant neural networks within diffusion probabilistic models (DDPMs). Our folding model, named Klotho, captures local atomic interactions and structural features using SO(3)-equivariant message passing layers with a point cloud data representation. Ablation studies confirm that Klotho’s model performance scales with higher dimensionality and improves with enriching the input with secondary structure information and sequence embeddings from RNA foundation models. Building on this foundation, we introduce RiboGen, a multi modal deep learning model to jointly generate both RNA sequence and all-atom 3D structure. RiboGen integrates Flow Matching and Discrete Flow Matching within a unified multi modal representation and employs Euclidean Equivariant Neural Networks to learn geometric features. Our results demonstrate that RiboGen can generate chemically plausible, self-consistent RNA molecules, highlighting the potential of co-generative models to explore the sequence–structure landscape of RNA in a unified, data-driven framework. Together, these contributions advance the field of RNA modeling by offering scalable, symmetry-aware architectures for prediction and design. They lay the groundwork for future generative systems in RNA biology, therapeutic development, and biotechnological innovations.

Enhancing a Data-Centric Framework for Predictive Maintenance of Wind Turbines

2026-01-23T15:35:00Z

Enhancing a Data-Centric Framework for Predictive Maintenance of Wind Turbines Pan, Raymond Predictive maintenance of wind turbines is a machine learning task aimed at minimizing repair costs and improving efficiency in the wind turbine and renewable energy industry. Existing machine learning solutions often fail to meet real-world deployment requirements due to fragmented pipelines, lack of domain integration, and reliance on black-box models. Zephyr, a data-centric machine learning framework, addresses these challenges by enabling Subject Matter Experts (SMEs) to incorporate their domain knowledge into the prediction process, and to leverage automated tools for labeling, feature engineering, and prediction tasks without requiring extensive technical knowledge. However, the current version of Zephyr still has limitations, including usability gaps and a reliance on external tools for certain steps. Case studies with real-world data from the renewable energy company Iberdrola demonstrate Zephyr’s potential to integrate domain expertise into wind turbine predictive maintenance (thus streamlining the process) but also expose a sub-optimal user experience. This thesis explores gaps in the current state of the Zephyr framework and proposes refinements to enhance its usability. Key improvements include the consolidation of current tooling and relevant external libraries into a single API, state management with careful logging and exception handling, and improved support for model evaluation. These enhancements aim to support seamless end-to-end predictive modeling workflows, and to provide a more refined and flexible user experience for the Zephyr user base.

Minimalist Approach to End-to-End Vision Language Navigation with Multi-Modal Foundation Model Features

2025-10-07T04:14:33Z

Minimalist Approach to End-to-End Vision Language Navigation with Multi-Modal Foundation Model Features Mishra, Kartikesh Recent vision-language navigation (VLN) approaches leverage large models, prompt engineering, and/or explicit reasoning for instruction interpretation and agent guidance. We introduce MiniNav, a minimalist framework employing frozen vision-language foundation models as patch-wise feature extractors, avoiding data and compute heavy fine-tuning and cumbersome language model reasoning. Our lightweight control policies (∼ 10⁵ trainable parameters) are trained on a compact dataset of language-based specified navigational behaviors (∼ 10² runs, ∼ 10⁴ frames per behavior). We demonstrate generalization to novel objects and scenes, including direct real-world transfer, despite training on only two objects in a single simulated environment. Through its simple and scalable design, MiniNav provides an alternative to computationally intensive pipelines for robust real-world instruction-following. Our solution can provide a reference for evaluating the effective edge of more complex and larger VLN policies.

Strategic Sampling: A Framework for Enhancing Speed and Performance of Financial Fraud Detection Models

2025-10-07T04:14:01Z

Strategic Sampling: A Framework for Enhancing Speed and Performance of Financial Fraud Detection Models Mitchell, Samuel Financial fraud detection is a high-stakes field where rapid inference is essential. While state-of-the-art fraud detection models vary in terms of architectural decisions and appear to exhibit unique computational bottlenecks, we highlight that their run-times are all dominated by extensive information-gathering steps. These steps involve aggregating information from a large set of nodes or edges within a graph, and these intensive steps are performed O(|V |) or O(|E|) times during an inference forward pass, on a graph with |V | nodes and |E| edges. We introduce Strategic Sampling, a general method to accelerate these information-gathering steps. Our approach tailors sampling strategies based on the specific objective function used in each model’s information-gathering process, selecting the most relevant pieces of information to use in each step. This ensures that critical information is retained while significantly reducing the amount of data processed, thus speeding up the computation. We conceptually demonstrate how Strategic Sampling can be applied to message-passing Graph Neural Networks, Graph Transformers, and TGEditor (a state-of-the-art graph editing algorithm). To showcase the effectiveness of our proposed Strategic Sampling method, we implement it in the TGEditor codebase. Our results show that Strategic Sampling not only significantly reduces computation time by more than an order of magnitude, but also improves the F1 score, enhancing both efficiency and performance. This study underscores the potential of Strategic Sampling to universally boost the performance of various financial fraud detection models, paving the way for faster and more accurate fraud detection.

Optimization Techniques for Trustworthy 3D Object Understanding

2025-10-07T04:14:29Z

Optimization Techniques for Trustworthy 3D Object Understanding Shaikewitz, Lorenzo Franceschini Autonomous machines require reliable 3D object understanding to interpret and interact with their environment. In this thesis, we consider two tightly coupled 3D object understanding problems. Shape estimation seeks a consistent 3D model of an object given sensor data and some set of priors. Pose estimation seeks an estimate of the object’s position and orientation relative to an invariant shape frame. In general, these problems are non-convex and thus difficult to solve. We present algorithms which nonetheless solve shape and pose estimation efficiently and with assurances in terms of of optimality, uncertainty, or latency. We begin in the multi-frame tracking setting, where we propose the certifiably optimal estimator CAST⋆ for simultaneous shape estimation and object tracking. CAST⋆ uses 3D keypoint measurements extracted from an RGB-D image sequence and phrases the estimation as fixed-lag smoothing. Temporal constraints enforce rigidity and continuous motion. Despite the non-convexity of this problem, we solve it to certifiable optimality using a smallsize semidefinite relaxation. We also present a compatibility-based outlier rejection scheme to handle outliers, and evaluate the proposed approach on synthetic and real data. Next, we focus on estimating the pose of an object given its shape and a single RGB image (no depth). Assuming only bounded noise on 2D keypoint measurements (e.g., from conformal prediction), we derive an estimator for the most likely object pose which uses a semidefinite relaxation to initialize a local solver. We pair this with an efficient uncertainty estimation routine which relies on a generalization of the S-Lemma to propagate keypoint uncertainty to high-probability translation and rotation bounds. The high-probability bounds hold regardless of the accuracy of the pose estimate, and are reasonably tight when tested on the LineMOD-Occluded dataset. Lastly, we propose a sub-millisecond solution to simultaneous estimation of object shape and pose from a single RGB-D image. Our approach converts the first-order optimality conditions of the non-convex optimization problem to a nonlinear eigenproblem in the quaternion representation of orientation. We use self-consistent field iteration to efficiently arrive at a local stationary point, finding solutions more than an order of magnitude faster than Gauss-Newton or on-manifold local solvers on synthetically generated data.

Joint Localization and Synchronization via User Cooperation in Non-Terrestrial Networks

2025-10-07T04:12:36Z

Joint Localization and Synchronization via User Cooperation in Non-Terrestrial Networks Morrison, James C. Next-generation (xG) wireless networks require accurate localization and synchronization for efficient resource management and emerging applications. Non-terrestrial networks (NTN) with low Earth orbit (LEO) satellites offer a promising alternative for positioning, navigation, and timing (PNT) by providing diversity and increasing the signal-to-noise ratio (SNR) over global navigation satellite systems (GNSS). However, the primary challenge in NTNbased localization with LEO satellites is the lack of precise clock synchronization, which introduces biases in time-of-arrival (TOA) measurements and limits localization accuracy. This paper introduces a joint cooperative localization and synchronization (JCLS) framework that addresses this challenge through spatiotemporal cooperation, soft information, and simultaneous synchronization. Furthermore, we propose a three-step algorithm for performing JCLS. The first step calculates a coarse position estimate using TOA measurements and the Gauss-Newton method. Then, this coarse estimate is updated using the LevenbergMarquardt method which performs joint localization and synchronization. Finally, we derive a soft information-based filter that is used to continuously refine the position and clock error estimates as new measurements are available. We characterize the fundamental performance limits of JCLS using Fisher information, which offers insight into its localization and synchronization accuracy bounds. Furthermore, simulation results based on TOA measurements of the 3rd Generation Partnership Project (3GPP) 5G New Radio positioning reference signal (PRS) demonstrate that the proposed algorithm for JCLS significantly improves localization and synchronization accuracy compared to non-cooperative methods.

Multipartite Quantum Clock Synchronization Via Collective Symmetric States

2026-01-16T19:10:16Z

Multipartite Quantum Clock Synchronization Via Collective Symmetric States Keskin, Ufuk This thesis investigates multipartite quantum clock synchronization (QCS) tasks using a class of quantum states, called collective symmetric (CS) states, which generalize Dicke and N00N states. Employment of CS states in previous QCS procedures is shown to improve synchronization performance in various network scenarios. The focus of the paper is on QCS procedures that, after the distribution of quantum states, rely exclusively on local operations and classical communication (LOCC), ensuring compatibility with highly noisy quantum channels. Two synchronization scenarios are considered: (i) synchronization between the two nodes of an arbitrarily chosen pair of nodes, and (ii) global synchronization where all nodes wish to synchronize their clocks to a common average time. First, a framework in which the previous procedures operate employing the CS states is introduced. Using such framework, possible limitations of the QCS procedures in terms of estimation ambiguity and lack of robustness are pointed out. Second, a procedure referred to as the tactical delay procedure (TDP) is proposed for each of the two synchronization scenarios. The TDP resolves the mentioned limitations and outperforms the state-of-the-art multi-partite QCS procedures in terms of synchronization precision without requiring additional quantum resources.

Accelerating Embedded HOWFSC Algorithms

2025-10-07T04:14:36Z

Accelerating Embedded HOWFSC Algorithms Eickert, Brandon The quest to directly image planets of other solar systems demands not only state-of-the- art coronagraphs, but also places extreme performance demands on space-based processors. Direct imaging requires precise wavefront control to acquire the 1010 contrast necessary to reveal a dim, Earth-like exoplanet. This precise level of control is only possible if high-order wavefront sensing and control (HOWFSC) algorithms are executed with enough speed to offset wavefront error accumulation. Of the many aspects that make high-contrast imaging difficult, a central bottleneck is the speed at which we can run these algorithms. At the center of this work, we aim to accelerate the execution of two foundational HOWFSC algorithms: optical modeling and Electric Field Conjugation (EFC). Optical modeling underpins both Jacobian-based EFC, and a relatively new variant of EFC, called adjoint-based EFC. The two main contributions of this thesis are to port bottleneck HOWFSC algorithms to the relevant computing environments, and quantify speedups attained by both algorithm choice and implementation optimization. This work explores the acceleration of optical modeling for a vector vortex coronagraph through the use of the FFTW library, and the acceleration of EFC by implementing adjoint-based EFC in an embedded context. We utilize functional analogs to radiation-hardened processors, using the NXP T1040 in place of the BAE RAD5545, and the NXP LS1046 in place of the LS1046-Space. We find that the FFTW library enabled a factor of six speedup for 4096 × 4096 fast Fourier transforms (FFTs), and a factor of five for 2048 × 2048 FFTs. With these significant speedups, the bottleneck within the vortex operations of the optical model shifts from the FFT to matrix multiplication. We additionally time the execution of the underlying routines of Jacobian-based EFC and AD-EFC to estimate that AD-EFC is 46 times faster than Jacobian-based EFC. Despite these speedups, AD-EFC is still a factor of 124 away from 100-second latency for our specific optical model. These results demonstrate that one to two orders of magnitude of speedup must be attained by either further optimizing algorithm implementations, or exploring other parallelization strategies, computing architectures, and mission paradigms to achieve a latency on the order of 100 seconds.

Formalizing Causal Models Through the Semantics of Conditional Independence

2026-01-21T18:53:55Z

Formalizing Causal Models Through the Semantics of Conditional Independence Zhang, Anna Many foundational tools in causal inference are based on graphical structure and can involve complex conditions that obscure the underlying causal logic. Given the inherent complexity and subtlety of cause-and-effect phenomena, establishing formal guarantees about these tools is both challenging and important. This thesis presents a semantics-driven formalization of causal models within the Coq proof assistant, enabling precise, mechanized reasoning about causal relationships. Central to this work is a new function-based definition of conditional independence, which captures how changes propagate through a causal graph. We prove that this semantic notion is equivalent to the standard graphical criterion of d-separation, thereby establishing a rigorous bridge between structural and semantic interpretations of independence. The formalization includes a library of graph-theoretic and causal-reasoning tools, encompassing key concepts such as mediators, confounders, and colliders. By linking the syntactic and semantic perspectives on causality, this work lays a robust foundation for formally verifying causal assumptions and guiding experimental design.

Contextual Knowledge Sharing in Multi-Agent Long-Horizon Planning Settings with Centralized Communication and Coordination

2025-10-07T04:14:31Z

Contextual Knowledge Sharing in Multi-Agent Long-Horizon Planning Settings with Centralized Communication and Coordination Zhang, Jackson Embodied multi-agent systems, comprising autonomous agents interacting within shared environments, enable intelligent, collaborative solutions for tasks requiring real-time coordination and adaptability. While applications span diverse fields, from disaster response to healthcare, planning in these systems remains challenging due to partial egocentric observations and limited environmental awareness. This work addresses these challenges by introducing a software module that synthesizes a shared world state from individual agent views, maintaining spatial information about objects and agents to support more effective joint action planning. Integrated into the LLAMAR framework, this module aims to improve planning accuracy and efficiency. The proposed approach is evaluated using metrics such as success rate, transport efficiency, and coverage performance. Our evaluation demonstrates that utilizing a perfect (oracle-generated) world state significantly enhances planning effectiveness. Notably, under these ideal conditions, the success rate of the LLAMAR planner improved by over 16%. These findings underscore the critical impact of accurate world state representation on multi-agent performance and highlight the potential for significant advancements in collaborative task execution in dynamic, unstructured settings.

Parametric Study of Novel Passive Thermal Control Technology for Spacecraft

2025-10-07T04:14:23Z

Parametric Study of Novel Passive Thermal Control Technology for Spacecraft Shafer, Emma Thermochromic variable emissivity materials (VEMs) are a relatively new passive thermal control technology used for spacecraft radiators. VEMs passively change their emissivity based on their temperature, with VEMs having low emissivity at low temperatures and high emissivity at high temperatures. This property of VEMs allows for spacecraft to have reduced heater power and less extreme temperature swings without adding active thermal control systems. There is a potential for VEM technology to become more widely used in spacecraft radiators. Because thermochromic VEMs are still a relatively new technology, there has not yet been a study with a parametric sweep of some possible VEM profiles and common spacecraft parameters to determine the best-case uses of particular VEM profiles. This thesis models a single-node spacecraft in an equatorial low Earth orbit, varying the spacecraft’s shape, surface area, and thermal mass using Thermal Desktop. The temperature history of the spacecraft in orbit, particularly its orbit minimum temperature, orbit maximum temperature, orbit average temperature, and orbit temperature range, is recorded, and twelve VEM profiles are compared against default black and white paint materials to see how the twelve VEM profiles change orbit minimum temperature, maximum temperature, average temperature, and temperature range. The desired outcome is for the VEMs to reduce the temperature range the most compared to black or white paint while keeping temperatures within typical temperature requirements for spacecraft components. It is found that, compared to white paint, VEMs always increase the orbit minimum temperature, maximum temperature, average temperature, and temperature range across all nodal thermal masses and surface areas studied. For spacecraft with lower surface areas, having only white paint decreases the temperature too much for typical spacecraft components, so even though white paint always decreases temperature range compared to VEMs, it is recommended to have VEMs instead of white paint for lower surface area spacecraft due to VEMs being better than white paint at keeping components within typical temperature requirements. When VEMs are compared to black paint, it is found that black paint has lower minimum temperatures and greater maximum temperatures than all VEMs at greater surface areas. For lesser surface areas, the node covered in black typically has minimum and maximum temperatures in the middle of the VEMs’ minimum and maximum temperatures. For all surface areas and thermal masses, the average temperature of the black node is typically in the middle of the average temperatures of the nodes with VEMs; in relation to the VEMs’ average temperatures, the black average temperature decreases as node height increases. For all node heights and thermal masses, VEMs always decrease the temperature range compared to black. VEMs are shown to be better than black paint in having spacecraft components stay within typical temperature requirements, and which VEM to choose depends on what the specific spacecraft component is and its specific temperature requirements. The biggest difference in individual VEM profiles compared to each other is the orbit average temperature; the lower the VEM’s transition temperature, the lower the average temperature. Only at the greatest nodal surface areas and smallest nodal heights is there a significant difference in temperature range between individual VEM profiles; typically, the lower the transition temperature of the VEM, the less its temperature range. Future work includes expanding on the parameters studied and studying spacecraft in different orbits, different spacecraft shapes, and different VEM profiles.

Path Planning for Autonomous Sailing Vessels: Developing Robust and Efficient Survey Strategies

2026-01-05T16:06:49Z

Path Planning for Autonomous Sailing Vessels: Developing Robust and Efficient Survey Strategies Ahlers, Matthew C. Autonomous sailing vessels offer a promising solution for maritime research, offering low maintenance and sustainable platforms for environmental monitoring and data collection. These vessels utilize wind power, eliminating the need for conventional fuel and enabling long-duration operations with minimal environmental impact. Their applications range from oceanographic studies to maritime surveillance, where persistent and autonomous data collection is essential. This thesis explores the challenges and methodologies associated with path planning for autonomous sailing, particularly in the context of survey operations. Unlike traditional motorized vessels, sailing autonomy must account for wind variability, sail dynamics, and limited maneuverability, requiring specialized path-planning techniques to ensure efficient and reliable navigation. The research investigates various sail and hull configurations, the dynamics of windpowered propulsion, and the application of autonomy frameworks such as MOOS-IvP. A key focus is on optimizing continuous coverage path planning (CPP) to maximize efficiency while adapting to environmental constraints. By integrating real-time wind data and vessel performance characteristics, the study refines survey strategies that enhance mission effectiveness. Different survey strategies are implemented and evaluated using both simulation and real-world testing on the Charles River. These trials demonstrate the feasibility of fixed-path decomposition approaches and adaptive moving horizon control methods, evaluating methods with the impact of wind conditions on autonomous sailing performance. The results contribute to the development of robust and efficient survey strategies that improve the autonomy and reliability of wind-powered marine vessels.

Reliable and Generalizable Real-World Planning with LLM-based Formalized Programming

2025-10-07T04:13:23Z

Reliable and Generalizable Real-World Planning with LLM-based Formalized Programming Hao, Yilun While large language models (LLMs) have recently demonstrated strong potential in solving planning problems, LLMs, as zero-shot planners themselves, are still not capable of directly generating valid plans for complex planning problems such as multi-constraint or long-horizon tasks. This motivates the needs to develop a robust and reliable planning system for complex real-world planning problems. Furthermore, many frameworks aiming to solve complex planning problems often rely on task-specific preparatory efforts, such as task-specific in-context examples and pre-defined critics or verifiers, which limits their cross-task generalization capability. This motivates the needs to extend the robust and reliable planning systems to have strong generalization capability. In this thesis, we first develop an LLM-based planning framework that formalizes and solves complex multi-constraint planning problems as constrained satisfiability problems and can reliably identify the unsatisfiable cores for unsatisfiable requirements, provide failure reasons, and offers personalized modification suggestions. Then, we generalize the paradigm by proposing a general-purpose framework that leverages LLMs to capture key information from planning problems and formally formulate and solve them as optimization problems from scratch, with no task-specific examples needed. Comprehensive experimental results have shown that our frameworks significantly outperform the baselines and have strong performance across tasks and LLMs.

Concentration-Dependent Thermodynamics and Kinetics in Lithium-Metal Battery Electrolytes: Implications for Coulombic Efficiency

2025-10-07T04:13:04Z

Concentration-Dependent Thermodynamics and Kinetics in Lithium-Metal Battery Electrolytes: Implications for Coulombic Efficiency Plaza Rivera, Christian O. Lithium (Li)-metal batteries (LMBs) present a promising avenue for high-energy applications. However, their practical adoption is constrained by challenges such as dendrite formation and unstable interphases. This study investigates the intricate interplay between electrolytedependent thermodynamics, kinetics, and transport properties in LMBs, focusing on the concentration effects in fluoroethylene carbonate (FEC) and 1,2-dimethoxyethane-based electrolytes containing lithium bis(fluorosulfonyl)imide. Due to FEC’s unique properties, these electrolytes facilitate significant upshifts in the Li redox potential and contribute to stable interphases and voltage profiles. Our findings reveal that the redox potential is primarily governed by the solvent’s electron-donating ability, reflecting underlying solvation dynamics, while the electrolyte permittivity influences reaction entropy trends. The results show entropy changes from increased molecular disorder at moderate concentrations to reduced entropy in highly concentrated regimes, driven by the formation of ion–solvent complexes. Kinetic analyses demonstrate a volcano-shaped dependence of exchange current density on concentration, centered at 2 M. Two prevailing perspectives propose that either kinetic–transport interplay or thermodynamic properties govern Coulombic efficiency (CE). However, separating these contributions is complex, since both higher exchange current density and upshifts in the Li redox potential enhance CE. Furthermore, CE strongly aligns with the combined effects of kinetics, thermodynamics, and transport, emphasizing the need for a holistic electrolyte design approach. Optimizing these three factors makes it possible to stabilize the interphase, promote uniform Li deposition, and elevate the overall safety and performance of next-generation LMBs.

1500W High Voltage DC-DC Converter for Electroaerodynamic Aircraft Applications

2025-10-07T04:13:48Z

1500W High Voltage DC-DC Converter for Electroaerodynamic Aircraft Applications Shevgaonkar, Mihir Electroaerodynamic (EAD) propulsion is a novel form of propulsion that is nearly silent and has no moving parts. The first functional untethered heavier-than-air EAD aircraft had an endurance of 90 seconds and could only fly in a straight line. To enable a practical fixed wing EAD aircraft that can fly outdoors with a payload for an extended period of time, improved power conversion technology is necessary. Prior work specifies a practical EAD aicraft as one with an endurance of 10 minutes, a payload capacity of 200 g, and full controllability. This work explores methods of increasing the specific power of power converters for EAD aircraft from 1.15 kilowatts per kilogram to over 2.0 kilowatts per kilogram. Such an increase can be achieved by utilizing magnetics integration and thermal management techniques, as well as adjustments in the operating point of the power converter. The power converter for the first generation EAD aircraft had an input voltage of 200 V, an output voltage of 40 kV, an output power of 600 W, a specific power of 1.15 kilowatts per kilogram, and an efficiency of 85 percent. In this work, a power converter with an input voltage of 200 V, an output voltage of 20 kV, an output power of 1476 W, a specific power of 2.7 kilowatts per kilogram, and an efficiency of 96 percent was demonstrated to work for a 40 second duration. At the end of the test, device temperatures continued to increase, so it has not been proven that the converter can work in thermal steady state as required for a 10 minute flight. Future work would involve modifying the test setup to allow for adequate ventilation of the ambient air around the converter, as well as modifying the converter with adequate thermal management so as to enable operation under thermal steady state.

Feasibility Analysis and Fuel Burn Benefits of Relaxing Constraints in High Altitude Cruise

2025-10-07T04:12:55Z

Feasibility Analysis and Fuel Burn Benefits of Relaxing Constraints in High Altitude Cruise Cezairli, Mina Operational interventions, such as enabling more fuel-efficient trajectories, are desirable in mitigating the environmental impact of air travel due to their relatively fast implementation potential. In particular, the vertical inefficiency arising from the altitude stratification in the airspace can be mitigated by relaxing vertical constraints. The feasibility of vertical flexibility is evaluated by quantifying the rate of close encounters and the frequency of alerts that would be needed to prevent them. Substantial diurnal variability in the number of close encounters was found in the airspace, with lower rates of events during the nighttime period. Furthermore, regional differences among Air Route Traffic Control Centers were observed in the number of close encounters. The frequency of controller intervention events that would have to occur was evaluated at 25 NM and 50 NM alerting distance levels, and it was found that, given sufficient technological capabilities for alerting at the 25 NM reaction distance, most centers would have fewer than 10 alerts per hour during the nighttime period. Boston, Miami, and Seattle appeared especially promising, with approximately one alert per hour for each region. Finally, the potential fuel benefit from enabling vertically optimal trajectories was estimated to be up to 100,000 gallons of fuel savings per month in the case of a CONUS-wide nighttime implementation.

Risk Management in Air Traffic Applications: Data-Driven Modeling, Prediction, and Generation of Realistic Weather Disruptions and Other Unfavorable Conditions

2025-10-07T04:13:59Z

Risk Management in Air Traffic Applications: Data-Driven Modeling, Prediction, and Generation of Realistic Weather Disruptions and Other Unfavorable Conditions Zhang, Joseph Understanding the interaction between weather and disruptions in complex air transportation network is important to the design and evaluation of preemptive measures and responses taken by air traffic managers. However, the occurrence of disruptive weather events is often rather limited compared to the amount of data available for nominal operations. Additionally, in large-scale systems with many known and unknown confounding factors, it can be difficult to identify the relevance of existing data to different underlying distributions of interest. Furthermore, existing work generally follows a frequentist paradigm in predicting disruptions based on weather, and does not easily lend itself to inferring the causes of disruptions, which can be important both in building models and using them to make predictions, and generate test cases to stress-test proposed design decisions. In this thesis, we develop a hierarchical Bayesian model for air traffic network operations, and investigate methods for learning these models in data-constrained settings, by extend existing work on retrospectively analyzing failures. We also include a guiding case study performed on LaGuardia Airport, in which a generative model is developed for the interaction between weather conditions and airport-level parameters within a single airport, trained on unlabeled historical data, and evaluated by simulating disruptions on historical schedules.

Interposing the Syscall Boundary: Transparent Python Execution in SigmaOS

2025-10-07T04:13:33Z

Interposing the Syscall Boundary: Transparent Python Execution in SigmaOS Wu, Ivy σOS aims to provide both serverless and stateful support to cloud applications while maintaining strong isolation, security, and efficient startup times and scheduling among multiple users. While σOS and its container startup times have been successfully benchmarked for tasks written, compiled, and statically linked in Golang and Rust, it currently lacks support for other languages, including interpreted ones like Python. To bridge this gap, this paper presents the first integration of an interpreted language into σOS, enabling native Python support without compromising the system’s core principles. Our design, σPy, achieves this through three key ideas: (1) system call interposition via LD_PRELOAD to enable just-in-time dependency management, where Python libraries are fetched on-demand from tenant-specified AWS S3 buckets, avoiding overhead during container initialization; (2) a multi-layered mount namespace that spans the local machine, a per-realm Docker container, and a per-proc σcontainer, enabling efficient dependency caching at the per-tenant granularity; and (3) a hybrid C++, C, and Python API layer that bridges σOS’s Protobuf-based RPC system with Python’s dynamic types. Preliminary benchmarks demonstrate that σPy achieves performance comparable to that of compiled languages like Golang when interacting with the σOS API, with only 0.2 - 0.3 additional milliseconds of overhead on all tested API calls, validating the success of Python programs on the σOS architecture.

Simulating LLM Runtime Latency

2025-10-07T04:14:06Z

Simulating LLM Runtime Latency Wang, Sarah Y. Large Language Models (LLMs) are expensive to run and can incur high latencies. Each LLM application has its own cost and latency targets. For example, AI voice assistants operate under low latency objectives, while large document batch processing jobs are typically cost-sensitive. However, navigating these trade-offs is not trivial, as LLM latency is highly task– specific and depends on factors such as the offered query load, the hardware configurations, request properties, and various model characteristics. To support the user in configuring their deployment according to their application needs, we introduce vLLMSim, an accurate simulator that estimates the latency of a given workload on different hardware configurations. vLLMSim advances two key avenues toward latency-aligned LLM deployments. First, the simulated latency metrics inform the user’s model and hardware choice, so they can use a configuration that is ideal for their workload. Second, our simulator enables researchers to quickly test latency-improving ideas, bypassing the need for time-consuming implementations before validating their effectiveness. In fact, vLLMSim is already used in two research projects with the goal of reducing latency and cost of LLM inference. In this thesis, we show how vLLMSim’s design allows it to accurately support the use cases above, while providing highly accurate runtime predictions. To support hardware exploration without GPU access, vLLMSim provides precomputed performance profiles that are sufficient to accurately simulate the user’s workload. The simulator code can be found here, and the instrumented vLLM code for creating profiles can be found here.

Methods for Latent Space Interpretation via In-the-loop Fine-Tuning

2025-10-07T04:13:58Z

Methods for Latent Space Interpretation via In-the-loop Fine-Tuning Wen, Collin With language models increasing exponentially in scale, being able to interpret and justify model outputs is an area of increasing interest. Although enhancing the performance of these models in chat mediums has been the focus of interaction with AI, the visualization of model latent space offers a novel modality of interpreting information. Embedding models have traditionally served as a means of retrieving relevant information to a topic by converting text into a high-dimensional vector. The high-dimensional vector spaces created via embedding offer a way to encode information that captures similarities and differences in ideas, and visualizing these nuances in terms of meaningful dimensions can offer novel insights into the specific qualities that make two item similar. Leveraging fine-tuning mechanisms, dimension reduction algorithms and Sparse Autoencoders (SAEs), this work surveys state-of-the-art techniques to visualize the latent space in highly interpretable dimensions. ConceptAxes, derived from these techniques, is a framework is provided to produce axes that can capture high-level ideas that are ingrained into embedding models. ConceptAxes with highly interpretable dimensions allow for better justification for the latent space and clusters. This method of increasing embedding transparency proves valuable in various domains: (1) AI-enhanced creative exploration can be more guided and customized for a particular experience and (2) high-level insights can be made more intuitive with vast text datasets.

Commanding, Telemetry, and Software Strategy for CubeSat Laser Infrared CrosslinK (CLICK) Mission

2025-10-07T04:13:21Z

Commanding, Telemetry, and Software Strategy for CubeSat Laser Infrared CrosslinK (CLICK) Mission Whitmore, Garrett This work outlines the software-related requirements necessary for successful operations of the NASA-sponsored Cubesat Laser Infrared CrosslinK (CLICK) B/C mission [1] [2]. This twin-cubesat mission will demonstrate peer-to-peer laser-communication capabilities novel at this small terminal scale. Optical laser communication terminals can have lower Size, Weight, and Power (SWaP) compared with traditional radio communication, as well as fewer licensing regulations and improved link security. CLICK-B/C follows from CLICK-A, a risk-reduction mission that successfully performed laser downlink with a ground station at MIT [3]. In addition to downlink, B/C will perform crosslink experiments at a data transmission rate over 20 Mbps at ranges between 20 and 580 km in Low-Earth Orbit (LEO). This thesis focuses on the software related to the function of the satellite payload, in particular, the improvements and additions made to the operating system, software systems that were ported over from CLICK-A, the integration and testing of these subsystems, and analyses done to prepare for in-flight operations before launch. An overview of the MIT & UF payload hardware and electronics is given before detailing interactions with components as necessary. A deep dive into the payload software libraries, internal and external communication channels, and operating system build details are given. A description of functional testing and its results are laid out as well as a template crosslink experiment script and further specifications for mission-related analyses and pre-launch preparations. This work on software upgrades, verification, and examination is necessary for CLICK-B/C to reach its stated mission goals, here on Earth and in its orbit.

Foundational Verification of Running-Time Bounds for Interactive Programs

2025-10-07T04:14:04Z

Foundational Verification of Running-Time Bounds for Interactive Programs Tockman, Andrew The field of formal methods has a rich history of practical application in verification of the correctness of software. Existing verification tooling operates at a wide range of rigor, from proving relatively weak properties via traditional static analysis to powerful theorem provers that can express very precise specifications. It is sometimes desirable to prove properties about programs that make reference to not just semantic behavior but also to other metaproperties of the program’s execution, such as runtime or I/O histories. There is also a wide variety of existing tooling for proving bounds on program runtime. However, there is no prior work on a maximally rigorous verification system that can prove predicates involving all of semantic behavior, runtime, and I/O. Our contribution is exactly that – we extend the existing Bedrock2 framework, which implements a C-like systems language within a powerful proof engine together with a verified compiler capable of expressing arbitrary proof conditions involving behavior and I/O, and augment it to add the capacity to reason about runtime as well. As a capstone proof of concept, we apply the new metrics machinery to an IoT lightbulb controller (already verified with respect to the previous framework) and produce a new specification with time bounds based on arrival of network packets.

Graph Neural Networks for City Policy Recommendations as a Link Prediction Task

2025-10-07T04:13:10Z

Graph Neural Networks for City Policy Recommendations as a Link Prediction Task Rozario, Consecrata Maria Graph Neural Networks (GNNs) have become a widely utilized tool in recommender systems in various contexts. While recommendation tasks can be approached using a multitude of data structures and types, graph-structured data is particularly well-suited for this domain, as graphs naturally capture a variety of relationships and interactions between entities. By leveraging graph representation learning, we can effectively encode these complex dependencies, enabling robust and context-aware recommendations. We use this methodology in the domain of policy recommendations for urban centers. To recommend policies, we would learn the complex local and global relationships between cities, their environmental features, and currently implemented policies. We construct a graph structure relating cities, implemented policies, and city features, and formulate the policy recommendation task as a GNN link prediction problem, demonstrating its potential to scale data-driven urban governance.

Automatic Detection of Landmark Acoustic Cues in Human Speech

2025-10-07T04:13:51Z

Automatic Detection of Landmark Acoustic Cues in Human Speech Park, Janette H. This study presents a framework for the automatic detection of the eight landmark acoustic cues in human speech. Landmarks are key articulatory events, produced as a result of minimal vocal tract constriction (e.g., vowels and glides) or closures and releases in the oral region (e.g., nasal, fricative, and stop consonants). A complete landmark detection system is a key step towards an overarching speech analysis system that relies on lexical acoustic cues, as landmarks guide the identification of other acoustic cues in speech. In the proposed framework, the acoustic properties of each of the eight landmark cues are modeled by extracting speech-related measurements and training Gaussian Mixture Models (GMMs). To remove the effects of speaker variability and different recording environments, methods for normalizing speech-related measurements are proposed and evaluated. For a new speech signal, the normalized speech-related measurements are extracted at each time frame and evaluated against the eight trained GMMs to compute the likelihood of each landmark. Using Bayes’ Theorem, the posterior probabilities are calculated to determine the most probable landmark (or absence thereof) at each time frame. The system’s performance is evaluated by comparing the detected landmarks to the manually labeled ground truth landmark annotations.

Single-Cell Language Model for Transcriptomics & Cell Type Annotation

2025-10-07T04:13:29Z

Single-Cell Language Model for Transcriptomics & Cell Type Annotation Lin, Vincent As single-cell transcriptomics datasets continue to grow in size and biological complexity, current models for cell type annotation remain limited in their generalizability and are often evaluated on only a small fraction of the standardized cell types defined in modern ontologies. Current state-of-the-art models for transcriptomic representation demonstrate that deep learning models can extract rich features on single-cell data but are evaluated on very few cell types and perform poorly on broader datasets. This work introduces a multimodal model architecture that integrates large language models (LLMs) with gene expression encoders to address this scalability gap in cell type annotation. Inspired by vision-language frameworks, our architecture combines a pretrained scRNA encoder with a Perceiver Resampler that maps gene expression profiles into the latent space of a large language model. We construct structured, ontology-grounded datasets of up to 197 cell types and evaluate our model's performance using instruction fine-tuning. Our experiments analyze the impact of integrating language modeling components with scRNA encoders and their benefit on cell type annotation performance for large, diverse datasets. Our results show that while a scRNA encoder may be sufficient for small datasets, our single-cell model leveraging LLMs consistently outperforms the scRNA encoder baseline on larger datasets, with a widening gap in classification performance as data complexity increases, demonstrating the scalability and improved generalizability of our multimodal architecture. We also provide further analysis of the tradeoffs associated with using the natural language domain for biological analysis.

Inference Time Search for Protein Structure Prediction

2025-10-07T04:13:03Z

Inference Time Search for Protein Structure Prediction Qi, Richard Scaling inference-time compute for deep learning models has led to superhuman performance in games and enhanced reasoning capabilities for language models. However, similar gains have not yet been made in the field of biomolecular structure prediction. We introduce a new paradigm for inference-time search by adding architectural components and a finetuning procedure to state-of-the-art structure prediction models that give rise to a discrete latent space. We implement algorithms for searching and sampling in this discrete latent space and conduct experiments on a small model, demonstrating an increase in oracle and top-1-selected accuracy for predicted protein-protein complex structures.

Designing a Localized Lower Body Negative Pressure Garment for Long-Duration Spaceflight

2025-10-07T04:13:39Z

Designing a Localized Lower Body Negative Pressure Garment for Long-Duration Spaceflight Chu, Kaitlyn A. Lower Body Negative Pressure (LBNP) has long been explored as a countermeasure to the physiological deconditioning and orthostatic intolerance associated with prolonged microgravity exposure. Traditional LBNP systems, however, are large, stationary devices that require astronauts to remain immobile during use, limiting their integration into daily spaceflight routines. Although more mobile LBNP solutions have emerged, they remain cumbersome and uncomfortable, ultimately still restricting multitasking and reducing operational feasibility. This study introduces the Soft Kinetics INterface (S.K.I.N.), a flexible, wearable structure designed to support the application of localized LBNP. The goal was to evaluate whether targeted negative pressure applied through the S.K.I.N. could replicate the fluid shift effects of a traditional LBNP chamber while improving comfort, mobility, and time-efficiency. The human thigh was chosen as the focus of this technology demonstration due to its known responsiveness to LBNP and its suitability for small-scale implementation. The development of the S.K.I.N. began with finite element modeling (FEM) to identify optimal material properties and structural geometry. Iterative physical prototyping resulted in a sinusoidal silicone waveform design, selected for its mechanical stability and user comfort. The final prototype was then evaluated in three experimental phases: (1) mechanical testing using pressure-sensitive film to assess structural integrity under vacuum, (2) an ex-vivo pig leg study to validate experimental protocols and assess the S.K.I.N.’s ability to induce fluid shifts, and (3) a human study (n=10) comparing fluid shifts between the S.K.I.N. and a scaled-down version of the traditional LBNP chamber. On average, results from the human study showed that the S.K.I.N. successfully induced localized fluid shifts similar to those of the chamber. However, response magnitude varied considerably across participants. Most of the observed effect was driven by female participants, who exhibited more pronounced fluid shifts, while most male participants showed minimal or no measurable response. FEM simulations supported this finding, suggesting that higher fat-tomuscle ratios — more common in women — may enhance tissue deformability and volume displacement, thereby facilitating greater fluid shifts under negative pressure. Although these differences limit generalizability, they also highlight the potential for the S.K.I.N. to serve as a more targeted countermeasure for specific physiologies or user groups. Although the current S.K.I.N. design’s limited surface area constrains its overall effect, the concept shows promise. The ability to deliver targeted fluid shifts in a more mobile, comfortable format could enable integration into dynamic operational settings. Future work should focus on expanding the system to cover larger areas, such as a whole-pants version, and incorporating a portable vacuum source for mobility in both spaceflight and terrestrial applications. Larger, more diverse participant cohorts will also be necessary to assess long-term usability, efficacy, and individual variability in response.

Mantis: A Screen Magnification Tool for Diagram Traversal

2025-10-07T04:13:18Z

Mantis: A Screen Magnification Tool for Diagram Traversal Patterson, Lydia J. Complex diagrams and charts can be difficult for people who use screen magnification to navigate. A sense of spatial context and of the diagram’s overall structure is oftentimes lost, as magnifiers can only magnify a fraction of the screen at any given time. So, while sighted users have both clarity and full context simultaneously, screen magnifier users often have to choose or split their attention between the two. Existing screen magnifiers are content-agnostic, so the current way of navigating visualizations is freeform and unguided. The burden of figuring out where to explore while retaining a mental model of the diagram is placed entirely on the user. In this paper, we present Mantis—six prototypes of an automatic, content-aware screen magnification tool designed to aid people who have low vision in the traversal of diagrams. Each design experiments with what sorts of information might be provided to help the user retain a sense of context. Further, they each explore how such a tool might use its knowledge of the diagram’s semantic structure to streamline traversal to and from areas of interest to the user. To this end, we evaluate how these proof-of-concepts improve the user’s navigational experience and reduce the user’s cognitive load.

Teacher-Centered Design in Educational Games: Iterative Improvements to the Tragedy of the Commons pSim Dashboard

2025-10-07T04:12:56Z

Teacher-Centered Design in Educational Games: Iterative Improvements to the Tragedy of the Commons pSim Dashboard Luong, Jacky K. Teaching tools such as the Tragedy of the Commons (ToC) participatory simulation, developed by MIT STEP Lab, have the potential to develop different skills or knowledge compared to single-player educational games. ToC illustrates the challenges of managing shared resources, but its existing teacher dashboard may not be well-suited to support its growing use across various classrooms. Through surveying and interviewing educators along with observing classroom usage, the software's shortcomings and opportunities for improvement were identified. This resulted in the design and implementation of a redesigned teacher dashboard, including a new “central bank” feature that provides structure to support more complex simulations. Additional enhancements improved usability and performance. Evaluations with teachers and controlled playtests demonstrated that these changes show promise in enabling richer classroom dynamics and making facilitation easier. The findings underscore the importance of teacher experience in educational game design.

All Therapies Are Equal - Unless You’re a Bot: Evaluating the Effectiveness of Four Therapy Schools for AI Chatbot Therapists

2025-10-07T04:13:25Z

All Therapies Are Equal - Unless You’re a Bot: Evaluating the Effectiveness of Four Therapy Schools for AI Chatbot Therapists Liu, Andi This thesis tests two design questions for Large Language Model (LLM) Chatbot Therapists: Which therapeutic school suits an LLM best, and does an explicit Theory-of-Mind (ToM) reflection improve outcomes? We prompted GPT-4.1-mini to act as eight therapists — CBT, Narrative, Psychodynamic, and SFBT, each with and without a ToM step — and held 240 simulated sessions with scripted AI patients. SFBT achieved the greatest projected PHQ-9 improvement (around 4 points), significantly higher than CBT, Narrative, or Psychodynamic approaches. Immediate distress (SUDS) fell modestly and uniformly across schools. ToM reasoning did not alter either measure. The findings show that extra “thinking time” might not automatically translate into therapeutic gain, but also highlight a current strength of LLMs: executing brief, rule-based therapies.

Automated Fiber Coupling with Actuated Mirrors

2025-10-07T04:13:13Z

Automated Fiber Coupling with Actuated Mirrors Vel, Vetri Senthil Almost all atomic physics experiments rely on precise alignment of lasers. For example, optical fields are used to cool, control, and image atoms in neutral atom arrays. In this thesis, we present a design for mirrors actuated by servos that allow the precise, repeatable alignment of lasers in free space optical setups. We then apply these actuated mirrors to automate fiber coupling, where laser beams are coupled from free space into a fiber waveguide. We present the theory of fiber coupling and use experimental data on the fiber coupling landscape to develop an accurate digital twin. Insights from the combination of the digital twin and experimental data are used to develop a fast and effective algorithm for automated fiber coupling.

ACED: Automatic Concourse Event Detection

2025-10-07T04:12:45Z

ACED: Automatic Concourse Event Detection Wagner, Luke A. Fans of the San Antonio Spurs often face long delays when traversing the arena or waiting for food. Automatic Concourse Event Detection (ACED) is a novel system designed for tracking these statistics in the Spurs’ arena in real time. We use existing machine learning models and introduce novel processing algorithms to identify the total number of people in each section throughout the arena in addition to tracking the wait times for different restaurants and restrooms. ACED collects and stores this information in a database, which could be used to present fans with up-to-date arena information in a live dashboard to assist them in their in-game decision making. This would improve the overall fan experience, which could encourage fans to buy tickets more frequently. We provide the San Antonio Spurs with a completed implementation of ACED, which is ready to be deployed within the Spurs’ arena.

Ultraviolet-C Powered Air Purifying Respirator (UVC PAPR)

2025-10-07T04:13:30Z

Ultraviolet-C Powered Air Purifying Respirator (UVC PAPR) Seeyave, Evan The global challenge posed by pandemics, notably COVID-19, has underscored the critical need for advanced personal protective equipment (PPE). This thesis details the development and evaluation of a multi-stage powered air-purifying respirator (PAPR) incorporating direct ultraviolet-C (UVC) germicidal irradiation. The proposed PAPR aims to provide enhanced protection by actively sterilizing air through this UVC chamber immediately prior to inhalation. This approach offers an advantage over traditional filter-based PAPRs by removing both the need to replace filters and pull air with high-power motors, while still neutralizing a broad spectrum of airborne pathogens, including viruses and bacteria. The primary objective of this research is to design, construct, and test a PAPR prototype capable of achieving a high inactivation rate (target 99.9%), thereby offering a robust solution for individuals in high-exposure environments. In addition to the UVC chamber, we also built an alternate ultraviolet-A (UVA) activated titanium dioxide (TiO2) photocatalytic oxidation (PCO) chamber. This work encompasses the overall design of the system, safety considerations, and testing to quantify its pathogen inactivation efficacy and to characterize system performance.

GridFix: A Desktop Application for the Correction of Algorithmically-Generated Beatgrids for Music

2025-12-15T15:52:46Z

GridFix: A Desktop Application for the Correction of Algorithmically-Generated Beatgrids for Music Shi, Iris Beatgridding is a technique meant to aid DJs in aligning the beats of two different songs. By overlaying a grid of beat markers (a “beatgrid”) on top of a waveform representation of the track being beatgridded, a song’s beats can be visualized and thus easily matched to another’s. State-of-the-art DJ software—like rekordbox by the company AlphaTheta—will algorithmically generate beatgrids for songs. However, these beatgrids are not always accurate and can often be difficult to correct with only the software-provided tools. GridFix is a desktop application designed to be an auxiliary tool for rekordbox, allowing users to correct rekordbox-generated beatgrids by providing additional functionality that rekordbox does not. GridFix’s main advantage is its ability to let users make local changes to small, isolated sections of a beatgrid, a task that is quite hard to achieve in rekordbox. GridFix is fully compatible with rekordbox and fairly easy to learn how to use, as shown by user testing.

Graph Metrics for Improving Cybersecurity on Software Dependency Networks

2026-01-16T20:08:01Z

Graph Metrics for Improving Cybersecurity on Software Dependency Networks Yao, Darren Z. Modern software ecosystems are deeply interconnected, allowing a vulnerability in a single component to propagate and affect many others. In this thesis, we model software ecosystems as directed graphs, and apply various graph-theoretic metrics to quantify security risk. We compare two deep learning frameworks (PyTorch and TensorFlow) with two traditional software frameworks (npm and PyPI), identifying critical properties of their dependency structures, which motivates several recommendations for improving software supply chain security.

Planning Robotic Cutting Operations

2025-10-07T04:12:38Z

Planning Robotic Cutting Operations Lunawat, Tarang Classical planning and most PDDL variants operate on the assumption that the number and types of objects present in the environment are known at the time of initialization and neither can nor do change during plan execution. However, there are many domains in which it is helpful and necessary to be able to capture action (or environment) effects that are able to change the existence of objects rather than just facts about these objects. PDDLStream already provides a framework for "certifying" new facts about the environment as necessary throughout plan execution; I propose using PDDLStream to construct a principled way to reason over not just added facts, but also added or removed objects in the environment. In order to do this, I will work within the domain of cutting operations in the kitchen, as this is a domain that both necessitates a lot of object change as objects are cut and often requires chains of these generated objects to be fully reasoned over. Additionally, I will lay the groundwork to use this principled way to reason over new objects to implement different types of cutting operations in the kitchen, with the eventual goal of a robot planner being able to sequence different provided actions to more efficiently work with knives in the kitchen in a human-like manner.

Adaptive Wavefront Estimation Algorithms for High-Contrast Imaging of Exoplanets

2025-10-07T04:15:05Z

Adaptive Wavefront Estimation Algorithms for High-Contrast Imaging of Exoplanets Manojkumar, Saikrishna The direct imaging of exoplanets orbiting stars outside our solar system remains one of the crucial tools we have available to answer whether there exists life beyond Earth. The light from an Earth-like exoplanet is approximately ten orders of magnitude dimmer than its host star and hence the imaging system of the telescope observing the exoplanet must be able to suppress the starlight to achieve a “contrast” of 10−10 in the image. This is typically achieved using a coronagraph, which blocks the light from the star while allowing the light from the planet to pass through. However, some starlight that leaks through the coronagraph needs to be further removed in the search region for the exoplanet; this region is referred to as the dark hole or dark zone (DZ). Creating a DZ requires the use of focal plane wavefront sensing and control techniques, which estimates the electric field of the starlight in the focal plane of the telescope using a camera and then informs the deformable mirrors (DMs) located upstream of the coronagraph to null these electric fields. Once the DZ is created with a desired contrast, there are still slow, high-order drifts in the optical system that cause the contrast to degrade over the long observation times of the science target. High-order wavefront sensing and control (HOWFSC) techniques are required to maintain the contrast in the DZ while observing a science target. Dark Zone Maintenance (DZM) is a technique that has demonstrated the ability to maintain the contrast in the DZ over long observation times. This algorithm utilizes an Extended Kalman Filter (EKF) to estimate the open-loop electric field at every pixel in the DZ and use this information to inform the control algorithm. The achievable contrast and contrast stability of DZM are determined by several key parameters: the optical system’s drift rate, the photon flux and associated shot noise in the measurement images, and the probe magnitude applied to the DMs for the estimation algorithm. This work quantifies the impact of the drift rate, photon rate, and probe magnitude on the performance of DZM by performing a parameter scan on high-contrast imaging testbeds. The parameter scan was performed on both the in-air High-contrast imager for Complex Aperture Telescopes (HiCAT) testbed at the Space Telescope Science Institute (STScI) and the in-vacuum Decadal Survey Testbed (DST) at the Jet Propulsion Laboratory (JPL). The parameter scan was run in both simulation and on the physical testbed using the contrast in the DZ as a performance metric, and evaluated relative to the photon-noise theoretical bounds to assess the efficacy of the DZM algorithm. The substantial difference between the theoretical bounds and experimental results, on average 70 times worse on HiCAT, motivated the development and implementation of a new DZM algorithm that utilized a separate EKF to estimate the modes of wavefront error derived from the DMs and use that information to correct for the aberrations. This new modal EKF algorithm was tested with a similar parameter scan on the HiCAT simulator demonstrating a nearly 5 times level of improvement relative to the original DZM algorithm simulation performance. The results of this work will inform the design of future algorithms to maintain high contrast during observations for upcoming space telescope missions such as the Habitable Worlds Observatory (HWO).

Incentivizing Data Contributions in Decentralized Collaborative Learning

2025-12-15T15:42:32Z

Incentivizing Data Contributions in Decentralized Collaborative Learning Wang, Yuxiao In a collaborative learning scheme such as the federated learning model, each user benefits from the data contribution of others. Previous work shows that the federated learning protocol can incentivize users to contribute more than in the competitive equilibrium by penalizing deviations. However, a central controller with access to all the data may raise privacy concerns. In this work, we construct a decentralized collaborative protocol in which users share data without relying on a centralized controller. We then extend this protocol to a repeated game and analyze the competitive equilibrium behavior, along with strategies users can implement to foster collaboration in the repeated setting of the protocol. We provide a quantitative analysis of free-rider behavior under decentralized protocols and compare the amount of information collected with decentralized protocols against that in the centralized protocol.

DisViz: Visualizing real-world distributed system logs with space time diagrams

2025-10-07T04:14:58Z

DisViz: Visualizing real-world distributed system logs with space time diagrams McMenamy, Josiah This thesis aims to provide an intuitive debugging and learning tool for distributed systems that communicate by message passing. Understanding and debugging distributed systems can be challenging and slow to iterate on, so there is a need for tools that can speed up the time it takes to diagnose the root cause of a bug. There exists significant prior work in creating tools that can aid in the visualization and debugging of distributed system executions, such as the ShiViz log visualizer [13]. This work builds on top of these tools to provide more debugging information, handle large log files, and be easily instrumented in existing systems. We demonstrate using the tool to debug issues in an implementation of the Raft consensus algorithm [34].

Casting Protein Structure Predictors as Energy-Based Models for Binder Design and Scoring

2025-12-15T17:19:54Z

Casting Protein Structure Predictors as Energy-Based Models for Binder Design and Scoring Nori, Divya Protein binder design has been transformed by hallucination-based methods that optimize structure prediction confidence metrics, such as the interface predicted TM-score (ipTM), via backpropagation. However, these metrics are imperfect proxies for binding affinity and do not reflect the statistical likelihood of a binder–target complex under the learned distribution. In this work, we propose a principled alternative: an energy-based framework that directly extracts the statistical likelihood of a predicted binder–target complex from a structure predictor’s internal confidence distributions. Building on the Joint Energy-based Modeling (JEM) framework, we introduce pTMEnergy, a statistical energy function over structures that is derived from predicted inter-residue error distributions. We incorporate pTMEnergy into BindEnergyCraft (BECraft), a hallucination-based binder design pipeline that maintains the same optimization framework as BindCraft but replaces ipTM with our energy-based objective. Across a diverse panel of challenging protein targets, BECraft achieves higher in silico success rates compared to BindCraft, RFDiffusion, and ESM3. Beyond design, we evaluate pTMEnergy as an unsupervised scoring function for retrospective virtual screening tasks. Without any task-specific supervision or retraining, pTMEnergy consistently outperforms baseline methods across both protein–protein and protein–RNA interaction benchmarks. Our results demonstrate that confidence-derived energy functions offer a powerful and generalizable signal for binder design and scoring.

Efficient Modeling, Optimization, and LLM-Assisted Decision Support for Geothermal Well Arrays

2025-10-07T04:14:45Z

Efficient Modeling, Optimization, and LLM-Assisted Decision Support for Geothermal Well Arrays Ouko, Edwin O. Geothermal well arrays, which organize multiple geothermal wells into carefully planned geometric configurations, provide an opportunity to enhance energy production capacity and increase fault tolerance of geothermal systems. Closed-loop geothermal systems (CLGS), a type of geothermal well design, promises to allow harnessing of geothermal energy in any location with minimal adverse environmental impact. I demonstrate how the development of these emerging geothermal technologies could be accelerated by recent advances in large language models (LLMs) in conjunction with high-level high-performance programming languages like Julia. In particular, I focus on how LLMs could be used in design brainstorming and to increase efficiency in numerical modeling. I assess the potential of state-of-the-art LLMs such as ChatGPT, Gemini, Claude, Grok, and a domain-specific model, AskGDR, as expert assistants in geothermal research. Owing to the unpredictable reliability of LLMs, there is a constant need for objective evaluation benchmarks in various domains. I propose a novel approach, leveraging Google’s recently introduced AI tool, NotebookLM, to accelerate the generation of quantitative geothermal benchmarks with only new unpublished questions. In addition, I propose the use of blackbox optimization as a computationally less costly alternative to approximate the optimal configuration of CLGS wells in a geothermal array to minimize thermal interference and improve heat energy production. I evaluate several optimization strategies such as Bayesian optimization, particle swarm optimization, natural evolution strategies, differential evolution optimization, Nelder-Mead, and simulated annealing on various performance characteristics such as convergence speed and highest production capacity attained.

A Transformer-Based Foundation Model for Human Microbiome Analysis

2025-10-07T04:14:28Z

A Transformer-Based Foundation Model for Human Microbiome Analysis Medearis, Nicholas A. The human microbiome plays a crucial role in maintaining our health. Alterations in the microbiome have been linked to various chronic conditions like autoimmune disorders, metabolic diseases, and cancer. While various tools have been developed to study the microbiome, each tool tends to be specialized for a specific task. To overcome this limitation, we report on the development of a foundation model pretrained on 13,524 human microbiome metagenomic samples. The model was then fine-tuned to predict the clinical status of the host. Our model was able to differentiate between healthy and diseased samples in 10-fold cross-validation on the training dataset with an accuracy of 83.7%. On an external validation dataset of 927 samples, our model had an accuracy of 74.9%. Notably, our model performed even better at differentiating diseases from one another. On the diseased samples in the training dataset, it classified samples with an accuracy of 93.3% in 10-fold cross-validation. Together, our results show that generative AI has the potential to transform microbiome research and advance personalized medicine.

Towards an Augmented Reality-based Cyber-Physical Production System Planner

2025-10-07T04:13:36Z

Towards an Augmented Reality-based Cyber-Physical Production System Planner Mueller, David Investment in automation by small and medium-sized enterprise (SME) manufacturers in the United States has lagged behind their larger counterparts for decades, despite comprising a majority of the nation’s manufacturing industry. The cyber-physical production systems (CPPSs) introduced by Industry 4.0 promise to bolster productivity and efficiency, but only for those enterprises which invest in constituent technologies. These technologies are not easily integrated in existing factories, typically requiring installation of invasive infrastructure and continuous technical support. Robotic integration is typically performed by specialized third-party firms or by in-house staff with extensive technical training, such as engineers. SME manufacturers are particularly sensitive to the complexities of robot integration due to limited access to technologists, and their need for frequent reconfiguration under economies of scope. This thesis introduces Marve: the Mobile Augmented Reality Visual Editor. Marve is a proof-of-concept Android application that enables line workers to directly configure and control an autonomous mobile robot (AMR)-backed hybrid intralogistics system using lowcost consumer hardware. Workers can use Marve’s augmented reality (AR)-based interface to define and visualize the essential geometry and components of such a system. Once configured, workers are able to simulate how the system would respond to their requests to move material throughout the factory. The use of AR enables extensive work to be done at the planning stage of CPPS integration by line workers themselves, bypassing the need for modeling by engineers. Marve relies exclusively on fiducials and visual-inertial odometry (VIO) for localization, and fiducial tags for object tracking, thus eliminating the need for supporting infrastructure. Taken together, these features make Marve an easy on-ramp for SMEs seeking to transition legacy production lines into the CPPSs of Industry 4.0.

Enabling Efficient ML Inference in SigmaOS with Model-Aware Scheduling

2026-01-16T19:18:11Z

Enabling Efficient ML Inference in SigmaOS with Model-Aware Scheduling Liu, Katie Machine learning inference in multi-tenant cloud environments leads to significant challenges when it comes to minimizing latency and resource contention, especially as models grow in size and complexity. This thesis addresses the cold start overhead and scheduling inefficiencies of multi-tenant ML serving by integrating the RayServe distributed model-serving framework into σOS, a cloud operating system that unifies container and serverless paradigms. The thesis also proposes two model-aware schedulers within σOS that intelligently routes inference requests to reduce the number of cold starts: Model Colocation, which prioritizes placing requests on machines where the required model is already loaded, and Centralized Model Registry, which tracks globally available models to inform scheduling decisions. These policies proactively reduce model load times by reusing cached models. Experimental results on language translation workloads in an 8-node cluster show that these schedulers achieve a ≈ 50% reduction in average inference latency and eliminates roughly 4–5 cold starts per workload, compared to σOS’s default scheduler. Through this model-aware approach to scheduling, our work enables more efficient, scalable, and low-latency ML inference serving in multi-tenant cloud settings.

Impact of efficiency-driven aircraft technology improvements on climate and air quality

2025-10-07T04:14:55Z

Impact of efficiency-driven aircraft technology improvements on climate and air quality Shukla, Aditeya The impacts of commercial aviation on global climate and air quality have led to an industry-wide movement to reduce its environmental impact. While technological developments in aircraft propulsion, materials, and aerodynamics aim to reduce fuel consumption and CO₂ emissions, these efforts often overlook the full climate and air quality impacts of aviation, especially emissions impacts of NOₓ, CO, HC, soot, and contrails. This study assesses the environmental constraints associated with advancements driven by fuel efficiency by modeling aircraft technologies across narrow-body, wide-body, and regional jet categories. By focusing on near-future technology insertions in materials, aerodynamics, and propulsion, we can compute quantifiable environmental metrics such as temperature changes, global warming potentials, and monetized environmental damages. Our modeling shows that certain propulsion technologies — such as increased component polytropic efficiencies or higher allowable turbine-metal temperatures — can reduce fuel consumption by more than 10% under favorable re-optimizations of engine design. However, they often raise engine core pressures or temperatures in ways that increase NOₓ emissions indices by more than 30%. This can lead to worse air quality damages, offsetting some of the CO₂ savings and in some cases result in a 2% increase in environmental damages on a total net present value (NPV) basis. Primary structure material upgrades consistently reduce both fuel burn and NOₓ emissions. These improvements in air quality from reduced NOₓ result in a 10% reduction of the total NPV from environmental impacts. This analysis shows that focusing on fuel efficiency alone can be an incomplete metric towards understanding the environmental impact of an aircraft. By offering a quantitative assessment of how near-future upgrades can affect both climate and air quality, this study also provides guidance on which technology paths are most effective in reducing the overall environmental impact of aviation.

Digital Symbol Digit Test: Multimodal Behavior Detection and Visualization

2025-10-07T04:14:47Z

Digital Symbol Digit Test: Multimodal Behavior Detection and Visualization Xu, Jessica J. Neurodegenerative diseases, such as Alzheimer’s, impact many people worldwide and currently have no cure, making early detection essential for effective symptom management and intervention. Traditional diagnostic practices often rely on subjective clinical evaluations that can vary between practitioners, highlighting the need for more objective methods. The digital Symbol Digit Test (dSDT), administered via the Cognitive Health App on an iPad and using the ETVision Eye Tracking System, aims to provide an automated, reliable method to analyze patient cognitive function to detect early signs of impairment through capturing handwriting and gaze data. This thesis builds upon previous work by automating the synchronization of these two data modalities, refining definitions of learning behaviors, and developing pipelines for data processing and visualization. By creating a synchronized multimodal dataset, we can visualize participant behavior for more intuitive interpretation and draw meaningful conclusions. These contributions provide an end-to-end framework for analyzing behavior during the cognitive assessment and lay the groundwork for future development of diagnostic models to detect early signs of neurodegenerative diseases.

Location Verification for Spoofing Detection in Non-Terrestrial Networks

2025-10-07T04:14:43Z

Location Verification for Spoofing Detection in Non-Terrestrial Networks Schatz, Ensign Nathan Caleb Reliable location awareness is essential for the development of new services and applications in non-terrestrial networks (NTN). The ability of malicious users to report false location information poses a significant threat to NTN performance. This threat introduces the need for a flexible and robust location verification system (LVS) that can reliably detect malicious users. This paper proposes a single-satellite LVS based on round-trip time and angle-of-arrival measurements. We characterize several sources of uncertainty unique to the NTN scenario and examine their combined effect on positioning error. To detect spoofing probabilistically, we approximate the likelihood function for the unknown user position using a Gaussian mixture model and employ a likelihood ratio decision rule for location verification. Results display receiver operating characteristic curves to evaluate the LVS performance under various satellite ephemeris error conditions, spoofing distances, number of measurements available to the system, and wireless channel properties. The proposed LVS is shown to reliably detect spoofing among malicious users.

Triangle Splatting

2025-10-07T04:14:41Z

Triangle Splatting Xu, Daniel We develop a differentiable rendering method for recovering 3D meshes of scenes from 2D images. Unlike existing approaches, our method does not rely on a differentiable renderers and is compatible with any standard mesh rasterizer. To our knowledge, it is the first mesh-based differentiable rendering method that is not reliant the use of visibility masks entirely. Beyond these conceptual advancements, we implemented a set of highly optimized kernels that enable efficient scene representation on a sparse voxel grid, effectively overcoming the cubic scaling bottleneck faced by similar methods. These innovations result in promising performance on unbounded real-world scenes with complex backgrounds.

Adaptive Control Strategies for Mitigating Spaceflight Fluid Shifts Using Lower Body Negative Pressure and Non-Invasive Fluid Shift Sensing

2026-01-16T19:55:06Z

Adaptive Control Strategies for Mitigating Spaceflight Fluid Shifts Using Lower Body Negative Pressure and Non-Invasive Fluid Shift Sensing Ortiz, Ciarra Celena Entering a microgravity environment induces cephalad fluid shifts that can lead to cardiovascular and renal-hormonal adaptations that can effect astronaut health and performance in space. The current monitoring strategies for fluid shift lack the ability to track regional fluid shift in real-time, which limits countermeasure efficacy. This thesis aims to highlight the investigation and validation of using prototype non-invasive radiofrequency (RF) sensors for regional fluid shift detection. Additionally, the integration of the feedback from these sensors into Lower Body Negative Pressure (LBNP) chambers could allow for the development of an adaptive Lower Body Negative Pressure regulation framework. Coaxial RF sensors were designed and characterized using tissue phantoms, and tested in a human subject study involving controlled LBNP exposure. Reflection coefficients (S₁₁ and S₂₂) were analyzed to detect regional fluid changes in arm and leg tissue. The preliminary results indicated a statistically significant decrease in the arm reflection coefficients (S₁₁) during active LBNP, which is consistent with fluid being pulled towards the lower body. The leg reflection coefficients (S₂₂) were more variable and did not exhibit statistically significant results, suggesting a need for more investigation with placement and sensor sensitivity. This work demonstrates the potential of using wearable RF sensors for non-invasive fluid shift monitoring and lays the foundation for integrating fluid sensor feedback into adaptive LBNP control protocols to improve astronaut health monitoring and countermeasure personalization.

Robust Inference via Optimal Transport Ambiguity Sets

2025-10-07T04:14:34Z

Robust Inference via Optimal Transport Ambiguity Sets Wang, Zheyu Uncertainty quantification is pivotal for ensuring the safety and reliability of predictive algorithms in high-stakes applications—ranging from cancer diagnosis to autonomous driving. This challenge is exacerbated by distribution shift, in which the true data–generating distribution diverges from the nominal distribution on which our statistical methods were trained. In this thesis, we formalize distribution shifts via ambiguity sets—metric neighborhoods in the space of probability measures defined by distances such as the Wasserstein metric—and demonstrate that leveraging these ambiguity sets endows two widely used statistical algorithms with distributional robustness. The Kalman filter enables accurate, real-time tracking of latent states by assimilating noisy, indirect measurements over time. Its performance relies on precise state-space models for both the evolution dynamics and the observation process. In practice, uncertainties in these models introduce errors that can significantly degrade filter accuracy. Here, we review two robust Kalman-filter variants that explicitly account for such errors via Wasserstein ambiguity sets. Split conformal prediction, hereafter referred to as conformal prediction, offers a powerful framework for quantifying predictive uncertainty by constructing prediction intervals with finite-sample, distribution-free guarantees. Despite its widespread success, ensuring its validity under train-test distribution shifts remains a significant challenge. We model distribution shifts using ambiguity sets defined by two optimal transport-based metrics and propose two robust conformal prediction algorithms that preserves validity under these shifts. First, we consider ambiguity sets defined by a pseudo-divergence derived from the LévyProkhorov (LP) metric, which captures both local and global data perturbations. We provide a self-contained overview of LP ambiguity sets and their connections to widely used metrics such as the Wasserstein and Total Variation distances. We then establish a natural link between conformal prediction and LP ambiguity sets: by propagating the LP ambiguity set through the scoring function, we reduce complex high-dimensional distribution shifts to manageable one-dimensional shifts, enabling exact computation of the worst-case quantile and coverage. Building on this foundation, we develop valid robust conformal prediction intervals under distribution shifts, explicitly relating LP parameters to interval width and confidence levels. Experimental results on real-world datasets demonstrate the effectiveness of the proposed approach. Next, we extend our analysis to robust conformal prediction over Wasserstein-2 ambiguity sets, deriving a theoretical characterization of the worst-case quantile. However, we identify intractability due to the dependence on the shape of the original score CDF and conclude with potential future directions.

Theoretical Limits of Quantum Ranging

2025-10-07T04:14:24Z

Theoretical Limits of Quantum Ranging Kartal, Bünyamin The ability to determine distances from dedicated measurements, namely active ranging, is crucial in a variety of systems including localization, radar, and lidar. This thesis establishes the quantum limits and determines the quantum advantage provided by single-beam displaced squeezed states in active ranging. Analytical expressions of the quantum Fisher information (QFI) are provided for monochromatic and continuous-mode waves passing through a thermal loss channel with arbitrary loss and noise conditions. The optimal allocation of system resources for performing displacement and squeezing operations is determined. The optimal allocation consists of apportioning all resources to perform either the displacement operation, providing no quantum advantage, or the squeezing operation. Analytical results are examined in optical and microwave regimes. The optimal gain, i.e., the ratio between the QFI obtained by optimal resource allocation and the QFI obtained by performing only the displacement operation, is derived for the optical and microwave regimes. Quantum advantage afforded by the prototypical heterodyne receiver is also investigated. The results of this thesis pave the way for establishing a foundation of active ranging and provide insights for system design employing currently available quantum technologies.

Generalized Policy Learning with Planning

2025-10-07T04:14:01Z

Generalized Policy Learning with Planning Yang, Ryan P. Generalized policy learning seeks to find policies that solve multiple tasks within a planning domain. We introduce methods to search for policies independently in a domain from empty initialized policies. As an extension, we also propose a problem setting to learn satisficing policies between domains. In an independent domain, we propose a score function to guide the policy search. Our approach, Policy-Guided Planning for Generalized Policy Generation (PG3), evaluates policies based on how well it can be used to plan. Empirically, we show that PG3 allows generalized policy learning to occur more efficiently than other baselines with PDDL-based problems and policies represented as lifted decision lists. Finally, our experiments show that policies independently learned are qualitiatively similar, prompting further investigation on the possibilities of further accelerating the policy search process.

Meta-Learning Exploration Strategies with Decision Transformers

2025-10-07T04:14:18Z

Meta-Learning Exploration Strategies with Decision Transformers Welch, Ryan The problem of pure exploration in sequential decision-making is to identify strategies for efficiently gathering information to uncover hidden properties of an environment. This challenge arises in many practical domains, including clinical diagnostics, recommender systems, and educational testing, where data collection is costly and the effectiveness of exploration is critical. Efficient exploration in these contexts strongly depends on exploiting underlying structural relationships within the environment. For instance, recognizing that multiple medical tests may provide overlapping information can reduce the number of tests required to make a diagnosis. Existing exploration approaches drawn from reinforcement learning and active hypothesis testing typically rely on heuristic strategies that require explicit prior assumptions about such structural information. However, when this information is unknown, heuristic methods often lead to redundant exploration, significantly limiting their practical utility in high-stakes domains. Furthermore, these existing approaches do not leverage past experience to improve their exploration efficiency over time. To overcome these limitations, we introduce In-Context Pure Exploration (ICPE), a novel meta-learning framework capable of autonomously discovering and exploiting latent environmental structures across related tasks to guide efficient exploration. ICPE leverages the in-context learning and sequence-modeling capabilities of transformers, combined with supervised learning and deep reinforcement learning techniques to learn exploration strategies directly from experience. Through extensive experiments on synthetic and semi-synthetic exploration tasks, we demonstrate that ICPE is able to efficiently explore in deterministic, stochastic and highly structured environments without relying on any explicit inductive biases. Our results highlight the potential of ICPE to enable more practical exploration strategies suitable for real-world decision-making contexts.

WhatWhen2Ask: Cost-Aware LLM Querying for Autonomous Robots in Uncertain Environments

2025-10-07T04:13:46Z

WhatWhen2Ask: Cost-Aware LLM Querying for Autonomous Robots in Uncertain Environments Thirumalai, Vittal Autonomous agents operating in real-world environments must make decisions under uncertainty, facing challenges such as partial observability, sparse rewards, and long-horizon planning. While reinforcement learning (RL) enables agents to learn from experience, standard policies often struggle to generalize in the presence of ambiguous tasks or incomplete information. Large language models (LLMs) can provide valuable semantic guidance, but their high computational cost and latency make constant querying impractical. This thesis introduces WhatWhen2Ask, a framework for cost-aware, confidence-driven querying of external multimodal large language models (MLLMs). The agent employs a Deep Q-Network (DQN) as its internal action planner, selectively querying open- and closed-source models (BLIP-2 and GPT-4o) in a hierarchical manner when its confidence is low and external guidance is likely to improve performance. Accepted hints are embedded and fused with structured state representations, supported by tailored reward shaping for improved learning in sparse environments. Evaluated in the HomeGrid environment, WhatWhen2Ask improves the success rate from 38% (DQN-only) to 54%, while querying in fewer than 6% of steps. Ablation studies show that semantic hints, confidence-based querying, selective hint filtering, and hierarchical fallback each contribute meaningfully to performance. These results suggest that principled, confidence-aware LLM querying can enhance decision-making in uncertain environments, offering a step toward more efficient and cost-aware language-augmented agents.

Detecting Errors in Financial Data: A Multi-Agent LLM and Synthetic Data Approach

2025-10-07T04:13:07Z

Detecting Errors in Financial Data: A Multi-Agent LLM and Synthetic Data Approach Liu, Katherine With the high volume of activity flowing through financial institutions, detecting potential errors remains a critical challenge. This paper addresses two key areas where errors may occur: business name registrations and transactions within valid accounts. Traditional string-matching methods struggle to accurately identify incorrectly written business names that closely resemble existing ones, while existing error detection models for transaction data often suffer from class imbalance, leading to reduced performance on minority incorrect transaction cases. To address these issues, this paper proposes two novel approaches. First, a hybrid method integrating multi-agent Large Language Models (LLMs) with existing string-matching techniques enhances the detection of incorrect business names by capturing subtle variations beyond conventional edit-distance metrics, improving the recall from 0.815 for the baseline model to 0.987 using the proposed method. Second, an improved tabular data generation method for credit card transactions is introduced, leveraging LLMs and class balancing to generate high-quality synthetic data. Using this data to train error detection systems results in a decrease of the false negative rate from 23.47% to 12.84%. Together, these methods enhance the performance of error detection systems, enabling financial institutions to enhance the experiences of their clients.

Switching State Space Modeling via Constrained Inference for Clinical Outcome Prediction

2025-10-07T04:14:11Z

Switching State Space Modeling via Constrained Inference for Clinical Outcome Prediction Su, Arnold C. In clinical settings, timely and accurate prediction of adverse patient outcomes can help guide treatment decisions. While deep learning models such as LSTMs have demonstrated strong predictive performance on multivariate clinical time series, they often lack interpretability. To address this gap, this thesis proposes a framework that combines the predictive strength of neural networks with the interpretability of latent variable models. Specifically, we develop a constrained inference approach to train a switching state space model—an autoregressive hidden Markov model (AR-HMM)—for outcome prediction. Our method leverages knowledge distillation: a high-capacity LSTM "teacher" model is first trained to predict a target clinical outcome of interest, and its predictive behavior is then transferred to an interpretable AR-HMM "student" model through a similarity constraint during inference. We implement a constrained variational inference approach to estimate the parameters of the student model while aligning its latent representations with that of the teacher model’s. We evaluated our approach using two real-world clinical datasets. Our approach demonstrates predictive performance comparable to state-of-the-art deep learning models, while producing interpretable latent trajectories that reflect clinically meaningful patient states.

Duality, Weight Decay, and Metrized Deep Learning

2025-10-07T04:14:20Z

Duality, Weight Decay, and Metrized Deep Learning Newhouse, Laker The Muon optimizer has shown convincing evidence that it is faster and more scalable than AdamW for deep learning training, setting speed records for training NanoGPT and scaling up to models with 16B parameters. The theory that led to Muon is called metrized deep learning, a method that suggests assigning norms to each part of a neural network. Chapter 1 begins with an accessible explanation of metrized deep learning, including one of its recurring tools: odd polynomial iterations that act directly on singular values. Chapter 2 reviews duality, a way to modify the gradient that seeks to decrease the loss the most while disturbing the model the least. Pedagogically, duality links four popular optimizers—SGD, Adam, Shampoo, and Muon—under a common framework, steepest descent under a norm. Practically, experiments suggest that duality-based optimizers train faster than AdamW and transfer learning rate across width. Chapter 3 develops tools to enforce weight norm constraints during training, conferring provable and upfront Lipschitz guarantees for transformers. We find that optimizer dynamics matter: switching from AdamW to Muon improves standard weight regularization methods—weight decay and spectral normalization—allowing models to reach equal performance with a lower Lipschitz bound. Leveraging that Muon’s update has a fixed spectral norm, we co-design a weight constraint method called spectral cap that improves the Lipschitz vs. performance tradeoff for MLPs and 2M parameter transformers. Our 4-Lipschitz transformer on Shakespeare text reaches validation accuracy 60%. Scaling to 145M parameters, our 600-Lipschitz transformer reaches 21% accuracy on internet text. However, to match the NanoGPT baseline validation accuracy of 39.4%, our Lipschitz upper bound increases to 10^274. Nonetheless, our Lipschitz transformers train without stability measures such as layer norm, QK norm, and tanh logit softcapping.

Modeling Sequence Uncertainty in Comparative Genomics with a Probabilistic DNA Representation

2025-10-07T04:13:56Z

Modeling Sequence Uncertainty in Comparative Genomics with a Probabilistic DNA Representation Zhao, Sarah Ann Uncertainty in nucleotide sequences is widespread in bioinformatics, arising from somatic mutations, population-level variation, sequencing errors, and ancestral state inference. Yet, standard formats like FASTA encode DNA deterministically using ASCII string characters, omitting this uncertainty and contributing to pervasive reference biases in genomics. Graph pangenomes have recently emerged to address these limitations by representing genetic variation across populations as bidirected graphs. While promising, these approaches are still developing and are not yet fully integrated with widely used linearly-referenced genomic tools and databases. To bridge this gap, I introduce pDNA (probabilistic DNA), a linearly-referenced data structure that encodes nucleotide-level uncertainty in a vector format compatible with traditional genomics workflows. Each position in a pDNA sequence is represented as a 4-dimension probability vector over the four possible DNA nucleotides, inspired by position weight matrices and one-hot encodings. I also introduce pFASTA, a binary file format for efficient storage of pDNA sequences, along with an open-source software package for generating, manipulating, and analyzing these data. This framework enables uncertainty-aware sequence analysis while maintaining compatibility with existing genomics infrastructure. I apply this framework to ancestral sequence reconstruction.

Online Acquisition of Simulatable Rigid Object Models

2025-10-07T04:14:13Z

Online Acquisition of Simulatable Rigid Object Models Yang, Ethan How can we build a robot that operates autonomously in a home environment over long periods of time? A key requirement is the ability to perceive and understand its surroundings, including the objects it will interact with. This thesis investigates how a robot can reconstruct previously unknown objects and integrate them into a physics simulation for planning. We explore two methods for reconstructing the 3D geometry of objects and test their performance in simulation and in real-world experiments. Our results demonstrate that a learned depth model enables 3D reconstruction of unknown objects and their successful integration into simulation environments. Additionally, we investigate methods for estimating an object’s inertial parameters, using its reconstructed mesh and through manipulation.

Scaling contrastive learning batch size by two orders of magnitude

2026-01-16T20:14:33Z

Scaling contrastive learning batch size by two orders of magnitude Tian, Betsy Contrastive learning has emerged as a powerful framework for unsupervised representation learning, allowing models to learn by maximizing agreement between related samples and distinguishing dissimilar ones. However, contrastive learning frameworks are fundamentally limited by the number of negative pairs a model can observe, and memory-intensive backbones constrain practical batch sizes. We introduce a three-phase, adapter-augmented training framework that scales contrastive batch sizes by two orders of magnitude – surpassing previous state-of-the-art learners in both accuracy and speed. First, we co-train the backbone and adapter on small batches to establish a strong initialization. Next, we freeze the backbone and train the adapter alone with very large batches, exposing it to an enlarged negative pool. Finally, we transfer large-batch adapter gradients back into the backbone via segmented backpropagation. We evaluate our method on the PlacesAudio dataset and show promising results for boosting retrieval performance at each phase. By exposing the model to substantially more negatives per effective batch, we achieve higher accuracy at a faster speed than optimizer-stepping baselines. Ultimately, this approach that scales batch size by hundreds of times can be integrated into any contrastive learning framework for more robust representation learning and abundant negative sampling.

Towards Fully Automated Volumetric Analysis of Lung Nodules in Computed Tomography

2025-10-07T04:14:04Z

Towards Fully Automated Volumetric Analysis of Lung Nodules in Computed Tomography Rubel, Evan Early detection of lung cancer significantly improves patient outcomes, and tracking the growth of lung nodules over time is key to understanding their progression and informing future treatment decisions. However, calculating nodule growth in computed tomography (CT) scans remains a highly manual and time-consuming task. In this work, we develop an automated end-to-end pipeline to compute lung nodule growth using state-of-the-art computer vision techniques. While modern advances in deep learning have all but solved many learning tasks in the domain of natural images, biomedical imaging presents unique challenges due to limited data availability, inconsistent annotations, and deployment constraints. We address these challenges by training robust detection and segmentation models using the LUNA16 and LNDb datasets. On the held-out UniToChest dataset, our methods generalize well, attaining a nodule recall of 77.49%, reducing false positives per scan by a factor of 11.3 compared to existing techniques, and achieving a mean nodule-wise Dice score of 0.6453. We then apply our methods to analyze nodule growth in 1,378 patients from the National Lung Screening Trial; we estimate a median nodule volume-doubling time of 791.23 days across all nodules from the patients that do not receive a cancer diagnosis and a median nodule volume-doubling time of 637.38 days across all nodules from the patients that do receive a cancer diagnosis. We also recall 82.20% of radiologist-annotated nodules that are directly associated with a cancer diagnosis and estimate a shorter median nodule volume-doubling time of 370.11 days for these nodules. By automating lung nodule growth quantification, this work lays the foundation for improved screening protocols, personalized treatment planning, and the development of novel imaging biomarkers. To encourage further work in this area, we release our full software pipeline at https://github.com/evanrubel/nodule_volumes.

Design and Analysis of a 80 GHz Hybrid CMOS Dielectric Resonator Oscillator

2025-10-07T04:13:26Z

Design and Analysis of a 80 GHz Hybrid CMOS Dielectric Resonator Oscillator Louie, Tiffany This work studies a high frequency, low phase noise, hybrid CMOS oscillator based on a cylindrical dielectric resonator coupled directly to an on chip structure. Dielectric resonators (DR) are known for their high quality factor, low cost, and high temperature stability which makes them a desirable frequency selecting element in design for millimeter-wave (mmWave) applications. Current dielectric resonator oscillators (DRO) have proven to be phase stable, but are limited in frequency (< 40Ghz) due to their implementation with discrete components. However, in increasing the operational frequency up to the GHz range, it is possible to reduce size of the DR and place it directly on top of a cmos chip. We demonstrate, using a 22nm FD-SOI process, the design of a 80Ghz DRO with an area of 4mm² and an oscillator power consumption of 1.95mW. The DRO achieves a simulated phase noise of -128 dBc/Hz at 1MHz and -148 dBc/Hz at 10MHz.

LEO: an LLM-Powered EDA Overview

2025-10-07T04:13:57Z

LEO: an LLM-Powered EDA Overview Zheng, Sophia Computational notebooks impose a linear structure that impedes data analysts’ sensemaking process with overwritten cells, dead-end code, and fragmented logic. This challenge is especially pronounced when analysts either encounter a notebook authored by someone else or revisit a self-authored notebook after significant time has passed. In both cases, understanding the analysis code becomes convoluted and laborious. To address these barriers, we introduce LEO, a computational notebook tool that operationalizes notebook summarization by leveraging large language models to (1) cluster analysis patterns and (2) trace variable use. LEO organizes code into a two-level hierarchy–General Level Sections and Code Level Actions—integrated with in-line textual summaries filtered on the variable-level, further supporting task-driven exploration. We evaluate the system’s effectiveness in a user study with five computational notebook users across two realistic use cases. Participants reported that LEO streamlined code comprehension and navigation of undocumented notebooks by allowing them to query variables and traverse code cells with greater ease.

Articulated 3D Scene Graphs from Egocentric Vision

2025-10-07T04:13:12Z

Articulated 3D Scene Graphs from Egocentric Vision Yu, Alan Robotic mapping systems typically approach building metric-semantic scene representations from the robot’s own sensors and cameras. However, these “first person” maps inherit the robot’s own limitations due to its embodiment or skillset, which may leave many aspects of the environment unexplored. For example, the robot might not be able to open drawers or access wall cabinets. In this sense, the scene graph is not as complete, and requires a more capable robot to fill in the gaps by remapping. We narrow these blind spots in current methods by leveraging egocentric data captured as a human naturally explores a scene wearing Project Aria glasses, giving a way to directly transfer knowledge about articulation from the human to any deployable robot. We demonstrate that, by using simple heuristics, we can leverage egocentric data to recover models of articulate object parts, with quality comparable to those of state-of-the-art methods based on other input modalities. We also show how to integrate these models into 3D scene graph representations, leading to a better understanding of object dynamics and object-container relationships. We finally demonstrate that these articulated 3D scene graphs enhance a robot’s ability to perform mobile manipulation tasks, showcasing an application where a Boston Dynamics Spot is tasked with retrieving concealed target items, given only the 3D scene graph as input.

Decentralized Declustering of Multiple Underactuated Autonomous Surface Vehicles

2025-10-07T04:13:54Z

Decentralized Declustering of Multiple Underactuated Autonomous Surface Vehicles Strømstad, Filip Traasdahl Multi-agent systems have seen a significant rise in research interest, enabled by the increasing availability of low-cost autonomous platforms and motivated by a wide range of emerging applications. However, the coordinated deployment of large numbers of autonomous vehicles in marine environments remains a nontrivial and high-risk problem, yet it is often overlooked in the literature. These vehicles are typically deployed from a single location, and their underactuated nature, close proximity, and susceptibility to external disturbances make it difficult to achieve a mission-ready configuration without collisions. In this thesis, we address the problem of transitioning a set of underactuated Autonomous Surface Vehicles (ASVs) from arbitrary and inconvenient initial conditions to a deconflicted set of deployed vehicles. We propose a decentralized and scalable method that calculates and assigns target positions to the vehicles, generates optimal paths that comply with minimum turning radius constraints, and ensures collision avoidance between the vehicles through a shared speed policy. Contributions also include a formal definition and quantification of clustering and declustering in multi-agent systems. The approach is implemented using the MOOS-IvP autonomy framework, and performance is evaluated through simulation with up to $64$ vehicles and extensive field trials with eight vehicles. Results demonstrate that our approach reduces the time to decluster for the most challenging initial conditions by 50% compared to the current manual method. By improving efficiency and robustness while eliminating human involvement, this work streamlines ASV fleet deployments, enabling more scalable multi-agent field operations.

DBOS Advanced Network Analysis Capability for Collaborative Awareness

2025-10-07T04:13:02Z

DBOS Advanced Network Analysis Capability for Collaborative Awareness Lockton, Sophia E. Collaborative cyber defense is an essential strategy for detecting and mitigating cyber threats [1]. As traditional intrusion detection systems struggle against increasingly sophisticated attacks, we propose embedding collaborative cyber defense directly into system infrastructure. This work presents a novel implementation of collaborative awareness within DBOS (a Database-Oriented Operating System), resulting in a platform that significantly accelerates application development while providing built-in security for transactional web services. By treating security as a first-class operating system service, our approach facilitates real-time comprehensive network observation and analysis without the need for external tools. The implementation supports the construction, aggregation, and analysis of traffic matrices using both Python and PostgreSQL-based workflows. These workflows extract and process IP-level metadata from DBOS applications, enabling multi-instance aggregation and analysis of network data. This integration represents the first instance of collaborative network analysis within an operating system runtime, demonstrating that secure-by-default infrastructure is both feasible and performant.

Minding the Politeness Gap in Cross-cultural Communication

2025-10-07T04:13:34Z

Minding the Politeness Gap in Cross-cultural Communication Machino, Yuka Misunderstandings in cross-cultural communication often arise from subtle differences in interpretation, but it is unclear whether these differences arise from the literal meanings assigned to words or from more general pragmatic factors such as norms around politeness and brevity. In this paper, we report three experiments examining how speakers of British and American English interpret intensifiers like “quite” and “very,” finding support for a combination of semantic and pragmatic factors. To better understand these differences, we developed a computational cognitive model where listeners recursively reason about speakers who balance informativity, politeness, and utterance cost. A series of model comparisons suggest that cross-cultural differences in intensifier interpretation stem from (1) different literal meanings, (2) different weights on utterance cost. These findings challenge accounts based purely on semantic variation or politeness norms, demonstrating that cross-cultural differences in interpretation emerge from an intricate interplay between the two.

Empirical Analysis of Neural Architectures and Side Information in Financial Time Series Forecasting

2025-10-07T04:12:39Z

Empirical Analysis of Neural Architectures and Side Information in Financial Time Series Forecasting Senthil, Swathi This thesis investigates the predictive capabilities of neural networks in financial time series forecasting, focusing on predicting the weekly close price of the SPY index. We explore the integration of options-derived features alongside traditional price data, compare recurrent architectures and transformer-based models, and evaluate multiple training strategies. Our key contributions include: (1) evidence that options-derived input features improve both error metrics and directional accuracy; (2) a comparison study of four training methods (one-step-ahead, direct multi-step, simulation error, and teacher-forcing); (3) the development of a bidirectional GRU-LSTM hybrid model that outperforms standard recurrent networks in multi-step forecasting; and (4) a novel coarse tokenization approach for discretizing continuous financial data, which improves first-week prediction performance when used in transformer models that use an asymmetric attention mechanism. Overall, this thesis illustrates the importance of input design, model architecture, and training methodology in neural financial forecasting. We conclude by outlining directions for future work, including cross-asset generalization and further exploration of tokenization schemes for transformer-based models.

Mitigating LLM Hallucination in the Banking Domain

2025-10-07T04:13:41Z

Mitigating LLM Hallucination in the Banking Domain Sert, Deniz Bilge Large Language Models (LLMs) offer significant potential in the banking sector, particularly for applications such as fraud detection, credit approval, and enhancing customer experience. However, their tendency to "hallucinate"—generating plausible but inaccurate information—poses a critical challenge. This thesis examines existing strategies for mitigating LLM hallucinations and proposes a novel approach to reduce hallucinations in the context of predicting customer churn using LLMs.

Layered Unlearning for Adversarial Relearning

2025-10-07T04:13:17Z

Layered Unlearning for Adversarial Relearning Qian, Timothy Our goal is to understand how post-training methods, such as fine-tuning, alignment, and unlearning, modify language model behavior and representations. We are particularly interested in the brittle nature of these modifications that makes them easy to bypass through prompt engineering or relearning. Recent results suggest that post-training induces shallow contextdependent “circuits” that suppress specific response patterns. This could be one explanation for the brittleness of post-training. To test this hypothesis, we design an unlearning algorithm, Layered Unlearning (LU), that creates distinct inhibitory mechanisms for a growing subset of the data. By unlearning the first 𝑖 folds while retaining the remaining 𝑘 − 𝑖 at the 𝑖th of 𝑘 stages, LU limits the ability of relearning on a subset of data to recover the full dataset. We evaluate LU through a combination of synthetic and large language model (LLM) experiments. We find that LU improves robustness to adversarial relearning for several different unlearning methods. Our results contribute to the state-of-the-art of machine unlearning and provide insight into the effect of post-training updates.

Mixed-Variable Bayesian Optimization using Prior-Data Fitted Networks

2025-10-07T04:13:52Z

Mixed-Variable Bayesian Optimization using Prior-Data Fitted Networks Qian, Janet Bayesian optimization (BO) is a powerful framework for optimizing expensive blackbox functions, widely used in domains such as materials science, engineering design, and hyperparameter tuning. Traditional BO relies on Gaussian processes (GPs) as surrogate models, but GPs face limitations in flexibility and scalability. Prior-Data Fitted Networks (PFNs) have recently emerged as a promising alternative, leveraging transformer architectures and in-context learning to approximate posterior predictive distributions (PPDs) in a single forward pass. By training on large amounts of synthetically generated data from sample-able function priors, PFNs can learn to rapidly predict PPDs across a wide range of function classes. In this thesis, we investigate the application of PFNs to mixed-variable BO, a particularly challenging setting due to the interplay between continuous and discrete inputs and the combinatorial complexity of the search space. We evaluate how PFNs perform when integrated with a range of mixed-variable BO strategies, including various encoding schemes and discrete-aware acquisition optimization. Additionally, we explore how finetuning PFNs on targeted function priors can enhance performance when prior knowledge about the objective is available. Our contributions include empirical evaluations of mixed-BO techniques, insights into PFN training, and a suite of mixed-variable benchmark problems.

Eliminating Hallucination-Induced Errors in Code Generation with Functional Clustering

2025-10-07T04:13:28Z

Eliminating Hallucination-Induced Errors in Code Generation with Functional Clustering Ravuri, Chaitanya Modern code–generation LLMs can already solve a large fraction of programming problems, yet they still hallucinate subtle bugs that make their outputs unsafe for autonomous deployment. We present functional clustering, a black-box wrapper that eliminates nearly all hallucination-induced errors while providing a tunable confidence score. The wrapper samples many candidate programs, executes each on a self-generated test suite, and clusters candidates whose I/O behavior is identical; the empirical mass of the largest cluster serves as an exact confidence estimate. A single scalar threshold on this estimate lets users trade coverage for reliability with exponential guarantees. On LiveCodeBench our verifier preserves baseline pass@1 on solvable tasks yet slashes the error rate of returned answers from ∼65% to 2%, and drives it to 0% at a conservative threshold while still answering 15.6% of prompts. Manual audits show that the few residual mistakes stem from prompt misinterpretation, not random generation noise, narrowing future work to specification clarity. Because the method requires only sampling and sandbox execution, it applies unchanged to closed-source APIs and future models, offering a practical path toward dependable, autonomous code generation.

Choosing Networks for Ride-Hailing Platforms

2025-10-07T04:13:09Z

Choosing Networks for Ride-Hailing Platforms Somsirivattana, Thana The development of autonomous vehicles is poised to reshape the landscape of transportation. As companies prepare to deploy these vehicles on ride-hailing platforms, a key operational challenge is determining the networks on which to train the vehicles. Our work contributes toward addressing this challenge on three fronts. First, we develop a theoretical model of the network selection problem and prove theoretical results that show the importance of two parameters: the detour factor and the fleet size. Second, we develop several approaches for selecting the networks. Third, we evaluate these approaches on empirical data. We find empirical support for the importance of the detour factor and the fleet size.

PyGridSim: A Functional Interface for Distributed System Simulation

2025-12-11T16:38:42Z

PyGridSim: A Functional Interface for Distributed System Simulation Zhao, Angela M. This thesis details the development of PyGridSim, an open source python module that leverages OpenDSS capabilities to provide an efficient and scalable functional interface for building distributed system simulations. Distributed power systems encompass all components that power an electrical system— from larger power plants to microgrids—and represent the network of electric consumption and production in a system. Simulations of such power systems allow experts to analyze potential faults and risks in a fast, reproducable, and cost-efficient way. Thus, the accessibility of such simulations is critical to supporting the safety and reliability of power systems. While existing packages built for distributed system simulation provide the necessary computing power and customizability of a distributed system simulator, their interfaces are hard to scale over many nodes and often have difficult-to-learn syntax. PyGridSim aims to build on these existing modules—maintaining customizability while providing a flexible, intuitive, and scalable syntax structure.

Improving the Programmability of A Distributed Hardware Accelerator

2025-10-07T04:13:22Z

Improving the Programmability of A Distributed Hardware Accelerator Shwatal, Nathan A. Sparse iterative matrix algorithms are critical to many scientific and engineering workloads, yet they perform poorly on conventional hardware. (Ōmeteōtl, a new hardware accelerator with a distributed-memory and task-based execution model, aims to address these performance bottlenecks. However, programming for (Ōmeteōtl is low-level, error-prone, and far removed from the simplicity of typical iterative formulations. This thesis presents Lapis, a domain-specific language and compiler that allows users to express sparse matrix algorithms in high-level Python code and automatically generates efficient C++ code for (Ōmeteōtl. Lapis abstracts away data partitioning and task orchestration, reducing implementation complexity: for example, it lowers lines of code by 30× for conjugate gradients and 46× for power iteration. Despite this abstraction, generated code achieves 75.7% to 92.6% of the performance of manually written implementations across several benchmarks.

Study of Thermochemical Non-equilibrium and Sensor Cavity Geometry in Hypersonic Flow

2025-10-07T04:12:44Z

Study of Thermochemical Non-equilibrium and Sensor Cavity Geometry in Hypersonic Flow Mao, Grace This work presents a computational investigation of the influence of geometric configurations within a hypersonic flow field on optical distortion, with a particular focus on the effects of window deformation and the role of thermochemical modeling compared to perfect gas assumptions. Turbulent RANS and conjugate heat transfer were used to model three 3D geometries in US3D, an unstructured-grid finite volume computational fluid dynamics (CFD) solver. The three investigated geometries are a flat plate with a flush-mounted sensor, an open cavity with a length-to-depth ratio of 2, and a closed cavity with a length-to-depth ratio of 16. The data demonstrate that the flat plate configuration has the best optical performance and that the closed cavity has the worst. Additionally, the inclusion of thermochemistry in the flow simulation results in a more pessimistic outlook on image quality compared to the perfect gas model. The results document optical distortion for several different geometries with and without thermochemical modeling within hypersonic flow that can inform future design decisions and research.

BlueVeri: Formal Security Verification for Bluespec Processor Designs

2025-10-07T04:13:28Z

BlueVeri: Formal Security Verification for Bluespec Processor Designs Wang, Shih-Yu There are numerous hardware security defense mechanisms designed to mitigate sidechannel attacks. However, ensuring that a defense can comprehensively protect against an entire class of attacks, while avoiding the introduction of new vulnerabilities that could lead to additional attack surfaces, remains a significant challenge. Although researchers have attempted to apply formal verification techniques to hardware security, these efforts have been hindered by scalability issues. In this paper, we introduce BlueVeri, a systematic and automatable approach for formally verifying the security of a Bluespec processor against speculative execution attacks. BlueVeri leverages the high-level information provided by Bluespec’s guarded atomic actions, simplifying and accelerating the verification process. We evaluate BlueVeri on out-of-order processors implemented in Bluespec, demonstrating that our approach substantially enhances verification scalability and is capable of proving the security properties of a minimal out-of-order processor within one hour.

Machine Learning Methods for Churn Prediction and Infrastructure Resilience

2025-10-07T04:12:35Z

Machine Learning Methods for Churn Prediction and Infrastructure Resilience Agrawal, Shreeansh This thesis investigates how advanced machine learning methods can effectively address two critical business challenges facing the telecommunications industry: short-term customer churn prediction and long-term infrastructure resilience to climate-driven disruptions. In the first part of this work, I develop an upgrades-informed churn forecasting model tailored specifically for marketing operations. Recognizing limitations in the existing aggregate forecasting methodologies, I create a cohort-based cascade model that explicitly integrates customer upgrade behavior across various contract tenures. To address data sparsity and longitudinal gaps in newer contract types, I employ synthetic data generation and imputation techniques, such as regression-based methods and Multivariate Imputation by Chained Equations (MICE). For forecasting churn and upgrade rates, I prioritize interpretability by applying linear regression enhanced with time-series forecasting techniques and macroeconomic indicators, including the Consumer Price Index. This approach significantly improves forecasting accuracy, aligns internal stakeholder objectives, and supports strategic decision-making around customer retention and promotional offers. The second part focuses on building predictive models and strategic frameworks for long-term infrastructure resilience in the face of increasing climate risks. Leveraging spatial-temporal clustering methods (DBSCAN) and advanced neural network architectures, I develop a model to attribute historical outages to extreme weather events. Further, I integrate this model with future climate scenarios from CMIP5 projections using Monte Carlo simulations, providing actionable insights into future infrastructure vulnerabilities. Employing SHapley Additive exPlanations (SHAP), I interpret model predictions, highlighting critical factors such as precipitation, windspeed, and atmospheric pressure. Additionally, I propose frameworks for quantifying financial impacts of future outages and recommend optimization strategies for proactive infrastructure hardening and emergency response. Collectively, these applications demonstrate the value of strategically employing interpretable and robust machine learning methodologies to enhance short-term operational decisions and long-term strategic planning within telecom organizations.

Segmentation Based Tracking for Aerial Robot Global Localization in Unstructured Environments with Oblique Monocular Camera Orientation

2025-10-07T04:13:14Z

Segmentation Based Tracking for Aerial Robot Global Localization in Unstructured Environments with Oblique Monocular Camera Orientation Shafferman, Hannah R. In the field of robotics, there has been a growing interest in multi-robot systems and their potential to improve the efficiency, scale, and reliability of tasks beyond what an individual robot can achieve. Global localization is a crucial task for autonomous robot navigation, specifically in the multi-agent scenario where robots need to localize within maps communicated by other agents. The scenario where vehicles are viewing their environments from the same perspective, or camera viewpoint, is well studied. However, when environments are mapped from different camera viewing angles, traditional methods fail to match visual features and thus fail to localize. The technical gap that this thesis addresses is when autonomous vehicles within a team are mapping the same environment from different viewpoints, specifically nadir and an oblique camera orientations in an unstructured environment. Many existing visual place recognition (VPR) methods fail to match visual features that look visually different due to appearance, illumination, or viewpoint changes and thus fail to localize. In this thesis, we demonstrate the shortcomings of previous work to generalize to an off-nadir camera angle and explore the benefits and challenges that arise with utilizing oblique imagery for visual feature detection and tracking. We propose a segmentation-based object tracking pipeline to improve tracking and environment mapping performance in this traditionally challenging scenario. Our approach consists of 1) a front-end auto-segmentation tracking pipeline followed by 2) a submap correspondence search, which exploits geometric consistencies between environment maps to align vehicle reference frames. We evaluate our approach on a challenging indoor, cluttered dataset and demonstrate a maximum precision 74% higher than traditional and learning-based baseline methods, with a map size 0.5% the size of the most memory conservative traditional baseline method.

An Aerocapture Guidance and Estimation Framework for Improved Robustness to Uncertainty

2025-10-07T04:13:00Z

An Aerocapture Guidance and Estimation Framework for Improved Robustness to Uncertainty Sonandres, Kyle A. Aerocapture is an orbital insertion maneuver that converts a hyperbolic approach trajectory into a desired captured orbit using the aerodynamic forces generated during a single atmospheric pass. While it offers major benefits, such as reduced interplanetary cruise time and lower propellant mass reserves, it also introduces significant risk due to extreme sensitivity to atmospheric and delivery state uncertainties. This drives the need for robust guidance algorithms and accurate environmental estimation techniques. This thesis presents approaches to address both of these needs, developing solutions to improve aerocapture performance and robustness to uncertainty. The first contribution is the development of ABAMGuid+, a novel aerocapture guidance algorithm that leverages simultaneous control over bank angle and angle of attack. Inspired by optimal control theory, the algorithm uses a four-phase structure to mimic the optimal control laws while maintaining tractability for online use. Optimal control theory is utilized to identify the optimal control solutions, and numerical optimization is used to validate the analytic solutions prior to integration into a guidance algorithm. Extensive simulation results of a Uranus aerocapture scenario, including over 140,000 Monte Carlo trajectories, demonstrate significant improvements in capture success rates and propellant efficiency compared to existing methods. The second contribution addresses environmental uncertainty directly by developing a deep learning-based approach to estimate the atmospheric density profile during flight. A long short-term memory (LSTM) neural network-based architecture is trained to predict atmospheric density given sequences of flight data. The trained model is integrated into the guidance loop and a curriculum learning process is used to refine in-flight performance. Monte Carlo results show that the LSTM-augmented guidance system reduces propellant usage compared to traditional estimation methods. In summary, this thesis presents two approaches that improve aerocapture performance and robustness to uncertainty. We show that this added robustness can be achieved both by expanding algorithmic ability and by improving environmental estimation approaches.

Mass and Distance Estimation Simulations for the Nancy Grace Roman Space Telescope Using PyLIMASS and a Case Study on Intellectual Property Frameworks in Space Collaborations

2025-12-10T00:31:37Z

Mass and Distance Estimation Simulations for the Nancy Grace Roman Space Telescope Using PyLIMASS and a Case Study on Intellectual Property Frameworks in Space Collaborations McGee, Carissma Gravitational microlensing is a phenomenon in which a foreground star or planet briefly magnifies light from a more distant background star. This effect enables the discovery of exoplanets that are otherwise undetectable, including those orbiting faint hosts and at large separations. Microlensing is well suited to characterizing exoplanets beyond the snow line, revealing mass ratios and orbital geometries inaccessible to transit or radial velocity methods. The Nancy Grace Roman Space Telescope will carry out the Galactic Exoplanet Survey to detect thousands of microlensing events with the cadence and precision necessary for statistical exoplanet population studies. To verify Roman’s ability to meet its core science requirement, recovering the lens mass and distance in at least 40% of planetary events with better than 20% uncertainty, targeted simulations are essential. Using the pyLIMASS inference framework and Fisher matrix-based uncertainty propagation, I demonstrate that for the well-characterized event OGLE-2013-BLG-0132Lb, the lens mass can be constrained to within 18.7% uncertainty, validating the feasibility of Roman’s requirement on a case-study basis. This thesis also addresses the legal and policy foundations needed to ensure global access to these simulation tools. By advancing open-source software models and proposing a space IP framework for equitable knowledge sharing, it supports collaborative scientific infrastructure for future international space missions.

Combined Steam Power Cycle and Turbofan Engine for Improvement in Aviation Climate Impacts

2025-10-07T04:13:45Z

Combined Steam Power Cycle and Turbofan Engine for Improvement in Aviation Climate Impacts Mueller, Anna Despite significant innovations in aviation technology over the last 70 years resulting in enormous efficiency improvement, the rising demand for air travel means that aviation carbon emissions continue to increase each year. The rate of improvement to aircraft propulsion engines is diminishing and additional improvements often add significant engine cost or weight. With the goal of reducing aviation’s contribution to global climate change, future aircraft engine designers must consider concepts that stray from the traditional turbofan engine. In this thesis, I develop an engine cycle model combining the turbofan engine with a steam power cycle and use the model to explore the benefits of applying this concept to aircraft engines. In order to study the impact to engine performance and emissions from adding a steam cycle, the engine model needs to be capable of representing the water phase changes and the heat exchangers required to drive those phase changes. My contribution is the development of such a model – with special attention to the modeling of water properties and phase change of water – which ties heat exchanger models into an engine thermodynamic model. The engine cycle as well as heat exchanger parameters including water-to-air ratio, combustor exit temperature, overall pressure ratio, and water pressure are varied to explore the impact to overall engine performance, including the impact of the added heat exchanger weight. This thesis covers the development and initial testing of this model, which enables future studies in engines with phase changing heat exchangers or water injection with the goal of assisting the search for the future engine technologies that will reduce harmful impacts of aviation while continuing to allow air travel.

An Aero-Thermo-Chemo-Mechanical Coupling Framework for the Analysis of Hypersonic Ablative Thermal Protection Systems

2025-10-07T04:13:43Z

An Aero-Thermo-Chemo-Mechanical Coupling Framework for the Analysis of Hypersonic Ablative Thermal Protection Systems Hoss, Summer A. There are countless challenges associated with the accurate modeling of the hypersonic flight of ablative thermal protection systems (TPS): resolving the relevant coupled physical phenomena through multi-physics simulations, the management of the disparate spatiotemporal scales associated with the fluid and solid responses, and establishing a reliable numerical model able to predict the response of ablative materials exposed to extreme gradients—to name a few. The two-way, loosely coupled framework presented in this thesis consists of ΣMIT, a multi-physics computational solid mechanics (CSM) code, coupled with US3D, a hypersonic computational fluid dynamics (CFD) solver, to form a complete aero-thermochemo-mechanical simulation framework. The ΣMIT-US3D coupling framework provides a step towards high-fidelity simulation capabilities for hypersonic vehicles with ablative TPS, establishing a strong foundation for the simulation of fluid-structure interaction (FSI) phenomena and computation of the mechanical response of porous ablators. The requirement of a robust numerical formulation for the solution of hypersonic pyrolysis problems was made apparent when encountering numerical convergence issues with legacy methods, which sparked the development of a robust semi-implicit pyrolysis material model. The so-called Linearized Pyrolysis model employs simplifying assumptions for the energy and mass balance equations and relies upon the time-lagging of chosen terms to achieve linear convergence and robust performance. The performance of the model has been validated against the Ablation Workshop Test Cases and has increased the range of allowable representative hypersonic boundary conditions significantly compared to the legacy approach. Together, the model and the coupling framework are applied to two aero-thermochemo-mechanical analyses contained within the thesis: a spherical-tipped nose cone and the Orion heat shield. Preliminary results identify the decomposition region as a zone in which high von Mises stress tends to occur—care must be taken to ensure that internal and external flight loads do not exceed allowable limits to prevent catastrophic TPS material failure in this region. However, perhaps the most significant insight resulting from the framework relates to the computation of mass fluxes through the porous ablative material, revealing that for an isotropic monolithic heat shield with at a zero angle of attack, pyrolysis gas flow is driven by the pressure gradient applied to the shield such that the flow exits at the edges of the shield rather than from the base.

Aeroverse: Aerospace Education in Extended Reality

2025-10-07T04:13:32Z

Aeroverse: Aerospace Education in Extended Reality Johnson, Mollie Aerospace education is a continuously evolving field that is increasingly dependent on digital tools. However, it is ambitious to shift the teaching paradigm to accommodate new cutting-edge technologies. Extended reality (XR), which encompasses augmented (AR) and virtual reality (VR), is an example of such technology. In recent years, VR has seen an increase in usage in education as a novel way to provide students with immersive learning experiences, and XR has a long history of use within the working aerospace industry. However, application in the overlap between the two— aerospace engineering education— remains largely unexplored to date. The themes addressed in this thesis are two-fold: first, the goal is to create VR learning modules to supplement the existing aerospace engineering curriculum. Second, the aim is to validate whether VR technology as a teaching medium can improve learning outcomes and student engagement within the MIT AeroAstro department. With these themes in mind, two experiments were conducted to explore this topic. The first experiment presents the design and execution of an experimental course aimed at aerospace engineering students to assess the educational impact of VR. Over the course of this study, ANOVA and Kruskal-Wallis tests found that there was no significant difference (p > 0.05) in performance between the VR and non-VR groups, save for a few exceptional cases. The second experiment details the integration of a single VR module into an existing course in which all students interacted with the VR activity. Students responded positively to this experiment, reporting increased feelings of engagement and a sense that it aligned well with the rest of the course. One-sample Wilcoxon tests reveal that these findings are largely significant (p < 0.05). This thesis advances the work on assessing VR use for aerospace education. The implications of this work may influence the decisions of other educators regarding the adoption of VR technology as supplements to their own teaching methodologies. As a whole, this thesis contributes to the broader conversation on integrating VR into the classroom.

Investigating the Role of Mission Architecture in Crew Socioemotional Health for Mars Exploration

2025-10-07T04:13:24Z

Investigating the Role of Mission Architecture in Crew Socioemotional Health for Mars Exploration MacRobbie, Madelyn Human space exploration is evolving rapidly, with commercial successes and NASA’s Artemis missions driving rapid growth and innovation. Plans for longer, larger, and more complex missions necessitate development of new mission architectures to sustain the crews needed to support these missions. Larger missions and multi-site architectures have become feasible with advances in commercial launch vehicles, and generate increased safety and redundancy for crewed operations. However, crew dynamics in these mission architectures have yet to be investigated. This thesis investigates the role of mission architecture (specifically single-site versus dual-site configurations) in subgroup formation and the resulting impacts to socioemotional well-being. We first develop a systematic approach for optimizing analog mission design, then apply this to design two analog missions to compare the effects of single-site and dual-site mission architectures on crew dynamics and psychosocial health. Results provide valuable insights for future Mars mission design, where crew structure and psychosocial adaptation are critical to mission success.

Towards a Strong, Human-Compatible Codenames AI Agent

2025-10-07T04:13:15Z

Towards a Strong, Human-Compatible Codenames AI Agent Zhu, Sebastian Current language models are limited in their ability to solve complex planning and reasoning problems without the aid of search procedures. While a large body of work has developed search procedures tailored to single-turn, single-user natural language interactions, language generation in multi-agent contexts involving multiple users, imperfect information, and partially misaligned objectives remains extremely challenging. We aim to build search procedures that will enable language models to assist with interactive, multi-agent decision-making in a diverse range of contexts. Using the word game Codenames as a benchmark, we will combine game-theoretic planning procedures with basic language model-based scoring methods to create agents that both play strong policies and play well with human policies. This work yields a set of practical text generation procedures, new evaluation benchmarks, and foundational algorithmic improvements in language model search.

An Investigation into Contrail Observability from Different Satellite Platforms

2025-10-07T04:13:06Z

An Investigation into Contrail Observability from Different Satellite Platforms Euchenhofer, Marlene V. Contrails are line-shaped ice clouds that can form behind aircraft engines and, under certain cold and moist conditions, spread into contrail cirrus that persists for several hours. By adding to the existing cloud cover, contrails can act to either cool or warm, with the latter, on average, being dominant, resulting in an overall warming effect. Although the effective radiative forcing from contrails is inferred to be of the same order of magnitude as that caused by aviation’s CO₂ emissions, large uncertainties remain around specific radiative forcing estimates. Observational studies of contrails, either to support climate impact assessments or operational contrail avoidance strategies, face trade-offs between spatial and temporal resolution. Many recent publications have relied on data from geostationary satellites accepting lower input data resolution in exchange for higher temporal resolution and greater spatial coverage. Limitations of the observability of contrails in the resulting images have not been sufficiently investigated and need to be assessed and quantified. This study aims to leverage the higher spatial resolution of VIIRS satellite imagery to identify potential limitations on contrail observability in lower-resolution GOES ABI imagery. We generate a dataset of human-identified contrails visible in false-color thermal infrared imagery from both GOES ABI and VIIRS for twelve scenes over the contiguous US. Based on this dataset, we investigate the number, cover, and appearance of the observed contrails. We find that GOES ABI does not resolve 80% of all contrails that can be identified in VIIRS imagery and only shows half of the total observed contrail length. Finally, incorporating an existing contrail-flight matching algorithm by Barbosa, we show that VIIRS tends to resolve more younger contrails than GOES ABI. The findings from this study help to bound the validity of current contrail simulations and modeling outputs that estimate contrail cover and occurrence.

MINCE: Dialect-Aware SQL Decomposition for Federated Query Execution

2025-10-07T04:12:36Z

MINCE: Dialect-Aware SQL Decomposition for Federated Query Execution Zhang, Sophie S. The increasing adoption of specialized database systems has led to the rise of heterogeneous data environments. While having multiple engines in a data infrastructure enables opportunities for workload optimization, SQL dialect incompatibility makes workload migration difficult. To address this challenge, we develop MINCE (Multi-dialect INtegration and Crossengine Execution), a technique that decomposes SQL queries into parts to enable federated execution across engines with differing SQL dialects. MINCE uses a rule-based method to partition a query into executable components that are assigned to different database systems. To evaluate different execution strategies, MINCE further implements a cost model that incorporates both on-engine query execution time and inter-system data transfer overhead. We evaluate MINCE on a TPC-H-based workload augmented with PostgreSQL-specific functions unsupported in Amazon Redshift. Experimental results show that MINCE produces the fastest execution strategy among our baselines for 72.1% of queries using estimated cardinality, achieving a 2× speedup over single-engine baselines. With perfect cardinality information available to our cost model, this value increases to 88.4%, with an average 2.8× speedup. These results demonstrate that our system not only enables more flexible federated query execution, but also reliably identifies performant execution strategies.

New results in canonical polyadic decomposition overfinite fields

2025-10-07T04:13:40Z

New results in canonical polyadic decomposition overfinite fields Yang, Jason Canonical polyadic decomposition (CPD) consists of expressing a tensor (multidimensional array) as a sum of several rank-1 tensors, each of which is an outer/separable product of vectors. The number of rank-1 tensors used in a CPD is called the rank of the CPD, and the minimum possible rank of a CPD for a given tensor is called the rank of the tensor. CPD is at the core of fast matrix multiplication, a computational problem with widespread implications across several seemingly unrelated problems in computer science. Much recent progress in this field has used randomized heuristic search to find new CPDs, often over a finite field. However, if these techniques fail to find a CPD with low enough rank, they cannot prove that no such CPD exists. Consequently, these methods fail to resolve certain long-standing questions, such as whether the tensor corresponding to 3 × 3 matrix multiplication has rank less than 23. To make progress on these problems, we develop a novel algorithm that preserves exactness, i.e. they can provably verify whether or not a given tensor has a specified rank. Compared to brute force, when searching for a rank-R CPD of a n0 × · · · × nD−1-shaped tensor over a finite field F, where n0 ≥ · · · ≥ nD−1, our algorithm saves a multiplicative factor of roughly |F| R(n0−1)+n0( P d≥1 nd) . Additionally, our algorithm runs in polynomial time. We also find a novel algorithm to search border CPDs, a variant of CPDs that is also important in fast matrix multiplication. Finally, we study the maximum rank problem and give new upper and lower bounds, both for families of tensor shapes and specific shapes. Although our CPD search algorithms are still too slow to resolve the rank of 3 × 3 matrix multiplication, we are able to utilize them in this problem by adding extra search pruners that do not affect exactness or increase asymptotic running time.

Parameter Estimation for Anonymous Hawkes Processes

2025-10-07T04:13:35Z

Parameter Estimation for Anonymous Hawkes Processes Wang, William Hawkes Processes are self-exciting point processes used to model many real-life networks in which an event from one agent causes the rate at which events occur from related agents to increase, such as in earthquake networks or social media. This project investigates the question of finding the underlying structure of the Hawkes Processes given a history of when events occurred. This problem has been studied extensively in the regime where the event labels are known, and the bulk of the literature involves parameterizing the model and passing it through statistical learning tools. Our proposed work focuses on the the same question in “anonymous" case where labels are not given. In this regime, the lack of information makes many previous approaches intractable and we develop novel non-parametric approaches for solving cases of the structure learning problem in algorithmic and information theoretic settings. Our results show the ability to learn the entire model under mild assumptions in the information theoretic regime, where we have access to an arbitrarily long Anonymous Hawkes Process transcript, whereas when we’re confined to a polynomially lengthed transcript, the situation is considerably more difficult.

Organization Infrastructure for Tokenized Asset Records

2025-10-07T04:13:27Z

Organization Infrastructure for Tokenized Asset Records Whartenby, Patrick E. The Tokenized Asset Record (TAR) represents a way to connect existing technology related to tokenized assets and asset schemas to real-world documents that validate the existence of an object. Exactly who should manage TARs and the properties of the related organization schemes remains an open question. Answering this question is crucial to furthering the existing digital economy. While existing solutions have sought to expand digital commerce through pioneering digital clearing houses, little work has explored support for other classes of real-world digitized assets with proof of ownership and existence. The research proposed here seeks to answer this question by suggesting possible solutions and developing a framework for uniformly analyzing the proposals. The research proposes and evaluates three models for the management of TARs. The first is a scheme that involves each industry setting up its own TAR database and managing the system independently from other industries. The second proposes hosting all TARs on a single blockchain. The third argues for an off-chain decentralized platform to host all, akin to the Data Spaces proposed by the European Union. The research finds, based on the proposed criteria, that a decentralized off-chain approach best meets the goals of a TAR management framework.

Unveiling Phenotype–Genotype Interplay with Deep Learning Foundation Models for scRNA-seq: A Quantitative Perspective

2025-10-07T04:13:23Z

Unveiling Phenotype–Genotype Interplay with Deep Learning Foundation Models for scRNA-seq: A Quantitative Perspective Thadawasin, Pakaphol Foundation models have emerged as powerful tools for analyzing single-cell RNA sequencing (scRNA-seq) data, leveraging large-scale pretraining to capture complex gene expression patterns. However, a comprehensive quantitative framework for understanding the interplay between phenotypes and genotypes remains underdeveloped. Such a framework is critical not only for validating model performance but also for uncovering previously unrecognized biological relationships. In this work, we present both traditional and deep learning-based quantitative analysis pipelines for PolyGene [1], a transformer-based scRNA-seq foundation model, aimed at disentangling the complex phenotype–genotype relationship. First, we implement a top-k classification and entropy evaluation pipeline to serve as a primary validation framework. Our results demonstrate that the pretrained PolyGene [1] is robust in top-k classification metrics and provides meaningful insights into the entropy landscape of human cells across different life stages. Second, we propose a novel deep learning gradientbased gene selection method designed to address limitations in traditional feature selection approaches, such as poor scalability and sensitivity to heterogeneity in high-dimensional data. Through empirical evaluations on benchmark scRNA-seq datasets, we show that our method enhances model interpretability and improves downstream performance, offering a more scalable and biologically relevant alternative to existing techniques. Overall, this work introduces a set of quantitative analysis tools that fill a critical gap in evaluating and interpreting scRNA-seq foundation models, contributing to a deeper understanding of the genotype–phenotype interplay through modern deep learning techniques.

Deepfake Face Detection: An Ensemble Framework for Generalized Classification in Biometric Verification Systems

2025-10-07T04:13:20Z

Deepfake Face Detection: An Ensemble Framework for Generalized Classification in Biometric Verification Systems Zen, Hilary Generation methods for deepfake images have advanced rapidly, and deepfake face images pose a critical security for biometric verification systems. Applications that rely on face recognition to grant access to sensitive data need to maintain high accuracy across a wide variety of deepfake generation methods, including novel and developing types that the application has not previously trained on. Current deepfake detection models achieve nearperfect accuracy on benchmark datasets, but do not perform as well on unseen types of deepfakes that were not part of their training dataset. We propose building an ensemble model with multiple base detectors, each trained on different generation model families to maintain high performance across many deepfake generation methods. Using four base models, including two models with the same architecture and training data, we exhaustively test all possible ensemble models. We find that combining similar base models trained on the same deepfake generation family does not improve performance compared to the individual base models. However, combining base models trained on different deepfake generation families leads to significant increases in accuracy and recall. Our ensemble framework provides a flexible and inexpensive solution in the ever-changing landscape of deepfake generation and security.

When Should Model Updates Propagate?

2025-10-07T04:13:16Z

When Should Model Updates Propagate? Struckman, Isabella Marguerite AI supply chains rely increasingly on downstream developers adapting pretrained upstream models. When upstream models are retrained with data deletions (which may be prompted by copyright violations, privacy compliance, or removal of illicit content), it’s unclear if all downstream developers must also undergo costly retraining. In this thesis, we investigate the propagation of data deletions through fine-tuned models within a controlled visual classification setting comprising dog-breed and plane-manufacturer recognition tasks. We show that not all model updates propagate equivalently to downstream tasks, and there is a strong relationship between the deleted data’s relationship to the downstream task and its affect on the downstream model. We demonstrate that neither simple performance metrics (accuracy or F1), nor output-level divergences, nor even embedding-based similarity metrics alone adequately predict when a deletion meaningfully impacts downstream tasks. To overcome these limitations, we introduce an information-theoretic metric grounded in Gaussian mixture modeling (GMM) of embedding distributions, capturing deeper representational shifts. Our proposed approach precisely distinguishes when deletions require downstream retraining, achieving high predictive accuracy and recall without directly accessing retrained downstream models.

Digital Twin Modeling for NV Magnetometry

2025-10-07T04:13:08Z

Digital Twin Modeling for NV Magnetometry Rich, John P. This thesis presents the development and application of a digital twin modeling framework for nitrogen-vacancy (NV) center-based magnetometry, advancing the field of quantum sensing. A surrogate model serves as a computational representation of the physical NV magnetometer system, enabling comprehensive exploration of parameter spaces to optimize device design. Leveraging machine learning techniques, this study optimizes control mechanisms, including the design of learned analog filters, to enhance system performance. This research investigates the fundamental limits of NV magnetometer performance, identifying strategies to minimize power requirements while maintaining high sensitivity. A dynamic framework is implemented to update the surrogate model’s parameters in real-time based on experimental measurements, ensuring accurate fidelity to the physical system. Additionally, the optimized control strategies are simulated within the digital twin environment, demonstrating their potential for advanced quantum sensing applications such as magnetocardiography (MCG) for heartbeat detection.

Fuzzing for User-Schedulable Languages

2025-10-07T04:13:11Z

Fuzzing for User-Schedulable Languages Moon, Kenneth Performance engineers restructure programs to use hardware as efficiently as possible. Even simple mathematical functions can become sprawling and complex programs when fully optimized, as the resulting code must often be precisely molded around specialized behaviors supported by the hardware. To help performance engineers deal with this complexity, userschedulable languages provide scheduling operations, which are abstractions of common steps taken to restructure programs. By composing these scheduling operations, performance engineers can concisely represent their intended optimizations to programs. Exo, being a user-schedulable language, provides this abstraction with the additional guarantee that any scheduling operation which passes Exo’s automated checks does not change the behavior of the program. Though this guarantee is useful for avoiding bugs while optimizing a program, the analysis required to provide such a guarantee is infeasible on programs in general. To make analysis feasible, Exo only allows users to write programs with a restricted set of behaviors. As a result, some programs are impossible to schedule using Exo, limiting the use cases of Exo. In this thesis, we explore how fuzzing can be used as an alternative to the existing analysis in Exo, with the goal of allowing Exo to analyze more complex programs. “Fuzzing” refers to a test case-driven approach to determining properties of a program, such as whether its behavior changes after a scheduling operation. If the program’s outputs do not change after the scheduling operation when provided the same inputs, the fuzzer concludes that the program’s behavior did not change. Since fuzzing only requires us to know how to evaluate the program, it can be applied to a much broader set of programs than the existing analysis in Exo. However, fuzzing can miss mistakes in scheduling if the fuzzer fails to find a test case demonstrating the issue with a scheduling operation, as it is a complete form of analysis rather than a sound form of analysis like the existing analysis in Exo. Additionally, fuzzing can be costly compared to the original analysis, as repeatedly running programs on many test cases for many scheduling operations can be slow. We explore ways to mitigate these issues throughout this work. Finally, we evaluate our implementation of the fuzzer and its performance on some example use cases for Exo.

Efficient Verifiable Computation Made Easy

2025-10-07T04:12:46Z

Efficient Verifiable Computation Made Easy Ma, Chengyuan Recent advancements in cloud computing, data privacy, and cryptography have sparked a growing interest in Verifiable Computation (VC) in both industry and academia. In particular, zero-knowledge proof (ZKP) algorithms are gaining rapid traction due to their strong privacy guarantees. However, they are notoriously computationally intensive, making performance a critical concern. Given the inherent data parallelism and heavy use of vector operations in ZKP computations, multicore CPUs and GPUs offer a promising acceleration path. Unfortunately, accelerated programming for ZKP remains challenging: ZKP algorithms evolve rapidly, their structures grow increasingly complex, and writing high-performance ZKP code is tedious, error-prone, non-portable, and unfriendly to algorithm developers. We present an end-to-end compiler framework, Zera, that lowers ZKP algorithms to parallel hardware for efficient acceleration, with minimal programmer effort. By effectively leveraging ZKP algorithm patterns and trends, we are able to automate the key performance optimizations, with a succinct linguistic extension and a set of practical compiler customizations. Consequently, with just 92 lines of trivial high-level annotation added to the original 7,000 lines of C++ code, our single-source code solution delivers 33.9× and 24.0× speedup on GPU over a highly optimized serial C++ implementation on CPU and an existing multithreaded Rust baseline on CPU, respectively. Compared to our hand-optimized GPU/CUDA implementation requiring an extra 2,000 lines of low-level code (roughly 60 programmer hours), our compiler-generated GPU implementation is only 58% slower (1.58× slowdown) on large inputs, demonstrating a compelling trade-off between performance and productivity.

Optimizing Partitioning for Efficient Parallel Reads

2025-10-07T04:13:03Z

Optimizing Partitioning for Efficient Parallel Reads Sragow, John Modern database management systems spend a significant portion of query execution time scanning data, so minimizing scanning latency is critical to maintaining high performance. As such, databases are partitioned into blocks so that queries can skip irrelevant tuples and avoid scanning the entire database. When this partitioning is optimized to minimize the number of blocks accessed by each query, smaller queries that access very few blocks fail to fully utilize the bandwidth because they cannot take advantage of parallel reading. However, reducing the size of each block in order to increase the number of blocks accessed by smaller queries slows down larger queries by forcing them to increase the number of I/Os they must perform. We propose a novel partitioning scheme that shuffles the row groups of blocks accessed by smaller queries so that they can read fewer tuples from multiple blocks in parallel without increasing the I/O cost of larger queries. Our experiments show that this technique allows smaller queries to be scanned up to twice as fast on larger block sizes as they would on a standard partitioning without significantly slowing down larger queries.

Integrating Functional Knowledge into Protein Design: A Novel Approach to Tokenization and Noise Injection for Function-Aware Protein Language Models

2025-10-07T04:12:32Z

Integrating Functional Knowledge into Protein Design: A Novel Approach to Tokenization and Noise Injection for Function-Aware Protein Language Models Tang, Adrina Designing novel proteins with specific biological functions remains a fundamental challenge in computational biology. While recent advances in protein language models have enabled powerful sequence-based representations, most models, including state-of-the-art systems like ESM3, fall short in effectively encoding functional context during protein generation. In this work, we present a multimodal protein co-design framework that conditions sequence generation on fine-grained functional annotations, specifically leveraging residue-level Gene Ontology (GO) term labels on sequences from the UniRef100 database. By explicitly associating functional signals with residue elements of proteins, our model learns to generate function-conditioned protein sequences that are biologically plausible and semantically consistent. Unlike prior approaches, which treat function as a secondary feature or a classification task, our method focuses on joint reasoning over function and sequence during the design process. This closes a critical gap in the current landscape of protein design tools, offering a scalable and generalizable approach to co-designing protein sequences with user-specified functional profiles.

Pairwise Matching of Intermediate Representations for Fine-grained Explainability

2025-12-10T00:52:29Z

Pairwise Matching of Intermediate Representations for Fine-grained Explainability Shrack, Lauren The differences between images belonging to fine-grained categories are often subtle and highly localized, and existing explainability techniques for deep learning models are often too diffuse to provide useful and interpretable explanations. We propose a new explainability method (PAIR-X) that leverages both intermediate model activations and backpropagated relevance scores to generate fine-grained, highly-localized pairwise visual explanations. We use animal and building re-identification (re-ID) as a primary case study of our method, and we demonstrate qualitatively improved results over a diverse set of explainability baselines on 35 public re-ID datasets. In interviews, animal re-ID experts were in unanimous agreement that PAIR-X was an improvement over existing baselines for deep model explainability, and suggested that its visualizations would be directly applicable to their work. We also propose a novel quantitative evaluation metric for our method, and demonstrate that PAIR-X visualizations appear more plausible for correct image matches than incorrect ones even when the model similarity score for the pairs is the same. By improving interpretability, PAIR-X enables humans to better distinguish correct and incorrect matches.

Deep Learning for Space Object Density Distribution Prediction

2025-10-07T04:12:29Z

Deep Learning for Space Object Density Distribution Prediction Sarangerel, Sumiyajav The rapid growth of artificial objects in Low Earth Orbit (LEO) has heightened concerns over orbital congestion and collision cascades, known as Kessler Syndrome. Traditional high-fidelity models, while accurate, are computationally intensive and poorly scalable. This thesis introduces a machine learning–based framework for forecasting the long-term evolution of space object density. A large dataset is generated, using the MIT Orbital Capacity Assessment Tool – Monte Carlo (MOCAT-MC), simulating thousands of scenarios across varying launch, disposal, and maneuver parameters. A Convolutional Gated Recurrent Unit (ConvGRU) is trained to predict density distributions over a 100-year horizon, achieving accurate forecasts with significantly reduced runtime. With a simple guidance mechanism, the generalization capability of the model across diverse scenarios is greatly improved. This approach offers a scalable and efficient tool for supporting future space traffic management and sustainability efforts.

Towards Understanding Privacy Leakage in Decentralized and Collaborative Learning

2025-10-07T04:12:32Z

Towards Understanding Privacy Leakage in Decentralized and Collaborative Learning Shi, Yichuan The emergence of large-scale machine learning (ML) models has highlighted a fundamental conflict: While computational demands push for the consolidation of data and models in vast, centralized data centers, real-world data continues to be distributed and fragmented across personal devices and private databases. How can we reconcile this contradiction without further monopolizing the ML ecosystem? What unique privacy and security risks arise from alternative ML orchestration system designs? Furthermore, how do these vulnerabilities and system failures inform our understanding of both how and what machines learn? This thesis attempts to explore these questions. It first examines key types of privacy leakages, evaluating their impact under realistic, cross-distribution settings. It then introduces a benchmarking analysis platform, SONAR, to investigate the relationship between privacy leakage (measured by attack performance), network topology, and data distribution. Finally, it presents Co-Dream, a novel algorithm for collaborative learning that offers improved privacy characteristics.

Prototyping a Scalable Proof Engine

2025-10-07T04:12:37Z

Prototyping a Scalable Proof Engine Rosario, Jon Formal verification is an exciting development in software engineering, enabling implementations of programs to be rigorously checked against mathematical specifications. Assuming the specification is well-defined, formal verification provides guarantees of a program’s correctness and freedom from bugs that are simply not possible with test-based methods. There’s just one catch: the process of verifying large programs in popular theorem provers such as Coq (now known as Rocq) or Lean is painfully slow. These proof assistants rely on proof engines to construct proofs of correctness for given properties, but to our knowledge, there is no widely available proof engine that offers strong performance guarantees. Even more frustrating is the lack of consensus on what “good” performance should even mean in this context. This thesis lays the groundwork for addressing that gap by presenting a proof engine design that achieves asymptotically linear-time performance with respect to several important variables. We illustrate the design and its performance characteristics with examples from an implementation of the design and outline directions for future work.

Stress-Guided Material Segmentation for Recycled 3D Printed Structures Using Finite Element Analysis

2025-10-07T04:13:01Z

Stress-Guided Material Segmentation for Recycled 3D Printed Structures Using Finite Element Analysis Paulin, Cole J. We present a simulation-driven method for optimizing the structural performance of 3D printed objects made with recycled and fresh filament. Although sustainable materials such as recycled PLA reduce environmental impact, they often exhibit degraded or inconsistent mechanical properties, making them less suitable for structurally demanding applications. To address this, we develop a finite element analysis (FEA) pipeline that simulates stress and strain distributions under user-defined loading conditions, enabling intelligent segmentation of the object into regions of high and low mechanical demand. These segmented regions can be assigned recycled or fresh material during fabrication. Our system leverages open-source tools (SfePy) for simulation and we validate its accuracy against Abaqus, a commercial industry standard. We also introduce methods for automatically identifying and correcting segmentation artifacts, such as small disconnected islands. Through comparative simulation studies and performance evaluation, we demonstrate that our approach enables more sustainable 3D printing without sacrificing structural reliability

Metaheuristic Optimization for Automatic Arrangement of Power Electronics Components in a Shipboard Electrical Distribution System

2025-10-07T04:12:33Z

Metaheuristic Optimization for Automatic Arrangement of Power Electronics Components in a Shipboard Electrical Distribution System Lohier, Sebastien This thesis proposes a novel methodology for the automatic placement of Power Electronics Building Blocks (PEBBs) in modular, integrated power corridor designs. These building blocks, which are created and tested offsite for a variety of applications, are currently placed manually during the design process, a method that is time-consuming and suboptimal. To address this challenge, we reduce the placement problem to a 2D bin-packing problem, leveraging a hybrid approach combining Genetic Algorithms and Simulated Annealing. This approach enables the generation of optimized placements that find the extremes of arbitrary heuristics, including minimizing routing distance and power density, effectively improving both design efficiency and system performance. The proposed methodology offers a significant step toward automating and optimizing the layout of power electronic components in complex systems.

An Interpretable Multimodal Framework for Regional Organ Transplantation Outcomes

2025-09-19T04:50:03Z

An Interpretable Multimodal Framework for Regional Organ Transplantation Outcomes Lee, Ju Young The demand for kidney transplants continues to outpace supply, with over 89,792 patients on the waitlist as of September 2024, yet only 27,332 transplants performed in 2023 [1], and 28% of recovered kidneys going non-utilized [2]. In this thesis, we highlight the use of large language model (LLM) embeddings combined with structured tabular data to build a predictive classifier that estimates offer outcomes for kidney donor-recipient matches. For each predictive model deployed, we provide further analysis on the interpretability of these black-box models using a custom-designed SHAP analysis framework. Our study focuses on three distinct U.S. regions (Regions 1,2,3) with markedly different demographics and amounts of data on organ acceptances (Region 1: 43,126 offers with 2.19% acceptance rate, Region 2: 394,640 offers with 1.57% acceptance rate, Region 3: 169,342 with 2.23% acceptance rate in years 2016-2019). Among the baseline XGBoost models, Region 3 achieved the highest performance, with a precision-accept score of 0.929 and accuracy of 0.993 in the test data. Building on this strong foundation, the multimodal TabText model in Region 3 achieved the best performance overall, with a precision-accept score of 0.959 and accuracy of 0.993 after fine-tuning for six epochs. Our findings suggest that increasing the number of text features, extending training epochs, and incorporating explicit numerical values led to improved model performance in Region 3. In Regions 1 and 2, the baseline model outperformed the TabText model, suggesting that data sparsity in these regions may have limited the effectiveness of the multimodal approach and that further hyperparameter tuning is needed. We also present several visualization techniques to enhance model interpretability. Specifically, we developed a novel SHAP explainer that illustrates feature interactions between multimodal inputs, including both tabular and textual data. Additionally, we explored methods to identify regions of high and low model fidelity by mapping per-sample prediction errors onto t-SNE embeddings. Overall, this thesis introduces new directions for transplant research in the context of transformer-based models and interpretable AI. Leveraging data-driven decision-support tools and refining allocation policies are essential steps toward addressing the persistent gap between supply and demand in the kidney transplant landscape.

Medium Access Control Protocol for Satellite Constellations

2025-09-19T04:50:02Z

Medium Access Control Protocol for Satellite Constellations Li, Brian Satellite internet constellations have emerged as a promising solution for providing global internet connectivity, especially in regions underserved by terrestrial infrastructure. However, as user demand increases, especially in densely populated urban areas, existing Medium Access Control (MAC) protocols face significant scalability challenges and fail to take advantage of advanced antenna processing techniques, including phased array nulling, as well as capacity sharing via inter-satellite links. We present both an offline linear program and a novel online greedy MAC protocol to assign satellite resources to users using either sequential service, capacity sharing, or interference-aware nulling. Our offline formulation provides an upper bound on system performance, and while our online protocol is sub-optimal compared to this optimum, it is designed to be implementable on a real-time system. Simulations demonstrate that incorporating nulling can increase effective capacity by up to 25 times, substantially boosting profit in high-demand scenarios. We further quantify the performance gap between the online protocol and the offline optimum under varying demand distributions, showing that our online approach achieves near-optimal results in low-peakiness settings and gracefully degrades under more extreme conditions. These results highlight the importance of spatial processing at the MAC layer and offer practical design insights for future satellite internet constellations.

Copilot Tutor: Automated Software Engineering Practice Augmented with LLMs

2025-09-19T04:50:00Z

Copilot Tutor: Automated Software Engineering Practice Augmented with LLMs Kong, Blisse In recent years, large language models (LLMs) have become more ubiquitous in the workplace. In software engineering, they are often realized as “copilots" which produce code given a prompt or existing code. Programmers using these tools to increase their coding productivity need to be proficient in inspecting and in understanding these copilots’ outputs. As engineers incorporate these tools to accelerate their workflows, they have a parallel opportunity to accelerate learning new programming languages. This thesis presents a tutor interface where students with some programming experience in an origin language can learn a target language while practicing how to critically read and fix a copilot’s output to write correct, safe programs. This work also introduces the automatic generation of exercises teaching syntax and semantics on which a programmer experienced in the origin language but not the target language should focus.

Strategic Physical Withholding of Renewable Energy Generators

2025-09-19T04:50:00Z

Strategic Physical Withholding of Renewable Energy Generators Irvine, Paul M. Renewable generators may have incentives to strategically withhold energy output in electricity markets, either to exercise market power or to avoid congestion pricing caused by transmission constraints. Although academic work often treats renewables as not downward dispatchable, renewable generators technically can, at least in principle, reduce their output by self-curtailing. This paper shows that a firm with a large, diverse portfolio could find it profit-maximizing to withhold renewables over conventional thermal generators once it accounts for constraints on ramp rates and minimum generation, as well as the costs associated with starting-up generators and the probability of detection on generator type by market monitoring authorities. Long-term forward contracts like pay-as-produced Power Purchase Agreements (PPAs) can blunt incentives to exercise market power by insulating individual generators from wholesale prices; however, since generators under PPAs typically bid into the wholesale market and influence competitive prices, they may actually encourage renewable withholding if contract prices are sufficiently low and the parent firm’s portfolio is exposed to wholesale prices. To screen for renewable withholding, this paper proposes three methods: (1) examining the distribution of aggregate output across export interfaces for suspicious bunching, (2) testing deviations from ex-ante forecasts, and (3) identifying the time intervals where generators encounter model structural changes compared to a benchmark presumed free of withholding. Together, this work prepares academics and regulators to more accurately model the behavior of renewable generators in electricity markets and to screen for potential market abuses.

Argos: Verifiable FHE Using Commodity Hardware

2025-09-19T04:49:59Z

Argos: Verifiable FHE Using Commodity Hardware Jepsen, Fisher We present Argos, a simple approach for adding verifiability to fully homomorphic encryption (FHE) schemes using trusted hardware. Traditional approaches to verifiable FHE require expensive cryptographic proofs, which incur an overhead of up to seven orders of magnitude on top of FHE, making them impractical. With Argos, we show that trusted hardware can be securely used to provide verifiability for FHE computations, with minimal overhead relative to the baseline FHE computation. An important contribution of Argos is showing that the major security pitfall associated with trusted hardware, microarchitectural side channels, can be completely mitigated by excluding any secrets from the CPU and the memory hierarchy. This is made possible by focusing on building a platform that only enforces program and data integrity and not confidentiality (which is sufficient for verifiable FHE, since all data remain encrypted at all times). All secrets related to the attestation mechanism are kept in a separate coprocessor (e.g., a TPM)—inaccessible to any software-based attacker. Relying on a discrete TPM typically incurs significant performance overhead, which is why (insecure) software-based TPMs are used in practice. As a second contribution, we show that for FHE applications, the attestation protocol can be adapted to only incur a fixed cost. Argos requires no dedicated hardware extensions and is supported on commodity processors from 2008 onward. Our prototype implementation introduces 3% overhead for FHE evaluation, and 8% for more complex protocols. In particular, we show that Argos can be used for real-world applications of FHE, such as private information retrieval (PIR) and private set intersection (PSI), where providing verifiability is imperative. By demonstrating how to combine cryptography with trusted hardware, Argos paves the way for widespread deployment of FHE-based protocols beyond the semi-honest setting, without the overhead of cryptographic proofs.

Automatic Conversion of C and C++ Programs to the BuildIt Multi-Stage Programming Framework

2025-09-19T04:49:58Z

Automatic Conversion of C and C++ Programs to the BuildIt Multi-Stage Programming Framework Kumar, Aryan BuildIt allows users to write C++ programs that can execute in multiple stages, where the output of one stage is the program source for the next stage, ending with some final output produced. This is particularly useful for writing specialized code and generating code for domain-specific languages. While there are other approaches to multi-stage programming, BuildIt has several advantages: it takes a library-based approach (so it requires no modifications to the compiler and is thus highly portable), and it has excellent ease of use as all the user has to do is change the declared types of variables in their C++ program. The goal of this thesis is to further improve BuildIt’s ease of use by simplifying this step: in particular, by developing a tool that will automatically convert existing C and C++ programs to the BuildIt framework. We show how to use Clang tooling in conjunction with modifications to the Clang compiler to perform non-trivial modifications to source, namely type-modification, to automatically convert code to its unstaged BuildIt equivalent. As the unstaged BuildIt code can be specialized by staging certain variables, this tool will ultimately enable more easily staging and optimizing C/C++ repositories with the BuildIt framework.

Association of GLP-1 Receptor Agonist Use with Kidney and Cardiovascular Outcomes in Stable Kidney Transplant Recipients

2025-09-19T04:49:57Z

Association of GLP-1 Receptor Agonist Use with Kidney and Cardiovascular Outcomes in Stable Kidney Transplant Recipients Jung, Emma Yejoo Recent surges in the use of glucagon-like peptide-1 receptor agonists (GLP-1RA) have shown promise in reducing cardiovascular events and improving kidney function in patients with type 2 diabetes. Due to these hopeful improvements, kidney transplant recipients (KTRs) have started using GLP-1RA. However, their effects in KTRs remain largely unstudied in clinical studies. This thesis uses a large-scale Electronic Health Record (EHR) database to perform a retrospective cohort analysis to study the association between GLP-1RA use and kidney and cardiovascular outcomes amongst stable KTRs. Primary outcomes include all-cause mortality, major adverse kidney events (MAKE), and major adverse cardiac events (MACE). Among stable KTRs, GLP-1RA users show reduced risk for all-cause mortality (adjusted hazard ratio [aHR]: 0.45; 95% confidence interval [CI]: 0.32-0.62) and MAKE (aHR: 0.69; 95% CI: 0.58-0.81), but no significant difference for MACE (aHR: 0.84; 95% CI: 0.67-1.05). In addition, users show increased risk for irritable bowel syndrome (IBS) (aHR: 2.11; 95% CI: 1.07-4.15) and urinary tract infection (UTI) (aHR: 1.53; 95% CI: 1.27-1.85). These results indicate the potential of GLP-1RA to reduce mortality and adverse kidney outcomes and increase IBS and UTI in KTRs.

Hardware Acceleration for Real-Time Compression of 3D Gaussians

2025-09-19T04:49:52Z

Hardware Acceleration for Real-Time Compression of 3D Gaussians Kahler, Kailas B. 3D Gaussian Splatting (3DGS) is a technique for novel view synthesis, where images of a scene from a specific viewpoint are generated using images from different viewpoints, that has gained popularity for its reduced computational overhead, resulting in faster training and rendering times compared to other methods like Neural Radiance Fields (NeRFs). Its applications outside of strictly novel view synthesis have also been explored, with monocular simultaneous localization and mapping (SLAM) in robotics being an emergent application. However, because of limited on-board battery capacity, the computer hardware used in small robots is much less capable than the high-powered GPUs that the 3DGS algorithm was originally developed on, having both less compute and memory capacity and bandwidth. While there has been work developing specialized compute for the rendering pipeline of 3DGS, memory remains an obstacle to deployment. The Gaussian map can occupy from 1MB − 700MB in memory, which is both too large to store on-chip within micro-robots and such that moving Gaussians from memory to compute can dominate power consumption. While there has been prior work on algorithms for compressing Gaussian representations, they are not yet capable of running in real-time on the hardware present in these robots, as would be required for SLAM. Thus, this thesis explores the limits of these compression methods on current hardware, resulting in an optimized CUDA implementation with better than 100× the throughput of prior work and achieving real-time operation on workstation-class hardware. However, after concluding that custom hardware is necessary for further improvement, this thesis also presents a hardware accelerator that nears real-time compression performance within a reduced power budget, outperforming an NVIDIA Jetson Orin Nano with 64% higher throughput while using 1/16th of the multipliers and drawing 38% of the power when running at 100MHz on an AMD UltraScale+ FPGA.

Personalization of AI Tutor Based on Knowledge Graphs

2025-09-19T04:49:55Z

Personalization of AI Tutor Based on Knowledge Graphs Huang, Sheng Personalized tutoring, tailored to the specific knowledge and needs of individual students, has been shown to significantly enhance academic performance. Research by Schmidt and Moust, for example, highlights that tutors who engage with students on a personal level are more effective in guiding them toward higher academic achievement [1]. Inspired by this principle, the Axiom group at the MIT Media Lab developed an AI tutor for their Intro to Programming courses. The initial version of the tutor, RAGS, relied on analyzing past conversations between students and the tutor, as well as course content, to generate personalized responses. While this approach showed promise, it faced scalability challenges, such as the need to store an ever-growing volume of conversation history and the risk of exceeding token limits in prompt context windows. Additionally, the model occasionally struggled with over-generalization, particularly when responding to vague questions based solely on historical interactions. To address these limitations, this thesis introduces a new approach: a student knowledge graph. Rather than relying on an expanding archive of past conversations, the knowledge graph uses weighted nodes to represent a student’s understanding of each concept. A weight of -8 indicates subpar understanding, while a weight of 8 signifies mastery. After pre-processing the course data, the graph maintains a fixed size, eliminating the need for additional storage over time. This innovation solves two critical problems: 1. Scalability: By leveraging a fixed-size PostgreSQL database, the student knowledge graph avoids the storage challenges associated with saving endless conversation histories. 2. Improved Personalization: Instead of sifting through old conversations, the tutor uses concept weights to generate more precise and contextually relevant responses, even to vague questions. Testing and evaluation of the implemented system demonstrate its effectiveness in both scalability and response quality. Over 60% of survey participants reported that the knowledge graph-enhanced tutor provided clearer and more relevant guidance, particularly when building on concepts they already understood. Additionally, over 80% of respondents noted improvements in the tutor’s ability to address weak areas and provide targeted practice, especially when preparing for quizzes or exams.

SPIRAL: Iterative Subgraph Expansion for Knowledge-Graph Based Retrieval-Augmented Generation

2025-09-19T04:49:44Z

SPIRAL: Iterative Subgraph Expansion for Knowledge-Graph Based Retrieval-Augmented Generation Hadjiivanov, Michael D. Large language models (LLMs) excel at generating fluent answers but are prone to hallucination when the prompt fails to anchor them to verifiable facts. Retrieval-augmented generation (RAG) mitigates this risk, yet existing graph-based retrievers either return bloated neighborhoods or incur prohibitive latency on large knowledge graphs (KGs). We introduce SPIRAL—Supervised Prior + Iterative Reinforcement with Adaptive Labelling—a lightweight two-stage framework that constructs compact, tree-shaped evidence subgraphs. This differs from previous work in its use of a trained, iterative policy network built on top of a prior over triples, delivering improved performance on multi-hop question answering tasks. Stage 1 trains a single-label GLASS-GNN on shortest-path heuristics, producing frozen, question-aware node embeddings at negligible runtime cost with significant local topology awareness around question entities. Stage 2 layers a GLASS policy—which re-labels the partial subgraph at each step—on top of these embeddings and optimizes it with proximal policy optimization. The policy scores only the 1-hop frontier, enabling sub-second inference even on million-edge graphs. On the multi-hop KGQA benchmark WebQSP, SPIRAL attains 0.95 triple recall and 0.97 answer recall while retrieving at most 50 triples—doubling the sampling efficiency of the strongest prior work. Coupled with Llama 3.1-8B, the retrieved trees boost Hit@1 by 2.5 % over SubgraphRAG. Ablation studies confirm that adaptive labels are critical for multi-hop reasoning. SPIRAL demonstrates that accurate and concise retrieval is achievable without resorting to massive models or expensive graph crawls, opening the door to real-time, KG-grounded assistants on modest hardware.

Injection of Domain-Specific Knowledge for Enterprise Text-to-SQL

2025-12-09T18:27:23Z

Injection of Domain-Specific Knowledge for Enterprise Text-to-SQL Choi, Justin J. This work examines the current state of using large language models (LLMs) to solve Text-to-SQL tasks on databases in an enterprise setting. Benchmarks on publicly available datasets do not fully capture the difficulty and complexity of this task in a real-world, enterprise setting. This study examines the critical steps needed to work with enterprise data as well as using knowledge-injection to enhance the performance of LLMs on Text-to-SQL tasks. We begin by evaluating the baseline performance of LLMs on enterprise databases, revealing that a predominant source of failure stems from a lack of domain-specific knowledge. To improve performance, we explore knowledge-injection: the process of incorporating internal and external knowledge. Internal knowledge consists of database-specific information such as join logic, while external knowledge refers to institutional acronyms or group names. We present a hybrid retrieval pipeline that combines embedding and text based searching with LLM-guided ranking to supply models with relevant external knowledge during Text-to-SQL generation. We evaluate the impact of the knowledge-injection by testing the performance of LLMs on the table retrieval task after being augmented with appropriate external knowledge. We demonstrate that knowledge-injection significantly improves accuracy on table retrieval using BEAVER: an enterprise-level Text-to-SQL benchmark. Our findings highlight the importance of domain-specific knowledge-injection and retrieval augmentation in bringing LLMs closer to deployment in enterprise-grade database systems, as well as common failure modes that occur when executing enterprise Text-to-SQL.

Evaluating the Feasibility of Transaction Scheduling via Hardware Accelerators

2025-09-19T04:49:37Z

Evaluating the Feasibility of Transaction Scheduling via Hardware Accelerators Chomphoochan, Thanadol As single-thread performance plateaus, modern systems increasingly rely on parallelism to scale throughput. Yet, efciently managing concurrency—particularly in transactional systems—remains a major bottleneck. This thesis explores the feasibility of accelerating transaction scheduling via hardware, leveraging FPGAs to ofoad scheduling logic from the CPU. We revisit Puppetmaster, a hardware transaction scheduler, and present a redesigned architecture emphasizing deployability, modularity, and evaluation. We implement both an optimized software baseline and a Bluespec-based hardware design, evaluating their performance across synthetic YCSB-style workloads with varying contention levels. Our hardware prototype demonstrates competitive throughput, achieving over 90% of peak throughput even under high-contention workloads. These results validate the potential of transaction scheduling as a target for hardware acceleration and highlight promising directions for future hybrid hardware-software concurrency-control systems.

Computational Exploration of Thermodynamic Models of Geological CO₂ Injection

2025-09-19T04:49:51Z

Computational Exploration of Thermodynamic Models of Geological CO₂ Injection Edelman, Jonathan This thesis investigates the behavior of carbon dioxide flow in porous media through high-fidelity computational modeling, with a specific focus on the impact of the Span-Wagner equation of state (EOS). Accurate modeling of CO₂ transport in subsurface environments is essential for applications such as carbon capture and storage (CCS). We model the entire flow from injection, down throughout a vertical pipe and into a porous reservoir. To this end, we utilize the MOOSE (Multiphysics Object-Oriented Simulation Environment) framework developed by Idaho National Laboratory to perform finite element simulations. A key contribution of this work is the successful coupling of a porous rock domain with a one-dimensional pipe flow simulation in Julia, enabling a broader representation of injection scenarios. The study examines how the thermodynamic accuracy of the Span-Wagner Equation of State influences flow characteristics, in comparison to the Ideal Gas Equation of State. Through a series of coupled pipe-reservoir simulations, we assess variations in pressure and density as CO₂ is injected from the pipe into the porous medium. The model can detect phase change conditions, allowing us to predict the maximum mass flux that can be achieved below the liquefaction threshold, as defined by the binodal curve in the CO₂ phase diagram at a given temperature. The results highlight the importance of EOS selection in predicting multiphase flow behavior, especially under conditions relevant to geological storage. Furthermore, we find that the Ideal Gas EOS underpredicts injection rates under the same conditions. This integrated modeling approach advances the understanding of thermodynamic effects in coupled subsurface flow systems and supports the development of reliable tools for large-scale carbon storage applications.

Clinical Text De-identification Using Large Language Models: Insights from Organ Procurement Data

2025-09-19T04:49:56Z

Clinical Text De-identification Using Large Language Models: Insights from Organ Procurement Data Dahleh, Omar This thesis presents a novel approach to the de-identification of clinical notes from Organ Procurement Organization (OPO) records, leveraging advanced natural language processing (NLP) methodologies. Specifically, we employ in-context learning using large language models (LLMs) to effectively identify and remove protected health information (PHI), aiming to maintain high data utility post-redaction. Our work systematically evaluates the performance of the LLM-based method against established baseline techniques, including traditional Named Entity Recognition (NER) and rules-based systems. Through a slew of experiments, we assesses the strengths and limitations of each method regarding precision and recall. This work will contribute to a uniquely extensive dataset, comprising millions of de-identified OPO clinical notes, which will facilitate ethical healthcare research and enhance compliance with contemporary data protection standards. Ultimately, this dataset holds significant potential for improving processes and outcomes within the field of organ donation and procurement.

Efficient ML Inference via Matrix-Vector Approximations

2025-09-19T04:49:55Z

Efficient ML Inference via Matrix-Vector Approximations Li, Daniel D. Efficient inference is a growing priority in deep learning, where large model sizes and increasing deployment demands pose challenges for latency, memory, and energy usage. This thesis presents a unified framework for evaluating approximation methods that accelerate inference by modifying weight matrices. We model each method as a function ƒ_c(A) that approximates a weight matrix A under a compression rate c, and assess its impact on both matrix–vector accuracy and downstream task performance. We conduct empirical evaluations across two representative models, AlexNet on CIFAR10 and DistilBERT on AG News, comparing quantization, sparsification, and low-rank approximations. Our analysis spans four perspectives: (1) how different methods trade off ℓ₂ error and compression, (2) how weight statistics and input distributions shape error, (3) how well ℓ₂ error predicts classification accuracy, and (4) how idealized compression differs from real memory savings. We find that sparsification offers a strong trade-off between storage and accuracy, particularly because it preserves task-relevant structure in the weights. We also show that ℓ₂ error is not always a reliable proxy for accuracy, especially when input data lie on low-dimensional manifolds. These results suggest that approximation quality must be evaluated not only by global distortion metrics, but also by how the method interacts with model structure and input distributions. Our findings offer practical guidance for deploying efficient deep learning models and shed light on how compression affects performance in real-world settings.

A Pedagogical Multimodal System for Mathematical Problem-Solving and Visual Reasoning

2025-09-19T04:49:50Z

A Pedagogical Multimodal System for Mathematical Problem-Solving and Visual Reasoning Lee, Jimin Effective reasoning often requires more than text or language. It requires visualizing, drawing, gesturing, and interacting for both humans and artificial intelligence (AI). Specifically in educational subjects, such as geometry and graphs, visual tools like auxiliary annotations and drawings can greatly help students understand abstract theories. This thesis explores and suggests how multimodal interaction between humans and AI helps humans engage with the system more naturally and effectively, leading to improved problem-solving in mathematical settings. Recent large multimodal models (LMMs) have the ability to facilitate collaborative reasoning by supporting textual, visual, and interactive inputs, diversifying methods of communication between humans and AI. Utilizing such advancements, this thesis also dives into the development of Interactive Sketchpad, a tutoring system that combines language-based explanations with interactive visualizations to enhance learning. It also reviews findings from user studies with Interactive Sketchpad, demonstrating that multimodality contributes to user task comprehension and engagement levels. Together, these contributions can reframe the role of AI in education as a visual and interactive collaborator that supports deeper reasoning rather than simply providing answers. Furthermore, this work demonstrates the potential of multimodal human-AI systems in fostering engagement and scaling personalized, visual learning across domains.

Fast and Scalable Subgraph Learning

2025-09-19T04:49:53Z

Fast and Scalable Subgraph Learning Liang, Derrick Graph Neural Networks (GNNs) are a powerful framework for learning over structured data, enabling predictive modeling across domains such as bioinformatics, recommendation systems, and financial fraud detection. While scalable systems like SALIENT++ have advanced the training of node-level GNN tasks at industrial scale, they do not support an emerging class of workloads: subgraph classification, which is increasingly common in real-world applications. Prior implementations address this gap by modifying both the data pipeline and the model architecture—but at the cost of composability, creating tightly coupled systems that slow further development. This thesis introduces MOSAIC, a lightweight data transformation that reframes subgraph classification as nodewise prediction by augmenting the graph with representative nodes. This approach enables direct compatibility with SALIENT++ and other nodewise systems while decoupling workload format, dataloader design, and model architecture. I demonstrate that MOSAIC enables modular reuse of architectures like GraphSAGE and subgraph-aware components from GLASS, while preserving SALIENT++’s system-level scalability. On the large-scale Elliptic2 dataset, this integration reduces training memory usage by 2.8× and epoch runtime from over 90 minutes to 0.4 seconds—while improving classification performance. I implement MOSAIC as a succinct (<100-line), reusable preprocessing script, enabling integration of the GLASS architecture into SALIENT++ in <10 lines of code, compared to Wang et al.’s tightly coupled 500+ line design. These results highlight the feasibility of scalable, composable experimentation for subgraph learning tasks in high-performance GNN systems.

Dynamic Scene Editing via Semantically Trained 3D Guassians

2025-09-19T04:49:42Z

Dynamic Scene Editing via Semantically Trained 3D Guassians Lam, Jordan Image-based 3D scene reconstruction continues to be a challenge as it involves solving both the sufficient 3D representation problem and the 3D reconstruction itself. One approach to tackle the rendering problem is 3D Gaussian Splatting because of its potential to produce fast and realistic renders via 3D Gaussian representation. With many applications in the entertainment industry, there is motivation in using 3D Gaussian Splatting for not only reconstructing 3D dynamic scenes but also editing them. However, extending the problem to dynamic 3D scenes proves to be a challenging task as it involves discerning the correct representation of a 3D scene while maintaining the capability to render in real time. State-ofthe-art methods have proposed methods that reconstruct dynamic scenes or edit static scenes, but the problem of editing dynamic scenes is still underexplored. This thesis analyzes the feasibility of editing semantically trained Gaussians for dynamic 3D scene editing. By training 3D Gaussians to represent the semantics across the time steps of a dynamic 3D scene, these primitives can be combined with an image editing pipeline to perform real-time, realistic 3D scene editing. Results show that editing segmented 3D Gaussians produces higher-quality and efficient renders as compared to editing without segmentation. However, when evaluated for mainstream applications, results show the impracticality of this pipeline and draw focus to memory and editing limitations that need to be further researched for future advances in 3D Gaussian Splatting.

Decentralized AI for Methylation Data with Applications to Precision Health

2025-09-19T04:49:50Z

Decentralized AI for Methylation Data with Applications to Precision Health Jamee, Mehrab S. Advances in precision health rely on integrating large-scale genomic data to identify biomarkers and predict health outcomes. However, sharing sensitive patient data between institutions like hospitals poses significant privacy and security challenges, limiting collaboration and the development of robust machine learning models. This thesis proposes a decentralized artificial intelligence framework for analyzing DNA methylation data, enabling institutions to collaboratively train models without exchanging sensitive information. By taking advantage of generative deep learning techniques and federated learning paradigms, the framework aims to impute missing biomarkers in fragmented datasets and improve the accuracy of downstream predictive tasks, like predicting chronological age, mortality, and cancer data. Two intermediate models are implemented and evaluated in this thesis. The first predicts age from DNA methylation data, and can be used for evaluation of the imputation model. The second is an imputation model that uses a conditional autoencoder architecture to reconstruct missing biomarker data in clinical datasets, which is designed to take advantage of contextual methylation embeddings, made available by recently published pretrained epigenomics foundation models. This work seeks to advance the use of decentralized AI in epigenomics, with the ultimate goal of improving personalized healthcare while preserving patient privacy.

Exploring Smallholder Field Delineation

2025-09-19T04:49:40Z

Exploring Smallholder Field Delineation Janjigian, Lily T. Accurate crop field delineation from satellite imagery is a critical component of agricultural monitoring. However, most existing models are developed and evaluated in large-scale, industrial agricultural regions, where field boundaries are relatively regular and high-quality annotated data is more readily available. In contrast, smallholder regions—where fields are smaller, more irregularly shaped, and often lack precise geospatial labels—remain underrepresented in both data and model performance. This thesis investigates model architectures, loss functions, and learning paradigms for improving segmentation performance in smallholder settings. Using datasets from Austria, India, and Rwanda, we evaluate several model configurations including ResUNet++ with Dice+BCE and Tanimoto+BCE losses, a meta-learned ResUNet++ using Model-Agnostic Meta-Learning (MAML), and SAM2 ViT-H, a large vision transformer released by Meta, evaluated in a zero-shot setting. We introduce a data processing pipeline that converts vector field boundaries from the FTW dataset into highresolution image–mask pairs suitable for supervised learning. Quantitative and qualitative results reveal that models trained on industrial-scale data perform poorly in smallholder regions without adaptation. SAM2 exhibits strong zero-shot performance, especially on larger fields, while ResUNet++ models trained directly on India perform more consistently across small-to-medium sized fields. MAML yielded underwhelming performance under resource constraints, highlighting the need for further tuning. These findings underscore the importance of geographically diverse, well-aligned training data and support the case for developing globally representative agricultural segmentation datasets.

You Only Look Twice: An Ensemble Deep Learning Model for Wildfire Detection Using Terrestrial Camera Networks

2025-09-19T04:49:48Z

You Only Look Twice: An Ensemble Deep Learning Model for Wildfire Detection Using Terrestrial Camera Networks Jones, John M. Wildfires represent a growing global threat that requires rapid detection and response to minimize environmental damage, economic losses, and human casualties. In the United States, California stands out as a particularly common wildfire hot spot. Recent fire seasons have shattered historical records and been particularly devastating. This work investigates innovative methods for classifying and localizing wildfires through terrestrial cameras positioned on elevated terrain, aimed at improving early detection capabilities and response times while maintaining computational efficiency and reliability for the U.S. Space Force in Southern California. We present YOL2, a novel ensemble approach that combines a fine-tuned ConvNeXt Convolutional Neural Network incorporating a Dynamic Tanh normalization layer with a fine-tuned YOLO11 model for precise localization. Using a comprehensive dataset of 33,636 time-sequenced images from terrestrial cameras across the United States and Europe, our system achieves 98% fire detection accuracy and 55% localization mean average precision [50:95]. The implementation of Dynamic Tanh normalization—applied for the first time in wildfire detection—enhances computational efficiency without sacrificing performance. The images used capture the spread of incipient fires over time, with most containing bounding boxes denoting the approximate location of fire, allowing our system to identify fires quickly while minimizing false positives. Importantly, our spatiotemporal system operates effectively without requiring individual models to rely on multiple time steps as input, enabling modular component replacement and adaptation. The use of pan, tilt, and zoom cameras in concert with our YOLO model provides a more computationally efficient confirmation of fire than alternative methods, showing that extracting better results from less information is possible. Beyond wildfire applications, the YOL2 ensemble methodology demonstrates profound implications for remote sensing more broadly. This work establishes a foundation for highly efficient visual detection systems applicable across numerous domains requiring rapid and accurate object identification and localization.

Towards transparent representations: on internal structure and external world modeling in LLMs

2025-09-19T04:49:32Z

Towards transparent representations: on internal structure and external world modeling in LLMs Hariharan, Kaivalya Large language models (LLMs) generalize far beyond their training distribution, enabling impressive downstream performance in domains vastly different from their pretraining distribution. In this thesis, we develop a data-centric view on machine learning. We suggest that the deep generalization of LLMs is best understood through studying the relationships between the four fundamental components of this data generalization: pretraining data, test-time inputs, model outputs, and internal structure. Of these, we present two full research studies characterizing test-time inputs and internal structure. Chapter 1 develops the data-centric view of machine learning, and outline the thesis. Chapter 2 presents Breakpoint, a method of generating difficult coding tasks for models at a large scale that attempts to disambiguate the factors that make problems at test-time difficult. Chapter 3 analyzes the structure of gradient-based jailbreaks in LLMs. We argue that even though GBJs are more out of distribution than even random text, they induce a low-rank, structured change in models. Finally, Chapter 4 discusses the recent rise of reasoning models and proposing some lines of future work in the data-centric view towards developing more robust understanding of LLMs.

Biomechanical Validation of Skeletal Tracking Data and Developing Action Recognition Models for Basketball: A Baseline for NBA Officiating Tools

2025-09-19T04:49:49Z

Biomechanical Validation of Skeletal Tracking Data and Developing Action Recognition Models for Basketball: A Baseline for NBA Officiating Tools Hong, Stephen S. Optical tracking technology in sports has advanced rapidly in recent years, enabling new opportunities for data-driven analysis and tools to enhance the game. This study presents a framework for processing and analyzing a new skeletal tracking dataset collected from NBA basketball games. The methodology includes biomechanical joint validation, anomaly detection, and region-based consistency analysis to assess the integrity of player motion data. Joint movement anomalies are used to detect tracking errors, while court region and stadium-level evaluations help identify where the optical tracking system may be underperforming. These patterns can guide data providers toward specific areas that require refinement, offering a clearer starting point for improving system accuracy. After cleaning the dataset of 117 NBA games, two action recognition models—a transformer-based model and a temporal graph neural network—are implemented to classify player actions, specifically dribbling, passing, shooting, and rebounding, from sequences of skeletal tracking frames. The objective is to establish a baseline for developing tools to support officiating decisions in the NBA. By leveraging spatiotemporal representations of joint motion, this work improves the reliability of skeletal tracking data and contributes to the advancement of automated decision support in professional sports officiating.

Towards Enhanced Proposals for PINN-Based Neural Sampler Training

2025-09-19T04:49:26Z

Towards Enhanced Proposals for PINN-Based Neural Sampler Training Erives, Ezra Sampling from distributions whose density is known up to a normalizing constant is an important problem with a wide range of applications including Bayesian posterior inference, statistical physics, and structural biology. Annealing-based neural samplers seek to amortize sampling from unnormalized distributions by training neural networks to transport a family of densities interpolating from source to target. A crucial design choice in the training phase of such samplers is the proposal distribution by which locations are generated at which to evaluate the loss. Previous work has obtained such a proposal distribution by combining a partially learned vector field with annealed Langevin dynamics. However, isolated modes and other pathological properties of the annealing path imply that such proposals achieve insufficient exploration and thereby lower performance post training. In this work we extend existing work and characterize new families of proposals based on controlled Langevin dynamics. In particular, we propose continuously tempered diffusion samplers, which leverage exploration techniques developed in the context of molecular dynamics to improve proposal distributions. Specifically, a family of distributions across different temperatures is introduced to lower energy barriers at higher temperatures and drive exploration at the lower temperature of interest. We additionally explore proposals based on Langevin dynamics involving non-Newtonian kinetic energies. We empirically validate improved sampler performance driven by extended exploration.

From Sketch to Stage: Tools for Prototyping and Exporting Collaborative DMIs on the Web

2025-09-19T04:49:38Z

From Sketch to Stage: Tools for Prototyping and Exporting Collaborative DMIs on the Web Luchko, Yaro This thesis presents tools and ideas for prototyping and exporting collaborative digital music instruments (DMIs) on the web, the primary purpose of which is to lower the barrier to making music and to enable easier collaboration. This is done in the context of the Creativitas website, which has become a tool of the MIT 21M.080 "Introduction to Music Technology" class to learn about music technology and audio on the web, and a tool for FaMLE (the Fabulous MIT Laptop Ensemble) to use in live performances. The website allows creators to execute code within an editor code box and partake in a practice known as live coding, ultimately creating both sound and visuals. Audio is primarily created with the Tone.js interactive web audio framework, and visuals are drawn on a provided canvas using p5.js. This thesis extends the Creativitas website by providing functionality for exporting the written code as a standalone website. The exported standalone websites serve as DMIs, with standard controls such as volume, tempo, and start and stop buttons. Furthermore, we discuss and implement strategies for synchronizing timing and instrument values. This includes state-of-the-art strategies, as well as ideas for creating extendable interfaces that can include more strategies as they are developed. We end with two examples of exported DMIs, which can be effectively used in performances.

Programmable Expressiveness in Non-Social Tasks: A Mixed-Methods Study of Middle School AI Learning

2025-09-19T04:49:21Z

Programmable Expressiveness in Non-Social Tasks: A Mixed-Methods Study of Middle School AI Learning Lei, Si Liang Background. Programmable expressive features—such as speech, facial expressions, and chatbot-style dialogue—are often promoted as tools to enhance engagement in educational robotics. While prior research shows benefits in socially-oriented tasks like storytelling or group collaboration, it remains unclear how student-controlled expressive blocks affect learning when the task itself is non-social. This study isolates the impact of such features in a context where expressiveness is not instructionally required. Method. We conducted a controlled, two-cohort study with 41 middle school students (ages 10–12) during a one-day AI-and-robotics workshop using the Doodlebot platform. Students in the experimental group had access to optional blocks enabling the robot to speak, emote, and use GPT-based responses. These features were hidden from the control group. All participants completed identical programming tasks (e.g., maze navigation, visual classification) that did not require social interaction. Data sources included pre/post surveys, facilitator notes, and student code. We applied the Mann–Whitney U test [1, 2] and reflexive thematic analysis [3, 4] to examine outcomes. Results. The expressive condition showed no significant gains in programming confidence or peer trust, but performed significantly worse on the post-workshop concept quiz (p = .007, r = .41). Qualitative data revealed that students in this group often used expressive blocks for entertainment rather than learning, leading to distraction, off-task behavior, and increased reliance on adult facilitation. Contributions. This study contributes (i) empirical evidence on the limitations of robot expressiveness in non-social learning contexts, (ii) a mixed-methods protocol for analyzing classroom robot deployments, and (iii) design guidance for aligning robot behavior with pedagogical intent. Implications. Expressiveness in educational robots should be contextually deployed—not assumed beneficial by default. In technical, goal-driven tasks that do not involve social reasoning, unscaffolded expressiveness may introduce cognitive overhead or divert attention. We propose a “dial-a-sociality” model, where robot behavior can be flexibly tuned to match the demands of the learning environment.

High-Speed Simulator for Millimeter-Wave Synthetic Aperture Radar

2025-09-19T04:49:33Z

High-Speed Simulator for Millimeter-Wave Synthetic Aperture Radar Kuka, Adrian The past few years have witnessed growing interest in using millimeter-wave signals for non-line-of-sight (NLOS) perception tasks, with applications in robotics, augmented reality, and smart-homes. However, existing systems suffer from a lack of large mmWave datasets, resulting in limited accuracy and generalizability compared to their line-of-sight, camera-based counterparts. We present the design, implementation, and evaluation of mmSim, a new, high-speed millimeter-wave (mmWave) simulator capable of producing large synthetic datasets to help drive the field of mmWave-based NLOS perception. mmSim introduces two main contributions to improve the speed over existing mmWave simulators. First, it pre-selects areas of the object, which will produce reflections towards each simulated antenna location, allowing it to minimize future computation. Second, it introduces a coarse-to-fine approach to allow early, less critical steps to operate at lower resolutions, while maintaining the high resolution in later steps required for high-accuracy images. These techniques, combined with other performance optimizations, allow mmSim to achieve an over 24x improvement in speed over state-of-the-art mmWave simulators.

Towards AI Safety via Interpretability and Oversight

2025-09-19T04:49:46Z

Towards AI Safety via Interpretability and Oversight Kantamneni, Subhash In this thesis, we advance AI safety through mechanistic interpretability and oversight methodologies across three key areas: mathematical reasoning in large language models (LLMs), the validity of sparse autoencoders, and scalable oversight. First, we reverse-engineer addition within mid-sized LLMs and discover that LLMs represent numbers as helices. We demonstrate that LLMs perform addition via the manipulation of these helices using a "Clock" algorithm, providing the first representation-level explanation of mathematical reasoning in LLMs, verified through causal interventions on model activations. Next, we rigorously evaluate sparse autoencoders (SAEs), a popular interpretability tool, by testing their effectiveness on the downstream task of probing. We test SAEs under challenging probing conditions, including data scarcity, class imbalance, label noise, and covariate shift. While SAEs occasionally outperform baseline methods, they fail to consistently enhance task performance, underscoring a potentially critical limitation of SAEs. Lastly, we introduce a quantitative framework to evaluate scalable oversight - a promising idea where weaker AI systems supervise stronger ones - as a function of model intelligence. Applying our framework to four oversight games ("Mafia," "Debate," "Backdoor Code," and "Wargames"), we identify clear scaling patterns and extend our findings through a theoretical analysis of Nested Scalable Oversight (NSO), deriving conditions for optimal oversight structures. Together, these studies advance our understanding of AI interpretability and alignment, providing insights and frameworks to progress AI safety.

Metagradient Descent: Differentiating Large-Scale Training

2025-09-19T04:49:38Z

Metagradient Descent: Differentiating Large-Scale Training Chen, Benjamin A major challenge in training large-scale machine learning models is configuring the training process to maximize model performance, i.e., finding the best training setup from a vast design space. In this work, we unlock a gradient-based approach to this problem. We first introduce an algorithm for efficiently calculating metagradients -- gradients through model training -- at scale. We then introduce a "smooth model training" framework that enables effective optimization using metagradients. With metagradient descent (MGD), we greatly improve on existing dataset selection methods, outperform accuracy-degrading data poisoning attacks by an order of magnitude, and automatically find competitive learning rate schedules.

A simplified approach to calculating personalized estimates for electric vehicle charging delays

2025-09-19T04:49:29Z

A simplified approach to calculating personalized estimates for electric vehicle charging delays Chen, Helen In the past decade, electric vehicles (EVs) have gained traction as a cleaner alternative to internal combustion engine vehicles, commonly referred to as gas-powered vehicles. To promote EV adoption, the government has implemented various regulations and incentives to support the transition to cleaner transportation. However, EV adoption in the United States has progressed more slowly than expected, with EVs accounting for less than 10 percent of new vehicle sales in 2023. Recent surveys indicate that a significant barrier is the perceived inconvenience and uncertainty surrounding EV charging, particularly the additional time required to charge during active use, which we call charging delay. Currently, there exist some models for estimating these charging delays, but these models require users to input a significant amount of information, such as their daily driving schedules, locations of charging stations, and exact distances of trips taken each year, which many users may not even remember. These more complex models are likely to overwhelm users, especially those who may be entirely new to EVs. To fill this gap, this thesis introduces a simplified model for estimating personalized annual EV charging delay using a set of easy-to-provide inputs, including typical driving behavior and access to home and work charging. The model logic captures delay from both routine usage, such as weekly driving patterns or typical trips, and occasional, high-energy long-distance trips, which, while not routine, are still important to account for. For weekly trips, the model considers four scenarios based on combinations of home and work charging access to determine driving and charging schedules. For long-distance travel, the model uses data from the 2022 National Household Travel Survey (NHTS) and performs multiple iterations of bootstrap resampling to create synthetic distributions of long-distance trips within a year. Data related to individual routine vehicle usage and charging delay is unavailable, so we are unable to validate the model’s performance through accuracy calculations. Instead, we performed a one-at-a-time sensitivity analysis to better understand how charging delay is affected by different factors. We found that access to private charging, such as home or work charging, improves charging delay robustness for regular weekly trips, with the exception that relying solely on work charging on workdays can cause stepwise increases in non-workday delays. Additionally, long-distance trip delays are no affected by private charging access and follow a stepwise pattern based on vehicle range. In general, the simplified approach presented in this thesis offers a more accessible way for current and prospective EV owners to clearly understand their own expected experience of EV ownership.

The Limits of Temporal Abstractions for Reinforcement Learning with Sparse Rewards

2025-12-05T17:48:39Z

The Limits of Temporal Abstractions for Reinforcement Learning with Sparse Rewards Li, Zhening Skills are temporal abstractions that are intended to improve reinforcement learning (RL) performance through hierarchical RL. Despite our intuition about the properties of an environment that make skills useful, there has been little theoretical work aimed to characterize these properties precisely. This work studies the utility of skills in sparse-reward environments with a discrete state space and finite action space. We show, both theoretically and empirically, that RL performance gains from skills are worse in environments where successful trajectories are less compressible. In environments with a highly incompressible distribution of successful trajectories, using unexpressive skills such as macroactions will provably worsen RL performance. We hope our findings can guide research on automatic skill discovery and help RL practitioners better decide when and how to use skills.

Self-Supervised ECG Learning for Multimodal Clinical Tasks

2025-09-19T04:49:36Z

Self-Supervised ECG Learning for Multimodal Clinical Tasks Chen, Peilin We present a multimodal clinical AI framework that integrates time series, images, and text to support robust diagnostic reasoning across diverse input combinations. We first introduce ECG-JEPA, a self-supervised encoder pretrained on multiple ECG datasets to learn generalizable time series representations. This unimodal pretraining improves ECG classification, achieving a 23-point AUC gain on the underrepresented Ga dataset. We then align and fuse these ECG embeddings with chest X-rays and EHR text using a vision–language model backbone, enabling end-to-end multimodal inference. Our results show that incorporating ECG signals meaningfully improves diagnostic performance, highlighting the value of multitask time series pretraining and modular fusion for clinical AI.

A Hierarchical Approach to Quantitative Portfolio Optimization for Technology Development Project Portfolios (OPTIM-H)

2025-09-19T04:49:27Z

A Hierarchical Approach to Quantitative Portfolio Optimization for Technology Development Project Portfolios (OPTIM-H) Huang, Roderick W. The use of Mean-Variance Portfolio Optimization (MVO) in Modern Portfolio Theory (MPT) has been a long-standing method to guide investment decisions for market-traded assets like stocks and bonds. Recent research shows that portfolio optimization developed using MPT could prove useful in investment decisions for technology projects. Traditionally, empirical data from past projects and statistically driven technology trends are used to predict the risk-return model necessary for MPT. This thesis introduces a new methodology, Optimizing Portfolios in Technologies Investments Methodology with Hierarchy (OPTIM-H), which extends MPT to make investment decisions within a hierarchical organizational structure of technology projects. An integrated dataset was developed to demonstrate this methodology, combining 19,000 data records from Techport and Small Business Innovation Research (SBIR) datasets. The dataset captures investment trends and maturity pathways across 17 taxonomy areas, revealing that most projects begin at Technology Readiness Levels (TRLs) 2–4, with average funding amounts near \$300,000. OPTIM-H effectively distinguishes between broader technology groups and their subcategories, showing the impact of community interest on investment decisions. Furthermore, this work investigates k-means clustering as a tool for classifying technology projects for targeted investment, with the analysis identifying seven clusters and achieving a mean utility score of 0.595 with a standard deviation of 0.651.

Integrating Canvas with a Large-Scale Social Annotation Platform

2025-09-19T04:49:41Z

Integrating Canvas with a Large-Scale Social Annotation Platform Heiberger, Henry R. The last decade has seen a growing interest in the use of collaborative annotation systems, educational tools that allow multiple users to asynchronously comment, highlight, and discuss digital content directly on the source material, transforming traditional classroom readings into a more engaging group activity. Originally developed by MIT CSAIL’s Haystack Group in 2012 under the direction of Professor David Karger, Nota Bene (NB) is a particular collaborative annotation tool that allows students to have annotated online discussions in the margins of textbooks, papers, and even webpages [1]. Though various studies have already proven its ability to succeed in a classroom setting, conversations with key stakeholders have revealed that the tool is missing a key feature found in many other popular collaborative annotation solutions: integration with the Canvas learning management system (LMS) [1–3]. Thus, this work sought to integrate the classroom management features that Canvas provides into the NB platform by supporting Canvas account linking, class importation and roster synchronization, and automatic grade uploading. By doing this, we hoped to improve NB’s quality as a classroom tool, enhancing its value to institutions, encouraging its wider adoption across the academic landscape, and aligning with a much broader trend of creating more integrated, efficient, and user-friendly educational technology solutions.

On Passive-Scoping as a method for Large Language Model Robustness to Jailbreaks and Adversarial Examples

2025-09-19T04:49:31Z

On Passive-Scoping as a method for Large Language Model Robustness to Jailbreaks and Adversarial Examples Hernandez, Adriano Artificial Intelligence (AI) and large language models (LLMs) not only present a challenge for adversarial robustness, but also the natural emergence of unwanted capabilities. Current approaches to safeguarding AI and LLMs predominantly rely on explicitly restricting known instances of these. However, this places a burden on model developers, because they cannot anticipate all the potential attacks and undesirable capabilities. To solve this problem, we leverage interdisciplinary knowledge. In the field of information security, the principle of least privilege provides guidance on how to defend from unknown threats. In AI, the principle could be implemented by ensuring that developers specify the knowledge and capabilities an AI system should retain, restricting all others by default. We call this application of the principle of least privilege, passive scoping. Our thesis makes two claims: 1. We argue that (a) passive scoping mitigates concerns about adversarial robustness and loss of control of AI systems and (b) passive scoping to edit the weights and activations at post-training time is underexplored by the literature. 2. Of possible approaches, our sparse autoencoder (SAE) filters can implement this underexplored type of passive scoping. They increase safety relative to LoRA finetuning and prompt engineering, but leave room for improvements. The thesis is structured as follows: 1. Chapter 2 elucidates the challenges with adversarial robustness and loss of control risk. Chapter 3 puts forward a conceptual argument for the benefits of passive scoping. Later, it analyzes the extent to which passive scoping has been attempted. These two chapters work together to defend claims 1a and 1b. 2. Chapter 4 defines our optimization problem. Chapter 5 defines our experimental methodology and metrics. These two define our success criteria for claim 2. Chapter 6 finalizes our defense of claim 2 based on our results. 3. Chapter 7 explores related work, Chapter 8 engages in a broader discussion, and chapter 9 summarizes the contributions of this thesis.

Explorations in AI and Creative Learning New Tools to Expand How Young People Imagine, Create, and Tinker with Scratch

2025-09-19T04:49:24Z

Explorations in AI and Creative Learning New Tools to Expand How Young People Imagine, Create, and Tinker with Scratch Huang, Alexis As generative AI tools become increasingly prevalent in young people’s lives, these technologies have a growing influence over the way that children learn. While much of the early work at the intersection of AI and education has focused on the development of intelligent tutoring systems designed to deliver content more efficiently, this thesis explores how generative AI might be used to support the creative learning process by sparking curiosity, encouraging exploration, and helping young people express themselves creatively. In this thesis, I explore ways of integrating generative AI with Scratch, the world's largest programming community for children, while remaining aligned with the core values of Scratch: creativity, playfulness, and self-expression. I designed three tools that extend the Scratch ecosystem: Scratch Connect, which explores using generative AI to help Scratchers discover projects that inspire them to create while opening the black box of recommendation systems; scrAItch, which investigates how people can iterate with generative AI by using text-based inputs to create and tinker with Scratch projects; and Scratch Spark, which reimagines the new learner experience by using generative AI to help users create personally meaningful “spark projects.” This thesis describes the process of imagining, creating, and reflecting on these tools, including many of the challenges and tensions that we encountered along the way. I discuss observations and feedback from creative workshops with young people, and conclude by reflecting on open questions and opportunities for future work in designing generative AI tools that support creative learning.

Effects of Hardware Design Choices on Neural Network Accuracy in Analog Inference Accelerators

2025-09-19T04:49:34Z

Effects of Hardware Design Choices on Neural Network Accuracy in Analog Inference Accelerators Forsythe, Eyan Analog accelerators can enable energy-efficient and high-throughput deep neural network (DNN) computations by computing in memory. Unfortunately, device and circuit nonidealities in these accelerators, such as noise and quantization, can also lead to low DNN inference accuracy due to computation errors arising from these non-idealities. These errors are largely a function of both the choice of DNN workload and different hardware design choices, such as circuit topology and DNN operand encoding. Different hardware design choices can affect the energy, throughput, and area of the system, so it is important to understand how these design choices interact with DNN inference accuracy. However, there is a lack of a systemic understanding of how each of these hardware design decisions affects accuracy and how they interact with other design decisions. To address these issues, we model how hardware design choices can lead to analog errors such as noise and quantization. Then, we explore these errors affect inference accuracy in analog accelerators and how tradeoffs can be made between inference accuracy, energy efficiency, area, and throughput. We find that analog errors generated from hardware design decisions can generate different amounts of accuracy loss depending on which layer in a DNN is subject to these analog errors. This leads to the structure of the DNN having a significant impact in how hardware design choices affect DNN inference accuracy, especially with respect to the individual layers of a DNN. We use knowledge of the relationships between device and circuit non-idealities to improve the accuracy of published analog accelerators and analyze the energy and area costs of the increased accuracy.

Design of High-Resolution SAR ADC for Detection of Sub-Cortical Neuron Action Potentials for BMI Applications

2025-09-19T04:49:28Z

Design of High-Resolution SAR ADC for Detection of Sub-Cortical Neuron Action Potentials for BMI Applications Guobadia, Omozusi E. The advancement of brain-machine interfaces (BMIs) requires neural signal acquisition systems that are capable of resolving both fast, low-amplitude action potentials (APs) and slow, higher-amplitude local field potentials (LFPs) under stringent power and area constraints. This thesis presents the design and simulation of a high-resolution, low-power successive approximation register (SAR) analog-to-digital converter (ADC) tailored for sub-cortical neural signal detection. To optimize dynamic range and reduce power consumption, a novel adaptive zoom-and-tracking architecture is introduced, enabling the ADC to dynamically adjust its reference window based on LFP trends while maintaining high-resolution capture of APs. The proposed system integrates a bootstrapped track-and-hold circuit, a differential capacitive DAC, and a strong-arm comparator in the analog front-end, alongside a digital FIR filter and SAR logic with zoom-range control in the digital domain. Simulations validate the functionality of each subsystem independently and in concert, demonstrating the system’s ability to dynamically isolate APs from LFP-dominated baselines while reducing analog power draw by over 60% compared to fixed-range ADCs. This work offers a promising approach for scalable, energy-efficient neural recording architectures suited to future BMI applications.

Transformer-Based Prediction of Coronary Artery Lumen Expansion Post Angioplasty Using Optical Coherence Tomography

2025-09-19T04:49:22Z

Transformer-Based Prediction of Coronary Artery Lumen Expansion Post Angioplasty Using Optical Coherence Tomography Gupta, Shreya Coronary artery disease is the leading cause of mortality globally, resulting in an urgent and critical need to better understand both vessel morphology and the processes of intervention. Angioplasty is an intervention which causes a previously constricted vessel to expand via placement of a stent, and is affected by numerous characteristics of the vessel such as calcium eccentricity and size, wall thickness, and prior lumen size. Being able to accurately assess whether a stent will properly expand allows cardiologists to pursue pre-stenting calcium lesion modification strategies that help avoid dangerous complications of improper stenting. This work introduces a pipeline for post-stenting lumen area prediction from pre-stenting optical coherence tomography (OCT) images. This pipeline includes morphological correction of OCT image segmentations, explainable feature extraction from OCT segmentations, and a predictive transformer network that combines morphological features with injected stent information. The aim is for such a pipeline to be used to support clinical decision making.

Complete Visual and Geometric Object Reconstruction via Autonomous Robotic Manipulation

2025-09-19T04:49:35Z

Complete Visual and Geometric Object Reconstruction via Autonomous Robotic Manipulation Fu, Evelyn Accurately simulating object dynamics based on real-world perception inputs has wide applications in digital twins and robotic manipulation. Yet, doing so requires practitioners to carefully measure and reconstruct the dynamic and geometric properties of the objects, which is time-consuming and requires domain expertise. This project proposes an automatic pipeline to construct 3D representations from a collection of real objects, which can further be used to generate assets with accurate visual texture and collision geometry to be used in simulation. This pipeline will be designed to have minimal hardware requirements and aim to be efficient in time for physical actuation to maximize data collection on minimal hardware.

Model-based Planning for Efficient Task Execution

2025-09-19T04:49:30Z

Model-based Planning for Efficient Task Execution Ding, Wenqi Robotic agents navigating 3D environments must continuously decide their next moves by reasoning about both visual observations and high-level language instructions. However, they plan in a high-dimensional latent space, opaque to human collaborators. Hence, it is difficult for humans to understand the agent’s decision-making process. This lack of interpretability hinders effective collaboration between humans and robots. The key question we are trying to answer in this thesis is: Can we build a unified planning framework that fuses visual and language into a single, interpretable representation, so that humans can interpret robots’ decisions? We propose a model-based planning framework built around pretrained vision-language models (VLMs). We show that VLMs can be used to plan in a unified embedding space, where visual and language representations can be decoded back to human-interpretable forms. Empirical evaluation on vision-language navigation benchmarks demonstrates both improved sample efficiency and transparent decision making, enabling human-in-the-loop planning and more effective human-robot collaboration.

Global Non-Convex Optimization with Integer Variables

2025-09-19T04:49:24Z

Global Non-Convex Optimization with Integer Variables Kriezis, Demetrios C. Non-convex optimization refers to the process of solving problems whose objective or constraints are non-convex. Historically, this type of problems have been very difficult to solve to global optimality, with traditional solvers often relying on approximate solutions. Bertsimas et al. [1] introduce a novel approach for solving continuous non-convex optimization problems to provable optimality, called the Relaxation Perspectification Technique - Branch and Bound (RPT-BB). In this thesis, we extend the RPT-BB approach to the binary, mixed-binary, integer, and mixed-integer variable domains. We outline a novel branch-and-bound algorithm that makes use of the Relaxation Perspectification Technique (RPT), as well as binary, integer, and eigenvector cuts. We demonstrate the performance of this approach on two representative non-convex problems, as well as two real-world non-convex optimization problems, and we benchmark its performance on BARON and SCIP, two state-of-the-art optimization solvers for non-convex mixed-integer problems. We observe that our algorithm, despite being more general, is able to outperform the state-of-the-art solvers on many problem instances.

Optimizing AI Agents for Automated Software Engineering with Palimpzest

2025-09-19T04:49:10Z

Optimizing AI Agents for Automated Software Engineering with Palimpzest Li, Jason The deployment of large language models (LLMs) as autonomous agents is transforming the software development landscape. Increasingly more engineers are using natural language agents to expedite and guide development workflows, while large organizations are investing heavily on building agentic systems for tasks such as code generation and code repair. A key challenge in developing such systems is tuning agent hyperparameters— settings that affect performance such as choice of model, temperature settings, and context window sizes. As system complexity grows, the hyperparameter space expands, complicating optimization under real-world compute and time constraints. In this work, we present Palimpzest[1] as an agentic optimizer able to balance cost and performance objectives by tuning agentic hyperparameters. We demonstrate that Palimpzest can tune our agent hyperparameters at 8.5 times lower cost and with 24 times greater time efficiency compared to the conventional grid search. By integrating our custom-built Debugger and Code Editor Agents as new operators within Palimpzest, we enhance the system’s ability to resolve real-world GitHub issues. And to facilitate hyperparameter selection, we also introduce File Coverage, Report Accuracy, and Patch Similarity along with the traditional SWE-Bench Score as quality evaluation methods used by Palimpzest’s optimization loop. When evaluated on the SWE-Bench Lite[2] benchmark, our optimized system achieves a 15% score at a significantly lower cost compared to previous approaches.

Integrating Gradient Boosting and Generative Models: Hybrid Approach to Address Class Imbalance and Evaluation Gaps in Real-World Systems

2025-09-19T04:49:13Z

Integrating Gradient Boosting and Generative Models: Hybrid Approach to Address Class Imbalance and Evaluation Gaps in Real-World Systems Lau, Mary Anomaly detection remains a persistent challenge in machine learning due to the extreme class imbalance, high cost of false negatives, and the need to regulate false positives in realworld settings at scale. This thesis introduces Tail-end FPR Max Recall, a business-aware evaluation framework designed for such constrained environments. Using this framework, we benchmark LightGBM—a gradient boosting method known for its computational efficiency and predictive accuracy—on an imbalanced dataset, comparing its performance against standard academic evaluation criteria. Our results demonstrate that Tail-end FPR Max Recall fills critical gaps left by standard academic criteria, providing a more realistic assessment of model performance that aims to maximize recall while enforcing a false positive rate budget. Beyond benchmarking, we propose two strategies that incorporate deep learning methods to augment the already strong performance of gradient boosting: (1) using generative models to produce synthetic minority-class samples that outperform traditional oversampling techniques, and (2) using neural embeddings to improve feature representation for anomaly detection. Together, these contributions offer a methodology for evaluating and improving anomaly detection pipelines in domains where rare, high-impact events must be detected while meeting strict operational demands.

FPGA Based Data Acquisition System for Cryogenic Device Verification

2025-09-19T04:49:13Z

FPGA Based Data Acquisition System for Cryogenic Device Verification Kandeh, Stephen In this work, a system of processors connected to an FPGA is interfaced with a custom analog frontend and used to create a verification environment for cryogenic devices. In particular, this thesis focuses on the technical structure of that system. Current validation efforts often rely on commercially available arbitrary waveform generators (AWGs) and oscilloscopes, which, while highly capable, are often prohibitively expensive and poorly suited for large-scale or parallelized testing environments. As noted in industry reports, scaling such instrumentation introduces significant challenges in cost, calibration, and signal synchronization, making them inefficient for high-resolution or high-speed analyses in multi-channel systems [1]. On the other hand, an FPGA provides the necessary performance to increase parallelism without a proportional increase in cost, greatly improving testing resolution and speed. When augmented with a set of processors, we introduce a level of accessibility and automatability not currently present in commercial products. To be clear, while the board was designed with the testing of nanowires in mind (and is not capable of measuring DC voltages), it can still be combined with separate lab equipment to interact with Josephson Junction based devices. That said, the flexibility of this system allows for a generalized application to any electronic that demands a specialized testing procedure involving arbitrary signal processing and generation. The money, time, and energy that this innovation will save on cryogenic electronic validation will significantly improve our progress in developing these technologies.

Energy Efficient Real-time Operating Systems on Chip

2025-09-19T04:49:12Z

Energy Efficient Real-time Operating Systems on Chip Kang, Ezra H. Autonomous micro-robots are crucial for several tasks, such as search and rescue, noknowledge mapping, and navigation. Without an external power connection, these robots are constrained by their on-platform energy capacity. The power consumption of actuation systems used in micro-robots is within the same magnitude of the power consumption of the compute system. Thus, the remaining factor for enabling these micro-robots is associated with the design of energy-efficient compute systems. Energy usage of compute systems is typically dominated by memory operations, which previous efforts have attempted to mitigate with memory efficient software and hardware. These efforts are enabled with the software/hardware interface, which is implemented as an Operating System (OS). However, Operating Systems for energy-efficient platforms have not been fully explored. Current approaches utilize full general-purpose Operating Systems such as Linux, which can incur large memory and compute overhead penalties. These overheads not only consume the typically limited memory resources of energy-efficient systems, but also increase the number of memory accesses and CPU cycles, both of which are significant contributors to energy consumption. To address these concerns, we propose the design of a computational and memory efficient Real-time Operating System (RTOS). Our RTOS is designed to minimize both memory footprint and compute cycle overhead. It achieves this primarily through direct physical memory access, cycle-efficient task scheduling, and minimal runtime services to avoid unnecessary processing. Additionally, the modular RTOS kernel includes only the components required by an application in the final binary, reducing code size and memory usage without compromising functionality. The design enables the utilization of energy-efficient hardware accelerators and software, allowing for execution of robotics workloads with minimal memory and cycle overhead. When comparing robotics algorithms implemented on our proposed RTOS and baseline OSes, our design was able to achieve a 99% reduction in memory footprint. Additionally, it achieved up to a 47% increase in throughput. Thus, our design demonstrates a direct reduction in memory and CPU cycle overhead, which in turn lowers total system memory and energy consumption. The proposed design was demonstrated and verified on a resource constrained system-on-chip on the AMD Virtex Ultrascale+ VCU118 FPGA.

Uncertainty and Generality of Transfer Learning Models in Predicting Signaling History

2025-09-19T04:49:11Z

Uncertainty and Generality of Transfer Learning Models in Predicting Signaling History Lu, Claire Proper cell-cell communication is essential for multicellular development, from embryogenesis to stem cell differentiation. To map these networks, we developed IRIS (Intracellular Response to Infer Signaling state), a semi-supervised deep learning method that fits conditional variational autoencoders (CVAE) to single-cell RNA sequencing (scRNA-seq) data. IRIS is able to annotate cellular signaling states of individual cells using only their gene expression. Currently, IRIS has been validated in developmental contexts, including gastrulation, early endoderm organogenesis, and mesoderm lineages in mouse embryos. However, its predictions often show extremely high or extremely low confidence, suggesting a need for methods to prevent overconfidence and better account for uncertainty. To generalize IRIS to broader cell-cell communication problems, we combined engineering and experimental approaches, integrating uncertainty quantification techniques with new biological datasets. We implemented three approaches for estimating uncertainty in IRIS predictions: stochastic sampling, Monte Carlo dropout, and ensemble prediction. These approaches were evaluated on two new endoderm and mesenchyme combinatorial perturbation screens. Across all methods, uncertainty values reliably reflected the varying difficulty of predicting different signaling pathways, driven by both biological complexity and dataset representation. Moreover, higher uncertainty was consistently associated with lower prediction accuracy, confirming uncertainty as a useful proxy for model confidence. All three methods identified similar high-uncertainty cell populations, supporting their consistency and validity. By incorporating uncertainty quantification into IRIS, we provide more robust and interpretable predictions that can guide future experiments and enhance the model’s applicability across diverse biological contexts.

Core Material Evaluation for Magnetic Energy Harvester Applications

2025-09-19T04:49:20Z

Core Material Evaluation for Magnetic Energy Harvester Applications Le, Khang D. Current transformer magnetic energy harvesters (CTMEHs) harvest magnetic energy from an AC current-carrying conductor and convert this energy into usable electrical energy for use by various low-power devices, such as sensors and microcontrollers. The amount of power harvested by CTMEHs is determined by the primary current passing through the conductor; however, variables such as the magnetic core’s dimensions, magnetic properties, and turn count also influence performance. Previous works have focused mainly on analytical or numerical modeling of CTMEH behavior or improving power harvest performance given a specific magnetic core material. Some existing research has compared the effects of different core materials on CTMEH power harvest in limited fashion; but a comprehensive, comparative study of high permeability, high saturation flux density CTMEHs had yet to be explored. This thesis establishes core material as the primary independent variable along with primary current and frequency during testing to isolate the effects of magnetic properties on determining the amount of power a magnetic core can harvest under different current conditions. The thesis concludes that nanocrystalline material excels at lower-current applications, while silicon steel material offers better performance at higher-current applications across all frequencies when used as CTMEHs, offering system designers enticing material choices depending on the nature of the application.

Eliciting Visualization Attitudes with Repertory Grids

2025-09-19T04:49:19Z

Eliciting Visualization Attitudes with Repertory Grids Hua, Dana Research in public data communication typically focuses on improving the processes of encoding and decoding, answering the question of how to design a visualization to best communicate information to an audience. However, by treating visual communications as simply conduits for information, we ignore an important aspect of how people interact with communications. We ignore the attitudes – the thoughts, feelings, and intentions toward action – a person may form from communicative artifacts based on their personal values and experiences. Recent research has demonstrated that—much like natural language—readers of visualizations make social attributions: inferences about the identities and characteristics of an artifact’s makers, modes of distribution, and tools of production. In this thesis, I contribute a method to systematically map the visualization attitudes of an individual and the associated ideologies of their sociocultural group, by adapting the repertory grid technique from clinical psychology, to the context of data visualization. I demonstrate the effectiveness of this mixed methods approach by eliciting both the attitudes towards a visualization most salient to an individual, and the design features of the visualization that inform each attitude. This method offers a new way of exploring the content and latent structure of visualization attitudes, which opens new avenues for socioculturally-informed and intervention-driven research in data visualization.

Optimizing scheduling for stream structured programming for StreamIt

2025-09-19T04:49:17Z

Optimizing scheduling for stream structured programming for StreamIt Dow, Nicholas Lee As straightforward increases in performance on general purpose CPUs slow down, the shift to application specific implementations and hardware has accelerated. This shift to towards specialization improves performance but often at the cost of developer productivity in learning these new tools. StreamIt is a Domain Specific Language developed to increase performance of streaming applications while being relatively user-friendly. While designed to be parallelized easily, the scheduling backend of the StreamIt compiler is not adapted to the heterogeneous and distributed nature of new accelerator hardware. This thesis details the design and development of a scheduler interface that enables hardware customized schedulers to be developed quickly. The scheduler interface allows for schedulers to take advantage of the unique compiler optimizations enabled by StreamIt’s structure. Two schedulers, one search based and another heuristic based, are built using this interface to schedule StreamIt workloads to optimize differing metrics such as throughput and latency. Our experiments evaluate the performance of these workloads, and details future direction for expanding the interface and scheduler designs that could take advantage of it.

Mitigating Electromagnetic Interference in Unshielded MRI: Implementation, Experimentation, and Future Directions

2025-09-19T04:49:16Z

Mitigating Electromagnetic Interference in Unshielded MRI: Implementation, Experimentation, and Future Directions Flynn, John M. Portable, Low-Field MRI broadens access and enables numerous new applications such as point-of-care. Operating outside an RF-shielded room introduces electromagnetic interference (EMI), degrading further the signal-to-noise ratio (SNR) which is already diminished due to the lower magnetic fields used in portable imaging. Existing methods to reduce EMI perform well in simple noise environments, but can struggle with more complex profiles. Relaxing the linear assumptions is hypothesized to bring more robust mitigation algorithms. A system-wide characterization of SNR challenges was carried out on a rebuilt 800G scanner, existing techniques were validated, and new signal processing approaches were explored to drive image quality upwards. Various analytical approaches showed promise, such as dynamic coils/preamps, averaging methods, calibration, and smoothing methods. Groundwork was laid for learning-based methods throughout the pipeline. This work serves as an important baseline for the numerous experiments necessary for the full-system optimization.

Single-Model Any-Subgroup Equivariance via Symmetric Positional Encodings

2025-12-09T18:18:14Z

Single-Model Any-Subgroup Equivariance via Symmetric Positional Encodings Goel, Abhinav The inclusion of symmetries as an inductive bias, known as “equivariance”, often improves generalization on geometric data (e.g. grids, sets, and graphs). However, equivariant architectures are usually highly constrained, designed for pre-chosen symmetries, and cannot be applied to datasets with different symmetries. This work constructs a single model that is simultaneously equivariant to several groups, by simply regulating a certain input feature. Starting with a permutation-equivariant base model respecting the full Sₙ symmetry group, we can obtain subgroup G ⊆ Sₙ equivariance by using a symmetry-breaking input that is G-symmetric. Under mild conditions, the resultant network is only G-equivariant. But finding an input with automorphism group exactly G is computationally hard, which can be overcome by relaxing exact symmetry breaking to approximate symmetry breaking. This is done by leveraging the notion of 2-closure to derive fast algorithms. This method is validated on symmetry selection, multitask, and transfer learning settings, demonstrating that a single network equivariant to multiple permutation subgroups outperforms both separate equivariant models or a single non-equivariant model.

Application of Precision Successive-approximation-register Analog-to-digital Converters for Digital Root-mean-square Calculation

2025-09-19T04:49:09Z

Application of Precision Successive-approximation-register Analog-to-digital Converters for Digital Root-mean-square Calculation Choi, Sun Mee The advancement of semiconductor manufacturing processes has allowed for the availability of powerful microcontrollers at lower costs, granting system designers the flexibility to select between analog and digital signal processing techniques. Enabled by recent developments in low-power successive approximation register (SAR) analog-to-digital converter (ADC) technology, a digital approach to root-mean-square (RMS) measurement is proposed. The work begins with an explicit accumulation and averaging approach, and a set of improvements were designed to increase measurement accuracy and reliability. Algorithms are compared using the metrics of error, power efficiency, latency, and digital overhead. High-performing and power-efficient digital RMS measurement methods could be valuable for decentralized instrumentation systems such as smart grids and factory automation where long-lasting handheld and portable solutions are becoming critical.

Hosting LLMs on Shared GPUs

2025-09-19T04:49:08Z

Hosting LLMs on Shared GPUs Choi, Kenneth K. Large language models (LLMs) have emerged as powerful tools for a wide array of applications. Serving multiple LLMs on shared GPUs has increasingly gained attention as single providers need to support multiple applications (summarization, chat, code generation), different model versions (A/B testing), and various types of customers. However, multi-model serving is particularly challenging, as static memory partitioning can lead to severe under-utilization, fragmentation, and latency spikes, while dynamic loading of model weights can cause unacceptable downtime due to high model loading overheads. To address these issues, we introduce hierarchical paging, a novel key-value (KV) cache management strategy, and we implement it within the vLLM serving engine. Hierarchical paging organizes GPU memory into a two-level hierarchy: large contiguous memory blocks allocated to individual models, which are then subdivided into smaller blocks that are allocated to different requests issued to that model. Our design enables dynamic memory sharing across models, improving model throughput and overcoming key problems of existing approaches. We detail our implementation and present end-to-end experiments that showcase these throughput improvements under different workloads. We include further evaluations on the runtime overheads of our hierarchical paging implementation, which show that the overheads are insignificant. Most importantly, we demonstrate that hierarchical paging is easy to implement, optimizing for implementation effort and maintainability.

A Topology-Guided Diffusion Process for Synthetic Tabular Data Generation

2025-09-19T04:49:06Z

A Topology-Guided Diffusion Process for Synthetic Tabular Data Generation Cheng, Emily Synthesizing realistic tabular data is crucial for any analytical application, including policy evaluation related to household energy use. However, detailed household-level consumption data, necessary for such evaluation, are scare at fine geographic scales, as public surveys like the U.S. Residential Energy Consumption Survey (RECS) provide too few observations. We address this gap by developing a topology-guided diffusion-based generative model that produces realistic synthetic household data, and our approach handles two key challenges in this setting: (1) mixed continuous and discrete features and (2) strong hierarchical dependencies among variables. To handle categorical features, we build upon recent advancements in discrete diffusion, particularly TabDDPM [1] and TabDiff [2], which discretize the diffusion process through noise transition matrices, effectively extending diffusion methods to discrete tabular domains. To address hierarchical dependence, we include (1) a structure-aware noise schedule that injects noise from the leaves to the root along an approximate Chow–Liu tree constructed from the variables and (ii) a masked self-attention denoiser that aligns with the same graphical structure. Extensive experiments show that our structured diffusion model outperforms the baseline TabDiff on data with tree-like dependencies, due to the inductive bias from our structure-aware noise schedule. On data that only approximately follows a tree, such as the RECS dataset, our model maintains competitive performance, only slightly outperforming standard diffusion methods. These results highlight the potential for future work to further optimize the tradeoff between structural approximation and estimation accuracy and for future work beyond the energy domain.

On Dynamic Treatment Regimes: Collaborative Search and LLM-Driven Decision Trees

2025-09-19T04:49:05Z

On Dynamic Treatment Regimes: Collaborative Search and LLM-Driven Decision Trees Gregory, Cale This thesis evaluates the validity of current dynamic treatment regime algorithms and presents a novel data structure for extracting treatment decisions from unstructured clinical notes. The main contribution is the Clinical Decision Tree (CDT) which uses large language models (LLMs) to extract key decisions in chronic disease treatment. This addresses the main pain points in dynamic treatment regimes of low interpretability and reliance on poorly collected data for traditional machine learning methods. This work contains extensive experiments on mortality prediction, time series forecasting, and synthetic patient modeling. Experiments show that vital-based representations do not capture enough meaningful data about a patient to accurately predict and evaluate new treatment methods. By utilizing latent embeddings and vector search, experiments show that the collected vitals of patients fail to differentiate the outcomes of the related patients. Conversely, the clinical notes contain complex and substantial information about clinical decision making. LLMs enable the valuable knowledge extraction from unstructured data. Utilizing LLMs, experimental results and expert evaluation indicates that CDTs can extract and distill interpretable treatment decisions. Thus, CDTs are a valuable tool that can be refined to increase confidence in treatment decisions and identifying rare and uncommon medical practices.

"Eliminating the Friction": An AI-powered Assistant for StarLogo Nova

2025-09-19T04:49:04Z

"Eliminating the Friction": An AI-powered Assistant for StarLogo Nova Han, Aileen Agent-based modeling is a technique that allows students to reason about and create models of real-life phenomena. However, the programmatic implementations of this technique, such as StarLogo Nova, often introduce “friction”; students may get stuck on the syntactical details of the implementation before being able to engage in the mechanistic thinking behind their models. In order to shift students’ focus towards the goal of understanding the systems they are building, we set out to create an AI-powered assistant for StarLogo Nova that can explain and debug students’ code. After identifying and experimenting with various parameters of AI models in an attempt to improve their performance, we were able to build the StarLogo Turtle Helper, an easily accessible assistant integrated into the platform that can produce accurate responses to StarLogo-related questions. Through this process, we discovered two key properties of these models: first, the method through which these models use provided documentation (called retrieval-augmented generation, or RAG) is quite rudimentary, so any background knowledge should be included in the prompt or the model’s system instructions instead. Second, these models perform best if they are designed to only serve one purpose, so creating multiple models and chaining them together may be the best way to achieve more complex functionality.

Predicting Progression of Metabolic Dysfunction-associated Steatotic Liver Disease

2025-09-19T04:49:07Z

Predicting Progression of Metabolic Dysfunction-associated Steatotic Liver Disease Li, Jonathan This work focuses on the progression from metabolic dysfunction-associated fatty liver to metabolic dysfunction-associated steatohepatitis, a more serious prognosis that can lead to liver failure and death. Additional adverse progressed outcomes include hepatic failure, fibrosis, cirrhosis, and malignant neoplasm of liver and intrahepatic bile ducts. We explore the possibility of using different machine learning techniques, including logistic regression, XGBoost, random forest, and decision trees to predict the likelihood of progression. We use data from Massachusetts General Brigham to train our models, incorporating demographics, physical measurements, lab results, and doctor notes. As a result of this project, we our best model was an XGBoost classifier with an AUROC of 0.800 with random forest at a similar performance of 0.786. However, all of our models had low AUPRC and sensitivity, indicating both overfitting and an imbalanced dataset.

Data Traceability via OTrace Concepts and Implementation

2025-09-19T04:49:01Z

Data Traceability via OTrace Concepts and Implementation Farooq, Ashar Financial transactions are commonplace in the modern world. Everyday consumers make purchases on many e-commerce sites and often use many third-party financial services, such as to predict your credit score, to obtain customized budget recommendations, and to find out which specific loan is the best for them. These financial services often need financial information from the consumer, which is not always clear to the consumer. In other words, consumer data are being used without their knowledge and consent. The proposed solution of using a traceability protocol called OTrace aims to mitigate this issue of not knowing where a consumer’s data is along with what is being done with it. This paper will aim to bolster OTrace to be more representative of a protocol that consumers can actually use as a service, and financial institutions can have trust that this will solve the problem of consumers not knowing which third-party financial services have their data. In other words, this work will create a more general traceable and accountable data sharing system specification that includes the OTrace layer on top of an OAuth layer that will be complemented with a model deployment example. The addition of more relevant OTrace API endpoints corresponding to a new specification along with an entire new OTrace Web implementation along with analysis will guide the data traceability world, data privacy world, open banking world, financial world and ultimately the global world forward. There will be a model deployment of an OTrace service on top of an OAuth protocol that can allow everyone to see it being used by various parties that can ultimately scale up to fix the problem of unintended data usage and lack of transparency of location of data.

Equivariant Autoregressive Models for Molecular Generation

2025-09-19T04:48:55Z

Equivariant Autoregressive Models for Molecular Generation Kim, Song Eun In-silico generation of diverse molecular structures has emerged as a promising method to navigate the complex chemical landscape, with direct applications to inverse material design and drug discovery. However, 3D molecular structure generation comes with several unique challenges; generated structures must be invariant under rotations and translations in 3D space, and must satisfy basic chemical bonding rules. Recently, E(3)-equivariant neural networks that utilize higher-order rotationally-equivariant features have shown improved performance on a wide range of atomistic tasks, including structure generation. Previously, we have developed Symphony, an E(3)-equivariant autoregressive generative model for 3D structures of small molecules. At each sampling iteration, a single focus atom is selected, which is then used to decide on the next atom’s position within its neighborhood. Symphony built on previous autoregressive models by using message-passing with higher-order equivariant features, allowing a novel representation of probability distributions via spherical harmonic signals. Symphony’s performance approached that of state-of-the-art diffusion models while remaining relatively lightweight. However, it continued to face challenges in error accumulation and determining bond lengths, and it was only evaluated against small organic molecules. Here, we expand on Symphony’s capabilities and make it more compatible with larger atomic structures. We add improvements to the embedders, split the radial and angular components when predicting atom positions, and increase the radial cutoff for atomic neighborhoods considered during prediction. We also increase Symphony’s training and inference speeds through a new implementation in PyTorch, making inference nearly 4x faster than previously. In addition, we demonstrate its effectiveness across a variety of tasks, including small molecule and protein backbone generation.

Vigilis: Leveraging Language Models for Fraud Detection in Mobile Communications and Financial Transactions

2025-12-09T18:09:25Z

Vigilis: Leveraging Language Models for Fraud Detection in Mobile Communications and Financial Transactions Das, Gaurab Although advances in security have strengthened defenses in digital financial systems, attackers increasingly rely on social engineering to achieve their goals. These attacks are difficult to detect and prevent with existing security measures. To address this, we propose Vigilis, a fraud-protected application that employs advanced language models to counter such attacks in calls, texts, and payments. We first collect and make available a corpus of fraudulent calls from the Internet and train lightweight transformer-based models that achieve fraud detection accuracies of up to 94% and 87% on transcript and audio modalities, respectively. We integrate these models into a real-time call system within Vigilis that operates entirely on-device, enabling accurate fraud detection in an efficient and privacy-preserving manner. We then extend Vigilis to incorporate context-aware transaction authentication, where the underlying social context behind a transaction is determined from calls, texts, and browsing history and used to infer the transaction’s validity. By uniquely incorporating social concepts into traditional cybersecurity techniques, we attempt to counter and mitigate issues related to social engineering attacks in financial fraud.

GDSVD: Scalable k-SVD via Gradient Descent

2025-09-19T04:48:51Z

GDSVD: Scalable k-SVD via Gradient Descent Gan, Emily We show that a gradient-descent with a simple, universal rule for step-size selection provably finds k-SVD, i.e., the k ≥ 1 largest singular values and corresponding vectors, of any matrix, despite nonconvexity. There has been substantial progress towards this in the past few years where existing results are able to establish such guarantees for the exact-parameterized and over-parameterized settings, with choice of oracle-provided step size. But guarantees for generic setting with a step size selection that does not require oracle-provided information has remained a challenge. We overcome this challenge and establish that gradient descent with an appealingly simple adaptive step size (akin to preconditioning) and random initialization enjoys global linear convergence for generic setting. Our convergence analysis reveals that the gradient method has an attracting region, and within this attracting region, the method behaves like Heron’s method (a.k.a. the Babylonian method). Empirically, we validate the theoretical results. The emergence of a modern compute infrastructure for iterative optimization coupled with this work is likely to provide a means of solving k-SVD for very large matrices.

Comparison of dispersion metrics for estimating transcriptional noise in single-cell RNA-seq data and applications to cardiomyocyte biology

2025-09-19T04:48:59Z

Comparison of dispersion metrics for estimating transcriptional noise in single-cell RNA-seq data and applications to cardiomyocyte biology Chen, Tina T. Transcription is a dynamic process with a multitude of characteristics, including transcript level, burst frequency, amplitude, and variability. Single-cell RNA sequencing data analysis often focuses on comparing transcription levels. However, these analyses capture only a portion of the wealth of information conveyed by transcription. The quantification and analysis of transcriptional variability poses an opportunity to study transcription and gene regulation from a new angle. Transcriptional variability has already been implicated in a number of biological processes, including in immune system development and in aging. Yet, the most appropriate method for measuring transcriptional variability in single-cell data has remained relatively unclear. Here, we simulated single-cell data with varying dispersion and dataset size to assess the relative responsiveness of the Gini index, variance-to-mean ratio, variance, and Shannon entropy to variability in single-cell counts. We found that the variance-to-mean ratio scales approximately linearly with increasing dispersion, and that it is scale-invariant. The Gini index displayed paradoxical behavior, and Shannon entropy was not scale-invariant. Thus, we applied the variance-to-mean to measure transcriptional variability in two publicly available datasets studying congenital heart defects in mouse models. We first found that change in transcriptional variability does not correlate with gene characteristics such as transcript level and evolutionary gene age. We also found that using change in transcriptional variability to focus GSEA and TF motif enrichment analyses revealed both genes with known involvement in cardiomyopathy and new genes and pathways as potential targets for future study. Notably, many of the genes and pathways identified through transcriptional variability analysis were not found by differential expression analysis, suggesting that transcriptional variability can provide additional biologically relevant information beyond what is observed from studying mean expression alone.

Expanding Annotation to Mixed-Media Types in a Large-Scale Social Annotation Platform

2025-09-19T04:48:44Z

Expanding Annotation to Mixed-Media Types in a Large-Scale Social Annotation Platform Heiberger, Harry G. In recent years, social annotation systems have become a popular and effective tool for hosting collaborative discussions on assigned readings. One such tool created by our lab is NB. Over the last twelve years, hundreds of instructors have incorporated NB within their classes, with over 50,000 students leaving millions of annotations [1]. While feedback for NB has mostly been positive, one major limitation is its difficulty in annotating documents with nested media types. As multimodal forms of learning beyond just text are becoming increasingly common in educational assignments, having the ability to annotate beyond simple text documents would greatly increase the utility of NB in the modern classroom. This work seeks to remedy this issue by expanding the types of documents NB can successfully annotate, specifically focusing on three mixed-media issue types: independently moving text components, image annotation, and video annotation. We will explore the design space of possible implementation strategies for these features and discuss the specific design decisions that were made when adding them to NB. We hope that by increasing the types of documents NB can annotate, we will better fulfill its goal of enhancing student engagement and learning.

Pareto Task Inference Analysis of Single-Cell RNASequencing of Human Placenta Reveals Biological Insightsinto Adverse Pregnancy Outcomes

2025-09-19T04:48:58Z

Pareto Task Inference Analysis of Single-Cell RNASequencing of Human Placenta Reveals Biological Insightsinto Adverse Pregnancy Outcomes Eppinger, Aria R. Adverse pregnancy outcomes (APOs), such as preeclampsia, fetal growth restriction, and preterm birth, occur in 10-15% of pregnancies. There is limited knowledge of how the cellular states in the placenta and decidua tissues are altered in women with particular APOs or may contribute to APOs. Single-cell RNA sequencing (scRNAseq) approaches have characterized cellular populations and interactions at the maternal-fetal interface using traditional dimensionality-reducing methods such as UMAP-based clustering. However, these techniques may generate limited representations of nuanced cellular functions and biological relationships among and within cell clusters. Pareto Task Inference (ParTI), a dimensionality reduction technique that fits data to an n-dimensional polygon or polytope, models how cells optimize among multiple biological functions and transition between states. We applied ParTI to assess its ability to identify nuanced cellular states and intercellular relationships and to highlight biological mechanisms underlying specific APOs. We analyzed scRNAseq data from 50 whole placental homogenates collected from healthy pregnancies and those complicated by fetal growth restriction (FGR), preterm preeclampsia (PrePET), spontaneous preterm birth (PTB), term preeclampsia or gestational hypertension (TermPET/GHTN), or type 1 diabetes (DM1). ParTI was applied to the dataset with 1) all main cell lineages (B-cells, trophoblasts, stromal, endothelial, Haufbauer, T-NK, maternal myeloid cells) and 2) syncytiotrophoblasts (SCTs), a sublineage of trophoblasts. Marker genes and gene set enrichment analysis for the ParTI polytope vertices, called archetypes, were performed to assess the biological states associated with the archetypes. We demonstrated that the ParTI polytope can separate both broad cell lineages and sublineages, suggesting that iteratively applying ParTI can serve as an alternative clustering approach when cell-lineage marker genes are previously known. Additionally, ParTI applied to SCTs separated healthy controls from pregnancies complicated by specific APOs. Gene set enrichment analysis of the cells proximal to the archetypes suggests biological differences in SCTs with specific APOs compared to the controls. Thus, ParTI can identify biological mechanisms underlying specific APOs and be applied to additional datasets to uncover biological relationships among and within cell-type clusters.

Modeling Recursion with Iteration: Enabling LLVM Loop Optimizations for Recursive Data Structure Traversal

2025-09-19T04:49:02Z

Modeling Recursion with Iteration: Enabling LLVM Loop Optimizations for Recursive Data Structure Traversal Cuevas, Elie E. Recursive algorithms are a natural and expressive way to traverse complex data structures, but they often miss opportunities for optimization in modern compiler infrastructures like LLVM. This thesis explores a novel technique that temporarily transforms recursive traversals into synthetic loop-like structures, enabling existing loop-specific optimizations to apply, before transforming them back. By extending Clang’s semantic analysis and implementing a custom LLVM transformation pass, recursive traversals are initially structured into synthetic loops that can benefit from existing loop analyses and optimizations. After these optimizations are applied, the transformation restores the original recursive semantics, preserving program behavior while incorporating performance gains. Evaluation across custom microbenchmarks shows that while general recursive traversals suffer a modest overhead, workloads designed to benefit specific loop-focused optimizations achieve up to a 30% performance improvement. This demonstrates that even though the approach requires temporarily "misrepresenting" code to the compiler, selective exposure of recursive patterns to loop-based optimization infrastructure is practical and effective. This work establishes a proof-of-concept for compiler transformations that bridge recursion and iteration, paving the way for future systems that better optimize real-world recursive code without sacrificing clarity or maintainability.

Grounding Time Series in Language: Interpretable Reasoning with Large Language Models

2025-12-09T18:21:52Z

Grounding Time Series in Language: Interpretable Reasoning with Large Language Models Chen, Lily Can large language models (LLMs) classify time-series data by reasoning like a domain expert—if given the right language? We propose a method that expresses statistical time-series features in natural language, enabling LLMs to perform classification with structured, interpretable reasoning. By grounding low-level signal descriptors in semantic context, our approach reframes time-series classification as a language-based reasoning task. We evaluate this method across 23 diverse univariate datasets spanning biomedical, sensor, and human activity domains. Despite requiring no fine-tuning, it achieves competitive accuracy compared to traditional and foundation model baselines. Our method also enables models to generate expert-style justifications, providing interpretable insights into their decision-making process. We present one of the first large-scale analyses of LLM reasoning over statistical time-series features, examining calibration, explanation structure, and reasoning behavior. This work highlights the potential of language native interfaces for interpretable and trustworthy time-series classification.

National crop field delineation for the United States

2025-09-19T04:48:54Z

National crop field delineation for the United States Chen, Zitong Comprehensive and accurate crop field boundary maps are crucial for digital agriculture, land management, and environmental monitoring. However, no high-quality field boundary dataset is publicly available in the United States. This thesis addresses this gap by creating a new, large dataset and training a deep learning model capable of mapping field boundaries. We built a dataset of over 15,000 image-mask pairs using high-resolution National Agriculture Imagery Program (NAIP) satellite imagery and curated field boundary labels. This dataset covers a variety of leading agricultural states and includes images taken at different scales to capture a wide variety of field sizes and layouts. We used this dataset to train an adapted ResUNet++ neural network model designed to segment crop fields. The trained model achieved around 0.8 for pixel-level accuracy, showing it can generally identify field areas well. However, its performance in matching predicted individual field instances with the ground truth instances (measured by mean instance Intersection over Union, or mIoU) was around 0.5. This lower instance score was largely due to the post-processing step, which converts the model’s probability predictions into separate field instances. Despite this, the field polygons produced by our approach are visually coherent with satellite field images and can be readily used with geospatial tools like Google Earth Engine. Our work provides a practical starting point for future research on mapping fields across the contiguous U.S. Potential directions for improvements may involve developing sharper boundary predictions, exploring direct instance segmentation models, refining post-processing methods, and expanding the dataset to include more challenging areas.

Design and Implementation of an Analog High Power Broadband Self Interference Canceller for In Band Full Duplex

2025-09-19T04:48:52Z

Design and Implementation of an Analog High Power Broadband Self Interference Canceller for In Band Full Duplex Hanly, Bianca Marie A Self-Interference Canceler is the principle component that allows for Simultaneous Transmit And Receive (STAR) of radio signal broadcasting. Previous research and designs by other groups have resulted in systems that either operate at high powers or are capable of cancellation over a wide bandwidth. This work seeks to build upon previous research in order to design an analog SIC that is capable of both high power (∼100W) and wide instantaneous bandwidth (∼1GHz) cancellation. The system is designed as a vector modulator using off-the-shelf hybrid couplers and switches with a custom variable attenuator designed using PIN diodes in a Waugh attenuator architecture. The system was fabricated on a four layer PCB and measured with a network analyzer. Simulated results for variable attenuator and overall vector modulator are presented.

Implementation of Semantic SLAM on a Mobile Manipulator System

2025-09-19T04:48:54Z

Implementation of Semantic SLAM on a Mobile Manipulator System Francis, Zachary R. In the field of robotics, the development of household robots capable of performing everyday tasks continues to be a major area of research and practical interest. Many domestic chores—such as picking up and moving objects from one location to another—have been successfully performed by stationary robotic manipulators paired with visual perception systems. However, accomplishing more complex, varied, and spatially distributed tasks in real-world home environments requires a mobile platform with a more human-like form factor. These tasks demand greater flexibility, spatial awareness, and interaction capabilities than fixed systems can typically provide. This work focuses on the RBY1 robot from Rainbow Robotics, a humanoid platform designed to support advanced manipulation and mobility. A range of tools and modules were developed to enhance its functionality, including software for semantic perception, task execution, and environment interaction. This thesis provides a technical overview of these tools, highlighting their roles in collecting new datasets that can be used for semantic SLAM research. In the future, these tools can enable the robot to operate more effectively in domestic settings, towards the ultimate goal of enabling more capable home-assistive robots.

Improving Introductory Computer Science Students’ Programming Process When Using a Generative AI Tutor (PyTutor)

2025-09-19T04:48:49Z

Improving Introductory Computer Science Students’ Programming Process When Using a Generative AI Tutor (PyTutor) Cunningham, Caroline K. This thesis examined students’ programming process while using PyTutor, a generative AI tutor for introductory computer science students. This thesis had the research questions: (1) How does the process of test case creation, with or without PyTutor’s Test Case Runner, impact students’ programming process while using PyTutor? (2) How can prompt engineering of PyTutor’s system prompt be leveraged to improve AI Chat response quality with respect to: (a) reducing the amount of code revealed in the answer, (b) improving the conciseness of responses, and (c) having the AI chat give the student test cases as a tool to understand code correctness? (3) How does PyTutor’s responses from the updated prompt affect the programming process for computer science students? A key finding from a focus group in the first stage (n=9) was apart from test cases and was that the majority of participants who asked questions to PyTutor received at least three lines of code, unideal for PyTutor’s pedagogical purpose. This discovery inspired the next phase of this thesis of prompt engineering PyTutor, which resulted in an updated prompt. Responses from the both the updated prompt and the original prompt were scored using an evaluation rubric. For the Students thinking through problem category of the evaluation rubric, it was statistically significant that the distribution of points for responses from the updated prompt was greater than the distribution of points for responses from the original prompt. Finally, participants were asked to solve a programming problem using either PyTutor with the updated prompt (n=10) or PyTutor with the original prompt (n=2). Across the focus groups from the first and final stage, I found that fewer participants who used PyTutor with the updated prompt received at least three lines of code. Furthermore, participants who used PyTutor with the updated prompt required a greater number of messages to first receive three lines of code. Additionally, all four participants who received at least three lines of code from PyTutor with the updated prompt asked majority high-level questions. As participant feedback suggested that PyTutor’s responses for high-level questions could be repetitive, this data highlights a new direction of improving PyTutor’s responses when answering high-level questions to benefit students’ programming process.

A set-theoretic approach to state estimation.

2025-10-30T15:50:06Z

A set-theoretic approach to state estimation. Hnyilicza, Esteban. Thesis: M.S., Massachusetts Institute of Technology, Department of Electrical Engineering, 1969; Bibliography: leaves 112-113.

High Precision Binary Trait Association on PhylogeneticTrees

2025-08-28T03:07:49Z

High Precision Binary Trait Association on PhylogeneticTrees Balogun, Ishaq O. Understanding how genetic variation drives microbial phenotypes is fundamental to advancing microbiology, particularly in pathogenicity, drug resistance, and host adaptation. Traditional genome-wide association study (GWAS) methods fail to account for shared evolutionary history, confounding association analyses. Microbial GWAS approaches emerged to address this, but modern methods often lack the statistical power to detect associations while controlling false discoveries, and face computational limits at scale. Here, we present SimPhyNI (Simulation-based Phylogenetic iNteraction Inference), a computational framework for detecting binary trait-trait associations in microbial populations. SimPhyNI uses stochastic simulations of trait evolution on phylogenetic trees to detect positive and negative associations with high precision and recall. Benchmarking on large synthetic datasets, SimPhyNI achieved a precision-recall AUC (PR AUC) of 0.987 and 0.975 for positive and negative interactions, respectively, indicating near-perfect discrimination of true from neutral associations. Competing methods showed substantially lower performance, especially for negative associations. We further applied SimPhyNI to empirical datasets, recovering known biology and generating plausible hypotheses for novel mechanisms. Though tested here on binary traits, SimPhyNI’s design supports future extension to multi-state and continuous traits using generalized models. Its high recall also makes it well-suited for constructing gene interaction networks and identifying co-evolving trait modules. By combining evolutionary modeling with scalable statistics, SimPhyNI advances our ability to uncover the genetic interactions that drive microbial function, ecology, and disease.

Foundations for Building an Innovation-Centric Product Development Framework for Medical Devices

2025-08-28T03:07:23Z

Foundations for Building an Innovation-Centric Product Development Framework for Medical Devices Rajan, Neena E. The medical device industry, governed by a tight regulatory landscape, often relies heavily on structured Product Development Processes (PDPs) to bring innovative solutions to market. These structured processes create significant challenges when integrating technological innovations that emerge in the later stages of the development cycle. This study explores the complexities of this "innovation paradox" within large United States-based medical device corporations, examining how the rigidity of traditional PDP models affects the incorporation of innovative changes to in-flight projects. Drawing upon insights from a comprehensive literature review and a quantitative analysis utilizing a Monte Carlo simulation, this research highlights the impact of integrating an innovative change on the overall project timeline and cost. The simulation results show that introducing innovative changes to the PDP typically extends project timelines and increases the total net present costs and are affected by the timing of the change and its technological maturity. Introducing changes in later project phases significantly increases both duration and cost compared to earlier phases. Changes with lower technological maturity led to greater duration and cost escalations, especially when introduced late in the development cycle. To balance regulatory requirements and PDP agility, large medical device companies can adopt hybrid PDP models, establish dedicated innovation assessment teams, create flexible product designs, and focus on value-driven innovations that meet patient and market needs.

Savaal: A system for automatically generating high-quality questions from unseen documents

2025-08-28T03:07:50Z

Savaal: A system for automatically generating high-quality questions from unseen documents Chandler, Joseph A. Assessing human understanding through exams and quizzes is fundamental to learning and advancement in both educational and professional settings. However, current solutions to automate the generation of challenging questions from educational materials and documents are insufficient, resulting in superficial or often irrelevant questions. While LLMs have been shown to excel in tasks like question answering, their usage on question generation is underexplored for general domains and at scale. This work presents Savaal, a scalable question-generation system that generates higher-order questions from documents, as well as a real-world system implementation for general use. Savaal accomplishes the following goals and objectives: (i) scalability, capable of generating hundreds of questions from any document (ii) depth of understanding, synthesizing higherorder concepts to test learners’ understanding of the material, and (iii) domain independence, generalizing broadly to any field. Rather than naively providing the entire document in context to an LLM, Savaal breaks down the process of generating questions into a three-stage pipeline. We demonstrate that Savaal outperforms the direct prompting baseline as evaluated by 76 human experts on 71 documents across conference papers and PhD dissertations. We additionally contribute a general system for serving Savaal in real-world scenarios. We demonstrate that our system is scalable, enabling fault-tolerant and horizontal scaling of each individual component in response to fluctuations in usage. Moreover, our architecture enables interactive usage from users and collaboration in groups, reflecting real-world organizations like classrooms or enterprises. We hope that the system enables scalable question generation for educational and corporate use-cases.

Multi-Objective Exploration of Refueling Architecture for Sustainable Crewed and Cargo Space Transportation

2025-08-28T03:07:38Z

Multi-Objective Exploration of Refueling Architecture for Sustainable Crewed and Cargo Space Transportation Terakado, Daiki This thesis presents a new integrated framework for evaluating in-space refueling architectures, focusing on their application to the human space missions such as Artemis. The framework tightly couples vehicle sizing with a boil-off control model, allowing the evaluation of various combinations of propellant types, refueling locations, and boil-off control. The model captures the dynamic interdependence between the components of the refueling system, the transport vehicle, the refueler, and the depot, using an iterative approach to ensure consistent mass estimates across configurations. The framework is applied to analyze human landing system (HLS) architectures with refueling in cis-lunar space. The key findings highlight the mass savings benefits of cryocoolers, the benefits of high Isp with Lox/LH2, the benefits with NRHO refueling for acceptable ΔV requirement, and positive and negative effects of reusability in mass and mission time. Furthermore, the study indicates that the number of required refueling events is more sensitive to payload and refueler capacity than to boil-off losses. To extend the framework toward long-term, scalable transportation solutions, the thesis compiles a comprehensive set of figures of merits (FoMs) and discusses future model extensions including risks, ISRU, and electric propulsion. Limitations such as lack of reusable configuration flexibility, and insufficient support for Mars mission parameters are identified as areas for future development. This work provides a foundational framework for the exploration of refueling architecture and solid next steps to design sustainable and scalable human space transportation systems.

Embedded Software-Defined Radio Architectures for 6G Cellular Networks

2025-08-28T03:07:13Z

Embedded Software-Defined Radio Architectures for 6G Cellular Networks Urbonas, Jonas Over the past decades, the widespread adoption of wireless communication technologies in the industrial, scientific, medical, defense, and commercial sectors has resulted in substantial advancements in digital radio technologies. Each new generation of cellular technology, beginning with 1G, has introduced novel use-case scenarios that have challenged the performance of the prevailing digital radio architectures. The newly proposed scenarios for 5G-Advanced, and the upcoming 6G cellular networks due to be standardized by 2030 are no exception. The emerging 6G network components, such as the space-air-ground integrated cell-less networks, as well as the artificial intelligence-native network architecture, drive the demand for flexible and fully reconfigurable radio units supporting multi-GHz instantaneous signal bandwidths, frequency agile radio architectures covering multi-octave frequency ranges, and highly sensitive receivers. To support these requirements, software-defined radios (SDR) are becoming an essential building block of next-generation radio networks. This thesis presents a review of softwaredefined radio technology, examines its history, proposes the requirements of SDR units for 6G cellular networks, and presents a quantitative performance analysis of over 2 million distinct SDR architectures that could be used in 6G communication networks. It does so by defining the key system architectural decisions and their options, including the data converters, filters, mixer and amplifier technologies. It also examines different radio transmitter and receiver architectural topologies, including baseband sampling, IF sampling, direct RF sampling, and fully digital RFSoC, and constructs a multi-attribute utility (MAU) to quantify the system performance. The MAU is used to build a tradespace of SDR architectures, enabling the identification of the Pareto frontier. Analysis of SDR system architectures on the Pareto frontier reveals that the performance of direct RF sampling SDR architectures is highly competitive with industry-standard IF sampling. The tradespace is also used to analyze the sensitivity of system performance to individual architectural decisions via a main-effect analysis, allowing quantification of connectivity and sensitivity of available architectural decisions. Sensitivity analysis reveals that system performance is highly sensitive to receiver architectural decisions, particularly analog-to-digital converters, indicating the need for continued advances in this technology to produce high-performance SDR systems.

Computational Design of Architected Lattices for Construction Applications

2025-08-28T03:07:47Z

Computational Design of Architected Lattices for Construction Applications Leamon, Sophie Architected lattices have been utilized in aerospace and research applications for their modularity, scability, reconfigurability, and high strength-to-weight properties. However, voxels have yet to find widespread integration in the residential or commercial construction industry because of the industry’s distinct system needs. This study identifies the pain points unique to the construction industry that have slowed or disabled the adoption of new practices, highlighting the importance of utilizing known materials, methods, and the transparency of the design process, as major hurdles to adoption of innovation in the industry. This study presents a computational approach to designing architected lattices that seeks to undermine these core issues by making building with architected lattice structures agnostic to material and manufacturing methodology. Three open source computational approaches to architectural design are proposed: 1) integration of support structures for additively manufactured structures; 2) parametric design of voxels from 2D material, their manufacturing molds, and optional alignment features; and 3) generation of two-dimensional cut files for assembly with 3D printable joinery. These files are computationally designed and arranged for instantaneous production to demystify the lattice architectural design process, establish a pathway for utilizing all available materials in lattice construction, reduce the overhead costs for experimentation with lattice structures, and eliminate barriers to the fabrication process by enabling accessible manufacturing methods.

Evaluating the Strategic Intent and Competitive Dynamics of China’s Satellite Communications Constellations

2025-08-28T03:07:33Z

Evaluating the Strategic Intent and Competitive Dynamics of China’s Satellite Communications Constellations Delkowski, Michal . This thesis examines the strategic, technical, and economic feasibility of China’s two flagship low Earth orbit (LEO) satellite megaconstellation programs, Guowang and Qianfan, in the context of the rapidly evolving global satellite communication (Satcom) market. Against the backdrop of SpaceX’s Starlink dominance and intensifying geopolitical competition, China’s efforts represent not only a telecommunications infrastructure push but also a broader assertion of technological sovereignty and global influence. This study uses a scenario-based analysis that integrates system throughput analysis and financial forecasting. Three deployment scenarios (base, optimistic, and pessimistic) are analyzed, accounting for satellite production rates, launch capabilities, and regional adoption patterns, particularly across Belt and Road Initiative (BRI) markets. The study also evaluates "system-of-systems" integration with China’s military objectives, and spectrum coordination challenges. Key findings reveal that Guowang becomes marginally viable only in the optimistic scenario, assuming deployment of at least 9,000 satellites, reduced satellite unit costs (targeting ~$300,000 per satellite), expanded gateway infrastructure, and realization of these targets by 2035, while remaining unviable in base and pessimistic cases. Qianfan faces greater commercial risk, achieving viability only with early adoption in BRI countries and government dual-use contracts, incurring a pessimistic-case NPV loss exceeding $76B. Resource allocation problem (RAP) modeling suggests that projected throughput may saturate early without major gateway expansion. Both constellations require China to scale reusable rockets and sustain a combined annual launch rate exceeding 1,000 satellites by the early 2030s. Neither constellation system meets China’s 2030 rural broadband targets under base-case conditions, over 40% of the 336M unconnected citizens remain underserved without terminal subsidies. Ultimately, China’s LEO Satcom strategy depends not on satellite count alone but on coordinated progress in launch economics, affordability, dual-use policy, and international partnerships.

Optimization of CPG budgets in Retailer-led marketing programs

2025-08-28T03:07:07Z

Optimization of CPG budgets in Retailer-led marketing programs Gandhi, Abhinav Grocery retailers and Consumer Packaged Goods (CPG) companies have a symbiotic relationship. Retailers need CPGs to supply the products, and CPGs need retailers’ customers to grow their brands. Since shelf space is limited, CPGs offer trade and marketing funds to prominently feature their brands. As part of loyalty programs, retailers offer coupons to customers that are often funded by CPGs. In return, CPGs expect a return on their investment(ROI). Since budgets are limited and are also expected to be utilized, it becomes a challenge for the retailer to find the right size of a mailer which can balance costs and relevance to customers. This thesis explores how knapsack problems can be used in an non-adaptive setting to help maximize the reach of print and email campaigns. Seeking inspiration from existing literature, multiple simulations were set up to evaluate budget-constrained allocation and compare two approaches, the multiple-choice Knapsack (MCK) and a greedy algorithm. Considering uncertainty in redemption, the Newsvendor model was also explored to review the possibility of over-allocation to improve budget utilization and increase reach. The preliminary analysis findings offer promising results and provide a setting for further research.

Exploration of design strategies and optimization for efficient mass timber structures as a function of column position

2025-08-28T03:07:41Z

Exploration of design strategies and optimization for efficient mass timber structures as a function of column position Gerken, Christoph The building sector is responsible for a large share in global carbon emissions. As the load bearing structure is particularly material-intensive, a decisive shift can be achieved by improving its design and decreasing its volume. This thesis examines how structural mass timber floor systems can be designed in an efficient, low-waste manner through a design-oriented approach that is immediately applicable within the context of conventional construction techniques and building practices. Reducing material in timber structures has economical and ecological benefits. Reduced timber demand entails significant cost savings and decreased building weight which considerably cuts embodied carbon. Since common floor systems mainly act in bending, this work focuses on the reduction of moment forces in standard setups comprised of timber slabs, beams, and columns. In principle, bending forces in beams and slabs can be reduced by moving the supports inwards, leading to overhanging structural elements. The original method presented in this thesis shows how this approach applies to conventional mass timber floor systems. This work provides an understanding of how informed column positioning can take advantage of this behavior and allows for material and embodied carbon reduction trough design. The consequent architectural implications of the resulting irregular column grid are explored in a floor plan design suggestion Material demand and embodied carbon are evaluated as a function of column position through finite element analysis and optimization as part of a computational model. By consulting a mass timber manufacturer’s catalogue to assign appropriate products to structural members, this approach enables material reduction in the design process rather than in the production. Bypassing slow-changing, inert fabrication procedures, this method can be realized instantaneously. This work identifies the optimal support position to reduce bending forces in beams and slabs to be at 41% of the distance from the element’s edge to its midspan. Furthermore, this research finds that the impact of ideal column position on material efficiency depends on required minimum effective spans. While being negligible in the absence of constraints, informed column positioning can reduce timber demand by 20% and embodied carbon by 16% when subjected to a minimum effective span requirement of 6 m – a common span in timber construction – in a building of 30x30 m and five floors. Building dimensions are found to have an insignificant impact on these results. This thesis illustrates the potential for architects and engineers to enhance structural efficiency of mass timber floor systems merely by deviating from the usual, regular column grid and taking advantage of straightforward structural principles through design.

Machine Learning for the Condition Assessment of Concrete Bridges

2025-08-28T03:07:27Z

Machine Learning for the Condition Assessment of Concrete Bridges Fayad, Fred The assessment of concrete bridge conditions is critical for ensuring structural integrity and public safety. Traditional inspection methods, which rely heavily on visual inspections and manual assessments, are time-consuming, subjective, and prone to human error. With the increasing number of aging bridges worldwide, there is a growing need for more efficient and accurate methods to assess bridge health. This thesis aims to explore the application of machine learning techniques for automating the bridge condition assessment process and improving the accuracy and reliability of bridge evaluations. This study investigates the development and implementation of a model consisting of two machine learning algorithms to predict the condition of concrete bridges based on data collected from various public sources. The first algorithm appraises the structural health of a bridge based on bridge rating and the second algorithm assesses the condition of a bridge after a specific failure mechanism. Specifically, this work focuses on using classification algorithms such as Random Forest (RF), XGBoost, and Neural Networks (NN) in both algorithms to achieve their purpose. The results of this study demonstrate that machine learning models can provide a decent performance in predicting bridge conditions. The overall model achieved a testing accuracy of 79%. This research contributes to the field of civil engineering by showcasing the potential of machine learning in infrastructure management. By automating the assessment process, the proposed models can help reduce the time and cost of inspections while providing more accurate data to guide maintenance planning and bridge rehabilitation efforts. Future work will focus on further optimizing the models, incorporating additional data sources, and deploying the system for real-time bridge monitoring.

A Comparison of Theoretical and Actual Coumarin Exudation Under Iron Limitation to Understand Root Exudation Mechanics

2025-08-28T03:08:08Z

A Comparison of Theoretical and Actual Coumarin Exudation Under Iron Limitation to Understand Root Exudation Mechanics Van Note, Lana Nutrient cycling is an important component of plants’ immune systems, largely driven by the act of exuding environmentally influential metabolites from roots. Root exudation may be driven by multiple unique mass-transport mechanisms, including active and passive transport types, though the latter is not well-studied despite being labelled a significant driver of low molecular weight metabolite exudation. This research investigates the generally accepted assumption that low molecular weight metabolites, including iron-fixing coumarins (scopoletin, fraxetin, etc.) are primarily exuded passively, and high molecular weight metabolites follow an active exudation approach. Scopoletin and scopolin exudation from Arabidopsis thaliana in low-iron and replete conditions is quantified to determine if the hypothesized passive diffusion mechanism is a significant contributor to coumarin exudation. LC-MS analysis suggests that passive diffusion of scopoletin and scopolin from roots plays a significant role in total coumarin exudation values. Further research should include investigating the implications of passive coumarin exudation on long-term iron storage and soil health in addition to the relationship between coumarin production and exudation.

Offshore floating solar with compressed air storage as a baseload power plant for a data center

2025-08-28T03:08:14Z

Offshore floating solar with compressed air storage as a baseload power plant for a data center Athanasopoulos, Panagiotis Rafail This thesis presents the conceptual design, technical modeling, and economic analysis of a novel offshore floating solar energy system integrated with Compressed Air Energy Storage (CAES) for reliable baseload power delivery to coastal data centers. The system architecture is modular, consisting of multiple “powercells,” each comprising a 5×5 photovoltaic (PV) array mounted above a matrix of submerged compressed air storage cylinders anchored below the floating platform, addressing the energy resilience and spatial constraints of coastal computing infrastructure. This scalable configuration enables distributed energy collection and localized storage, tailored to meet site-specific demands. Detailed thermodynamic modeling of both charging and discharging cycles is conducted, with analytical solutions validated against a full numerical implementation. Results show that under realistic operating assumptions, the temperature inside the storage vessels remains nearly isothermal due to the long charging duration and large heat exchange surface, enabling a simplified energy balance model. A techno-economic analysis evaluates both structural steel requirements and photovoltaic investment, benchmarked against market data from 2024. Key metrics such as structural cost per unit energy ($/kWh) and per rated power output ($/kW) are derived. The hybrid system is found to be economically competitive with lithium-ion (Li-ion) battery alternatives, offering extended lifespan (20–30 years), lower material costs, and enhanced sustainability through avoidance of critical minerals. Environmental and mooring considerations for offshore deployment are also addressed, demonstrating the feasibility of integrating energy generation, storage, and maritime infrastructure. This work advances the development of resilient, decarbonized energy systems aligned with global renewable energy targets and the rising demand for sustainable data center operations.

Destructive Behaviors in Naval Shipyards: A STAMP and System Dynamics Analysis

2025-08-28T03:08:10Z

Destructive Behaviors in Naval Shipyards: A STAMP and System Dynamics Analysis Brower, Braden C. United States Navy Refueling and Complex Overhauls (RCOHs), and other extended maintenance availabilities, present uniquely demanding environments where Sailors face elevated risks for destructive behaviors, including suicide and substance abuse. Prolonged exposure to harsh industrial conditions, significantly degraded Quality of Service, demanding workloads, and critical manning shortfalls create cumulative stress distinct from operational duty. These destructive behaviors severely impact personnel’s well-being, erode force readiness through attrition and morale issues, and indicate systemic contributing factors as highlighted by recent investigations into carrier suicides during shipyard periods. This thesis utilizes Causal Analysis based on Systems Theory (CAST), grounded in systems thinking, to analyze the USS George Washington RCOH events and identify the underlying safety control structure flaws that contributed to this hazardous environment. Insights from the CAST analysis were then integrated with a qualitative System Dynamics model to better understand the feedback loops and dynamic interactions driving system behavior, particularly revealing a capability trap dynamic exacerbated by resource constraints and personnel pressures. The analysis identified critical, interacting systemic flaws across multiple organizational levels that contributed to the accident: (a) inadequate strategic resourcing and manning prioritization for RCOH personnel support, (b) deficient planning, risk management, and oversight processes that were ineffective at protecting Sailor well-being amidst budget and schedule pressures, (c) ineffective feedback mechanisms that prevented critical information from reaching decision-makers, (d) and reliance on flawed assumptions regarding the RCOH environment, Sailor resilience, and standard process adequacy. Based on these findings, the thesis provides actionable, systemically focused recommendations aimed at strengthening the Navy's safety control structure by improving decision makers’ mental models, enhancing feedback and oversight, enforcing well-being constraints, and fostering organizational learning. Combined, these recommendations empower leaders to proactively manage risks, reduce destructive behaviors, and ensure a safer, more resilient environment during future RCOHs.

Enhancing Community Risk Preparedness for Flooding Emergencies: A System Dynamics Approach for the U.S. Army Corps of Engineers

2025-08-28T03:08:15Z

Enhancing Community Risk Preparedness for Flooding Emergencies: A System Dynamics Approach for the U.S. Army Corps of Engineers Hoyt, Thomas S. Flooding events pose a significant and growing threat to communities in the United States, particularly as climate change alters weather patterns and sea levels continue to rise. This thesis examines how the U.S. Army Corps of Engineers (USACE) can enhance community preparedness for flood emergencies through improved risk communication strategies. Focusing on the New England District as a representative case, it integrates data from the Federal Emergency Management Agency’s (FEMA) National Household Survey and the National Flood Insurance Program (NFIP) claims archive to develop and calibrate a System Dynamics model of flood risk perception and preparedness. The model built in this thesis incorporates key variables and captures the feedback loops that influence community preparedness over time. Scenario testing demonstrates that monthly to quarterly engagements by USACE help sustain risk awareness and reduce flood-related damage, whereas less frequent engagement demonstrates minimal improvement above the baseline. By contrast, barriers to action, such as complex procedures or limited access to information, can substantially slow the adoption of preparedness measures. High levels of trust in authorities further amplify the effectiveness of risk communication and foster community engagement. This model quantifies the importance of frequent engagement, low barriers to action, and trust-building initiatives in reducing flood impact. Through calibration against historical claims and survey data, the model provides a robust framework that can guide USACE and partner agencies in refining their own flood risk communication strategies to bolster community resilience.

Structural engineering model of irregular and efficient concrete beams: Application to topology optimized shapes and integrated textile reinforcement

2025-08-28T03:08:13Z

Structural engineering model of irregular and efficient concrete beams: Application to topology optimized shapes and integrated textile reinforcement Stribos, Sophia Concrete remains one of the most widely used construction materials due to its strength, durability, and availability. However, it is responsible for a large share of the global carbon emissions. Within the 40% of the global emissions attributed to the building sector, 5-8% alone accounts for the production of cement, a key component in concrete. As the construction industry seeks innovations towards sustainable practices, alternative beam designs that improve material efficiency and introduce nontraditional reinforcement systems are emerging as promising potential. However, accurate structural models capable of predicting and validating the performance of these innovative beams are often lacking, limiting their implementation in the industry, primarily due to safety and code compliance. This thesis bridges this gap by developing and validating a structural engineering model to predict the shear and flexural capacities and the deflection of irregular, efficiently shaped concrete beams, including those with alternative reinforcement and formwork. The model discretizes a 3D beam geometry into 2D sections to perform a geometric and structural cross-sectional analysis along the beam’s length. The structural engineering model is applied to two case studies: a topology-optimized steel-reinforced concrete beam and an integrated knit textile reinforced concrete beam, using experimentally measured material properties and beam testing data. The predicted engineering model results are compared against experimental data to validate the model’s accuracy. While the model could accurately capture the behavior of the topology-optimized steel-reinforced beam, it slightly overestimated the strength of the knit-textile reinforced beam. The engineering model for the topology-optimized beam had a close alignment in flexural capacity and had a slightly conservative estimate in shear and deflection due to the nature of the design equations. However, the model showed a minor overprediction in the flexural capacity and deflection of the integrated knit textile beam. Discrepancies in this model were linked to inaccurate material properties, experimental imperfections, and prestressing effects. To ensure complete accuracy and reliability, additional beam analysis using this model is needed. This research advances structural design by offering a tool for predicting the capacity and serviceability of irregular, efficiently shaped concrete beams, including those with alternative reinforcement. This thesis enables designers to validate and optimize their innovative beam designs and support their ideas as sustainable solutions within the concrete construction industry.

Assessment of Decarbonization Pathways of Japan

2025-08-28T03:08:11Z

Assessment of Decarbonization Pathways of Japan Suto, Sadami Developing realistic pathways for decarbonization is crucial for the success of climate change mitigation actions. To evaluate Japan’s pathways toward achieving carbon neutrality, this study enhances the MIT Economic Projection and Policy Analysis (EPPA) model and analyzes a suite of policy scenarios that combine domestic mitigation measures such as emissions targets from the updated Japan’s Nationally Determined Contribution (NDC), power mix goals, and availability of carbon capture and storage (CCS) with international emissions trading. The impacts on CO₂ emissions, GDP, consumption, carbon prices, and sectoral output in Japan between 2030 and 2050 are assessed. Under the baseline scenario, emissions over time remain flat at about 1,000 MtCO₂e, far exceeding the carbon neutrality goal. Even when Japan’s 2030 and 2040 NDC for CO₂ and power mix targets are fully achieved, residual emissions of 100 – 200 MtCO₂e remain, which calls for a need of carbon offsets. Relying on domestic-only measures is costly for Japan. In high-ambition domestic-only scenarios without CCS, carbon prices soar to over $46,000/tCO₂ by 2050, leading to GDP losses exceeding $1.5 trillion (23% of GDP) and significant contractions in key sectors of the economy. In contrast, scenarios incorporating international emissions trading enable Japan to achieve comparable total emissions reductions by partially relying on imported carbon credits. This mechanism significantly lowers marginal abatement costs, allowing carbon prices to stabilize at $20 –$30/tCO₂ and reducing GDP losses to about $100 billion (1.6% of GDP) by 2050. Scenarios that emphasize domestic reductions while flexibly using international credits emerge as manageable pathways. These scenarios achieve domestic emissions reductions of 40 – 60% by 2050, with carbon prices ranging from $140 to $340/tCO₂ and GDP losses contained between $150 and $290 billion (2.3% and 4.3% of GDP). Importantly, these scenarios incorporate the deployment of CCS, which plays a critical role in reducing marginal costs and enabling deeper abatement in hard-to-decarbonize sectors. Most industrial sectors maintain stable output, while carbon-intensive sectors undergo gradual structural transitions. Overall, these findings suggest that Japan can achieve carbon neutrality through an integrated strategy that combines strengthened domestic action, technological deployment, and international cooperation. This study provides a robust quantitative foundation for designing feasible, equitable, and cost-effective climate policies.

Impact-Induced Bridge Failures: Analyzing Structural Vulnerabilities and Optimizing Pier Designs for Enhanced Resilience

2025-08-28T03:08:05Z

Impact-Induced Bridge Failures: Analyzing Structural Vulnerabilities and Optimizing Pier Designs for Enhanced Resilience Ren, Daisy Due to the rise in global traffic patterns in recent years, bridge failures due to impact effects are becoming an increasing concern, especially for aging infrastructure. Following the recent collapse of the Francis Scott Key Bridge, issues regarding bridge vulnerabilities and design deficiencies arose, which highlighted the need for better design codes and protection for bridge piers. This study aims to address these issues by better understanding bridges' impact-related structural failure mechanisms by developing a comprehensive optimization framework to enhance the resilience of structures to dynamic impact forces using three phases: (i) statistical analysis of bridge failure data from the Multidisciplinary Center for Earthquake Engineering Research (MCEER), with data focusing on the frequency, bridge types, and bridge material trends associated with different bridge failures across the United States, (ii) development of a compliance-based optimization for trusses using MATLAB that is applied to 2D representations of pier structures for different truss configurations (2X3, 3X4, 3X5) under stress, load, and volume constraints to simulate large magnitude impact conditions, and (iii) design and validation of optimization results through mathematical calculations of compliance and strain energy to ensure consistency between numerical results and structural mechanics principles. Both fail-safe and shape optimization strategies are employed and compared across all truss configurations, revealing distinct design methodologies between maximum and minimum compliance optimizations and the trade-offs between stiffness and energy dissipation. Maximum compliance optimization designs demonstrate increased redundancy and strain energy capacity, while minimum compliance optimization designs showed increased efficiency but were more prone to brittle failure. The final study utilizing volume constraints further examined material distribution under realistic impact loads and highlighted the importance of distributed load paths and deformation capacity in structural performance. This work provides a design framework for energy-absorbing pier geometries and aims to offer insight into improving current design standards for pier designs to account for extreme events and help guide retrofitting efforts that could prevent future failures.

Computing Economic Equilibria and Their Applications to Market Games

2025-08-28T03:08:01Z

Computing Economic Equilibria and Their Applications to Market Games Bruce, Samuel G. The emergence of new technologies such as e-payments and tokenized assets, distributed ledgers, smart contracts and encryption have created new opportunities for improving access and equity in financial institutions. These new tools can be used to build better infrastructure and improve economic efficiency, especially in previously underdeveloped countries. The use of these tools in various applications however requires and intimate link between economics and computer science to ensure an implementation that is both computationally efficient and improves social welfare. There has been significant research in the field of computer science concerning the computation of economic equilibria, specifically Nash Equilibria and Correlated Equilibria. These algorithms, however, have not been used in many financial applications. Further, while research exists on various methods of computation for Correlated Equilibria, little exploration has been done evaluating the quality of these equilibria in terms of economic efficiency in specific mechanisms. This work provides a sweeping view of the existing literature on equilibrium computation as well as an analysis on the economic and algorithmic tradeoffs of different approaches. The discussion begins with simple 2-player, finite action games, then moves to more complex machine learning based method for equilibrium computation in difficult settings. One of these methods is then extended to a limit-order market game explicitly described by Dubey [1] and implemented, with small modifications, by SPEEDEX [2]. This limit-order game offers a continuous, vector-valued action space with complex payoff functions, causing tension with many of the equilibrium computation algorithms explored previously. This paper identifies these tensions, then offers modifications to algorithms which allow tractable, welfare improving approximate Coarse Correlated Equilibrium computation. Finally, there is a discussion on future work which aims to generalize the developed framework. The code corresponding to the equilibria computation will be released publicly in this repository [3].

Optimizing SigmaOS for Efficient Orchestration of Fault-Tolerant, Burst-Parallel Workloads

2025-08-28T03:08:00Z

Optimizing SigmaOS for Efficient Orchestration of Fault-Tolerant, Burst-Parallel Workloads Chang, Ryan SigmaOS is a multi-tenant cloud operating system designed for efficient orchestration of fault-tolerant, burst-parallel workloads. It provides users with isolated cloud environments called realms, where resources are accessed through a Unix-like filesystem interface, and supports applications built from procs—lightweight, rapidly-spawnable programs that can be both short-lived for bursty tasks or long-running and stateful for persistent services. However, the current prototype exhibits performance bottlenecks that hinder its scalability for larger, more demanding applications. This thesis addresses these limitations by introducing two key optimizations: (1) a rearchitected watch API, enhancing its efficiency and scalability for monitoring directory changes crucial for inter-proc coordination and event notification, and (2) a new ft/task server, providing a robust and high-performance mechanism for managing fault-tolerant bags of tasks, essential for applications like MapReduce. Through these enhancements, this work demonstrates significant improvements in SigmaOS’s performance on the MapReduce benchmark, showcasing improved scaling capabilities for larger cluster deployments, larger inputs, and more granular tasks. These optimizations are crucial steps towards enabling SigmaOS to effectively realize its vision as a scalable and performant platform for complex cloud workloads.

Data-Driven Modeling and Real-Time Optimal Control of Continuous Manufacturing Processes

2025-08-28T03:07:56Z

Data-Driven Modeling and Real-Time Optimal Control of Continuous Manufacturing Processes Gomez, Samuel John When faced with complex disturbances, continuous manufacturing processes require robust control and adaptability to maintain product quality and operational efficiency. Although advanced control strategies such as linear quadratic regulator, model predictive control, and adaptive control have demonstrated strong performance, many industrial processes still rely predominantly on classical proportional-integral-derivative (PID) controllers because of their simplicity, ease of implementation, and sufficient results. This thesis investigates the effectiveness of data-driven modeling techniques in capturing system dynamics more accurately than traditional physics-based models. It further examines using a high-fidelity digital twin, constructed from experimental data via linear system identification and nonlinear deep learning (NARX) approaches, to optimize PID controller parameters through simulation-based gradient descent methods. A comprehensive experimental platform was developed to collect synchronized sensor and video data from a roll-to-roll continuous manufacturing system, specifically targeting disturbance scenarios that cause process interruptions. The digital twin created from these data was validated against physical experiments and shown to outperform conventional physics-based models when predicting the system’s dynamic response to disturbance inputs. Optimal control of the system was explored by implementing a virtual PID controller that closely replicates the physical controller. Optimal gain settings were identified through simulation and applied to the physical manufacturing process. The experimental results showed a significant reduction in the mean squared error and the maximum web deviation. These results demonstrate the substantial potential of digital twin-driven, data-centric control approaches in enhancing resilience, efficiency, and adaptability in manufacturing processes. This research also lays the foundation for the future development of real-time, adaptive, and autonomous control strategies in industrial applications.

Power and Progress in Japan: The Past, Present, and Future of Japan as a Tech Powerhouse

2025-08-28T03:08:07Z

Power and Progress in Japan: The Past, Present, and Future of Japan as a Tech Powerhouse Maruyama, Shun This paper analyzes Japan’s economic and technological history since the Meiji Restoration through the framework of Power and Progress proposed by Acemoglu and Johnson (2023), focusing on the concepts of direction of technology and productivity bandwagon. A historical review reveals that technological progress and the distribution of its benefits were not determined solely by market mechanisms or technological inevitability, but were shaped by the power dynamics among governments, companies, workers, and others. Periods when workers held strong bargaining power and inclusive social institutions were in place saw the emergence of a virtuous cycle, in which the direction of technology moved toward broad-based innovation and the productivity bandwagon functioned effectively. Conversely, after the collapse of the bubble economy, a shift in the power balance in favor of companies led to a rise in short-term cost-cutting, resulting in a divergence from inclusiveness and innovation in the direction of technology, as well as a breakdown of the productivity bandwagon. This ultimately undermined Japan’s ability to leverage the strengths of its production system and led to a decline in technological capabilities. Currently, a new wave of technological innovation centered on AI is emerging. However, its impact remains heavily dependent on existing employment practices and corporate behavior models, making a short-term shift in direction unlikely. In the medium-to-long term, however, the societal will and collective action may create an opportunity to rebuild a virtuous cycle. This paper proposes action guidelines for companies, workers, and the government, and argues that realizing true prosperity from technological progress requires reassessing existing power structures and actively choosing new pathways as a society.

Bluespec Language Server: Adapting Rust Analyzer for Bluespec SystemVerilog

2025-08-28T03:08:06Z

Bluespec Language Server: Adapting Rust Analyzer for Bluespec SystemVerilog Chan, Martin The Language Server Protocol (LSP) and popularity of VS Code have facilitated the current ubiquity of smart code editing features like hover or goto-definition. These features are powered by language servers, which are programs that perform compiler-like functions at keystroke latency on potentially incomplete code. Mainstream languages like Rust or Python have the large userbases to motivate the creation of bespoke language servers like Rust Analyzer or Pylance. However, smaller languages like Bluespec SystemVerilog, used in computer architecture classes at MIT, often need to make do without a language server. As students come to expect smart code editing features, they may miss the convenience when working with languages like Bluespec. In this thesis, we present a Bluespec Language Server forked from Rust Analyzer. This involved adapting the Rust Analyzer parser, HIR, and other internals to work for Bluespec SystemVerilog. The resulting artifact supports the full suite of typical smart editing features for classroom-grade Bluespec projects and continues to mostly work for industrial-grade projects. We discuss the many changes and challenges required to adapt this language server to work for a different language than it was designed for. Further, to address the current gap in the literature covering language server implementation, we include thorough discussion of the overall system architecture and several important subsystems with significant overlap with Rust Analyzer's internals. Finally, we conclude with a discussion of current limitations of our language server and directions for future work.

A Microelectromechanical-Cantilever Hydrogen Sensor with Palladium-Driven Bending and Piezoresistive Readout

2025-08-28T03:07:46Z

A Microelectromechanical-Cantilever Hydrogen Sensor with Palladium-Driven Bending and Piezoresistive Readout Andrade, Marco A. Hydrogen gas (H₂) is considered a promising source of environmentally friendly and sustainable energy of benefit for global decarbonization. However, given the flammable and explosive nature of H₂, highly sensitive and selective detection systems with fast response are needed to enable leakage monitoring to ensure safe deployment and use. To address this need, we propose a microelectromechanical (MEMS) platform for H₂ sensing with the aim of achieving sub-1-ppm sensitivity. Our platform employs a MEMS structure that has H₂-responsive palladium (Pd) features. Once exposed to H₂, the Pd lattice expands as H₂ diffuses into it. This results in the structural deflection of a mechanically-mobile feature, in particular a cantilever. This deflection is measured using piezoresistors, which are embedded in the cantilever using a spin-on glass doping process. Piezoresistors enable rapid high-accuracy detection and quantification of H₂, as will be shown in this thesis through a combination of modeling, sensor development, sensor fabrication, and basic experimental characterization. In this thesis, we have successfully developed a fabrication plan, demonstrated the two key aspects of our fabrication, namely beam release and piezoresistor fabrication, shown beam bending driven by absorption of hydrogen by palladium, and shown that our piezoresistors respond to beam bending. Our physical results match our theoretical predictions for a beam of size 100 µm by 20 µm and a resistor with resistance 115 kΩ fabricated on SOI chips. This beam could be used to detect H₂ below 1 ppm.

Emergent Dynamics in AI-Enhanced Entrepreneurship Education: A System of Systems Study of the Orbit Tool

2025-08-28T03:07:35Z

Emergent Dynamics in AI-Enhanced Entrepreneurship Education: A System of Systems Study of the Orbit Tool Dale, William The convergence of artificial intelligence and entrepreneurship education has opened a novel frontier in pedagogical innovation. The deployment of Orbit—a bespoke generative AI tool—within MIT’s 15.390 entrepreneurship course, which follows the structured Disciplined Entrepreneurship framework, is examined through a System-of-Systems perspective. This approach reveals how the tool functions not as an isolated feature but as an integrated element within a multifaceted educational ecosystem. Drawing on quantitative usage data across three consecutive academic semesters (Spring 2024-Spring 2025) complemented by course evaluation metrics, our mixed-methods approach reveals the multidimensional impact of AI-enhanced entrepreneurial education. The findings demonstrate that Orbit, particularly in its refined v2 iteration, functions as a powerful External Enabler that significantly reduces both the opacity and agency-intensity inherent in complex entrepreneurial frameworks. This enabling function manifested through measurable increases in student adoption, idea generation, and iterative engagement with critical DE steps. Beyond efficiency gains, we identify a substantive Transformation of Learning where students developed distinctly different engagement patterns—characterized by increased iteration, greater willingness to tackle complex entrepreneurial challenges, and enhanced overall course experiences. This transformation appears to deepen rather than merely accelerate learning, as evidenced by improved course evaluations alongside increased time investment in coursework. However, our analysis reveals that this transformation operates within the constraints of what we term AI’s "Jagged Frontier"—an uneven landscape of capabilities leading to differentiated impacts across DE tasks and student segments. The evolution from Orbit v1 to v2 underscores how thoughtful system design and curriculum integration critically influence the effectiveness of educational AI tools. This research contributes a nuanced understanding of how specialized AI tools can enhance entrepreneurship education while highlighting that their benefits depend on deliberate design choices, strategic pedagogical integration, and recognition of current technological limitations. The SoS framework proves instrumental in capturing these emergent dynamics, offering valuable insights for educational technologists, entrepreneurship educators, and institutions navigating the AI-enhanced learning landscape.

System Design and Evaluation of Spectrum Management Architectures for Co-Primary Sharing in the 37 GHz Band

2025-08-28T03:07:12Z

System Design and Evaluation of Spectrum Management Architectures for Co-Primary Sharing in the 37 GHz Band Alsehali, Mohammed S. This thesis presents a system design framework for evaluating spectrum management architectures enabling co-primary access in the 37 GHz band. Motivated by increasing demand for mid-band and mmWave spectrum, and recent policy directions for federal-commercial sharing, this research investigates the trade-offs between utilization efficiency, coordination overhead, and interference performance across thousands of feasible spectrum management system. Using a morphological matrix, eight key architectural decisions were defined, including coordination topology, licensing mechanism, frequency planning, sensing mode, and access priority. A parametric event-driven simulation model was developed in Python to evaluate 2,808 valid architectures under low, medium, and high spectrum demand scenarios. The performance metrics, Spectrum Utilization Efficiency (SUE), Coordination Index (Cindex), and Blocking Probability (BP), were used to generate multi-dimensional tradespaces and identify Pareto-optimal solutions. Results indicate that semi-dynamic spectrum management systems with decentralized or hybrid coordination topologies consistently dominate the Pareto frontier across all demand levels. Compared to fully dynamic systems, semi-dynamic designs achieve 80–90% of the utilization efficiency with way less than 50% of the coordination cost. The results validate key hypotheses about performance trade-offs and offer actionable insights for regulators and system designers. This thesis recommends semi-dynamic, co-primary frameworks for initial 37 GHz implementation and proposes future research directions, including agent-based modeling, economic behavior integration, and acuarate physics modeling.

Architecting Innovation in Healthcare: A Case Study Review in Digital Pathology

2025-08-28T03:07:31Z

Architecting Innovation in Healthcare: A Case Study Review in Digital Pathology Jezewska, Martyna The Mayo Clinic, a renowned non-profit organization, has long been at the forefront of healthcare innovation. This thesis explores the implementation of digital pathology within the Mayo Clinic, focusing on its potential to enhance diagnostic accuracy, increase efficiency, enable remote collaboration, and ultimately improve patient care. By leveraging the Architecting Innovative Enterprise Strategy (ARIES) framework, this research provides a comprehensive analysis of the socio-technical aspects of digital pathology implementation. The study begins with a literature review on innovation and its application in healthcare, followed by an in-depth case study of the Mayo Clinic's journey with digital pathology. Key findings highlight the importance of organizational design, stakeholder engagement, and continuous improvement in successfully integrating digital pathology into existing healthcare systems. The research concludes with recommendations for future innovations and insights on how healthcare institutions can better prepare for and adapt to disruptive technologies.

Optimization of Renewable Energy Siting Decisions through Vertical Axis Wind Turbine Integration

2025-08-28T03:07:14Z

Optimization of Renewable Energy Siting Decisions through Vertical Axis Wind Turbine Integration Suresh, Nithyaharini The rapid increase in wind energy deployment is critical to achieving net-zero carbon emissions in the United States. However, conventional Horizontal Axis Wind Turbines (HAWTs) face deployment constraints due to their large spatial requirements stemming from their size itself and turbine spacing to accommodate wake interference. Their large footprint makes it impractical to deploy in densely populated and restricted areas, such as military zones and urban regions. This setback results in the underutilization of available wind resources, limiting wind energy’s full potential. To overcome these constraints, Vertical Axis Wind Turbines (VAWTs) offer a spatially compact alternative, enabling deployment in space-constrained areas. This study investigates the feasibility of VAWTs as a complementary wind technology by integrating them into a renewable energy siting optimization framework. This framework considers HAWTs, Solar Photovoltaics (PV), battery storage, etc., within the New England region, assuming a 100% decarbonized power system. The model utilizes an analysis that aims to minimize total system costs to assess VAWTs under varying capital expenditures and land-use restrictions. A novel feature of this study is the usage of the land availability cutoff and land restriction cases that are introduced to realistically mimic real-world land use constraints that influence wind turbine siting. The land availability cutoff defines the minimum area of land usable within the parcel for it to be considered for HAWTs and Solar PV deployment, given their larger spatial footprint. Parcels below this land cutoff are excluded from those technologies and only consider VAWTs due to the lower land available within the parcel, representing constrained regions. This methodology offers a more technical modeling of spatial constraints for renewable energy siting and allows for a realistic assessment of VAWT feasibility. Results indicate that, at current commercial costs, VAWTs are less competitive withm HAWTs and solar PV, primarily due to their early stage in the technology development and their significantly higher CAPEX, which is approximately ten times that of HAWTs. To test the technology’s viability with hypothetical utility-scale costs, where VAWT costs fall within the range of $1,300–$1,500/kW, the model still preferentially selects HAWTs due to their higher capacity factors. However, when the model considers different land use restriction cases for VAWT technology, as compared to HAWTs and Solar PVs, VAWTs become significantly more viable. VAWT placement becomes notable in these cases, increasing its share in the energy mix by 2.61% to 10.32% in favorable conditions. At high levels of land availability on a per-parcel scale, specifically, when more than 70% of the land identified as technically suitable remains available for any deployment, high-quality sites with favorable wind resources and high capacity factors continue to support HAWTs as the dominant technology given their lower Levelized Cost of Energy (LCOE). However, when the land availability cutoff increases beyond 70%, reducing siting opportunities for HAWTs and solar PV, the reliance is shifted towards VAWTs, amplifying the impact of their higher LCOE on overall system costs and making cost differentials between technologies more critical. These findings emphasize that while CAPEX reductions are critical in scaling VAWTs and driving up their competitiveness, land-use policies and spatial constraints are primary determinants of deployment feasibility. The study highlights the need for targeted policy intervention for flexible siting policies and continued research to optimize VAWT deployment strategies, ultimately enhancing wind energy integration in land constrained regions within New England and maximizing wind resource potential.

Computational Targeted Codon Optimization and Translation with Deep Learning

2025-08-28T03:07:06Z

Computational Targeted Codon Optimization and Translation with Deep Learning Chemparathy, Anugrah Codon optimization—the task of recoding a protein’s underlying DNA sequence to maximize expression in a target organism—is a complicated biological optimization problem. Each gene brings a dynamic combination of local and long-range dependencies along with globally imposed constraints specific to the organism. While most existing tools for systematic codon optimization are restricted to optimizing under the constraint of a fixed amino acid sequence, recent architectural advancements in deep learning have made it possible to introduce partial modifications to the amino acid sequence without affecting protein function during the codon optimization process. Such approaches greatly increase the search space of feasible sequences, potentially opening up pathways to previously unconsidered DNA sequences with significantly greater expression rates. In this thesis, we seek to understand and improve the inverse-folding codon optimization model CodonMPNN, the behavior and performance of which have not yet been fully evaluated. We present a detailed empirical evaluation of CodonMPNN, characterizing its performance across reconstruction and translation tasks and demonstrating that it captures higher-order codon usage patterns. We produce evidence that the CodonMPNN’s training has successfully captured nontrivial aspects of the codon distribution for 1000 unique organisms, and are able to better characterize the optimal tasks that CodonMPNN’s non-synonymous nature may be able to solve. Then, by a combination of improved pretraining and a new inference-time evolutionary algorithm we are able to modestly improve the base performance of CodonMPNN from its original publication. Together, these contributions yield a measurable improvement in CodonMPNN’s practical performance and provide actionable guidance for its application in constrained codon design. More broadly, this work highlights the importance of application-aware evaluation when deploying machine learning models in synthetic biology and motivates the design of future architectures that are better aligned with real-world usage constraints.

The First Signs of Vision

2025-08-28T03:07:26Z

The First Signs of Vision Chang, Cathy There has been a lot of research on the evolution of eyes through the lens of biology; however, there have been a distinct lack of research in simulating what animals saw as their eyes evolved. This project aims to create interactive simulations of the evolution of animal visions from the Cambrian Explosion to present day through the use of extended reality (XR) environments. Our goal is to communicate and educate about the evolutionary timescale to help our audience understand 1) the history of vision and intelligence and 2) how vision came to be and why it is the way it is. In addition, we want to bridge the gap between technology and vision research to help people better understand and visualize this evolutionary process. We have also collaborated with the Museum of Science and the MIT Museum to display this work in events at their venues.

The Steerability of Generative Models: Towards Bicycles for the Mind

2025-08-28T03:07:19Z

The Steerability of Generative Models: Towards Bicycles for the Mind Bentley, Sarah Generative models have rapidly advanced in their ability to produce diverse, high-quality outputs. Yet their practical utility often falls short: users frequently struggle to guide models toward desired outputs, even when the model is capable of producing those outputs. This thesis argues that unlocking the full potential of generative AI requires not only improving what models can produce (producibility), but also how effectively users can guide them toward producible outputs (steerability). In short, how can we make the entire producible sets of generative models easily accessible to humans? Our contributions are fourfold. First, we formally define steerability and introduce a framework for evaluating it independently of producibility. Second, we instantiate this framework through benchmarks on the steerability of text-to-image and language models. We find that not only is steerability poor, but steering doesn’t reliably improve with more attempts. Third, we propose a framework for designing and optimizing steering mechanisms – tools that help users articulate and achieve their goals with models – and introduce Reinforcement Learning for Human Steering (RLHS) to systematically optimize these mechanisms. Finally, we instantiate this framework in a new steering mechanism for image generation that enables users to steer via images rather than text prompts. This mechanism achieves over 2x improvement over traditional text-based prompting on our benchmark. Our mathematical framework provides a generalizable path forward for measuring and improving the steerability of generative models, while our implementations of that framework empirically demonstrate its utility and viability. Overall, we define a new axis – steerability – upon which we can vastly improve generative models not only as tools for automation, but as bicycles for the mind.

Safety Analysis and Design Improvement for Semi-Automatic Train Operation (STO) in High-Speed Rail Using STPA

2025-08-28T03:07:09Z

Safety Analysis and Design Improvement for Semi-Automatic Train Operation (STO) in High-Speed Rail Using STPA Suzuki, Wataru In Japan, the Tokaido Shinkansen, a major high-speed rail corridor, plans to introduce Grade of Automation 2 (GoA2) through Semi-Automatic Train Operation (STO). While partial automation promises advantages such as reduced driver’s workload and enhanced efficiency, it also creates new risks due to increasingly complex interactions among automated control systems, human operators, and physical infrastructure. This thesis aims to systematically identify and address potential hazards arising from STO in high-speed rail. By using the Tokaido Shinkansen’s announced plan as a model case, the research seeks to uncover scenarios in which normal, non-failed system behaviors can still lead to unsafe outcomes, and to propose design solutions that mitigate those risks early in development. To achieve this, the study applies Systems-Theoretic Process Analysis (STPA). Rather than isolating hardware and function failures, STPA models the entire system as a hierarchical control structure, examining each controller’s possible unsafe actions and their feedback pathways. The analysis reveals hazard scenarios that traditional failure-based methods might overlook. Examples include cases where a passenger is not detected between the train and platform doors at departure, or where verbal and signal instructions conflict and delay the driver’s response. These scenarios can happen even without any component failure. Drawing on these insights, the thesis recommends a variety of design improvements, such as new monitoring functions for subsystems, modifying instruction interfaces, and strengthening the software logic of automation systems. These findings demonstrate the value of conducting a holistic safety analysis using STPA at the conceptual design stage, before late-stage changes become more expensive. Moreover, this research provides a comprehensive, system-level railway hazard analysis, and the proposed measures can be broadly applicable to high-speed rail systems with automation.

Biomechanical Golf Swing Analysis using Markerless Three-Dimensional Skeletal Tracking through Truncation-Robust Heatmaps

2025-08-28T03:07:44Z

Biomechanical Golf Swing Analysis using Markerless Three-Dimensional Skeletal Tracking through Truncation-Robust Heatmaps Taylor, Benjamin F. The efficient generation and transfer of energy in the golf swing has long been a subject of biomechanical interest, with a particular focus on the concept of the kinematic sequence, which is the coordinated segmental rotation of the pelvis, torso, arms, and club. While previous studies have modeled aspects of this sequence using high-end laboratory setups or proprietary systems, few have provided open, quantifiable, and time-resolved measurements of angular kinematics across the full swing cycle. This thesis seeks to address this gap by implementing a markerless temporal skeletal tracking approach built on the open-source MeTRAbs computer vision framework to model and measure joint angles and angular velocities throughout the golf swing. Using two-dimensional video footage of right-handed golfers performing driver swings, the MeTRAbs pose estimation model and supplemental cross-frame temporal motion sequencing code were used to reconstruct three-dimensional joint trajectories and compute rotational kinematics of key body segments. This study demonstrates the feasibility of using markerless pose estimation to extract golf swing signatures and angular velocity profiles without requiring expensive or inaccessible motion capture equipment. Preliminary analysis suggests that joint coordination patterns and temporal characteristics of body segment angular velocities may reveal quantifiable insights into the kinematic sequence, laying the groundwork for further research and instructional applications. Ultimately, this thesis contributes a replicable and cost-effective framework for analyzing golf swing biomechanics using open-source tools and computer vision.

Opportunities in Advanced Wireless Integrated Circuits

2025-08-28T03:07:37Z

Opportunities in Advanced Wireless Integrated Circuits Fareed, Mo The continued evolution of wireless communications, novel compact radars, and power electronics has driven demand for high-performance semiconductor materials capable of operating at higher power density, fast switching speeds, and improved efficiency. Gallium Nitride (GaN) has emerged as a leading candidate due to its superior electrical properties compared to traditional silicon (Si), silicon carbide (SiC), and gallium arsenide (GaAs). GaN’s high power density, thermal stability, and high-frequency operation make it an ideal candidate for applications in 5G/6G infrastructure, satellite communications, defense radar, electric vehicles, and power electronics. However, widespread commercial adoption of GaN faces significant barriers, including high production costs, supply chain constraints, and integration challenges within existing silicon-based fabrication processes. This thesis explores the opportunities and challenges associated with GaN-based integrated circuits (ICs) in the context of advanced wireless systems by utilizing Dr. Eugene Fitzgerald’s innovation framework – Technology, Markets, and Implementation (TMI). A comparative analysis of monolithic vs. board-level GaN integration is conducted. The research highlights that scaling GaN wafer production to approximately 10,000 wafers per year (200mm sized wafers) is necessary to achieve cost-effective monolithic integration, yet current defense-driven demand is insufficient to drive economies of scale. Instead, commercial applications—such as telecommunications, power electronics, and consumer RF devices—are target audiences that can take advantage of monolithic integration in high volume. The findings indicate that while defense applications have led non-monolithic GaN adoption (that is, discrete GaN transistor adoption), they cannot sustain large-scale production alone due to small volume. The semiconductor industry must navigate manufacturing bottlenecks, cost reduction strategies, and foundry availability to ensure GaN’s transition from a niche, high-cost technology to a commercially viable solution. By mapping the TMI intersections and addressing economic and technical barriers, this thesis provides strategic insights into how GaN technology can achieve scalable production, unlock new market opportunities, and shape the future of advanced wireless integrated circuits.

Stock-constrained design of pseudo-standard walls from studs off-cuts

2025-08-28T03:07:17Z

Stock-constrained design of pseudo-standard walls from studs off-cuts Fontaine, Anouk The AEC industry is responsible for 40% of global greenhouse gas emissions and 38% of EU waste, much of which is landfilled. The AEC waste represents an immense portion of resources that could be used instead of new materials. Many ongoing research projects have explored ways of reusing irregular components in construction, from whole steel trusses to single elements, triangulated subparts, or even irregular wood offcuts in order to mitigate the intensive recycling and deconstruction processes. However, the research has focused on general methodologies or one-off prototypes. This paper introduces a systematic approach to repurpose discarded steel and timber studs - components that make up to 10% of waste on local sites (Parigi, 2021) - into modular, steel-frame, load-bearing walls, providing a way to build new structures for the growing global demand for housing and infrastructure, while minimizing the creation of new emissions through the use of waste elements. Through a topdown and stock-constrained design approach, geometry optimization through a matching algorithm is combined with topology optimization to generate and evaluate various configurations to minimize new emissions and maximize structural efficiency. A human-scale prototype further assesses costs, architectural and structural flexibility, construction feasibility, robotic efficiency, and embodied emissions, offering a promising pathway for sustainable construction through effective waste reuse. For the available inventory, a human-scale prototype gives data on the workflow. This approach tackles the issues of existing waste stock with the growing demand for infrastructure and minimizes embodied emissions through structurally efficient resource use by pushing forward a systematic implementation of reuse in common construction practices.

Ensuring Security of Supply while Decarbonizing Islanded Heavy Industrial Electricity Systems

2025-08-28T03:07:03Z

Ensuring Security of Supply while Decarbonizing Islanded Heavy Industrial Electricity Systems Kumar, Prashant Electricity is set to become the central pillar of both energy production and consumption in the global effort to achieve net-zero emissions. As key sectors—transportation, chemicals, and heavy industry—seek to decarbonize by electrifying their operations, industrialized nations face mounting strain on their electricity systems. This strain is further compounded by the rising demand for electricity driven by data centers and artificial intelligence applications, heralding a future of potentially unrelenting load growth. In such a context, it becomes not merely prudent but essential to approach decisions regard- ing investment and operation in the electricity sector with analytical rigor. Advanced capacity expansion models provide the tools for this task. In this thesis, the GenX model is employed to study Taiwan’s electricity system—an islanded, industrially-intensive grid—evaluating the evolution of its capacity mix, generation profile, prices, emissions, and overall costs. Our findings suggest that a reliable path to decarbonization lies in a considered combination of natural gas-fired generation with carbon capture and storage (CCUS), renewable sources such as solar and wind, and energy storage systems. Furthermore, this study finds that integration of nuclear and geothermal technologies significantly improves the cost-effectiveness of achieving decarbonization targets. This thesis also addresses the “missing money” problem endemic to energy-only electricity markets, examining how the introduction of a capacity market influences both investment and operational outcomes. We find that the efficacy of capacity markets is highly sensitive to the design parameters of the demand curve and the capacity credit values of the resources. For islanded systems such as Taiwan’s, a pragmatic approach to ensuring security of supply may involve retaining existing natural gas infrastructure as a strategic reserve, paired with a capacity market design that avoids excessive conservatism, leveraging the absence of policy interactions and competition with neighboring electricity markets, as observed in Europe.

Grid Enhancing Technologies: Optimization and Benefit for Distribution Grids

2025-08-28T03:06:59Z

Grid Enhancing Technologies: Optimization and Benefit for Distribution Grids Anastos, Daniel One of the largest existential challenges the US and other countries face is climate change. And maybe no other system is more crucial to combatting climate change than the grid. Increasingly more requirements have been put onto the transmission and distribution grids to play an even larger role than they have in the past; consider AI, EV, residential solar, electrification of heat, decarbonization of buildings, increasing energy rates, old infrastructure. Improving the grid is a necessity to decarbonize and innovate. However, utilities, backed up by state regulation, usually, but not always, use traditional techniques to expand grid capacity and increase resiliency as opposed to investing in modern grid technology that would more quickly allow for future innovations and decarbonization. These technologies, or techniques, are broadly called grid enhancing technologies, or GETs. There are rational reasons why GETs are not used more often. Utilities are correctly, highly risk averse because they must safely and adequately supply power directly to people. Utilizing new technologies, even if proven, can be a risk that utilities are unwilling, or not allowed, to take given their role and responsibility. But these risks are largely avoided with the technologies discussed in this paper and one could argue these technologies could not only make the grid cheaper to expand but also give the grid more resilience. This paper explores how a particular grid section can increase its solar penetration by avoiding traditional hosting capacity limitations and use not even innovative GETs but GETs that are largely tested and proven. Traditionally, at some limit, the utility will stop allowing solar in an area due to various grid constraints. This paper explores how a utility may solve these constraints using new methods to avoid large grid expansion CAPEX costs and utilize new technologies or techniques. Some of the techniques explored here are commercial scale energy storage support at substations, PV curtailment, and volt-var optimization control.

Geothermal Energy Planning Considerations for Military Operational Energy Demands

2025-08-28T03:06:58Z

Geothermal Energy Planning Considerations for Military Operational Energy Demands Seckfort, Cody L. Contingency locations are temporary military bases that are often established in austere or contested environments. These locations rely heavily on diesel fuel for electrical power, which creates logistical vulnerabilities and increases the risk to personnel conducting fuel resupply missions. While the Department of Defense has made progress in adopting renewable energy technologies, many of these systems remain too large, inefficient, or underdeveloped for widespread use in operational environments. Geothermal energy presents a promising but underexplored alternative for generating reliable, on-site electrical power without the need for continuous fuel resupply. This thesis evaluates the feasibility of geothermal energy systems for military operational energy demands and introduces a modified power planning process that incorporates geothermal considerations. The research focuses on closed-loop geothermal systems, utilizing an example system called the “Mil-Loop”, which is designed to minimize the system surface footprint and support remote installations. The planning process integrates existing geothermal tools, including GEOMAP/TEST for resource estimation and GEOPHIRES for system modeling and performance analysis. The Mil-Loop System Model incorporates each step of the planning process to produce a site-specific power system profile. A case study using site-specific data from Bagram Airfield was used to assess the performance of a hybrid geothermal-diesel power system. The results suggest that geothermal system integration could reduce diesel fuel consumption by up to 42.9 percent over a 40-year site lifecycle. A sensitivity analysis indicates that geothermal system power output, drilling time, and installation costs are the most critical parameters affecting system viability. Advances in drilling technology and heat extraction have the potential to reduce installation costs and timelines, making geothermal more competitive with diesel generation. This thesis also identifies a gap in military energy planning resources, specifically the lack of frameworks that include geothermal options for operational environments. It recommends that the DoD begin integrating geothermal technologies into its energy planning strategies and develop modular systems that can be deployed in contested or resource-constrained areas. While this research is limited by simplified power demand modeling and generalized tool assumptions, it offers a practical framework for evaluating geothermal viability in future defense applications. This study demonstrates that geothermal energy systems, particularly closed-loop configurations, can serve as a viable and strategically beneficial power source for military operations. When paired with targeted technology development and thoughtful integration into planning processes, geothermal systems can reduce logistical burdens, improve energy resilience, and enhance mission success in operational environments.

Levelized Cost of Fuel (LCOF) studies for microreactors using TRISO fuel in hydride and beryllium-based composite moderators in open and closed fuel cycles

2025-08-28T03:07:02Z

Levelized Cost of Fuel (LCOF) studies for microreactors using TRISO fuel in hydride and beryllium-based composite moderators in open and closed fuel cycles Balla, Sai Prasad This study provides a comprehensive techno-economic evaluation of a specific class of nuclear batteries—high-temperature gas-cooled 10 MW_th microreactors (HTGRs) with TRISO fuel in prismatic- and pebble-bed cores—using four composite moderator concepts (MgO–Be, MgO–BeO, MgO–YH, MgO–ZrH). These options are compared against a prismatic graphite benchmark, under both once-through and continuous-recycle fuel cycles. In once-through prismatic systems, hydride-based moderators can reduce overall fuel-cycle costs by up to about 20% relative to graphite, whereas beryllium-based moderators may remain 40–50% costlier due to higher raw material expenses. Shifting from prismatic blocks to pebble beds decreases moderator usage and increases burnup, thus making advanced moderator options more competitive. Adopting a continuous-recycle strategy replaces enrichment with reprocessing and can further lower fuel-cycle costs by roughly 30%. Coupling a sodium-cooled fast reactor (SFR) to supply transuranic’s further reduces the cost: SFR driver fabrication and reprocessing can account for the bulk of total costs, rendering microreactor-level variations comparatively minor. Meanwhile, pebble-bed designs propose ultra-high burnups and extended residence times, which could yield significant economic gains, contingent on demonstrated long-term TRISO fuel integrity. Waste handling also factors into the analysis. Deconsolidation—removing the inert moderator before disposal—can shrink spent-fuel volumes by more than 90%, easing repository demands. Continued R&D into advanced additive manufacturing, high-burnup TRISO performance, and streamlined waste management will be crucial for capitalizing on these potential cost advantages.

Robust Dexterous Manipulation Enabled by Learning at Scale inSimulation

2025-08-28T03:07:00Z

Robust Dexterous Manipulation Enabled by Learning at Scale inSimulation Bhatia, Jagdeep Singh Robots with robust bimanual dexterity have the potential to transform industries such as manufacturing and healthcare by performing complex tasks at human-level proficiency. While end-to-end learning methods have shown promise in achieving this goal, scaling these approaches remains challenging. Existing paradigms suffer from high costs associated with collecting large-scale, high-quality demonstrations on physical systems and face performance saturation due to reliance on offline data. We propose a task-agnostic pipeline that leverages robotics simulation to overcome these limitations. In particular, we introduce DART, a cost-effective, augmented reality, robot teleoperation platform for scalable data collection. We demonstrate through user study that it enables twice the throughput of existing systems. We also present a learning algorithm that integrates real-world demonstrations with reinforcement learning to surpass performance plateaus. Finally, we design a method that zero-shot transfers policies trained in simulation on real robots using only RGB input. Together, these contributions provide a practical and scalable path toward achieving general-purpose dexterous robot manipulation.

Image Registration and Gantry Tracking System of Clytia hemisphaerica

2025-08-28T03:08:03Z

Image Registration and Gantry Tracking System of Clytia hemisphaerica Bunch, Bradley Understanding nervous system function and evolution requires detailed behavioral analysis of model organisms such as the jellyfish Clytia hemisphaerica. However, its size and rapid, free-swimming nature pose significant tracking challenges. This work presents a platform for the XY gantry system developed to overcome these hurdles for high-resolution behavioral monitoring. Separately, to prepare for downstream neural analysis, we developed an automated neuron segmentation pipeline - tailored for image registration purposes. Together, the tracking system and the analysis preparation pipeline provide powerful, distinct tools for high-throughput behavioral quantification and facilitate future studies linking behavior to underlying neural dynamics in Clytia hemisphaerica.

Digital Twin Technology Applied to Automotive Diagnostics

2025-08-28T03:07:45Z

Digital Twin Technology Applied to Automotive Diagnostics Mwarage, Jessy Mbagara There is currently a lot of interest in the area of Digital Twin (DT) Technology. Physical product oriented organizations are increasingly looking for ways to stay ahead of the technological innovation curve in order to not get disrupted by more agile entrants. Therefore, the promise of a technology like DT is alluring for the sake of maintaining a competitive edge. This thesis seeks to explore the potential benefits of DT technology alongside what challenges might be faced in implementing one. To this end, a problem statement is formulated in the field of automotive diagnostics. This is a key value addition field for automotive companies seeking to better manage the diagnosis and repair of their automobiles in the field or the manufacturing environment. The problem is further concretized with a study of some user-driven use cases and needs in a real automotive company. From these needs, a set of requirements is formulated to guide the architecture and design of a DT demonstration. The process of architecting and designing the DT is documented. This includes a deep dive on the modeling approaches considered, the solution space for the architecture and the detailed design and implementation of a DT demonstration from a selected architectural concept. The DT demonstration is then operated under controlled conditions in order to showcase some of its capabilities. Pursuant to all this, a reflection on the effectiveness of the demonstration and the lessons learned about the implementation process are discussed. The results of the study and demonstration show some promise for organizations seeking to adopt DT technology, in this particular case for automotive diagnostics. The benefits are mainly in terms of better system architecture planning and the increased potential for better incorporating lessons learned from products operating in the field back into the design process. These benefits are weighed against the socio-technical challenges of implementing DTs from the outset of a system design exercise.

Data Acquisition for Enhancing Human-Informed Topology Optimization

2025-08-28T03:07:38Z

Data Acquisition for Enhancing Human-Informed Topology Optimization Wang, Zach This thesis presents a survey application designed for the future development of HumanInformed Topology Optimization (HiTop) towards the deeper integration of optimization and real-world feasibility. Topology optimization produces high-performance designs by optimally distributing material, but its application in professional environments remains limited due to fabrication constraints and inflexible design workflows. To address this, the Carstensen Group developed HiTop, which integrates optimization algorithms with human experience, allowing engineers to modify the computer design based on their professional judgment. Thus, the future development of HiTop requires real-world data on human preferences. This project introduces a web-based survey app integrated with Qualtrics. It presents users with various design scenarios and computer-optimized designs, and records their modifications and reasoning. A preliminary survey collected responses from 13 professionals and engineering students. Preliminary findings suggest that engineers consistently focus on similar regions of interest, even when motivated by different reasons. However, the sample size is too small to make any statistically significant conclusions. While the platform mostly performed as intended, a bug related to data storage was discovered during analysis. The issue has since been resolved, and the tool is now fully functional and ready for broader deployment.

Investigating Motivational Drivers of Participation in W3C’s Web Standards Development Process

2025-08-28T03:07:25Z

Investigating Motivational Drivers of Participation in W3C’s Web Standards Development Process Lauber, Emily This research investigates the motivational drivers for companies and individuals to participate in the World Wide Web Consortium’s Web standards development process. Motivational drivers are identified through a literature review, primary sources, and interviews. Thirteen semi-structured interviews were conducted with questions related to participants’ experience with the World Wide Web broadly, Web standards in general, the organization of W3C, and game modeling of the process. W3C was selected as the case study of Web-related standards bodies because of its unique model of paid membership yet open standards available royalty-free. The W3C standards process requires consensus-building, horizontal review, and proof of implementation before the organization officially recommends the specification. Existing research documents the history and value of standardization across industries, the modeling of various Standards Development Organizations (SDOs) in information industries, and the negotiation of international Internet governance. This thesis does not attempt to prove a societal benefit of Web standards but instead focuses on an individual’s belief in societal benefit and how that belief drives their engagement with W3C. Initial findings point to members seeking economic, philosophic, and moral value through participation in Web standards development. A game theory framework evaluates the economic value of different players within the ecosystem and identifies that Web browser vendors and long-time consortium members have greater power for their preferred specification outcomes than Web developers or newcomers. Despite changes in the Web ecosystem in the past 30 years, W3C members continue to be drawn to the Web for the same philosophical intents that Sir Tim Berners-Lee designed the Web for. There are shared concerns though that the economic power players identified in the game modeling has damaged or will threaten the philosophy of an open, safe, accessible Web. Interviewees shared personal beliefs that there was a moral responsibility to engage in Web standards development and enable W3C’s mission of “empowering humanity”. Further research is required to catalogue more motivational drivers, evaluate drivers across other Web-related Standards Development Organizations, and rank the priority of motivations when the different drivers are in tension.

Multi-Objective Generation of Pareto-Optimal Perception Architectures for Autonomous Robotic Systems

2025-08-28T03:07:55Z

Multi-Objective Generation of Pareto-Optimal Perception Architectures for Autonomous Robotic Systems Putnam, Rachael M. Designing perception systems for autonomous robots and vehicles requires balancing sensor performance against cost, complexity, and integration constraints. This thesis introduces GO4R (Generation and Optimization of Perception System Architectures for Robotics), a multi-objective framework that jointly designs sensor selection, placement, against volumetric, entropy-based utility metric H (-) and monetary cost M ($). Perception Entropy H is formalized as a volumetric measure of uncertainty across a voxelized regions of interest (ROI), which naturally rewards coverage, overlap, and redundancy required for robust sensor fusion and calibration. NSGA-II is implemented with custom mixed-variable operators to specifically handle both continuous (e.g. sensor poses) and discrete (e.g. sensor type/count) decision variables found in this problem. Two case studies, long-range outdoor navigation on a Clearpath Jackal and short-range indoor navigation on ANYmal-C, demonstrate the framework’s ability to generate Pareto-optimal sensor architectures under vastly different ROI definitions and operating conditions. In the Jackal study, GO4R converges to a population of 11 novel Pareto-optimal designs, and revealing sensitivity to voxel size and importance weighting. In the ANYmal-C study, the compact, uniformly weighted ROI yields a flatter Pareto front with 25 Pareto-optimal designs, and underscores how intrinsic sensor parameters (e.g. angular resolution, and Field of View) dominate design trade-offs when baseline coverage is already high. Key architectural decisions are analyzed, quantified by their impact on Pareto front shape, and ordered according to the GO4R method to successively reduce uncertainty. The resulting guidelines provide practitioners with a rigorous, reusable process for tailoring perception systems to task-specific requirements. Finally, GO4R provides a publicly available NVIDIA Isaac Sim extension to aid practitioners in following the GO4R method, no matter their Autonomy application. Future work will extend GO4R to dynamic environments, improve fidelity of generated designs, and incorporate additional cost metrics such as computational load and maintainability.

Agricultural Waste Utilization: Life Cycle Assessment for Selecting Carbon-Management Best Practices on a Global Scale

2025-08-28T03:07:59Z

Agricultural Waste Utilization: Life Cycle Assessment for Selecting Carbon-Management Best Practices on a Global Scale Shao, Yu-Tong Crop residues are a widely available form of agricultural waste with several possible reuse applications, including use as biofertilizers, animal feed, biofuels, and for carbon sequestration. However, in many parts of the world, large quantities of these residues are still burned in the field, releasing significant amounts of greenhouse gases (GHGs) and air pollutants to the atmosphere. This study aims to evaluate alternative and carbon-efficient strategies for reusing crop residues – especially focusing on rice straw and wheat straw – by conducting life cycle assessments (LCA) of multiple utilization pathways. Different alternative scenarios for utilizing crop residues are assessed: incorporating residue in field, animal usage for feeding, pyrolysis for electricity generation, pyrolysis for carbon sequestration, and electricity generation through residue combustion. Specifically, for the scenarios of pyrolysis and electricity generation through residue combustion, the maximum feasible transport distances of crop residues from agricultural fields to processing facilities are modeled for different logistics methods, providing information for the locations for establishing centralized facilities while maintaining GHG benefits for the scenarios. The results of this study highlight that electricity generation using crop residues, either through pyrolysis or direct residue combustion, offers the greatest climate benefits among all evaluated options. Carbon sequestration through pyrolysis also yields substantial GHG reductions, although slightly lower than the benefits from electricity generation. While crop residue-based electricity emits 4.35 to 31.25 times more GHGs per unit of electricity generated than renewable sources and 50.00 to 67.57 times more than nuclear sources, it still performs better than fossil fuels and provides added value in terms of agricultural waste management, resulting in 30.56 to 66.67% lower GHG emissions. Moreover, transportation emissions account for only a small share of the total life cycle global warming potential (GWP) in the electricity generation scenarios, ranging from 0.66% (via ships) to 16.40% (via trucks) for every 1000 km traveled. This makes long-distance residue transport viable. The findings of this study underscore the potential for crop residues to play a meaningful role in climate mitigation and sustainable agricultural waste management.

Strategic Roadmapping and Technology Portfolio Selection for Heating Decarbonization in Canada

2025-08-28T03:07:57Z

Strategic Roadmapping and Technology Portfolio Selection for Heating Decarbonization in Canada Shalash, Karim Heating systems contribute significantly to Canada’s greenhouse gas emissions, accounting for approximately 117 megatons of CO₂ equivalent. demanding urgent decarbonization to meet national climate targets. This thesis employs the Advanced Technology Roadmap Architecture framework, integrating strategic roadmapping and technology portfolio selection methodologies to evaluate pathways for transitioning Canada’s heating sector to net-zero emissions by 2050. By analyzing historical emissions, forecasting adoption trends for key technologies like heat pumps, and conducting stakeholder-driven scenario analysis, this research identifies critical barriers to scaling low-carbon solutions, including high upfront costs, infrastructural limitations, and regional climatic constraints. Seven representative heating architectures—air-source heat pumps, ground-source heat pumps, district heating, hydrogen-based systems, electric resistive heating, and conventional gas-fired furnaces—are evaluated comprehensively. Among these, district heating is particularly emphasized due to its potential for significant emissions reductions and minimal consumer-bearing initial cost of ownership, especially when strategically integrated with waste heat recovery from data centers. This integration utilizes otherwise wasted thermal energy, creating a robust symbiotic opportunity for urban and industrial decarbonization. To support the practical deployment of these architectures, the thesis establishes a targeted technology portfolio comprising essential enabling and supporting technologies. Enabling technologies include centralized supervisory control systems, urban-scale district heating networks, inverter-driven compressors, advanced refrigerants, ground heat exchangers, and circulation pumps with variable frequency drives. Critical supporting technologies identified encompass building information modeling integration kits, cybersecurity modules, digital permitting platforms, smart thermostats, and thermal energy storage systems, among others. This thesis further explores technology trade-offs, focusing on structural complexity, technology readiness, and associated risks of deployment. Through detailed modeling and stakeholder-informed scenario analysis, the thesis concludes that effective decarbonization of heating in Canada necessitates substantial policy interventions, robust financial incentives, targeted infrastructure investments, and region-specific strategies. The analysis indicates that a carefully allocated $8 billion catalyst investment could close approximately 60% of Canada’s heating emissions gap by 2050. Ultimately, district heating coupled with waste heat recovery emerges as a particularly promising strategic option, underscoring its transformative potential within a diversified approach to achieving Canada’s sustainable heating future.

Proximity and Prenatal Care: Geographic Accessibility to Healthcare Facilities in N’Djamena, Chad

2025-08-28T03:07:52Z

Proximity and Prenatal Care: Geographic Accessibility to Healthcare Facilities in N’Djamena, Chad Alkhalil, Kabbod Access to prenatal care is critical for reducing maternal and neonatal mortality rates. Yet, accessibility to healthcare facilities remains an understudied challenge in many sub-Saharan African countries. This study examines the spatial accessibility to healthcare facilities in N’Djamena, Chad, across various transportation modes, as well as the relationship between travel time and adherence to WHO-recommended prenatal care visits. This analysis utilized a mixed-methods approach. A geospatial analysis was conducted to estimate travel times and distances to the nearest healthcare facility across the city of N’Djamena using various transportation modes to uncover areas of low accessibility. This analysis was supplemented with survey data collected from interviews with 67 pregnant women across three different hospitals. Findings show that 72% of the surveyed population use motorcycles or cars and benefit from high accessibility. 95% of these patients have travel times under 26 and 30 minutes, respectively. In contrast, pedestrians have poor accessibility, especially when patients only attend district or national hospitals. This behavior is very likely – 81% of the surveyed population reported bypassing closer facilities, advancing familiarity and quality of care as the main reasons. In this instance, 20% of the population have travel times greater than one hour on foot. While adherence to WHO guidelines was high in early pregnancy (below 20 weeks), it declined in later stages. The study found no statistically significant correlation between travel time and adherence. Improving accessibility for pedestrians will require a combination of health system improvements, better facility distribution, and transport subsidies. The Ministry of Public Health and urban planners can employ similar data-driven approaches to plan the placement of new healthcare facilities and develop outreach strategies to ensure equitable access in a growing urban context.

Proof-of-Work Mitigation Strategy for DNS-Based Amplification Attacks

2025-08-28T03:07:49Z

Proof-of-Work Mitigation Strategy for DNS-Based Amplification Attacks Bansal, Umang Distributed Denial of Service attacks, and particularly DNS Amplification attacks, have seen a steady rise in deployment over the past few decades. DNS Amplification attacks, in particular, are challenging to identify and mitigate because of their apparent similarity to legitimate DNS traffic. This thesis proposes a new Proof-of-Work mitigation strategy that provides a defense against DNS Amplification attacks and shifts the burden of mitigation to the attackers. Through our experiments, we show that our Proof-of-Work strategy is effective in reversing the impact of DNS Amplification attacks on the victim’s ability to service legitimate clients. We also provide an evaluation framework to evaluate the mitigation strategy’s impact on the victim’s quality of service.

Modeling Human-Informed Variables in Medical Data

2025-08-28T03:07:48Z

Modeling Human-Informed Variables in Medical Data Abu Daoud, George In the Age of Information and Artificial Intelligence, data plays a major role in analyzing and understanding underlying trends and patterns as well as informing processes and operations. Medical data often captures information beyond mere patient conditions and state, but also human behavioral aspects of the medical process, affecting the data itself and the decisions informed by it. Modeling these variables could help us understand how they influence decisions in the field and potentially augment our models for better and more nuanced predictions. In the first study, we look into how external non-medical factors might affect decision-making by investigating the effect of 30-day mortality metrics on discharge rates following surgeries in Cardio-Vascular Intensive Care Units (CVICU) using data from the MIMIC-IV dataset. In the second study, we examine data extraction from human-notes for enhancing organ procurement decision processes.

Soil Moisture Dynamics and Thresholds for Surface Energy Balance Regime Transitions: An Observational Analysis at a U.S. Grassland Site

2025-08-28T03:07:43Z

Soil Moisture Dynamics and Thresholds for Surface Energy Balance Regime Transitions: An Observational Analysis at a U.S. Grassland Site Verensia, Ria Understanding how soil moisture declines following rainfall—when the soil progressively dries due to evaporation and plant uptake—is critical for assessing plant water stress, surface energy partitioning, and land–atmosphere interactions. These periods of moisture loss, commonly referred to as soil moisture drydowns, provide a valuable window into the transition from wet to dry surface conditions. This study focuses on the critical soil moisture threshold (θ*), which marks the transition from energy- water-limited surface evaporation regimes. This transition reflects a key shift in surface energy balance and controls the extent to which evaporation is constrained by moisture availability. While previous research has typically treated θ* as a static value based on soil texture, emerging evidence suggests that it may vary depending on environmental conditions, particularly seasonal climate. This study investigates whether θ* is a fixed property or a dynamic threshold influenced by seasonal variation and available energy. Using in situ data from the Soil Temperature and Moisture Profile (STAMP) system and Infrared Thermometer (IRT) measurements at a semi-arid grassland site in Oklahoma, USA, I identify and analyze soil moisture drydown events. I estimate θ* by applying piecewise linear regression to the relationship between soil moisture and diurnal surface temperature range, isolating the breakpoint that indicates the transition from energy-limited to water-limited evaporation. Results reveal that θ* exhibits systematic temporal variations, particularly across seasons and temperature regimes, suggesting that surface temperature dynamics during drydowns are most likely a response to changes in soil moisture content. These findings challenge the assumption that θ* is solely texture-dependent and highlight the need to account for dynamic environmental controls in modeling surface energy exchange. This research provides new insights into soil moisture-temperature coupling and offers implications for land surface model development, drought forecasting, and vegetation response assessments under a changing climate.

MOBLLM: Model Building LLMs via Symbolic Regression and Experimental Design

2025-08-28T03:07:20Z

MOBLLM: Model Building LLMs via Symbolic Regression and Experimental Design Binbas, Berkin Large language models (LLMs) have recently emerged for daily use and have already been extensively utilized for various tasks. They are shown to be able to carry out more and more complex tasks every day, including those that require a high level of formal/mathematical reasoning at human or superhuman levels. In particular, their in-context learning capabilities and the domain-specific knowledge they have via their vast pretraining corpus, as well as their fine-tunability for specific tasks drove a lot of attention and research in the field. However, applications of LLMs to the frontiers of scientific research remains an underexplored direction. In this work, we investigate how one can leverage LLMs to aid with building compact mathematical models and experimental design. Specifically, we propose a framework for using LLMs as a guide to concurrently handle the experimental design and symbolic regression tasks for data obtained from 1) a black box 1D function and 2) a black box physical system. We propose further modifications to our base framework, and perform experiments to analyze how it performs under different experiment variants, across different LLM tiers. Our experiments reveal that while larger models (of around 70b parameters) do not always achieve better downstream performance compared to smaller models (of around 8b parameters), they are able to utilize the given information and/or physical context when designing experiments and proposing symbolic expressions, and perform better than random-design baselines. We also observe that natural language constraints do not consistently improve symbolic regression accuracy. These results underscore both the challenges and the potential of integrating LLM agents into the scientific discovery process, particularly as proposers of experiments and symbolic expressions.

I-Con: A Unifying Framework for Representation Learning

2025-08-28T03:07:32Z

I-Con: A Unifying Framework for Representation Learning Alshammari, Shaden As the field of representation learning grows, there has been a proliferation of different loss functions to solve different classes of problems. We introduce a single information-theoretic equation that generalizes a large collection of modern loss functions in machine learning. In particular, we introduce a framework that shows that several broad classes of machine learning methods are precisely minimizing an integrated KL divergence between two conditional distributions: the supervisory and learned representations. This viewpoint exposes a hidden information geometry underlying clustering, spectral methods, dimensionality reduction, contrastive learning, and supervised learning. This framework enables the development of new loss functions by combining successful techniques from across the literature. We not only present a wide array of proofs, connecting over 23 different approaches, but we also leverage these theoretical results to create state-of-the-art unsupervised image classifiers that achieve a +8% improvement over the prior state-of-the-art on unsupervised classification on ImageNet-1K. We also demonstrate that I-Con can be used to derive principled debiasing methods which improve contrastive representation learners.

System thinking to analyze the Market penetration of Two-Wheeled vs Four-Wheeled EVs in India

2025-08-28T03:07:10Z

System thinking to analyze the Market penetration of Two-Wheeled vs Four-Wheeled EVs in India Kumbhare, Piyush This thesis analyzes the disparate market penetration rates of electric two-wheelers (E2Ws) and electric four-wheelers (E4Ws) in India, using systems thinking approaches to understand the underlying dynamics and propose strategic interventions. In 2024, while E2Ws have achieved 4.43% market penetration, E4Ws lag significantly at 1.91%, despite similar policy support. Through force field analysis and stakeholder value mapping, this research identifies key factors driving this disparity and evaluates their temporal evolution over three time horizons. The analysis reveals that E2Ws benefit from stronger driving forces, including urban suitability, favorable total cost of ownership, and simpler charging solutions, with 91% of users relying on home charging. In contrast, E4Ws face more substantial barriers, particularly in upfront costs, charging infrastructure requirements, and range anxiety. Technical modeling of key Figures of Merit (FOMs) demonstrates how different optimization challenges affect each segment's market acceptance. The research culminates in recommendations for accelerating E4W adoption, emphasizing the need for India-specific models priced similar to internal combustion engine (ICE) vehicle, localized manufacturing ecosystems, robust charging infrastructure, and innovative financing solutions. The findings suggest that while E2W adoption will continue to grow naturally, E4W penetration requires coordinated interventions across manufacturing, technology, infrastructure, policy, and consumer awareness dimensions. This research contributes to understanding how systems thinking can inform strategic planning for electric vehicle adoption in emerging markets, with specific implications for India's goal of 30% EV penetration by 2030.

Designing Generative Multi-Agent Systems for Collective Intelligence and Resilience

2025-08-28T03:07:28Z

Designing Generative Multi-Agent Systems for Collective Intelligence and Resilience Dao, Nguyen Luc Large Language Models (LLMs) have been increasingly adopted by businesses to support their workflows, driving significant investment in developing generative agents. These agents can collaborate and exchange information to solve complex problems. Previous research has found that the benefits of such multi-agent systems include better performance and the potential emergence of collective intelligence characterized functionally as leadership, debate, and feedback. However, expanding multi-agent systems to include agents beyond trusted boundaries introduces the risks of malicious agents that provide incorrect or harmful information to deteriorate collective decisions or cause systemic failure. This study investigates how architectural decisions, including group size, agent prompting, and collaboration schemes, impact the system's resilience against malicious agents. Our experiment results show that increasing group size improves both accuracy and resilience at the cost of more tokens. Step-back abstraction prompting enhances accuracy and mitigates the likelihood of hallucinations induced by malicious agents. Group Chat topology is highly vulnerable to malicious interferences. Reflexion, Crowdsourcing, and Blackboard topologies offer safeguards against such risks. Eventually, we expand our research to investigate accountability gaps in generative AI systems. Designing generative multi-agent systems requires careful consideration of the trade-offs between performance, cost, resilience, and accountability.

National Space Power Analysis Through Organizational and Market Evolution

2025-08-28T03:07:29Z

National Space Power Analysis Through Organizational and Market Evolution Deline, Carrie B. The space domain is undergoing fundamental changes and unprecedented growth. Once dominated by state-led missions, the space sector is now home to commercial competition, rapid innovation, and evolving models of public-private collaboration. These changes call to question how space power is built and maintained, especially during the raising geopolitical climate and power competition in space. The rise of agile commercial industry has driven down launch costs, accelerated technology development and opened new markets and business cases forcing legacy institutions to re-evaluate their strategies and business models. This thesis is motivated by the need to understand how organizations are responding to these changes, and how their choices collectively shape the United States as a national space power. Through the application of a theoretical space power model based on war strategy and Schumpeterian innovation theory, the different elements of space power will be explored in today’s context. It seeks to identify the organizational drivers of change, tensions and synergies between legacy enterprises and new entrants, and the implications of the dynamic space ecosystem. This thesis includes a mixed-methods analysis starting with a historical understanding of the evolution of the sector. By identifying current market trends, government policies and initiatives, the applied theoretical model is presented. The model is supported by market data, a force field analysis of organizational shifts, and qualitative interview insights from industry leaders. The research aims to contribute insights for government strategists and industry leaders concerned with America’s future as a space power and their organization’s role within it.

Noisy with a Chance of Mislabels: A Local and Training Dynamics Perspective on Detecting Label Noise in Deep Classification

2025-08-28T03:07:08Z

Noisy with a Chance of Mislabels: A Local and Training Dynamics Perspective on Detecting Label Noise in Deep Classification Chentouf, A. Anas Noisy labels are a pervasive challenge in modern supervised learning, especially in highstakes domains such as healthcare, where model reliability is critical. Detecting and mitigating the influence of mislabeled data is essential to improving both performance and interpretability. Building on insights from training dynamics, we propose Local Consistency across Training Epochs (LoCaTE), a class of data-filtering methods that leverages over-parameterized and over-trained neural networks to distinguish clean samples from mislabeled ones. Our approach integrates both local neighborhood information and the behavior of samples across training epochs to identify noise and enhance model robustness. We evaluate our method on real (human) and synthetic label noise across three classification datasets, finding that it achieves competitive F₁ of label error detection and improved downstream accuracy using a lightweight classifier with low added computational cost.

Analyzing Multimodal Interactions through Improved Partial Information Decomposition Estimation

2025-08-28T03:07:22Z

Analyzing Multimodal Interactions through Improved Partial Information Decomposition Estimation Balachandran, Adithya S. Multimodal AI aims to build comprehensive models by integrating information from diverse sensory inputs such as text, audio, and vision. However, significant challenges remain in understanding how these different modalities interact and contribute to downstream tasks. In particular, we seek to characterize how modalities complement each other, overlap in the information they convey, or contribute jointly to patterns that are not clear from any single modality alone. To address this, we propose novel methods for quantifying these multimodal interactions using information-theoretic techniques. Specifically, we will introduce a novel estimator for Partial Information Decomposition (PID) using normalizing flows, with the ability to scale well to high-dimensional data. We also develop a new framework for estimating pointwise PID, which provides insights into how individual data points contribute to information sharing and interactions across modalities, and show how to apply this framework for anomaly detection. We demonstrate the effectiveness of our methods on a variety of high-dimensional datasets, including both synthetic and real-world multimodal data such as videos.

Explanation Alignment: Quantifying the Correctness of Model Reasoning At Scale

2025-08-28T03:07:34Z

Explanation Alignment: Quantifying the Correctness of Model Reasoning At Scale Bang, Hyemin To improve the reliability of machine learning models, researchers have developed metrics to measure the alignment between model saliency and human explanations. Thus far, however, these saliency-based alignment metrics have been used to conduct descriptive analyses and instance-level evaluations of models and saliency methods. To enable evaluative and comparative assessments of model alignment, we extend these metrics to compute explanation alignment — the aggregate agreement between model and human explanations. To compute explanation alignment, we aggregate saliency-based alignment metrics over many model decisions and report the result as a performance metric that quantifies how often model decisions are made for the right reasons. Through experiments on nearly 200 image classification models, multiple saliency methods, and MNIST, CelebA, and ImageNet tasks, we find that explanation alignment automatically identifies spurious correlations, such as model bias, and uncovers behavioral differences between nearly identical models. Further, we characterize the relationship between explanation alignment and model performance, evaluating the factors that impact explanation alignment and how to interpret its results in-practice.

Octavio: A Distributed System for the Sensing, Storing, and Retrieval of Piano Playing Data

2025-08-28T03:07:04Z

Octavio: A Distributed System for the Sensing, Storing, and Retrieval of Piano Playing Data Abdulrezak, Ayyub MIT has a wealth of pianos spread across its campus. These instruments are owned by various groups and MIT organizations. Every day, students, faculty, and extended members of the MIT community play and practice with them. However, there currently exists no available data on their usage. This project aims to create the infrastructure for capturing this data. To this end, we installed sensing equipment on pianos across campus, constructed a matching database and filesystem of all playing sessions across time, and established a public API for the retrieval of this data. The collected data will later be used to power a publicly accessible webpage of real-time and historical visualizations, as well as serve to bolster research efforts into the characteristic piano playing of MIT.

Policy and Technical Recommendations for Integrating Autonomy into Military Offensive Cyberspace Operations

2025-08-22T03:06:15Z

Policy and Technical Recommendations for Integrating Autonomy into Military Offensive Cyberspace Operations Wettstein, Benjamin As AI technologies and autonomy grow, the transition to application in the military, specifically enhancing military cyberspace operations, has become both a strategic imperative and an adoption challenge. This thesis explores the challenge of effectively integrating autonomous cyber weapons systems into offensive military cyberspace operations. I offer both technical and policy recommendations to ensure autonomous technology development does not outpace its ability to be integrated. This thesis analyzes historical case studies, such as loitering munitions and escort jammers, to examine the potential for integrating autonomous cyber weapons systems into military offensive cyberspace operations. This analysis finds that the more autonomous and lethal a weapon is, the more difficult it is to integrate it into military operations. Subsequently, the current state of cyberspace operations is analyzed by discussing two cyberspace attacks, Stuxnet and Conficker. This analysis reveals that cyberspace operations currently demonstrate low to medium levels of autonomy and low levels of lethality. Therefore, there is a significant opportunity to adopt autonomous systems in the current context of offensive cyberspace operations. However, as the domain of cyberspace is transforming with the growth of complexity in technology, there are evolving legal, ethical, bureaucratic, and technical concerns. This thesis contains policy recommendations around technical standards, investment and acquisitions, and regulations regarding using autonomous cyber capabilities to address these challenges. Along with the policy recommendations, the core technical recommendation that enables autonomous cyber systems is the safe and effective deployment of human-machine interfaces to direct and control them. This thesis argues that interfaces are not merely supporting tools but are, in fact, the central technical mechanism for enabling traceability, oversight, and control in autonomous cyberspace operations. The future development and integration of autonomous cyber systems must prioritize interface design tailored to varying degrees of autonomy and operator control. The technical portion of this thesis explores different interfaces for autonomous cyber systems, utilizing distinct models of autonomy within the Cyber Operations Research Gym (CybORG) simulation environment. Each interface corresponds to the three human-machine relationships discussed, which include a semi-autonomous interface (human in the loop), a supervised autonomous interface (human on the loop), and a fully autonomous interface (human out of the loop). These interfaces serve as a proof of concept, providing evidence that different levels of autonomy can be implemented on the same autonomous cyber system. Additionally, the use of LLMs to explain the actions taken by autonomous cyber systems is explored. Ultimately, this thesis contributes technical and policy recommendations for navigating the future of autonomous cyber warfare. As autonomous systems evolve in sophistication and capability, the U.S. military must adopt policy and technical mechanisms that enable autonomy without sacrificing oversight, accountability, or effectiveness.

Response of Arabidopsis to bacterial presence under iron stress

2025-08-22T03:06:25Z

Response of Arabidopsis to bacterial presence under iron stress Kitzinger, Katherine A. Iron availability is essential for the normal function of plants, but it becomes less available for uptake under drought. A lack of iron can lead to early senescence, fewer and less nutritious crops, and in extreme cases, plant death. In response to these stressful conditions, microbial interactions can lead to improved plant health, however the mechanism by which this occurs is not understood. In this study we cocultured an Arabidopsis MTP8 knockout line, which is susceptible to iron stress, as well as a subset of a previously established synthetic microbial community derived from healthy Arabidopsis roots. We cocultured the Arabidopsis lines and bacteria under three different iron levels in a hydroponics system and measured the dry weight and chlorophyll content ten days post inoculation. This study aims to narrow the potential mechanism of the beneficial effects of bacteria on plants experiencing nutrient stress.

Interactive Topology Optimization with Hybrid Truss and Continuum Elements Types

2025-08-22T03:06:19Z

Interactive Topology Optimization with Hybrid Truss and Continuum Elements Types Zhang, Eileen Topology optimization is a rising tool in structural design that can improve material efficiency and promote sustainability. However, currently topology optimization is not greatly used in the industry due to the nature of its user-unfriendly, high computation cost and difficulty in manufacturability. This thesis proposes a new framework combining traditional discrete topology optimization with truss elements and continuum elements topology optimization in creating a more informed algorithm suitable for more practical design scenarios. In addition, the drawing toolkit is also introduced in helping users better interact with the system in outputting their desired outcome. The hybrid element type topology optimization is achieved by creating separate local stiffness matrices and mapping them respectively to the same global design space to perform optimization together. The interactive drawing functions are used as add-in truss members that users can select the amount and draw in the length and locations of them in the design space. This framework is tested on multiple topology optimization classic problems including cantilever beam with bracings and MBB beam. All draw-in truss hybrid topology optimized results show a more efficient design results with lower compliance and overall lower material quantity.

Analyzing Inconsistent Results of Table Transformer for Improved Data Extraction in Childhood Obesity Intervention Literature

2025-08-22T03:06:24Z

Analyzing Inconsistent Results of Table Transformer for Improved Data Extraction in Childhood Obesity Intervention Literature Neupane, Pragya Tables in scientific literature are rich sources of structured data, yet their complex and variable formats pose challenges for automated extraction. This thesis focuses on improving the reliability of Table Structure Recognition (TSR) using the Table Transformer (TATR) model, with a specific application to childhood obesity intervention studies. While fine-tuning TATR on a domain-specific dataset improves detection metrics, persistent errors such as overlapping rows and misclassified header columns remain. Through a systematic post-hoc error analysis of 175 scientific tables, we identify these dominant failure modes and develop lightweight post-processing modules: an overlap-aware row filtering algorithm and an OCR-enhanced column boundary correction method. Importantly, instead of relying on computationally expensive large language models (LLMs), this approach leverages efficient, interpretable techniques tailored to the domain-specific structure of public health tables. Our combined method reduces the proportion of structurally erroneous tables from 46.3% to an estimated 9.7–12.6%, improving the semantic alignment and interpretability of model outputs. This work contributes a transparent and scalable pipeline that enhances the trustworthiness of automated table extraction systems, with direct relevance to evidence-based decision-making in public health.

Assessing Opportunities to Reduce Carbon Dioxide Emissions from Electric Arc Furnace Steelmaking in the United States

2025-08-22T03:06:08Z

Assessing Opportunities to Reduce Carbon Dioxide Emissions from Electric Arc Furnace Steelmaking in the United States Colcord, Christopher C. Steel is energy- and CO₂ emissions-intensive to produce, but it is also a crucial material for infrastructure, defense, and the energy transition. This thesis focuses on Electric Arc Furnace (EAF) steelmaking, which accounts for roughly 70% of steel production in the United States. Decarbonization levers for EAF producers are diverse—encompassing energy efficiency (EE) measures, fuel switching, material input substitution, development of onsite carbon-free electricity (CFE) generation, CFE procurement through power purchase agreements (PPAs) or unbundled renewable energy credits (RECs), and negative-emissions credit purchases, among others. We first construct a techno-economic model that analyzes costs and emissions of individual EAF facilities in the United States under a business-as-usual (BAU) scenario for the years 2025 through 2035. We then calculate the Levelized Cost of Carbon Abatement (LCCA) of various decarbonization levers against the BAU counterfactual. We build aggregate LCCA curves to draw insights on least-cost emissions abatement strategies for facilities and opportunities for policy to accelerate decarbonization decisions. We find that the modeled levers collectively deliver a 46% reduction in EAF CO₂ emissions versus the BAU case—equivalent to a reduction of roughly 1.7% of national industrial CO₂ emissions. Voluntary CFE procurement has the greatest potential to abate EAF emissions, but comes with large uncertainties. Onsite CFE and PPAs have negative LCCAs in most cases, whereas unbundled RECs have positive LCCAs. EE measures provide modest emissions reductions and costs are negative on a levelized basis under a wide range of assumptions. EE opportunities, onsite CFE, and PPAs may be bound by non-financial constraints. Direct reduced iron (DRI) with carbon capture has lower variable costs and produces fewer emissions versus hydrogen-based DRI in most cases. While the challenges to decarbonize EAF steelmaking are immense, we find EAF facilities can take actionable steps in the near term—supported by federal and state policies—to abate carbon emissions while reducing levelized costs.

Investigating the Capacity of Generative AI to Learn Genotype-by-Environment Interactions in Brachypodium distachyon

2025-08-22T03:06:12Z

Investigating the Capacity of Generative AI to Learn Genotype-by-Environment Interactions in Brachypodium distachyon Neufeldt, Charlie Climate change exacerbates environmental stressors such as drought, challenging the resilience of agricultural systems and highlighting the need to understand plant genomic architecture and its responses to such environmental variation. A key molecular mechanism underlying these responses is transcriptional plasticity: environment-induced changes in gene expression that vary among genotypes, representing one way that genotype-by-environment (GxE) interactions manifest at the molecular level. While transcriptomic data offers a unique and powerful view into these responses, traditional modeling approaches often rely on linear assumptions, limiting their ability to detect complex, nonlinear patterns of regulation. This thesis investigates whether generative machine learning modeling, specifically the use of transformers, can extract biologically meaningful representations of gene expression dynamics in plants. Inspired by the successes of the scGPT model for human genomics, I developed and trained a compact transformer architecture, the PlantGeneEncoder, on bulk RNA-seq data from two natural accessions of Brachypodium distachyon grown under drought and control conditions. The model was trained on binned expression values using both a baseline configuration and a set of regularized variants incorporating noise injection, co-expression preservation, entropy-based sample weighting, and masked gene modeling as a self-supervised objective. While baseline models achieved perfect reconstruction accuracy, they failed to preserve meaningful biological structure in the latent space. Regularized models achieved a better trade-off, maintaining high reconstruction fidelity while demonstrating improved genotype classification performance and modestly better alignment with the original expression structure. However, environmental condition signals remained difficult to capture across all configurations, with classification accuracies only marginally above random chance. These findings highlight the promise and limitations of transformer-based generative modeling for plant transcriptomics and provide a flexible framework for future efforts to model transcriptional plasticity and regulatory responses to environmental stress.

Numerical Analysis of Human-Informed Topology Optimized Lateral-Load-Resisting Systems of Tall Buildings under Seismic Excitation

2025-08-22T03:06:18Z

Numerical Analysis of Human-Informed Topology Optimized Lateral-Load-Resisting Systems of Tall Buildings under Seismic Excitation Blaze, Edie In the construction industry, structural, architectural, and environmental considerations can often be at odds with each other, leading to inefficient structures and, consequently, material waste. Topology optimization has shown promise as one potential solution to this problem, offering designs that are both structurally efficient and aesthetically interesting. However, topology-optimized designs are often difficult to manufacture or do not take into consideration other aspects that are crucial in the construction industry. Human-informed topology optimization, or HiTop, is a previously-developed algorithm that allows users to edit areas of interest, providing a computationally-efficient solution to address concerns with the designs. This paper uses MATLAB to apply HiTop to the design of the lateral-load-resisting systems of tall buildings, comparing results to those of three other designs: a “human” design with standard cross bracing, a optimized design using classical topology optimization, and a previously-developed algorithm which optimizes designs under a sum of modal compliances formulation, similar to how structures are analyzed in seismic codes. The designs are evaluated quantitatively, comparing natural periods, modal displacements, sum of modal compliances using modal decomposition, as well as computation time. They are also evaluated qualitatively, as HiTop is used to modify designs to improve constructability and aesthetics. The HiTop algorithm successfully created manufacturable, aesthetic designs in line with the user’s goals across a range of H/B ratios within a brief time frame. HiTop designs also performed similarly to the classically optimized designs, indicating that modifications to an optimized design to improve manufacturability, aesthetics, or other potential goals of a user do not significantly decrease structural performance under seismic loading.

Peatland burning identification among other wildfires across different ecozones in Canada

2025-08-22T03:06:16Z

Peatland burning identification among other wildfires across different ecozones in Canada Chen, Ming The unprecedented severity of the 2023 Canadian wildfires highlights growing concerns about the vulnerability of global peatlands—key ecosystems storing substantial amounts of terrestrial carbon. Peatlands, traditionally resistant to burning, are increasingly at risk due to climate-induced warmer and drier conditions. This study specifically investigates the extent and characteristics of peat burning in the 2023 Canadian wildfires based on available remote sensing data. The primary objective is to determine whether fires on peatlands demonstrate distinct fire behavior compared to fires on non-peatland. To achieve this goal, this study utilized statistical tools and machine learning algorithms, including power-law relationship estimates, Mann-Whitney U test, K-means clustering, and generalized additive model (GAM) to identify the contribution of peat presence to fire behaviors. Key findings demonstrate that fires on peatland are significantly more intense, longer-lasting, and associated with higher carbon emissions. Even though peat combustion can not be confirmed without field validations, these results underscore the critical importance of the potential impact of peat on wildfire growth and management. By highlighting the disproportionate impact of peat burning, this study provides a foundation for future research aimed at developing targeted remote sensing techniques and policy responses to mitigate peatland vulnerability and preserve vital carbon stores in the context of global climate change.

Sustainable Engineering of Polyethylene Fiber Materials: Advancing Functional Properties of Diverse Textile-Based Structures

2025-08-22T03:06:23Z

Sustainable Engineering of Polyethylene Fiber Materials: Advancing Functional Properties of Diverse Textile-Based Structures Huynh, Amy This thesis explores pathways to circularity for polyethylene-based textiles through an integrated framework that combines material experimentation, systems-level policy analysis, and cultural innovation. Focusing on olefin block copolymer (OBC) filaments—engineered with semicrystalline polyethylene hard segments and elastomeric soft blocks—the study evaluates their mechanical behavior across a range of stitch-based textile geometries. Cyclic and postfatigue tensile testing reveals how formulation and structure shape energy dissipation and durability, informing design strategies for high-performance applications such as intra-vehicular spacesuits and wearable technologies. To understand the broader systems context, the thesis analyzes barriers to integrating recycled polyethylene (rPE) into textile supply chains, identifying economic, legal, institutional, technological, firm-level, and societal constraints. It proposes targeted strategies based on global policy trends, EU case studies, and a geospatial analysis of U.S. recycling infrastructure. Finally, the work explores how generative AI can revitalize traditional craft practices—such as bobbin lace—by co-creating patterns designed for both aesthetic and functional performance in new materials. Together, these efforts propose a model for advancing sustainable textile innovation that bridges material science, circular design, and policy transformation.

Fueling Conflict: A Global Dataset of Energy Protests

2025-08-22T03:06:04Z

Fueling Conflict: A Global Dataset of Energy Protests Harrison, Ethan How do popular grievances about (the lack of) access to energy lead to political violence and instability? I use a mixed-methods approach to answer this question, based on a qualitative case study in Sri Lanka and a quantitative framework for tracking energy protests worldwide. Specifically, through an analysis of the 2022 Aragalaya protest movement in Sri Lanka, I elaborate on how breakdowns in state capacity to provide energy to its citizens can trigger civilian unrest. Building on this case study, as well as insights from the empirical literature on the drivers of instability related to energy access, I then pilot a machine learning (ML) framework to identify energy-related protest events in the Armed Conflict Events Database (ACLED) based on context-specific keywords, which results in the creation of the first global dataset on energy protests. This novel source of evidence, in turn, will open new avenues for research on the conflict-energy nexus, particularly on the impact of market shocks on civilian unrest and instability in low- and middle-income countries – a topic for which current empirical work is limited. I show how the ML framework I develop here can be used to enable continuous monitoring of protest activity related to energy access, as well as how the framework can be extended to other forms of political violence, offering a promising tool for peace-building initiatives across contexts. Therefore, such a framework could inform key evidence to support policymakers, practitioners, and researchers in the design of strategic policies that facilitate the provision of energy while mitigating the risk of conflict and instability worldwide, particularly in "energy-poor" countries.

Towards a Circular Lunar Economy: Incentivizing the Design of Multi-Purpose Reusable Lunar Landers and Rovers

2025-08-22T03:06:07Z

Towards a Circular Lunar Economy: Incentivizing the Design of Multi-Purpose Reusable Lunar Landers and Rovers Khan, Nadia Rehman Both NASA and ESA have committed to establishing a lasting presence on the Moon by 2030. However, lunar surface debris has already exceeded 200,000 kg, prompting concerns about the environmental, operational, and economic viability of future missions. This thesis proposes that circular economy principles—particularly reusability, modularity, and interoperability—must be embedded in early mission architecture to reduce waste, improve system longevity. To evaluate these goals, this thesis introduced a novel decision-support framework, the Lunar Exploration Impact Assessment (LEIA), alongside a policy-informed set of Lunar Surface Sustainability Guidelines (LSSG). Both decision support tools were designed help mission designers and space policy stakeholders to incentivize the design of resilient reusable lunar landers and rovers. In this thesis the LEIA framework, was applied to two case studies: NASA JPL’s EnduranceA autonomous lunar sample return rover, and ESA’s multi purpose Argonaut lander to evaluate the sustainability of each spacecraft after the EOL/M phase of each mission. Scores were computed using a Multi Criteria Decision Analysis (MCDA) approach. Seven Impact Assessment Indicators (IAI)s were considered, to assign a sustainability rating for each mission: Cost-effectiveness, environmental impact, science value, redundancy, resilience, strategic value, and technological feasibility. The Endurance-A mission achieved a sustainability score of 66.4%, based on a sample collection post primary mission scenario, indicating moderate sustainability across some categories such as cost-effectiveness 18.9% and technological feasibility 12%. However, the environmental impact score was limited to 7.7%, due to the out-gassing and launch emissions associated with the SpaceX Starship lander. The rovers redundancy and maintainability ratings also constrained the overall sustainability rating – highlighting a gap in the availability of tools suitable for EVA-based repairs on the lunar surface. Subsystems most at risk of degradation—mobility, thermal, and power—require enhanced design for long-term reuse scenarios. Each of these factors were made salient through the Argonaut case study, indicating that in the short to medium term in order to prevent the accumulation of lunar surface debris lunar rovers and landers must be designed to be more resilient to the conditions of the lunar environment. To supplement the LEIA framework, a set of policy recommendations were developed in order to address the lack of End of Life (EOL) procedures in place to manage lunar surface debris – in the form of retired lunar missions. The guidelines detailed how economic policy mechanisms adopted in circular economy systems could be leveraged to incentivize the design of sustainable lunar surface missions and operations.

Beam Mechanism Failure in Multistory Steel Frame Structures

2025-08-22T03:06:17Z

Beam Mechanism Failure in Multistory Steel Frame Structures Hashbarger, Brad Engineers must ensure that building structures are not in danger of collapse, so analyses always include safety factors that create redundant yet materially inefficient buildings. This has been common practice for most of structural history, but today, growing concerns for carbon emissions force designers to cut material usage while retaining the same level of safety. Processes opt into one of two processes: an overall lighter unit or stiffening specific internal systems to encourage a load path. The problem with either of these options lies in progressive collapse in the event of structural damage. If one column is lost, stresses propagate either until equilibrium or a larger collapse occurs. Progressive collapse remains a popular research area to identify specific vulnerabilities, often with numerical models for a visualization of each stress state and redundant capacity. Previous studies used analytical and experimental performance to observe the critical effects of losing an external versus internal column and the role of other components, such as joints, joists, and composite slabs, to carry additional loads. However, designs and analyses are bound by assumptions that govern model behavior. To understand the sensitivity and limits of these assumptions, this thesis predicts the performance of steel moment-frame structures of varying bay geometries, proposing deflection fields to inform modern practice in all phases of project development. Instead of numerical simulations, the process follows an analytical approach based in the fundamental methods of equilibrium and the conservation of work and energy. By designing sections for their elastic capacity, their operational performance is directly linked to their failure response. This suggests the dominance of design preferences in stability, even with changes in beam spans or floor loading. Results support an optimal span ratio for plasticity under two-way load distributions that favors bay geometry ratios (L1/L2) between 1 and 2 but varies based on failure locations and how many columns have been lost. This also emphasizes the weaknesses out of plane as span ratios range from 0.5 to 1. Project layouts can utilize the free strength provided by bay geometries as part of the structural design process. If large deflections or span lengths are expected, beam depth and section thickness should increase together to ensure beams utilize their full plastic capacity to achieve additional redundancy from catenary action. Overall, the thesis demonstrates that such considerations in the early design stage can enable steel structures to achieve greater safety with less material.

Estimating Aboveground Biomass (AGB) Throughout the Pacific

2025-08-22T03:06:14Z

Estimating Aboveground Biomass (AGB) Throughout the Pacific Domingo-Kameʻenui, Joy P. Aboveground biomass (AGB) is a signiﬁcant carbon pool in forests, making AGB a good indicator of forest health and carbon storage. AGB has been studied on multiple scales, in which allometric equations were developed to ﬁnd relationships between AGB and tree parameters. However, despite the presence of AGB studies for speciﬁc sites in the Paciﬁc Islands, there is a lack of AGB comparative studies or data syntheses focused on the Paciﬁc Islands as a whole. This study synthesized data on AGB, tree height H, land cover, and Paciﬁc Island forest community to develop allometric equations using linear and polynomial regression models for trees in the Paciﬁc based on H as the main parameter. This study found polynomial relationships between AGB and H for shrub and herbaceous covers. Speciﬁcally, AGB = 1.76 H^2 + -51.01 H + 346.53 for shrub cover (adjusted R^2 = 0.94, n = 39), and AGB = 1.11 H^2 + -81.97 H + 1167.20 for herbaceous cover (adjusted R^2 = 0.71, n = 79). However, future research and data collection would be necessary to develop allometric equations for tree cover and barren land cover. No signiﬁcant correlation was found between AGB and H for Paciﬁc Island forest community. This study may help with forest management and conservation practices, along with carbon sequestration and storage practices in forests, in the Paciﬁc Islands. This study may also contribute to Paciﬁc-led climate change mitigation and adaptation methods and initiatives.

AI-powered Data Mining for the Development of Sustainable Concrete Materials

2025-08-22T03:06:00Z

AI-powered Data Mining for the Development of Sustainable Concrete Materials Duan, Yifei Data mining has become essential to contemporary industrial and scientific research, playing a pivotal role in uncovering insights from large-scale industrial datasets and literature collections. The sustainable transition of the concrete industry, a major contributor to global CO₂ emissions, demands both operational optimization and scientific innovation. This thesis presents comprehensive data mining frameworks for both industrial and literature source data to support the development of more sustainable concrete materials. Focusing on concrete manufacturing, we develop AI-powered methodologies tailored to real-world industrial data and complex scientific literature. For industrial data mining, we propose to incorporate interpretability and realistic engineering design scenarios to enhance the reliability of both predictive and prescriptive modeling of concrete mixes containing supplementary cementitious materials (SCMs). A domain-informed amortized Gaussian process and a shallow multi-layer perceptron (MLP) are shown to possess superior scientific consistency in predicting time-varied compressive strength, and time-invariant slump and air content properties, respectively. The explainable surrogate property models are applied in mix design optimization under a variety of realistic scenarios considering different engineering design requirements and SCM costs and densities. The importance of the comprehensive property constraint set is demonstrated in comparison against a baseline using only 28-day strength constraint which results in unreasonable property values. The necessity to differentiate realistic scenarios is also highlighted through the differences of optimized mixes and their production costs and climate impacts. Higher design strength, higher design slump, lower design air content, higher SCM density, and higher SCM unit cost can drive up the production costs. Though stratification patterns in the production costs of optimized mixes are observed across different scenarios, the mix-wise climate impacts are not clearly stratified, indicating that substantial emission reduction can be achieved without significantly increasing costs, regardless of the realistic scenarios. For literature mining, a novel method that finetunes lightweight large language models (LLMs) (pythia-2.8B) with multichoice instructions is developed. With the multifaceted linguistic complexity of communication within the domain rendering it infeasible to adopt the conventional named-entity-recognition approach, the new method successfully achieves great information inference accuracy in a time-, cost-, and computation-efficient manner, outperforming the GPT-3.5 in-context learning baseline by over 20%. A knowledge graph is constructed with the literature-mined data, offering insights to promote alternative material substitution strategies in concrete production as the current commercial SCMs are not comprehensively sustainable in the longer term. Statistical summary and temporal trend analyses are adopted to provide both static and dynamic insights into the research landscape. Although SCMs have remained a research hotspot, results revealed a systematic shift in recent studies from commercial SCMs to other materials. Geopolymer and fine aggregate studies have surged in the recent period, while clinker feedstock and filler studies have declined. A node similarity metric is modified to develop a model-free link prediction algorithm, enhanced with random graph perturbation for robustness and uncertainty quantification. Through link prediction, the currently underexplored lime-pozzolan cement application emerges as a potentially promising future research direction.

Metabolic Scaling Analysis of Building Energy Efficiency: A Case Study of Massachusetts Institute of Technology

2025-08-22T03:06:02Z

Metabolic Scaling Analysis of Building Energy Efficiency: A Case Study of Massachusetts Institute of Technology Hsu, Yu-Hsuan The building sector plays a critical role in global energy consumption and carbon emissions, accounting for 21% of global GHG emissions (12 GtCO₂-eq) and 31% of global final energy demand (128.8 EJ) in 2019 (Cabeza et al. 2022). This reality underscores the urgent need to enhance energy efficiency within the sector. This research applies ecological metabolic scaling principles to building energy analysis, utilizing the Massachusetts Institute of Technology (MIT) campus as a case study. Analogous to biological systems, where an animal’s metabolic rate scales to 3/4 power of its mass, our findings indicate that larger buildings, similar to larger organisms, are inherently more energy efficient. Furthermore, an analysis of overall energy consumption at MIT from 2009 to 2020 reveals a steady decline, though not proportionally, as the scaling exponent fluctuated with a decreasing trend (<3/4), indicating improved efficiency in larger buildings. However, the COVID-19 pandemic in 2020 acted as a major shock, disrupting this trend. This disruption was likely driven by operational and behavioral changes, including reduced occupancy, increased remote work, and adjustments to ventilation and heating systems to ensure health and safety. These shifts highlighted the system’s tendency to return to the baseline scaling exponent of 3/4, demonstrating regression to the mean and ultimately pushing efficiency back to its prior baseline level of 25%. Additionally, the study includes case analyses of specific buildings on the MIT campus to provide deeper insight into comparative energy performance. While several guidelines for energy systems have been proposed, certain limitations remain. Future research should focus on expanding the dataset to help validate the applicability of these findings to other contexts while also accounting for variations in building types. Ultimately, this study aims to facilitate the development of more effective policies and innovations in building energy management.

Scaling Carbon-Cement Supercapacitors for Energy Storage Use-Cases

2025-08-22T03:06:13Z

Scaling Carbon-Cement Supercapacitors for Energy Storage Use-Cases Grewal, Darshdeep The urgent global transition to renewable energy is constrained by the intermittent nature of solar and wind sources, highlighting the critical need for scalable energy storage solutions. This thesis presents a comprehensive investigation into the development of structurally integrated supercapacitors based on carbon-doped cement composites, known as EC3 cells. These multifunctional materials combine structural performance with electrochemical energy storage capabilities, enabling integration directly into civil infrastructure. The research focuses on three essential challenges for real-world deployment: (1) replacing laboratory acrylic casings with hydrophobic sealants compatible with cementitious systems, (2) quantifying and mitigating shrinkage and swelling in nanocarbon cement matrices under electrolyte exposure, and (3) identifying corrosion-resistant current collectors that maintain conductivity and mechanical durability under harsh conditions. Bitumen-based coatings were found to be promising sealants for moisture containment. Shrinkage studies [ are underway, I will complete this part shortly]. Meanwhile, corrosion testing of various collector materials revealed that graphene sheets and stainless steel–reinforced graphillic papers offered optimal trade-offs between conductivity, corrosion resistance, and mechanical performance. The thesis concludes with two field-implementation design proposals—a vertical column and a vaulted arch—both of which leverage compression to improve electrochemical contact and stability. Altogether, this work establishes a foundational framework for embedding energy storage directly into the built environment.

Preserving Human Autonomy in AI-Mediated Negotiations

2025-08-22T03:05:58Z

Preserving Human Autonomy in AI-Mediated Negotiations Chen, J. Alvin The rapid integration of generative artificial intelligence (AI) into negotiation and conflict resolution processes raises critical ethical concerns about the erosion of human autonomy, particularly when AI systems navigate irreconcilable “sacred” values (non-negotiable moral principles) alongside transactional “mundane” interests. This thesis investigates whether generative AI can be designed to recognize and respect important values and beliefs while preserving human agency in decision-making. Drawing on datasets from a repository of large language model (LLM) prompts tested in simulated negotiation scenarios, this study employs a mixed-methods approach to evaluating AI’s efficacy in balancing efficiency with ethical imperatives in negotiation. Quantitative metrics (enumerating the outcomes of two-party negotiations) are analyzed alongside qualitative assessments of values such as transparency and consent, drawn from Kantian ethical frameworks. My analysis reveals that while AI negotiating bots excel in trades across mundane, tradable interests they struggle to navigate beliefs and values without oversimplifying moral reasoning or obscuring cultural considerations. These findings inform policy recommendations, including a call for human-in-the-loop validation and technical safeguards for protecting important values in efforts to incorporate AI-assistance into negotiations. By bridging technical analysis and ethical theory, I hope this research contributes to improvements in designing autonomy-preserving AI systems for use in a range of negotiating settings, prioritizing human dignity alongside computational efficiency.

Characterizing Effective System Architectures for Cislunar Space Situational Awareness

2025-08-22T03:06:10Z

Characterizing Effective System Architectures for Cislunar Space Situational Awareness Rude, Connor D. Achieving Space Situational Awareness (SSA) in the Cislunar region—the area between the geosynchronous belt and the Moon's gravitational boundary—poses significant technological and organizational challenges. Instead of proposing new theoretical systems, this thesis employs the Architecting Innovative Enterprise Strategy (ARIES) Framework to evaluate existing SSA architectures and previously suggested solutions. ARIES provides a structured assessment through its elements (strategy, information, infrastructure, products, services, processes, organizations, and knowledge), identifying infrastructure, acquisition strategies, policy-driven timelines, and communication structures as key areas for improvement. Stakeholder objectives, current initiatives, and operational needs guide the characterization of an ideal SSA architecture. Four prior system proposals for cislunar SSA are assessed using qualitative analysis of existing literature and first-order physics-based simulations. These evaluations correlate specific design features with enhanced system suitability. Particularly beneficial are constellation proximity to targets, strategic constellation placement and phasing, sensor orbital diversity, and orbital stability. Additionally, certain design strategies consistently yield higher suitability, including focusing on underserved SSA regions, leveraging heritage technology, and optimizing designs for ride-share launch compatibility.

LLM-Supported Natural Language to Bash Translation

2025-08-22T03:05:59Z

LLM-Supported Natural Language to Bash Translation Westenfelder, Finnian Ellis The Bourne-Again Shell (Bash) command-line interface for Linux systems has complex syntax and requires extensive specialized knowledge. Using the natural language to Bash command (NL2SH) translation capabilities of large language models (LLMs) for command composition alleviates these issues. However, the NL2SH performance of LLMs is difficult to assess due to inaccurate test data and unreliable heuristics for determining the functional equivalence of Bash commands. We present a manually verified test dataset of 600 instruction-command pairs and a training dataset of 40,939 pairs, increasing the size of previous datasets by 441% and 135%, respectively. Further, we present a novel functional equivalence heuristic that combines command execution with LLM evaluation of command outputs. Our heuristic can determine the functional equivalence of two Bash commands with 95% confidence, a 16% increase over previous heuristics. Evaluation of popular LLMs using our test dataset and heuristic demonstrates that parsing, in-context learning, in-weight learning, and constrained decoding can improve NL2SH accuracy by up to 32%. Additionally, we consider military use cases for NL2SH models and discuss the limitations of current Department of Defense documentation standards for LLMs. We write and publish documentation for our models and datasets to promote safe use. Our findings emphasize the importance of dataset quality, execution-based evaluation, translation method, and proper documentation for advancing NL2SH translation and enabling responsible use. Our code is available at https://github.com/westenfelder/NL2SH.

Redefining “Space Sustainability” for Launch Vehicles: Forecasting the Atmospheric Impact of the Commercial Space Launch Industry in 2050

2025-08-22T03:06:03Z

Redefining “Space Sustainability” for Launch Vehicles: Forecasting the Atmospheric Impact of the Commercial Space Launch Industry in 2050 Ma, Clara Z. Discussions on “space sustainability” have largely centered on orbital debris, the burnup of vehicles during atmospheric reentry, and the resulting emissions. However, few studies have examined emissions from the launches themselves. Along with reentry burnup, rocket launches are the only source of high altitude anthropogenic emissions. At such high altitudes, emitted particles can remain in circulation for years. With the annual growth rate of the commercial launch industry averaging 14.6% in the last 4 years and over 211 launches in 2023 alone, our research on the atmospheric impact of launch vehicles comes at a crucial point in the policy debate on space sustainability. This thesis outlines several potential future scenarios of the launch industry in 2050, with all the vehicles in each scenario using the same fuel type. We examine these four launch scenarios—a kerosene (RP-1) launch industry, a methane (CH4) launch industry, a hydrogen (H2) launch industry, and a control or “baseline” scenario without launches. For each scenario, we estimate the number of launches for a distribution of heavy-lift launch vehicles across origin spaceports. We simulate the chemical interactions of the launch plumes with the atmosphere using the global atmospheric chemistry model GEOS-Chem High Performance (GCHP). Finally, we quantify the steady state impact of launch emissions on stratospheric ozone and surface air quality. We find that the black carbon emitted by kerosene and methane rockets causes an indirect increase in stratospheric ozone due to the removal of NOx, with ozone column change averaging 5.07 Dobson Units (DU) and 1.26 DU respectively; hydrogen rockets cause a net decrease in ozone column averaging -0.11 DU. The population-weighted average surface ozone impact is -0.286 ppb, -0.068 ppb, and 0.023 ppb for RP-1 rockets, CH4 rockets, and H2 rockets respectively. The population-weighted average surface PM2.5 impact is -0.031 μg/m3, -0.004 μg/m3, and 0.002 μg/m3 for RP-1, CH4, and H2 rockets respectively. Although RP-1 and CH4 rockets decrease surface ozone and surface PM2.5, H2 rockets have the smallest magnitude impacts on the atmosphere overall. Our findings have important implications for commercial launch providers, research institutions, and policymakers including the Federal Aviation Administration (FAA) and NASA.

AI Trust and Technology Optimism in the Workforce: Data-Driven Insights into Regional Variation

2025-08-22T03:05:55Z

AI Trust and Technology Optimism in the Workforce: Data-Driven Insights into Regional Variation Velonia Bellonia, Maria Eleni Automation and AI systems are reshaping the workplace. How these technologies make a difference varies according to local contexts. Workers’ willingness to trust and embrace these technologies is shaping how this transformation unfolds in practice. Some workers trust AI more than others, and interestingly, trust levels differ from one region to another. Drawing on a far-reaching 2024 worker survey spanning different countries, and on a rich body of literature on technology, trust, and change, this work examines how key factors influencing workers’ AI trust and technology optimism interweave, shaping their perspectives on new technologies and automation. The focus is on understanding how the industrial and regulatory landscape in which workers operate, combined with their personal experiences with AI, shapes their AI optimism, with a particular emphasis on the US and Europe. While external market innovation indicators provide limited understanding of workers’ technology optimism, individual interaction and familiarity with AI, alongside organizational AI adoption and a worker’s industry of employment, emerge as key factors shaping AI trust. Additionally, the regulatory environment, encompassing technology governance, social safety nets, and workers’ institutional trust, all seem connected with how workers think about the impact of new technologies on society, the economy, and their jobs. Interpersonal trust propensity contributes to AI trust formation, though its relevance exhibits regional variation. By offering insights into the critical factors shaping the relationship between workers and AI, this study aims to provide evidence that supports societies in unlocking the value of emerging technologies, while empowering the workforce to confidently embrace and excel alongside them.

Predicting Ridership and Travel Time Impacts of Bus Service Changes Using Sketch Planning Methods

2025-08-12T03:07:00Z

Predicting Ridership and Travel Time Impacts of Bus Service Changes Using Sketch Planning Methods Lim, Tiffany M. Bus service changes range in scale, and understanding their impacts on ridership and travel times can inform decision-making as changes are considered for the bus network. Budgetary limitations are at the heart of service change decisions, resulting in the need for analysts to assess different scenarios and accommodate quick turnarounds. This thesis provides a sketch planning framework for predicting ridership and travel time impacts of bus service changes, with a focus on direct demand models and the use of an open-source multimodal routing algorithm. The framework is designed to be streamlined with the use of data sources and capabilities, such as exporting a General Transit Feed Specification (GTFS) feed of a given bus network scenario, that agencies may have access to through existing transit planning tools. Direct demand models are developed to estimate bus ridership at the level of approximately one-mile route-segments and time-of-day periods. This level of analysis provides a more disaggregated evaluation of bus ridership than past direct demand models. The models are sensitive to both route and network improvements. New variables designed to capture the relationship between bus routes, including the competitive and complementary nature of routes, are introduced and incorporated in the model development process. These models are developed for the Washington Metropolitan Area Transit Authority (WMATA). A case study analyzing two scenarios in WMATA's Better Bus Network Redesign (BBNR) is presented, with selected route examples to illustrate how the models capture different types of service changes. These routes fall under three categories: routes with no major service changes, routes with improvements in frequency, and routes with re-routing and other improvements. An open-source multimodal routing algorithm, available through an R package called r5r, is used for travel time analysis. r5r calculates a distribution of door-to-door travel times for a given origin-destination (OD) matrix and returns a selected percentile value from the distribution for each OD pair. The percentile parameter is calibrated through a comparison of estimated travel times and actual travel times recorded in origin-destination-interchange inference (ODX) data. Low percentile values were found to provide travel times close to actual travel times. Additional guidance is provided for interpreting travel times from r5r, and use cases related to calculating travel time impacts between scenarios and evaluating rail competitiveness for a given bus network are explored.

Sensing and Predicting Urban Rail Platform Crowding Using Emerging Data Sources

2025-08-12T03:07:28Z

Sensing and Predicting Urban Rail Platform Crowding Using Emerging Data Sources Fiorista, Riccardo Rail platform crowding poses serious challenges to passenger safety, operational performance, and service quality in urban rail transit systems. This thesis investigates the short-term forecasting of platform-level crowding, focusing on enhancing prediction accuracy, spatial granularity, and operational interpretability through multi-source data integration. We first employ a gradient-boosted tree regression model (LightGBM) to leverage fare card transaction, vehicle location, weather, and public event data from the Washington Metropolitan Area Transit Authority (WMATA) to forecast platform-level occupancies 15–60 minutes ahead of time. Our results show significant improvements over a WMATA-internal baseline while providing a robust data preparation and prediction pipeline. Subsequently, we explore integrating platform-level CCTV data to overcome the lack of real-time crowding estimates. Using a custom-collected image dataset and three computer vision methods, namely object detection (YOLOv11, RT-DETRv2) and head counting (APGCC), crowd-level classification (Crowd-ViT), and semantic image segmentation (DeepLabV3), we demonstrate that estimated counts from calibrated image segmentation maps enable accurate real-time estimation of platform crowding. Additionally, we show that these estimates can correct and improve 15-minute horizon predictions when incorporated with a stochastic gradient-boosted tree learner such as LightGBMLSS. Finally, we extend the time series modeling framework by incorporating network-wide causal influences through an analysis driven by Empirical Dynamic Modeling and Convergent Cross Mapping. We show that accounting for network effects improves predictive performance, particularly for platforms characterized by regular low-occupancy patterns, improving the prediction of anomalies. The work presented in this thesis extends the existing literature on short-term platform crowding prediction, offering new methodologies to incorporate emerging CCTV data and causal network effects for increased prediction accuracy.

Spatial Thinking as an Analytical Lens for Bilateral International Development: Lessons from the Harbor Reconstruction Project in Jamestown, Accra

2025-08-12T03:06:53Z

Spatial Thinking as an Analytical Lens for Bilateral International Development: Lessons from the Harbor Reconstruction Project in Jamestown, Accra Avis, Victoria This thesis examines transcalar tensions that emerge from urban infrastructure development projects funded through bilateral foreign assistance mechanisms. Using a mixed-methods case study approach to gather data from a wide variety of historical and contemporary primary and secondary sources, this research centers a harbor revitalization and port reconstruction project in Jamestown, a historic fishing community in Accra, Ghana. Having coordinated plans with the Ghanaian national government, a Chinese state-owned construction firm began working on the port in 2020. In 2024, the revitalized harbor and expanded port were officially handed over to the government of Ghana in a widely attended ceremony. The spatial implications of this physical urban infrastructure project across international, national, municipal, and local levels are complex and interrelated. Therefore, this case study is especially relevant at a historical moment when the nature of bilateral engagement may be undergoing significant transformation. This thesis argues that spatial thinking, a foundational concept in urban planning, is a necessary analytical lens to incorporate within international development practice. Despite its relevance, spatial thinking has not been meaningfully incorporated into international development policy or implementation. Therefore, this thesis seeks to bridge epistemic gaps between urban planning and international development by advancing a spatial thinking framework, adapted for use in international development contexts. In doing so, this thesis envisions a future for bilateral development assistance that delivers equitable and sustainable development outcomes across scales of engagement. This approach, rooted in spatial thinking, intends to respond to local community needs and aspirations, capacitate municipal governments, align with national priorities, and accommodate geopolitical dynamics that facilitate bilateral project implementation.

Solid-State NMR Characterization of a PET Ligand Binding Sites in AD Tau Fibrils

2025-08-12T03:07:02Z

Solid-State NMR Characterization of a PET Ligand Binding Sites in AD Tau Fibrils Angehrn Rodas, Frida Nicole Aggregation of the tau protein into fibrils is a key feature of Alzheimer's disease (AD) and many other neurodegenerative disorders. Developing small molecules that bind these tau fibrils is important for the diagnosis and treatment of tauopathies. This thesis revolves around a study on the binding sites of a positron emission tomography (PET) ligand, PI-2620, to a recombinant tau construct that adopts the C-shaped AD fold. Using solid state NMR experiments in combination with other techniques such as Transmission Electron microscopy (TEM) as well as docking simulations allowed a better understanding of the binding sites of this PET agent. Specifically, 13C-19F REDOR experiments were used to identify nearby residues to the ligand. PI-2620 was found to bind two primary sites within the C-shaped structure. The docking simulations allowed the proposition of several possible binding poses. Additional 2D NMR experiments suggest that PI-2620 alters the protofilament interfaces. The stoichiometry of PI-2620 binding to tau fibrils was determined to be approximately 20 mol%, with varying degrees of ligand mobility. These findings offer insights into the interaction of this PET tracer with tau fibrils and have implications for the design of improved imaging agents.

Demand‑Driven Decarbonization: Impact of Voluntary 24/7 Low-Carbon Power Procurement

2025-08-12T03:06:55Z

Demand‑Driven Decarbonization: Impact of Voluntary 24/7 Low-Carbon Power Procurement Ali, Adam This thesis examines the impact of voluntary 24/7 (hourly) low-carbon power procurement on grid-wide emissions and investment strategies in generation technologies. Recognizing the growing number of businesses and government agencies making voluntary commitments to reduce greenhouse gas emissions (GHGs) through increased procurement of low-carbon power, this study investigates the effectiveness of these commitments, particularly those aiming for hourly matching of low-carbon energy with consumption. This study employs GenX, an open-source capacity expansion model, to simulate an electricity market with two classes of buyers. Buyers in one class commit to reduce the carbon intensity of their electricity procurement by some amount, while buyers in the other class procure electricity at minimum cost without any regard to carbon emissions. This setup allows for a detailed examination of how different levels of ambition in voluntary hourly low-carbon commitments influence the electricity system and investment strategies. The study tests both a simpler model without storage and demand-response capabilities and a more complex model that incorporates these elements to assess their impact on meeting hourly clean energy targets. Our findings suggest that at low to moderate ambition levels of hourly low-carbon electricity procurement, the buyers with voluntary commitments can primarily "reshuffle" built low-carbon generation without incentivizing new clean capacity additions or achieving measurable reductions in system-wide emissions. Significant shifts in generation investments and decreases in total carbon emissions are observed only when commitments exceed a critical threshold, ranging from approximately 70% to 96%, depending on the facts of the system, which happen to be reflected in different model set-ups. Even then, cost-minimizing behavior in voluntary procurement can distort investment, spurring excessive wind and solar builds that exceed what a least‑cost, socially-optimal zero‑carbon portfolio would require. In conclusion, for voluntary 24/7 procurement to cut emissions materially—and avoid misallocating capital—either ambition must be extremely high or participation must broaden enough to share costs and benefits. Otherwise, committed buyers bear steep costs, non‑participants enjoy spill‑over gains, and the system drifts toward a sub‑optimal technology mix.

Clarifying Decision Making Processes: Tools for Interdependency Modeling

2025-08-12T03:07:01Z

Clarifying Decision Making Processes: Tools for Interdependency Modeling Baker, Ellie F. Tools for problem specification in AI Decision making are underdeveloped at present. I propose two new tools for this purpose; first, a model of AI Decision Making, which supports problem identification and mitigation. Second, a Bill of Assumptions for Data Production. Data is an important component of AI Decision Making Systems, and data is necessarily produced by making a series of assumptions. My Bill of Assumptions for Data Production is a new approach to communicating these assumptions that facilitates collaboration, data transparency, and reduction of harmful bias. I illustrate this new approach by developing a dataset that estimates the distribution of Government education spending in the US across income deciles. My dataset informs existing Distributional National Accounts (DINA), which are a primary measure of income inequality in the US (Piketty et al., 2018). My estimate shows Government education spending is more progressive than assumed in current DINA. Furthermore, I show that removing federal education funding to postsecondary institutions would produce substantial harm.

Transforming geospatial textual data into narrative storytelling visualization

2025-08-12T03:06:57Z

Transforming geospatial textual data into narrative storytelling visualization Ma, Ruixian Current large language models (LLMs) often struggle to integrate geospatial data into dynamic, interactive visualizations, relying instead on text-based outputs. This limitation hinders the full potential of geospatial data to convey complex information through narrativedriven communication, making it difficult for users to interpret the data easily. Meanwhile, existing data visualization tools typically depend on static dashboards and rigid scientific formats, which have a steep learning curve and lack engagement through narrative elements. Audiences, however, are increasingly drawn to story-driven presentations, as seen in platforms pioneered by the MIT Senseable City Lab, and widely popularized by The New York Times and the Washington Post, which use narrative data visualization formats to attract and immerse readers. This gap between the capabilities of current LLM-based tools and users’ preferences presents a unique opportunity to develop a narrative-based geospatial visualization tool that meets these needs. This tool could transform how we communicate spatial data, particularly in fields such as journalism, travel planning, and urban planning, where the ability to convey complex patterns in an engaging manner is essential.

Quantifying the Post-Pandemic Urban Activity & Mobility Regime: Implications for Adaptation and Future Planning of Cities and Public Transit Systems

2025-08-12T03:06:51Z

Quantifying the Post-Pandemic Urban Activity & Mobility Regime: Implications for Adaptation and Future Planning of Cities and Public Transit Systems Leong, Chee Weng Michael Between 2019 and 2022, a pattern break during the COVID-19 pandemic introduced consequential changes to the trajectory of urban activity and mobility patterns. This thesis advances both theoretical and practical understandings of this evolving post-pandmemic regime of activity and mobility, as well as its implications for the future of cities and public transit systems, using high-resolution location-based services data and a case study within the Washington, DC metropolitan area. First, a custom analysis framework is developed where geographical units - subcenters and neighborhoods - are designed to provide insight at an interpretable scale that corresponds to policy and business decision making. Second, a custom suite of twelve mobility metrics are curated to distill the applicability of post-pandemic changes in travel patterns to business problems (site selection, network planning, and operations planning) and societal outcomes (social fabric, quality of life, and environmental sustainability). To complement spatial analysis, these metrics are also regressed on socio-economic attributes to provide greater explanatory power. Lastly, key trends in post-pandemic activity and mobility are distilled into eight mega-trends, and their implications for the adaptation of public transportation systems and future urban development are discussed, including complexity from divergent definitions of success among different stakeholders.

Small Stores, Big Obstacles: Understanding Constraints and Opportunities for Micro-retail Firms

2025-08-12T03:06:58Z

Small Stores, Big Obstacles: Understanding Constraints and Opportunities for Micro-retail Firms Cervantes Gil, Sergio Yael Micro and small enterprises (MSEs), particularly informal micro-retailers known as nanostores, play a vital role in developing economies but remain largely underserved by traditional financial institutions and overlooked in economic policy. In Mexico, nanostores account for more than 95% of businesses and over 10% of national employment, yet face high closure rates, low productivity, and limited access to formal credit. This thesis asks: What structural and contextual factors determine the survival and performance of nanostores, and how can policy better support high-potential firms within this segment? To answer this, the study constructs a longitudinal panel of nanostores using microdata from the Mexican Economic Census (2009, 2014, and 2019), and combines it with municipality-level contextual data including crime, infrastructure, unemployment, electricity costs, and business regulations. It applies survival models to estimate firm closure dynamics and implements a misallocation framework to quantify distortions in capital and labor usage. The results reveal that misallocation—particularly of capital—is pervasive and systematically linked to institutional weaknesses and credit access constraints. In response to the limited real-time data available for this sector, the thesis proposes the LIFT Performance Index, developed by the MIT Low-Income Firms Transformation Lab (MIT LIFT Lab), as a diffusion-based tool for monitoring micro-retailers’ business sentiments using structured operational surveys. A pilot implementation in Argentina demonstrates the index’s potential to generate timely and actionable insights for policymakers and private stakeholders. Overall, this work contributes a novel empirical foundation for understanding heterogeneity within the micro-retail sector and offers a scalable framework for designing targeted, data-driven interventions to support inclusive economic development.

Maximizing Flexibility and Efficacy of Undersea Wireless Power Transfer Systems

2025-08-12T03:06:56Z

Maximizing Flexibility and Efficacy of Undersea Wireless Power Transfer Systems Gess, Derek Autonomous underwater vehicles (AUVs) are an ever-increasingly essential tool for ocean-based applications, whether it be scientifically, economically, or militarily. To advance the capabilities of AUVs, it is crucial to improve the mission time and length of these vehicles. One proposed way to achieve this is with remote undersea wireless power transfer (WPT) systems to allow AUV charging from remote areas of the ocean floor. While there has been significant research in WPT system design, these projects often tailor the design specifications towards a specific AUV shape, size, or power requirement. These point designs have wildly different power outputs, efficiencies, coupling coefficients, sizes, and more, making it difficult to understand how the design parameters affect each of these properties. This paper aims to address this knowledge gap in current undersea WPT systems by designing an equivalent circuit framework for a WPT system with a targeted power output of ~1 kW to show how design parameters such as input voltage, coil size, transfer gap, coupling coefficient, and load resistance affect the power output and efficiency of the charger. Furthermore, the effects of misalignment in vertical and lateral directions for two separate compensation networks – series-series (SS) and series-parallel (SP) – are compared to determine which compensation network would perform best under specified circumstances. The paper then addresses the losses associated with a conductive environment by coupling the circuit model with an electric field model in seawater. The impact of undersea losses on system metrics is quantified, showing a 3% decrease in efficiency as compared to in air. Finally, the study investigates the use of magnetic cores in WPT systems for EM shielding and field-shaping characteristics. A design methodology is introduced to rank material properties based on the desired system performance characteristics. Suggested materials are then chosen according to this ranking and tested using the models derived in the study. By mapping both electrical and magnetic-core design spaces in a conductive seawater environment, this thesis delivers a unified methodology for designing scalable, efficient undersea wireless chargers.

City as Seed: The Urban Resonance Field and the Case for Sonic Awareness in Ecological Renewal

2025-08-12T03:06:54Z

City as Seed: The Urban Resonance Field and the Case for Sonic Awareness in Ecological Renewal Navarro, Cadine Seeds, the “abominable mystery” (Darwin, 1897), hold our past and potential future. They also hold sound. Much like cities, they are sites of growth, transformation, and resilience. This thesis draws parallels between laboratory research on the sensing capacities of seeds and embodied experiences of sensing within urban landscapes, exploring how living systems interact with sound and vibration. Through both scientific and poetic approaches, it examines how seeds respond to sonic environments and how this sensitivity can inform human engagement with acoustics in the urban context. The investigation of intangible forces, vibration, resonance, and sound reveals a shared responsiveness between seeds and cities, documented through graphs, sound spectra, and reflective narratives that bridge science and art. Focusing on sound as a strategic lens, this work brings attention to often-overlooked sensory domains, inspiring a more ecologically and socially responsive urbanism. Ultimately, it advocates for practices of deeper listening as a method to engage openly and imaginatively with human and nonhuman worlds, and to reimagine urban environments as spaces of attunement, dialogue, and co-existence.

Critical Vulnerabilities of AI in Latin America

2025-08-12T03:06:48Z

Critical Vulnerabilities of AI in Latin America Dobles Camargo, Claudia Artificial Intelligence (AI) is rapidly reshaping societies, economies, and governance systems worldwide. While it offers tools for addressing critical challenges—such as climate change, health care, and educational inequity—it also risks deepening historical inequalities, undermining democratic institutions, and exacerbating global technological dependencies if not ethically governed. Latin America faces unique vulnerabilities for the development and use of AI, currently underexplored in existing scholarship—such as informal data work or the territorial principle and its implications on AI law enforcement—this study investigates AI's critical vulnerabilities within the Latin American context in order to determine and provide regional and national policies to advance on an inclusive, strategic, and ethical approach for developing and deploying AI systems in Latin America. The study seeks to answer the question through a cross-analysis and comparative case study of six countries (Brazil, Chile, México Costa Rica, El Salvador and Honduras) drawing on existing and recent global and regional benchmarks, including the Stanford HAI AI Index (2024), UNESCO’s Recommendation on the Ethics of AI (2021), and the Latin American AI Index (ILIA 2024). The countries were selected based on a broad range of AI readiness levels, focusing on mapping institutional, regulatory, and socio-political contexts as well as metrics and input from relevant sources. The analysis shows structural inequality as the core vulnerability shaping AI’s impact in Latin America, alongside governance gaps, limited regional cooperation, and minimal public participation. The analysis identifies ten critical vulnerabilities—including the use of AI in surveillance, increase in inequality, increase in disinformation, AI-use in organized crime, and environmental exploitation—that, if unaddressed, may accelerate democratic erosion and technological dependency. Ethical principles are shown to be deeply interconnected and grounded in human rights, yet their implementation remains aspirational. This research underscores a call for action toward regional coordination, inclusive education strategies prioritizing gender policies and rural areas, and aligned industrial policies in the countries of the region. A Latin American context-specific, collective approach ensures that AI serves the public interest, strengthens sovereignty, and supports equitable development in Latin America.

Measurements on contact potentials of metals

2025-10-30T17:03:46Z

Measurements on contact potentials of metals Zisman, William A. Thesis: M.S., Massachusetts Institute of Technology, Department of Physics, 1928; Includes bibliographical references (leaves 59-60).

A department store fore the Hudson Bay Company Edmundton, Alberta, Canada

2025-08-07T03:07:29Z

A department store fore the Hudson Bay Company Edmundton, Alberta, Canada Thrift, Eric W. Thesis: M. Arch., Massachusetts Institute of Technology, Department of Architecture, 1938

The Community Retrofit Trust: Incentivizing Deep Energy Retrofits in Massachusetts' Triple Deckers

2025-07-30T03:06:43Z

The Community Retrofit Trust: Incentivizing Deep Energy Retrofits in Massachusetts' Triple Deckers Chuttani, Milan To meet its 2050 net-zero carbon emissions goals, Massachusetts must rapidly retrofit its aging stock of three-story multi-family homes, also known as “Triple Deckers.” However, high upfront capital costs, disparities between subsidized gas and electric energy rates, complex eligibility criteria, and misaligned incentives for landlords and renters constrain the widespread adoption of deep energy retrofits (DERs) in small multi-family homes. Drawing on energy democracy and reparative planning theory, this thesis reframes Triple Decker retrofits as a pathway to social and spatial transformation that empowers residents through cooperative participatory processes. This project proposes a practical framework for a “Community Retrofit Trust” which uses systems of distributed energy savings, community ownership of DER assets, and cooperative governance to ensure tenants, building owners, and neighbors in environmental justice communities share benefits from DERs while maintaining rental affordability. A proposed values-based decision-making process also helps community cooperatives adapt the Retrofit Trust’s framework to their unique social contexts. Descriptive case studies of two community solar initiatives illustrate how cooperative approaches that build trust, bundle projects and local expertise, and expand opportunities for participation can efficiently distribute energy benefits across a community while increasing investment and lowering costs. A feasibility analysis of a Community Retrofit Trust in Boston examines the strengths, challenges, and contradictions of incentivizing Triple Decker DERs through a cooperative approach.

Rebuilding Civic Infrastructure for Equitable Development: Intermediary Solutions for Transforming Resource-Extractive Economies in Rural Southwest Arkansas

2025-07-30T03:08:32Z

Rebuilding Civic Infrastructure for Equitable Development: Intermediary Solutions for Transforming Resource-Extractive Economies in Rural Southwest Arkansas Bradford, Mo Southwest Arkansas, a rural and mineral-rich region, is entering a new wave of resource-driven economic activity fueled by lithium extraction. While local leaders are pushing for rapid industry development to counter long-standing socioeconomic decline, this research asks a critical question: Can these pro-industry strategies truly deliver equitable and lasting public benefits, or will they repeat historical patterns of extraction that have sidelined local communities? This study critiques neoliberal development schemes and neoconservative, sectionalist ideologies that deprioritize equity-driven agendas and prioritize deregulation and private sector efficiency, arguing that such approaches often weaken institutional civic organizing and reduce responsiveness to public needs. As an alternative, it proposes civic infrastructure as a strategic solution, one that strengthens the networks of community institutions, local governments, and intermediary organizations essential for advancing equity in extractive economies. The research further explores the role of intermediary organizations in bridging institutional and capacity gaps in Southwest Arkansas. These organizations can support under-resourced communities by providing convening power, technical assistance, and financial resources. Through policy analysis, case studies, and field interviews, this work examines how civic infrastructure and intermediary support can work together to shift economic development toward more just and inclusive outcomes in resource-extractive economies.

When Girls Just Wanna Have Fun, How Do They Go? A Mixed Methods Study of Nighttime Leisure Travel in Boston

2025-07-30T03:08:31Z

When Girls Just Wanna Have Fun, How Do They Go? A Mixed Methods Study of Nighttime Leisure Travel in Boston Dy, Raelene Ina Bianchi Louise Mendez When we think of urban living and its depictions in popular culture, many shows and movies depict characters in leisure activities, such as meeting friends, going on dates or pursuing hobbies, often at night. Despite the prominence of the night as a key theme in depictions of urban leisure, transportation planners have rarely focused on nighttime leisure travel as an area of intensive study beyond the lens of safety. This thesis investigates the nighttime leisure travel patterns of residents and students in Greater Boston through statistical analysis and data sculpture with a focus on how these vary by gender. To create a baseline understanding of travel patterns, I focused on the Boston Metropolitan Area and used the most recent version of the Massachusetts Department of Transportation’s Household Travel Survey from 2011. I limited my analysis to a fixed set of leisure activities during a fixed nighttime period to understand associated travel behaviors. I also implemented a data sculpture method to investigate how a subset of MIT students made decisions around their travel modes. I found that women travelled differently from men, in that they spent more time walking and were more likely to be passengers in a car. In contrast, men were more likely to be behind the wheel and travel further. Both men and women showed a preference for walking over all other modes when leaving an activity. Together, these findings indicate that nighttime leisure travel is not a simple extension of daytime patterns. To better design nighttime transportation that accommodates gender differences, planners need to respond to the special qualities of the city after dark.

An Economic Reevaluation of Navi Mumbai and the Indian Satellite City

2025-07-30T03:08:06Z

An Economic Reevaluation of Navi Mumbai and the Indian Satellite City Thomas, Archer Navi Mumbai, a municipality in the Mumbai Metropolitan Region, is the largest satellite city project in India. Nevertheless, it has been seen within the planning discipline as underperforming its original ambitions. Drawing upon the goals enumerated in the city’s original development plan, this thesis proposes a series of quantitative metrics corresponding to said goals and then utilizes data drawn from surveys, censuses, official reports, financial statements, and remote sensing datasets to propose an updated evaluation of Navi Mumbai’s performance over the past half-century. This thesis argues that, contrary to earlier perceptions, Navi Mumbai has largely succeeded in fulfilling its ambitions, and that this can be attributed to shifting suburbanization patterns in India, the prescient decision to prioritize office-based service industries over manufacturing, and the ongoing reconfiguration of transportation and logistics networks within the Mumbai region. Reflecting on the history of urban and economic planning in India, this thesis then suggests the implications of Navi Mumbai’s apparent success for satellite city projects in India and across the Global South, focusing on questions of financing and governance.

The Private Sector in Public Transit: Evaluating Early US Experience in P3s

2025-07-30T03:08:00Z

The Private Sector in Public Transit: Evaluating Early US Experience in P3s Farabow, Web Problems in US public transit are well documented: transit providers struggle to develop new infrastructure, face high project costs and long implementation timelines, pursue designs that prioritize ease of delivery over value to the public, and struggle to sustain their operations. In response to these challenges, Public-Private Partnerships (“P3s” or “PPPs”) have been promoted as a way to deliver more infrastructure on faster timelines at lower cost and higher quality. As P3s have been increasingly considered for major transit projects, this thesis investigates their ability to deliver on promotional claims, and their ability to address key challenges in American public transportation. First, the thesis contextualizes contemporary P3s within a history of private sector involvement in US public transit. In addition to detailing how existing infrastructure came to be, this history intends to sharpen an understanding of contemporary P3s by considering how forms of private involvement have changed over time. It proceeds to develop detailed case studies for three major infrastructure projects that have proceeded under a P3 model: RTD’s Eagle P3 in Denver, Maryland MTA’s Purple Line in Southern Maryland, and Los Angeles Metro’s Sepulveda Transit Corridor Project. Combining historic research and contemporary case study analysis, the thesis seeks to understand the circumstances under which contemporary P3s have emerged, and to draw lessons from early experience. American transit providers have considered P3s for a variety of reasons, but have been primarily motivated by limited administrative and financial capacity, and by a perceived ability of private firms to deliver projects on faster timelines. Early P3s have facilitated provision, enabling projects that otherwise may not have been built, and have demonstrated their potential to ensure sustainable operations over long-term contract periods. But P3s have achieved mixed results in accelerating project timelines, and their ability to reduce lifecycle project costs remains unclear. While P3s seek to increase private involvement in transit provision, the model places a higher burden on upfront public planning compared to conventional delivery strategies. Public infrastructure owners can design P3s to leverage private sector resources and capacity, but the model comes with tradeoffs that should be carefully weighed against likely benefits. Ultimately, P3s can address a number of acute challenges in American public transit, but are unlikely to provide a workaround to fundamental political and financial challenges that limit transit development more broadly.

Reparative Preservation through Immersive 3D Documentation: Cultural Memory, Spatial Justice, and Gullah Geechee Futures on Daufuskie Island

2025-07-30T03:08:18Z

Reparative Preservation through Immersive 3D Documentation: Cultural Memory, Spatial Justice, and Gullah Geechee Futures on Daufuskie Island Jones, Wil This thesis advances a reparative framework for cultural preservation by combining immersive documentation with co-authored digital storytelling to support Black spatial memory and community sovereignty. Grounded in fieldwork on Daufuskie Island, South Carolina—a historic Gullah Geechee community confronting dispossession and cultural enclosure—the project co-creates Daufuskie3D (https://daufuskie3d.org/), an interactive website that presents annotated 3D scans, oral histories, ambient videos, and symbolic interface design rooted in Gullah epistemologies. It is guided by two research questions: How can immersive documentation support reparative preservation for communities at risk of spatial erasure? And what frameworks—technical, ethical, and political—ensure digital practices reflect Black cultural values, descendant authorship, and community control? Drawing from Black geographies, wake work, vernacular cartography, and speculative design, the thesis introduces a conceptual distinction between visualization and analysis tools to examine how different modes of spatial capture shape visibility and authority. The project finds that immersive tools, when grounded in ethical design and descendant authorship, can function not simply as representational media but as reparative infrastructure—supporting visibility, stewardship, and spatial return in communities confronting erasure. The Daufuskie3D website serves as both platform and method. Its spatial interface draws on Gullah visual language, including Underground Railroad quilt codes and spiritual symbolism, while its non-linear navigation resists conventional heritage taxonomies. Rather than flattening culture into content, the site embraces ambiguity, withheld spatial detail, and narrative restraint as ethical design principles. Developed in partnership with Ms. Sallie Ann Robinson, a sixth-generation Gullah cultural steward, the project repositions preservation as participatory, situated, and future-facing. It offers Daufuskie3D as both a working prototype and a methodological contribution toward reparative immersive practice—centering digital preservation as a strategy of memory, sovereignty, and cultural regeneration within the Black diaspora. Keywords: Immersive Documentation, 3D Scanning / LiDar / Photogrammetry, Cultural Preservation, Gullah Geechee, Daufuskie Island, Reparative Preservation, Black Geographies, Digital Heritage, Speculative Design, Counter Cartography, Counterpublic, Spatial Justice, Oral History, Afrofuturism, Digital Public, Digital/ Web Archive, Cultural Stewardship, Ethical Design, Participatory Design, Underground Rail Road, Return

Toward a political economy of the power sector: green capitalism, eco-socialism, and co-operative power in decarbonized climate policy

2025-07-30T03:08:17Z

Toward a political economy of the power sector: green capitalism, eco-socialism, and co-operative power in decarbonized climate policy Jin, Brooke The political economy of the power sector has been characterized by a putative transition from fossil capitalism to green capitalism in an attempt to mitigate the worst effects of anthropogenic climate change on nature and society. In recent years the rise of green industrial policy, such as the passage of the Inflation Reduction Act of 2022, has sought to stimulate domestic economic development of green-technology projects and implement protectionist trade policies with the normative intent of protecting the geopolitical hegemony of U.S. industry. Yet the objectives of such industrial policies, which function less to reduce carbon emissions than to increase resource- and carbon-intensive consumption patterns, run antithetical to putative state objectives of the decarbonization of the power grid and industrial operations, and in fact green capitalism does not exist without the continued influence of fossil capital. In this thesis I look to Marxist theories of the state, capital, labor, and nature to illustrate the crises of capitalism that have been occurring due to the exponential increase in power demand by data centers and large technology companies. In reshaping the governance of power markets, electricity generation, and transmission and distribution infrastructure through this increase in demand, called load growth, I show the illusion of sustainability under a green-capitalist political economy that purports to advance decarbonization goals, yet which in actuality facilitates conditions for the centralization and monopolization of private capital, as well as the continued destruction of nature and exploitation of workers. However, this crisis of load growth and the issue of governance that it raises open a window for experimentation into new state systems, socialized modes of production, and labor and environmental solidarity in the creation of a new climate policy: one that prioritizes equity, welfare, ecological preservation, and a truly decarbonized society. I propose a socialization of the power sector to increase community autonomy over their energy needs and to begin to dismantle the technocratic influence of fossil-fuel and large technology companies over electricity generation and access.

Decarbonization at the Neighborhood Scale: Challenges, Learnings and Opportunities in an Emerging Model

2025-07-30T03:08:14Z

Decarbonization at the Neighborhood Scale: Challenges, Learnings and Opportunities in an Emerging Model Cina-Sklar, Zoë Decarbonizing residential buildings in the United States is critical for reaching climate goals and has significant public health and energy justice benefits if accessible to all. To date, building electrification has been individual-level and market-driven, with some financial incentives at the state and federal level. This model is generally inaccessible to low-income homeowners and renters who are unable to afford the upfront costs of building improvements and new electric appliances. Neighborhood-scale building decarbonization has been proposed as an alternative in which new developments would be built allelectric or existing buildings would be electrified at the block or neighborhood scale. In the latter use case, neighborhood-scale building decarbonization is often tied explicitly to decommissioning gas lines. Specifically, proponents posit that these projects could be funded through avoided gas line repair and replacement costs. Investor-owned utilities are seen by some experts in the space as key to the success of neighborhood-scale building decarbonization because of their financing capabilities and existing role in providing heating and/or electric service to customers. In recent years, a number of state policymakers have passed legislation approving utility-funded neighborhood-scale building decarbonization and state utility commissions have promulgated regulations approving cost recovery for these projects. Utilizing desk research and informant interviews, this paper analyzes what has enabled and hindered existing utility-funded neighborhood-scale building decarbonization pilot projects in California, Massachusetts, and New York. I diagnose strong and specific climate goals, the passage of enabling legislation, an engaged state utility commission, and strong advocacy ecosystems as key factors for initiating neighborhood-scale pilot projects. Through informant interviews, I identify costs, financing, community buy-in and planning as central determinants for the success of pilot projects and the future of the model. I close by offering recommendations and outstanding research areas for planners interested in pursuing future neighborhood-scale building decarbonization projects.

Can planning, a tool for colonization, be decolonized? MIT’s funding at the expense of Indigenous Peoples through the Morrill Act

2025-07-30T03:08:11Z

Can planning, a tool for colonization, be decolonized? MIT’s funding at the expense of Indigenous Peoples through the Morrill Act Barrera Gonzalez, Devora This thesis questions whether planning and the activities the profession’s umbrella covers are beneficial or harmful. The project analyzes the role of planning in the colonization of Turtle Island by materializing and legitimizing the seizure of Indigenous Land through planning practices like urbanization, enclosure, the creation of Indian reservations, and tools like cartography, lawfare, and landscape architecture and design. I make an argument in this thesis about how there is no such thing as sustainable or beneficial urbanization because urbanization equals death, that planning is inherently harmful because it was born as a tool of colonization, and that there is no way to decolonize the profession, given that the profession upholds the current land system, I make an argument that the only solution to reverse and undo the harm done by planning and urbanization is to give Land Back to Indigenous Peoples. For this, building my argument, I will walk you through the narrative built to dispossess land, the concept of imaginary geography, how planning enabled and legitimized diferent ways for land dispossession, and finally, the modification of land (urbanization). A chapter is dedicated to looking closer at one piece of lawfare in particular: the morrill act, revealing the history of the foundation of MIT at the expense of Indigenous Peoples, the role that universities play in the maintenance and strengthening of the systems of oppression in place. Using that information to answer the calls for decolonization of the profession, this thesis makes an argument and underscores that, given that planning is born as a tool for colonization, the profession can’t be decolonized and demands Land Back as the only solution. The thesis presents the information on two parcels that belong to the Confederate Tribes of Coos, Lower Umpqua, and Siuslaw Indians, located in the state of Oregon, that were seized and, through the morrill act, resold with the proceeds benefitting MIT, calling for the restitution of the parcels and giving Land Back.

Equity and Climate Resilience in Bogotá's Public Space Policy: A Critical Policy Review

2025-07-30T03:08:09Z

Equity and Climate Resilience in Bogotá's Public Space Policy: A Critical Policy Review Duque Añez, Silvia In Bogotá, where long-standing spatial and social inequalities intersect with growing climate risks, public space policy holds the potential to either reinforce exclusion or promote resilience and justice. Decisions about parks, plazas, and green corridors are not neutral; they reflect political priorities, embedded values, and power dynamics. This thesis asks: To what extent, and in what ways, does Bogotá’s public space policy framework incorporate criteria of equity and climate resilience? Through this question, the research examines how policies define and implement these concepts, what types of interventions they promote, and what limitations may emerge. While prior research has emphasized the importance of inclusive and adaptive public spaces, there is limited analysis of how these principles are embedded in policy instruments in Latin American cities. Addressing this gap, this thesis develops an analytical framework informed by literature on urban environmental justice and climate adaptation. This framework serves as both an evaluative tool and a resource for policymakers seeking to move beyond vague commitments and toward actionable pathways for equity and climate resilience. The framework is used to analyze two key policy instruments: the District Public Space Policy (Política Pública Distrital de Espacio Público 2019-2038) and the Master Plan (Plan de Ordenamiento Territorial: Bogotá Reverdece 2022-2035). The evaluation reveals that both perform well, reflecting a genuine political effort to prioritize these issues. However, the findings also show that narrow or inconsistent interpretations of equity and climate resilience can lead to unintended consequences, and that significant implementation challenges remain. By grounding its analysis in a Global South context, this thesis contributes to international conversations on urban sustainability, offering both a critical lens and a practical tool. Ultimately, this research advocates for a shift in public space governance, one that treats equity and resilience not as aspirational ideals, but as measurable, structural commitments to a more just and climate-ready urban future.

Interest Group Politics in U.S. "Social Housing" Experiments

2025-07-30T03:08:04Z

Interest Group Politics in U.S. "Social Housing" Experiments Davidson, Zak The rising cost of housing has renewed interest in public sector-led models of mixed-income housing production. Advocates, local governments, and state lawmakers are exploring strategies to involve the public sector more directly in the residential development process by capitalizing revolving loan funds, leveraging public land, and creating new public authorities. While a universal definition for “social housing” remains elusive, most policymakers and supporters agree that social housing is permanently affordable for economically and racially diverse households and includes elements of resident self-governance. This research analyzes how key interest groups—including affordable housing developers, tenant advocates, labor unions, market-rate developers, and pro-housing coalitions—shape and respond to emerging social housing initiatives. Drawing on interviews and case studies of Seattle, Montgomery County (MD), California, New York, Atlanta, and Chattanooga between 2019 and 2025, this thesis examines how political context, institutional constraints, and coalition dynamics influence how social housing proposals are framed, negotiated, and either supported or resisted by key stakeholders. Four key themes emerge from these case studies. First, existing affordable housing developers often interpret new mixed-income, permanently affordable proposals as competition, particularly amidst resource scarcity and institutional constraints. This constitutes a substantial roadblock for the social housing movement. Second, proponents’ theory of change, initiative branding, and their ability to participate in multi-issue bargaining notably impact how affordable housing interest groups respond. Third, private sector actors’ support appears dependent on the public sector’s willingness to partner and how proponents describe the problem they are solving. Fourth, while collaborations around social housing may trigger fault lines between YIMBYs and tenant justice groups regarding revenue neutrality and the value of new market-rate supply, social housing represents an opportunity for bridge-building and collaboration across the housing movements. As interest in these models grows, this research offers practical insights for advocates and policymakers seeking to design locally tailored, politically viable approaches to public-led, mixed-income housing production.

Shifting Spaces: Housing and Urban Change in Kabul

2025-07-30T03:07:56Z

Shifting Spaces: Housing and Urban Change in Kabul Ghanizada, Bibi Khadija This thesis explores the evolution of Kabul’s housing landscape with a focus on the emergence of Shahraks (planned townships) after 2001. Drawing on historical research, four case studies (Aria City, Khwaja Rawash Township, Khushal Khan Mena Blocks, and Omid-e-Sabz Township), and interviews with residents and experts, it analyzes how Shahraks have reshaped urban development in a rapidly growing city. Inspired by Soviet-era Mikrorayons, Shahraks introduced formal infrastructure, legal recognition, modern amenities, and opportunities for new economic activity. They helped expand Kabul’s formal housing stock and created pockets of urban community identity. However, the research finds that Shahraks also deepen spatial and socioeconomic inequalities. Largely built through private investment and targeting wealthier residents and civil servants, they remain inaccessible to the majority of Kabul’s population. Many Shahraks were developed on contested or illegally grabbed land, raising concerns about tenure security and governance. Despite improved infrastructure compared to informal settlements, Shahraks often suffer from poor climate responsiveness, environmental degradation, limited green spaces, and energy-intensive designs. Their weak integration with Kabul’s broader urban fabric further exacerbates issues of spatial fragmentation. Looking ahead, the thesis argues that Kabul must learn from both the achievements and shortcomings of Shahraks as it plans future projects like Kabul New City. Their model is not inherently unsustainable or inaccessible, but without deliberate reforms, Kabul risks reproducing a cycle where contemporary urban development becomes synonymous with exclusion, fragmentation, and missed opportunity. Key recommendations include prioritizing affordable and expandable housing models, enforcing transparent land governance, promoting climate-adaptive design, strengthening connections between housing and employment centers, and carefully structuring public-private partnerships to align private investment with public goals. As Kabul embarks on projects like Kabul New City, it must learn from the partial successes and profound shortcomings of past developments. The challenge is not simply to build new cities, but to build a more inclusive, adaptable, and sustainable urban future for all Kabulis.

When Public Space Goes Digital: Rethinking Urban Planning with Insights from Letra Ese

2025-07-30T03:07:46Z

When Public Space Goes Digital: Rethinking Urban Planning with Insights from Letra Ese Chiappero, Sofia Belen Digital public spaces have become vital for organizing, belonging, and community-building, particularly for marginalized groups such as the LGBTQ+ community, who are increasingly excluded from both physical and online public spaces. Yet, the design of these digital spaces is largely shaped by profit-driven interests rather than the needs of the communities that rely on them. This thesis addresses this gap by asking: What if we treated digital spaces with the same care and intention we demand from our physical public spaces? To explore this question, the thesis brings together frameworks from urban planning, LGBTQ+ advocacy, and digital design. It proposes a reframing of “urban planning” to include “digital urban planning,” grounded in principles of rights, care, safety, and collective memory. Through a feminist urbanist lens and systems thinking, the work challenges the separation between physical and digital cities. Methodologically, the project moves beyond traditional research approaches, incorporating Conversational Design and the Relational User Framework to co-create knowledge with activists. The resulting contributions include both a prototype and a roadmap for a digital public space that supports and amplifies LGBTQ+ advocacy; not as a technical fix, but as a speculative and participatory framework for reimagining digital public infrastructure. This research is grounded in a case study of Letra Ese, an activist-led LGBTQ+ organization in Mexico. The case illustrates how such groups navigate systemic neglect while leveraging technology to document violence and sustain community. Ultimately, the thesis offers a starting point for rethinking the design of digital public spaces and argues for the inclusion of digital environments within the domain of urban planning, recognizing that for many, especially marginalized communities, much of life is already lived online.

Flooding as Remembering: A Trickster’s Guide to Fugitive Ecology, Revolutionary Recall, and Speculative Worldbuilding Beyond the Plantationocene

2025-07-30T03:07:04Z

Flooding as Remembering: A Trickster’s Guide to Fugitive Ecology, Revolutionary Recall, and Speculative Worldbuilding Beyond the Plantationocene Delaney, Simone Hope Since the early days of conquest, Black, Indigenous, and Afro-Indigenous peoples of the Lower Mississippi River Delta have survived recurrent processes of settler colonial un-worlding by re-worlding sovereign lifeways rooted in reciprocal relationships to other colonized peoples and the environment. Un-worlding occurred to Black and Indigenous peoples through dispossession of land, capture into enslavement, and genocide. This process was intertwined with the un-worlding of the landscape’s agency, which was captured and enclosed into property by arresting waterways’ movements through constrictive engineering using coercive labor. In the Bas de Fleuve swamps (today known as the Louisiana Central Wetlands), self-emancipated fugitives that had escaped enslavement formed autonomous inner worlds in the unenclosed territories between the Mississippi River and Lake Borgne. Known as Maroons, they were led by a leader named Juan San Malò and forged interdependent networks that extended to Indigenous settlements, enslaved Africans on plantations, and free Blacks in New Orleans. By living outside eurosettler logics of property and re-establishing reciprocity with the more-than-human web of life, they demonstrated that the liberation of captive people is bound to the liberation of captive landscapes. Their re-worlding was also reminiscent of the pan-African trickster figure: anarchistic heroes that overturn the dominant oppressive world order for more liberatory realities Today, the destruction of wetlands across Southeast Louisiana means that descendants are facing an un-worlding of the sovereign livelihoods their ancestors re-established generations before. This is due to anthropogenically induced land loss, flooding, storm surge, and saltwater intrusion influenced by extractivist industries. Through revolutionary recall, reclaiming the logics of re-worlding established by Juan San Malò’s band of Maroons offers pathways to resist the intensifying threats of climate change that represent afterlives of slavery. Common Ground Relief is one collective that has drawn from Maroon legacies to lead bottom-up disaster response, mutual aid initiatives, and citizen-led wetland restoration. Drawing from creative land reclamation projects led by Utē Petit, Monique Verdin, the Nanih Bvlbancha Builders, and the Descendants Project, a constellation of small, site-specific projects are also presented to demonstrate how revolutionary recall can become a form of speculation for broader land-based liberation in the Lower Mississippi Delta.

Accelerating Mass Timber Adoption in Greater Boston, Massachusetts: A Practical Study for Local Real Estate Developers

2025-07-30T03:07:35Z

Accelerating Mass Timber Adoption in Greater Boston, Massachusetts: A Practical Study for Local Real Estate Developers Cerny, Faith W. Today’s real estate development strategy must incorporate decarbonization to mitigate the built environment’s detrimental impact on climate change. Beyond required climate action, developments are increasingly seen as responsible for improving occupant health and wellbeing. Furthermore, industry stakeholders are tasked with efficiently delivering sustainable, high quality, and affordable housing in dense, urban areas to meet a growing demand. As the stakes intensify and demands of real estate development increase, projects face multiple barriers to implementation. This thesis explores mass timber construction as a viable solution to modern development challenges. While research content derives from multiple geographies within North America, a particular focus on the relevance and utility for Greater Boston, MA, USA is maintained. The thesis comprises five chapters. Following an introduction, the second chapter provides an overview of mass timber as an evolving building technology with an emphasis on how and why it is gaining momentum as a viable and preferred alternative to traditional building materials. The section conversely discusses commonly cited drawbacks delaying industry acceptance. The third chapter explores mass timber adoption at multiple scales, including studies of innovative projects proving achievement of development objectives despite challenges. Guided by insights from interviews, this chapter discusses stakeholders’ current understanding of the material and motivations for its use, perceived feasibility constraints as well as believed opportunities for its incorporation and proliferation, with a focus on Greater Boston. The fourth chapter considers methods to accelerate the rate of mass timber adoption, including facilitation of local development strategy. The section builds on research and interview findings to establish key considerations when evaluating a mass timber project and to propose an analytical framework for real estate developers to holistically assess the value of incorporating the material in their projects. The concluding chapter speculates the local arc of adoption and subsequent impacts of widespread mass timber project implementation for the city and region.

Understanding Micromobility in New York City: An Examination of Vehicle Type Use and User Behavior in Protected Bicycle Facilities

2025-07-30T03:06:41Z

Understanding Micromobility in New York City: An Examination of Vehicle Type Use and User Behavior in Protected Bicycle Facilities Boeri, Jake A shift towards the use of micromobility vehicles (MMVs), specifically motorized two-wheeled vehicles in urban mobility networks, has gained significant attention over the past decade. Many have commented on a perceived increase in MMV use in New York City (NYC) in particular, a trend that appears to have accelerated in the wake of the COVID-19 pandemic and in response to the expansion of high-quality bicycle facilities across the city. However, the extent to which different types of MMVs are used and related rider behavior is poorly understood, forcing policymakers, planners, elected officials, and community members to develop policies and infrastructure with inadequate information. Through direct observation of 9,629 vehicles across five locations, this thesis provides a degree of ground truth and an initial understanding of the prevalence of different MMV types used in protected bicycle facilities in NYC and related user behavior, including commercial application of these vehicles, helmet use, and passenger presence. The findings of this study point to a surprisingly high use rate of motorized MMVs in protected bicycle facilities in NYC, with motorized vehicles comprising nearly three-quarters (73.96%) of all vehicles observed. E-bikes were the largest class of vehicles observed (63.85%), followed by conventional, non-motorized bicycles (25.76%), e-scooters (6.69%), and mopeds (1.96%). Commercial-use vehicles made up nearly one-quarter (23.20%) of observations. A very small proportion of observations were cargo vehicles (2.89%), indicating their limited use for both personal and commercial purposes. Users were significantly more likely to wear a helmet when using a non-motorized vehicle than a motorized one, with helmet use varying substantially across vehicle classes. Modal split of MMV types, commercial use, and cargo vehicle use varied by both location and time of day, pointing to uneven distribution across the mobility network. There were substantial differences between the manual count from this study and automated bicycle counts generated by the New York City Department of Transportation over the same period, indicating a systemic undercounting of MMV use by the automated count system. In response to these findings, a series of recommendations are provided for how NYC and other cities with both developed and developing MMV networks can promote and guide safe, equitable, and sustainable mode shift as micromobility use expands. These proposals include policy and spatial planning improvements that should be part of a response to widespread MMV adoption, and the ongoing transformation of how protected bicycle facilities are used.

Housing in European Metropolises: supply dynamics and planning frameworks in large Urban Areas of the EU

2025-07-30T03:07:33Z

Housing in European Metropolises: supply dynamics and planning frameworks in large Urban Areas of the EU Berra Sandin, Mikel Europe’s housing affordability crisis presents significant territorial challenges, particularly as housing demand increasingly spills over from inner cities to surrounding municipalities at the metropolitan scale. This study addresses key policy questions regarding the coordination of housing supply and planning instruments in large urban areas of the European Union. Focusing on 23 large Functional Urban Areas (FUAs), the research follows a three part approach: a quantitative analysis of municipal-level housing production and demographic growth between 2011 and 2021 based on Census data; an analysis of the effects of housing supply on housing prices; and an AI-powered quantitative examination of urban plans, at municipal, metropolitan, and regional scales to observe whether they establish housing supply goals. This methodology generates evidence on the spatial dynamics of housing development, by creating an EU-wide database at municipal granularity, while providing a novel focus and analytical approach to institutional urban plans as drivers of housing supply. Findings prove mixed alignments between housing supply and demographic growth, with Southern and coastal urban areas falling short on housing supply. In most cases, there is a pronounced metropolitan effect, where peripheral municipalities experience larger housing and population growth. When analyzing the plans, more frequent planning relates to larger housing provision. In addition, the research highlights that housing goals are usually determined at local plans, showing a mismatch between planning efforts and housing dynamics, which tend to be metropolitan or regional. Therefore, the research deepens the understanding of European housing provision and the planning of urban territories, highlighting the need for stronger housing policy mechanisms at the metropolitan level.

Miles Matter: Demographics, Distance, and Decision-Making

2025-07-30T03:07:52Z

Miles Matter: Demographics, Distance, and Decision-Making El-Sisi, Kareem H. In this thesis, I investigate which variables have the strongest influence on an individual's travel mode choice depending on the purpose and level of urgency (leisure, essential, emergency) of the trip. I analyze the relationship between spatiotemporal costs conditioned by demographic segmentation using data on population mobility patterns in auto-centric Los Angeles and multimodal New York City. Through a synergistic three-pronged methodology consisting of spatial (time and distance analysis complemented by a spatial interaction model), statistical (multinomial logistic regression model), and machine learning-based (graph neural networks and extreme gradient boosting) analysis, I explore the multifaceted nature of decision-making processes in different urban environments. The hidden patterns revealed by artificial intelligence show that distance is the key determinant of mode choice, depending on the urban form of the city and its adaptation to multimodality.

Producing a Black Oeuvre: Narratives of Black Grassroots Cultural Organizing in Boston

2025-07-30T03:07:24Z

Producing a Black Oeuvre: Narratives of Black Grassroots Cultural Organizing in Boston Hunsen, Alula Amidst a bevy of nonprofits and governmental actors that support and facilitate cultural and aesthetic production in the City of Boston, a vanguard of Black artists and cultural organizers are developing structures and organizations to help local members of Boston’s Black communities steer their own cultural production. This thesis develops an understanding of actions being taken by these organizers and organizations through interviews, and builds a set of participatory action research frameworks by partnering with these organizations (specifically: Thrill, Black Cotton Club, and 5Thou), to conduct further research as to how Black Bostonians can continue to self-determine in the realms of arts and culture. Drawing from a lineage most directly traceable to the Black Arts Movement of the late 1960s, and to hip-hop cultural production in ensuing decades, these organizers are furthering Black-led, community-controlled arts, and fostering community-building. Borrowing theorist Henri Lefebvre’s conception and declaration of a right to creative expression and participation, characterized as oeuvre and as a critical aspect of a “right to the city,” I hypothesized that these actions toward cultural self-determination could be seen as the establishment of a Black oeuvre. This assertion was expanded upon by research partners, to include a broader array of strategies and conceptual frameworks for producing Black place, community, and culture in Boston.

Envisioning Regional Futures in Southeast Los Angeles: Understanding Barriers to Implementing Transit-Oriented Communities along the Forthcoming Southeast Gateway Line

2025-07-30T03:07:49Z

Envisioning Regional Futures in Southeast Los Angeles: Understanding Barriers to Implementing Transit-Oriented Communities along the Forthcoming Southeast Gateway Line Martinez, Alejandra A. The first 14.5-mile phase of the Southeast Gateway Line (SEGL), a planned light rail project through Southeast Los Angeles and the Gateway Cities region, is expected to be completed by 2035. The rail line aims to improve transit access while being complemented by a regional planning framework and station area planning that seeks to promote transit-oriented communities around station areas and drive equitable community development along the corridor. However, it remains uncertain whether the frameworks and governing bodies responsible for implementing the rail project, including the Los Angeles County Metropolitan Transportation Authority (LA Metro), the Gateway Cities Council of Governments (GCCOG), and cities along the corridor, will effectively align the transit investment with these land use and development goals. Given these uncertainties, this thesis focuses on the Southeast Los Angeles (SELA) subregion, where a history of structural challenges underscores both the urgency and the complexity of realizing visions for transit-oriented communities tied to the forthcoming rail investment. Drawing on semi-structured interviews with LA Metro and GCCOG staff, along with officials and staff from cities hosting future stations, this research explores the emerging political, economic, and structural barriers to implementing transit-oriented land use around two future SEGL stations: Florence/Salt Lake Station in Huntington Park and Firestone Station in South Gate, both stations of which have multi-jurisdictional spheres of influence. This thesis also proposes a collaborative framework that encourages SELA stakeholders to engage in incremental, low-stakes planning and establish accountability mechanisms before the rail arrives, laying the foundation for sustained stewardship over the vision of transit-oriented communities and broader equitable community development goals throughout the rail's lifespan.

The Path Forward: Gentrification Management Strategies in Rural Trail-Based Outdoor Recreation Economies

2025-07-30T03:07:09Z

The Path Forward: Gentrification Management Strategies in Rural Trail-Based Outdoor Recreation Economies Smith, Mistaya Rural communities in the United States face economic challenges due to a combination of factors including the decline of the extractive sector, the departure of manufacturing, the agglomeration of farmland, and the regionalization of key public services. To some policymakers, this economic decline, in combination with the nation’s rural-urban political stratification, serves as reason to further abandon rurality and promote migration to urban areas. These policies overlook the interdependence between rural and urban ecosystems and ignore rural America’s unique assets. In capitalizing on rurality’s existing natural beauty and land access, the trail-based outdoor recreation economy functions as a form of asset-based economic development in rural communities. In connecting recreators to the land, serving as the setting of social connection, and creating place-based connections across time, trails further benefit rural communities through the construction of place attachment. Investment in trails as a form of economic development, however, commodifies nature so as to attract external interest in rural places. Externally-driven population increases and wealth influxes in rural communities can cause physical gentrification in the form of rising property values and resident displacement. This gentrification process also contains a cultural component as the commodification of nature and the demographic shift in rural places erodes place attachment between longtime residents and the land through the displacement of local place-based knowledge, changes in traditional land access, and disruption to recreational use patterns. Research suggests that those with deeper place attachments exhibit greater civic engagement, a deeper sense of community and belonging, and more care for their community and environment. Therefore, cultural gentrification can also lead to a decline in community care and a risk to rural vitality. This thesis examines five rural Northeastern towns with trail-based outdoor recreation economies to discern how each community approaches the risks of physical and cultural gentrification.

Wildfire Risk Management for Informal Settlements in Chile

2025-07-30T03:07:29Z

Wildfire Risk Management for Informal Settlements in Chile Sakai, Yuri This thesis explores the critical intersection of wildfire risk and informal settlement development in Chile, focusing on the municipality of Viña del Mar. This city experienced the deadliest wildfires in the nation’s history in 2024 and holds the nation’s highest concentration of informal settlements. Despite this double vulnerability, the city has inadequately integrated wildfire resilience into its disaster risk management (DRM) framework, creating an urgent need for policy reform. Through combined statistical and geospatial analyses, the author documents informal settlements’ expansion trajectories, especially between 2011 and 2024, and systematically assesses their wildfire exposure. Utilizing unregularized community datasets, wildfire risk classifications, and municipal planning documents, the analyses revealed that the growth of informal settlements outpaces regularization interventions. They also unveiled that all of the informal communities in the city, including their wildland-urban interface zones, face significant fire risk. These findings further led the research to evaluate the current Chilean wildfire governance under Law 21.364 (enacted 2021) to provide comprehensive DRM across national, regional, and municipal administrative levels. Additionally, the study examines the disaster response mechanisms for the 2024 Chile Wildfires. This policy and evidence-based analyses identify inherent still reactive approaches to disasters even 4 years after the policy transition, and reveal a systematic marginalization of informal settlements. Based on these findings, the research culminates in phase-specific actionable policy recommendations addressing the compound vulnerabilities of informal communities through: 1) enhanced shelter capacity estimation methodologies; 2) formalized private sector involvement; 3) integrated tsunami-wildfire warning systems; 4) periodic intergovernmental learning opportunities; and 5) technical support in reconstruction. Given the 2024 tragedy and Chile’s transition toward comprehensive DRM, these interventions are particularly crucial to accelerate its transition and establishment.

Still Working: Re-examining America’s Urban Working Waterfronts

2025-07-30T03:07:18Z

Still Working: Re-examining America’s Urban Working Waterfronts Zhang, Mabelle While American urban waterfronts once served as critical sites of production, they are now disappearing, reflecting larger de-industrialization trends. This thesis argues for a critical re-examination of the continued and evolving role that waterfronts play as sites of work. It expands the definition of urban working waterfronts to include sites of industry, production, and economic activity, thereby aligning with these sites’ historic and ongoing uses. This thesis examines four working waterfronts in the Northeastern United States, a region with over 400 years of urban development driven by and around its waterfronts. This thesis examines this through four case studies: Central Waterfront in Portland, ME; Waterfront District in New Bedford, MA; Waterfront at Port Morris, NY; and Waterfront at Sunset Park, NY. Through analyzing these cases, this thesis proposes a typology of working waterfronts :the Traditional Working Waterfront, the Industrial Working Waterfront, and the Hybrid Working Waterfront—based on key differences in uses, forms, and governance. This thesis argues that the central issue is not merely protecting working waterfronts, but understanding how they are adapting to new realities. State and community-driven protections through zoning help protect existing working waterfronts, however; these sites are not stagnant relics of historic working waterfronts—rather, they are ever-evolving in response to new economic realities through incorporating new industries, technologies, and public access into their sites.

Pedestrian Accessibility and Individual’s Subjective Happiness

2025-08-25T18:54:42Z

Pedestrian Accessibility and Individual’s Subjective Happiness Shikida, Aika Cities in many countries are taking steps to use happiness as a formal policy measure of well-being, in addition to more commonly used economic indicators such as Gross Domestic Product. Economists and public policy and public health scholars have researched the factors that are associated with happiness, linking higher self-reported happiness outcomes with financial status, gender, social interactions, personal health, and sense of security. However, the link between happiness and the built environment around one’s home or workplace has been understudied and remains poorly understood. While location quality — particularly pedestrian accessibility to commercial, recreational, institutional, educational, and transportation facilities — is known to affect home location values, how the same set of location attributes that affect housing prices may have a relationship with happiness remains unclear. In theory, more convenient home locations offer individuals the capacity for independent living (e.g., walking access to destinations), social interactions (e.g., chance encounters with community members), and a sense of belonging (e.g., through self-sufficient neighborhood amenities) — qualities that should also contribute to happiness. This thesis reports on an exploratory analysis of location quality and self-reported happiness in the United States and Japan. Using a customized pedestrian accessibility metric, this thesis examines how access to daily destinations is related to individuals’ subjective happiness, controlling for socio-demographic variables. In the U.S. data, we found that people living in areas with higher pedestrian accessibility to destinations were not necessarily more likely to report being happier, on average. In fact, there was a small tendency for individuals in these areas to report slightly lower happiness levels, on average, after accounting for other influences such as age, income, and marital status. Note that the relationship between pedestrian accessibility and happiness may be more complex than expected and may involve other factors (e.g., presence or absence of greenery). We conducted an additional analysis by dividing the Census tracts into two groups based on population density. In areas with lower population density, the relationship between pedestrian accessibility and happiness remained negative and statistically significant and showed the same strength as the overall analysis. For Nagasaki, Japan, there was not a statistically significant relationship between happiness and pedestrian accessibility, but this might be due to a problem in the street network data, so further investigation is required. In addition, a qualitative analysis of Nagasaki reveals that residents report that problems with the walking environment (e.g., narrow sidewalks, slopes and stairs, darkness at night, road surface differences, distance to facilities) influence their travel behavior and happiness. Nevertheless, although the results of this thesis have limitations, as described above, promoting pedestrian accessibility should remain an important consideration for policy makers when setting public policy goals, since pedestrian accessibility could, for instance, lead to improved physical and mental health, as well as other benefits. For both the U.S. and Japan, future work is necessary to understand the complex experiences of individuals that include spatial, psychological, and environmental factors related to the built walking environment.

How Listeria monocytogenes crosses host cell barriers

2025-07-30T03:07:23Z

How Listeria monocytogenes crosses host cell barriers Hanna, Ruth Listeria monocytogenes is a bacterial pathogen that causes listeriosis, a severe food-borne illness that can cause severe complications and mortality in immunocompromised or pregnant people. Listeria is able to cross several host barriers to cause severe disease, including the intestinal barrier, the blood-brain barrier, and the placental barrier. This is mediated by a diverse range of bacterial factors. In this review, I outline the key host barriers encountered by Listeria during host infection and the mechanisms by which Listeria crosses each barrier.

Oakland's Preservation Park: Planning for the Future

2025-07-30T03:07:20Z

Oakland's Preservation Park: Planning for the Future Kaufman, Samantha Preservation Park in Oakland is an anomaly. It is neither a green park nor strictly an office park, 16 historic homes, carefully renovated and maintained, are arranged around an internal way and studded with a central fountain in the Victorian style. Seeds for this park were initially planted by the city's Landmark Preservation Advisory Board in 1976 and with fits and starts, it opened in 1991. As Interstate Highway 980 was built, the park was created as a way to save a few of the most beautiful homes under threat by the Oakland Redevelopment Authority's urban renewal clearance and construction of the highways. Interstates 580, 880, and 980 were lashed across Oakland to bring suburban commuters over the bridge to San Francisco, cutting up a city of neighborhoods and destroying thousands of homes and small businesses. Oakland envisioned this acre and a half as a permanent site for community based organizations and non-profits to revitalize the edge of downtown and West Oakland. Since 1991, the office space has been rented to tens of non-profits and hosted hundreds of weddings, conferences, and other public and private events. In 2004, the community development corporation, East Bay Asian Local Development Corporation purchased the park from the city and continued to manage the property as a successful office park and event space. The COVID-19 pandemic irrevocably changed how many people work, and for the first time, Preservation Park vacancies increased and have remained substantially below 100%, presenting a challenge to EBALDC and its portfolio. This thesis seeks to provide the client with a framework to assess possible redevelopment and reprogramming schemes which is sensitive to the community goals of EBALDC and requirement for the property to sustain itself. By considering, financial feasibility and partnerships, a multi-phase roadmap with a 20-year time horizon is presented to EBALDC to consider. This will also provide a potential framework for more non-profit firms to pursue commercial real estate management and redevelopment as a strategy for community wealth-building and neighborhood stability.

Co-Governing Care in Astoria, Queens: The Role and Responsibility of the City in Supporting Community-Led Solidarity Networks

2025-07-30T03:07:06Z

Co-Governing Care in Astoria, Queens: The Role and Responsibility of the City in Supporting Community-Led Solidarity Networks Kleinbock, Yvette In the spring of 2020, as COVID-19 spread across New York City and the United States, an inadequate government response and an overburdened social safety net left millions facing unemployment, eviction, and food insecurity with limited institutional support. Yet alongside these systemic failures, mass acts of solidarity emerged, as unprecedented numbers of people mobilized mutual aid eff orts to help their neighbors survive. While many mutual aid groups have since disbanded or experienced burnout, others have sustained the work, helping to establish alternative infrastructures of collective care. Taking Astoria, Queens as a case, this thesis examines the political lessons that have emerged in the aftermath of the COVID-19 pandemic, focusing on what it takes to sustain community-led solidarity networks and considering City’s role and responsibility in supporting urban infrastructures of care more broadly. To conceptualize this relationship between local community eff orts and the City, I further consider the possibilities of co-governance as a framework for community care. This research utilizes a community-centered, relational, qualitative approach that draws on oral history and ethnographic traditions, including thematic analysis of key informant interviews, document review, and participant observation. Tracing the trajectory of mutual aid and other community-led eff orts in Astoria and exploring the possibilities and challenges of collaborative governance, this research imagines how planning, policy, and governance strategies in New York City can deepen collective capacity, foster resilience, and advance more just and caring urban futures.

Implementing a Digital Common Application for Affordable Housing in Massachusetts

2025-07-30T03:06:53Z

Implementing a Digital Common Application for Affordable Housing in Massachusetts Moss, Emily The need for affordable housing in Massachusetts is immense, with fragmented housing application processes further compounding barriers for low-income residents to access stable housing. To address these challenges, the Massachusetts Executive Office of Housing and Livable Communities (EOHLC) initiated the development of a digital common application (Common App) in 2024 to streamline tenant application and selection processes for privately owned publicly subsidized housing opportunities throughout the state. This client-based thesis offers an implementation roadmap for EOHLC to successfully operationalize the Common App within the agency. The roadmap is structured around three topics as requested by EOHLC: (1) organizational design considerations as the Common App scales, including internal staffing models, external vendor relationship management, and budget planning; (2) long-term technical integration opportunities, including identifying relevant data systems likely to interact with the Common App and potential areas for alignment; and (3) compliance mechanisms to ensure housing providers’ participation in the Common App, including a review of Massachusetts fair housing regulations as one possible strategy to require or incentivize providers to use the platform. Each topic draws from a review of state policies as well as academic literature in organization studies, information systems, and public administration; stakeholder interviews; and case study research on digital affordable housing search and application platforms in Massachusetts, Detroit, San Francisco, and the Bay Area—culminating in a series of recommendations for EOHLC to effectively administer the Common App over the long term.

Ensuring Equitable Tenant Outcomes: Case Studies of Building Decarbonization Initiatives in Greater Boston, Massachusetts

2025-07-30T03:07:11Z

Ensuring Equitable Tenant Outcomes: Case Studies of Building Decarbonization Initiatives in Greater Boston, Massachusetts Wong, Nicole U.S. cities are ramping up building decarbonization initiatives to reduce greenhouse gas emissions from buildings. However, these programs and policies generate complex challenges at the intersection of housing, climate, and environmental justice, especially for cities that face barriers to adopting strong renter protections. This thesis offers two case studies regarding tenant-related equity concerns that emerged during the implementation of building decarbonization initiatives in greater Boston, Massachusetts: Boston’s building performance standard the Building Emissions Reduction and Disclosure Ordinance (BERDO) and Everett’s energy efficiency incentive program Electrify Everett. This thesis also identifies strategies that residents, community organizations, and city officials highlight as important to advance building decarbonization without generating unintended consequences for tenants. Key equity concerns include the potential impacts of building decarbonization on rental affordability, displacement, and energy burden, whereas strategies include broad tenant protections such as rent control, renter protections attached to building decarbonization subsidies, and robust enforcement mechanisms. This research illuminates the need to build power to win essential tenant protections, focus decarbonization on housing with existing affordability protections, and advance alternative, decommodified forms of housing.

Neutronic Performance and Thermal Hydraulic Analysis of the MIT Reactor Fission Converter Experimental Facility Using High-Density U-10Mo Low-Enriched Uranium Fuel Elements

2025-07-30T03:07:00Z

Neutronic Performance and Thermal Hydraulic Analysis of the MIT Reactor Fission Converter Experimental Facility Using High-Density U-10Mo Low-Enriched Uranium Fuel Elements Sears, Caroline Julia The MITR fission converter (FC) is a core-driven subcritical assembly at the MIT Nuclear Reactor Laboratory, located on the MIT campus in Cambridge, MA. The assembly is made of eleven partially-depleted MITR-II fuel elements in a separate cooling tank attached to the side of the core-tank graphite reflector. The FC serves to boost the thermal flux from the core and send a hardened neutron spectrum to an irradiation target, providing a fission energy flux spectrum without the need to put a sample inside the core tank. It was previously used for boron-neutron capture therapy clinical trials before its decommissioning in the 2010s. Recently, it has been modified from a medical beamline to a general-use engineering and materials testing facility. The new FC-based experimental facility has roughly one cubic meter of empty space downstream intended to contain large experiments, called the m³. This work is a safety and performance study aimed at quantifying the impact of modifying the facility’s geometry as part of the FC’s recommissioning, as well as the impact of changing its fuel from HEU to LEU fuel as part of the MITR LEU conversion project. Neutronics and thermal hydraulics analysis of the renovated facility have been performed using the codes MCNP5 and STAT7, respectively. This analysis quantified the FC’s k_eff, power distribution, multi-group neutron flux, and conditions which cause onset of nucleate boiling (ONB). It was determined that the FC assembly will remain subcritical (k _eff < 0.9) and low power (≤200 kW) under a wide range of performance conditions, including with both types of fuel and a variety of materials on the target-side of the FC tank. The HEU-fueled FC is expected to require no changes to the limiting safety system settings (LSSS) outlined in the original technical specifications document. The LEU fuel is expected to increase the FC performance, but as a tradeoff, will require minor changes to the LSSS setpoints to maintain margin to ONB under the most limiting thermal-hydraulic conditions. Additionally, this study evaluates the feasibility of using the FC for in-assembly fuel experiments, particularly as a pathway for testing the new LEU fuel elements at low power. This study indicated that this proposed FC configuration with one LEU and ten HEU elements is feasible and maintains wide safety margins.

Development of a Supervisory Control System as a Transition Technology Towards Autonomous Reactor Plant Operations

2025-07-30T03:06:48Z

Development of a Supervisory Control System as a Transition Technology Towards Autonomous Reactor Plant Operations Fortier, Lauren G. The economic viability of small and microreactors depends on reducing energy generation costs. The implementation of autonomous reactor control systems provides an avenue for reducing operations and maintenance expenses. Advanced reactor designs with enhanced passive safety features, reduced source terms, and digital instrumentation and control systems, directly support autonomous controllers. In these plants, where the need for human operators is already reduced, the introduction of supervisory control systems (SCS) for dynamic operations further lessens operator dependence while building trust in these systems, laying a solid foundation for the transition to fully autonomous reactor control. Finite state automata (FSA) provide a framework for engineering fully verifiable and validatable supervisory controllers, and thereby facilitate the transformation to autonomous operations in nuclear power plant operations. FSA serve as a foundational mathematical tool for modeling discrete event systems (DES). Properties such as nonblocking and controllability can be formally demonstrated and verified by leveraging the extensive set of mathematical proofs within the scope of regular languages. Furthermore, a DES can be directly linked to reactor plant systems and operational procedures within a hierarchical architecture by using a graded functionalization approach analogous to that of complex dynamic systems, such as self-driving vehicles. In this scheme, feedback controllers can regulate low-level actuation functions while a supervisory controller can govern high-level plant state transitions. A generic supervisory controller was developed as a transition technology toward autonomous reactor operations. This controller was then tailored for application on a limited feedback model, for initial proof-of-concept testing, and then was scaled for use on light water reactor (LWR) simulators. In the absence of advanced reactor simulators for operational testing, LWR simulators were used because they provide realistic feedback and controls within a more conservative operating margin than advanced reactors. These supervisory controllers successfully executed operational procedures within a fully verifiable framework, establishing the foundation of this modeling approach and laying the groundwork for its implementation in advanced reactor designs. This scalable model thus facilitates a smooth transition from functioning as an operator aid to fully autonomous operation as a comprehensive plant controller, increasing the economic viability of nuclear power.

Critical Water, Wastewater, and Thermal Infrastructure Development for a Resilient Neighborhood in War-Affected Ukraine

2025-07-30T03:06:56Z

Critical Water, Wastewater, and Thermal Infrastructure Development for a Resilient Neighborhood in War-Affected Ukraine Gendler, Isaac A. The Central Ukrainian municipality of Tetiiv is experiencing an influx of migrants due to its relatively safe position amid the Russian invasion. Tetiiv, in collaboration with the Ukrainian NGO Vid Sertsya Budova, is building a new neighborhood to accommodate internally displaced people, refugees, war veterans, and local residents. The neighborhood will require water, wastewater, and thermal infrastructure that satisfies European Union requirements given Ukraine’s ambition to join the economic bloc. This thesis performs a pre-feasibility study to help Tetiiv and Vid Sertsya Budova create an optimal configuration of water, wastewater, and thermal infrastructure for the new neighborhood. For water infrastructure, the report calculates water consumption using the BREEAM framework, quantifies storage requirements, analyzes water quality, estimates rainwater harvesting potential, and identifies optimal water source locations within 30 km using the DRASTIC methodology combined with geospatial analysis. For wastewater infrastructure, the study estimated wastewater generation, analyzed different wastewater treatment options, and used a decision matrix to identify the most optimal wastewater system for the site, a moving bed biofilm reactor system. The thermal infrastructure study developed a conceptual heating system for the new neighborhood, incorporating ground-source heat pumps in each row house and single-family home, vertical boreholes, a thermal energy network, and a wastewater heating system for the multifamily co-living units. This study offers a blueprint for Ukraine and other regions recovering from urbicidal conflict and disaster to rebuild in alignment with the new climate paradigm.

Affect in Resiliency Planning: A Conversation with Broad Channel

2025-07-30T03:06:39Z

Affect in Resiliency Planning: A Conversation with Broad Channel Fiol, Olivia Planning for climate change is more relevant than ever, as the earth continues to warm, sea levels rise, and no global policy or political will is in sight. In order to plan under hostile circumstances, it is of the utmost importance that planners turn our attention to the hyper-local scale, continuing momentum in our personal and professional relationships. In this thesis, I argue that centering affective experiences of place is essential in conversations about the future of places under climate change, especially in communities and neighborhoods resistant to the conversation about climate change’s impacts on their futures in the first place. This project focuses on Broad Channel, the only inhabited island community in New York City’s Jamaica Bay, which is on the front lines of sea level rise and tidal flooding in the city. I interviewed city leaders, community members, artists, planners, and activists to understand how we can move through and with affect when considering the future of a place. This can open up conversation about climate change previously inaccessible. These conversations also surfaced the need for planners to regroup and understand how their own affective positions impact difficult conversations about climate change. I offer these insights and recommendations for future resiliency planning work, reflecting both inward and outward.

Between Fields and Cities: The Politics of Land Use Changes in Punjab, India

2025-07-30T03:08:22Z

Between Fields and Cities: The Politics of Land Use Changes in Punjab, India Kodzis, Trevor Quigley This thesis examines the urbanization of agricultural lands in the State of Punjab, looking for patterns that explain the type of development that is occurring while embedding these transformations in a larger political and economic context. The study will focus on both transportation infrastructure and the real estate developments surrounding it, as a way of situating Punjab within a larger discourse on infrastructure and urbanization in the Global South. Through the case studies of three Punjabi cities: Mohali, Bathinda, and Ludhiana, this paper will employ remote sensing to analyze recent transformations from agricultural to developed land across different land use zones, revealing two primary patterns. First, highway infrastructure projects have been delayed because of land acquisition problems and a contentious political environment. Second, with the exception of Ludhiana, most of the real estate in Punjab is concentrated in the residential sector. This apparent stagnation of manufacturing growth in Punjab results from a wide range of political and economic factors including high land prices, protest movements, emigration, fiscal policies, geography, and competition with other states. In contrast to the rest of the state, Ludhiana has successfully attracted industrial growth, illustrating how cities that urbanized earlier follow a different path of economic development.

Ozarkitecture: Shaping the Sense of a Region

2025-07-30T03:08:15Z

Ozarkitecture: Shaping the Sense of a Region Jones, Rubin Contemporary planning often invokes a “sense of place,” yet the deeper work of placemaking remains largely unfulfilled. In its absence, cities and regions fracture into landscapes that appear whole but feel hollow. These are spaces stripped of the sensory depth and symbolic meaning that make dwelling possible. This thesis thus returns to the concept of the genius loci—the spirit of place—not as a nostalgic embellishment, but as an ethical and practical imperative. It traces the philosophical and historical foundations of place, examines how contemporary practice has diluted its meaning, and explains why a new approach is necessary. From this foundation, the project engages Kevin Lynch’s operational models and develops a reframed approach—shifting from a visual image to an embodied experience—to ground planning practice in the textures of memory, movement, and belonging. Five new concepts—anchor, patch, joint, seam, and trail—offer a vocabulary for cultivating places that hold meaning across time and transformation. This framework is applied in Northwest Arkansas, a region where rapid growth threatens to outpace the character of its communities. By strengthening sensory experience, rooted memory, and collective authorship, this project aims to offer a different way forward through regional transit—where planning not only shapes space, but safeguards access to the ongoing, unfinished project of place itself.

Navigating Identity and Place: The Role of Displacement Camps in Community Rebuilding and Identity Preservation in Sudan

2025-08-06T18:54:21Z

Navigating Identity and Place: The Role of Displacement Camps in Community Rebuilding and Identity Preservation in Sudan Sati, Maysaa Displacement camps are often framed as zones of impermanence; spaces of waiting designed to contain crises, not cultivate futures. Yet, in Kalma Camp in South Darfur, displacement has given rise to a self-organized, complex urban environment shaped by collective labor, cultural resilience, and everyday acts of spatial and political agency. This thesis explores how communities in Kalma have remade space, redefined home, and preserved identity in the face of prolonged uncertainty. Drawing on ethnographic fieldwork, spatial analysis, and critical urban theory, it situates Kalma not as an exception, but as a generative urban formation—an emergent city born from the margins. Through chapters that trace the camp’s spatial evolution, intergenerational understandings of belonging, informal governance, cultural production, and political expression, this research challenges dominant humanitarian paradigms that treat camps as temporary and peripheral. It argues that residents are not passive recipients of aid, but planners, builders, and cultural producers who contest displacement through care, memory, and infrastructure. By threading together theoretical insights from scholars such as Malkki, Bhabha, Roy, and Simone with grounded narratives from Kalma, the study reveals how displacement can also be a site of urban possibility. In reframing camps like Kalma as sites of urban life, not despite the crisis, but through it, this thesis calls for a fundamental shift in how urban planners, humanitarian actors, and scholars engage with protracted displacement. It invites us to see resilience as planning, care as governance, and the camp not as a space of suspension, but as a place where new urban futures are already being forged.

Characterization of a Diamond Proton Recoil Telescope for DT Neutron Measurements in the LIBRA Experiment

2025-07-30T03:08:09Z

Characterization of a Diamond Proton Recoil Telescope for DT Neutron Measurements in the LIBRA Experiment Edwards, Emily The LIBRA project investigates tritium breeding using beam-target style DT neutron generators to irradiate molten salt vessels. A critical aspect of understanding this process is the characterization of the energy and flux anisotropies within the neutron environment, which are inherent to the beam-target neutron generation method. These spectral and flux characteristics directly impact tritium production and the interpretation of experimental results, which makes the neutron field characterization essential for a complete understanding of the tritium breeding system. This paper presents the use of an sCVD diamond detector and an sCVD diamond proton recoil telescope to characterize the neutron environment produced by the DT neutron generator employed in the LIBRA experiments. The results of these measurements provide insight into the neutron flux and energy distributions incident on the breeding salt, enabling a more complete understanding of the neutron input in the LIBRA experimental tritium breeding process.

Contested Values of Eco-Developments: Leveraging Private Finance to Integrate Biodiversity into Nusantara’s City Development Framework

2025-07-30T03:08:08Z

Contested Values of Eco-Developments: Leveraging Private Finance to Integrate Biodiversity into Nusantara’s City Development Framework Leung, Yu Hang (Hannah) Rapid expansion of urban populations has spurred the construction of new cities, contrasted with the heightened urgency to adopt climate risk mitigation and disaster resilience strategies. Along with the global need for Nature-based Solutions (NbS), new eco- developments which are planned within biodiversity hotspots should adopt resilient climate adaptation strategies for long term benefits. However, these projects are often not financially justified or positioned to sustain long investments and holding periods. This thesis examines development of Ibu Kota Nusantara (IKN) in Indonesia as an evolving eco-development case study on how biodiversity could be repositioned as a key aspect in investment frameworks. Developing new cities and eco-developments tend to rely on external investments, as internal structures are navigate the challenges of rapid growth while seeking a self-sustainable equilibrium. For IKN, private investors hesitate to invest in a project that is situated in an unstable political landscape, while low government expenditure and poor governance structures has marred development progress. Based on the inherent need to build to support a growing urban population, this multidisciplinary thesis explores three components that are needed to design an eco-development project - namely consistent way to value biodiversity in comparison to development values, proper environmental governance, and sustainable financial instruments to support the initial and operational expenditures of a project. Measurement approaches such as GBS-FI and S&P NBS are able to streamline corporation’s dependency value of biodiversity, based on valorization models developed by SEEA-EA and the United Nation’s Integrated National Financing Framework. A mixed-methods approach of qualitative case study analysis and in-depth review of existing and potential financial instruments is used to understand the demand and supply side of eco-developments. A Contingent Valuation Method of assessing buyers’ Willingness-To-Pay in addition to qualitative questionnaire on perceived values of biodiversity provides insights on local understanding and WTP of premiums in support of elevated costs of eco-developments. The intention of this research is to explore how biodiversity could be recentered as a foundational element to sustainable development of cities. More broadly, this research seeks to synthesize the interdisciplinary discussions around development, environmental policy and ecological planning, while evaluating the feasibility of innovative financial mechanisms to mobilize capital for large-scale eco-development projects.

Data-Driven Assessment of Digital Age Inclusion: Topic Modeling Seoul’s Digital Governance Platform to Evaluate Elderly Representation

2025-07-30T03:07:42Z

Data-Driven Assessment of Digital Age Inclusion: Topic Modeling Seoul’s Digital Governance Platform to Evaluate Elderly Representation Lim, Sungmoon This paper examines the intersection of population aging and digital civic government in Seoul, South Korea. As cities worldwide digitize and age simultaneously, understanding elderly citizens' representation in digital governance platforms becomes critical for inclusive urban governance. As a leader in both aging and urban technologization, Seoul serves as an ideal case study. Combining computational analysis of civic queries with qualitative interviews, this study investigates whether elderly residents' concerns are adequately represented in Seoul's e-government platform. Comparing these datasets reveals significant disparities in how elderly concerns are represented digitally: despite Seoul's technological sophistication and digital inclusion efforts, substantial gaps remain in representing elderly citizens' concerns in governance forums, signaling gaps that may undermine age-inclusive development. This research contributes to theoretical understandings of digital democracy and urban aging while offering practical insights for designing more inclusive systems that address the realities of dual urban phenomena—aging and digitization—as they coalesce in cities.

Computational Fluid Dynamics Modeling of Compact Steam Generators

2025-07-30T03:08:01Z

Computational Fluid Dynamics Modeling of Compact Steam Generators Jiragoontansiri, Witiwat Compact Steam Generators (CSGs) are vital components in Small Modular Reactors (SMRs), particularly within Integral Pressurized Water Reactor (iPWR) configurations where compactness and high performance are essential. This thesis explores the use of Multiphase Computational Fluid Dynamics (M-CFD) to simulate two-phase flow boiling in CSGs based on Printed Circuit Heat Exchanger (PCHE) technology. Using the commercial CFD code STAR-CCM+, two modeling approaches—the Volume of Fluid (VOF) model and the Two-Phase Thermodynamic Equilibrium (TPTE) model—are applied to simulate both adiabatic and heat transfer conditions within mini-channels. The simulations are validated against experimental data from two sources: an R-134a-based vertical test loop developed at MIT’s Greenlab and a water-based PCHE test section from Kromer’s prior work. Key two-phase flow parameters such as void fraction, pressure drop, and heat duty are evaluated and compared to experimental benchmarks. Calibration methodologies are implemented to improve predictive accuracy. The validated models are then used to simulate realistic CSG operating conditions based on Babcock \& Wilcox and NuScale reactor designs. Results indicate that PCHE-based CSGs, despite being smaller, are capable of delivering favorable thermal and hydraulic performance, with slightly better results compared to the existing steam generator design. Overall, the study demonstrates the potential of M-CFD tools to support the design and optimization of CSGs for next-generation nuclear applications.

Farebox Freedom: An analysis of centralized fare policy interventions relative to the suburbanization of poverty

2025-07-30T03:07:45Z

Farebox Freedom: An analysis of centralized fare policy interventions relative to the suburbanization of poverty Chachra, Vir The United States is witnessing a shift in its geography of poverty, with suburban communities experiencing greater increases in poverty rates relative to urban cores. However, transit service and fare policies have not kept pace with this demographic shift, inadequately meeting the needs of a growing population of lower income riders in the suburbs, particularly those served by higher-cost modes like commuter rail. This thesis confronts this evolving dynamic, bridging a research gap between transit fare policy and the suburbanization of poverty, analyzing seven transit systems across the US through a Spatial Difference in Differences research approach, revealing mode specific shifts in transit cost burdens from 2019 to 2021 and impacts of these shifts on social vulnerability as defined by the CDC. The thesis also explores federal policy pathways to create greater fare equity in light of this dynamic, either through supporting operations costs for transit agencies or through a flat-fare national transit pass for riders, akin to Germany's Deutschlandticket (D-ticket) program. Focusing on suburban commuter rail communities across the sampled networks, the analysis finds that in 2021, communities with only commuter rail access and higher-than-average social vulnerability scores were associated with approx. an 11% additional increase in transit cost burdens compared to all other groups while also experiencing an increase in transit cost burdens overall. Furthermore, a two-fold increase in transit costs as a share of median income in 2021 was correlated with an additional 7.4% rise in social vulnerability index scores for commuter rail communities, relative to those with access to other modes that are closer to the urban core. While these communities have a 38% lower social vulnerability score, the analysis estimated a 60% increase from 2019 to 2021, highlighting a disproportionate increase and challenging the assumption of the wealthy commuter rail suburb. This increasing sensitivity to transit cost burdens points to a significant ongoing interaction between national trends of suburbanization of poverty and fare policy. Given that many transit agencies face funding constraints and are nationally inconsistent in their low-income fare programs, they may be structurally limited in their ability to address these disparities on their own. This analysis considers lessons from historical policies such as the National Mass Transportation Assistance Act of 1974 and recent international programs like Germany’s D-ticket, to suggest that federal support for transit operations—paired with inclusive, mode-agnostic fare programs—would help address these emerging inequities in transit affordability amid the suburbanization of poverty.

Evolving Concepts of the Public Interest in Comprehensive Planning

2025-07-30T03:07:34Z

Evolving Concepts of the Public Interest in Comprehensive Planning Tagliani, Jessie The public interest is an important, yet contested, concept in the field of planning. On the one hand, it offers a normative criterion against which planning decisions can be evaluated and is traditionally viewed as the source from which planners derive their authority. However, the precise nature of the concept is fiercely debated by both planning practitioners and theorists, with some going so far as to denounce its existence. Today, the increasingly pluralist and complex nature of communities lead to questions over the concept’s relevance and applicability. In the second half of the twentieth century, planning theoreticians began assembling a body of literature surrounding this concept, mostly in the form of typologies of the definitions that have been ascribed to the public interest However, my review of the literature revealed that the study of the public interest as a normative criterion for planning has almost entirely taken place in the realm of planning theory. Therefore, I sought to add to the empirical scholarship concerning the public interest by analyzing it from two angles: first, I sought to understand how the public interest as a historical concept has changed and evolved alongside the field of planning throughout the twentieth century. Second, I chose the field of comprehensive planning as my analytical lens due to its longevity across the history of the planning profession and its close affiliation to the concept of the public interest. Specifically, I sought to analyze how the public interest is manifested in a series of comprehensive plan documents and thereby illustrate how the concept’s operationalization has evolved over the course of the past half century of planning. I began my analysis by drawing on over fifty years of scholarship to construct my own typology of the main definitions of the public interest. I then applied these definitions to four different models of comprehensive planning that were developed between 1962 and 2012. I also obtained a second perspective on the evolution of the concept of public interest by examining a series of comprehensive plans adopted by the City of Annapolis between 1964 and 2022. The two analyses revealed very different trajectories in the evolution of the public interest as an empirical concept. On the one hand, the four models demonstrate a fairly linear evolution in what is constituted to be the substance and process of constituting the public interest, which can be broadly classified as achieving social equity, the responsible stewardship of natural resources, and authentic citizen involvement. By contrast, the five Annapolis comprehensive plans did not neatly follow the same evolution. Instead, a recurring concern for many of the Annapolis plans is the conservation of the physical city through the control of the city’s growth, the careful maintenance of its economy, and the preservation of its urban fabric. However, the more recent plans demonstrate a stronger commitment to the social values and processes espoused by the four planning models, indicating that there is growing consensus in the field of planning today regarding an empirical understanding of the public interest.

Prioritizing Sidewalk Accessibility Improvements for the Aging Population and Individuals with Disabilities: A Case Study of Bandung, Indonesia

2025-07-30T03:07:58Z

Prioritizing Sidewalk Accessibility Improvements for the Aging Population and Individuals with Disabilities: A Case Study of Bandung, Indonesia Kurniaputri, Aulia While walking is fundamental to inclusive urban mobility, major cities in Indonesia continue to face challenges in providing barrier-free pedestrian infrastructure, even for individuals without physical impairments. As the population of older adults in Indonesia continues to grow, the risk of disability within this demographic will increase, contributing to the overall number of individuals with disabilities. In Bandung City, there is a rising awareness across various sectors of society regarding the rights of older adults and individuals with disabilities to navigate sidewalks safely. These trends highlight the importance of improving inclusivity on city streets, where people travel daily to reach their essential and desired destinations. This thesis explores an evidence-based methodology to prioritize sidewalk accessibility improvements for older adults and individuals with physical disabilities, aiming to develop a prioritization strategy that targets maximum impacts. Accessibility scores and pedestrian flow counts are calculated with the Urban Network Analysis (UNA) toolbox. Three types of user groups—non-disabled individuals, cane or crutch users, and wheelchair users—were assigned penalties for each type of barrier on a sidewalk segment, resulting in varying perceived distances. Those with physical mobility limitations perceived longer distances than those without. To identify priority locations, a system-selection ranking was applied that considered sidewalk segments with both high-frequency usage and significant discrepancies between actual and perceived lengths. The methods outlined in this thesis are scalable for use in other neighborhoods and cities, thereby supporting data-driven decision-making in pedestrian infrastructure improvements.

Relationality and Reciprocity in Civic Design: Public Engagement and Offshore Wind Development in the Gulf of Maine

2025-07-30T03:08:05Z

Relationality and Reciprocity in Civic Design: Public Engagement and Offshore Wind Development in the Gulf of Maine Bendixen, Amanda Offshore wind projects are inherently complex, requiring the integration of social, environmental and technical planning. Meaningful engagement with communities is critical to ensuring procedural fairness, trust and equity throughout the development process. Yet, the role of civic design in shaping these outcomes remains unexplored. This thesis investigates how relationality and reciprocity are fostered through the civic design of public engagements for offshore wind development in the Gulf of Maine. Through qualitative analysis of public meeting transcripts – using thematic coding and memo writing in Atlas.ti – this study identifies civic design elements and recurring engagement themes. The findings highlight relational accountability as a mechanism for building trust, transparency and procedural fairness. They also explore how civic design can support reciprocity, while revealing how structural barriers can undermine relationality. This research demonstrates the possibilities and limitations of civic design in fostering relational and reciprocal public engagements. It concludes with recommendations for incorporating civic design elements that promote sustained, reciprocal relationships, accountability and long-term community involvement in offshore wind development.

Beyond Safety and Surveillance: New Possibilities for Public Light After Dark

2025-07-30T03:07:47Z

Beyond Safety and Surveillance: New Possibilities for Public Light After Dark Corlett, Lucy As cities refocus planning and design goals in response to evolving global standards for urban well-being, sustainability, and spatial equity, research on best practices and innovative considerations for the public realm has expanded. As a result, a new movement in research and guidance on public light has emerged. Rather than continuing to view lighting as a punitive means of enforcing surveillance and public safety, this movement in research and practice advances radically inclusive, responsive design methods that use light to redress inequality in the built environment. This thesis builds on a growing body of research that establishes the powerful influence of light on human experience and perception, initiating a dialogue between different models for place-based approaches to lighting design in shared public spaces. Drawing on in-depth studies of these models, interviews with stakeholders, scholarship, policy, and design and planning practice, this thesis recommends that city planners serve as the bridge between ideation and implementation in a new era of urban illumination.

Community Benefits Agreements for Equitable Renewable Energy Siting: The Importance of Negotiation Power and Stakeholder Engagement

2025-07-30T03:08:02Z

Community Benefits Agreements for Equitable Renewable Energy Siting: The Importance of Negotiation Power and Stakeholder Engagement Paul, Sanjana As renewable energy development accelerates across the United States, conflicts over project siting have become increasingly common; often rooted not in opposition to clean energy itself, but in concerns over fairness, community inclusion, and long-term accountability. This thesis investigates how Community Benefits Agreements (CBAs) can serve as tools to address these challenges, focusing on how negotiation dynamics, mediation, and stakeholder engagement shape the equity and enforceability of CBAs in renewable energy siting. Using a mixed-methods approach, this research draws on qualitative case studies, stakeholder interviews, and legal-policy analysis, alongside a limited quantitative assessment of CBA implementation outcomes. The study examines both the procedural and structural conditions that influence how benefits are negotiated, formalized, and monitored. By analyzing cases that include third-party facilitation, amendment mechanisms, and diverse stakeholder participation, the thesis identifies best practices for designing CBAs that move beyond performative engagement and toward genuine community empowerment. Ultimately, this research offers a multidimensional understanding of CBAs as emergent governance instruments situated at the intersection of infrastructure planning, environmental justice, and public accountability. It concludes by proposing a model state-level regulatory framework to support equitable CBA development and embed principles of justice into the future of renewable energy siting.

Public Health Governance at the Watershed Scale: Exploring Opportunities for Multi-sector Governance to Advance Planetary Health in Northeastern Massachusetts

2025-07-30T03:07:41Z

Public Health Governance at the Watershed Scale: Exploring Opportunities for Multi-sector Governance to Advance Planetary Health in Northeastern Massachusetts Morales, Daniela Many health and environmental regulations apply only within specific political or administrative boundaries, creating a mismatch between the spatial scale of natural systems which impact health and the spatial extents of relevant regulations. For example, in Massachusetts, local Boards of Health govern specific public health and environmental issues through spatialized regulatory powers that carry significant weight in both local and larger geopolitical contexts. Despite the fact that watershed management influences regional public health outcomes through impacts to water quality, water quantity, and climate resilience measures, the organizations focused on watershed management do not have influence that matches the power of public health entities. This thesis explores how watershed management decisions could have similar weight to other public health governance decisions by exploring the specific speculative case of what interest there is in, and what barriers there are to, watershed management organizations in Northeastern Massachusetts working as public health governing units, such as local Boards of Health. Using a mixed methods approach, combining organizational and policy analyses with semi-structured key informant interviews and surveys, I assessed the opportunities, barriers and interest for multi-sector watershed and health governance to advance Planetary Health in Northeastern MA. The findings showed low receptiveness towards adopting a new regional governance system due to both perceived and actualized legal, organizational and social barriers. The findings also highlighted an interest towards strengthening existing regional partnerships and building new collaborations across the fields of public health and watershed management for more effective approaches towards environmental health decision making. These results suggest a need for additional interdisciplinary training for both sectors, and the creation of new spaces and relationships for collaboration between actors involved in public health, watershed management, and related fields.

From Silence to Sankofa: The Role of Archives in Addressing Urban Renewal’s Displacement History

2025-07-30T03:07:54Z

From Silence to Sankofa: The Role of Archives in Addressing Urban Renewal’s Displacement History Mohamed, Menatalla In the post-World War II era, urban renewal was designed as a path towards the revitalization of American cities through public investment into the redevelopment of ‘blighted’ areas. Through eminent domain takings, urban renewal projects led to the forced relocation of residents from their homes and neighborhoods, with a disproportionate impact on Black, immigrant, and low-income communities across the country. The archives of the renewal period hold the story of this widespread displacement and are of significant value for contemporary planning practice. Through the lens of two case studies, this thesis explores how and why urban renewal archives are being revisited today to address this displacement history through institutional and community approaches to memorialization. In Cambridge, MA, the Cambridge Redevelopment Authority (CRA) is an example of an agency drawing on its own archive to publicize its role in past forced relocation through its use of eminent domain. In Rochester, NY, Clarissa Uprooted is a public history and community building project centered around the story of Clarissa Street, a historically Black neighborhood that was demolished for renewal in the 1960s. Through document analysis and interviews, I examine how these efforts to activate urban renewal archives and better understand the scope and impact of forced relocation provide avenues for planners and community members to remember the past, acknowledge systemic harms, and reflect on repair. Despite the different positionalities of the CRA and Clarissa Uprooted, a comparative approach also highlights how both organizations have created opportunities to unearth histories of dissent to urban renewal, more fully recognize the legacy of commercial displacement, and imagine avenues to planning, policy, and institutional change. This research demonstrates the significance of local archival initiatives that draw upon the past to better position planners and communities to face the urban challenges and inequities of the present and future.

Synthesis and oxidation behavior of Cr alloyed uranium borides at high temperatures

2025-07-30T03:07:27Z

Synthesis and oxidation behavior of Cr alloyed uranium borides at high temperatures Moeykens, Riley S. Following the nuclear accident at Fukushima Daiichi Power Station in 2011, an urgent need for safer, more economical, and versatile nuclear fuels has arisen. In recent years, uranium boride (as a tetraboride and diboride) has been further investigated as a candidate fuel form for its high thermal conductivity, high melting point, high uranium loading, and potential for dual use as a fuel and burnable absorber. In this work, the synthesis, structural behavior, and oxidation behavior of uranium borides and chromium- and yttrium- alloyed uranium borides are investigated. The structure of the synthesized uranium borides and chromium- and yttrium- alloyed uranium borides were probed using synchrotron X- ray Powder Diffraction (XRD) and Pair Distribution Function (PDF) analysis with in-situ heating. The methods and challenges in synthesizing uranium boride and chromium- and yttrium-alloyed uranium boride, as well as the consequential thermophysical and oxidation properties of these potential fuel forms, are elucidated in this work.

Towards Semi-Autonomous, Highly Automated, and Remotely Operated (SAHARO) Nuclear Reactors

2025-07-30T03:07:51Z

Towards Semi-Autonomous, Highly Automated, and Remotely Operated (SAHARO) Nuclear Reactors Hallinan, Aidan M. In the United States, comprehensive reactor design certification, site permitting, and operating licensing processes exist to ensure the safe and reliable operation of nuclear power plants (NPPs). Most of these plants have belonged to the same design class: large, centrally located Light Water Reactors (LWRs). Thus, our regulatory processes were tailored for their phenomenology and the unique challenges associated with their operation and maintenance. However, these types of plants may be impractical for specific energy markets, where smaller, non-LWR, highly flexible, and multi-faceted NPPs can be more optimal. The novelty of these designs and their use cases has further inspired new operating paradigms, which will be referred to as Semi-Autonomous, Highly Automated, or Remote Operations (SAHARO) in this thesis. While some of these new reactors have seen limited progress in design certification and licensing efforts under current regulatory practices, there remains little precedent for these novel operating approaches. To facilitate discussion, guide designers, and inspire regulatory progress, I begin by looking at existing regulations, licensing practices, technical guidelines, and other rules that govern the NPP design and operations. I then dive into current applications and discussions of the sub-components of SAHARO, across different technical domains as well as nuclear power, to gather technical, operational, and regulatory insights. To provide reactor design evaluators with an additional tool, I define a Risk-Complexity Score (RCS), which couples simple system complexity quantification with existing risk measures and can support risk-informed system analyses. I then conduct an internet network Quality of Service (QoS) test to demonstrate one of the many important considerations for remote operations stress-testing, which proposes an approach for evaluation within the SAHARO licensing process: the “SAHARO Coping and Minimum Inventory Assessment Strategy.” Lastly, based on my literature and industry reviews, I have constructed a framework that informs reactor designers on how to iterate through the SAHARO-based design process, while also enabling vendor-regulator collaboration and shared learning. Ultimately, I aim to help designers and regulators in the nascent fields of autonomous, automated, and remote NPP operations identify the key questions these technologies and systems must address to ensure safe, effective, and practical application.

Decentralizing Power: Enabling Local Energy Resilience and Equity in Accra

2025-07-30T03:06:55Z

Decentralizing Power: Enabling Local Energy Resilience and Equity in Accra Kulkarni, Nikita Over 600 million people in Sub-Saharan Africa lack access to electricity. While Ghana is projected to achieve universal access by 2030, this national milestone obscures lived experiences of energy insecurity— particularly in urban centers like Accra. Despite a reported 91% grid connection rate, only 17% of Accra’s households consider their electricity supply reliable (Afrobarometer, 2022). Traditional, binary metrics— focused solely on grid connection—fail to capture essential social dimensions such as reliability, affordability, equity, and resilience, particularly under intensifying climate and urban pressures. My thesis investigates persistent energy insecurity in Accra, Ghana’s capital, through the lens of dumsor—a term used to describe recurring power outages that disrupt daily life and expose the fragility of the centralized electricity system. Drawing on the frameworks of splintered urbanism and the techno-politics of infrastructure failure, the thesis explores how dumsor reflects institutional fragmentation, political contestation, and inequality in the energy infrastructure space. In response to dumsor, I examine whether decentralized energy systems, particularly solar, can offer a pathway to local energy resilience—defined here as the place-based capacity to withstand dumsor through cleaner, more affordable alternatives for sustainable and reliable power. The study combines a technical assessment of Accra’s solar potential with a critical analysis of policy frameworks, climate finance mechanisms, and political agendas. Grounded in fieldwork and interviews with stakeholders across the energy value chain—from regulators and municipal actors to utilities, solar providers, financiers, residents, and advocacy groups—my thesis identifies on-the-ground barriers to and opportunities for the energy transition. While distributed solar presents a promising alternative with broad reach, persistent challenges in affordability, coordination, and delivery capacity threaten its scalability. Without targeted policy interventions, there is a risk of reinforcing a new form of energy infrastructure splintering—where only the affluent benefit. My thesis concludes that addressing energy insecurity in Accra requires strategic institutional and policy reforms to reconfigure governance, empower municipalities, and enable inclusive financing and policy at the most local level to enable solar alternatives. Energy decentralization offers a promising path forward, but the thesis underscores the ongoing role of the state as a critical enabler of an energy transition that is sustainable and just.

From Vacant to Valuable: Building Community Wealth through Brownfield Redevelopment in Legacy Industrial Cities

2025-07-30T03:07:31Z

From Vacant to Valuable: Building Community Wealth through Brownfield Redevelopment in Legacy Industrial Cities Jex, Sara Lynn Recent federal investments in domestic manufacturing have renewed economic interest in legacy industrial cities across the United States. As these places attract new development, it is critical to safeguard against repeating the harms of the 20th-century exodus of industry and manufacturing jobs—when offshoring, suburbanization, and discriminatory housing policies deepened spatialized racial and economic inequalities. How can communities retain the wealth generated by new industrial investments, even if companies leave? This thesis explores how industrial brownfield redevelopment might utilize community wealth-building (CWB) strategies to advance equitable economic development. Focusing on the work of the Site Readiness for Good Jobs Fund in Cleveland, Ohio—a nonprofit preparing long-vacant industrial land for job-dense uses—it examines the potential for mission-driven organizations to use brownfield redevelopment to anchor wealth locally and proactively resist displacement. By analyzing case studies in Buffalo, Milwaukee, Chicago, and Philadelphia, the research tackles three questions: How do mission-driven organizations deliver community benefits through industrial brownfield redevelopments? In what ways do CWB models reshape how capital flows through redevelopment projects? And, what questions and decisions must the Site Readiness Fund consider to build lasting community wealth in Cleveland? Findings suggest that industrial brownfield redevelopment, when paired with strategic partnerships, site control, and a clear vision, offers a unique opportunity to implement CWB models. These strategies can help mission-driven organizations redistribute the risks and rewards of necessary public investments in brownfields and build trust with the community, ensuring that residents surrounding these reactivated sites benefit not just from new jobs, but from ownership and long-term economic power over their futures. The thesis concludes by applying these lessons to the Site Readiness Fund, outlining potential paths forward that embed economic democracy in the redevelopment of Cleveland’s legacy industrial areas.

Burning S(e)oul: A Body for Cremation

2025-07-30T03:06:49Z

Burning S(e)oul: A Body for Cremation Kwun, Namhi Every year, there are over 70,000 fatalities around Seoul, with only two operating crematoria in the city, that is over 100 bodies a day each institution needs to process efciently. By May 26, it would have been six years since my grandfather was gone in those fames. Threading the remnants of mourning, Burning S(e)oul, in forms of a short flm, is a dialogue between “absences” of bodies and architecture. It is presented as a triptych along three parallel timelines divided into fve tableaux. Narrating the aftermaths of death, it refects the bereaved, the deceased, and the workers’ perspective along three mandatory days of grieving. Absence in this paradigm is not solely physical or emotional but rather phenomenological— what appears a quotidian existence of oneself is stripped of its corpse, reafrming that the inherent genius loci of the crematorium instead refect a broader infuence that institutions have experienced since post-war Korea. It argues that the systematized practice of death processing is an apparatus used to sever the genealogy of individual bodies from their role in afrming personal and communal kinships. Embedded within its architectural design, this alienation dismantles time by shifting the condition of death processes as an engineered state, rather than historical or material one. This detachment is emblematic of the country’s postwar trajectory, where rapid modernization prioritized efciency over continuity, severing longstanding rituals that once bond personal grief to communal memory. The friction between an engineered present and an inherited past manifest as a form of cultural desynchronization— one where the ostensibly modern remains haunted by the traditional. This shift extends beyond mere technical or practical concerns; it represents a deliberate method of assimilating a nonlinear societal modernization—one that in its pursuit of progress, distances itself from historical trauma. Yet this tension does not merely mark a transition; it accumulates as a generational melancholy, where the urgency of progress leaves grief suspended in an unresolved state, neither fully severed nor meaningfully preserved.

Analyzing Risks in Voluntary Forest Carbon Offsets Using Open Data: A Hybrid Framework Integrating Retrieval-Augmented Generation in LLMs and Geospatial Analytics

2025-07-30T03:07:36Z

Analyzing Risks in Voluntary Forest Carbon Offsets Using Open Data: A Hybrid Framework Integrating Retrieval-Augmented Generation in LLMs and Geospatial Analytics Xu, Ziqing (Becky) The credibility of voluntary carbon markets hinges on the quality of carbon offset projects, particularly in forestry and land-use sectors where claims of additionality and emissions reductions are often disputed. This paper introduces a novel, open-source approach to evaluating carbon offset projects by integrating open datasets, satellite-based remote sensing, and large language models (LLMs). Focusing on additionality and baseline integrity, the study examines existing challenges—including inflated baselines, inconsistent standards, leakage risks, and limited transparency—and proposes a system to automate early-stage project assessment. The platform combines AI-driven document analysis and geospatial data processing to evaluate risk factors such as additionality, leakage, and policy compliance, offering stakeholders an accessible, scalable tool to identify high-integrity carbon credits and mitigate greenwashing. This work aims to enhance transparency, accountability, and trust in the voluntary carbon market through data-driven, user-friendly decision support.

Reduction of Radiation Produced in Ion Implantation Devices, and Measurement of Some Relevant Cross-Sections

2025-07-30T03:07:10Z

Reduction of Radiation Produced in Ion Implantation Devices, and Measurement of Some Relevant Cross-Sections Zangi, Arthur S. Ion implantation devices, machines which can very precisely dope semiconductors using beams of accelerated charged particles, have in recent years begun to be used in implanting high energy light ions, with energies greater than 1 MeV. This has caused unprecedented production of neutron and gamma radiation, particularly of neutrons from the ¹³C(alpha,n)¹⁶O reaction, creating an unacceptable radiation hazard. To address this issue, we undertake dose mapping and modeling efforts to create simulation tools in Geant4 which can accurately predict dose rates on the Axcelis VXE LT. Existing physics tools for modeling nuclear reactions have been shown to produce non-physical results at incident particle energies of 1 - 2 MeV, as these tools are frequently used for modeling reactions which may have energies into the GeV or even TeV range. To address these deficiencies, we construct a new drop-in physics model which uses relativistic kinematic equations to precisely predict the energy and angular distributions of secondary particles produced in Geant4 at low energies. This model relies on accurate cross-section data to describe the reaction; to address gaps in the literature on the two neutron producing reactions of interest to this work, we measure the angular dependent cross-section of the ¹³C(alpha,n)¹⁶O reaction over 7 angles, at the 2.605 and 2.670 MeV resonances, and we measure the total cross-section of the ²⁹Si(alpha,n)³²S reaction at 2.6 and 2.7 MeV. By implementing the new physics model and adding new cross-section data to the model of the ion implantation device, we are able to produce a high-fidelity simulation of radiation production and transport in ion implantation devices. Using this tool, we then propose solutions to mitigate radiation production within the ion implanter, reducing the radiation hazards of high energy ion implantation devices.

Radiation Effects on Thermal Properties of Advanced Nuclear Materials

2025-07-30T03:07:40Z

Radiation Effects on Thermal Properties of Advanced Nuclear Materials Johnston, Maren Understanding the effects of irradiation on critical thermophysical properties is fundamental for the advancement of next-generation nuclear systems operating in high-flux neutron and gamma environments. Zirconium hydride (ZrH) and yttrium hydride (YH) have emerged as promising neutron moderating materials due to their exceptional hydrogen density leading to superior moderating power. Yet, the radiation-induced microstructural evolution and its correlation to macroscopic thermal transport phenomena remain insufficiently characterized. In this work, ZrH and YH specimens were characterized pre- and post-irradiation via laser flash analysis, high-resolution dilatometry, and differential scanning calorimetry. Comparative analysis revealed that even low-fluence neutron irradiation induced complex defect clusters that degraded thermal diffusivity, while the crystallographic lattice parameters, vibrational energy states (inferred from thermal expansion measurements), and heat capacity exhibited an inconclusive response to radiation damage. To address limitations in current characterization methods for large-scale, anisotropic composite nuclear materials, we developed an advanced thermal transport measurement facility using infrared photothermal excitation. This platform enables spatially-resolved thermal diffusivity mapping of silicon carbide (SiC) composites—materials with complex three-dimensional fiber arrangements being evaluated for accident-tolerant fuel cladding applications. Complementary Thermal Conductivity Microscopy (TCM) measurements conducted at Idaho National Laboratory provided microscale resolution of constituent thermal properties, establishing a multi-scale characterization approach that bridges microscopic thermal transport mechanisms with bulk composite performance. These findings advance the qualification of advanced nuclear materials, enabling more accurate thermomechanical modeling and performance prediction under the extreme conditions of next-generation reactors.

Bragg Coherent Diffraction Imaging of Metal Microcrystals Using a Multipurpose In Situ Cell Design

2025-07-30T03:07:23Z

Bragg Coherent Diffraction Imaging of Metal Microcrystals Using a Multipurpose In Situ Cell Design Hultquist, Riley J. Structural materials are a key limiting factor in the safety, longevity, and efficiency of nuclear power plants. Advanced metal alloys show great promise for use in reactor environments, but ensuring their reliability requires a fundamental understanding of their microstructural evolution under extreme conditions. In situ X-ray experiments offer a powerful means to investigate nanoscale defect evolution under reactor-relevant conditions. Bragg coherent diffraction imaging (BCDI), a synchrotron X-ray technique, enables high-resolution 3D imaging of degradation processes. Combined with an experimental electrochemical cell, BCDI is a promising tool for providing insight into the problems facing advanced materials in next-generation reactor designs. In this work, a custom designed electrochemical cell, successfully adapted for use at four beamlines, was developed and used to demonstrate in situ corrosion and hydrogen embrittlement (HE) of nickel (Ni) and copper (Cu) microcrystals. HE experiments confirmed the hydrogen evolution reaction (HER) at Cu surfaces and bulk embrittlement, using a removable silver/silver chloride (Ag/AgCl) electrode to maintain a stable reference potential. The cell’s chemical durability was demonstrated during more than 30 hours of operation, wherein Ni microcrystals were subjected to boric acid (B(OH)3) and lithium hydroxide (LiOH) to simulate the corrosive coolant chemistry of pressurized water reactors (PWRs). BCDI revealed the evolution of phase and dislocations in a Ni microcrystal under these conditions, affirming its power as a nanoscale measurement tool. Furthermore, BCDI provided direct evidence of lattice expansion in Cu in response to cathodic reduction of hydrogen. Additional analysis reveals a selective beam relaxation effect on Ni microcrystals, providing further insight into radiation-material interactions. The findings of this work lay important groundwork for future advanced alloy development utilizing user-friendly in situ experimental cells.

Enabling Car-Free Living: Shared Micromobility and Public Transit Interactions in Chicago

2025-07-30T03:06:58Z

Enabling Car-Free Living: Shared Micromobility and Public Transit Interactions in Chicago Joyce-Johnson, Seamus C. Shared micromobility/bikeshare services and public transit both offer travel alternatives to the automobile in urban areas. While these services might be viewed as competitors in the urban mobility space, this thesis argues that each benefits from the other as part of a “package of options” available to the car-free or car-lite urban resident that together provide a comprehensive replacement for auto-mobility. This work centers on the Chicago mobility context. It compares shared micromobility systems in Chicago, Los Angeles, Austin, Pittsburgh, and Washington, D.C., each of which have varying levels of transit integration, ridership, ownership models, and fares. It finds that transit agency ownership of shared micromobility systems appears not to be a panacea and that truly integrated fares are not present even in agency-owned systems. It also finds that lower fares are present in systems with greater levels of public subsidy, regardless of the ownership model. The second part of the thesis characterizes the specific interactions between Divvy, Chicago’s main scooter- and bikeshare system, and the Chicago Transit Authority (CTA). It tests the suitability of novel data sources, including CCTV footage and CTA farecard transactions, for inferring transfers between the two systems and finds that existing spatiotemporal inference methods do not capture the wide heterogeneity in transfer rates among rail stations. Although Divvy has stations near most CTA rail stations, there is room for improvement in the rapidity of these transfers. Using GIS and open-source routing tools, the thesis finds an average walk time of 2.1 minutes from CTA entrances to the nearest Divvy station and suggests high-priority relocations. The third part of the thesis presents preliminary results from a survey of Chicago-area residents probing their attitudes and behaviors regarding shared micromobility and public transit. The survey results showed some evidence of complementary use between the two modes. The thesis concludes with a set of recommendations for the CTA regarding improvements in its integration with Divvy.

Subaltern Spaces in the Ancient City: Cultural Identity, Spatial Memory, and Networks of Meaning in Roman Pompeii

2025-07-30T03:07:13Z

Subaltern Spaces in the Ancient City: Cultural Identity, Spatial Memory, and Networks of Meaning in Roman Pompeii Dufour, Curtis This thesis is about subaltern spaces and identities in the Roman colony of Pompeii—an ancient city notably destroyed and preserved by the eruption of Vesuvius in 79 CE; one that has been widely studied for its preservation of a Roman urban environment that was ‘frozen in time’. The excellent preservation of the site reveals a colonial material record that has long encouraged terminal narratives of Roman acculturation, so-called Romanization, which have devalued the plurality of identities and meanings found in the dispersed spaces and imageries of the ancient city. Rejecting this unilinear narrative of colonization, this thesis instead examines the networks of meaning tied to subaltern spaces, architectures, and imageries of Pompeii under Roman colonial rule. In doing so, this thesis adopts a middle-range approach to the study of Pompeii’s spaces—giving attention to the distinct elements of the material record while acknowledging their interrelations that form networks of meaning stretching across time, space, and culture. These networks shaped and collated the distinctive spatial and imagistic elements constructed in the city under Roman rule—creating cohesive and legible spaces that recursively engaged with the diverse population of the city. Engaging in a ‘peopling’ of the past—that is, reimagining the lived experiences of subaltern Pompeian residents within the ancient colonial city—this thesis explores how networks of meaning led to the persistence, subsidence, and emergence of subaltern identity spaces within the ancient colonial city—spaces that were erased, appropriated, and peripheralized under Roman colonial rule. Through a detailed analysis of the networked spaces in the city—employing methodological frameworks from urban planning, social geography, and urban ethnography—this thesis tracks the presence of the proposed networks of meaning attached to subaltern spaces within the spatial and imagistic environment of the Colonia Cornelia Veneria Pompeianorum. In doing so, this thesis finds that the plurality of identity spaces in Pompeii cannot be understood through top-down, unilinear narratives of domination and erasure; rather, they must be apprehended as dynamic social and spatial features wherein subaltern Pompeian identities persisted within the very frameworks intended to marginalize them—producing hybridized spaces, syncretized architectural forms, and alternative discourses of place defined by the networked meanings that made the city legible to the diverse individuals who inhabited it.

From Parking to Parcels: The Potential for Microhubs in New York City’s Parking Garages

2025-07-30T03:06:52Z

From Parking to Parcels: The Potential for Microhubs in New York City’s Parking Garages Fabris-Green, Sarafina This thesis employs a site planning and policy perspective to explore how parking garages can serve as last-mile microhubs for e-commerce package deliveries in New York City. During the COVID-19 pandemic, deliveries accelerated, prompting a proliferation of “last-mile facilities,” the destination where parcels go just prior to final delivery. This surge of activity has prompted residents to raise complaints about trucks and vans driving through their neighborhoods and blocking streets or sidewalks when unloading their goods. In response, New York City government has been forced to think more proactively about the freight supply chain and its impact on the urban environment. New York and other cities have begun experimenting with the use of microhubs. Microhubs are small spaces in which packages are unloaded from vans and trucks onto smaller, more sustainable modes such as cargo bikes and handcarts. A commonly identified but understudied location for microhubs is the parking garage. London stands out as a city with this form of hub. This thesis employs three primary research methods—site observations, interviews, and case studies—to argue that parking garages could provide a solution to better utilize dense urban space in dense cities and improve quality of life for residents by reducing the negative impacts of existing last-mile warehouses and delivery vehicles, all while requiring minimal funding. This is shown through an analysis of existing microhub sites in London and how they relate to their urban surroundings. These findings are then applied to two distinct contexts and garage designs in New York City. Finally, the thesis offers site planning criteria that connect land use policy to the design of the facilities and the surrounding public realm through the concept of “planning at the interface.”

Financing Inclusive Resilience: Beyond the Economics of Infrastructure in Accra, Ghana

2025-07-30T03:07:16Z

Financing Inclusive Resilience: Beyond the Economics of Infrastructure in Accra, Ghana Goyal, Shubhi Global infrastructure losses from disasters now exceed an estimated US$700–845 billion annually, disproportionality affecting cities in the Global South (CDRI, 2023). Accra, as a rapidly urbanizing coastal city, faces recurring floods, coastal erosion, and rising vulnerabilities that erode development gains and entrench existing socio-economic inequalities. Climate-related disasters alone cost the city US$118 million in annual losses (CDRI, 2023), disproportionately affecting informal settlements. Infrastructure financing remains underfunded: the city needs US$37.9 billion annually to meet infrastructure needs by 2047 (GNIP, 2018), while a US$900 million gap undermines its Climate Action Plan (AMA, 2025). Despite increased national investment and brewing/blooming/?? global climate finance mechanisms, Accra struggles to attract and equitably deploy resources for inclusive resilience (CPI, 2023). Projects like the Greater Accra Resilient and Integrated Development (GARID) project expose systemic issues – prioritizing asset protection over community-centered design, with inadequate participation and social co-ownership (GARID PAD, 2019). This thesis critically examines how infrastructure financing mechanisms in Accra shape the potential to build inclusive resilience. Mapping the city’s financing landscape, it analyzes how institutional, financial, and governance arrangements influence the selection, distribution, and implementation of investments. Using GARID as a case study, the thesis applies a critical justice framework – drawing on distributive justice (who benefits and who bears the costs), procedural justice (who has voice and decision-making power), and epistemic justice (whose knowledge systems are valued in infrastructure planning) (Carolini, 2022) – to evaluate current infrastructure financing practices and explore opportunities to embed these justices in efforts to build resilience. Findings reveal that infrastructure financing decisions are dominated by centralized donor-driven and ministerial priorities, constrained by fiscal austerity, and evaluated through technocratic frameworks that marginalize community participation and local knowledge. Ultimately, the thesis argues that building inclusive resilience in climate-vulnerable cities like Accra requires transforming infrastructure financing systems to prioritize social inclusion, participatory governance, and knowledge pluralism – alongside, not subordinate to, economic efficiency.

Breaking the Loop: Climate-Driven Urbanism for America's Climate Migration Hubs

2025-07-30T03:07:03Z

Breaking the Loop: Climate-Driven Urbanism for America's Climate Migration Hubs Wagner, Cale As sea level rise and other climate impacts force millions across the U.S. to increasingly relocate in coming decades, how receiving cities accommodate this growth will significantly impact future emissions trajectories. This thesis examines the climate migration feedback loop, where climate migrants relocate to urban areas with carbon-intensive development patterns, inadvertently accelerating the climate change driving their displacement. Through analysis of three contrasting metropolitan areas—Atlanta, Portland, and Buffalo—this research demonstrates how different development approaches could either perpetuate or disrupt this feedback loop. Using a spatial methodology based on the urban transect model, the study compares Business-as-Usual scenarios that follow current development trends with Climate-Driven Reform scenarios that redirect growth toward transit-accessible, walkable locations. The research reveals that Climate-Driven Urbanism can meaningfully reduce both land consumption and emissions compared to conventional development patterns. These reductions stem not from technological advancement or behavioral change, but from strategic spatial reorganization of the same migrating population, with each metropolitan area demonstrating unique implementation pathways. By connecting regional migration flows to metropolitan development scenarios and neighborhood design interventions, this thesis offers planners, designers, and communities a framework for evaluating alternative futures that transform population growth from a spatial challenge and emissions liability into a catalyst for sustainable urbanism.

Reimagining the Role of City Owned Assets as Multifunctional Infrastructure: Serving Community Needs Through Collaboration

2025-07-30T03:06:45Z

Reimagining the Role of City Owned Assets as Multifunctional Infrastructure: Serving Community Needs Through Collaboration Smith, Alessandra This thesis investigates how city governments can reconceptualize infrastructure to reshape value creation for communities, using the City of Atlanta as a case study. By examining various departments and executive offices within Atlanta’s municipal structure, the research highlights the complexities of urban governance, where value is not uniformly defined or understood even within a single city. The central question guiding this work is: How can Atlanta’s city agencies collaborate across departments to identify opportunities to create more value through city-owned assets? Through stakeholder interviews and a mapping of publicly owned assets, this thesis explores an alternative, strategic approach to infrastructure one that supports not only urban planners but also city practitioners seeking to enhance residents’ quality of life through a value-based lens. The study also acknowledges the often overlooked, expanded value of built assets, which remains difficult to capture through conventional metrics. In doing so, it argues for a broader, more inclusive understanding of infrastructure’s role in urban life. This research offers a framework to view and explore infrastructure and values in a more comprehensive and holistic way compared to traditional methods. The framework centers strategy around prioritizing infrastructure planning, its relative outcomes, the spatial relationships and function of infrastructure, and the relationships that influence how people interact with infrastructure from a value-based lens.

“Whose Bronx?” Regime Politics and the Evolution of Community Power at the Kingsbridge Armory

2025-07-30T03:07:22Z

“Whose Bronx?” Regime Politics and the Evolution of Community Power at the Kingsbridge Armory Phillips, Natalie This thesis traces the 30-year history of redevelopment activities at the Kingsbridge Armory in the Northwest Bronx, as community groups have mounted an expanding challenge to development-as-usual in New York City. Using urban regime theory as a lens, I deploy archival research and interviews to assess the tensions that emerge when regime politics collide with a building movement of community power at the Kingsbridge Armory over time. I argue that New York City’s predominant urban economic development regime is not structured to accommodate an organization that is both a grassroots leader and a developer, and that as community power continues to evolve, the regime’s traditional arrangements become increasingly untenable. I ultimately assert that the increasingly structural movement of community power at the Kingsbridge Armory requires a reimagining of the informal processes, logics, and roles that have defined New York economic development.

China Dispossession Watch: Making Visible the Human Costs of Forced Land Expropriation in Urbanizing China

2025-07-30T03:07:07Z

China Dispossession Watch: Making Visible the Human Costs of Forced Land Expropriation in Urbanizing China Wu, Franny Xi This thesis critically examines China's land expropriation regime through a mixed-methods approach that integrates ethnographic investigation, quantitative economic analysis, and practical interventions developed in collaboration with affected communities. Drawing on extensive fieldwork in the Yangtze Delta Region, including 50 in-depth interviews with dispossessed residents, the research documents how China's urbanization strategy systematically captures land value through a dispossession machinery operating at the intersection of state power, market mechanisms, and contested citizenship. The ethnographies reveal a sophisticated system of dispossession enabled by a network of actors whose complementary roles maintain procedural appearances while facilitating extralegal tactics. Quantitative analysis demonstrates systemic under-compensation and value capture that leaves dispossessed households with livelihood disruption and housing insecurity. The research examines how affected communities navigate severe constraints through adaptive resistance strategies to overcome power asymmetries and institutional manipulation, and documents their economic, social, and health outcomes. Moving beyond analysis to practice, the thesis introduces two pragmatic interventions developed through collaborative design with affected communities: a digital humanities platform hosting multimedia ethnographic archives and a quantitative data dashboard; and an anti-displacement handbook which operationalizes research findings into actionable guidance calibrated to the specific challenges identified by community partners. These practical outputs, established as the China Dispossession Watch social venture, reflect a theory of change focused on addressing information asymmetries while building horizontal knowledge networks and long-term movement capacity.

An evaluation of the main harbors of Puerto Rico as to their potential for the location of port industries, with special reference to Jobos Harbor

2025-10-30T15:50:06Z

An evaluation of the main harbors of Puerto Rico as to their potential for the location of port industries, with special reference to Jobos Harbor Martinez-Sandin, Owen. Thesis: M.C.P., Massachusetts Institute of Technology, Department of City and Regional Planning, 1960; Includes bibliographical references.

Theoretical electron work functions of film coated metals

2025-10-30T15:50:05Z

Theoretical electron work functions of film coated metals Levine, Jules David. Thesis: M.S., Massachusetts Institute of Technology, Department of Nuclear Engineering, 1961; Includes bibliographical references (leaves 47-48).

Heat transfer from immersion heaters to boiling liquids

2025-10-30T17:03:43Z

Heat transfer from immersion heaters to boiling liquids Simpson, H. C. Thesis: M.S., Massachusetts Institute of Technology, Department of Chemical Engineering, 1951; Includes bibliographical references (leaves 161-163).

Effects of degradation rate and crosslink density of artificial skin on wound contraction

2025-10-30T17:03:43Z

Effects of degradation rate and crosslink density of artificial skin on wound contraction Lee, Elaine. Thesis: M.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1986; Bibliography: leaves 93-94.

Continual Learning for Engineering: Benchmarking and Exploring Strategies for 3D Engineering Problems

2025-07-08T03:06:49Z

Continual Learning for Engineering: Benchmarking and Exploring Strategies for 3D Engineering Problems Samuel, Kaira M. Engineering applications of machine learning often involve high-dimensional, computationally intensive simulations paired with limited and evolving datasets. As new designs and constraints emerge, models must adapt to incoming data without frequent retraining, which is often infeasible due to the cost of generating engineering data. Continual learning (CL) offers a promising alternative by enabling models to incrementally learn from sequential data while mitigating catastrophic forgetting, in which there is a loss of performance on previously seen examples. This thesis investigates the application of continual learning to regression-based engineering tasks, with an emphasis on surrogate modeling. We begin by benchmarking several foundational CL strategies, including regularization-based and rehearsal-based methods, across five diverse engineering datasets. To support this analysis, we construct nine new regression-focused continual learning benchmarks designed to reflect practical engineering scenarios. Results show that Experience Replay, a simple rehearsal method, consistently achieves strong performance, approaching "joint training" performance baseline of retraining from scratch, while substantially reducing computational cost. To further explore how rehearsal strategies can be made more efficient and effective, we propose two adaptive replay methods that prioritize memory samples based on forgetting dynamics. These methods extend previous adaptive replay strategies by using input clustering and representations from TabPFN, a foundation model for tabular data, to guide more informed sample selection without knowledge of experience boundaries. We evaluate their performance on both complex engineering datasets and controlled synthetic tasks. In scenarios where forgetting is unevenly distributed, the adaptive methods offer clear advantages, highlighting the potential for more intelligent replay under constrained resources. This work positions continual learning as a practical and effective strategy for handling dynamic engineering datasets, and offers new insights into how adaptive replay can enhance efficiency in data-limited, high-cost learning environments.

High-Order and Wavelet-Adaptive Immersed Methods for PDEs on Complex Domain Geometries

2025-07-08T03:06:47Z

High-Order and Wavelet-Adaptive Immersed Methods for PDEs on Complex Domain Geometries Shen, Changxiao Nigel The development of immersed methods brings a promising solution to the numerical simulation of interface-coupled multi-physics problems, such as multi-phase flows and fluidstructure interactions. This renders necessitates the design of novel high-order and efficient solvers based on immersed methods. This thesis examines two pivotal aspects of these methods: firstly, the acceleration of computational processes via adaptive resolution strategies; and secondly, the enhancement of accuracy order while sustaining numerical stability. To achieve the former, we develop a novel wavelet transform algorithm applicable to computational domains with arbitrary geometries. This wavelet transform maintains the order of the wavelet and serves as an indicator for local truncation error (LTE), resulting in an adaptive resolution strategy with explicit error control. To address the latter, we introduce a fifth-order upwind finite difference (FD) scheme that sustains numerical stability across any immersed interface discretization.

Constrained and High-dimensional Bayesian Optimization with Transformers

2025-07-08T03:06:46Z

Constrained and High-dimensional Bayesian Optimization with Transformers Yu, Rosen Ting-Ying This thesis advances Bayesian Optimization (BO) methodology through two novel algorithms that address critical limitations in handling constraints and high-dimensional spaces. First, we introduce a constraint-handling framework leveraging Prior-data Fitted Networks (PFNs), a foundation transformer model that evaluates objectives and constraints simultaneously in a single forward pass through in-context learning. This approach demonstrates an order of magnitude speedup while maintaining or improving solution quality across 15 test problems spanning synthetic, structural, and engineering design challenges. Second, we propose Gradient-Informed Bayesian Optimization using Tabular Foundation Models (GITBO), which utilizes pre-trained tabular foundation models as surrogates for high-dimensional optimization (exceeding 100 dimensions). By exploiting internal gradient computations to identify sensitive optimization directions, GIT-BO creates continuously re-estimated active subspaces without model retraining. Empirical evaluation across 23 benchmarks demonstrates GIT-BO’s superior performance compared to state-of-the-art Gaussian Process-based methods, particularly as dimensionality increases to 500 dimensions. Together, these approaches establish foundation models as powerful alternatives to Gaussian Process methods for constrained and high-dimensional Bayesian optimization challenges.

Enabling Starting Material-Oriented Strategies in Computer-Aided Synthesis Planning With a Bidirectional Search Algorithm

2025-07-08T03:06:56Z

Enabling Starting Material-Oriented Strategies in Computer-Aided Synthesis Planning With a Bidirectional Search Algorithm Yu, Kevin Retrosynthesis, in which one proposes a reaction pathway towards a target molecule from simpler starting materials, is a fundamental task in synthetic chemistry. Current computational search methods assume the sufficiency of reaching arbitrary building blocks but fail to address the common real-world constraint where the use of specific starting materials is desirable. To this end, this thesis reformulates computer-aided retrosynthesis as a starting material-constrained problem, in which one or more starting materials are given as input in addition to the target structure. Under this formulation, we are able to apply novel strategies to more efficiently navigate the combinatorial explosion of reactions to consider during synthesis planning. First, we demonstrate how training on multi-step synthesis routes inferred from a reaction base allows a neural network to predict the number of steps needed to synthesize targets from other specified building blocks. Using this learned value function in combination with recent advances in bottom-up synthesis planning, this thesis proposes a novel bidirectional CASP algorithm, DESP (Double-Ended Synthesis Planning). We demonstrate the utility of DESP through a number of empirical benchmarks and case studies.

Putting Lipstick on a PIG: Modeling Pine Island Glacier (PIG) Shear Margin Collapse with Compressive Arch Failure and Observations

2025-07-08T03:06:55Z

Putting Lipstick on a PIG: Modeling Pine Island Glacier (PIG) Shear Margin Collapse with Compressive Arch Failure and Observations Wells-Moran, Sarah Pine Island Glacier (PIG) drains 10\% of the West Antarctic Ice Sheet and has undergone rapid change in the observational record, contributing to uncertainty in sea level rise projections. The Pine Island Ice Shelf (PIIS), which provides a key buttressing force that slows the flux of ice across the grounding line, has accelerated 800 m/yr (an approximate 20\% increase in speed) between 2015 and 2024, accompanied by a visible increase in damage in the Southern shear margin, indicating a partial loss of buttressing. We examine this loss of buttressing to determine the mechanisms through which ice shelves collapse. Buttressing allows an ice shelf to increase in thickness to a point at which the stresses within the ice would exceed the tensile yield strength without the compression provided by buttressing. Following the Compressive Arch Theory proposed by \textcite{doake_breakup_1998}, we hypothesize that when a calving event decouples the ice shelf from a buttressing region, the thicker ice shelf is thrown into tension and rapidly collapses, as happened with the Larsen B Ice Shelf in 2002. We use the Ice-sheet and Sea-level System Model to investigate the instantaneous stress response to loss in buttressing on an idealized glacier, with the goal of finding the changes in shear margin buttressing that most accurately recreate observed changes. In our model, we are only able to replicate observed changes in stress regime by decoupling both shear margins, suggesting the PIIS is currently providing negligible buttressing, allowing PIG to accelerate, thin, and retreat. We construct a timeline of shear margin evolution and collapse over the PIIS from 2015 to 2024 using model outputs of stress field response to changes in buttressing, coupled with observed changes in velocity, effective and principal strain rates, and calving events. Despite losing buttressing from both shear margins, the PIIS is still intact, contrary to our initial hypothesis on compressive arch failure. We re-frame Compressive Arch Theory to better capture the timescales involved in loss of buttressing. We posit that compressive arch failure from loss of buttressing on short time scales leads to rapid ice shelf disintegration, whereas compressive arch failure occurring on longer time scales allows the ice to viscously relax, leading to ice shelf thinning instead of collapse. This new framework for investigating loss of buttressing allows us to better assess the stability of ice shelves and more accurately model future Antarctic contributions to sea level rise.

Integration of Zip-formwork and conventional formwork systems for shape-optimized concrete in large scale construction

2025-07-08T03:06:53Z

Integration of Zip-formwork and conventional formwork systems for shape-optimized concrete in large scale construction Zhuang, Yingjia Cast-in-place concrete production plays a dominant role in the architecture, engineering and construction (AEC) industry, particularly in large-scale projects, contributing significantly to global material consumption, construction costs, and embodied carbon emissions. Shape optimized concrete has been developed as a solution for more affordable and sustainable construction using less material to create efficient structures that meet structural demands. Although extensive research and development has focused on applying shape optimization to prismatic concrete beams, these beams are often limited by the constraints of available formwork and are primarily designed as pre-cast components. This paper presents the results of optimizing the Zip-Form, a digitally fabricated formwork system made from mild steel, designed for forming shape-optimized concrete beams, and its integration with conventional formwork equipment. The study evaluates the structural performance, embodied carbon, and cost of the Zip-Form integrated system in comparison to a traditional formwork platform used for prismatic beams. The findings highlight the Zip-Form’s potential for forming shape-optimized concrete beams using cast-in-place methods, making it a viable solution for sustainable large-scale construction projects in the current industry. The methodology outlined in this thesis provides a comprehensive design process, beginning with the structural design of the shape-optimized concrete beams, followed by the design of the Zip-Form integrated formwork system to cast the beams, and concluding with an embodied carbon and cost analysis to evaluate the environmental and financial benefits. This thesis aims to bridge academic research and innovation with practical, real-world applications.

First Visible Wavelength Lightcurves for the Northern Hemispheres of Titania and Oberon

2025-07-08T03:06:52Z

First Visible Wavelength Lightcurves for the Northern Hemispheres of Titania and Oberon Colclasure, Abigail M. The most recently published lightcurves of the large Uranian satellites were published in 1989 and there have been no published lightcurves of the satellites’ northern hemispheres. In this work, I present the first visible-wavelength lightcurves of the northern hemispheres of Titania and Oberon. Observations of the Uranian satellites are inherently difficult given their proximity to Uranus. Contamination from stray Uranian light is a major challenge and the background near the satellites must be well characterized. I mitigated the effects of stray Uranian light using point spread function photometry. I modelled Uranus with a Lorentzian with the same full width at half max as the stellar point spread function. I also determined that Uranus’s profile is poorly modeled with a Gaussian or with the stellar empirical point spread function. After accounting for Uranian light in this way, there remains significant correlation between the photometric measurements of Titania and Oberon. I considered what may be causing this correlation and suggest several paths forward.

Traversing Rugged Domains: Explorations in Non-convex Optimization Theory and Software

2025-07-08T03:06:50Z

Traversing Rugged Domains: Explorations in Non-convex Optimization Theory and Software Dixit, Vaibhav Kumar This thesis introduces theoretical and computational frameworks for nonlinear, nonconvex optimization problems in statistics, machine learning, and optimal control. Disciplined Geodesically Convex Programming (DGCP) extends convexity verification to Riemannian manifolds, enabling optimization on curved spaces with global optimality guarantees. We develop rules and atoms for Cartan-Hadamard manifolds, particularly symmetric positive definite matrices, transforming non-convex problems into tractable ones through Riemannian geometry. We also present Optimization.jl, a unified interface for diverse optimization methods that supports specialized implementations for specific problem classes. Its modular architecture integrates automatic differentiation with an extensible plugin system. The framework’s capabilities are demonstrated through a GPU-accelerated hybrid method combining Particle Swarm Optimization with L-BFGS, and an augmented Lagrangian approach with stochastic inner optimizers that connects constrained optimization with machine learning techniques. Our work combines theoretical foundations with practical implementation, providing researchers tools to use advanced optimization methods without specialized mathematical knowledge.

Accurate Protein Function Prediction with Graph Transformer-Based Function Localization

2025-07-08T03:06:48Z

Accurate Protein Function Prediction with Graph Transformer-Based Function Localization Mitra, Shania Protein function prediction is a fundamental challenge in biology, crucial for understanding biological processes, disease mechanisms, and accelerating drug discovery. While computational methods leveraging sequence or structural information have advanced, accurately translating protein structure to function and pinpointing the specific residues responsible remain significant hurdles. Many existing deep learning approaches fall short, often relying on post-hoc analyses that lack specificity or fail to directly integrate functional site identification into the prediction process. In this study, we introduce the Protein Region Proposal Network (ProteinRPN), a novel graphbased deep learning framework designed to address these limitations. ProteinRPN is the first model to integrate the proactive identification of functional regions within the Gene Ontology term prediction pipeline. The core of the model is a Region Proposal Network module that processes protein structure graphs (residues as nodes, contacts as edges) to identify potential functional regions, termed anchors. These anchors are subsequently refined using a multi-stage process involving a novel differentiable node drop pooling layer that incorporates domain knowledge. A functional attention layer further enhances the representations of predicted functional nodes, and a Graph Multiset Transformer aggregates this localized information into a comprehensive graph-level embedding for final prediction. The model is optimized using a combination of cross-entropy classification loss, supervised and self-supervised contrastive learning losses (SupCon and InfoNCE) for robust representation learning. Evaluated on standard benchmarks derived from the DeepFRI/HEAL datasets, ProteinRPN demonstrates state-of-the-art performance, consistently outperforming existing sequencebased and structure-based methods across all three Gene Ontology domains (Molecular Function, Biological Process, Cellular Component) based on standard CAFA metrics (Fmax, AUPR, Smin). Notably, ProteinRPN achieves significant improvements over strong baselines like HEAL, with AUPR (Area under Precision Recall curve) gains of approximately 15.4% (BP), 8.5% (CC), and 1.3% (MF). Furthermore, ablation studies validate the contribution of each key component, particularly the region proposal mechanism. Qualitative analysis confirms the model’s ability to accurately localize known functional residues within protein structures, offering enhanced interpretability. By directly modeling and identifying functionally relevant structural regions, ProteinRPN presents a robust, interpretable, and high-performing approach to structure-based protein function prediction. This work contributes a novel framework that bridges the gap between structural information and functional annotation, offering potential for deeper biological insights and advancing computational tools for understanding the proteome.

Axial vibration of steam turbine buckets

2025-10-30T17:03:43Z

Axial vibration of steam turbine buckets Ewert, Richard H. Thesis: M.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1938; Includes bibliographical references (leaf 56).

The deflection of steam turbine diaphragms

2025-10-30T15:50:03Z

The deflection of steam turbine diaphragms Prohl, Melvin Albert. Thesis: M.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1938

A new railway labor plan

2025-10-30T17:51:28Z

A new railway labor plan Gilman, Jonathan C. Thesis: M.S., Massachusetts Institute of Technology, School of Industrial Management, 1963; Includes bibliographical references (leaves 119-121).

Transient analysis of marine steam turbine, propeller and ship dynamics.

2025-10-30T15:50:05Z

Transient analysis of marine steam turbine, propeller and ship dynamics. Stang Lund, Emil. Thesis: M.S., Massachusetts Institute of Technology, Department of Naval Architecture and Marine Engineering, 1965; Bibliography: leaves 79-91.

Strengthening Value Chains for Developing and DeployingBatteries in the Global South

2025-07-01T03:05:08Z

Strengthening Value Chains for Developing and DeployingBatteries in the Global South Munjal, Mrigi This thesis presents an integrated assessment of the elements required to strengthen the battery industry in emerging markets. It articulates a synergistic approach to fostering resilient battery value chains that are critical for the sustainable energy transition in the Global South. The first part touches upon building a more diversified and secure raw material base is essential for robust battery value chains in developing economies. It establishes the groundwork by proposing a potential pathway to diversify the global lithium supply chain by examining the potential of lithium mining in Arkansas through stakeholder analysis and policy recommendations. The second part underscores the importance of technology adaptation and process innovation in developing cost-effective battery chemistries suitable for the distinct conditions of the Global South. This part of the thesis addresses the technological challenges in scaling up battery production, focusing on sodium-ion batteries (SIBs) as a promising alternative to lithium-ion systems. Through an innovative application of natural language processing, this analysis distills the vast landscape of SIB research to identify scalable solutions for electrode design and manufacturing. The final part of the thesis converges on the deployment aspect of batteries, scrutinizing the role of Battery Energy Storage Systems (BESS) in three distinct emerging markets: India, South Africa, and Malawi. It offers a granular perspective on the application of BESS within varied energy landscapes, advocating for the customization of storage solutions to local market realities. This illuminates the transformative potential of BESS for enhancing grid stability and enabling renewable energy integration, thereby empowering the Global South to leapfrog to a resilient and green energy paradigm. This thesis coalesces into a comprehensive framework that underscores the multifaceted aspects of value chain enhancement—from mineral sourcing and battery chemistry innovation to end-use applications.

A computer program for assessing the economics of hie[r]archical systems : an application in engineering project evaluation

2025-10-30T17:03:43Z

A computer program for assessing the economics of hie[r]archical systems : an application in engineering project evaluation Hagen, Arnulf. Thesis: M.S., Massachusetts Institute of Technology, Department of Ocean Engineering, 1990; Title as it appears in the M.I.T. Graduate List, Feb. 1990: A computer model for assessing the economics of hierarchical systems.; Includes bibliographical references (leaves [73]-85).

BIOSENTERO: Bioinspired Soft Enteroscopic Robot for Facilitating Locomotion, Steering, and Intervention in the Deep Small Intestine

2025-07-09T03:14:03Z

BIOSENTERO: Bioinspired Soft Enteroscopic Robot for Facilitating Locomotion, Steering, and Intervention in the Deep Small Intestine Jebran, Ahmad Mujtaba Diagnosing and treating small intestinal disorders such as bleeding, inflammatory bowel disease, and tumors pose significant challenges due to limitations in accessing this anatomical compartment. To address these challenges, we develop BIOSENTERO, a bioinspired soft enteroscopic robot, to facilitate deep small intestine procedures, which addresses challenges associated with locomotion, steering, and intervention faced by existing soft robotic systems. BIOSENTERO features a hollow-cylinder design consisting of a linearly deformable soft pneumatic actuator as the robotic body, two radially expandable soft pneumatic actuators wrapped with Kirigami sleeves as the robotic head and tail units, a central hollow channel for housing accessory endoscopic tools, and a control box and joystick for navigation. The robot's body is a fiber-reinforced actuator with four inflatable chambers, enabling versatile movements, including axial expansion and contraction and bending over 90 degrees for 360-degree planar access. The dynamic Kirigami sleeve design achieves clinically acceptable friction force on intestinal mucosa with radial expansion, while minimizing tissue distention. A reinforced central channel supports the passage of tools to facilitate diagnostic and therapeutic interventions. A control box supports efficient locomotion and steering, achieving autonomous speeds of ~100 mm/min in vitro and ~43 mm/min in ex vivo intestinal tissue, and an assisted speed of ~200 mm/min in pig studies, without overdistention. Through in vivo pig studies, we demonstrated BIOSENTERO's potential for tissue biopsies, localized drug delivery, and real-time visualization in the deep intestinal region, without causing tissue overdistention and damage.

Structural Wireless Delamination Sensor

2025-07-09T03:13:56Z

Structural Wireless Delamination Sensor Ghosh, Aniruddha Composite materials, particularly laminated fibre reinforced polymer composites, have gained widespread acceptance in various industries due to their superior strength-to-weight ratio and corrosion resistance. The phenomenon of cracking between plies/laminae of such a layered composite is commonly referred to as delamination and occurs due to various reasons, such as corrosion and fatigue of the structure. Structural integrity can be enhanced by monitoring delamination and, ideally, to have a sensor that can continuously monitor the delamination extent. The delamination sensor proposed in this thesis (termed as Wireless Interlaminar Nano Sensor, or WINS) is an LC resonant circuit (resonant frequency fₛ = 1 MHz), and unlike prior sensors, is comprised solely of structural materials: a structural epoxy and carbon nanotubes (CNTs). The delamination crack causes a change in capacitance of the sensor leading to a change in its resonant frequency. The wireless sensor operation was demonstrated using an LC resonant circuit implemented on a printed circuit board, which is termed the sensor emulator (SE). A wireless sensing circuit and reader provided by Analog Devices Inc. was used for the initial measurements using the SE and later, the proof-of-concept (PoC WINS) devices. The PoC WINS device is a CNT-polymer nanocomposite based parallel plate capacitor, adhesively bonded between two composite laminates, and connected in parallel to the capacitor of the SE. The PoC WINS device was subjected to loading in the Mode-I configuration to induce delamination crack growth. The quality factor Q of the SE was varied (Q = 18, 3.2, 1.6, 0.8) by adding different external resistors, and a signal was acquired wirelessly for each value of Q as the delamination crack propagated. The wirelessly acquired signal was also sampled (sampling frequency Fₛ = 100 MHz) and analyzed to estimate the resonant frequency of the sensor. The effect of low sampling frequency was studied by downsampling of the acquired signal by a factor of 100. When Q was large (Q= 18), a change of∼2 kHz in the resonant frequency could be detected, corresponding to a change in capacitance of∼100 pF. At smaller values of Q∼1, challenges encountered in wireless signal acquisition were the too-rapid decay of the sensor signal and low signal-to-noise ratio (SNR). A wireless sensing circuit was designed and developed to enable signal acquisition at Q ≤1. The SE was used in the feedback system of a modified Armstrong oscillator (MAO) to obtain a sinusoidal signal of constant amplitude (∼1 V, SNR∼100 dB) even at Q = 0.8. The frequency (f_AO) of the signal wirelessly acquired from MAO is a non-linear function of the capacitance and the quality factor Q of the sensor and was observed to be in the range of 2 MHz. The MAO was tested for its performance using PoC WINS devices. It was observed that capturing the output signal for a duration of∼100 µs was sufficient for the accurate estimation of frequency (standard deviation∼3 Hz). At Q = 0.8 of the sensor, the MAO was able to detect a change in capacitance of 100 pF. To enable the use of low sampling rate (Fₛ = 1 MHz) for wireless signal acquisition, enhance the sensitivity of detecting change in capacitance, and provide direct readout of the change in capacitance of the sensor, the MAO was made part of another circuit termed MAO+. In the MAO+, mixer and filter circuits were used to modulate fAO from∼2 MHz to∼180 kHz and then to∼25 kHz, allowing the use of sampling frequency as low as 50 kHz to estimate the frequency. A phase-locked loop was made part of MAO+ which enabled direct readout of the change in capacitance of the sensor through a 4 1⁄2 digit digital display. The MAO+ was independently tested using PoC WINS devices and was able to detect a change in capacitance (at Q= 0.8 of the SE) of∼10 pF, corresponding to∼200 microns crack advance. This thesis presents the design, implementation, and operation of a wireless sensing circuit that allows signal acquisition at a low quality factor (Q ≤1) without compromising the SNR, demonstrating the first practical (wireless, made out of structural materials) delamination sensor for advanced composites.

High-Efficiency Soft-Switched Pulsed Plasma Bias Supply System

2025-07-09T03:14:00Z

High-Efficiency Soft-Switched Pulsed Plasma Bias Supply System Estrin, Julia Radio Frequency (RF) generators play a crucial role as bias voltage sources in plasma-enhanced semiconductor manufacturing processes. Employing pulsed waveforms to generate plasma offers significant improvements in manufacturing precision. However, producing these waveforms is challenging due to the need for high voltages (kilovolt range), high frequencies (hundreds of kilohertz to low megahertz), precise timing, and broadband frequency content. Traditional methods to generate these waveforms are limited by semiconductor voltage ratings, leading to either low-voltage waveforms or complex circuits to achieve higher pulse voltages. This work presents a simple, compact, and efficient method for generating a pulsed bias voltage for plasma processing. The approach involves synthesizing the pulsed waveform at a low, convenient voltage and then using a transformer to step up the voltage to the desired level. A low-leakage inductance coaxial cable-based transformer is developed to provide scaling with sufficient fidelity across a wide frequency range. Zero voltage switching (ZVS) is achieved on all devices, ensuring highly efficient operation. The proposed system is validated through a lab bench prototype that generates pulses of 2.1 kV at a frequency of 400 kHz. Additionally, this system allows for adjustments in pulse duty ratio and slew rate, offering enhanced control and versatility for various applications.

Development and Evaluation of a Potentially Wearable Device for Circulating Cell Monitoring

2025-07-09T03:13:53Z

Development and Evaluation of a Potentially Wearable Device for Circulating Cell Monitoring Jang, Kyuho Monitoring circulating cells is crucial for assessing cancer metastasis and evaluating the efficacy of chimeric antigen receptor (CAR) T-cell therapies. Traditional blood-draw methods face challenges such as discontinuous monitoring and potential cell degradation, leading to inaccurate estimations. In vivo flow cytometry (IVFC), which measures real-time cellular response to laser illumination such as fluorescence, presents a viable alternative. However, its application in humans has been limited by the bulky design of existing devices and configurations unsuitable for larger organisms. This thesis introduces a novel, wearable fluorescence IVFC device tailored for human use, featuring a compact laser diode and silicon photomultiplier (SiPM) to enhance portability and functionality. The device includes a specialized optical system similar to a fluorescent microscope, which optimizes the signal- to-noise ratio by maximizing cellular fluorescence and minimizing background interference. Experimental determination of the limit of detection (LOD) for the SiPM and device establishes their detection capabilities and operational stability. Theoretical evaluations confirm that while the device can detect individual fluorescent cells in vitro, its current configuration does not support this sensitivity in vivo. The thesis also proposes strategies to improve the device’s sensitivity, aiming for reliable in vivo detection of single fluorescent cells.

Geometrically Programmed Nano-Resistors for Ultra-Robust Artificial Neural Network Accelerator

2025-07-09T03:13:54Z

Geometrically Programmed Nano-Resistors for Ultra-Robust Artificial Neural Network Accelerator Lee, Giho Despite the transformative advance in artificial intelligence (AI), the AI processing hardware have not matched the speed and power-efficiency requirement, restricting the realization of the full potential of AI and requiring innovation in AI hardware. Data transmission bottleneck between memory and processor has been pointed out as main source of poor computing speed and power efficiency. By embeding neural weights in hardware to minimize data transmission, non-volatile memory (NVM)-based in-memory computing have expected to have several orders of speed and power-efficiency boosts. However, its practical implementation as a next generation AI hardware has been not successful due to the non-idealities in NVMs including unstability, poor state resolution, challeng in programming, and systemon-a-chip (SoC) incompatibility. This thesis introduces ultra-accurate and ultra-robust geometrically programmed nano-resistor (GPNR) that can overcome NVM non-idealities and enable commercial AI accelerator based on analog in-memory computing. The state-of-theart 6-bit conductance state resolution and 8-bit stability of nano-resistor was realized by channel geometry optimization and thermodynamically stable material, while SoC imcompatible programming in NVM devices is omited. To evaluate the computing performance, experimental vector-matrix multiplication (NVM) operation were performed, showing 5-bit accuracy operation with 28x28 GPNR array without selectors. Finally, AI inference simulation was performed with simplifed 5x5 cropped MNIST digit image classification task. GPNR-based final classification layer demonstrates 91.0 % accuracy, comparable to the software limit of 93.2 %. The outcomes of this research not only bolster the feasibility of GPNR technology in practical applications but also highlight the potential for future advancements in AI accelerators that can fully harness the capabilities of analog in-memory computing.

Proton Exchange Membrane Electrolysis Applied to the Dehydration of Cow Milk

2025-07-09T03:13:59Z

Proton Exchange Membrane Electrolysis Applied to the Dehydration of Cow Milk Morice, Peter G. The dehydration of cow milk to powder form extends product shelf life and reduces product shipping costs and emissions. However, the existing thermal methods commonly employed by the dairy industry produce harmful emissions in the combustion of fossil fuels. This work explores the potential role of an electrochemical alternative method of proton exchange membrane (PEM) electrolysis in the process of concentrating milk solids. Although the thermodynamic specific energy of electrolysis at [mathematical notation] is high compared to existing thermal methods around [mathematical notation], experimental results for PEM electrolysis assisted by mechanical centrifugation suggest a specific energy closer to [mathematical notation] is possible. The energy competitive PEM electrolysis method has the additional benefit of zero emissions when supplied by renewable energy sources. Analysis of milk solids processed by the electrolysis assisted method shows promising levels of high fat, mineral, and total protein content, with liquid chromatography quantifying both casein and whey protein types retained in the solid product.

Design/System Technology Co-optimization of Gallium Nitride High Electron Mobility Transistors for Next-G 3DIC Heterogeneous Integration of Gallium Nitride and Si CMOS

2025-07-09T03:14:02Z

Design/System Technology Co-optimization of Gallium Nitride High Electron Mobility Transistors for Next-G 3DIC Heterogeneous Integration of Gallium Nitride and Si CMOS Yadav, Pradyot Singh With data rates pushing into the Tbps, there is an urgent need for the use of mmWave and subterahertz RF front ends and transistors. Gallium Nitride (GaN) transistors have continued to push the limits of high-power density, high frequency semiconductor devices. The future of GaN radio frequency (RF) circuit technology is at the intersection of device engineering, advanced packaging, and circuit design. Currently, these are three separate fields with little-to-no communication between them, resulting in critical limitations to today’s technology. These fields need to collaborate, crosspollinate, and intersect in order to modernize and advance innovation for the next generation of RF front ends. To design the most efficient W-G Band devices and systems, we must embrace a design/system-technology co-optimization (DTCO/STCO) approach, that combines innovative GaN transistors with engineered linearity, novel heterogeneous integration with state-of-the-art Silicon (Si) bias and control circuitry, and advanced physics-based modeling. This thesis presents the development of a 3DIC consisting of GaN HEMTs and Si CMOS BEOL, in particular W-band GaN HEMTs, Si CMOS BEOL circuits in Intel16, and advanced packaging of dielets. The full chip continuum is investigated and innovated upon.

Al-Ni Nanofilm Powered Miniature Linear Actuator for Medical Devices

2025-07-09T03:13:57Z

Al-Ni Nanofilm Powered Miniature Linear Actuator for Medical Devices Cotey, Samuel A. Amedical device is sought to improve drug delivery options available to healthcare providers and patients; our initial focus is to develop a piston that can provide the power necessary to do an injection from an ingestible device. While many methods to administer drugs currently exist, the administration method in many cases is largely driven by factors that supersede ease, convenience, or comfort for the patient [1]. Many patients are saddled by cumbersome drug regimens that expose them to the risk of complex and painful drug administration paths and dependence on medical sharps [2, 3]. For these patients, being able to take injectable drugs orally allows them to use what appears to them to be simple, traditional drug delivery methods in lieu of injections that are painful and inconvenient. In order to perform an injection with a device that fits within an ingestible form factor, a novel piston is required. A concept design for an Al-Ni nanofilm powered miniature linear actuator has been developed in order to perform jet injections from within the gastrointestinal anatomy of a patient. This actuator consists of a small pressure vessel filled with liquid alcohol that undergoes a phase change to gas and generates pressure that can be used to cycle a piston in a drug loaded cylinder. Via exothermic reaction, nanofilm deposits thermal energy into the alcohol filled pressure vessel in order to generate the pressure needed to perform a jet injection. Cylindrical pressure vessel chambers with a diameter of 7mm and heights ranging from 3mm to 7.5mm were 3D printed and used to measure peak internal pressure vessel pressure as well as work output. The piston was used to push incompressible fluid through a nozzle in order to characterize the actuator’s work output. By using Bernoulli’s Equation, pressure on the piston head as a function of piston location along the stroke length was determined to characterize actuator performance as a function of pressure vessel size. The pressure vessel and the piston were modeled theoretically and empirically in order to identify the relevant design parameters so the piston can be effectively incorporated into the overall injection device.

Droplet Based Microalgae Photobioreactor for Biofouling Prevention

2025-07-09T03:14:00Z

Droplet Based Microalgae Photobioreactor for Biofouling Prevention Callan, Tess A. Microalgae have a wide variety of applications aiding in sustainability, yet during the cultivation process, photobioreactor biofouling remains an issue. It blocks light from entering the reactor and necessitates reactor cleaning, ultimately reducing overall reactor productivity and increasing cultivation costs. Here we investigate a new type of reactor that removes the possibility of biofouling by growing the algae in aqueous droplets surrounded by oil that preferentially wets the reactor surface. We first look into growing the algae in droplets and discuss major parameters that will be impacted. Then, we show a droplet-based reactor that demonstrates the potential to scale the system with similar growth rates to industry. Finally, we investigate the impact on major costs to confirm the economic viability of transitioning to this reactor. Overall savings in the cultivation process, mainly from power reduction and biofouling prevention, are shown.

A Business and Redevelopment Outline for the Re-Use of a Prime Site in South Boston

2025-06-17T03:13:05Z

A Business and Redevelopment Outline for the Re-Use of a Prime Site in South Boston Proman, Zachary D. This development and business plan considers the neighborhood context and current market conditions characterizing the subject site’s redevelopment potential. The subject site, further defined in this thesis, is a prime parcel of land in the South Boston neighborhood of Boston, MA currently improved and used for quick-serve restaurant operations. Proximate to the Seaport, Fort Point, and Dorchester, South Boston is surrounded by demand drivers resulting in explosive growth that make it one of the most desirable and expensive housing submarkets in the entire City of Boston. Development considerations are fully defined in the report including zoning, equity, financial projections, ground lease, and market-level factors. A conclusion is made on the feasibility of the proposed project with recommendations for next steps resulting from the modeled base-case scenario. Market assumptions and any unresolved development issues are clearly identified and discussed.

Optimization of four independently-actuated, complementary-sized control valves and nozzle groups to improve steam turbine efficiency

2025-10-30T15:50:04Z

Optimization of four independently-actuated, complementary-sized control valves and nozzle groups to improve steam turbine efficiency Yeaple, Thomas L. Thesis: M.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1984; Bibliography: leaf 79.

An experimental investigation of magnet geometry and hysteresis on simultaneous lift and guidance ferromagnetic suspensions

2025-10-30T18:06:07Z

An experimental investigation of magnet geometry and hysteresis on simultaneous lift and guidance ferromagnetic suspensions Farley, Holt Leonard. Thesis: M.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1978; Includes bibliographical references.

The impacts of labor-intensive roads in Colombia : a framework for analysis, research design and preliminary test.

2025-10-30T18:06:08Z

The impacts of labor-intensive roads in Colombia : a framework for analysis, research design and preliminary test. Borrero Mutis, Santiago. Thesis: M.S., Massachusetts Institute of Technology, Department of Political Science, 1978; Bibliography : leaves 150-153.

Winter weather types of the eastern North Pacific and adjacent coastal and island areas

2025-10-30T17:51:28Z

Winter weather types of the eastern North Pacific and adjacent coastal and island areas Kosco, George Francis.; Dorsett, John O. F. Thesis: M.S., Massachusetts Institute of Technology, Department of Meteorology, 1940; Includes bibliographical references (leaves [44]-[45]).

Aircraft leasing and airline corporate strategy

2025-12-06T03:20:32Z

Aircraft leasing and airline corporate strategy Setyopurnomo, Rudy. Thesis: M.S., Massachusetts Institute of Technology, Sloan School of Management, 1992; Includes bibliographical references (leaves 130-131).

An interactive statistics package for the social sciences.

2025-12-06T03:20:35Z

An interactive statistics package for the social sciences. Lebling, Peter David. Thesis: M.S., Massachusetts Institute of Technology, Department of Political Science, 1973; Bibliography: leaf 92.

International political behavior: historical analysis of Scandinavia and the Netherlands.

2025-12-06T03:20:34Z

International political behavior: historical analysis of Scandinavia and the Netherlands. Deber, Raisa Rebecca Sarah Berlin. Thesis: M.S., Massachusetts Institute of Technology, Department of Political Science, 1971; Bibliography: leaves 176-185.

Effects of dietary protein deficiency during different stages of pregnancy on fetal development and maternal body composition and behavior

2025-12-06T03:20:37Z

Effects of dietary protein deficiency during different stages of pregnancy on fetal development and maternal body composition and behavior Zartarian, Gary Michael. Thesis: M.S., Massachusetts Institute of Technology, Department of Nutrition and Food Science, 1979; Includes bibliographical references.

An examination of private capital available to the railroad industry.

2025-12-06T03:20:36Z

An examination of private capital available to the railroad industry. Wait, Barbara Rust. Thesis: M.S., Massachusetts Institute of Technology, Sloan School of Management, 1979; Bibliography: leaves 103-106.

The effect of eddies on fCO₂ in the North Pacific surface ocean

2025-12-06T03:20:30Z

The effect of eddies on fCO₂ in the North Pacific surface ocean Padalino, Christine We investigate the impact of mesoscale eddies in the North Pacific on surface ocean fCO₂ using the in-situ measurements from the Surface Ocean CO₂ Atlas (SOCAT) to inform the importance of the mesoscale dynamics on global CO₂ fluxes. We sort SOCAT measurements from 2000-2019 by whether or not they are in an eddy, per- form basin scale analysis, and present case studies. The results show lower fCO₂ in both anticyclones and cyclones compared to the background ocean, with the mag- nitude of the anomaly varying seasonally and spatially. Due to the many potential mechanisms of the eddy impacts, we analyze a temperature normalized fCO₂ to tease apart the impact of altered temperature from a biological response or mixing. With this method, we find evidence that eddies are increasing the background biological activity. To further attempt to separate the different effects eddies could have on sur- face fCO₂ and CO₂ fluxes, we identify two long lived eddies with many measurements over their lifetimes to use as case studies. We find that both the anticyclonic and cyclonic eddy initially increase fCO₂, but at the end of the lifetime mixing likely plays a role in counteracting temperature effects. The investigation of the varying effects the mesoscale can have on CO₂ fluxes not only allows for a better understanding of how eddies will affect surface fCO₂ but also provides insight into the potential impact on global scale estimates. Our analysis shows that on average, while mesoscale eddies modulate surface ocean fCO₂, they do not have a detectable enhancement of the CO₂ flux in the North Pacific.

Nothing Unwanted: Prototyping Matter out of Place

2025-12-06T03:20:29Z

Nothing Unwanted: Prototyping Matter out of Place Wang, Yiqing What we discard never truly disappears. Accompanying the societal shift from post-war scarcity to a consumerist culture, the contemporary building industry relies on abundant virgin materials, machinery, and a global transportation network. Immersed in this culture of convenience, architecture has limited agency to engage responsibly and intimately with reclaimed materials. The design of waste, inevitably, often symbolizes the separation between society and its waste, marked by an intention to remove, re-form, and re-standardize. Zero-waste systems and circular economy often inadvertently create hidden wastes, labor, and carbon footprints, leading to an uneven distribution of environmental harms. The thesis explores the unique materiality of municipal waste, linking human living with their unwanted with an architectural prototype. The new "unwanted" architecture integrates local waste into an adaptive inventory, avoiding over-precision, over-purification, and over-modularization. Based on the characteristics of US municipal waste, local-sourced garbage, including e-waste, plastics, wood, paper, metal, dust, and food waste, is studied, calibrated, and assembled to create building components and rooms. The bottom-up approach offers a way to compute heterogeneous materials with digital methods and low-tech on-site operations to minimize environmental impact. The richness of space blurs the boundaries between domesticity and abjection and between the sublime and the disgusting. The prototypes aim to rebuild both the Functional and Emotional Unwanted and re-imagine a scalable and operable building system. The design contrasts the previously visible waste in architectural design with today's invisible waste stream due to sophisticated waste management. It demonstrates an intimate approach to the gigantic amount of urban waste, emphasizing its cultural, personal, and collective dimensions.

Mobile Multi-Bounce LiDAR

2025-12-06T03:20:21Z

Mobile Multi-Bounce LiDAR Somasundaram, Siddharth Single-photon avalanche diodes (SPADs) are emerging sensors that can measure the propagation of light in a scene, capturing higher-order reflections, shadows, and light transport that ordinary cameras are unable to. Measurement of these multi-bounce light paths is especially useful for non-line-of-sight (NLOS) imaging. The increasing availability of SPAD sensors on mobile devices (e.g. iPhone Pro LiDAR) raises the potential to enable NLOS capabilities on consumer devices in the future. Currently, these sensors are primarily employed for LiDAR-based depth estimation, with untapped potential in other applications. In light of recent advances in SPAD device development, the timing is opportune to revisit the applicability of multi-bounce LiDAR techniques on consumer-grade mobile devices. This thesis extends the applicability of multi-bounce LiDAR techniques from research-grade SPAD hardware to consumer-grade mobile LiDARs. First, we enable single-shot capture of two-bounce signals and remove the need for laser scanning by developing a tomographic formulation for two-bounce non-line-of-sight imaging. Second, we enable real-time non-line-of-sight capture at eye-safe laser power under object and camera motion. Our approach is inspired by principles from burst photography. We implement and evaluate the proposed algorithms in simulations and on experimental SPAD hardware. We also demonstrate real-time non-line-of-sight tracking on a consumer-grade smartphone LiDAR. Potential future applications of our results include "X-ray vision" in AR/VR, full-body tracking for AR headsets, room scanning for hard-to-reach areas, collision avoidance for autonomous vehicles, and robotic navigation.

Toward the Understanding of Brain’s Molecular Language

2025-11-20T03:14:57Z

Toward the Understanding of Brain’s Molecular Language Zoghi Tavana, Sara What underlies the extraordinary capacity of neurons to process information, form memories, and orchestrate complex behaviors? Over a century of research has established that proteins are the central functional molecules of the cell, yet translating this knowledge into an understanding of emergent neural phenomena and effective treatments for neurological disorders remains elusive. We argue that this paradox stems from studying proteins in isolation, overlooking how their function is fundamentally shaped by spatial context and interactions with DNA, RNA, other proteins, lipids, carbohydrates, and metabolites. This coordinated molecular interplay, we posit, ultimately gives rise to the complex neural circuits and behaviors observed in higher organisms. Intriguingly, Alfred Binet foreshadowed this perspective as early as 1889 when he suggested that even simple, single-celled organisms—lacking anatomically defined nervous systems—might harbor a "diffuse nervous system" of molecular interactions within their cytoplasm enabling complex behaviors. However, the historical progression of neuroscience, largely dictated by available methodologies and oscillating between siloed reductionist molecular approaches and systems-level analyses, has not yet been able to fully capture this intricate molecular choreography underlying neural function. In this review, we examine how studying molecular species in isolation, while yielding important insights, has ultimately proven insufficient for understanding emergent neural functions. We propose that recent technological advances in expansion microscopy, molecular anchoring, machine learning-enabled protein detection, and cryo-fixation now make it possible to map molecular networks in their native context. This integrative approach promises to illuminate the molecular "language" of the brain, shedding light on how collective interactions among biomolecules give rise to neuronal emergent abilities—and guide future therapeutic innovations.

Mechanism of hydrolysis of triphenylsilyl fluoride

2025-11-20T03:14:58Z

Mechanism of hydrolysis of triphenylsilyl fluoride Esteve Campderá, Ramón María. Thesis: M.S., Massachusetts Institute of Technology, Department of Chemistry, 1948; Includes bibliographical references (leaves 32-33).

Extractive and azeotropic distillation

2025-11-20T03:14:59Z

Extractive and azeotropic distillation Hughes, Richard R. Thesis: M.S., Massachusetts Institute of Technology, Department of Chemical Engineering, 1947; Bibliography: leaves 94-95.

Railroad reliability and freight car utilization : an assigned fleet model.

2025-11-20T03:15:01Z

Railroad reliability and freight car utilization : an assigned fleet model. Assarabowski, Richard John. Thesis: M.S., Massachusetts Institute of Technology, Department of Civil Engineering, 1976; Bibliography: leaves 132-133.

Brewing Resilience: A Case Study in Adapting Small Business Strategy with Systems Thinking

2025-11-12T05:01:55Z

Brewing Resilience: A Case Study in Adapting Small Business Strategy with Systems Thinking Jones, Andrew C. This thesis explores how systems thinking—a methodology often reserved for large organizations—can be effectively applied to small businesses facing complex challenges. Using Lamplighter Brewing Co., an independent microbrewery in Cambridge, Massachusetts, as a case study, the research examines how the brewery adapted to the disruptions of the COVID-19 pandemic and the evolving economic landscape that followed. It documents the iterative application of systems thinking principles to identify root causes, leverage points, and actionable solutions to address issues such as declining revenue, rising costs, and misaligned organizational structures. Lamplighter's interventions ranged from restructuring its management and marketing teams to pivoting its sales and production strategies. By leveraging tools such as causal loop diagrams and stock-and-flow models, the brewery uncovered systemic dynamics driving its performance. The research highlights the importance of iterative learning, targeted interventions, and holistic analysis in fostering resilience and sustainability in resource-constrained environments. While focused on the craft brewing industry, the findings offer transferable insights for small businesses in similarly dynamic sectors, demonstrating that systems thinking can empower smaller organizations to navigate complexity, adapt strategically, and thrive amidst uncertainty.

Deep Learning-Based Classification of Phonotraumatic Vocal Hyperfunction Severity from Stroboscopic Images

2025-11-12T05:01:41Z

Deep Learning-Based Classification of Phonotraumatic Vocal Hyperfunction Severity from Stroboscopic Images Balaji, Purvaja Phonotraumatic vocal hyperfunction (PVH) is a vocal disorder characterized by damaged vocal folds from excessive or abusive voice use. Clinical assessment of PVH relies on timeconsuming videostroboscopy examination, which poses challenges for large-scale clinical studies. We address the need for more efficient clinical assessment tools by proposing deep learning approaches for automatically detecting PVH severity from stroboscopic images. One of the main challenges in building deep learning models for this task is a lack of labeled stroboscopy data. Motivated by this challenge, we explore two approaches: direct classification and segmentation-then-classification. In the segmentation-then-classification approach, we first train a model to segment the glottis, a clinically relevant part of the vocal fold anatomy. Then, we use the predicted segmentation along with the stroboscopic image as inputs into a classification model. This approach helps to guide the model towards key anatomical features. We achieve up to 0.53 accuracy in four-class PVH severity prediction with the direct classification approach. Incorporating glottal segmentations improves the accuracy to 0.64, underscoring the value of providing anatomically-informed segmentations when assessing PVH severity. By creating an automated PVH severity tool, our work has the potential to help clinicians more efficiently monitor disease progression and to facilitate large-scale screening, thereby contributing to improved patient care.

Intuitive Audio Interaction and Control in Multi-Source Environments

2025-11-12T05:01:34Z

Intuitive Audio Interaction and Control in Multi-Source Environments Oduniyi, Erick O. In an increasingly noisy world, managing auditory focus is a persistent challenge. This thesis explores how embodied interactions—primarily head tracking, alongside experiments with gaze tracking, speech commands, and audio-visual segmentation—can enhance user control over complex auditory environments. By linking head orientation to volume adjustments, we investigated whether natural, instinctive movements could serve as intuitive, hands-free mechanisms for isolating and amplifying relevant sounds. User studies revealed that head tracking is effective in structured audio contexts, such as music, where distinct sources are easily separable. However, its utility diminishes in dense, overlapping conversations, highlighting the need for finer control mechanisms. While gaze and segmentation offer promising refinements, cognitive load and system responsiveness remain key challenges. These findings underscore that embodied audio interaction must be adaptive, content-aware, and seamlessly integrated with user intent.This research contributes to human-computer interaction by demonstrating both the potential and limitations of movement-based audio control. Future work should refine multimodal fusion, improve segmentation accuracy, and enhance accessibility to create systems that dynamically respond to users’ natural behaviors—reducing cognitive strain and enabling more fluid, user-centric auditory experiences.

Digital Thread Maturity in Manufacturing: A Cross-Industry Study Using the Model-Based Enterprise Capability Assessment Framework

2025-11-12T05:01:12Z

Digital Thread Maturity in Manufacturing: A Cross-Industry Study Using the Model-Based Enterprise Capability Assessment Framework Peters, Michael Scott Modern-day manufacturing organizations find themselves in volatile and competitive markets with increasing pressure to deliver products faster, at lower cost, and with increased quality. In response to this pressure, many organizations are considering how technological advancements may improve the efficiency of their product development operations. Leading organizations have digitally transformed their businesses by shifting away from manual processes, static documents, and siloed operations toward automation, model-based data, and interconnectivity enabled by a digital thread. Accordingly, organizations pursuing the competitive edge offered through the digitalization of their business operations have often used different assessment tools to benchmark their current capabilities and define their vision for the future of their organizational operations. This thesis proposes a set of model-based and digital thread capabilities that are central to the long-term success of product development operations, along with a corresponding maturity model that may be used to identify gaps between current- and future-state capability implementation. Using the proposed capability maturity model, known as the Model-based Enterprise Capability Assessment Framework (MECAF), this study evaluated and compared capability maturity across various organizations in the Aerospace and Defense, Automotive, and Heavy Machinery industries. Through interviews with each participating organization, this thesis also explores the expected benefits, common challenges, and anticipated value of implementing model-based capabilities. Additionally, this thesis proposes an approach to bridging the gap from strategy to implementation based on the lessons learned and best practices of the organizations studied.

Productivity in the Workplace for Product Development Teams

2025-11-12T05:01:01Z

Productivity in the Workplace for Product Development Teams Farfan Perdomo, Jorge Productivity is a measure of the value generated for every hour worked. In a product development team, productivity can be affected by endogenous and exogenous factors, such as biological rhythms, work style, availability, work interruptions, team size, location, and the management strategies taken in a project. These factors will have an effect on the amount of effective work value generated in a workweek. A mathematical model and a Monte Carlo simulation were used to quantitatively assess the impact of these factors on the estimated cost and duration of a product development project. Based on the model results, we determined that workweek capacity and interruptions in the workplace are central to productivity. In addition, we demonstrated that combining different management strategies could be used to bring the project back on schedule and within budget to reduce the effects of these inefficiencies due to diverse endogenous and exogenous factors. For these reasons, this case study on a product development project will provide insight to engineering managers and project leaders about the effects of these inefficiencies in the workplace. The findings will help pave the way toward a more accurate project estimation and better modeling of project dynamics to reduce the amount of uncertainty in product development teams.

Design and Implementation of a Nonblocking Randomized Work Stealing Scheduler

2025-11-12T05:00:47Z

Design and Implementation of a Nonblocking Randomized Work Stealing Scheduler Ali, Sabiyyah This thesis presents FLCN (Free of Locks, Cilk is Now), a nonblocking work-stealing runtime scheduler that supports Cilk multithreaded programming. The existing OpenCilk runtime system uses lock-based synchronization and thus suffers from lock contention, does not provide progress guarantees, and can experience performance degradation with high worker counts and in multiprogrammed scenarios. FLCN leverages the existing runtime system’s provably efficient scheduling algorithm and introduces several new data structures and concurrency protocols to form a correct and performant lock-free system. In addition to enabling fork-join task parallelism, FLCN supports other Cilk features such as reducer hyperobjects. Through analyzing the performance of FLCN on various canonical benchmark programs, I find that for programs with low amounts of work, FLCN performs worse than the existing runtime. However, for most programs, I find that FLCN is either competitive with or marginally outperforms the existing runtime. Additionally, FLCN consistently exhibits higher scalability than the existing runtime, performing especially better when using hyperthreads and in multiprogrammed environments. I also outline future work that could make FLCN a more comprehensive and performant system, including ideas for improving FLCN’s work efficiency that would in turn better its performance on programs with low amounts of work.

Exploring the Role of Foundation Models for Training Generalist Robot Learning Policies

2025-11-12T05:00:31Z

Exploring the Role of Foundation Models for Training Generalist Robot Learning Policies Feng, Eugenia Y. Numerous methodologies to solving goal-conditioned short-horizon tasks require hundreds of expert demonstrations, but these demonstrations are effort-intensive to collect, reducing the scalability of these approaches. Even with approaches that do work, they may have difficulty generalizing to slightly different settings. In this work, we explore two approaches to training generalist robot learning policies using large-scale foundation models. The first approach aims to use a video foundation model to generate task-conditioned synthetic demonstrations at scale from a single expert demonstration. The objective is to leverage these synthetic demonstrations as proxy for expert demonstrations to train models that learn rewards from expert videos for solving complex visual RL problems. The second approach seeks to improve upon the generalization ability of behavior cloning policies. Moving away from the use of videos for training, we explore using privileged representations such as keypoints or object-poses learned using open-set foundation models. By tracking pose or keypoint correspondences, the aim is to minimize the required number of demonstrations to achieve task completion and improve generalization within classes of objects.

Prompt Injection Generation Using Small Language Models with Reinforcement Learning with Artificial Intelligence Feedback

2025-05-20T12:38:32Z

Prompt Injection Generation Using Small Language Models with Reinforcement Learning with Artificial Intelligence Feedback Gupta, Aneesh Large language models (LLMs) have become an integral part of many fields from customer support automation to research assistants. However, despite their growing adoption, they face significant challenges, particularly when it comes to safety in sensitive contexts. Existing methods like Reinforcement Learning with Human Feedback (RLHF) and keyword filtering have contributed to improving the robustness of these models, but these approaches are very resource-intensive and the models can still be vulnerable to malicious attacks like prompt injections and jailbreaking. One notable limitation in testing defenses against such attacks is the scarcity of appropriate datasets. This thesis investigates the use of small language models (SLMs) to generate goal hijacking messages, a subset of prompt injection messages. Techniques such as LoRA fine-tuning and full fine-tuning of even smaller models are employed in this short form text generation model. We also introduce a fine-tuned SLM enhanced with Reinforcement Learning with Artificial Intelligence Feedback (RLAIF), which removes reliance on slow human feedback by using faster AI-generated feedback instead. By optimizing the reference model and reward functions, we improve alignment with ground truth prompt injection messages while addressing issues such as mode collapse and overfitting. These findings show promise, and further research is necessary to determine how well the approach can generalize to other domains and perform in real-world scenarios. Future work is likely to focus on multilingual datasets and distributed computation to further extend the applicability and efficiency of the method.

Learning Diffusion Models to Enable Efficient Sampling for Task and Motion Planning on a Panda Robot

2025-11-12T05:00:21Z

Learning Diffusion Models to Enable Efficient Sampling for Task and Motion Planning on a Panda Robot Johnson, Quincy A search then sample approach to bilevel planning in the context of task and motion planning is one method of effectively solving multi-step robotics problems. In this planning framework, high-level plans of abstract actions are refined into low-level continuous transitions by sampling controller parameters associated with each action. Efficiently sampling these parameters remains a significant challenge, as exhaustive searches often become computational bottlenecks, especially for tasks requiring complex or multimodal parameter distributions. Moreover, relying on samplers hand-designed by humans is both impractical and limiting. To address these challenges, we propose using diffusion models to learn efficient sampling distributions from demonstrations. By avoiding the limitations of hand-specified and naïve sampling methods, our approach enhances planning efficiency and achieves superior performance across diverse tasks that require learning multimodal parameter distributions to solve successfully.

Modeling, Design, and Assembly of Spring Tires

2025-11-12T05:00:17Z

Modeling, Design, and Assembly of Spring Tires Lu, Michael With a renewed interest in the Moon and the need for autonomous lunar rovers that drive longer distances and operate over extended durations, designing efficient and robust mobility systems is paramount. Created by NASA Glenn Research Center, the spring tire is a compliant airless tire engineered for planetary rover missions in lunar and Martian environments. It consists of hundreds of coiled springs woven together to create a toroidal-shaped mesh wheel that can deform to uneven terrain, providing additional durability and traction. This work aims to apply this technology to two robotic testbeds: ERNEST, an autonomous lunar traversal rover built at NASA Jet Propulsion Laboratory, and IPEx, a lunar regolith mining robot built at Kennedy Space Center. This thesis discusses the modeling of these spring tires with numerical methods along with the design of two spring tire prototypes for use on the aforementioned rover platforms. A streamlined assembly process for these compliant wheels is also outlined as well as the results of compression testing, rough terrain driving, and drawbar pull testing to assess their performance.

Study of High Harmonic Fast Waves Interactions in the Scrape off Layer of NSTX-U

2025-05-20T12:38:29Z

Study of High Harmonic Fast Waves Interactions in the Scrape off Layer of NSTX-U De Levante Rodriguez, Ricardo Antonio High-harmonic fast wave (HHFW) heating experiments in the National Spherical Torus Experiment (NSTX) at Princeton Plasma Physics Laboratory (PPPL) have shown that up to 60% of the injected power can be lost in the Scrape-Off Layer (SOL) when the fast wave is able to propagate in front of the antenna [Hosea, Phys. Plasmas 15, 056104 (2008))]. This work discusses progress in modeling HHFW propagation and losses in the divertor region using more realistic SOL plasmas in the NSTX-U SOL 2D geometry. Previous RF studies assume density is a function only of magnetic flux, decaying exponentially, which may be insufficient to accurately determine the wavefield, especially in the divertor and high-field side plasma regions. In this work, the temperature profile is first evaluated by solving the non-linear heat conduction equation using a finite element approach in the Petra-M workbench assuming axisymmetry. A 2D density profile is then obtained from a prescribed outer midplane radial profile assuming pressure is uniform on a flux surface. This approach results in density and temperature profiles in which the strong asymmetric nature of diffusion is successfully captured. In particular, it is shown that for a parallel to perpendicular heat conduction anisotropy ratio of up to 10⁸ the expected exponentially decaying temperature profile is obtained using a non-linear iterative solver with proper mesh refinement conditions. Furthermore, this work focuses on investigating the effect of the SOL plasma density profile on the fast-wave propagation at different antenna phasing. The simulation results show that the gradient of the midplane density profile affects the wavefield pattern. As the density profile broadens, the wavefield intensity is reduced in the SOL and increased in the core. Finally, HHFW power in the plasma was studied by adding electron-ion collision power dissipation as a proxy for HHFW power deposition. The simulation results show that increasing the density gap width between the antenna and the core results in more power deposited in the SOL relative to the core.

Scalable and Sustainable Microwave Power Beaming to Mobile Lunar Surface Assets

2025-11-12T05:00:04Z

Scalable and Sustainable Microwave Power Beaming to Mobile Lunar Surface Assets Ng, Chu Pang Alex Lunar missions are hindered by the challenges of maintaining continuous operation, especially during the 14-day lunar night, when solar power sources may be unavailable, causing significant mission delays and limiting efficiency. Frequent returns to charging stations supplied by fixed lunar surface power plants further disrupt workflows and restrict the operational range of lunar vehicles. To address these issues and enhance lunar mission performance, a continuous, secure, and shareable power source is essential. While nuclear power and larger battery systems are viable options for continuous lunar energy supply, they pose challenges such as safety risks, complex deployment, and limited scalability. This thesis focuses on exploring microwave-beamed power systems as a flexible and scalable solution for sustained lunar operations. Ideally, the power source would enable 24/7 operations without requiring vehicles to return to base stations, allowing for unrestricted navigation across the lunar surface, including in permanently shadowed regions (PSR). In addition, it would support the construction of critical infrastructure, accelerating the development of the lunar economy. This thesis aims to support sustained lunar exploration and infrastructure development by exploring the design space for microwave-beamed power systems under three different demand use cases of increasing scale, loosely corresponding to the three phases of the Artemis program: Local (Shackleton Crater), Regional (navigation between equatorial regions and South Pole), and Global (entire lunar surface). A case study focused on the YUTU-2 lunar rover investigates alternative architectures for each use case, comparing power beaming from tall towers vs. satellites. Evaluation reveals that the most effective solution for the Local use case is a tower-based approach featuring a single 100m tower, >10,000 solar modules, and using 1 GHz operating frequency, at a cost of $3.4M/W. For the Regional use case, a satellite-based solution is preferred, utilizing 6-7 satellites per plane, 210,000 solar modules, and a frequency range of 1.0 GHz, at a cost of $1.7M/W - $1.8M/W. The Global use case also favors a satellite-based approach, employing 6 satellites per plane across 5 polar planes, with varying numbers of solar modules and utilizing a frequency of 1 GHz, at a cost of $0.8M/W. The trade studies showed that larger receiver antenna areas and lower frequencies improve performance and cost-effectiveness. Furthermore, larger microwave-beamed power systems leverage economies of scale, lowering the cost per watt by an average of $1M/W when scaling from the Regional to the Global power system, with potential for further reductions through future expansions.

Detecting Expertise Influence on Teamwork in Sustainable Urban Design Workshops through a System Model

2025-05-20T12:38:28Z

Detecting Expertise Influence on Teamwork in Sustainable Urban Design Workshops through a System Model Li, Chen The design of sustainable urban communities near transportation hubs, such as train stations, may play a vital role in enhancing neighborhoods by fostering new jobs, encouraging mixed-use developments, and promoting a cleaner environment. The engagement of experts and non-experts is often promoted as part of the urban planning process, yet workshops, while motivating, do not necessarily affect the systems design and long-term sustainability of the neighborhood in a substantive way. Prior studies present methods for detecting teamwork during the design of complex systems, including model-based co-creation and urban design workshops. While interactive model-based workshops promote increased engagement of non-experts, the traditional role of experts in framing the design options and the workshop dialogue remain. This thesis research seeks to examine how expertise shapes decision-making in urban sustainability contexts using enhanced system models. The research approach focuses on sustainable urban design workshops for compact city development, following three key steps. First, a neighborhood system model incorporating a commute flow simulator is developed to support collaborative exploration and design decision-making processes. Second, during a pilot experimental workshop, participants are divided into control and treatment groups, challenged to design a vibrant community with economic, social, and environmental benefits. The treatment group receives an expert-proposed, advocated solution to assess its impact on exploration and decision-making. Finally, results are analyzed using Large Language Models (LLMs) and statistical methods to assess how expert-driven solutions impact teamwork collaboration, decision-making speed, and final design alignment with the advocated solution. While the pilot workshop primarily serves to validate the approach and test the methodology, conclusive results cannot be drawn due to its exploratory nature. Nevertheless, this research successfully developed a robust urban design system model, enabling stakeholders to generate innovative solutions that foster a thriving community. Additionally, it established a methodology to advance the understanding of expertise in teamwork dynamics, laying a strong foundation for future studies in teamwork analysis and urban design challenges.

Investigation of Multi-Z Impurity Transport in Tokamaks using Neural Networks

2025-05-20T12:38:27Z

Investigation of Multi-Z Impurity Transport in Tokamaks using Neural Networks Johnson, Jamal Achieving clean, sustainable energy at scale is a pressing global challenge. Fusion of light elements holds significant potential to address this critical need. While only experimental fusion reactors are currently operational, significant progress is being made in the research and design of near-future tokamak fusion power plants. Reactor success will depend on a comprehensive understanding of heat and particle transport, including the role of impurities. This thesis focuses on the development of machine-agnostic neural network surrogates for TGLF, designed to predict impurity transport coefficients alongside heat and electron particle fluxes in DD plasmas. Training data are derived from synthetic fluxes generated for L, H, and I confinement modes in Alcator C-Mod, DIII-D, and ASDEX-Upgrade. To reduce training complexity, shot data are discretized by radius, and networks are developed at six ρ coordinates: 0.2, 0.4, 0.6, 0.7, 0.8, and 0.9. Fifteen plasma parameters are selected as inputs to the neural networks after examining TGLF flux sensitivities across all five output channels. Predicted impurity fluxes for arbitrary charge states and masses, ranging from 4He to 184W, are used to derive diffusive and convective transport coefficients. Three types of synthetic TGLF data are created and applied to network training to produce accurate models. The primary synthetic data type approximates experimental data by sampling within a perturbation range of ±10% around a given shot. Supporting data types enhance network performance by improving trends in single-parameter (1D) scans and addressing areas of highest network uncertainty. Hyperparameter optimization and testing resulted in highly accurate networks. Testing set relative errors averaged over ρ = 0.4–0.7 and 0.9 show approximate deviations of 0.12 ± 0.029 for heat flux and 0.42 ± 0.095 for particle flux channels. However, error metrics at ρ = 0.2 and 0.8 require location-specific tuning and potentially more data to match the accuracy achieved at other radii. The networks are used to analyze boron and carbon impurity peaking within machinespecific H-modes. Their predictions are then compared to published results. Qualitative results for boron peaking correlations in ASDEX-Upgrade are clearly reproduced, while carbon peaking trends in DIII-D are weaker. Sparse DIII-D data, which also includes atypical advanced modes, is believed to have contributed to reduced accuracy in these cases. Using H-mode shots spanning low to high local collisionality, impurity diffusion trends with charge state (Z) in ITG and TEM dominated plasmas were examined, showing good agreement with published studies. Additionally, analysis of network-derived convective transport shows that Z-sensitivity increases with collisionality. Network scans of the ion and electron heat flux responses to temperature gradients also reveal the clear presence of a critical gradient at all radii. These results demonstrate that the neural networks developed in this work can reliably reproduce TGLF results and deliver fast predictions of heat, electron particle, and impurity transport in tokamaks.

Urban Mining & Regenerative E-Waste Ecosystems: Visions towards Sustainable Entrepreneurial Futures for Informal Settlements and Recycling Communities

2025-05-20T12:38:24Z

Urban Mining & Regenerative E-Waste Ecosystems: Visions towards Sustainable Entrepreneurial Futures for Informal Settlements and Recycling Communities Pierre, Georine In the face of the growing challenge of urban waste, especially within rapidly expanding informal settlements projected to house over 45% of the global population by 2050 (United Nations Department of Economic and Social Affairs, 2022), innovative solutions are imperative. The thesis proposes a paradigm shift towards urban mining, emphasizing the significant value embedded in discarded electronics—where a tonne of circuit boards can hold ten times more precious metals than traditional ore (Minnesota Center for Environmental Advocacy, 2022). The global distribution of off-shored e-waste has led to the emergence of informal settlements that depend on e-waste recovery to support livelihoods and income generation. These communities have become prime examples for urban mining, embracing circular economic strategies to find adaptive ways to repurpose e-waste. Accra, Ghana’s Old Fadama, home to one of the largest e-waste sites in the world, has become a vital economic hub for informal e-waste processing. With a population of over 100,000 dwellers, local and migrant workers have built resilient communities through innovative recycling practices, tech repairs, and DIY digital fabrication methods. However, they face imminent environmental risks, health hazards, and displacement threats. Focusing on Old Fadama, the thesis will address the narratives of urban mining communities and look toward a systematic sympoiesis between economic, environmental, and social realities. By doing so, the thesis seeks to answer how we can foster nurturing and circular relationships for informal settlements and develop regenerative ecosystems for urban mining in the city environment. As an integrated field research, case study, and implementation, the thesis will: conduct key urban analysis for understanding e-waste sites and urban mining communities; identify technology interventions and policy recommendations that can improve local conditions; and utilize data-driven communication to advocate for new opportunities for urban systems tied to e-waste extraction through immersive multimedia as part of a public exhibition. Using a novel methodology, the thesis adopts the learnings from the economic, physical, and community-based interventions observed in informal e-waste recovery processes. The thesis combines quantitative data from satellite imagery and remote sensing with qualitative insights gathered through crowdsourced GIS mapping, films, interviews, and creative capacity-building workshops. These combined insights aim to enhance urban models, nurturing the innovation potential already present within urban mining communities. The thesis research will contribute to the previous work of MIT City Science Group’s “Power of Without” initiative, a comprehensive roadmap for understanding and collaborating with informal settlements and proposing non-Western decentralized infrastructure solutions. The thesis aims to provide practical insights for implementing innovations in urban mining communities by developing sustainable e-waste recovery strategies and supporting micro-industries in cities, which could serve as a model for similar contexts globally.

Leveraging Blockchain Technology for Enhancing Genomic Data Management: A Multidisciplinary Framework for Privacy, Trust, Identity Protection, and Equity

2025-05-20T12:38:18Z

Leveraging Blockchain Technology for Enhancing Genomic Data Management: A Multidisciplinary Framework for Privacy, Trust, Identity Protection, and Equity Niu, Yuner A. The effective adoption of blockchain technology in genomic data management is influenced not only by its technical advantages but also by external factors such as regulatory conditions, and the demands of consumers and patients. This thesis explores the critical factors required for blockchain platforms to thrive in managing genomic data, focusing on how these systems can be structured to address the high-priority needs of various stakeholders, including patients, healthcare providers, regulators, and researchers. Through a comprehensive examination of privacy, security, regulatory compliance, and equity concerns, the research develops a multidisciplinary framework that balances technological innovation with real-world stakeholder expectations. By conducting an in-depth stakeholder analysis and analyzing existing blockchain platforms used for genomics, the thesis presents a roadmap for creating blockchain solutions that are both technologically viable and aligned with the complex social, legal, and ethical landscape of genomic data management. This framework aims to maximize value for all stakeholders while mitigating associated risks, positioning blockchain as a viable tool in the future of personalized medicine.

Causal Representation Learning for Predicting Genetic Perturbation Effects on Single Cells

2025-05-20T12:38:17Z

Causal Representation Learning for Predicting Genetic Perturbation Effects on Single Cells Liu, Emily Advances in sequencing technologies have significantly deepened our understanding of gene regulation in cells. Among these, Perturb-seq has emerged as a powerful technique, enabling high-resolution profiling of transcriptomic responses to genetic perturbations at the single-cell level. Such insights have profound implications for functional genomics and the identification of therapeutic targets. This thesis investigates the efficacy of mechanistic computational models for predicting the effects of previously unseen genetic perturbations on cellular expression profiles. While existing deep learning approaches excel at interpolating within observational data, they often struggle to extrapolate to novel perturbations. To address this limitation, this study introduces a hybrid framework that integrates a linear causal model, grounded in the gene regulatory network, with variational deep learning techniques. The proposed mechanistic model utilizes a learned gene regulatory network to represent perturbational effects as shift interventions that propagate through the network. This approach operates within a low-dimensional gene space, effectively capturing the essential information needed to reconstruct full transcriptomic profiles. By incorporating this mechanistic causal model into a variational autoencoder (VAE), the framework generates detailed and comprehensive transcriptomic responses while maintaining the capacity to handle noisy, large-scale single-cell data. Two deep variational architectures are explored within this framework, corresponding to different output distributions. The single cell variational inference (SCVI) architecture, employing a zero-inflated negative binomial output distribution, demonstrates challenges in learning perturbational data distributions. In contrast, a standard VAE architecture with a Gaussian output distribution on normalized gene expressions, when paired with the structural causal model, achieves superior performance compared to current state-of-the-art methods. This hybrid approach, termed the Single-Cell Causal Variational Autoencoder (SCCVAE), demonstrates robust capabilities in both interpolation and extrapolation. For observed perturbations, the SCCVAE framework reveals latent representations that identify functional perturbation modules and simulate single-gene knock-down experiments across varying penetrance levels. These findings highlight SCCVAE as a powerful tool for interpreting and predicting perturbational responses at the single-cell level, advancing the integration of causal and variational approaches in computational biology.

Quantum Economic Advantage Calculator: An Extension of the Quantum Tortoise and Classical Hare Framework

2025-05-20T12:38:16Z

Quantum Economic Advantage Calculator: An Extension of the Quantum Tortoise and Classical Hare Framework Mejia, Frederick For some algorithmic problems, quantum computation has the potential to provide enormous speedups over classical computers. However, the drastic slowdowns associated with running error-free quantum hardware make achieving these theoretical advantages challenging. Researchers and industry leaders planning for the future would benefit from understanding when it will be both feasible and advantageous to switch to quantum computing platforms. This thesis builds on the framework by Choi, Moses, and Thompson (2023) to evaluate the feasibility and timeline for achieving Quantum Economic Advantage (QEA)—the point at which quantum hardware can outperform comparably-priced classical machines for specific computational tasks. This thesis substantially extends and deepens this framework and introduces a calculator to make these analyses accessible. The model incorporates parameters from quantum hardware vendors, such as physical-logical qubit ratios and overall connectivity, alongside the computational complexities of specific problems, to estimate the year of QEA. Most of the parameters in the tool are freely adjustable, allowing users to explore how varying assumptions about quantum improvement and technological advancement influence the projected timeline for QEA.

Structure, Function, and Interaction in Protein Language Models

2025-05-20T12:38:15Z

Structure, Function, and Interaction in Protein Language Models Zheng, Jared In recent years, transformer architectures have shown remarkable capabilities in learning meaningful representations from text and images. This approach has been extended to the realm of protein sequences through pretrained protein language models, which have excelled in various protein engineering tasks. In this thesis, we investigate a pre-trained protein language model’s ability to predict protein structure and the effects of mutations. For many advanced protein understanding tasks, such as predicting protein function and protein-protein interactions, fine-tuning of the model is essential. We explore methods to fine-tune the Evolutionary Scale Modeling (ESM2) model, a pretrained protein language model, for predicting protein functions structured as Gene Ontology terms and predicting protein-protein interactions. Notably, we develop a novel method of modeling the hierarchy constraint in GO term prediction that improves training convergence and test performance while making the model hierarchically consistent with GO. This research aims to enhance our understanding of protein language models in decoding complex biological information, thereby contributing to advancements in computational biology.

Healthcare Agents: Large Language Models in Health Prediction and Decision-Making

2025-05-20T12:38:13Z

Healthcare Agents: Large Language Models in Health Prediction and Decision-Making Kim, Yubin Large Language Models (LLMs) are transforming healthcare, yet utilizing them for clinical applications presents significant challenges. In this thesis, we explore two critical aspects in healthcare AI: (1) leveraging LLMs for multimodal health prediction from wearable sensor data and (2) developing collaborative AI framework for medical decision-making. We first introduce a Health-LLM framework that performs multimodal fusion of temporal physiological signals from wearable devices with contextual metadata to predict health outcomes. By implementing novel context enhancement strategies, our framework demonstrates significant improvements in prediction accuracy across multiple health domains compared to existing benchmarks. Furthermore, we present MDAgents, an adaptive framework that optimizes multi-agent LLM collaboration for complex medical reasoning tasks. MDAgents dynamically configures agent roles and interaction patterns based on task complexity, implementing a hierarchical consensus mechanism that emulates clinical team dynamics. Through comprehensive evaluation on medical diagnosis and reasoning tasks, MDAgents exhibits superior performance in multimodal medical reasoning compared to single-agent approaches. Our findings demonstrate that LLMs, when architected for multimodal integration and strategic collaboration, can serve as robust agents in healthcare systems, advancing both preventive medicine through continuous health monitoring and clinical decision support through distributed AI reasoning.

Strategizing against online learners in normal form repeated games

2025-05-20T12:38:11Z

Strategizing against online learners in normal form repeated games Assos, Angelos With the advent of machine learning and AI, learning algorithms are becoming more and more prevalent in online learning settings, where sequential decision-making is required. In such settings, the decisions of each agent can affect the utilities (or losses) of the other agents, as well as influence the decisions made by other agents later on in the interaction. Therefore, if an agent is good at anticipating the behavior of the other agents, in particular how they will make decisions in each round as a function of their experience thus far, he could try to judiciously make his own decisions over the rounds of the interaction so as to influence the other agents to behave in a way that ultimately benefits his own utility. In this thesis, we study repeated two-player games involving two agents: a learner, which employs an online learning algorithm to choose his strategy in each round; and an optimizer, which knows the learner’s utility function, parameters and the learner’s online learning algorithm. The optimizer wants to plan ahead to maximize his own utility while taking into account the learner’s behavior. We study this setting in zero-sum and general-sum games. In zero-sum games, we provide algorithms for the optimizer that can efficiently exploit a learner that employs a specific online learning algorithm in discrete and continuous-time dynamics. Specifically, the learner employs the Multiplicative Weights Update (MWU) algorithm for the discrete-time games, and the Replicator Dynamics in the continuous-time games. In general-sum games, we provide a negative result. Our negative result shows that, unless P=NP, there is no Fully Polynomial Time Approximation Scheme (FPTAS) for maximizing the utility of an optimizer against a learner that best responds to the history in each round. We additionally provide exponential-time algorithms that efficiently strategize against a learner that uses MWU, as well as a new way of thinking about strategizing against online learners via calculus of variations.

Enabling Semantically Grounded, Long Horizon Planning and Execution for Autonomous Agents

2025-05-20T12:38:11Z

Enabling Semantically Grounded, Long Horizon Planning and Execution for Autonomous Agents Covarrubias, Lucian Robots have been playing an ever increasing role in complex environments, often in coordination with teams of systems or humans. Autonomous systems of the future will need to be tightly grounded in the real world, drawing information directly from their environment to develop an understanding of the world. They will need to maintain a semantic understanding of their environment, including the kinds of objects they observe and their relationships to each other. At the same time, they must be able to reason over diverse constraints related to their tasks, such as time limits and resource usage. While there are existing approaches which enable robots to execute tasks with semantic goals, such as finding a certain type of object in a room, they often fail to consider the multitude fo task specific constraints which are vital to robust performance. On the other hand, planners which consider task specific constraints require a human to provide all information about the environment manually. These systems are too cumbersome to model complex tasks, requiring hours of manual effort which is prone to errors. This thesis presents an architecture for semantically grounded planning which leverages the strengths of constraint based planners while automating the environmental modeling step with an advanced semantic perception engine. By automating environmental modeling, we are able to create a system which executes complex semantically grounded tasks such as navigating to certain objects within a certain room, without major user input which is typically required of these systems.

Transformers as Empirical Bayes Estimators The Poisson Model

2025-05-20T12:38:10Z

Transformers as Empirical Bayes Estimators The Poisson Model Jabbour, Mark We study the ability of transformers to perform In Context Learning (ICL) in the setting of Empirical Bayes for the Poison Model. On the theoretical side, we demonstrate the expressibility of transformers by formulating a way to approximate the Robbins estimator, the first empirical Bayes estimator for the Poisson model. On the empirical side, we show that transformers pre-trained on synthetic data can generalize to unseen prior and sequence lengths, outperforming existing methods like Robbins, NPMLE, and ERM monotone in efficiency and accuracy. By studying the internal behavior of the representations of the intermediate layers of these transformers, we found that the representation converges quickly and smoothly over the layers. We also demonstrate that it’s unlikely transformers are implementing Robbin’s or NPMLE estimators in context.

Lifting 2D Vision Models into Structured Scene Representations

2025-05-20T12:38:09Z

Lifting 2D Vision Models into Structured Scene Representations Tang, George Intelligent agents can leverage structured scene representations capable of capturing object compositionality, affordances, and semantics as a world emulator. However, 3D scene data is limited, rendering supervised and self-supervised methods ineffective. Recent advances in 2D foundation models exhibit remarkable performance and generalization. Concurrently, several works have demonstrated lifting feature maps produced by these models into a 3D feature representation. This thesis further explores how lifting can be effectively employed to construct pixel-level fidelity structured scene representations. Learned scene representations such as NeRF and Gaussian Splatting do not support additional functionality besides novel view rendering. The world is compositional: a scene can be described in terms of objects. Correspondingly, we present a lifting solution for efficient open-set 3D instance segmentation of learned scene representations. Compared to previous approaches, our solution is more than an order of magnitude faster and can handle scenes with orders of magnitude more instances. Toward identifying affordances, we tackle the problem of zero-shot mesh part segmentation. Learning-based mesh segmentation does not generalize due to a lack of diverse mesh segmentation datasets, while traditional shape analysis methods are overfitted to previous benchmarks. We present a lifting solution for mesh part segmentation that overcomes these limitations, showing comparable performance to top-performing shape-analysis methods on traditional benchmarks while exhibiting much better generalization on a novel mesh dataset curated from an image-to-3D model. Beyond feature fields, lifting can be used for a variety of applications, including scene understanding and editing. However, current lifting formulations are inefficient and often exhibit additional unintended modifications. To address these deficiencies, we generalize lifting to semantic lifting, which incorporates per-view masks indicating relevant areas. These masks are determined by querying corresponding per-view feature maps derived from feature fields. However, it is impractical to store per-view feature maps, and the scene representations can be expensive to store and query. To enable lightweight, on-demand retrieval of pixel-aligned relevance masks, we introduce a Vector Quantized Feature Field. We demonstrate the effectiveness of semantic lifting with our method on complex indoor and outdoor scenes from the LERF dataset.

Toward Affordance-Based Generation for 3D Generative AI

2025-05-20T12:38:07Z

Toward Affordance-Based Generation for 3D Generative AI Wang, Sean Recent advances in 3D content creation with generative AI have made it easier to generate 3D models using text and images as input. However, translating these digital designs into usable objects in the physical world is still an open challenge. Since these 3D models are generated to be aesthetically similar to their inputs, the resulting models tend to have the visual features the user desires but often lack the functionality required for their use cases. This thesis proposes a novel approach to generative AI in 3D modeling, shifting the focus from replicating specific objects to generating affordances. We trained models that allow users to create point clouds that satisfy physical properties called affordances, which are properties that describe how an object should behave in the real world. By ensuring that the generated objects have the expected affordances, we explore how existing tools can be augmented to generate 3D objects whose functionality is consistent with their appearances.

Exploring Fine-Tuning Techniques for Removing Tamper-Resistant Safeguards for Open-Weight LLMs

2025-05-20T12:38:07Z

Exploring Fine-Tuning Techniques for Removing Tamper-Resistant Safeguards for Open-Weight LLMs Zhang, Sarah Open-source models present significant opportunities and risks, especially in dual-use scenarios where they can be repurposed for malicious tasks via adversarial fine-tuning. In this paper, we evaluate the effectiveness of Tampering Attack Resistance (TAR), a safeguard designed to protect against such adversarial attacks, by exploring its resilience to full-parameter and parameter-efficient fine-tuning. Our experiments reveal that while TAR enhances tamper resistance compared to models without safeguards, it remains susceptible to variability. Specifically, we observe inconsistencies where the same adversarial attack can succeed under some initializations and fail under others. This is a critical security risk as even a single instance of failure can lead to models being exploited for harmful purposes. These findings highlight the limitations of current tamper-resistant safeguards and emphasize the need for more robust safeguards to ensure the safe and ethical deployment of open-source models.

The SpaseCroissant Oven: Automatic Metadata Generation For Open-Source Space Weather Datasets

2025-05-20T12:38:06Z

The SpaseCroissant Oven: Automatic Metadata Generation For Open-Source Space Weather Datasets Chen, Edenna H. The rise of machine learning (ML) algorithms has led to a parallel rise in ML-ready datasets. A novel metadata schema released by OpenAI and MLCommons called Croissant, which is specifically designed for ML-ready datasets, aims to increase data accessibility, user understanding of data, and accuracy of claims based on data. However, current methods to automatically generate Croissant metadata present difficulties, such as involving manual entries. This can be especially difficult when attempting to preserve information about large ML-ready datasets, which are often derived from large scientific repositories belonging to organizations such as National Aeronautics and Space Administration (NASA). These major scientific repositories provide their own metadata standards, such as NASA’s Space Physics Archive Search and Extract (SPASE) schema but context from this metadata can often be lost during data processing. This thesis presents a novel, improved approach to Croissant metadata generation which involves a hybrid parsing logic and Large Language Model (LLM) inference approach, as well as recommendations for future Croissant standards and SPASE to Croissant schema metadata conversion, that aims to retain this lost context.

Applied Plankton Image Classification for Imaging FlowCytobot Data

2025-10-20T03:17:11Z

Applied Plankton Image Classification for Imaging FlowCytobot Data Duckworth, Barbara R. As the ability to gather vast quantities of data from oceanographic bioimaging sensors increases, so too does the need to process, analyze, and store that data in a consistent, standard way that enables replicability and accessibility for future studies. The Imaging FlowCytobot (IFCB), an automated submersible flow cytometer, produces high resolution images of plankton at rates up to 10 Hz for months or years, resulting in billions of images. This project compares various methods to categorize incoming images of plankton gathered by the IFCB - Convolutional Neural Nets (CNNs), Vision Transformers (ViT), and self-supervised learning (MAE). The benefits and downsides of each model are analyzed and discussed for future IFCB operators to process their data using the methods that best align with their research questions, along with step-by-step explanations about the pros and cons of each method depending on the use case.

A Computational Tsirelson's Theorem for All Compiled Nonlocal Games

2025-05-20T12:38:05Z

A Computational Tsirelson's Theorem for All Compiled Nonlocal Games Falor, Chirag Nonlocal games, defined as cooperative tasks between spatially separated players, have been a foundational tool in the study of quantum advantage and have been useful in classically verifying quantum computations. To address the challenge posed by the spatial separation assumption, Kalai et al. (STOC' 23) introduced a compilation procedure that compiles any nonlocal game into an interactive game between a classical verifier and a computationally bounded quantum prover. This compilation preserves classical soundness and quantum completeness, though quantum soundness has been established only in the asymptotic limit of the security parameter or for specific classes of games. In this work, we advance towards a concrete framework to bound the quantum value of compiled nonlocal games. Building on the notion of nice sum-of-squares certificates, introduced by Natarajan and Zhang (FOCS' 23) to bound the value of the compiled CHSH game, we extend the niceness framework and construct a hierarchy of semidefinite programs that searches exclusively over nice certificates. We show that this hierarchy converges to the optimal quantum value of the game. Additionally, we present a transformation to make any degree-1 sum-of-squares certificate nice. This approach provides a systematic method to reproduce known bounds for special classes of games and showcases the general applicability of the framework to low-degree certificates. Source code: https://github.com/chiragfalor/ Nice-SoS-SDP

Instructify: Demystifying Metadata to Visual Instruction Tuning Data Conversion Supplementary Materials

2025-10-20T03:16:47Z

Instructify: Demystifying Metadata to Visual Instruction Tuning Data Conversion Supplementary Materials Hansen, Jacob A. Visual Instruction Tuning (VisIT) data, commonly available as human-assistant conversations with images interleaved in the human turns, are currently the most widespread vehicle for aligning strong LLMs to understand visual inputs, converting them to strong LMMs. While many such VisIT datasets are available, most of them are constructed via ad hoc techniques, separately proposed by different groups, commonly poorly documented, without available (reproducible) code, and employing paid closed-source model APIs like GPT-4, Gemini, or Claud to convert image metadata (labels) to VisIT instructions. This incurs significant cost and difficulty to scale, improve quality, or produce VisIT data for new datasets. In this work, we address these challenges and propose an open and unified recipe and approach, Instructify, for converting available metadata to VisIT instructions using open LLMs. Our multi-stage Instructify features an efficient framework for metadata grouping, quality control, data and prompt organization, and conversation sampling. We show that our approach can reproduce or improve the data quality of the available VisIT datasets when applied to the same image data and metadata sources, improving GPT-4 generated VisIT instructions by ∼3% on average and up to 21% on individual benchmarks using open models, such as Gemma 2 27B and LLaMa 3.1 70B. We further show that our approach enables effective performance scaling (in terms of resulting LMM performance on a large variety of benchmarks) of the produced VisIT data both in terms of quantity and quality. In addition, we explore the impact of multiple factors, including conversation format, base model selection, and resampling strategies.

Leveraging Single-Cell ATAC-Seq for Genomic Language Models and Multimodal Foundation Models

2025-10-20T03:16:43Z

Leveraging Single-Cell ATAC-Seq for Genomic Language Models and Multimodal Foundation Models Kim, Dong Young Single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) has emerged as a powerful tool for profiling chromatin accessibility at single-cell resolution. By capturing epigenomic landscapes, scATAC-seq provides critical insights into the regulatory elements that govern gene expression. However, the sparsity of scATAC-seq data, resulting from its low sequencing depth relative to the genome’s potential complexity, poses significant challenges for effective and accurate modeling. To advance the utility of scATAC-seq in modern biology, we explore its integration into deep learning frameworks through two innovative applications. First, we demonstrate how incorporating scATAC data enhances the performance of existing genomic language models by providing complementary context about chromatin accessibility. Specifically, we introduce scATAC to improve SegmentNT, a DNA segmentation model that leverages the Nucleotide Transformer (NT) to predict 14 types of genomic and regulatory elements from DNA sequences up to 30kb at single-nucleotide resolution. Second, we introduce a novel multimodal foundation model that extends existing scRNA-seq foundation models by integrating scATAC-seq data. This model captures crossmodal relationships between gene expression and chromatin accessibility, establishing a unified framework that can be fine-tuned for diverse downstream tasks, including cell type classification and cross-modal imputation. Our work highlights the potential of incorporating scATAC-seq data into existing genomics deep learning strategies, providing a framework for integrating regulatory DNA analysis more seamlessly into genomic modeling.

Unsupervised Time Series Anomaly Detection Using Time Series Foundational Models

2025-10-20T03:16:14Z

Unsupervised Time Series Anomaly Detection Using Time Series Foundational Models Nguyen, Linh K. The rapid generation of time series data across a wide array of domains—such as finance, healthcare, and industrial systems—has made anomaly detection a critical task for identifying irregular patterns that could signal significant events like fraud, system failures, or health crises. Traditional approaches to time series anomaly detection, including statistical models like ARIMA and deep learning methods, have proven effective but often require an extensive training phase, which can be both data and time-consuming. In recent years, the emergence of foundational models, including large language models (LLMs) and specialized time series models, has opened up new possibilities for anomaly detection. These models, pre-trained on vast and diverse datasets, offer the potential to perform tasks with minimal task-specific training. This thesis investigates the feasibility of leveraging these foundational models for time series anomaly detection, with the aim of determining their effectiveness in detecting anomalies without the traditional training requirements. We also aim to investigate whether foundational models pretrained specifically on time series data yield better results compared to large language models (LLMs) that were not pretrained for time series tasks.

First-Person Teleoperation of a Bimanual Robotic System

2025-10-20T03:16:01Z

First-Person Teleoperation of a Bimanual Robotic System Thakur, Nandini First-person teleoperation of robots is a large field of research that could serve many benefits for automation. Teleoperation is a popular method to collect demonstrations for imitation learning that are easily learned by the robot, and thus it’s important to create teleoperation systems that are intuitive and enable human-like perception of a scene. Adding a first-person component to basic teleoperation systems is key to improving operators’ visual perception and making teleoperation possible for extended periods of time. Existing teleoperation systems do not integrate elements that provide the operator with a good perception of the task space, such as a first-person VR view and the ability to leverage the neck to search around the space. They rely on techniques such as third-person view of the space, or provide a first-person view but without the ability to move the neck to look around. This thesis proposes a VR-based teleoperation system with an actuated 5-DoF neck for enabling human-like perception and improving the ability to perform high quality demonstrations for use in imitation learning.

Scalable Embedded Tiny Machine Learning (SETML): A General Framework for Embedded Distributed Inference

2025-10-20T03:16:24Z

Scalable Embedded Tiny Machine Learning (SETML): A General Framework for Embedded Distributed Inference Vidal, Justice The growth of machine learning applications has increased the necessity of lightweight, energyefficient solutions for resource-constrained devices such as the STM32C011F6 microcontroller. However, such devices struggle with supporting larger models even after miniaturization techniques such as quantization and pruning. To facilitate machine learning inference on such devices, this work introduces Scalable Embedded Tiny Machine Learning (SETML), a general framework for distributed machine learning inference on microcontrollers. Furthermore, the framework is designed to be compatible with sensor-based applications that can take advantage of small hardware, such as gesture recognition, by testing binary size constraints with an accelerometer and its supporting library. This work evaluates the latency, power consumption, and cost trade-offs of using multiple small and efficient devices versus a larger device. The STM32C011F6 microcontroller is used as the primary hardware in the tested device network, while evaluation of the system is done in comparison with a device using a similar core processing element, the Seeeeduino XIAO SAMD21.

Investigation of the energy transfer network in upconverting nanoparticles

2025-10-20T03:15:27Z

Investigation of the energy transfer network in upconverting nanoparticles Zheng, Yuxuan Upconverting nanoparticles (UCNPs) have emerged as promising luminescent materials for a wide range of applications, including bioimaging, drug delivery, and photovoltaics. The intricate network of energy transfer processes within UCNPs enables their unique ability to convert low-energy infrared (IR) radiation into higher-energy visible light through photon upconversion, presenting significant challenges for accurate modeling. Despite their broad applications, theoretical models of UCNPs remain incomplete, and current models fail to accurately reproduce all experimental results. This thesis presents a comprehensive comparison of prevalent modeling approaches with the aim of developing improved models that more faithfully reproduce experimental observations. Using the Judd-Ofelt theory, we calculated essential transition rate parameters, including electric dipole (ED), magnetic dipole (MD), multiphonon relaxation (MPR), and energy transfer (ET), using constants sourced from the literature. We implemented both Monte Carlo models and Ordinary Differential Equation (ODE) models. Using the calculated rate parameters, we simulate the energy transfer pathways in Yb³⁺-Er³⁺ and Yb³⁺-Tm³⁺ UCNPs. Simulation results from all models were compared with experimental data to evaluate their effectiveness in capturing key luminescent properties such as population evolution, lifetime, saturation curves, and spectral purity.

Assessing Blood-Based Laboratory Diagnostics for Alzheimers’s Disease: A Systems Approach

2025-10-12T03:17:23Z

Assessing Blood-Based Laboratory Diagnostics for Alzheimers’s Disease: A Systems Approach Peralta Walker, Stephanie Christine This thesis adopts a systems approach to analyze the complex network of stakeholders involved in adopting blood-based laboratory screening tests for Alzheimer’s disease (AD). Traditional diagnostic methods, including cerebrospinal fluid (CSF) testing and positron electron tomography (PET) brain imaging, are invasive, costly, and inaccessible to many. Blood-based tests offer a less invasive and more cost-effective alternative, yet they remain underutilized in clinical practice. By conducting a literature review, stakeholder interviews, and a Kano analysis, the thesis identifies and evaluates the key stakeholder needs to support the widespread adoption of these tests, such as the need for demonstrated clinical performance of these tests, reimbursement, broader education of patients and health care professionals, and safe, effective medicines to treat AD. The research highlights two emerging tests that have published studies demonstrating clinical validation, a key parameter of clinical performance. A stakeholder tension analysis is included with proposed tension resolutions using stakeholder saliency to guide prioritization. Addressing these stakeholder needs could facilitate broader implementation, improve early diagnosis, and support emerging therapeutic interventions for AD, thus reshaping the diagnostic landscape for this increasingly prevalent disease.

Enhancing Coast Guard Infrastructure Management: A Multi-Criteria Framework for Prioritizing Maintenance Projects

2025-10-12T03:17:20Z

Enhancing Coast Guard Infrastructure Management: A Multi-Criteria Framework for Prioritizing Maintenance Projects Ballard, Zachary N. The United States Coast Guard is currently transforming its decision-making process for prioritizing shore infrastructure maintenance and repair projects. Current decision-making subjectivity appears to be generating inadequate project prioritizations. Stakes are high for an aging infrastructure portfolio in harsh coastal conditions, with increased national reliance on the Coast Guard in a fiscally constrained budgetary environment. Data availability, quality, and fidelity continue to increase, supporting the rationale for more robust and data-informed decision-making frameworks. The research begins with examining Coastal and Shore Operations (CSO) funding history, along with a thorough description of the current Centralized Planned Obligation Prioritization (C-POP) process. The complex, sociotechnical nature of the problem is highlighted by identifying all involved stakeholders and categorizing them through the leading view of stakeholder theory and salience. A detailed review of the governing asset management literature is conducted, gradually narrowing from a broad, international, and asset-type neutral perspective to more tailored infrastructure cross-asset prioritization material. Requisite framework data substance, collection, and analyses are described, and recommendations for data processing improvements are made. Two leading prioritization models are examined: the Importance and Urgency Quadrant Model and the Value Focused Multi-Criteria Decision Model. Their respective data visualizations are generated and analyzed. Using the multi-criteria analysis rooted in multi-attribute utility theory, four portfolios of measurably increasing value are constructed, compared with a baseline portfolio reflecting actual project selections in December 2023. These portfolio iterations include a linear programming solution to the Knapsack Problem of selecting projects that maximize overall portfolio utility within a budget limit while incorporating some of the more social and qualitative system properties. A traceable, adaptable, defendable, and objective data-informed multi-criteria framework is proposed, which aims to facilitate the effectiveness of the overall Coast Guard shore infrastructure portfolio in the long term.

A Systems-Theoretic Approach to Organizational Design and Analysis

2025-10-12T03:17:18Z

A Systems-Theoretic Approach to Organizational Design and Analysis Gutierrez, Lauren E. A significant challenge for large organizations lies in organizational design, particularly for public sector bureaucracies and the largest of industry’s private firms. Organizations tend to turn to organizational design improvements when facing effectiveness and efficiency issues. Unfortunately, these large organizations struggle with organizational design because of the sheer size and complexity of their organization which results in a fragmented and often times faulty approach to improving their organization. Organizations, at their core, are a special type of system or a set of components that operate or work together to achieve some common purpose. Organizations are purely social systems in that their elements are not technical or engineered. Systems Theory provides a lens through which these types of social systems can be studied. Just like in engineered systems, an organization's emergent behavior is determined by its internal elements' complex interactions. Traditional organizational design and analysis methods focus on optimizing these internal elements in the hopes of re-integrating optimized elements in pursuit of organizational-level optimal behavior. Just like in traditional systems engineering, component-level optimization does not yield system-level optimal behavior. This thesis codifies a systems-theoretic approach to organizational design and analysis using the language of Systems Theory and the semantics of Systems-Theoretic Accident Model and Processes. By extending traditional Systems-Theoretic Process Analysis (STPA), a tool for hazard analysis used primarily for engineered systems, this work refines STPA’s concepts and terminology to be more accessible for analyzing social systems. Building off this extension, this thesis leverages a contemporary Department of Defense reorganization effort as a case study, illustrating Systems-Theoretic Organizational Design and Analysis (STAODA) as a tool to assess organizational design options.

Efficient Deep Learning Systems for Visual Perception on the Edge

2025-10-12T03:17:06Z

Efficient Deep Learning Systems for Visual Perception on the Edge Yang, Shang Deep learning for visual perception on edge devices has become increasingly critical, driven by emerging applications in autonomous driving and AR/VR. Typically, sparse convolution on 3D point clouds and Visual Language Models (VLMs) for image processing are two important methods for visual understanding and reasoning. However, the limited compute resources and memory on edge devices pose significant challenges, necessitating specialized system support for deep learning models. Specifically, the efficiency challenges for edge visual perception are twofold: First, the sparsity and inherent irregularity of point cloud data introduce substantial complexity for parallel processing. Second, the colossal model sizes and amount of computation of LLMs and VLMs render edge deployment particularly challenging. In this thesis, we aim to address the efficiency issues of on-device deep learning via system-algorithm co-design. We first introduce TorchSparse++, a high-performance inference engine for sparse convolution on GPUs. Unlike existing sparse convolution systems, TorchSparse++ well balances the efficiency and implementation simplicity, achieving the best performance across different application scenarios. Specifically, we first create a highly efficient Sparse Kernel Generator that generates performant sparse convolution kernels at less than one-tenth of the engineering cost of the current state-of-the-art system. On top of this, we design the Sparse Autotuner, which extends the design space of existing sparse convolution libraries and searches for the best dataflow configurations for training and inference workloads. Consequently, TorchSparse++ achieves 2.9×, 3.3×, 2.2× and 1.7× measured end-to-end speedup on an NVIDIA A100 GPU over state-of-the-art MinkowskiEngine, SpConv 1.2, TorchSparse and SpConv v2 in inference; and is 1.2-1.3× faster than SpConv v2 in mixed precision training across seven representative autonomous driving benchmarks. It also seamlessly supports graph convolutions, achieving 2.6-7.6× faster inference speed compared with state-of-the-art graph deep learning libraries. Furthermore, to democratize the power of large foundation models in edge AI, we propose AWQ and TinyChat, a hardware-friendly full-stack solution for efficient on-device LLM and VLM deployment. AWQ is a novel quantization method based on the insight that not all weights in an LLM are equally important. Protecting only 1% salient weights can greatly reduce quantization error. Specifically, AWQ employs an equivalent transformation and scales up the salient weight channels to reduce the weight quantization error, during which the scale is determined by collecting the activation statistics offline. Alongside AWQ, we further introduce TinyChat, an efficient and flexible inference framework tailored for 4-bit on-device LLM/VLMs. With on-the-fly dequantization, extensive kernel fusion and platform-aware weight packing, TinyChat offers 2.7-3.7× speedup over the Huggingface FP16 implementation on both desktop and mobile GPUs. It also enables the deployment of the 70B Llama-2 model on mobile GPUs. Together, these techniques significantly reduce the computational and memory costs for deploying deep learning models on edge devices, increasing the accessibility of deep learning for practical application. We hope that this thesis can inspire future research on efficient edge AI across diverse modalities.

Diagnosing Supply Chain Threats to Defense Innovation

2025-10-12T03:16:53Z

Diagnosing Supply Chain Threats to Defense Innovation Schneider, Donald E. As the U.S. Department of Defense (DoD) shifts focus to an era of global power competition, the demand for rapid innovation and disruptive technologies has grown significantly. Prototyping remains a vital tool for advancing technological innovation, enabling early learning and risk reduction in developing complex systems. However, persistent supply chain challenges threaten the success of defense prototyping projects, causing schedule delays, and diminished effectiveness. This research identifies the underlying causes of supply chain disruptions specific to Federal Acquisition Regulations (FAR) governed prototyping efforts, offering a socio-technical systems analysis that accounts for stakeholder relationships, market dynamics, and regulatory frameworks. Through extensive data collection, including stakeholder interviews across agencies, organizations, and supply chain roles, 181 issues were identified and analyzed, revealing over 500 contributing factors. The disciplined analysis of these factors identified three systemic root causes: (1) the misapplication of production management strategies that focus on efficiencies at scale and low tolerance for risk; (2) pooled supply chain management functions, which marginalizes prototyping’s unique demands and creates inefficiencies; and (3) regulatory and organizational barriers to entry that deter non-traditional suppliers, hindering innovation. To address these systemic challenges, the thesis recommends restructuring organizations to better align with the unique demands and risks of prototyping while simultaneously creating pathways to reduce barriers for new suppliers. Resolving these issues will require a coordinated effort across the prototyping ecosystem. By addressing these root causes, the DoD can improve the efficiency and effectiveness of prototyping programs, ultimately sustaining U.S. technological superiority in an increasingly competitive global environment.

A framework for determining remote sensing capabilities for ecosystem services valuation

2025-10-12T03:16:46Z

A framework for determining remote sensing capabilities for ecosystem services valuation Sampath, Aparajithan Nature provides vital services—clean water, air purification, and climate regulation—to human societies thanks to the "natural capital" like forests and lakes on our planet. Accurately measuring and valuing these ecosystem services is crucial for informed economic and development decisions. Remote sensing (RS) technology offers a powerful way to monitor natural capital (e.g., mapping forest cover, assessing water quality). However, current data lack the accuracy and precision needed for robustly monitoring the value of these services. This deficiency has impeded the use of natural capital assessment data in economic decision-making. This research partly addresses this challenge by developing a new framework to investigate the necessary sensor characteristics (spectral, radiometric, temporal, spatial) for effectively monitoring natural capital and quantifying ecosystem services. The framework first identifies the different types of services provided by an ecosystem, then uses a physics-based approach to identify crucial physical parameters and determines the necessary measurements that need to be made from a sensor for their quantification. The sources of uncertainty impacting quantification and value estimation are also analyzed in detail. The approach is integrated to formulate a system utility function that is used to compare performance of existing and proposed RS systems, and the overall results are subsequently used in proposing required capabilities for future remote sensing systems for natural capital monitoring. The framework is demonstrated on a case study focused on the flood attenuation function (service) provided by wetlands. Water budget models are utilized to identify essential parameters for monitoring water storage by wetlands. Using a study area encompassing the Fall Lake Creek reservoir (Oregon, USA), water storage capacity is measured and monitored by integrating USGS digital elevation models with Sentinel-1 synthetic aperture radar, Sentinel-2 optical data, and Planet Scope optical data. Results are validated against USGS published ground truth measurements. A strong correlation (r² of 0.95) was observed with all three datasets. An uncertainty analysis was conducted, using the random fields method, in which synthetic spatially autocorrelated errors were added to the RS datasets. Radiometric uncertainties were studied through addition of gaussian noise as a percentage of reflectance values, and results showed effects of < 2.5% on estimated water volume. Elevation data uncertainties (which were approximated to simulate uncertainties in globally available DEMs) showed higher effects, and errors in estimated storage volumes increased proportionally. A study of inundation (for a case study over Miami, FL) revealed that as the root mean square error of the DEMs increased from 2m to 7 m, the risk of flooding (defined as water depth accumulation of greater than 90 cm) increased more than 3 times. A utility function was developed to evaluate sensors based on their ability to estimate wetland water volumes. This function considers sensor characteristics like spatial, radiometric, and temporal resolution. Notably, the function estimates that a future optical system with 2x improved spatial and 4x improved temporal resolution (compared to Sentinel-2) can increase utility 7-fold.

The Uncanny Valley: An Empirical Study on Human Perceptions of AI-Generated Text and Images

2025-10-12T03:16:34Z

The Uncanny Valley: An Empirical Study on Human Perceptions of AI-Generated Text and Images Kishnani, Deepali This thesis explores how the uncanny valley phenomenon—historically tied to near-human robots—applies to text-based AI interactions and AI-generated images. While the concept has been predominantly studied in the context of robotics, the advent of generative AI reveals that text and visuals that are 'almost, but not quite' human can also provoke unease. Two experiments structure the study. The first examines GPT4-Turbo (GPT4o) text conversations. Sixty participants engaged with one of three “chatbots”: an “Uncanny-Valley Bot” (prompt engineered to fall in the uncanny valley), a “Human-Like Bot” (prompt engineered to converse like humans), or a human control. Godspeed Questionnaire results indicate that the uncanny valley effect surfaces in text-only form: participants consistently rated the “Uncanny-Valley Bot” lowest in anthropomorphism, animacy, likeability, and perceived intelligence. Furthermore, the experiment revealed that the distinction between GPT and humans is becoming increasingly blurred, with 60% of participants mistaking a human for GPT and 40% mistaking GPT for a human. Lastly, results highlighted a strong user preference for naturalness, human imperfections, and vulnerability. While human flaws enhance relatability, deviations that disrupt perceived humanity trigger the uncanny valley. The second experiment investigates AI-generated images produced by Stable Diffusion XL at varying degrees of realism. Fifty-six participants ranked each image’s “strangeness,” revealing that highly realistic or clearly stylized outputs raise fewer concerns. By contrast, images that inhabit the uncanny valley elicited discomfort. To quantify these findings, recognized metrics like Frechet Inception Distance (FID) and Kernel Inception Distance (KID) were used to compare real and AI-generated images. Both metrics strongly correlated with human perceptions, suggesting that distance metrics can be used to determine realism. The study also shows that image generation models can detect visual features associated with the uncanny valley. However, performance drops when the prompt calls for subtle, “mid-range” realism, indicating the model’s difficulty in maintaining comfort and believability at intermediate levels. Collectively, the two experiments confirm that uncanny valley responses are not confined to physical robots but persist in text-based dialogue and AI-synthesized images. Yet challenges remain. Short interaction windows, small participant samples, and reliance on selected AI models call for studies on the generalizability of these findings. Future work should adopt longitudinal designs, larger samples, and multiple AI systems. Addressing the uncanny valley in both textual and visual content is essential for advancing user trust, and comfort in AI.

Battery Pack Design and Transient Performance Modeling for High-Power Legged Robots

2025-10-12T03:16:09Z

Battery Pack Design and Transient Performance Modeling for High-Power Legged Robots Evagora, Christopher K. Legged robotics has recently shifted toward advanced optimization-based control methods, such as Model Predictive Control (MPC), to generate agile and energy-efficient locomotion. By casting the control problem as an optimization task, robotic systems can account for complex robot dynamics and operational constraints, including joint limits and actuator capabilities. However, high-performance maneuvers also demand rigorous consideration of onboard battery constraints. This work presents an empirically derived lithium-ion battery model that captures transient voltage sag and time-dependent internal battery state, enabling more accurate prediction of feasible power delivery. Additionally, a custom high-power battery pack was designed to meet the power demands of the MIT Humanoid, emphasizing power density, safety, and maintainability. Although the work presented in this thesis does not integrate the battery model into a trajectory optimization framework, it establishes the foundation for future research that aims to couple battery and robot dynamics in robot control. Ultimately, this approach will facilitate safer and more capable legged robots by ensuring that planned trajectories respect both physical and electrochemical constraints.

Multimodal Graphical User Interface for 3D Model Fabrication Through Generative AI

2025-05-20T12:37:47Z

Multimodal Graphical User Interface for 3D Model Fabrication Through Generative AI Báez Alicea, Isabel In recent years, three-dimensional model generation and manipulation through generative AI has seen significant developments. Current projects enable the generation of threedimensional assets from natural language prompts and input images, as well as functionalityaware model manipulation through mesh segmentation and categorization. However, all these workflows lack a coherent, unified platform that caters to users’ needs and each method’s technologies. Programs that rely on terminal-based commands lack the graphics needed for model interactions, and plugin extensions for 3D modeling applications are unintuitive and hard to extend for new functionalities. Additionally, both approaches require users to have prior computer engineering and/or 3D graphics knowledge. For this thesis, I propose the creation of a web-based, multimodal graphical user interface that consolidates all these different technologies in a single platform. By supporting model stylization and model generation (both from text prompts and input images), users can utilize combined workflows and expand the range of output possibilities for 3D asset creation. Other features in our interface include model uploading, saving, and downloading to enable a continuous stream of work on a single 3D asset. Apart from all this, we expand the current capabilities of existing image-to-3D generation programs by enabling users to combine up to six images together and create a merged 3D object. Each of these images corresponds to a view angle from which the outputted mesh will be built.

Convergence of the Arnoldi Iteration for Estimating Extreme Eigenvalues

2025-05-20T12:37:46Z

Convergence of the Arnoldi Iteration for Estimating Extreme Eigenvalues Chen, Cecilia Krylov subspace methods, like the Arnoldi iteration, are a powerful tool for efficiently solving high-dimensional linear algebra problems. In this work, we analyze the convergence of Krylov methods for estimating the numerical range of a matrix. Prior bounds on approximation error often depend on eigenvalue gaps of the matrix, which lead to weaker bounds than observed in practice, specifically in applications where these gaps are small. Instead, we extend a line of work proving gap-independent bounds for the Lanczos method, which depend only on the matrix dimensions and number of iterations, to the more general Arnoldi case.

GIM: Guidance as Initialization Method

2025-10-04T03:17:47Z

GIM: Guidance as Initialization Method Duitama Cortes, Juan Sebastian This work makes two contributions: the evaluation of early stop guidance for deep Fully Connected Networks (FCNs) and the introduction of guidance as an initialization method (GIM). Network initialization has been a meaningful and challenging topic in the field of machine learning (ML) for a long time. Many initialization methods exist, ranging from data-independent to data-dependent approaches. Initializations allow for a better understanding of model behavior and improvements in model performance. The novel guidance tool enabled us to propose GIM, a new technique that initializes a model by leveraging representational similarity with respect to models of different architectures. A model with an architecture that performs poorly in a specific task can be initialized with guidance from a model with an architecture that performs well in the respective task. We focus on the case of FCNs in the task of image classification and provide experimental results to validate our approach.

Simulating Weather For A Mixed Reality Platform

2025-10-04T03:17:07Z

Simulating Weather For A Mixed Reality Platform Ni, Hao Complex systems are inherently difficult to teach in a traditional classroom setting. The We’re In This Together (WIT) project aims to provide a different teaching strategy by using AR/VR headsets to situate the students directly inside the system. WIT’s first game attempts to tackle common weather concepts including precipitation and fronts; however, the most recent version fails to demonstrate and model the concepts in an accurate and comprehensible way. This project focuses on developing a brand-new simulation layer for the game that better captures the causes behind common weather phenomena. The new simulation uses a particle-based approach to model the movement of air in the atmosphere and creates a more thorough and interactive experience to help students explore the various aspects of weather.

Realistic Tactile Stylization for Digital Fabrication using Enhanced UV Unwrapping Method

2025-10-04T03:16:53Z

Realistic Tactile Stylization for Digital Fabrication using Enhanced UV Unwrapping Method Wong, Zoe While recent advances in Generative AI enable visual stylization of 3D models using image prompts, they typically neglect tactile properties. TactStyle addresses this limitation by enabling creators to enhance 3D models with both visual and tactile properties derived from texture images. Using a fine-tuned image-generation model, TactStyle generates highly accurate heightfields that faithfully replicate the tactile properties of input visual textures and applies them to 3D models. However, applying textures to 3D models presents challenges, such as ensuring even texture resolution, avoiding texture warping, and minimizing visible seams. TactStyle’s current implementation often struggles with significant texture stretching and distortion caused by poor UV mapping, compromising the accurate heightfields and diminishing the tactile fidelity of printed models. Our research systematically evaluates various UV unwrapping methods, including alternative UV projections and an optimization-based neural UV mapping, to improve the realism and accuracy of texture application on 3D models in digital fabrication. Building on these findings, we will release a Blender plugin that integrates the optimal UV unwrapping methods with TactStyle, enabling creators to easily customize their 3D models with accurate tactile properties using only reference texture images. This work enhances the practicality and accessibility of tactile 3D model customization, bridging the gap between visual and tactile design elements.

Identifying the Role of Transcription Factor RFX3 in 9PDeletion Syndrome

2025-10-04T03:16:39Z

Identifying the Role of Transcription Factor RFX3 in 9PDeletion Syndrome Edwards, Lilly 9p deletion (9p-) syndrome is primarily characterized by intellectual disability, developmental delays, and autism. This project investigated how much of the neuronal phenotypes of 9p- syndrome could be attributed to RFX3, a transcription factor and autism risk gene. Bulk RNA-seq data of iPSC-derived neurons from patients with 9p- syndrome and CRISPRengineered cell lines was analyzed using Principal Component Analysis, Differential Gene Expression analysis, and Functional Enrichment analysis. The findings indicate that RFX3 plays a significant role but is not the sole driver of the neuronal phenotypes. SMARCA2, a gene linked to intellectual disability and part of the SWI/SNF complex, was identified as a direct target of RFX3 in the commonly deleted region of chromosome 9p. Notably, the combined deletion of RFX3 and SMARCA2 led to greater dysregulation of SMARCA2 expression and SWI/SNF complex components than the deletion of either gene alone. These findings highlight the potential synergistic effects of RFX3 and SMARCA2 in 9p- syndrome and suggest their combined disruption may underlie the neuronal phenotypes observed.

Investigating Model Editing for Unlearning in Large Language Models

2025-10-04T03:16:30Z

Investigating Model Editing for Unlearning in Large Language Models Hossain, Shariqah Data regulations on the Right to be Forgotten such as that in the General Data Protection Regulation (GDPR) of the European Union protect the right of users to remove private information from organizations. With the increasing usage and influence of large language models (LLMs) that are trained on personal data, a question of how to implement the removal of information within these models arises. In addition, large language models (LLMs) are trained on a large corpus of data that is usually scraped from the Web. A current challenge with ensuring reliable and safe outputs from LLMs is false, toxic, harmful or biased information from Web data that is captured in the knowledge of the model. Machine unlearning aims to remove unwanted information from a model, but many methods are inefficient for models with large numbers of parameters or fail to remove the entire scope of information without harming performance in the knowledge that is to be retained. Model editing algorithms solve a similar problem of changing information in LLMs, but they focus on redirecting inputs to a new target rather than removing that information altogether. Despite the parallels between model editing and unlearning, there has yet to be a thorough investigation of the potential of model editing approaches within this setting. In this work, we explore ROME, IKE, and WISE editing algorithms and design new editing targets for an unlearning setting. For evaluating the potential of the model editing algorithms, we focus on unlearning fictitious information using the Task of Fictitious Unlearning (TOFU) benchmark. Through this investigation, we show that model editing approaches can exceed the performance of current unlearning methods at removing information depending on the setting. They share the limitation of traditional unlearning of being unable to encapsulate the scope of what is to be unlearned without damage to overall model performance. We hope to leverage this information to improve methods for unlearning model knowledge and therefore improve the reliability of LLMs.

Toward An Explainable Electric Power Grid Operation Assistant Using Large Language Models

2025-10-04T03:16:23Z

Toward An Explainable Electric Power Grid Operation Assistant Using Large Language Models Ravichandran, Anish This thesis explores potential applications of LLMs for assisting the analyses and decisionmaking of complex electric power grid operators. The power grid is a critical piece of infrastructure currently challenged by increased electrification, integration of renewable energy sources, and distributed energy resources (DERs). Human operators struggle to process the massive amounts of data produced by modern smart grids and need innovative solutions to handle the increased complexity of operational decisions. This thesis investigates the potential role of Large Language Models (LLMs) in grid operation tasks, focusing on interpretability and generalizability while exploring how LLMs can assist operators by providing actionable insights and recommendations. Multiple versions of LLM agents were developed, including naive and tool-assisted designs, and were evaluated on the Learn to Run a Power Network (L2RPN) benchmark for steady-state and cascading failure scenarios. While the LLM agents performed better in scenarios requiring exploratory decision-making, they struggled in steady-state operation and were constrained by their integration with tools and the testing environment. This work was limited by compute constraints, which affected the choice of model and the length of evaluation scenarios, and future work is needed toward seamless interaction of LLMs and power systems simulators, however LLMs have the potential to transform future grid operation, paving the way for more resilient and sustainable energy sector of the 21st century.

CIRCUIT: A Benchmark for Circuit Interpretation and Reasoning Capabilities of LLMs

2025-10-04T03:16:16Z

CIRCUIT: A Benchmark for Circuit Interpretation and Reasoning Capabilities of LLMs Skelić, Lejla The role of Large Language Models (LLMs) has not been extensively explored in analog circuit design, which could benefit from a reasoning-based approach that transcends traditional optimization techniques. In particular, despite their growing relevance, there are no benchmarks to assess LLMs’ reasoning capability about circuits. Therefore, we created the CIRCUIT dataset consisting of 510 question-answer pairs spanning various levels of analog-circuit-related subjects. The best-performing model on our dataset, GPT-4o, achieves 48.04% accuracy when evaluated on the final numerical answer. To evaluate the robustness of LLMs on our dataset, we introduced a unique dataset design and evaluation metric that enable unit-test-like evaluation by grouping questions into unit tests. In this case, GPT-4o can only pass 27.45% of the unit tests, highlighting that the most advanced LLMs still struggle with understanding circuits, which requires multi-level reasoning, particularly when involving circuit topologies. This circuit-specific benchmark introduces a scalable and reliable automatic evaluation method, transferable to other reasoning domains, and highlights LLMs' limitations, offering valuable insights for advancing their application in analog integrated circuit design.

ALFA-Chains: An Artificial Intelligence Approach to Exploit Chain Discovery in Networks

2025-10-04T03:15:56Z

ALFA-Chains: An Artificial Intelligence Approach to Exploit Chain Discovery in Networks Tulla Lizardi, Miguel A. Exploit chains play a crucial role in advanced persistent threats (APTs) and other malicious cyber campaigns. Sophisticated attackers can navigate across a network, escalate their privileges, and compromise valuable targets by executing the right exploits in the right order. However, finding these exploits chains is a challenging task requiring a broad knowledge of the vulnerabilities present in computer systems and the exploits that take advantage of them. Networks can be complex, with many hosts and intricate software stacks. Moreover, the range of known exploits and vulnerabilities is constantly growing, complicating the process of determining how they can be linked. This thesis introduces a solution, ALFA-Chains, that automates the discovery of exploit chains by leveraging classical AI planning, Large Language Models (LLMs), and existing exploit/vulnerability databases. ALFA-Chains describes networks and exploits using the Planning Domain Description Language (PDDL), a formal language to represent planning problems. This allows us to use optimized off-the-shelf planners that have been developed by the AI planning community over many years. Our system takes natural language descriptions of exploits and classifies them into categories based on their preconditions and effects. From this intermediary representation, we can programmatically generate PDDL that captures the requirements needed to run the exploit and the access gained by the attacker. Due to this automated approach, ALFA-Chains is able to consider a vast set of exploits when determining if a network is susceptible to exploit chaining. We show how ALFA-Chains can process 1,880 Metasploit exploits and their corresponding 2,002 CVEs to detect exploit chains in a variety of realistic network configurations. We proceed to discuss potential applications of ALFA-Chains, including automated penetration testing and vulnerability prioritization.

On the Inductive Biases of Conditional Diffusion Models

2025-09-03T03:35:59Z

On the Inductive Biases of Conditional Diffusion Models Yu, Christina Diffusion models have achieved remarkable progress in recent years across various domains and applications, but how diffusion models generalize is still not well understood. While prior work predominantly focuses on unconditional diffusion models, in this thesis we focus on understanding generalization for conditional diffusion models, which is especially relevant for modern text- or observation- conditioned applications. In particular, we are interested in the inductive biases of conditional diffusion models which predispose them to certain forms of interpolation in regions outside the support of the training data. We observe that neural networks are capable of learning qualitatively different forms of interpolation, which may be influenced by the architecture and capacity of the network and other aspects of neural network training. We develop a potential framework to model the interpolation behavior of neural networks via nonparametric estimation, which happens to have the property of being schedule consistent, or truly denoising at every time step. We find that, assuming a neural network with sufficient capacity, conditional diffusion models are biased towards smoothing, which can lead to non-schedule consistent behavior away from the training data and has a number of interesting consequences.

All Pass Readout With Ring Resonators for Qubit Measurement

2025-09-03T03:35:57Z

All Pass Readout With Ring Resonators for Qubit Measurement Zang, Alicia Quantum computers may advance computing by solving some NP complexity problems, such as factoring and simulating quantum systems. Superconducting qubits, configurable artificial atoms comprised of circuit elements, are a leading platform to create quantum computers. Many schemes for superconducting qubit readout include a weakly coupled port as a capacitor in the feedline, which allows for directionality in the readout signal. However, this impedance mismatch creates problems with resonator linewidth variation, standing waves, and voltage nodes in the feedline, leading to challenges in scaling to larger frequency multiplexed systems. This thesis proposes an all-pass readout scheme that utilizes ring resonators that do not require a weakly coupled port, allowing for more modular qubit readout architectures.

Verification of Go Channels

2025-09-03T03:35:46Z

Verification of Go Channels Zhang, Jessica Goose is a tool for translating a subset of the Go programming language into Perennial/Iris, which is an extension of Coq. However, Goose did not support channels, which are an important synchronization tool that Go is well known for. This thesis presents an extension to Goose to support channels, including a model to represent Go channels and operations in GooseLang, the language defined in Perennial/Iris that Goose translates into, an extension to the Goose translator to support channels, and a library of separation logic specifications that define the expected behavior of channel operations on open channels. Finally, this thesis evaluates how effective this model and library is for verifying Go code containing channels, and discuss some limitations and potential future work.

Laboratory testing and design of a mill for the treatment of a gold ore from Porcupine, Ontario

2025-10-30T15:50:05Z

Laboratory testing and design of a mill for the treatment of a gold ore from Porcupine, Ontario Loo, Pang Chieh. Thesis: M.S., Massachusetts Institute of Technology, Department of Mining Engineering and Metallurgy, 1917

A study of industry financing of a new jet transport for U.S. domestic airline service

2025-10-30T18:06:09Z

A study of industry financing of a new jet transport for U.S. domestic airline service Evani, Sunder Rayma Murthy. Thesis: M.S., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 1978; Includes bibliographical references.

Review of intervention programs for pre-schoolers in Venezuela.

2025-12-17T03:47:08Z

Review of intervention programs for pre-schoolers in Venezuela. Eskenasy, Sandra Patricia. Thesis: M.S., Massachusetts Institute of Technology, Department of Nutrition and Food Science, 1978; Bibliography: leaf 147.

Fast Multi-query Planning in Graphs of Convex Sets

2025-04-07T09:05:10Z

Fast Multi-query Planning in Graphs of Convex Sets Morozov, Savva Planning in Graphs of Convex Sets (GCS) is a recently developed optimization framework that seamlessly integrates discrete and continuous decision making. It naturally models and effectively solves a wide range of challenging planning problems in robotics, including collision-free motion planning, skill chaining, and control of hybrid systems. In this thesis, we study the multi-query extension of planning through GCS, motivated by scenarios where robots must operate swiftly within static environments. Our objective is to precompute optimal plans between predefined sets of source and target conditions, in an effort to enable fast online planning and reduce GCS solve times. Our solution consists of two stages. Offline, we use semidefinite programming to compute a coarse lower bound on the problem’s cost-to-go function. Then, online, this lower bound is used to incrementally generate feasible plans by solving short-horizon convex programs. We demonstrate the effectiveness of our approach through a variety of experimental domains: collision-free motion planning for a warehouse robot arm, item sorting for a top-down suction gripper, and footstep planning for a bipedal walker. In particular, in a warehouse-like scenario involving a seven-joint robot arm, our method generates higher-quality paths up to 100 times faster than existing motion planners.

Structuring Representation Geometry in Self-Supervised Learning

2025-04-07T09:25:49Z

Structuring Representation Geometry in Self-Supervised Learning Gupta, Sharut The central promise of deep learning is to learn a map 𝑓 : 𝒳 → ℝ_𝑑 that transforms objects 𝒳—represented in their raw perceptual forms, such as images or molecular strings—into a representation space ℝ_𝑑 where everything that is hard to do with raw perceptual data becomes easy. For instance, measuring the similarity between two objects [scientific notation] expressed as tensors of pixel intensities is non-trivial in their raw form, but becomes straightforward if 𝑓 maps these objects to a space where simple Euclidean distances, ‖𝑓(𝑥₁) − 𝑓(𝑥₂)‖₂ are meaningful measures of similarity. While this simple recipe has shown standout success in a range of tasks, certain applications require representations that encode richer structural relationships beyond pairwise similarity. For instance, tasks that encode relational information— such as “𝑋 is a parent of 𝑌 ” or “𝐴 is a treatment for 𝐵”—require embedding spaces that capture richer structural relationships. In this thesis, we explore what 𝑓 should encode in order to be useful for a range of unknown downstream tasks, from the point of view of the geometric structure of representation space. We investigate this question in the context of self-supervised learning, a paradigm that extracts meaningful representations by leveraging the structure of the data itself without relying on explicit labels. Specifically, we propose adding additional geometric structure to the embedding space by enforcing transformations of input space to correspond to simple (i.e., linear) transformations in the embedding space. To this end, we introduce an equivariance objective and theoretically prove that its minima forces transformations on input space to correspond to rotations on the spherical embedding space. Our proposed method significantly improves performance on downstream tasks, and ensures sensitivity in embedding space to important variations in data (e.g., color, rotation) that existing contrastive methods do not achieve.

Scalable and Modular Manufacturing of Insect-Scale Aerial Robots Towards Swarm Flight Demonstrations

2025-04-08T04:50:00Z

Scalable and Modular Manufacturing of Insect-Scale Aerial Robots Towards Swarm Flight Demonstrations Hsiao, Yi-Hsuan Insects demonstrate remarkable capabilities in navigating complex environments and executing tasks such as pollination and coordinated object transport. Inspired by these biological feats, insect-scale micro aerial vehicles (MAVs) have been developed with advanced flight functionalities, including collision resilience and aerial acrobatics. Despite these advancements, MAVs weighing less than a gram continue to face critical challenges in design, assembly, and repair. Additionally, limitations in sensing and control have prevented the realization of swarm-like behaviors, thereby constraining research on collective actions and potential applications such as distributed sensing. To overcome these obstacles, this work introduces a scalable and modular fabrication method for sub-gram MAVs. A parametric design algorithm automatically generates laser cutting templates from a minimal set of design parameters, while stereolithographic 3D printing is employed to fabricate static components such as airframes and connectors, significantly streamlining the production process. This modular approach improves assembly efficiency and repairability, reducing fabrication time by more than half. Using this methodology, two sub-gram MAVs successfully demonstrated controlled hovering and coordinated payload transport. These results represent a significant step toward enabling insect-inspired robotic swarms, providing a platform for future studies on collective flight behaviors and swarm robotics.

Goal Inference from Open-Ended Dialog

2025-04-07T09:13:54Z

Goal Inference from Open-Ended Dialog Ma, Rachel Embodied AI Agents are quickly becoming important and common tools in society. These embodied agents should be able to learn about and accomplish a wide range of user goals and preferences efficiently and robustly. Large Language Models (LLMs) are often used as they allow for opportunities for rich and open-ended dialog type interaction between the human and agent to accomplish tasks according to human preferences. In this thesis, we argue that for embodied agents that deal with open-ended dialog during task assistance: 1. AI Agents should extract goals from conversations in the form of Natural Language (NL) to be better at capturing human preferences as it is intuitive for humans to communicate their preferences on tasks to agents through natural language. 2. AI Agents should quantify/maintain uncertainty about these goals to ensure that actions are being taken according to goals that the agent is extremely certain about. We present an online method for embodied agents to learn and accomplish diverse user goals. While offline methods like RLHF can represent various goals but require large datasets, our approach achieves similar flexibility with online efficiency. We extract natural language goal representations from conversations with Large Language Models (LLMs). We prompt an LLM to role play as a human with different goals and use the corresponding likelihoods to run Bayesian inference over potential goals. As a result, our method can represent uncertainty over complex goals based on unrestricted dialog. We evaluate in a text-based grocery shopping domain and an AI2Thor robot simulation. We compare our method to ablation baselines that lack either explicit goal representation or probabilistic inference.

Multi-Subject Image Generation

2025-04-08T04:17:08Z

Multi-Subject Image Generation Yin, Tianwei Diffusion models excel at text-to-image generation, especially in subject-driven generation for personalized images. However, existing methods are inefficient due to the subject-specific fine-tuning, which is computationally intensive and hampers efficient deployment. Moreover, existing methods struggle with multi-subject generation as they often blend identity among subjects. In this thesis, we present FastComposer which enables efficient, personalized, multi-subject text-to-image generation without fine-tuning. FastComposer uses subject embeddings extracted by an image encoder to augment the generic text conditioning in diffusion models, enabling personalized image generation based on subject images and textual instructions with only forward passes. To address the identity blending problem in the multi-subject generation, FastComposer proposes cross-attention localization supervision during training, enforcing the attention of reference subjects localized to the correct regions in the target images. Naively conditioning on subject embeddings results in subject overfitting. FastComposer proposes delayed subject conditioning in the denoising step to maintain both identity and editability in subject-driven image generation. FastComposer generates images of multiple unseen individuals with different styles, actions, and contexts. It achieves 300–2500 speedup compared to fine-tuning-based methods and requires zero extra storage for new subjects. FastComposer paves the way for efficient, personalized, and high-quality multi-subject image creation.

Genetic algorithm gradient ascent (GAGA) optimization of compact symmetry-breaking photonic crystals

2025-04-08T04:37:02Z

Genetic algorithm gradient ascent (GAGA) optimization of compact symmetry-breaking photonic crystals Gold, Hannah T. Fundamental limits of thermal radiation are imposed by Kirchhoff’s law, which assumes the electromagnetic reciprocity of a material or material system. Thus, breaking reciprocity can enable breaking barriers in thermal efficiency engineering¹. This thesis presents 1D photonic crystals composed of Weyl/Dirac semimetal and dielectric layers, whose structures are optimized to maximize the nonreciprocity of infrared radiation absorptance/emittance in planar and compact designs. Two different mechanisms to enable nonreciprocal infrared absorbers/emitters are simulated and compared – anomalous Hall effect in Weyl semimetals 2 and electric-current-induced Fizeau drag in either Dirac or Weyl semimetals3 . To engineer an ultra-compact absorber structure that does not require gratings or prisms to couple light, a genetic algorithm (GA) was used to maximize nonreciprocity in the design globally, followed by the application of the numerical gradient ascent (GAGA) algorithm as a local optimization to further enhance the design. The first absorber design takes advantage of the intrinsic nonreciprocity of time-reversal symmetry (TRS) breaking Weyl semimetals due to their pseudomagnetic field in momentum space. GAGA methodology is then applied to design and optimize a flat absorber using inversion (IS) breaking Weyl/Dirac semimetals as active layers, in which tunable nonreciprocity is induced through an applied DC current bias. This momentum bias imparts plasmon Fizeau drag, the drag of an electrical current on propagating surface plasmon polaritons (SPPs). A semi-classical theory recently developed is used to model SPP transport along interfaces of 3D semimetals under Fizeau drag3 . Lastly, in both cases the optimization algorithm accounts for both s- and p-polarized absorptance spectra to create a final design suitable for thermal applications, which maximizes the nonreciprocal absorptance of p-polarized light and simultaneously minimizes the parasitic, reciprocal absorptance of s-polarized light.

Tailored Mechanical Response of 3D Microgranular Crystals with Hierarchical Architecture

2025-04-08T04:36:27Z

Tailored Mechanical Response of 3D Microgranular Crystals with Hierarchical Architecture Figueroa, Samuel D. Granular media exhibit extraordinary impact-mitigating properties due to their nonlinear grain-to-grain interactions, enabling efficient energy dissipation and wave perturbation under dynamic loading—behaviors unattainable in conventional monolithic materials. Recent efforts have sought to engineer granular systems with tunable mechanical responses, though few have begun to realize them as functional architected materials. Here, we introduce a two-level architected granular framework that programs spherical microgranular media across both grain-level (ellipsoidal microvoids) and bulk granular packing-level architectures, offering surprising control over static and dynamic properties. Using nanoindentation experiments, we reveal tunable quasi-static stiffness behavior, where hollow architected granular packings can exhibit superior mass-normalized energy dissipation compared to their fully dense counterparts. Finite element simulations uncover a structurally engineered Poisson effect, enabling nonlocal contact mechanisms that enhance load-bearing capacity across different packing structures. Future custom direct impact experiments demonstrate a potential route the effectiveness of our multi-scale design in dynamically programming energy dissipation. Our findings demonstrate that a hierarchical granular crystal exhibits enhanced specific energy absorption at a fraction of the weight of their fully dense counterparts and unique nonlocal stress redistribution, surpassing classical granular mechanics through architectural design. This work establishes a path toward lightweight, tunable, and impact-resistant metamaterials, with broad applications in nonlinear waveguiding, energy dissipation, and protective systems.

A Variational Lower Bound to Mitigate Batch Effect in Molecular Representations

2025-04-08T04:13:36Z

A Variational Lower Bound to Mitigate Batch Effect in Molecular Representations Wang, Chenyu High-throughput drug screening – using cell imaging or gene expression measurements as readouts of drug effect – is a critical tool in biotechnology to assess and understand the relationship between the chemical structure and biological activity of a drug. Since large-scale screens have to be divided into multiple experiments, a key difficulty is dealing with batch effects, which can introduce systematic errors and non-biological associations in the data. We propose InfoCORE, an Information maximization approach for COnfounder REmoval, to effectively deal with batch effects and obtain refined molecular representations. InfoCORE establishes a variational lower bound on the conditional mutual information of the latent representations given a batch identifier. Experiments on drug screening data reveal InfoCORE’s superior performance in a multitude of tasks including molecular property prediction and molecule-phenotype retrieval. Additionally, we show results for how InfoCORE offers a versatile framework and resolves general distribution shifts and issues of data fairness by minimizing correlation with spurious features or removing sensitive attributes.

Congestion Control for DNN training clusters

2025-04-07T08:56:14Z

Congestion Control for DNN training clusters Narang, Sanjoli The modern DNN workloads generate network traffic having striking differences with the conventional data-center traffic. DNN training jobs generate periodic traffic pattern where all subsequent flows depend on the completion of the currently running flow. Although this periodic behavior calls for a new non-conventional congestion control protocol for DNN training clusters, it also creates an unprecedented opportunity to approximate optimal schedule for DNN jobs in a distributed manner without requiring priority queues, centralized information, or switch hardware support. Prior work on MLTCP proposed updates to existing congestion control algorithms to make them capable of minimizing network congestion when DNN jobs compete for the network. In this thesis, we propose several techniques to expand the scope of prior work to support DNN jobs with more complex communication patterns or parallelization strategies, and further improve the performance speedup over TCP. With two straightforward ideas of updating the congestion control parameters, we expand the performance benefits of MLTCP to a wider set of periodic DNN jobs. Augmenting existing congestion control algorithms with MLTCP provides an effective guiding mechanism to a random search to find the optimal interleaved schedule for competing DNN jobs. Our contributions boost this guided search to improve performance further. We provide detailed theoretical analysis and extensive flow-level simulations to take a deep dive into the convergence, performance speedup, and fairness of MLTCP with the proposed changes.

Input Adaptive Allocation of Language Model Computation

2025-04-07T09:13:10Z

Input Adaptive Allocation of Language Model Computation Damani, Mehul Computationally intensive decoding procedures—including search, reranking, and self-critique— can improve the quality of language model (LM) outputs in problems spanning code generation, numerical reasoning, and dialog. Existing work typically applies the same decoding procedure for every input to an LM. But not all inputs require the same amount of computation to process. Can we allocate decoding computation adaptively, using more resources to answer questions whose answers will be harder to compute? We present an approach that predicts the distribution of rewards given an input and computation budget, then allocates additional computation to inputs for which it is predicted to be most useful. We apply this approach in two decoding procedures: first, an adaptive best-of-k procedure that dynamically selects the number of samples to generate as input to a reranker; second, a routing procedure that dynamically responds to a query using a decoding procedure that is expensive but accurate, or one that is cheaper but less capable. Across a suite of programming, mathematics, and dialog tasks, we show that accurate computation-allocation procedures can be learned, and reduce computation by up to 50% at no cost to response quality, or improve quality by up to 10% at a fixed computational budget.

Enhancing Robotic Manipulation of Liquid Using a Digitally Fabricated Intelligent Wearable Device

2025-04-07T08:57:31Z

Enhancing Robotic Manipulation of Liquid Using a Digitally Fabricated Intelligent Wearable Device Lee, Young Joong Despite recent exponential advances in computer vision and reinforcement learning, it remains challenging for robots to interact with liquids due to visual obstructions, transparent liquids, and fine-grained splashes. Yet, a substantial opportunity exists for robotics to excel in liquid identification and manipulation, given its potential role in chemical handling in laboratories and various manufacturing sectors such as pharmaceuticals or beverages. Recent advancements in electronic wearables, designed to replicate or surpass the functions and attributes of human skin, and their convergence with machine learning have provided opportunities to enhance the capabilities of robotic systems. Here, we present a novel approach for liquid class identification and position estimation with the robotic wearable device that can ‘see through’ the container, leveraging electrical impedance sensing. We design and mount a digitally embroidered electrode array to a commercial robotic gripper. Coupled with a customized impedance sensing board, we collect data on liquid manipulation with a swept frequency sensing mode and a frequency-specific impedance measuring mode. Our developed learning-based models achieve an accuracy of 93.33% in classifying 9 different types of liquids (8 liquids + air) and 97.65% in estimating the liquid position in the cup without any vision system present. We investigate the effectiveness of our system with a series of ablation studies. These findings highlight our work as a promising solution for enhancing robotic manipulation in liquid-related tasks.

Design and Optimization of Tunneling Nanoelectromechanical Switches

2025-04-08T04:11:30Z

Design and Optimization of Tunneling Nanoelectromechanical Switches Dang, Tong As silicon complementary metal-oxide-semiconductor (CMOS) technology nears its scaling limits, nanoelectromechanical (NEM) switch relays have emerged as promising candidates for complementing CMOS technology due to their superior characteristics, including zero leakage, steep subthreshold swings, high on-of current ratios, and robustness in harsh environments. However, the practical integration of NEM switches still faces challenges such as high actuation voltages, stiction, and slower switching speeds compared to CMOS. One promising strategy to mitigate these issues is the integration of a self-assembled monolayer (SAM) to create tunneling NEM switches. Such switches could achieve nanometer-scale mechanical modulation of gaps between electrodes, showing the potential to overcome the limitations of a conventional NEM switch by exhibiting low actuation voltages, high switching speeds, and minimizing stiction. Nevertheless, the tunneling NEM switches reported to date still show limited performance and require intricate fabrication processes. Additionally, functional tunneling NEM switches demonstrated are limited to two-terminal architectures. This thesis explores innovative designs, fabrication techniques, and material choices to address these limitations and to develop tunneling NEM switches with enhanced performance and reliability for next-generation NEM logic applications. To this end, switches with various structures have been fabricated and investigated, and their respective characteristics are analyzed. In a three-terminal lateral structure fabricated using entirely conventional nanofabrication techniques, switching is demonstrated in both contact and tunneling modes. While operation in direct contact mode shows a high on-of ratio, the integration of the SAM leads to a significantly reduced actuation voltage of 2 V and a lower hysteresis. Further, two-terminal vertical structured devices are studied in tunneling mode, and they consistently demonstrate operation cycles exceeding 100, with a maximum of over 7000, which manifests the reliability prospects of SAM. The trends in IV characteristics indicate that the SAM might have experienced physical deformation due to compression, highlighting a potential area for future research in the molecular engineering of the self-assembly monolayer.

Near-Optimal Learning and Planning in Separated Latent MDPs

2025-04-08T04:08:56Z

Near-Optimal Learning and Planning in Separated Latent MDPs Chen, Fan We study computational and statistical aspects of learning Latent Markov Decision Processes (LMDPs). In this model, the learner interacts with an MDP drawn at the beginning of each epoch from an unknown mixture of MDPs. To sidestep known impossibility results, we consider several notions of δ-separation of the constituent MDPs. The main thrust of this paper is in establishing a nearly-sharp statistical threshold for the horizon length necessary for efficient learning. On the computational side, we show that under a weaker assumption of separability under the optimal policy, there is a quasi-polynomial algorithm with time complexity scaling in terms of the statistical threshold. We further show a near-matching time complexity lower bound under the exponential time hypothesis.

VoxelPrompt: A Vision-Language Agent for Grounded Medical Image Analysis

2025-04-07T08:52:58Z

VoxelPrompt: A Vision-Language Agent for Grounded Medical Image Analysis Hoopes, Andrew We present VoxelPrompt, an agent-driven vision-language framework that tackles diverse radiological tasks through joint modeling of natural language, image volumes, and analytical metrics. VoxelPrompt is multi-modal and versatile, leveraging the flexibility of language interaction while providing quantitatively-grounded image analysis. Given a variable number of 3D medical volumes, such as MRI and CT scans, VoxelPrompt employs a language agent that iteratively predicts executable instructions to solve a task specified by a natural language input prompt. These instructions communicate with a vision network to encode image features and generate volumetric outputs (e.g., segmentations). VoxelPrompt interprets the results of intermediate instructions and plans further actions to compute discrete measures (e.g., tumor growth across a series of scans) and present relevant outputs to the user. We evaluate this framework on diverse neuroimaging tasks and show that the single VoxelPrompt model can delineate hundreds of anatomical and pathological features, measure many complex morphological properties, and perform open-language analysis of lesion characteristics. VoxelPrompt carries out these objectives with accuracy similar to that of fine-tuned, single-task models for segmentation and question-answering, while facilitating a large range of tasks.

Sub-Bottom Profiling Using an Autonomous Underwater Vehicle Equipped With a Sound Source and Towed Hydrophone Array

2025-04-07T08:41:59Z

Sub-Bottom Profiling Using an Autonomous Underwater Vehicle Equipped With a Sound Source and Towed Hydrophone Array Pfenninger, Paige Sub-bottom profiling using an autonomous underwater vehicle equipped with a source and a towed array is an excellent method to finely survey large areas of the ocean bottom with minimal interference from the water column. This approach has the benefit of being able to determine the range dependence of the sub-bottom on a meter-by-meter scale rather than assuming constant sub-bottom properties over a large range. This thesis conducts theoretical and experimental studies to investigate the feasibility of using the arrival times of acoustic signals from an autonomous underwater vehicle source to a short, 16-element towed hydrophone array to determine the sound speed and layer thickness of the seabed through Bayesian geoacoustic inversion. This method provides range-dependent geoacoustic parameters with a resolution on the order of 10 meters. Numerical studies indicate that, for timing data with low variance, arrival times can be used to accurately estimate seabed properties. However, the performance of the Bayesian inversion model deteriorates as the variance of the timing data increases. Experimental data were collected during the Seabed Characterization Experiment at the New England Mud Patch and the New England Shelf Break. This thesis attempts to improve the arrival times through the use of sub-array focusing but concludes that this method is not feasible due to the experimental data exhibiting a high level of variance in the sub-bottom timing returns, likely due to the presence of scatterers in the sediment layer. Therefore, the mean and variance of the direct path, bottom, and sub-bottom timing returns were calculated using Gaussian process regression. Furthermore, the results show that layer thickness and sound speeds are highly coupled, making it challenging to uniquely determine seabed properties.

Design-technology Co-optimization for Sub-2 nm Technology Node Based on 2D Materials

2025-04-07T09:20:09Z

Design-technology Co-optimization for Sub-2 nm Technology Node Based on 2D Materials Yao, Aijia Emerging disruptive technologies such as Artificial Intelligence (AI) and 6G communications have driven stringent demands for hardware components that enable faster and more energy-efficient computation. With the diminishing returns of traditional silicon-based scaling and the escalating complexity of advanced semiconductor processes, two-dimensional (2D) materials offer promising opportunities when developed through Design-Technology Co-Optimization (DTCO). This thesis presents a comprehensive study of DTCO with a novel framework tailored for 2D material-based electronics that addresses critical challenges in material synthesis, device design, and circuit integration. In this framework, experimental material and device data are integrated into the design and optimization of MoS₂-based multichannel transistors (MCTs). With the help of DTCO, we have achieved record performance for double-gate, single-channel MoS₂ transistors as well as the first demonstration of high-performance, functional double channel MoS₂ transistors. Based on the results of MCTs, a Process Design Kit (PDK) is developed to facilitate circuit-level integration. These advancements constitute a promising foundation for the development of next-generation electronics beyond sub-2 nm technology node.

Encoder-Agnostic Learned Temporal Matching for Video Classification

2025-04-08T04:30:54Z

Encoder-Agnostic Learned Temporal Matching for Video Classification Ho, Darryl In recent years, large transformer-based video encoder models have greatly advanced stateof-the-art performance on video classification tasks. However, these large models typically process videos by averaging embedding outputs from multiple clips over time to produce fixed-length representations. This approach fails to account for a variety of time-related features, such as variable video durations, chronological order of events, and temporal variance in feature significance. While methods for temporal modeling do exist, they often require significant architectural changes and expensive retraining, making them impractical for offthe-shelf, fine-tuned large encoders. To overcome these limitations, we propose DejaVid, an encoder-agnostic method that enhances model performance without the need for retraining or altering the architecture. Our framework converts a video into a variable-length temporal sequence of embeddings, which we call a multivariate time series (MTS). An MTS naturally preserves temporal order and accommodates variable video durations. We then learn pertimestep, per-feature weights over the encoded MTS frames, allowing us to account for variations in feature importance over time. We introduce a new neural network architecture inspired by traditional time series alignment algorithms for this learning task. Our evaluation demonstrates that DejaVid substantially improves the performance of a state-of-the-art large encoder, achieving leading Top-1 accuracy of 77.2% on Something-Something V2, 89.1% on Kinetics-400, and 88.6% on HMDB51, while adding fewer than 1.8% additional learnable parameters and requiring less than 3 hours of training time.

Reducing Transformer Key-Value Cache Size with Cross-Layer Attention

2025-04-07T08:57:54Z

Reducing Transformer Key-Value Cache Size with Cross-Layer Attention Brandon, William Key-value (KV) caching plays an essential role in accelerating decoding for transformer-based autoregressive large language models (LLMs). However, the amount of memory required to store the KV cache can become prohibitive at long sequence lengths and large batch sizes. Since the invention of the transformer, two of the most effective interventions discovered for reducing the size of the KV cache have been Multi-Query Attention (MQA) and its generalization, Grouped-Query Attention (GQA). MQA and GQA both modify the design of the attention block so that multiple query heads can share a single key/value head, reducing the number of distinct key/value heads by a large factor while only minimally degrading accuracy. In this work, we show that it is possible to take Multi-Query Attention a step further by also sharing key and value heads between adjacent layers, yielding a new attention design we call Cross-Layer Attention (CLA). With CLA, we find that it is possible to reduce the size of the KV cache by another while maintaining nearly the same accuracy as unmodified MQA. In experiments training 1B- and 3B-parameter models from scratch, we demonstrate that CLA provides a Pareto improvement over the memory/accuracy tradeoffs which are possible with traditional MQA, potentially enabling future models to operate at longer sequence lengths and larger batch sizes than would otherwise be possible.

Precision Pointing for the CubeSat Laser Infrared CrosslinK (CLICK) Mission

2025-04-07T08:48:19Z

Precision Pointing for the CubeSat Laser Infrared CrosslinK (CLICK) Mission Forester, Paige Advances in Free Space Optical Communications have led to numerous missions that have demonstrated optical space-to-ground links, however, fewer missions have demonstrated optical space-to-space links. NASA’s CubeSat Laser Infrared CrosslinK (CLICK) Mission aims to be the first to demonstrate optical space-to-space communication on a CubeSat scale using Commercial Off the Shelf (COTS) components that include a micro electromechanical system (MEMS) fine steering mirror for precision pointing. The first phase of the CLICK mission, CLICK-A, launched in September 2022 to demonstrate optical downlink. The second phase, CLICK-B/C, aims to demonstrate optical crosslink between two spacecraft: CLICK-B and CLICK-C. Optical crosslink communication requires precision pointing for both spacecraft to close the link. The development of the CLICK-B/C Fine Pointing, Acquisition, and Tracking (PAT) is presented in this thesis, as well as the analysis of disturbance rejection and evaluation of expected spacecraft disturbances. This thesis also asses the slewing required for differential drag control which is used to maintain the crosslink range between the two CubeSats. Preliminary results are presented from the CLICK-B/C flight hardware integration and testing phases, as well as findings from simulation of the lasercom payload’s performance.

On Solving Larger Games: Designing New Algorithms Adaptable to Deep Reinforcement Learning

2025-04-07T09:27:19Z

On Solving Larger Games: Designing New Algorithms Adaptable to Deep Reinforcement Learning Liu, Mingyang In this thesis, we explore the design of algorithms capable of handling large games where the state space is too large to store strategies in a tabular format from a theoretical perspective. Specifically, we focus on developing algorithms suitable for deep reinforcement learning in two-player zero-sum extensive-form games. There are three critical properties for effective deep multi-agent reinforcement learning: (last/best) iterate convergence, efficient utilization of stochastic trajectory feedback, and theoretically sound avoidance of importance sampling corrections. Chapter 3 introduces Regularized Optimistic Mirror Descent (Reg-OMD), which provably converges to the Nash equilibrium (NE) linearly in last-iterate. Chapter 4 shows that algorithms based on regret decomposition enjoy best-iterate convergence to the NE. Chapter 5 proposes Q-value based Regret Minimization (QFR), which achieves all three properties simultaneously.

Polymer Deconstructability and Recyclability via Introduction of Cleavable Si−O Bonds

2025-04-07T08:46:20Z

Polymer Deconstructability and Recyclability via Introduction of Cleavable Si−O Bonds Johnson, Alayna The synthesis of a new polysilylether via entropy-driven ring-opening metathesis polymerization (ED-ROMP) of cyclic bifunctional silyl ether-based monomers is reported. High molecular weight polymers (up to 100 k) with narrow dispersities were achieved at modest temperature. These polymers display excellent thermal stability and ultra-low T_g (–88 ºC). The polymers are both rapidly deconstructable via the cleavage of the labile silicon-oxygen linkages with either acid or fluoride triggers and partially depolymerizable by the addition of exogenous metathesis catalyst. Analysis of the deconstructed polymer products provided insight into the polymer microstructure, showing that the ED-ROMP process was regiorandom. Altogether, this work offers a new class of deconstructable polymers with a range of potential applications. Incorporation of these bifunctional silyl ether-based monomers into copolymers could aid in the triggered deconstruction of otherwise nondegradable hydrocarbon backbones.

Modeling and Analysis of Voltage Feasibility Problems for Cost-Effective Microgrids

2025-04-07T09:12:49Z

Modeling and Analysis of Voltage Feasibility Problems for Cost-Effective Microgrids Jones, Aaron Jerome Global efforts to mitigate climate change have led to a significant increase in the integration of renewable energy resources into the electricity grid. This transition not only necessitates the adoption of renewable energy technologies but also requires rethinking and redesigning existing power grid infrastructures to accommodate the unique characteristics of these resources. This research focuses on modeling techniques which can assist in analyzing the feasibility of microgrid topologies. Microgrids have emerged as a flexible and efficient approach to implementing novel grid topologies that support higher levels of renewable energy penetration. They also support the integration of distributed energy resources (DERs), such as photovoltaic (PV) systems, thereby promoting a more sustainable and efficient energy grid design. This thesis utilized sanitized load and system topology data from a real world microgrid located in Illinois to test the feasibility of increasing the number of PV units the system can utilize for reactive power support. In these systems, ensuring feasibility is a crucial concern due to power mismatches caused by the inherent variability of renewable resources. This work focuses of maintaining voltage within the constraints while increasing PV penetration on the system. We simulate the implementation of microgrids with PV generation using Alternating Current Optimal Power Flow (AC-OPF). The results of this thesis show the limits of feasible reactive power support from distributed PV units on a utility disconnected microgrid based on our voltage constraints. The study shows that there exists a limit to reactive power support provided by distributed PV units. Beyond this limit we see voltage collapse shown as infeasibility of power flow solutions. In order to avoid this problem we optimize the reactive power support from PV so that a solution exists within the constraints. The lesson learned for practical use of this result is that operators should use AC-OPF to compensate for reactive power using PV. Future research will explore the challenges and opportunities associated with the widespread adoption of microgrids, such as dynamic voltage instabilities that can occur with high levels of PV integration and complexities in inverter control strategies.

Design and Engineering of Protected Superconducting Qubits

2025-04-07T09:16:28Z

Design and Engineering of Protected Superconducting Qubits Kim, Junghyun Building extensible quantum information processors becomes increasingly promising as the qubits exhibit longer coherence times. To this end, realizing protected qubits, whose Hamiltonians are inherently resilient to both relaxation and dephasing, has attracted strong interest. In this thesis, we primarily explore the soft 0 − π qubit, a leading candidate for implementing superconducting qubit protection with current fabrication techniques. To enhance protection, the soft 0 − π qubit requires its two major modes, the charge-mode (θ) and the flux-mode (ϕ), to satisfy an asymmetric condition: maximizing charge-mode capacitance while minimizing flux-mode capacitance. The main challenge is therefore reducing stray capacitance from the large charge-mode capacitor, which hinders the reduction of flux-mode capacitance. To address this challenge, we depart from the conventional coplanar interdigitated capacitor design and use parallel-plate capacitors (PPC) with small footprints, achieving the desired large charge-mode capacitance while reducing unwanted stray capacitances. By reducing the capacitor area by a factor of approximately 50, the PPC 0−π qubit has achieved an estimated Eᵠ_C /Eᶿ_C ratio of 30–50, placing it among the highest reported. Additionally, we propose enhanced mode-selective control of the soft 0−π qubit using these parallel-plate capacitors. Finally, we discuss the remaining challenges of the soft 0−π qubit and introduce alternative parameter regimes that can potentially improve Raman-based control and qubit readout.

Accelerating the Discovery of Novel Metal Organic Chalcogenolates: A Computational and Machine Learning-Driven Approach

2025-04-07T08:38:53Z

Accelerating the Discovery of Novel Metal Organic Chalcogenolates: A Computational and Machine Learning-Driven Approach Ladera, Adriana J. Metal Organic Chalcogenolates (MOChas) are a class of robust, self-assembling, and hybrid materials featuring inorganic metalo-chalcogen frameworks that are scaffolded by organic ligands. These low-dimensional structures exhibit tunable optoelectronic properties, making them promising candidates for various applications, including optical sensors and nanotechnology. This tunable relationship between MOCha structural arrangements and targeted properties opens up a vast yet challenging search space for novel MOCha structures. Density Functional Theory (DFT) can predict properties of materials with good accuracy, making it a powerful choice for even hypothetical materials. However, the discovery of novel MOChas structures is constrained by poor scalability of DFT relaxation times for large systems and a lack of high-throughput design methods that can capture the complex geometries of MOChas. In this work, we employ DFT calculations to investigate the energetic and electronic properties of various MOChas, and provide insight into the optical behavior and kinetic favorability of such structures. To address the computational bottlenecks of high-throughput design and DFT workloads, we discuss the use of machine-learned interatomic potentials and various generative models that can enable rapid prototyping of novel MOCha structures.

Development of Dual Extruder Biomaterial 3D Printer

2025-04-07T08:24:35Z

Development of Dual Extruder Biomaterial 3D Printer de Alva, Jesse P. This research presents the design and fabrication of a novel dual-extruder biotic 3D printer for the precise deposition of natural biocomposites using organic materials such as pectin, chitosan, and cellulose. Unlike traditional FDM printers that rely on thermoplastic extrusion, this printer employs a syringe-based mechanical extruder capable of depositing viscous biomaterial hydrogels. The integration of a first-of-its-kind dual-extruder system enables the fabrication of multi-material prints and the exploration of biomaterial composites and complex geometric structures, thereby advancing sustainable, bio-inspired manufacturing. This thesis emphasizes the machine engineering aspects of the printer's development, including project motivation, systematic design methodology, component design and fabrication, testing, and exploration of future work. Notable features of the system include user-friendly operation for non-experts, open-source accessibility, and compatibility with a wide range of biomaterials. By addressing existing limitations in biomaterial 3D printing technology, this work provides a robust platform to support future research in biomaterials, sustainable additive manufacturing, and bio-inspired design. Furthermore, the open-source nature of the printer fosters innovation and collaboration, accelerating the adoption of sustainable materials and manufacturing methods.

Annealing Techniques for Color Center Formation

2025-04-08T04:31:10Z

Annealing Techniques for Color Center Formation Christen, Ian Color centers in diamond have emerged as leading atom-like quantum systems for applications spanning from quantum repeaters to sensors. However, the optical and spin properties of engineered diamond color centers are limited by crystal damage produced during ion implantation, crystal irradiation, and annealing. In this thesis, we develop advanced material processing methods and characterization techniques to address critical challenges in the formation of high-performance diamond color centers to advance towards the efficient creation of desired dopant-vacancy centers with minimal formation of deleterious multi-vacancy clusters.

Responsible Computational Text Generation: AI Content Classification and Policy Framework

2025-04-07T09:23:04Z

Responsible Computational Text Generation: AI Content Classification and Policy Framework Jung, Minseok Recent advances in generative AI, particularly in producing human-like text, have blurred the lines between human and AI authorship. Since these AI tools rely on stochastic generation rather than traditional scientific reasoning, concerns about misinformation and reliability have emerged, highlighting the need for AI detection tools and policy guidelines. In response, this study proposes a dual approach: (1) the application of adaptive thresholds to improve the use of AI text detectors and (2) an AI policy framework based on user patterns and opinions. To enhance detector performance, we present a threshold optimization algorithm that adapts to diverse subgroups, such as those based on text lengths and stylistic features, thereby reducing discrepancies in error rates. The commonly used method relies on a single universal threshold, which has led to inconsistent results across various text types because of different probability distributions. Our approach addresses these shortcomings by tailoring thresholds to the specific characteristics of each group. In parallel, the study examines the pressing need for comprehensive AI guidelines, given the rise of misinformation and academic integrity issues. While a few institutions have introduced comprehensive policies, many institutes lack approaches grounded in user patterns and opinions. To remedy this problem, we propose a policy framework based on a user study. The findings of this research will provide practical solutions for more effective AI text classification and a reliable framework for the necessity of AI writing policies.

Uncovering the link between twin-twin interactions and damage nucleation in an (α+β) Ti alloy

2025-04-07T08:32:41Z

Uncovering the link between twin-twin interactions and damage nucleation in an (α+β) Ti alloy Cooper, Megan F. L. Recently, a (α+β) Ti alloy was developed with an outstanding combination of both high strength and high ductility; however, the plasticity micromechanisms that lead to damage nucleation for this alloy had not yet been investigated in detail. In this work, post-mortem analysis and an in-situ SEM-EBSD tensile experiment were conducted to determine where damage was nucleating most frequently in the microstructure, and what deformation modes were associated with damage nucleation. Damage within primary α grains was found to be the most common, with most of these damage incidents occurring along {10̅12} twin-twin boundaries with a ~60° misorientation. The {10̅12} twinning mode is only activated in the localized neck, and twin activation is strongly dependent on initial crystallographic texture. The twinned domains are rotated such that prismatic slip is easier to activate, but prismatic slip transfer is unlikely across ~60° twin-twin boundaries due to geometric incompatibilities. The in-situ test revealed that a crack formed along a ~60° twin-twin boundary where slip was blocked. These findings provide new insights into how twin-twin interactions in Ti alloys can lead to damage nucleation and impact overall ductility.

Design Concepts for High-Acceleration Linear Actuators for Precision Motion

2025-04-08T04:28:08Z

Design Concepts for High-Acceleration Linear Actuators for Precision Motion Kim, Adam K. Advances in semiconductor photolithography scanners have made it possible to produce smaller, more affordable chips with higher throughput. Some of the key lithographic scanner components supporting these advancements are electromagnetic actuators responsible for positioning the long-stroke (LS) and short-stroke (SS) stages of the reticle stage in its scan direction. Such actuators need to provide the highest thrust at the deceleration and reacceleration phases when the stages turn around at the ends of the scanning trajectory. Thus, enhancing their acceleration capability and force output is essential for boosting chip throughput. However, the improved performance may demand large current densities that are unsustainable in terms of the associated power dissipation generated by ohmic losses in the copper coils. In this thesis, we continued a previous study conducted in our lab that explored the use of mechanical contact forces managed by a piezoelectric stack actuator (PEA). In this configuration, intermittent contact by the PEA can be used to apply forces to decelerate and reaccelerate the SS stage with respect to the LS stage during turnaround events. With such force assist, the non-contact precision actuators responsible for positioning the SS stage with respect to the LS stage no longer need to generate large thrusts for the deceleration and reacceleration. As a result, we can in principle decrease the weight and power loss of the SS-stage precision actuators, which thus lowers the thrust requirements for the LS-stageAdvances in semiconductor photolithography scanners have made it possible to produce smaller, more affordable chips with higher throughput. Some of the key lithographic scanner components supporting these advancements are electromagnetic actuators responsible for positioning the long-stroke (LS) and short-stroke (SS) stages of the reticle stage in its scan direction. Such actuators need to provide the highest thrust at the deceleration and reacceleration phases when the stages turn around at the ends of the scanning trajectory. Thus, enhancing their acceleration capability and force output is essential for boosting chip throughput. However, the improved performance may demand large current densities that are unsustainable in terms of the associated power dissipation generated by ohmic losses in the copper coils. In this thesis, we continued a previous study conducted in our lab that explored the use of mechanical contact forces managed by a piezoelectric stack actuator (PEA). In this configuration, intermittent contact by the PEA can be used to apply forces to decelerate and reaccelerate the SS stage with respect to the LS stage during turnaround events. With such force assist, the non-contact precision actuators responsible for positioning the SS stage with respect to the LS stage no longer need to generate large thrusts for the deceleration and reacceleration. As a result, we can in principle decrease the weight and power loss of the SS-stage precision actuators, which thus lowers the thrust requirements for the LS-stage actuators responsible for accelerating both the LS and SS stages, resulting in lowered power consumption. Using the single degree-of-freedom experimental setup previously built in our lab, we conducted several characterization experiments to develop a PEA position feedback controller augmented by a hysteresis-compensated feedforward trajectory to shape the contact compression and forces. We find that introducing a viscoelastic contact interface is essential for stabilizing the PEA controller and slowing the contact dynamics to remain within the controller bandwidth. Our feedforward trajectory successfully brings a 0.84 kg mass moving towards the PEA with an initial speed of 60 mm/s to zero velocity in approximately 1.5 ms using 36 µm of PEA stroke length. These results demonstrate the feasibility of using PEAs as mechanical assist devices for high-acceleration turnaround events in lithography tools.

Forecasting the lift of a randomly maneuvering airfoil under dynamic stall conditions, Re ∼ 10⁵

2025-04-07T08:28:27Z

Forecasting the lift of a randomly maneuvering airfoil under dynamic stall conditions, Re ∼ 10⁵ Kim, Donghyun Dynamic stall is the abrupt flow separation from airfoils rapidly changing their orientation. This phenomenon, characterized by a delayed stall followed by a sharp drop in lift, has prompted efforts to prevent or delay it. This study aims to predict the lift of an airfoil randomly maneuvering under dynamic stall conditions by utilizing sparse surface pressure measurements, which we believe can maximize the effectiveness of various dynamic stall suppression techniques. Using data from large eddy simulations, we demonstrate that a long short-term memory network, fed with raw surface pressures, delivers accurate predictions. Also, a new method introduced here, IdDM, conclusively links the characteristic frequency range of pressure fluctuations that emerges during the dynamic stall to the chord-lengthscale vortex dynamics. However, further analysis suggests that the forecast predominantly relies on the lower frequency components tied to the airfoil motion, possibly because the vortex dynamics are dependent on and sensitive to the airfoil motion. Meanwhile, specific sensor locations are proven to be more informative than others in this random, unsteady flow, and we show that optimal sensor placement can be quickly determined using mutual information alone. It reveals that two pressure sensors positioned near the leading edge, one on each side of the airfoil, capture most of the information needed to predict lift. The lift can be predicted with sparse sensors because surface pressures are strongly correlated across the airfoil, with large-scale flow structures dominating the forces.

Designing Visual Intelligence from Photons to Action

2025-04-08T04:20:08Z

Designing Visual Intelligence from Photons to Action Young, Aaron For embodied agents to perceive and effectively act within their environment, they must sense the world around them and translate this information into meaningful and safe actions; a process fundamental to both biological and human-engineered systems. Nature has evolved highly attuned visual systems, resulting in diverse and efficient eyes capable of facilitating complex behaviors. Conversely, roboticists have engineered sophisticated cameras and sensors, enabling robots to perform tasks beyond the capabilities of natural systems. This thesis explores the design of visual intelligence by integrating insights from both biology and engineering in two complementary parts. In Part I, we computationally recreate the evolution of vision within simulated embodied agents. By evolving the physical and neural aspects of vision in simulation - and training these visually-capable agents with deep reinforcement learning - we demonstrate that task-specific environmental pressures lead to distinct eye morphologies and behaviors, mirroring observations in biological evolution. This in silico approach enables us to investigate the fundamental principles underlying the emergence of animal eyes and provides a framework for exploring novel sensor designs subject to both biological (e.g., survival) and engineering constraints (e.g., manufacturability). In Part II, we leverage visual cues not typically used in nature (i.e., active illumination and multi-bounce light) to demonstrate enhanced robotic navigation via non-line-of-sight imaging. Using single-photon LiDARs, we capture the temporal propagation of individual photons, enabling the detection of objects around corners. This sensing capability allows us to develop robots that effectively anticipate and avoid hidden obstacles, reducing navigation time by 50% and overall trajectory length by 33%. Together, these works demonstrate how the synthesis of biologically-inspired design principles with advanced sensing modalities can enhance embodied agents' capabilities, while providing insights into both natural vision evolution and robotic perception.

The Archean origin of assimilatory sulfate metabolisms provides novel insight into redox conditions of early Earth environments

2025-04-07T09:26:51Z

The Archean origin of assimilatory sulfate metabolisms provides novel insight into redox conditions of early Earth environments Payette, Jack G. Dissimilatory sulfur metabolisms recording differing biological isotopic fractionation are well studied, important components of sulfur cycling (Mateos et al., 2023). Assimilatory sulfur metabolisms and genes across life provide a complementary window into sulfur biogeochemistry with individual pathways having specific isotopic fractionations acting on distinct redox states (e.g. sulfate, sulfide, sulfite) for anabolism (Liu et al., 2012). An assimilation pathway exists, which starts with sulfate adenylyltransferase (sat/ATP sulfurylase) catalyzing a reaction of adenosine triphosphate (ATP) and sulfate (SO42-) resulting in adenosine 5’-phosphosulfate (APS), and incorporation of more reduced sulfur into biomolecules. This sat/ATP sulfurylase enzyme represents the first step required by life to incorporate sulfate and informs our understanding of biological processes performing this fundamental chemical reaction. A phylogenetic and molecular clock analysis of the sat/ATP sulfurylase protein family (E.C. 2.7.7.4) was performed to determine the age of sulfate assimilation proteins. Extant diversity of sat proteins was estimated to have a last common ancestor ~3.24 Ga (95% CI 3.52–3.06 Ga) using relaxed molecular clocks calibrated with eukaryotic and cyanobacteria age ranges from previously published fossil calibrated investigations. These results suggest sulfate cycling in Paleoarchean environments, despite extensive evidence of low marine sulfate concentrations (Crowe & Canfield et al., 2014). Archean sulfate biogeochemical cycling could result from microbial sulfur oxidation and sources could include abiotic oxidation of volcanic sulfur, hydrothermal processes or pyrite (Canfield, 2001, Lyons et al., 2024). This phylogenomic evidence of sulfate during Archean times provides an independent complement to geochemical records and indicates that sulfur redox chemistry during the Archean was likely more complex than previously described.

Falling isn't the End: Reimagining Demolition as a Creative Practice

2025-04-08T04:12:35Z

Falling isn't the End: Reimagining Demolition as a Creative Practice Lee, So Jung This thesis investigates resilience not as an endpoint but as a condition of continuous transformation. It critiques the shortcomings of current architectural discourse in addressing climate disasters, waste, and carbon footprints. While these crises are widely acknowledged, architecture often operates within restrictive economic, legal, and cultural systems, relegating resilient design to the periphery or diminishing its potential impact. Collapse, traditionally perceived as failure, is reimagined here as a generative moment—an opportunity to rethink materials, systems, and the narratives that shape them. Central to this exploration is the concept of assembly, where materials are designed with deliberate life spans—some transient, others enduring. By anticipating the gaps and shifts that arise when permanence is no longer assumed, this thesis proposes new possibilities for adaptive design and architectural resilience within the evolving rhythms of life. To articulate these ideas, the thesis employs speculative scenarios and temporal media. These tools position architecture as a system in flux, evolving in tandem with societal and environmental changes. Through narrative-driven methodologies, this work seeks to expand architectural discourse, prompting reflection on the discipline’s foundational assumptions while connecting it to broader cultural and systemic challenges. Ultimately, this thesis redefines resilience—not as resistance or mere survival but as a dynamic and imaginative practice. It advocates for architecture’s leadership within the broader zeitgeist of sustainability, transforming pressing global challenges into opportunities for creative agency and systemic reinvention.

American (Ise): On the Lifecycle of Stadiums in the United States

2025-04-08T04:40:25Z

American (Ise): On the Lifecycle of Stadiums in the United States Wang-Xu, Mackinley When the Kingdome in Seattle was completed in 1976, it was celebrated as a marvel of modern engineering, expected to last for centuries. Yet, in an ironic twist, it was demolished by implosion in 2000, surviving only twenty-four years. The Kingdome epitomizes the issue of short lifespans that has plagued American stadiums since the post-war era. A broad survey of these structures reveals an average lifespan of just three decades—a startlingly brief tenure for buildings of their scale and significance. These stadiums also follow a distinctive model of renewal. Similar to the Shikinen Sengu ritual at the Ise Shrine, a new stadium is often constructed adjacent to its predecessor. However, unlike Ise, where materials from the old shrine are reused and disseminated throughout Japan’s network of shrines, old stadiums are almost always demolished and discarded. This thesis seeks to superimpose Ise as a model onto American stadiums, envisioning an architecture that embraces both impermanence and longevity through circularity. Investigations into the barriers to circularity specific to stadiums serve as the foundation for design proposals, spanning scales from the detail to the site. The project ultimately imagines a stadium in a constant process of disassembly and renewal, where its spatial and programmatic potential challenge paradigms of completeness. In the context of a climate crisis demanding waste reduction, and for a typology notorious for its excess, stadiums can learn to do more with less.

Building Insurance

2025-04-07T08:25:53Z

Building Insurance Janson, Charles Perot Over the past 350 years, the building insurance industry has been shaped by a series of major urban fires, each incrementally standardizing risk assessment and property valuation as financial products of risk management. In recent years, however, climate change has introduced unprecedented weather events that challenge the fine tuned models of insurance; in particular, the rise of wildfires in California and the Pacific Northwest have led to local withdrawal of insurance altogether. Within these contexts, the spatial conditions inherited by a highly insured past continually sustain separation, individual prosperity, and standard assemblies as inheritances of expansionist agendas. At this juncture of system failure, this thesis asks: how can architecture rethink more cooperative forms of building and living together that localize risk sharing, responsibility, and stewardship? While wildfire defense strategies put forth by insurance companies and building code armor stick-frame American single family home and its aesthetic traditions, this thesis proposes a new building typology entirely: a neighborly cooperative of adjoined homes. Under a single roof, property lines are transformed into sites of mutual stewardship, manifesting insurance no longer as an abstract response to risk, but as a series of social and spatial relationships between neighbors.

Evaluating Chongqing Tiandi Project: An Asset Management Perspective

2025-04-08T04:31:31Z

Evaluating Chongqing Tiandi Project: An Asset Management Perspective Yang, Junsi This thesis uses the Chongqing Tiandi project as a case study to analyze the entire process of development and asset management for large-scale urban renewal projects in China's second-tier cities. It focuses on the motivations and outcomes of Shui On Land's transition from an asset-heavy to an asset-light model. Based on theoretical analysis (Chapter 2), corporate-level financial analysis (Chapter 3), and project-level in-depth studies and interviews (Chapter 4), the thesis explores the logic and impact of this strategic transformation from multiple perspectives. The theoretical analysis summarizes real estate lifecycle management theory, portfolio theory, and corporate strategic transformation theory, providing a framework to examine Shui On Land's strategic decisions. The financial analysis reveals that, from 2015 to 2017, Shui On Land faced significant financial pressure with high debt ratios and cash flow constraints, necessitating systematic asset disposals. While the company disposed of multiple assets during this period, Chongqing Tiandi's 79.2% equity disposal was particularly strategic due to its position as a high-risk, low-return asset within the company's portfolio. The project-level analysis and interviews demonstrate that replicating successful development models from first-tier cities in second-tier markets faces unique challenges. In Chongqing Tiandi's case, these challenges manifested in multiple ways: limited residential price premiums due to local land supply policies, substantial investment requirements for super high-rise developments exceeding $1 billion, and persistently low office rental rates in the local market. These factors compromised the project's financial self-sustainability and made it particularly vulnerable in Shui On's portfolio, especially when compared to projects in other second-tier cities like Wuhan. The development and subsequent equity sale of Chongqing Tiandi not only provided essential financial support for Shui On Land but also reflected a strategic decision to divest from a project where market conditions created both immediate challenges and future uncertainties. This research provides valuable references for the development of large-scale projects in China's second-tier cities, emphasizing the need for developers to utilize funds efficiently, adapt flexibly to market changes, and focus on achieving long-term value. These insights hold significant implications for sustainable development in complex market environments.

Cooling Innovation and Circularity: Addressing Water Stress in the Age of AI-Driven Data Centers

2025-04-07T09:18:33Z

Cooling Innovation and Circularity: Addressing Water Stress in the Age of AI-Driven Data Centers Kseibati, Reem This thesis examines the growing demand for data centers and the critical challenges posed by their water and energy consumption. As artificial intelligence (AI) technologies expand, the infrastructure supporting these systems has become essential. The study highlights the projected increase in data center capacity driven by AI workloads and focuses on the impact in water-stressed regions across the United States. Given the resource-intensive nature of data centers, the research explores cooling technologies aimed at reducing environmental impact. Traditional air cooling is compared with innovative liquid and evaporative cooling techniques. Additionally, the thesis promotes circular economy principles, emphasizing resource efficiency, reuse, and regeneration as a pathway to sustainable operations.

Co-Living in Seoul: Addressing Housing Needs and Redefining Rental Market Trends

2025-04-07T08:26:11Z

Co-Living in Seoul: Addressing Housing Needs and Redefining Rental Market Trends Park, Suhyeon Co-living emerged as a novel asset class in the mid-2010s, addressing the housing needs of urban residents affected by rising housing costs, increasing urban migration, and the growing prevalence of single-person households. In South Korea, co-living has gained attention as a viable alternative to traditional housing, driven by unique local dynamics, including the decline of the dominant Jeonse system and a significant shortage of housing tailored to single-person households. With a growing preference for monthly rental systems over the Jeonse systems, both local conglomerates and start-ups have capitalized on the opportunity to offer company-operated co-living spaces. As the market grows, major international investors and global co-living providers have also entered, reflecting a unique market environment where institutionalized housing options are expanding alongside a notable shift in rental transaction systems. In this new era of urban housing, co-living is rapidly expanding and gaining popularity. This thesis seeks to answer the following question: What factors have driven the emergence and growth of the co-living market in Seoul, and what is its growth potential? To address this, it starts with an analysis of market drivers, provider strategies, and regulatory developments, followed by projections of market potential and an assessment of potential threats and mitigation strategies for long-term viability of co-living in Seoul. The goal is to offer insights for co-living providers to optimize their spaces and services. The findings suggest that while co-living addresses unmet housing demand, its long-term success depends on balancing operational efficiency with tenant satisfaction. While these strategies are applicable in other cities, they are particularly critical in Seoul, where the Jeonse system remains a strong and historically preferred alternative. In Seoul, co-living serves a dual mission: introducing an innovative housing model and reshaping the paradigm of the Wolse rental housing system. To succeed, co-living operators must clearly articulate their unique value proposition, addressing both the housing needs of urban residents and the broader evolution of the rental market.

Ending Well, Making the Harvest-Paths of Our Values

2025-04-07T09:22:58Z

Ending Well, Making the Harvest-Paths of Our Values Kpodo, Courage Dzidula Kwaku Any single story shrinks all others. In a place historically cultivated for the cocoa cash crop, this thesis proposes reorienting architectural practice towards a plural valuing of land and its constituent spirits. The journey begins in 2022 with my acquisition of a 99-year lease for a 5-acre land in Ghana. Prior to the conception of an academic proposal, this was to preserve and grow ecological and financial value through time. Located on a hill-cluster in the Eastern Region, this place is crucial as the birthplace of Ghana’s cocoa industry, which became the world’s largest exporter by 1911. Spurred by economic and colonial incentives, farmer-settlers acquired and cultivated forest land including the one I presently steward. They forged communities that live on despite a subsequent decline of cocoa production in the region. Five centuries of colonial influence in West Africa reduced a plural landscape into singular extractive narratives, creating place-names like the Gold Coast, renamed Ghana after independence. The capitalist framework of monocultural extraction, one reliant on a colonial government and its land survey department, continues under contemporary African states. Architecture and planning—a practice historically tied to power and capital—remains instrumental in this system, often overlooking other ways of valuing land. This thesis confronts the dispositions of an inherited profession by foregrounding the practices and materials of a socio-cultural paradigm. It is epitomized by the tree called Newbouldia laevis (African boundary tree) and its plural meanings in West Africa. It follows a cocoa harvest-path from a community named after a farmer-settler, Yaa-Aso, and ascends the hills, crossing the land limits of 7 farmers. It ends on the land I hold, with a lease ending in CE 2122. In July 2024, I led a convocation of the farmers along the path in the defunct cocoa distribution building, toward framing futures based on other values apart from capital. 3 languages were spoken in that gathering - Twi, Anlo-Eʋe and English. It resulted in a 7-foot expansion of the path, and the pacification of a seasonal spirit-stream that crosses it. They set the context for imagining a series of 5 moments, herein recorded, that explore a value system of things spiritual and communal, offered by the transgressions of a widened path and the land I hold at its end.

Designing Sustainable Recommender Systems

2025-04-07T09:14:07Z

Designing Sustainable Recommender Systems Huang, Lei Recommender systems are widely deployed to serve users with content they like. However, content must be created and insufficient demand dampens a creator’s production incentive. We argue that the canonical recommender system may not be sustainable if, by promoting the content each user likes the most, it suppresses the creation incentive of the less popular but still valuable content. We propose a “sustainable recommender system” solution – subsidize creators with demand according to their “sensitivity,” which measures how easily a creator can be incentivized by demand, and their “contribution,” which measures how important a creator is to users overall. Theoretically, we prove that this algorithm maximizes long-term user utility by internalizing the externality of user choice on other users. Computationally, our main innovation is to estimate creator contribution using computer vision, where we train a deep-learning model to compute how creator distribution affects system-wide user utility. Analyzing data from a large content platform, we show that our algorithm incentivizes valuable creators and sustains long-term user experience.

Empowering Place: Unlocking Value for Investors by Integrating Indigenous Values in Luxury Hospitality

2025-04-07T09:02:36Z

Empowering Place: Unlocking Value for Investors by Integrating Indigenous Values in Luxury Hospitality Peragallo, Nadra Alia The luxury hospitality industry has long been attuned to shifting consumer preferences, particularly as travelers increasingly seek unique, meaningful experiences. In today’s global market, trends centered on personalization, wellness, authenticity, and regeneration—further accelerated in the post-pandemic travel era—present both challenges and opportunities for real estate investors. This shift raises a critical question: How and where can value be unlocked in this evolving landscape? This thesis explores how real estate investors can maximize value creation in the luxury hospitality sector by leveraging traditional performance metrics alongside a complementary framework designed to uncover underexplored opportunities and enhance collaboration among stakeholder groups. Through the analysis of two case studies—Salterra Resort & Spa in South Caicos, Turks & Caicos Islands, British West Indies, and Puntacana Resort and Club in the Dominican Republic—the study demonstrates the practical application of this framework in tropical, coastal, and island regions, where the interaction between tourism, local communities, and fragile ecosystems is particularly pronounced. By showcasing its success, this research provides adaptable stakeholder rubrics and qualitative system dynamics causal loop diagrams as templates, while broadening the scope for innovation and inspiring further exploration of sustainable, value-driven approaches in luxury hospitality.

CO₂ Capture with Lithium Oxide in Molten Salt Media : A Case Study of CO₂ Capture via Electrochemically Produced Metal Oxide

2025-04-07T09:19:18Z

CO₂ Capture with Lithium Oxide in Molten Salt Media : A Case Study of CO₂ Capture via Electrochemically Produced Metal Oxide Byun, Gi Hyun As the unprecedented temperature rise originating from anthropogenic carbon dioxide (CO₂) emission intensifies, the development of post-combustion carbon capture technologies has been urged. Although its maturity, conventional thermal swing processes using aqueous amines, suffer from significant limitations, including high energy requirements and sorbent degradation. Electrochemical CO₂ capture technologies, which use electrical energy instead of thermal energy, have emerged as an energy efficient way to capture CO₂. This shift not only improves energy efficiency but also reduces reliance on fossil fuels, further contributing to reduction in CO₂ emissions. This work explored the potential of electrochemical metal oxide formation for CO₂ capture, a promising alternative to amine-based systems due to its exceptional sorbent (i.e., metal oxide) stability. Li₂O in eutectic mixture of potassium nitrate (KNO₃) and lithium nitrate (LiNO₃) was chosen as a case study due to the relatively well-understood chemistry of the system and the potential synergistic effects between metal oxide and the molten salt. Primarily, we investigated the synergistic effect of Li₂O in nitrate molten salt via thermal gravimetric analysis. Next, electrochemically produced Li₂O by reduction of oxygen gas was tested as a CO₂ sorbent while investigating parameters affecting its conversion to lithium carbonate (Li₂CO₃). Through this study, we suggested dissolution model as a crucial pathway for conversion. Lastly, we explored the effect of adding nitrite ion (NO₂⁻) to the molten salt. Irreversible side reaction between NO₂⁻ and CO₂ was confirmed with X-ray diffraction and NOₓ measurement. This thesis demonstrates the feasibility of electrochemical metal oxide-based CO₂ capture, highlighting some considerations in the capture step.

Cost Optimized Logistics for Commercial Operations in Low Earth Orbit and Cislunar Space

2025-04-08T04:38:57Z

Cost Optimized Logistics for Commercial Operations in Low Earth Orbit and Cislunar Space Brown, Ireland Designing profitable mission and logistics architectures is necessary to establish a profitable commercial market and support a robust space economy. It is the goal of the National Aeronautics and Space Administration (NASA) to establish such an economy in low Earth orbit (LEO) through the implementation of commercial LEO destinations and to commission self-sustaining lunar infrastructure through the Artemis missions. The ISS and the Apollo lunar landers demonstrated the ability to provide safe and reliable habitation, but the cost to support these missions has been on the order of billions of United States Dollars (USD). Minimizing the operational costs of commercial space systems will be required if commercial companies expect to generate a profit from their services. To address this, this thesis derives and demonstrates a manual cost optimization method for space system mission architectures, with respect to logistical and system design. In tandem, a computational tool called the Cost model for Space system Operations (COST-O) was developed. The demonstration included the iteration of a logistics and system design vector for two cases: a commercial LEO space station, and a commercial lunar in-situ resource utilization (ISRU) liquid oxygen generation system. These mission architectures were modelled and simulated in SpaceNet which first analyzed for feasibility and then were processed by COST-O. This data was used to make financial forecasts and were analyzed for cost sensitivity. The results suggest that for a commercial LEO space station, a closed loop ECLSS, large stockpile of resources, reduced resupply cadence, and a combination of tourists and visiting crew would be a profitable architecture at the crew capacity of at least three paying customers present on the station per day with an annual operational cost of 1,129,731,710 USD. Profits would be achieved by the end of ten years of steady state operations at the current market price of 3.12 million USD per crew member per day. Attempts to minimize this cost should first be made in the cadence of funded astronaut technician flights, as crew launches contribute most to the overall operational cost. Future work should address ways to minimize this, such as reducing the required amount of astronaut technicians that must be present at any given time. For a commercial lunar ISRU liquid oxygen generation system, an architecture supporting a closed loop system, using Starship as the launch and landing vehicle, a prepositioned stockpile of resources at the lunar surface, and a hydrogen reduction agent is most cost optimal, with an annual operating cost of 19,275,486,559 USD, and profitability achieved at the design rate of twenty metric tons of liquid oxygen produced and sold per year. At the current market price of 1.2 million USD per kilogram, the system would be profitable by the end of the first year of steady state operations. Attempts to minimize this operational cost further should improve the recyclability of the system. Future work should evaluate added robustness to the architecture by delivering multiple systems and should model deliberate cargo packing decisions.

For and Beyond the Plaques: Sustainable Certification Adoption and Its Impact on Real Estate Decision-Making in the Boston-Cambridge Market

2025-04-08T04:09:19Z

For and Beyond the Plaques: Sustainable Certification Adoption and Its Impact on Real Estate Decision-Making in the Boston-Cambridge Market Huang, Shenglin As demand for green and healthy buildings grows, real estate developers face complex decisions regarding building certification adoptions, which have become influential in real estate market dynamics. This thesis investigates how developers in the competitive Boston-Cambridge area navigate the sophisticated certification landscape—focusing on LEED, ENERGY STAR, WELL, Fitwel, and WiredScore/SmartScore—to gain competitive advantages, attract and retain tenants, maximize financial performance, and align with regulatory requirements and ESG goals. Using a mixed-methods approach, including quantitative analysis of certification overlaps and trends, along with qualitative insights from industry interviews, the study provides a comprehensive understanding of how real estate developers strategically use certifications to influence asset value while meeting tenant and investor expectations. Findings offer potentially actionable insights into how certifications shape market positioning and inform the decision-making process in real estate development.

Using AI to Improve Price Transparency in Real Estate Valuation

2025-04-08T04:14:36Z

Using AI to Improve Price Transparency in Real Estate Valuation Xu, Cunjia This thesis explores the integration of artificial intelligence (AI) into real estate valuation, focusing on visual property attributes to enhance traditional Hedonic models. By incorporating Vision Language Models (VLMs) and generative AI, the research evaluates the potential of these technologies to assess non-standard variables like aesthetic appeal, condition and cohesiveness of interior and exterior property photos. The study contrasts traditional hedonic regression models, which rely on quantifiable factors such as square footage and location, with a new approach that includes AI-generated scores derived from property photos. The study employs three distinct models: the No_Rubric Model, the Composite Model, and the Verbose Model with the Hedonic model serving as the baseline for evaluating their performance. The results demonstrate that incorporating visual data significantly improves model accuracy, aligning valuations more closely with buyer preferences and sold prices. This shift addresses the industry's need for price transparency and highlights how developers can design properties that better meet market demands.

Microfluidic Platform for Vascularized Tissue Models

2025-04-07T09:05:53Z

Microfluidic Platform for Vascularized Tissue Models Johnson, Matthew This thesis presents a microfluidic platform designed to support 3D vascularized tissue models for microphysiological systems. The platform delivers pneumatic pressure and vacuum signals to drive fluid flow and pressure on tissue culture devices with integrated pumps and back-pressure regulators. The mechanical performance of the pumps and back-pressure regulators is characterized. Tissue compartments in each device contain endothelial and stromal cells suspended in a hydrogel during culture. An oxygenating reservoir stores and replenishes oxygen in circulating cell culture media. During assembly, screws are used to compress an elastomeric membrane, forming a seal and transmitting pneumatic pressure signals from the connection manifold to acutate the fluidic control elements. After a biological experiment the tissue culture devices can be disassembled, cleaned, and re-used, thus enabling cost-effective experimentation and prototyping. Each of the 4 layers of the tissue culture devices arc ma.de of thermoplastic polymers, and their design is translatable to injection molding for future production at scale. The design and manufacturing methods for the platform and individual device features are discussed. Two major biological experiments are presented to demonstrate the platform's ability to support emergent vascularization in the tissue culture device over 7 days. Microscope images show development of perfusable microvessel networks.

Characterizing Engineered Skeletal Muscle Rings as Actuators Using Strain Sensing Methods

2025-04-07T08:26:30Z

Characterizing Engineered Skeletal Muscle Rings as Actuators Using Strain Sensing Methods Rosado, Laura M. A novel instrument was designed to characterize a force exertion model of engineered skeletal muscle rings. The instrument uses strain gauges to transduce a muscle ring contraction and has a verified resolution of 5 μN and 1.4 μm over the ranges of 5 μN and 1400 μm respectively. Experiments were carried out with four muscle ring specimens at six different structural stiffnesses. Each ring was excited at 1 Hz for 30 seconds while force and displacement was monitored. It was determined that the relationship between muscle contractile distance and force is related by a negative power function.

The Use of System Theoretic Process Analysis (STPA) onNovel Tiltrotor Aircraft to Prevent Mode Confusion

2025-04-08T04:26:56Z

The Use of System Theoretic Process Analysis (STPA) onNovel Tiltrotor Aircraft to Prevent Mode Confusion Basnight, Natalie Ann Initiatives are underway to develop tiltrotor and vertical take-off and lift (VTOL) aircraft that enhance commercial and military aviation’s autonomy, capability, and survivability. These designs integrate rotary and fixed-wing elements, introducing distinct safety considerations. These safety concerns are largely due to the differing mental models of operators trained in either rotary or fixed-wing aviation, alongside the rising reliance on autonomy. The traditional hazard analysis techniques (e.g., Fault Tree Analysis and Failure Models and Effects Criticality Analysis) do not adequately account for system component interactions or human factors in complex new aircraft designs. System Theoretic Process Analysis (STPA) is a powerful new hazard analysis technique for novel tiltrotor aircraft that includes their unique safety requirements. It is a top-down system hazard analysis technique that identifies loss scenarios (N. G. Leveson and J. Thomas Mar2018). It satisfies the tasks described in MIL-STD-882E (Department of Defense 2023). This research demonstrates the use of STPA to identify and mitigate potential instances of mode confusion between the operator’s mental model and the autonomy’s decision logic in the uniquely dynamic tilt-rotorcraft environment. Two previous tiltrotor aircraft accidents are analyzed utilizing Causal Analysis based on System Theory (CAST) to help set a framework for the importance of human and machine collaboration in systems. These accidents show a trend in the dangers of aircraft system mismanagement between various controllers. The CAST results for these accidents help provide information about how to prevent these types of incidents in the future, setting the stage for the use of STPA on novel tiltrotor aircraft, as demonstrated in this thesis. STPA can be used before design, implementation, and fielding, allowing for better early design of systems and reducing the cost of later redesign or modification.

Design and Testing of a Hovercraft with Electroaerodynamic Propulsion

2025-04-08T04:31:42Z

Design and Testing of a Hovercraft with Electroaerodynamic Propulsion Quiram, Matthew Electroaerodynamic (EAD) multistaged ducted (MSD) thrusters are a novel solid-state thruster architecture that has been shown to provide order-of-magnitude improvements in thrust density compared to single-stage EAD thrusters. This makes MSD thrusters well-suited for use in EAD hovercraft, where generating sufficient pressure is crucial for hovering. This study explored the feasibility of a wire-to-airfoil corona discharge MSD thruster powered hovercraft through a scaled-down prototype and final design. The hovercraft was tethered to a ground-based power supply and carried a payload mass to simulate having on-board power electronics to limit the scope of the project. The design of an EAD hovercraft involved applying the principles of hovercraft lift to a design optimization that implements the recently developed EAD MSD thruster model. A hovercraft prototype was designed and constructed to validate the models applied during the design phase and to test hovering capabilities without a payload. Using the manufacturing lessons and insights gathered in the prototype testing, a full-scale model was designed and built to hover while having an additional payload capacity that would be representative of a set of power electronics.

A Business and Redevelopment Outline for the Re-Use of a Prime Site in South Boston

2025-06-09T15:22:12Z

A Business and Redevelopment Outline for the Re-Use of a Prime Site in South Boston Proman, Zachary D.

Design Exploration of a Miniaturized Stirling Engine

2025-04-07T09:04:59Z

Design Exploration of a Miniaturized Stirling Engine Hee, Ryann Increased interest in long-term space exploration has increased demand for small yet powerful energy sources, especially for remote and harsh environments where traditional power sources may be impractical. In such scenarios, space probes and high-reliability systems necessitate innovative solutions to meet their growing power and thermal management requirements while maintaining small form factors. Presently, micro power systems fall short of achieving the desired efficiencies for these applications, typically hovering around 2% [1]. Stirling engines, with their proven capability to attain high thermodynamic efficiency (30-40%), offer a promising solution if this efficiency can be maintained in a miniaturized form [2]. This study delves into the design space of a miniaturized Stirling engine with a target input of 2Wth, which could be tailored for small-scale (mesoscale ~cm3 ) high-efficiency power generation or micro-cooling. Previous research has laid the groundwork for understanding the thermodynamics of miniaturized Stirling engines, exposing substantial challenges, including overwhelming parasitic losses at this scale. The current study endeavors to mitigate these losses and explore the path to optimal efficiencies through Simulink modeling. Simulations have demonstrated design spaces capable of producing mechanical efficiencies as high as 14% with a 2Wth input, marking significant progress in addressing the limitations of current micro power systems. The research's innovative approach has significant implications for enabling the power generation required for small space probes, particularly for long durations and need self-sustaining power over extended periods [3], [4]. As the study advances, it holds the promise of developing a physical prototype using the findings from the design space study, which helps push the field forward for future power generation and micro-cooling in small-scale space technology. This thesis aims to map the design space of a miniaturized Stirling engine focusing on mitigating parasitic losses to achieve markedly greater efficiency compared to existing technologies.

The Missing Megawatts Problem: Improving Modelling Practices to Prepare for an Uncertain Future

2025-04-07T08:49:38Z

The Missing Megawatts Problem: Improving Modelling Practices to Prepare for an Uncertain Future Bhatt, Nirmal K. Long-term energy system planning is one of the most pressing challenges for the power sector, which must maintain reliability while decarbonizing. Currently, no unified regulatory, modelling, or market framework exists in the United States to facilitate planning in pursuit of a clean and reliable grid. Variable renewable energy (VRE) generation can produce cheap power but they increase the grids exposure to interannual variability in demand and VRE generation. This raises questions about how grid planners will value VRE and clean firm power (such as nuclear power). This thesis evaluates the importance of considering interannual variability and clean firm power in long-term energy system planning. I use GenX, an open-source capacity expansion model, to model the U.S. New England region in 2050 assuming a high degree of electrification and various technology availability and emissions reduction pathways. I find that clean firm power will reduce the cost of decarbonizing the New England grid but that grid planners must consider decades of weather and demand data if they are to make appropriate investments. I also present a novel outputs-based timeseries clustering method which allows models like GenX to optimize grids using longer timeseries of weather and demand data. Based on my work, I recommend that policymakers, grid operators, and market designers establish rigorous standards around energy modelling for long-term planning that includes multiple scenarios and appropriately values technologies such as firm power.

If These Hills Could Speak

2025-04-08T04:18:01Z

If These Hills Could Speak Bayowa, Tejumola If these hills could speak, what would they reveal, and how would they express it? This central question guides this thesis, which examines three hills in the heart of Ibadan, Southwest Nigeria— each occupied by the ruins of colonial monuments. Before the construction of these structures, the hills served as sanctuaries, providing water, food, and safety. However, under British colonial rule, architecture was utilized to disrupt this harmonious relationship. Over the course of 50 years, three monuments were erected that mark Britain’s colonial imprint on the city: a neoclassical courthouse (1925), built to assert control over the central market; a 60-foot tower (1936), which displaced the surrounding forests; and a theater (1977), built during a time of national struggle for unity and identity. Today, at the foot of these hills, a community has forged a way of life within a broken system. By repurposing and subverting structures in ways their creators never intended, this community embodies a praxis and poiesis of adaptive creativity within the built environment. This process represents a transformative act of pidginization—a collective tactic for repair, resistance, and reappropriation in response to an ongoing, imposed socio-political order. For these hills to speak again, the ruins must be transformed. This thesis begins that process by applying acts of pidginization learned from below to the three ruins. It proposes their conversion through deconstruction and de-monumentalization, with the aim of fostering economic development, ecological restoration, and cultural production in the city.

On Hing Travel Agency Fictional Archive of Disappearing Hong Kong

2025-04-07T09:27:37Z

On Hing Travel Agency Fictional Archive of Disappearing Hong Kong Wu, Ina Hong Kong, shaped by rapid transformation and precarious land ownership, is a city where erasure defines its urban landscape. Amid this flux, a place I once called home was demolished, prompting the question: “How can one return to a place that no longer exists?” This thesis explores the transformative potential of disappearance, reframing it as a generative force that creates space for imagination, resistance, and continuity. Through On Hing Travel Agency (OHTA), demolished buildings "travel" into fictional worlds, becoming vessels of memory and imagination. Rooted in Hong Kong’s literary tradition—where fiction resists erasure and archives aspirations—the project employs fiction as both a tool of preservation and a site for belonging. Fictional destinations, inspired by Hong Kong novels, such as The Permanent City (1959), The Floating City (1986), and The Vanished Cities (2010), reflect pivotal historical moments while offering pathways to reconcile personal loss and master alternative spatial logics. The project culminates in the Lost Traveler’s Guide to Hong Kong, a publication curating maps, brochures, and layered narratives to immerse travelers in speculative thinking. By bridging the past and future, real and imagined, OHTA is a attempt to demonstrates how fiction can reclaim agency within the politics of disappearance, transforming loss into a catalyst for new narratives and creative engagement. Even in absence, Hong Kong’s disappearing spaces retain their resonance, generating new narratives and underscoring the creative potential of loss.

Sweating Details: Labor of “Los Constructores del Valle”

2025-04-07T08:43:24Z

Sweating Details: Labor of “Los Constructores del Valle” Andrade, Gabriel “You should always be grateful for the work you can find, so make sure you prove you deserve it.”- Commonly heard growing up amongst the Builders of the Valley in Orange, NJ. The necessary attitude that fuels the built environment. This thesis proposes a dialogical method of tectonics through exploring the embodied experiences of those who physically build the city and its architecture, positioning architectural design as fundamentally tied to the labor that makes buildings possible. It centers on two primary questions: “Who builds this architecture?” and “How does this design impact a builder’s occupational livelihood?” To challenge professional standards that perpetuate a disconnection between designers and builders, this thesis reconnects me, as a designer, with my educators from Orange, NJ. These individuals—professional construction workers—shaped my earliest understanding of the built environment and how to navigate it socially and professionally. Through this process, learning more about who they are, how they entered construction, and how the work has affected them over the years. This education with ongoing dialogue pushes towards future opportunities of working together, focusing on designing better for the act of building by prioritizing the physical, mental, and financial longevity of my Educators. The culmination of this research and communication is materialized through four architectural details within a workspace, designed to showcase my Educator’s expertise and affinities as professionals. These details reimagine occupational choreography, opening up for future workflows that think through both lessening and healing the musculoskeletal disorders that many builders face after years of laboring across the tristate area.

An Instrument for the Measurement of Soft Material Nonlinear Mechanical Response

2025-04-08T04:52:50Z

An Instrument for the Measurement of Soft Material Nonlinear Mechanical Response Unikewicz, Brendan M. Soft material research has seen significant growth in recent years, with emerging applications in robotics, electronics, and healthcare diagnostics where understanding material mechanical response is crucial for precision design. Traditional methods for measuring nonlinear mechanical properties of soft materials require specially sized samples that are extracted from their natural environment to be mounted on the testing instrument. This has been shown to compromise data accuracy and precision in various soft and biological materials. To overcome this, the Volume Controlled Cavity Expansion (VCCE) method was developed. This technique tests soft materials by controlling the formation rate of a liquid cavity inside the materials at the tip of an injection needle, and simultaneously measuring the resisting pressure which describes the material response. Despite VCCE’s early successes, expansion of its application beyond academia has been hindered by cost, size, and expertise. In response to this, the first portable, bench-top instrument utilizing VCCE is presented here. This device, built with affordable, readily available components and open-source software, streamlines VCCE experimentation without sacrificing performance or precision. It is especially suitable for space-limited settings and designed for use by non-experts, promoting widespread adoption. The instrument’s efficacy was demonstrated through testing Polydimethylsiloxane (PDMS) samples of varying stiffness. This study not only validates instrument performance, but also sets the stage for further advancements and broader applications in soft material testing. All data, along with acquisition, control, and post-processing scripts, are made available on GitHub.

The Dynamics of Diversity, Equity, and Inclusion Practice Adoption

2025-04-07T09:12:35Z

The Dynamics of Diversity, Equity, and Inclusion Practice Adoption Yadama, Aishwarya Pandey Despite the widespread adoption of Diversity, Equity, and Inclusion (DEI) initiatives in corporate America, significant disparities persist in the representation, compensation, and treatment of women and racial minorities. This paper investigates why well-intentioned DEI efforts often fail to achieve their intended outcomes and identifies managerial barriers to progress. This research employs a qualitative dynamic modeling approach to analyze the complexities of DEI practice implementation within organizations. I conducted a scoping review, focusing on longitudinal and experimental designs to identify key mechanisms influencing the outcomes of DEI practices. The interplay between organizational processes and individual cognitive and behavioral responses can be illustrated via reinforcing and balancing feedback loops that I map onto a causal loop diagram, which reveals how DEI initiatives interact with existing organizational processes and cultural dynamics. This paper introduces a dynamic perspective on DEI practice implementation, highlighting the feedback mechanisms that can either hinder or facilitate progress toward diversity goals. The model reveals that certain DEI practices may inadvertently trigger reinforcing loops that perpetuate inequality. By mapping DEI practices and their effects, this study provides a framework for understanding how DEI outcomes can diverge significantly depending on different implementation strategies. It underscores the importance of considering the endogenous feedback effects of DEI initiatives and offers insights into strategic interventions that can disrupt undesirable reinforcing cycles and promote progress toward organizational diversity, equity, and inclusion.

Precisely Loose: Unraveling the Potential of Particles

2025-04-07T08:25:28Z

Precisely Loose: Unraveling the Potential of Particles Yoon, Jeonghyun Random, irregular, erratic, arbitrary, unspecifiable, and unpredictable—particles. In a post-extractive future, our reliance on standardized materials, continuously sourced through the exploitation of raw resources, will no longer be sustainable. Instead, architecture will increasingly contend with materials that defy standardization. This thesis focuses on these non-normative materials—particles, encompassing construction demolition debris, manufacturing defects, naturally occurring gravels, and locally sourced mineral waste. Ubiquitous yet underutilized, these materials hold potential not only for use, but also for reuse. However, they are often dismissed as rigid and unpredictable ingredients that require precise manipulation and cumbersome processing in order to achieve predictable results. What kind of architecture could emerge if we embraced the inherent nature of these particles, not as rigid materials to be controlled, but as dynamic, fluid entities? By embracing their uncertainty as a generative design agent, how would design approaches and construction processes transform? This thesis presents a catalogue of precisely loose methods for engaging with particles. These methods offer an alternative design approach that moves beyond the obsession with refinement and control over material behavior. By pouring, pushing, reconfiguring, and containing—in lieu of identifying, cutting, placing, and stacking—this series of interactions explores the potential of plurality, investigating how loosely controlled particles can adapt to collaborative construction processes. In doing so, this thesis redefines architectural material culture rooted in rubble, offering a framework to reimagine our relationship with the irregular, the unpredictable, and the overlooked.

Multi-Agent Hybrid Prediction in Autonomous Driving

2025-04-07T09:20:29Z

Multi-Agent Hybrid Prediction in Autonomous Driving Yau, Tiffany Yee Kay In autonomous driving, the hybrid task of predicting both high-level actions and lowlevel trajectories of human behaviour is fundamental to safe downstream decision-making. Much of the existing work in behaviour prediction tackle this problem without sufficiently modelling agent-agent interactions, limiting their ability to capture the full range of possible joint outcomes. Another key challenge in multi-agent prediction is the intractable prediction space that grows exponentially in the number of agents and duration of the prediction horizon. As a result, scalability is a major challenge. This thesis presents two approaches to address these challenges in multi-agent hybrid prediction. In our first approach, we model interactions and address scalability by learning to factor the joint prediction distribution. We observe that agents do not interact with all other agents in the scene, but rather, there are groups that strongly interact. Therefore, we group agents and represent the high-level interaction outcomes of groups with discrete variables. We additionally assume that inter-group interactions are sparse and can be sufficiently represented with a directed acyclic graph. These assumptions enable us to factor the distribution into a product of factors, effectively reducing the prediction space, and providing an order in which to easily sample discrete values. We evaluate the performance of this method on a large-scale autonomous driving dataset and show that it exceeds prior methods in coverage of possible interaction outcomes by 24% to 48% on various multi-agent validation data splits, while maintaining state-of-the-art prediction error. Our second approach represents agents in a traffic scene as a set of concurrent hybrid models and assumes a collision avoidance model of interactions, rather than learning the model from data like the first approach. Our method begins enumeration based on a simpler collision-agnostic prior distribution. Based on our factored representation, we determine the next best assignment to the prior. We extract bounding conflicts to correct the prior and increasingly reduce the error between the distribution used by enumeration and our collision-aware posterior distribution. Our experiments show that enumeration using A* with bounding conflicts (A*BC) is faster than A* and is therefore better at addressing scalability. In terms of prediction metrics, we find that our collision-aware posterior performs worse than the collision-agnostic prior and suggest future directions for improvement.

Impact of Environmental Regulation on Data Center Valuation

2025-04-07T08:26:29Z

Impact of Environmental Regulation on Data Center Valuation Lee, Donghyun Artificial intelligence has become one of the defining trends of modern society, with applications spanning virtually every industry. This societal shift has also influenced the real estate landscape. While data centers have existed for decades, it is only in recent years that they have garnered significant attention, demonstrated by their strong rent growth and compressed cap rates.1 Along with the attention over data centers, there also has been extensive research on how data centers impact the environment, such as "Quantifying the Sustainability Impact of Data Center Availability" by Manish Marwah et al. which present how data center power architecture may impact the environment and "The Environmental Footprint of Data Centers in the United States" by Md Abu Bakar Siddik, Arman Shehabi, and Landon Marsto. This research delves into quantifying the environmental impacts of data centers, specifically focusing on carbon and water footprints. However, what remains unexplored is how environmental regulations influence the valuation of data centers as a distinct real estate property type. This thesis examines how data center valuations could be impacted if existing environmental regulations were applied to regions where data centers are concentrated. The findings reveal a complex dynamic: while penalties under these regulations would reduce net operating income (NOI), potentially devaluing these assets, the same regulations would discourage new development, exacerbate the already constrained supply, and ultimately drive-up market rents for these properties. As a result, these opposing forces create ambiguity regarding the net impact of such regulations on data center valuations, with the outcome depending on which force prevails. What is clear, however, is that tenants would bear the brunt of these regulations, as landlords are likely to pass on increased costs through higher rents. On the other hand, while the environmental impacts of data centers and AI applications is critical to achieving sustainability goals, the societal benefits of AI solutions—ranging from advancements in healthcare to increased operational efficiencies—must also be considered. Balancing these competing priorities presents a unique challenge for policymakers and investors, with significant implications for the future of real estate and the digital economy.

Examining the Economic Impact of Anti-Warehouse Development Policies in California: A Case Study of the San Diego Market

2025-04-07T08:58:10Z

Examining the Economic Impact of Anti-Warehouse Development Policies in California: A Case Study of the San Diego Market Ghasemlou, Peggy This thesis conducts a detailed examination of the implications of anti-warehouse development policies in San Diego, focusing on their impacts on key economic indicators from 2024 to 2034. The research provides an overview of the U.S. industrial market, addressing crucial topics such as logistics market size, job creation, and the growth of e-commerce, while also exploring the NIMBY phenomenon and its influence on community opposition to developments, including a discussion of Bill 98 and its legislative implications. A specific focus on the industrial market in Southern California reveals important insights into job growth, rental rates, and market dynamics in San Diego. Through a comprehensive analytical approach, the study addresses the effects of development policies by presenting ten distinct scenarios that project delivery volumes, uncovering potential reductions ranging from 10% to 90% compared to a baseline scenario without restrictions. The analysis anticipates vacancy rates and job losses across various years, utilizing the LINEST function for forecasting key market indicators, including asking rents and asset valuations. Additionally, the research highlights the critical importance of logistics categories and decarbonization strategies to meet net-zero goals, as well as contemporary warehouse design trends and transportation innovations. The conclusions drawn from this research emphasize the complexities of balancing community interests with economic growth and sustainability in the region, as well as the broader economic implications of restrictive development policies on San Diego's warehouse industry, which could adversely affect the economic vitality of the warehouse sector.

Dynamic Markers

2025-04-08T04:44:21Z

Dynamic Markers Ortiz, Evan When I was a child, I was certain that all clouds came from New Jersey. After passing through the Lincoln Tunnel, I-95 would gradually ascend, lifting our car to eye level with the billowing clouds emerging from beneath us. These clouds rose from the Meadowlands, a great marsh just two miles west of Manhattan, a landscape that has become defined by the infrastructure that occupies it. Nearly equal in land mass and opportunity to Manhattan, this landscape managed to resist holistic transformation due to our inability to control its water. Rather than becoming a prosperous site for agriculture in the 19th century, or the next metropolis in the early 20th, the Meadowlands fell out of focus and became a site to absorb the infrastructural networks needed to uphold rapid development at its edges. The Meadowlands was sutured shut by the networks interlaced through it in an attempt to erase the failures of the past. Utilizing this landscape as an urban sponge neglected that the marsh hosted a series of ecological infrastructures of its own. The Meadowlands' soft, uncertain ground once managed variations in the water level, but the draining of the ground that came with development reduced its capacity, making pump stations essential for managing water in inhabited areas. Unlike the other forms of infrastructure in the Meadowlands, the presence of the pump station is subdued, its invisibility upholds the illusion that the developments within this landscape are not threatened by their surroundings. However, steady sea level rise and an increase in storm surges have caused these pumps to fail, pulling the veil on their existence and more importantly, the essential role they play in our continued occupation of this landscape. The urgent need to increase the capacity of the pump station provides an opportunity to reconsider their agenda. This thesis proposes the Dynamic Marker, a new type of infrastructure that redefines the relationship between human systems and ecological flows. Grafted onto existing pump stations in the Meadowlands, it releases water as mist from 800 feet in the air, transforming the hidden mechanics of water management into a moment of wonder. The Dynamic Marker fosters microclimates and ecological connections, transforming infrastructure into a dynamic process that evolves with its surroundings. Over time, it becomes both a memorial to the marsh and a provocation for the future, inviting a rethinking of infrastructure as a participatory and adaptive force that responds to its surrounding ecology.

Green Herrings in a Yellow Room: A Counter Production of The Yellow Wallpaper

2025-04-07T09:04:12Z

Green Herrings in a Yellow Room: A Counter Production of The Yellow Wallpaper Aulgur, Leanah Sloan Charlotte Perkins Gilman’s The Yellow Wallpaper is a designer’s work of critical fabulation. Published in 1892, the short story follows an unnamed woman prescribed a “rest cure” by her husband, John. Confined to a room wrapped in gothic yellow wallpaper, the narrator becomes obsessed with its patterns. As her mind deteriorates, she sees a woman trapped behind the paper. This production reimagines Charlotte’s bedroom as not yellow, but green—a rich, vibrant green laced with the medium responsible for its provocative coloration: arsenic. The toxic pigment, invented in the late 18th century, induces bodily ailments, mental instability, and even death when used in textiles. Interiors threatened tenants with toxins as this green spread through 19th-century Europe before reaching New England and our narrator. Though known as an author and suffragette, Charlotte was first a designer. As a student in the inaugural class of the Rhode Island School of Design, she studied the arts just miles from the ports where the green pigment began its early residence. Her writing draws from arsenic publications, her scenes mimic medical case studies, and archives suggest she was aware of these toxic walls. This theatrical table reading positions the authoring of The Yellow Wallpaper within the simultaneous stories of the arsenic wallpaper. Why does the author mimic material traces of the green while redirecting her readers to the yellow? When does the color transition from literal to abstract? This work recontextualizes the foundational feminist text by unfabulating the story through design—questioning Charlotte’s literary misdirections and the public discourse surrounding the toxic color.

Marketplace Multiculturalism

2025-04-07T09:21:27Z

Marketplace Multiculturalism Chowdhary, Harris Picture Texas. No longer simply cowboys, footballs, and firearms, this land today is sustained by a daily choreography of cross-border commerce, managed by entertainment media turned handheld surveillance, and peppered with enclaves of immigrants from the world over. A contact zone where logistical and legislative apparati warp to serve consumer comfort, Texas today is the world tomorrow: forget the Alamo, it’s highways, tax-incentives, and backyard barbecue on the 21st century frontier. This thesis responds to a call for roadside service stations along a planned international tourist corridor in the Texas-Mexico borderlands with six interventions: a panoramic viewing tower disguised as a billboard, a sunken stadium for athletic agonism, a photovoltaic drive-in charging cinema, an international culinary incubator, a showroom for automated fulfilment, and a customs and border patrol welcome center. These structures are testing grounds for modes of relation and value exchange that edge beyond the outdated positivisms of globalization. They ask how architecture might produce new possibilities and publics by working within and taking advantage of contemporary systems of control. As tourist destinations, the stops suggest the nation’s true mythos lies not in static symbols but in choreographies of transaction and contact. Articulating in built form the dynamic processes that define a territory of sprawl, this proposal suggests that Texas’s most authentic monuments are the stops we make along the way.

Improved Complexity Analysis for the Proximal Bundle Algorithm Under a Novel Perspective

2025-04-07T08:35:40Z

Improved Complexity Analysis for the Proximal Bundle Algorithm Under a Novel Perspective Fersztand, David The proximal bundle algorithm (PBA) is a fundamental and computationally effective algorithm for solving optimization problems with non-smooth components. We investigate its convergence rate in two settings. We first focus on a composite setting where one function is smooth and the other is piecewise linear. We interpret a sequence of null steps of the PBA as a Frank-Wolfe algorithm on the Moreau envelope of the dual problem. In light of this correspondence, we first extend the linear convergence of Kelley's method on convex piecewise linear functions from the positive homogeneous to the general case. Building on this result, we propose a novel complexity analysis of PBA and derive a O (epsilon^-4/5) iteration complexity, improving upon the best known O (epsilon^-2) guarantee. This approach also unveils new insights on bundle management. We then present the first variant of the PBA for smooth objectives, achieving an accelerated convergence rate of O(epsilon^-1/2 log(epsilon^-1)), where epsilon is the desired accuracy. Our approach addresses an open question regarding the convergence guarantee of the PBA, which was previously posed in two recent papers. We interpret the PBA as a proximal point algorithm and base our proposed algorithm on an accelerated inexact proximal point scheme. Our variant introduces a novel null step test and oracle while maintaining the core structure of the original algorithm. The newly proposed oracle substitutes the traditional cutting planes with a smooth lower approximation of the true function. We show that this smooth interpolating lower model can be computed as a convex quadratic program. We finally show that Nesterov acceleration can be effectively applied when the objective is the sum of a smooth function and a piecewise linear one.

Impact of Introducing Technical Design Elements in Makerspace Trainings

2025-04-07T08:40:35Z

Impact of Introducing Technical Design Elements in Makerspace Trainings Barakat, Layal A. Makerspaces are used as a tool in higher education to support curricular, hands-on projects and encourage student extracurricular and personal projects. Because access to making is more self-driven, there is a gap between what makerspace trainings teach students and what students are expected to know by the time they reach capstone courses in engineering. To test the effects of introducing a technical makerspace training to students, several steps were taken. First, known barriers to making were explored and organized into categories. Second, Design Expertise was defined as a means to combat these barriers: it is a combination of (1) knowledge, (2) skill, (3) perspective, and (4) motivation. Third, a rigorous framework, the Design-Fabrication-Performance (DFP) matrix was created to break down design expertise into manageable chunks. Next, existing makerspace trainings at MIT were characterized using the DFP matrix. Afterwards, the DFP matrix was used to design a new, experimental training which would incorporate engineering design thinking and expertise with the typical makerspace machine training structure. Finally, 23 student participants were recruited, surveyed using a Likert scale (1 = strongly disagree, 5 = strongly agree), and interviewed to understand the impact of the training on participant perspectives, engineering identity, and maker motivation. Initial results suggest that student self-efficacy increases as a result of the training, This outcome is shown by the highest average differential of all survey responses (M = 0.78, SD = 0.85) for question 15: “I am confident in my ability to use GIR level knowledge to design and make things that perform as intended”. The maker training reinforced the motivation to make things for a majority of students, with the average score for the associated question being 4.48 (SD: 0.85). The training also positively impacted some traditionally marginalized groups in STEM. For the statement "I feel comfortable in engineering at MIT", women averaged 3.27 and men 3.90 before the training. The average differentials in the post- and pretraining scores to this question for these groups were 0.4 and 0.91 respectively. The training also appears to level playing field for students with less advanced backgrounds in engineering and science. For the question “I am confident in my ability to solve GIR level problems on my own”, students with parents with graduate degrees or higher averaged 4.44 before the training, while those with parents with undergraduate degrees or lower averaged 3.57. The average differentials are 0.22 and 0.64 respectively. Although students saw the value in modeling systems before design and fabrication, several questions demonstrated that students found modeling to be tedious and preferred to test and iterate on their designs in the makerspace; further work is needed to eliminate barriers to sustain student interest and participation in the long term. A longitudinal study following these students would also be needed to reveal long term outcomes such as STEM retention and long-term makerspace usage.

Fabrication and Characterization of Horizontally Aligned Carbon Nanotube Thermoplastic Bulk Nanocomposite Laminates

2025-04-07T09:14:21Z

Fabrication and Characterization of Horizontally Aligned Carbon Nanotube Thermoplastic Bulk Nanocomposite Laminates Lin, Yuying Carbon nanotubes (CNTs) have advantaged mass-specific mechanical properties and excellent thermal and electrical conductivity, making them an attractive reinforcement for composite systems. Due to an increasing need for more sustainable materials, incorporation of CNTs into thermoplastic matrices presents a promising solution for recyclable and repairable polymer nanocomposites (PNCs). This thesis presents an approach to fabricating and characterizing thermoplastic PNCs that incorporate ultra-high volume fractions of horizontally-aligned carbon nanotubes (HA-CNTs). An MIT-developed bulk nanocomposite laminating (BNL) process was adapted to fabricate multi-ply, unidirectional composites with poly(methyl methacrylate) (PMMA) and acrylonitrile butadiene styrene (ABS) matrices. For the HA-CNT/PMMA system, the BNL process was tailored to fabricate 4-ply and 8-ply laminates with fiber volume fraction v_f > 45 vol.%, using a 9 wt.% PMMA in anisole solution. Through characterization via X-ray microcomputed tomography (µCT), scanning electron micrography (SEM), thermogravimetric analysis (TGA), Fourier transform infrared (FTIR) spectroscopy, and polarized Raman spectroscopy, HA-CNT/PMMA laminates were shown to be free of micro-scale voids with weak or non-existent process-structure interactions, i.e., the CNTs had negligible effect on the polymer structure. TGA and IR helped demonstrate that the BNL process did not lead to decomposition or chemical changes to neat PMMA, and FTIR also revealed that the fabrication process did not induce covalent bonding between CNTs and PMMA. The crystalline behavior of PMMA was studied via dynamic scanning calorimetry (DSC) as well as X-ray diffraction (XRD), which demonstrated that BNL processing temporarily lowers neat PMMA glass transition temperature T_g by 4 ◦C with no permanent change after removal of thermal history. However, CNT inclusion leads to higher laminate T_g by 11 ◦C as shown through both DSC and dynamic mechanical analysis (DMA), which can be explained by CNT constraints on polymer chain movement as opposed to any crystallinity changes in the PMMA. Storage modulus of 8-ply HA-CNT/PMMA laminates was shown to be more than 600% of neat PMMA via DMA, while a decrease in tan(δ) of the laminate compared to neat PMMA indicates an increase in elastic behavior due to CNT inclusion. 4-ply laminates were subjected to a minimum radius of curvature test showing a ∼ 50% increase in yield strain compared to neat PMMA. Electrical properties of 4-ply HA-CNT/PMMA laminates were measured via 4-point probe testing, which demonstrated good Ohmic contact between CNTs, with conductivity of ∼ 2 × 10⁴ S m⁻¹ and anisotropy ratio of 1.2. A preliminary investigation was completed to evaluate the feasibility of using the BNL process for the HA-CNT/ABS system. Uniform suspensions of ABS in anisole were developed to use the BNL polymer infiltration method of spin-coating and vacuum-assisted infusion. It was shown that the nature of the ABS suspension led to uneven polymer distribution over the HA-CNTs. This work has demonstrated the successful incorporation of high volume fractions of aligned CNTs into PMMA thermoplastic matrices as well as the electrical conductivity of such composites, opening an avenue to the development of other high v_f thermoplastic PNCs and exploration into additional multifunctional capabilities.

Remembering Energic Connectivities: Appropriate Technology and Domestic Infrastructure in the Energy Crisis

2025-04-07T09:09:32Z

Remembering Energic Connectivities: Appropriate Technology and Domestic Infrastructure in the Energy Crisis Adornetto, Turner Day The electric grid is a large, complex machine. And yet, it represents but one, narrow framework for energic relations. Visions for just and sustainable futures – for social and ecological repair – should wander further afield. One place they could go is home. In this essay, the Appropriate Technology Small Grants Program, an oft-forgotten chapter of U.S. energy history, shows us how small-scale, place-based inventors transformed homes and neighborhoods into converters and conductors of nearby flows and potentials. At the height of the energy crisis of the 1970s, these inventors pursued a distributed solution to shortage. Along the way, they re-wired the material and conceptual strictures of the modern dwelling and broke into a vast reserve of lowcost, renewable power. Home, they showed, was a workshop to understand and design energic connectivities. But tracing the effects of home-based appropriate technology leads us somewhere else – to the frontiers of energy extraction, where social justice activists proved that small-scale, place-based energy systems could replace unjust mines and dams. What emerged, then, through renewed attention to the possibilities for home and energy, was a powerful counter to the logics of sacrifice at both ends of the energy continuum. Today, as we chart our own response to crisis, it helps to remember how others tried to create solidarities and resist tradeoffs with small-scale, place-based infrastructures. We can, I think, do more with energy.

Mining Multifaceted Customer Opinions from Online Reviews

2025-04-07T08:44:59Z

Mining Multifaceted Customer Opinions from Online Reviews Mao, Chengfeng Online reviews are a valuable source for studying customer needs and preferences. Previous studies focus on extracting a set of a priori defined constructs such as product attribute perception or explicit customer needs from reviews. Such a priori focus circumvents the limitations of certain natural language processing algorithms but discards valuable information in reviews that are not in the scope of the predefined construct. This study proposes a new method of extracting customer opinions and opinion targets from reviews with the Aspect Sentiment Triplet Extraction (ASTE) algorithm and then identifying theoretical constructs critical for product development with a posteriori interpretation method. We demonstrate the value of our proposed method by identifying granular opinion targets and expressions to find infrequent but important phenomena such as user innovations and delights.

Geographies of Selective Surveillance: Analyzing the Lived Experiences of Street-Level Trans Sex Workers and Muslims in India through the Matrix of Domination

2025-04-07T09:17:21Z

Geographies of Selective Surveillance: Analyzing the Lived Experiences of Street-Level Trans Sex Workers and Muslims in India through the Matrix of Domination Radhakrishnan, Radhika In this paper, I present a study of public and private CCTV surveillance of urban public spaces in India, which I term as ‘geographies of selective surveillance’ — areas where state power is discretionarily exercised and abused, and the presence of the state is experienced principally through police pickets and everyday violence unleashed on marginal occupants, rather than by access to civic amenities and systems of justice. I analyze these experiences of surveillance from the standpoint (Harding, 1992) of minoritized communities of street-level trans sex workers in Kolkata and Muslims in Mumbai. I then situate these experiences within the Matrix of Domination (Collins, 1990), a theoretical framework that explains how systems of power are configured. Defining empowerment as the power to gain control of and/or benefit from a scenario by weakening the Matrix of Domination, I analyze the structural determinants that make surveillance empowering or disempowering for these communities. I find that on the one hand, surveillance can be an empowering tool for minoritized communities as evidence of harm and innocence in cases of false accusations or when police officials typically refuse to believe their experiences due to discriminatory attitudes. On the other hand, surveillance also offers new opportunities for the private exploitation of the instruments of state power through corruption as well as community-based moral policing to be done with greater success and efficiency. I argue that what ultimately determines how surveillance is experienced is not laws and policies, but rather how power is discretionarily exercised on the ground, refracted through the influence of cultural and political beliefs, and discourse.

Commodifying and Consuming Endocrine Drugs in Republican China (1920s–1940s)

2025-04-07T08:58:30Z

Commodifying and Consuming Endocrine Drugs in Republican China (1920s–1940s) Wang, Thelma Yuanzhi Since the introduction of hormone pharmaceuticals into China during the early twentieth century, these substances became objects of fascination for a growing urban elite class. Drawing from newspapers, medical journals, and advertisements, this article examines the unique trajectories of hormone medicine in China. In conversation with previous scholarship on the dynamics of advertising and consuming hormones in China, this article examines specifically the discourses around the production and science of hormones. The circulation of hormones was informed by ideas of traditional Chinese medical cosmologies and enrolled in a nationalist movement encouraging the consumption of hormones produced by emerging Chinese medical entrepreneurs. This article provides a case study in a postcolonial context that problematizes historiographies depicting a linear transition of global hormone science from backwards to scientific, from traditional to modern.

Enabling AI Copilots for Engineering Design With Parametric, Graph, And Component Inputs

2025-04-07T08:25:08Z

Enabling AI Copilots for Engineering Design With Parametric, Graph, And Component Inputs Zhou, Rui Engineering design demands the synthesis of multimodal and often incomplete data—ranging from detailed parametric specifications, assembly graphs, visual references, and textual descriptions. Despite growing interest in generative models for design ideation and exploration, state-of-the-art approaches struggle with incomplete inputs, lack of support for modalities other than text and image, and limited controllability. This thesis addresses these gaps by unifying two complementary advances: First, we introduce a graph-guided diffusion approach for parametric data completion. By coupling Graph Attention Networks with a diffusion-based imputation mechanism, our method acts as a highly accurate and creative design auto-completion system for incomplete partial designs. On a dataset of 12,500 bicycles, this design imputation framework achieves a root mean square error (RMSE) of approximately 0.92 on numerical features and an error rate of around 0.18 for categorical attributes, outperforming both classical imputation methods such as MissForest, hotDeck, PPCA and advanced diffusion-based baselines such as TabCSDI. Moreover, it achives a Diversity Score of 3.10, surpassing all baselines, illustrating that the imputation process transforms incomplete data into multiple creative designs. Second, we develop a multimodal control architecture that can extend foundation models to condition their generation processes with all or a subset of parametric inputs, assembly graphs, component images, and textual constraints. This model tremendously enhances both the controllability and precision of the generation process of foundational generative models, enabling controlling modalities that were not possible before. We first show that our model excels at tasks that state-of-the-art models struggle on. We further validate the performance of our model with surrogate models that investigate individual features. Our model achieves 95% or greater R^2 scores on different continuous parameters. Further, we show that our model is able to generate creative and novel designs while maintaining a high level of precision. This enables engineers to guide generative outputs along precise dimensional, aesthetic, and functional targets. Across numerous trials of different settings, we observe that our pipeline robustly fuses tabular parametric information, assembly graphs, and reference component images to produce results aligned with both specification precision and creativity. Together, these contributions establish a coherent framework for AI-augmented design exploration. By viewing missing parameters as an opportunity for data-driven design autocompletion and by tightly integrating multimodal control over foundation models, this work elevates generative AI from a niche conceptual tool to a reliable design copilot. The implications of this thesis are profound: we show the possibilities and the pathways to AI copilot systems that can reduce data bottlenecks, broaden design spaces, and offer more thorough, constraint-adherent design candidates. As engineering problems grow in complexity and scale, the synergy of high-fidelity parametric imputation and multimodal control promises to accelerate innovation, cut development cycles, and guide human designers toward more inventive and manufacturable solutions.

Learning from Past Market Outcomes: Evidence from the Music Industry

2025-04-08T04:42:56Z

Learning from Past Market Outcomes: Evidence from the Music Industry Du, Jason We leverage unique features of music albums to investigate how musicians learn from current products when developing new products. We find that songs on a musician’s next album tend to be more similar to the songs that are more successful on that musician’s current album. This effect is stronger when the musician has less experience, and when the song on the current album is more novel (for that musician). Our findings suggest that musicians learn from the success of previous songs when developing new songs, and that learning is stronger if the musician has more need to learn, and when the song contains more new information.

Shut Up and Dribble? Exploring the Real Estate Strategies and Trends of NBA Teams

2025-04-07T09:06:30Z

Shut Up and Dribble? Exploring the Real Estate Strategies and Trends of NBA Teams Nguyen, Viet NBA teams have always had to think about real estate through one certain lenses: the arena they play their 41 home games in (plus any subsequent playoff games). But now, NBA teams have evolved past only just thinking about the arena. Teams have increasingly gotten involved in real estate development. This thesis seeks to explore the impact of real estate as a revenue driver for NBA teams, trends observed, and strategic decisions that teams must consider. This thesis will explore current real estate activities of all 30 NBA teams and will examine the choices that teams must make regarding arenas, real estate development, and practice facilities. The findings will help teams and municipalities understand best practices for team-driven real estate, and how strategies can vary team by team based on their situations.

Navigating RAD Conversions: Suggestions for Public Housing Rehabilitation

2025-04-08T04:38:17Z

Navigating RAD Conversions: Suggestions for Public Housing Rehabilitation Yan, Yu Public housing in the United States, a critical resource for nearly 1.7 million residents, faces significant challenges due to aging infrastructure and chronic operating funding shortfalls. The Rental Assistance Demonstration (RAD) program, authorized by Congress in 2012, aims to address these issues by leveraging private financing to rehabilitate and modernize public housing properties. Although the RAD program has been around for more than a decade and leveraged over $18.5 billion of construction investments, close to 75% of the more than 2500 eligible local PHAs are yet to benefit from it. This thesis examines the evolution of RAD programs, including the two newer tools, RAD/Section 18 Blend and Faircloth-to-RAD, and their adoption by public housing authorities (PHAs). The research incorporates a review of HUD program and policies, RAD implementation data, and interviews with industry practitioners, including PHAs, developers, and consultants, to understand the hurdles preventing the adoption of the program and the characteristics of successfully structured projects. This thesis offers insights into how specific strategies are used to overcome the hurdles and provides practical recommendations for PHAs seeking to leverage RAD for public housing preservation and development. Key findings highlight the importance of utilizing available funding sources to achieve financial feasibility and enhancing organizational skills and capacity.

Using Systems Architecture and the EVDT Framework for Monitoring Methane Emissions in Rio de Janeiro

2025-04-08T04:31:52Z

Using Systems Architecture and the EVDT Framework for Monitoring Methane Emissions in Rio de Janeiro Ajisafe Jr., Frederick Henry Oladimeji Methane is a powerful greenhouse gas that has important implications for climate change. Over the past decade, satellites have rapidly improved their ability to detect this gas from above the atmosphere. This Thesis uses two Systems Engineering frameworks, Systems Architecture and EVDT, to examine a case study of methane monitoring in Rio de Janeiro, Brazil. Data from one of these novel satellite systems, GHGSat, is taken over the Seropédica landfill near the city, and compared to Rio’s own IPCC- and GPC-derived greenhouse gas inventory. This is followed by a participant observation in the summer of 2024 involving interviews, discussions, and site visits. A near-doubling of methane was observed over Seropédica, raising questions about the cause of this increase. The direct engagement with Stakeholders provided by this study contributes to a literature gap in satellite monitoring of urban landfills in southeastern Brazil.

Designing Sparse Representations for Efficient Planning in Uncertain Environments

2025-04-07T09:19:01Z

Designing Sparse Representations for Efficient Planning in Uncertain Environments Veys, Yasmin We would like to enable robots to navigate efficiently in large, outdoor environments, where the traversabilities of many regions are unknown prior to planning. If we reason about the uncertainty in the environment instead of assuming that all unknown space is free to move through, we can generate policies that result in, on average, more efficient navigation. However, designing models that enable intelligent and efficient reasoning about environmental uncertainty is challenging. We would like our model to capture the underlying navigation problem and accurately represent the relevant uncertainty, yet remain as sparse as possible, so that planning remains tractable. Higher model expressiveness improves plan quality but reduces computational efficiency in planning, whereas higher model sparsity improves efficiency at the cost of plan quality. Balancing model expressiveness and model sparsity, thus, is crucial for generating high quality plans efficiently. In this thesis, we describe several useful models for planning under uncertainty and justify our decision to use weighted stochastic graphs with probabilistically traversable edges. We then present a novel method of efficiently generating sparse stochastic graphs given coarse information derived from overhead images of our environments. We test our approach in several simulated environments, demonstrating that our graphs effectively trade off between plan quality and planning efficiency for uncertainty-aware agents navigating in the graph. We then deploy our algorithms in a real-world environment on real-world hardware for single-agent and multi-agent teams. We discuss the challenges associated with using our approach in the field and the implications of our model assumptions not matching the real world. Finally, we present preliminary results for adding cost uncertainty to our graph-based representation.

A Power Efficient Analog Front End for Continuous Ultrasound Imaging of the Bladder

2025-04-07T08:30:15Z

A Power Efficient Analog Front End for Continuous Ultrasound Imaging of the Bladder Manohara, Mohith Continuous bladder monitoring is important for the monitoring of bedridden patients. One method to continuously monitor the bladder is to capture ultrasound images and use machine learning processing to measure the bladder volume from these images. Circuits for implementing these functions can be integrated onto a wearable device, and each of these functions can be integrated onto a single chip. In this thesis, we analyze ultrasound imaging in the context of the bladder to come up with algorithms and hardware to perform continuous bladder monitoring. We first assemble a discrete setup which can form ultrasound images. Using this setup, we describe a new algorithm for generating an ultrasound image by to power gate the hardware during the imaging process to save additional power when capturing the image. We combine these concepts into a single Analog Front End (AFE) chip that can capture images in a power efficient manner.

Quantum free games

2025-04-07T08:30:02Z

Quantum free games Zhang, Tina The complexity of free games with two or more classical players was essentially settled by Aaronson, Impagliazzo, and Moshkovitz [AIM14]. In the quantum world, there are two complexity classes that can be considered quantum analogues of classical free games: (1) AM⇤, the multiprover interactive proof class corresponding to free games with entangled players, and, somewhat less obviously, (2) BellQMA(2), the class of quantum Merlin-Arthur proof systems with two unentangled Merlins, whose proof states are separately measured by Arthur. In this work, we make significant progress towards a tight characterization of both of these classes. 1. We show a BellQMA(2) protocol for 3SAT on n variables, where the total amount of communication is Õ(√n). This answers an open question of Chen and Drucker [CD10] and also shows, conditional on ETH, that the algorithm of Brandao, Christandl and Yard [BCY10] for optimizing ˜ over separable states is tight up to logarithmic factors. 2. We show that AM*[ⁿprovers = 2, q = O(1), a = poly log(n)] = RE, i.e. that free entangled games with constant-sized questions are as powerful as general entangled games. (In contrast, [AIM14] shows that classical free games are much weaker than general classical games.) We show this using a question “hyper-compression” theorem that iteratively applies the introspection technique of Ji et al. [JNV⁺20]. Our result is a significant improvement over the headline result of Ji et al., whose MIP⇤ protocol for the halting problem has poly(n)-sized questions and answers. 3. By the same techniques, we obtain a zero-gap AM* protocol for a P2 complete language with constant-size questions and almost logarithmically (O(log n · log* n)) large answers, improving on the headline result of Mousavi, Nezhadi and Yuen [MNY21]. 4. Using a connection to the nonuniform complexity of the halting problem we show that any MIP* protocol for RE requires W(log n) bits of communication. It follows that our results in item 3 are optimal up to an O(log* n) factor, and that the gapless compression theorems of [MNY21] are asymptotically optimal. We conjecture that these bounds can be saturated in the gapped case as well.

Guiding Navigation of Unknown Environments with Distant Visual Cues

2025-04-07T09:13:25Z

Guiding Navigation of Unknown Environments with Distant Visual Cues Fahnestock, Ethan Kendall While navigating unknown environments, robots rely primarily on proximate features for guidance in decision making such as depth information from lidar or stereo to build a costmap, or local semantic information from images. The limited range over which these features can be used can result in poor robot behavior when assumptions made by motion planning about the cost of the map beyond the range of proximate features misguide the robot. Integrating “far-field” image features that originate beyond these proximate features into the mapping pipeline has the promise of enabling more intelligent and aware navigation through unknown terrain. To navigate with far-field features, key challenges must be overcome. As far-field features are typically too distant to localize precisely they are difficult to place in a map. Additionally, the large distance between the robot and these features makes connecting these features to their navigation implications more challenging. In this thesis we propose FITAM, an approach that learns to use far-field features to predict navigation costs to guide navigation through unknown environments from previous experience in a self-supervised manner. Unlike previous work, our approach does not rely on flat ground plane assumptions or range sensors to localize observations. We demonstrate the benefits of our approach through simulated trials and real-world deployment on a Clearpath Robotics Warthog navigating through a forest environment.

Integrated Visible-Light Liquid-Crystal-Based Modulators and Grating-Based Antennas

2025-04-07T08:27:26Z

Integrated Visible-Light Liquid-Crystal-Based Modulators and Grating-Based Antennas Garcia Coleto, Andres Current developments in integrated visible-light photonics have led to advancements in applications such as augmented-reality displays and quantum systems. However, the development of crucial integrated-photonics devices such as integrated gratingbased antennas and integrated optical modulators has predominantly focused on the infrared spectrum, leaving a gap in visible-light technologies. This thesis addresses this gap by designing and experimentally demonstrating integrated visible-light liquidcrystal-based (LC-based) modulators and grating-based antennas. First, we provide a thorough design guide for integrated visible-light grating-based antennas and experimentally demonstrate five antennas with varying advanced capabilities, including the first visible-light unidirectionally-emitting grating-based antennas for integrated optical phased arrays (OPAs), facilitating the use of integrated OPAs for new visible-light applications. Second, we discuss the fabrication processes, considerations, and evaluation techniques for successful packaging of integrated LC modulators, supporting the broader integration of LC into silicon-photonics platforms, enabling more compact and efficient on-chip modulation. Third, we experimentally demonstrate the first integrated visible-light LC-based variable-tap amplitude modulators, enabling a compact and low-power solution to integrated visible-light amplitude modulation for high-density integrated visible-light systems. Fourth, we experimentally demonstrate the first 300-mm wafer-scale platform and fabrication process that results in mechanically-flexible photonic wafers and chips, enabling the field of integrated photonics to advance into new application areas that require flexible photonic chips.

Contact Free Monitoring of Cell Density in a Bioreactor with Magnetic Resonance Relaxometry

2025-04-07T08:24:50Z

Contact Free Monitoring of Cell Density in a Bioreactor with Magnetic Resonance Relaxometry Gaensbauer, Hans Frequent, low-latency measurements of bioreactor culture growth are critical for achieving maximum culture efficiency and productivity. Typical cell density and viability measurements are made by removing a sample from the culture, but this approach is both slow and unsuitable for small culture volumes that cannot support frequent destructive sampling. In this work, magnetic resonance relaxometry measurements taken through the walls of the bioreactor tubing are used to monitor the cell density in near real-time. Using intracellular iron as the marker, the system detects variations in cell density in minutes, enabling rapid intervention to save the culture that would be impossible with the once-daily measurements taken by a traditional sampling-based culture analysis system. Given the biochemical importance of intracellular iron, these measurements have the potential to provide phenotypic information on cells without disrupting the bioreactor culture.

Computational and Statistical Detection of High-Dimensional Latent Space Structure in Random Networks

2025-04-07T08:44:36Z

Computational and Statistical Detection of High-Dimensional Latent Space Structure in Random Networks Bangachev, Kiril A probabilistic latent space graph PLSG (n, Ω, D, σ) is parametrized by its number of vertices n, a probability distribution D over some latent space Omega, and a connection function [mathematical function] such that [mathematical formula] almost surely with respect to D. To sample from [mathematical notations], first for each node [mathematical formula] an independent latent (feature) vector x_i is drawn from Omega according to D. Then, for each pair of vertices i and j an edge is drawn independently with probability sigma(x_i,x_j).$ Interest in settings of high-dimensional latent spaces $\Omega$ has surged in recent years due to the rise of high-dimensional data and powerful compute. The features x₁, x₂, . . . , xₙ are oftentimes hidden due to privacy considerations or absence of measurement. This gives rise to many challenging statistical tasks. A prerequisite for nearly any more sophisticated inference and estimation task is the following simple hypothesis testing question. When can we even test for the presence of high-dimensional latent space structure? When is there a computationally efficient test and what could this computationally efficient test be? We address the following aspects of these questions in the thesis. Chapter 2: We focus on the canonical geometric setting when latent vectors are distributed uniformly over the sphere [mathematical formula] where Tₚ is such that expected graph density is p. A conjecture that has witnessed continuous interest and progress in the past 15 years is that the information-theoretically optimal test for detecting the spherical random geometric graph is the signed triangle count. We contribute to the existing literature by confirming that the signed triangle count is computationally optimal among low-degree polynomial tests. Our main technical ingredient is a strategy for bounding Fourier coefficients of random geometric graphs based on a representation of spherical random geometric graphs as Erdős-Rényi with few planted edges. This part of the thesis is based on [BB24b]. Chapter 3: The conjectured optimality of the signed triangle count and the relavance of triangle-based statistics to the axiomatic triangle inequality of metric spaces have led to the conventional wisdom that triangle-based statistics are optimal in monotone random geometric graphs. We break this intuition by showing that in the case of a sup-norm geometry over the torus, the signed 4-cycle count is strictly stronger than the signed triangle count and is, furthermore, optimal among low-degree tests. Our main technical contribution is a novel strategy for bounding Fourier coefficients of random geometric graphs mimicking the cluster-expansion formula from statistical physics. This part of the thesis is based on [BB24a]. Chapter 4: While random geometric graphs over the sphere with Euclidean geometry and the torus with sup-norm geometry are interesting mathematically, they are perhaps too simplistic to describe real-world networks. Hence, one should ask to what extent the results and techniques used for these models generalize to other probabilistic latent space graphs. We introduce a new family of probabilistic latent space graphs which we call random algebraic graphs. In random algebraic graphs, Omega is an algebraic group and sigma is compatible with the group structure. This family captures the aforementioned random geometric graphs as well as instances of the stochastic block model and random subgraphs of Cayley graphs. We have two sets of results. First, we develop a general criterion based solely on the magnitudes of Fourier coefficients of sigma for the statistical hardness of detecting a random algebraic graph when the underlying group is the Boolean hypercube. We use this result to provide a uniform approach to many previously known results in the literature, but also highlight that certain structural properties of the connection function such as non-trivial symmetries and non-monotonicity yield novel behavior. Second, we exhibit a universal behavior for the impossibility of detecting a random algebraic graph based solely on the group size but not on the group structure. The result can be equivalently phrased in terms of the local structure of typical Cayley graphs. This part of the thesis is based on [BB23].

Factorization and Compositional Generalization in Diffusion Models

2025-04-08T04:30:30Z

Factorization and Compositional Generalization in Diffusion Models Liang, Qiyao One of the defining features of human intelligence is compositionality—the ability to generate an infinite array of complex ideas from a limited set of components. This capacity allows for the creation of novel and intricate combinations of arbitrary concepts, enabling potentially infinite expressive power from finite learning experiences. A likely prerequisite for the emergence of compositionality is the development of factorized representations of distinct features of variation in the world. However, the precise mechanisms behind the formation of these factorized representations in the human brain, and their connection to compositionality, remain unclear. Diffusion models are capable of generating photorealistic images that combine elements not co-occurring in the training set, demonstrating their ability to compositionally generalize. Yet, the underlying mechanisms of such compositionality and its acquisition through learning are still not well understood. Additionally, the relationship between forming factorized representations of distinct features and a model’s capacity for compositional generalization is not fully elucidated. In this thesis, we explore a simplified setting to investigate whether diffusion models can learn semantically meaningful and fully factorized representations of composable features. We conduct extensive controlled experiments on conditional diffusion models trained to generate various forms of 2D Gaussian data. Through preliminary investigations, we identify three distinct learning phases in the model, revealing that while overall learning rates depend on dataset density, the rates for independent generative factors do not. Moreover, our findings show that models can represent continuous features of variation with semi-continuous, factorized manifolds, resulting in superior compositionality but limited interpolation over unseen values. Based on our investigations, we propose a more data-efficient training scheme for diffusion models and suggest potential future architectures for more robust and efficient generative models.

Fabrication and Testing of A Middle-Ear Implanted Microphone

2025-04-08T04:19:30Z

Fabrication and Testing of A Middle-Ear Implanted Microphone Wawrzynek, Emma Cochlear implants are devices that can restore hearing to people with sensorineural deafness. Despite their name, cochlear implants rely on an external unit which contains components such as a microphone. This work presents the design, fabrication, and testing of an implantable middle-ear microphone called the “UmboMic” that measures the displacement of the tympanic membrane at the umbo. Particular consideration is paid to the biocompatability of the microphone and its long-term durability in the body. The work discusses biocompatible materials, methods of encapsulation, and techniques for testing device robustness. The UmboMic is a piezoelectric displacement sensor that is implanted in the middle ear cavity and contacts the umbo. As the umbo moves, it displaces the UmboMic, resulting in a charge that is amplified with a custom amplifier. The active area of the UmboMic is a triangular shaped cantilever made from two layers of piezoelectric thin film called polyvinylidene fluoride (PVDF). The bimorph design reduces common mode noise as compared to our previous microphone designs. Extensive bench testing and experiments in fresh human cadavers demonstrates excellent microphone performance despite the use of biocompatible materials. The UmboMic sensor is well shielded against electromagnetic interference, tolerant to implantation variations, and can be repeatably fabricated with little difference between sensor performances. It demonstrates high sensitivity from 100 Hz to above 8 kHz, with a sensitivity of 58 fC/Pa at 1 kHz and 230 fC/Pa at 2 kHz when including the outer ear. The noisefloor of the UmboMic normalized over 1/3 octave bins is 10⁻² fC, and the A-weighted equivalent input noise of the UmboMic with the outer ear is 82.4 dB SPL from 100 Hz to 7 kHz. When tested in five different human cadavers, the UmboMic sensors work reliably despite anatomical differences. Internalizing the entire cochlear implant would greatly improve the quality of life of wearers. In its current form, cochlear implants cannot be used during sleep and vigorous activity, are susceptible to noise from wind, and function poorly in loud environments. Implanting the device would mitigate these problems and provide users with the discretion of an invisible device. Our prototype demonstrates the feasibility of an implanted microphone and is an important step towards developing a totally implantable cochlear implant.

Structured Handwritten Input for Dementia Classification

2025-04-07T08:57:51Z

Structured Handwritten Input for Dementia Classification Flores, Gerardo We explore the use of deep learning to score the Digit Symbol Substitution Test (DSST), a paper-and-pencil behavioral test useful in diagnosing Alzheimer’s. We train a model to classify Alzheimer’s based on the subject’s responses to any one of the 108 queries in the test. We then combine predictions across the test to produce a new classifier that is considerably stronger. We also make an extensive search of architectures and optimization techniques that have proved useful in other settings. The ultimate result is a very strong classifier, with AUC for a response to a single question of 86% and for an overall patient of 97.25%.

Spatially-Adaptive LiDAR and Underwater Communications Using Integrated Optical Phased Arrays

2025-04-07T08:27:29Z

Spatially-Adaptive LiDAR and Underwater Communications Using Integrated Optical Phased Arrays DeSantis, Daniel Markus Silicon-photonics microsystems have enabled advanced optoelectronic capabilities in applications spanning from sensors to communication systems. In particular, integrated optical-phased-array-based (OPA-based) technologies, such as solid-state LiDAR and free-space optical communications (FSOC) systems, show promise to revolutionize the way we sense and communicate. This thesis enables new integrated-OPA-based solid-state beam-steering capabilities for these existing applications, as well as emerging spatially- and spectrally-demanding applications. First, we develop and experimentally demonstrate a novel multi-beam solid-state OPA-based LiDAR system capable of detecting and ranging multiple targets simultaneously, passively, and without rastering. Through this work, we demonstrate a new spatially-adaptive sensing modality for solid-state LiDAR that promises to reduce the data deluge associated with LiDAR sensing for autonomous systems. Second, we show the first, to the best of our knowledge, spiral integrated OPAs, enabling emission of focusing beams with tunable variable focal heights for the first time. This work introduces a first-of-its-kind integrated OPA architecture and, as such, enables new functionality for emerging applications of OPAs that require focusing operation, such as biophotonic optical tweezers and chip-based 3D printers. Third, we show the first visible-light integrated-OPA-based FSOC transmitter and use it to experimentally demonstrate the first integrated-OPA-based underwater-wireless-optical-communication (UWOC) link. This integrated OPA transmitter chip can reduce the size, weight, and mechanical complexity of apparatus for UWOC systems.

Distributional Private Information Retrieval

2025-04-07T09:02:44Z

Distributional Private Information Retrieval Lehmkuhl, Ryan A private-information-retrieval (PIR) scheme lets a client fetch a record from a remote database without revealing which record it has fetched. Classic PIR schemes treat all database records the same but, in practice, some database records are much more popular (i.e., commonly fetched) than others. We introduce distributional private information retrieval, a new type of PIR that can run faster than classic PIR—both asymptotically and concretely—when the popularity distribution is heavily skewed. Distributional PIR provides exactly the same cryptographic privacy notion as classic PIR. The speedup comes from providing a relaxed form of correctness: distributional PIR guarantees reliable retrieval for PIR queries that follow the popularity distribution, but only “best-effort” retrieval for out-of-distribution queries. We give several constructions of distributional-PIR schemes that make black-box use of existing standard PIR protocols. On a popularity distribution drawn from real-world Twitter data, distributional PIR reduces compute costs by 5.1–77× compared to existing techniques. Finally, we build CrowdSurf, an end-to-end system for privately streaming social-media posts, and show that our PIR schemes reduce the end-to-end server cost by 8×.

Methods for Enhancing Robustness and Generalization in Machine Learning

2025-04-07T09:24:00Z

Methods for Enhancing Robustness and Generalization in Machine Learning Schechter, Amit We propose two methods for improving subgroup robustness and out of distribution generalization of machine learning models. First we introduce a formulation of Group DRO with soft group assignment. This formulation can be applied to data with noisy or uncertain group labels, or when only a small subset of the training data has group labels. We propose a modified loss function, explain how to apply it to data with noisy group labels as well as data with missing or few group labels, and perform experiments to demonstrate its effectiveness. In the second part, we propose an invariant decision tree objective that aims to improve the robustness of tree-based models and address a common failure mode of existing methods for out-of-domain generalization. We demonstrate the benefits of this method both theoretically and empirically. Both these approaches are designed to enhance machine learning models’ performance under distribution shift.

Characterizing the Epistemic Uncertainty of Predictive Action Models and Sampling-Based Motion Planners for Robotic Manipulation

2025-04-07T08:44:25Z

Characterizing the Epistemic Uncertainty of Predictive Action Models and Sampling-Based Motion Planners for Robotic Manipulation Shaw, Seiji A. We derive methods to represent the epistemic uncertainty of models used in long-horizon robot planning problems in autonomous manipulation. We develop a representation of epistemic uncertainty for two types of models: uncertainty over the physical parameters of a model that predicts the observed outcome of a manipulation action and uncertainty over a geometric graph built by a sampling-based motion planner as a representation of the configuration space to answer a motion planning query. We propose a simple planning system that integrates these uncertainty characterizations to reason about the informational value of executing a manipulation action or allocating a number of samples to a sampling-based motion planner.

Burst Imaging with Learned Continuous Kernels

2025-04-07T08:59:17Z

Burst Imaging with Learned Continuous Kernels Biscarrat, Camille Burst imaging is a technique that consists of taking multiple images in quick succession and merging them into one output image. By aligning and combining data from multiple frames, we can increase resolution, attenuate noise, reduce motion blur and expand the dynamic range to obtain a higher quality image. In this thesis, we propose a method that learns continuous kernels to process and merge burst frames. We show that the learned kernels adapt to local image information and take advantage of sub-pixel sample location information to demosaic, denoise and merge the burst into a high quality output.

Magnetic Weyl Semimetals for Spintronic Applications

2025-04-07T08:41:31Z

Magnetic Weyl Semimetals for Spintronic Applications He, Zhiping Magnetic Weyl semimetals are a category of topological materials that hold promise for spintronic applications due to their unconventional transport properties, which arise from both bulk and surface topological states, as well as the rich interplay between band topology and magnetism. Among the family of semimetallic materials, the antiferromagnetic Weyl semimetals Mn₃X (X=Sn, Ge, etc.) and the ferromagnetic Weyl semimetal Co₂MnGa have attracted significant interest. So far, despite extensive theoretical and experimental investigations, the magnetic dynamics of Mn₃X and the spin-polarized tunneling in Co₂MnGa based spintronic devices remain not fully explored. In this thesis, I establish a theoretical framework to describe the low energy dynamics of strained Mn₃X. Using perturbation theory, I identify three distinct dynamic modes and derive a Landau-Lifshitz-Gilbert (LLG)-like equation to describe uniform mode dynamics. I also analyze the excitation of dissipative spin waves and the spin superfluidity state in Mn₃X by extending the model to include spatial inhomogeneity. The analytical results are validated against numerical simulations based on fully coupled LLG equations, where good agreement is achieved. In addition, I study fully epitaxial magnetic tunnel junctions (MTJs) composed of Co₂MnGa. By growing Co₂MnGa/MgO/Co₂MnGa stacks under different conditions, I develop a series of MTJs with varying degrees of chemical ordering in the Weyl semimetal electrodes and compare their tunneling magnetoresistance (TMR). I find that the TMR is enhanced with the improvement of the chemical ordering in Co₂MnGa. Our results reveal the relationship between the spin tunneling in MTJs and the chemical order of Co₂MnGa electrodes, offering insights into further enhancing TMR through Weyl semimetal engineering.

Superparamagnetic Tunnel Junctions for Reliable True Randomness and Efficient Probabilistic Machine Learning

2025-04-07T08:50:45Z

Superparamagnetic Tunnel Junctions for Reliable True Randomness and Efficient Probabilistic Machine Learning Koh, Dooyong Physical devices exhibiting stochastic functions with low energy consumption and high device density have the potential to enable complex probability-based computing algorithms, accelerate machine learning tasks, and enhance hardware security. Recently, superparamagnetic tunnel junctions (sMTJs) have been widely explored for such purposes, leading to the development of limited-scale sMTJ-based systems. Existing sMTJs face significant scalability and reliability issues, however, because their intrinsically low energy barrier and correspondingly small device area result in high sensitivity to external perturbations, as well as large variations from device to device. Here, we present an experimental demonstration of three-terminal sMTJs as reliable and potentially scalable sources of true randomness in the field-free regime. By leveraging dual-current controllability and incorporating feedback, we stabilize the switching operation of superparamagnets and reach cryptographic-quality random bitstreams. The realization of controllable and robust true random sMTJs underpin a general hardware platform for computing schemes exploiting the stochasticity in the physical world, as demonstrated by the generative artificial intelligence example in our experiment. Furthermore, we experimentally demonstrate a novel method of utilizing sMTJs as stochastic analog-to-digital converters (sADCs) in a crossbar array architecture for neural network acceleration, showing performance comparable to software implementations. This work highlights the potential of sMTJs to revolutionize energy-efficient computing and provides a foundation for future advancements in probabilistic computing and hardware security.

Large Language Model Tools for Project-based Learning

2025-04-07T09:24:42Z

Large Language Model Tools for Project-based Learning Ravi, Prerna Project-Based Learning (PBL) has emerged as a prominent educational approach that im- merses students in meaningful, real-world tasks, fostering deep and lasting learning experiences. Unlike traditional instructional methods, PBL emphasizes a student-centered pedagogy, where learners actively construct knowledge through exploration, collaboration, and reflection. This approach not only nurtures a love of learning but also encourages students to form personal con- nections to their academic experiences, making education more relevant and impactful. How- ever, while PBL offers significant educational benefits, it also presents challenges for educators, including the complexities of designing and managing projects, assessing student learning, and balancing student autonomy with guided instruction.. The advent of artificial intelligence (AI), particularly large language models (LLMs), holds promise for addressing these challenges by en- hancing personalized learning, automating administrative tasks, and providing real-time feed- back. To ensure that these AI tools are sustainable and conducive to diverse classroom contexts, it is crucial to involve educators in the design process from the outset. This thesis contributes to the intersection of PBL and generative AI by documenting a co- design process with interdisciplinary K-12 teachers aimed at integrating AI into PBL pedagogy. Through need-finding interviews, collaborative workshops, and iterative tool design, this re- search explores how AI can support teachers in implementing high quality PBL while maintaining the integrity of student-centered learning. We also investigate how this technology can augment the current roles of teachers without replacing them, and support their professional growth. The thesis is structured around three key objectives: exploring the challenges educators face with PBL, co-designing AI tools that address these challenges, and proposing design guidelines for future AI tools in PBL classrooms. By refining the design of AI-powered PBL tools, enhancing teacher professional development resources, and ensuring these tools are accessible and equitable, educators will be better equipped to foster engaging, student-centered learning environments. These contributions not only encourage future research and development of AI educational tools, but also aim to foster a more immersive and constructionist learning approach in classrooms.

Interpretable and Automated Bias Detection for AI in Healthcare

2025-04-07T09:05:01Z

Interpretable and Automated Bias Detection for AI in Healthcare Alexiev, Christopher Biases in artificial intelligence systems and the data they operate over are a major hurdle to their application in clinical and biomedical settings. Such systems have frequently been shown to fail to generalize from their training data to the real world environment and often display differing levels of accuracy over different population subgroups, which has detrimental effects on patients' quality of care and on healthcare equality. Here, we introduce an automated framework for identifying and understanding nontrivial sources of bias in healthcare datasets and AI models. Our framework is data and model agnostic and does not rely on human-developed heuristics or assumptions to uncover bias. We demonstrate its effectiveness by uncovering serious and nontrivial sources of bias in three widely used clinical datasets and one biomedical dataset, over the diverse tasks of diabetes risk prediction, lung cancer risk prediction, and biomolecular toxicity prediction. Our framework is used to uncover biases caused by patient BMI and computed tomography (CT) scanner type in the data used by a cutting-edge lung cancer risk prediction AI model, causing AUC drops on the order of ten percent.

Optical Characterisation of Strain and Defects in 2D Photonic Materials

2025-04-07T08:37:16Z

Optical Characterisation of Strain and Defects in 2D Photonic Materials Mukherjee, Abhishek Strain and defect engineering have shown to be powerful tools in modifying optoelectronic properties of semiconductors. This thesis aims to advance the fundamental understanding of electronic and optical properties in material systems with broken inversion symmetries and to use this understanding to engineer in-situ, localized strain fields for tailoring photonic responses at the nanoscale. We will address the fundamental question: How can we characterize the effect of strain and defects in two-dimensional photonic materials? To this end, we open with a review of current strategies in strain engineering, its fundamental consequences on electronic, optical, and magnetic properties, and the state-of-the-art applications of this technology in achieving band-gap-engineered straintronic devices. Touching on the advent of strain engineering for flexoelectricity - a spontaneous material polarization produced by a strain gradient that lifts the inversion symmetry, which can enable a bulk photogalvanic effect, we posit the aspect of meta-valent bonding in materials having a key role in this, by showing that the majority of prime material candidates known to have exhibit large photogalvanic response exhibit this characteristic. The rest of the thesis focuses on characterizing layered metal thio(seleno)phosphates, a family of materials known for their magnetic, electronic, and nonlinear optical properties. We show how the optical properties of these materials can be modulated via different means of defects and strain. These photoactive materials can be pivotal to a future comprising of strain-engineered flexoelectric devices, which take advantage of the bulk photogalvanic effect, to develop a new family of practical, deployable, self-powered, and low-cost photodetectors, and integrated arrays with limits-breaking performance in the UV-to-LWIR spectral bands.

High-accuracy, speed-optimized positioning system for electron beam lithography

2025-10-30T17:03:43Z

High-accuracy, speed-optimized positioning system for electron beam lithography Dadok, Luděk. Thesis: M.S., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 1982; Vita.; Includes bibliographical references.

Transfer function of heavy duty gas turbine combustor components.

2025-10-30T18:06:09Z

Transfer function of heavy duty gas turbine combustor components. Farrell, Thomas Dominic. Thesis: M.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1978; Includes bibliographical references.

A study of iron tricarbonyl complexes.

2025-10-30T18:06:07Z

A study of iron tricarbonyl complexes. Fanelli, Joseph John. Thesis: M.S., Massachusetts Institute of Technology, Department of Chemistry, 1978; Includes bibliographical references.

The effect of braking on automobile vehicle dynamics.

2025-10-30T18:06:07Z

The effect of braking on automobile vehicle dynamics. Evans, David Gordon. Thesis: M.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1978; Includes bibliographical references.

Fractographic investigation of crack-closure.

2025-10-30T18:06:08Z

Fractographic investigation of crack-closure. Faral, Michel. Thesis: M.S., Massachusetts Institute of Technology, Department of Materials Science and Engineering, 1978; Includes bibliographical references.

An accident and seismic containment reliability study including statistical uncertainty

2025-10-30T18:06:08Z

An accident and seismic containment reliability study including statistical uncertainty Fardis, M. N. (Michael N.) Thesis: M.S., Massachusetts Institute of Technology, Department of Nuclear Engineering, 1978; Bibliography: leaves 180-183.

A piezoelectric force measuring system for human mobility analysis.

2025-10-30T18:06:09Z

A piezoelectric force measuring system for human mobility analysis. Estey, Paul Norman. Thesis: M.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1978; Bibliography: leaves 178-182.

A study of methods of determining flood damages and of evaluating flood control benefits

2025-10-30T17:03:42Z

A study of methods of determining flood damages and of evaluating flood control benefits Lampert, James B. (James Benjamin), 1914-1978. Thesis: M.S., Massachusetts Institute of Technology, Department of Civil and Sanitary Engineering, 1939; Includes bibliographical references (leaf 101).

Thermal shock resistance of ceramics.

2025-10-30T15:50:03Z

Thermal shock resistance of ceramics. Goodof, Robert Steven. Thesis: M.S., Massachusetts Institute of Technology, Department of Metallurgy and Materials Science, 1973; Includes bibliographical references.

Innovative Structural and Mechanical Satellite Systems

2025-04-08T04:46:55Z

Innovative Structural and Mechanical Satellite Systems Thomas, Annika This thesis covers two topics within the field of satellite mechanical engineering. The first topic covered is the structural and thermal design and validation BeaverCube 2 Earth-imaging CubeSat. The second topic covered is the electromagnetics modeling and simulation of inductive spin drive for a novel magnetically levitated spherical control moment gyroscope for satellite attutide control. For the first topic on BeaverCube 2, the key tasks were to design and assemble the structure of the CubeSat, ensure that subsystems maintain their operating temperatures on orbit, and validate the structural integrity of the CubeSat structure during launch. We design and manufacture 24 components that integrate all subsystems of BeaverCube 2 and meet the size requirements of a 3U (3 x (100cm3)) CubeSat, including a chassis, panels, payload structure and connectors for the stack of boards. Next, we ensure that all subsystems of the satellite do not exceed their temperature limits through analytical and simulated thermal analysis, showing that during worst case hot (70∘ beta angle) and worst case cold (70∘ beta angle) orbits, no subsystem reaches within 5 ∘C of its operating temperature limits. Finally, we analyze the structure of BeaverCube 2 to validate that the components can structurally withstand the 4-7 G linear accelerations, 13.5 rad/s radial accelerations, 1200 N side rail loads, and random vibration environment that may be experienced during launch [1]. The design is shown to be robust in these conditions, with margins of safety of stress ranging from 19.97 to 37.56 and deformation of the stack of circuit boards not exceeding 0.05 mm. The minimum frequencies of modes of vibration throughout the structure occur at 623 Hz, which is well above the allowed minimum mode of 100 Hz. For the second topic of modeling the spherical control moment gyroscope, the key tasks were to design an actuation method using inductive drive and to experimentally validate a closed-loop controller for suspension of a prototype. For the actuation method, we present the electromagnetics modeling of an inductive spin drive, including analytical derivations of a bulk conductivity model and a skin current model. The analytical skin model shows that inductive drive with a rotating dipole magnetic field can generate a peak value 130 𝜇Nm of torque. We simulate both models with a rotating dipole and a rotating quadrupole stator drive configuration. Next, we successfully magnetically levitate a permanent magnet rotor prototype. We develop an analytical plant model for the system and a controller for closed-loop suspension with 40 Hz crossover and 20∘ phase margin, then we present preliminary experimental results.

Screen Time

2025-04-08T04:41:06Z

Screen Time Landman, Jeffrey In Times Square, architecture is inextricable from mediated representations. The place is dislocated by the screens that envelop its buildings and the other screens, around the world, upon which its image is ceaselessly presented. The neighborhood itself is named after the Times Tower, which was opened in 1905 as the office and printing press of The New York Times, and remains at the center of the square today, entirely empty, voided by the advertising value of its screens. But this condition is not a contemporary anomaly. If the screens, flowed through by consumer desire, currently vaporise the building’s edge, in 1904, before it was even occupied, the building summoned the city with the results of the general election, broadcast to the metropolis via searchlight. The building has always extended its edge, projecting public messages while concealing private concerns. This thesis understands the building as one actor in a media apparatus: a network of interconnections between broadcasting devices and media, infrastructure, public and political events, development policy and financial systems. The Tower indexes 20th century architecture’s participation in this media apparatus, telling a story in which communication and the distribution of power predate and outlast inhabitation, a story in which occupation is not part of the program. The thesis tracks the tower through six innovative broadcasting devices which the building sponsored, including the world’s first moving electric sign, the New Year’s Eve Ball, the world’s first changeable architectural screen, and the world’s largest open architectural competition. The form of the thesis is a short movie that uses found footage and computer generated animations to apprehend the Tower amid its myriad images. In designing for animated representation the thesis is positioned in a lineage of paper architectures, proposing a form of architectural production which embraces and redirects the forces of the media apparatus. The movie reconfigures, misaligns and misuses its historical sources to reproduce and subvert the Screen Time from which architecture can now never be distinct.

Product Purity Prediction and Anomaly Detection for an Automated Peptide Manufacturing Platform

2025-04-08T04:46:19Z

Product Purity Prediction and Anomaly Detection for an Automated Peptide Manufacturing Platform Yang, Liudi This thesis aims to develop and deploy a method of predicting product purity and automating anomaly detection for Mytide Therapeutics’ peptide manufacturing platform. A baseline study revealed how early purity prediction and anomaly reporting could decrease the production cycle time, manual data review, and chemical waste produced by the synthesis process. The most important tool for making purity predictions is UV absorption on the byproducts and excess reagents that come out of the reactor, where the peptides are made. A large part of this thesis was improving the quality of the UV data in order to make purity predictions using the improved UV traces. Sensor data from historical runs, including pressure, temperature, and flow rates, were analyzed to characterize several common anomalies. The reporting system takes in live data and alerts the relevant parties when the limits are reached, so that corrective action can be implemented quickly. The anomaly tracking code also generates a report to either be viewed on the user interface or stored in the backend database with the run’s historical data. Implementation of the described system improvements had several positive impacts on the workflow. The live anomaly alerts allowed for issues to be reported to the relevant parties upon occurrence, which increased the uptime of the system. The anomaly report, which is tagged to each peptide synthesis run, allows for historical data evaluation and easy decision-making for advancing the peptide to the next step of the process. The purity prediction allowed for earlier identification of certain poor-purity peptides by 27% of the production time. Together, these system improvements helped to advance the company’s peptide manufacturing platform towards total automated decision-making.

“Biopolitics from below?” — Lessons of Emergent Urban Governance Trend Under Covid-19 in China

2025-04-08T04:49:38Z

“Biopolitics from below?” — Lessons of Emergent Urban Governance Trend Under Covid-19 in China Shao, Yu This thesis interrogates COVID-19 emergent urban governance trends in China in response to the COVID-19 crisis, with a particular focus on the use of the narratives of epidemic and state emergency, as well as the governance strategies during the pandemic and in the socalled post-COVID era. More importantly, this thesis intends to investigate people’s responses towards emergency policies—the compliances and creative strategies that people have adopted to demonstrate their resistance. Using a combination of ethnographic data and archival research, this thesis covers five major themes: a) the impacts that different outbreak narratives perpetuated on the Internet; b) left-wing scholars’ view (or hope) for the rise of socialism and how the Chinese state has used the socialist narrative to build up its international image; c) the strong comeback of capitalist practices the pandemic exacerbated the precariousness of work; d) how the pandemic has been used as a justification to impose panoptic surveillance and control on Chinese citizens and asked for absolute obedience towards government policies, as well as how the formulaic practices dominated the post-COVID landscape; and finally, e) people’s response and sentiments to government policies such as lockdowns and social distancing displayed on social media platforms. It concludes by arguing that even in an autocratic state with increasingly tightened control justified by the epidemic, people are not passive recipients of such policies. They have come up with creative strategies to express their resistance and exhibit negotiation with the policies. It further argues that in China, COVID-19 has aroused a new wave of active civil participation, for citizens to discuss politics openly, starting from pandemic related topics to the freedom of speech at large. Complicating what Panagiotis Sotiris terms biopolitics from below, it suggests that the creative posts on social media platforms are a savvy means of claiming back our bodies.

Applying Statistical Analysis and Machine Learning to Improve the Ice Sensing Algorithm

2025-04-08T04:16:12Z

Applying Statistical Analysis and Machine Learning to Improve the Ice Sensing Algorithm Herron, Lucas A. The detection of sea ice is a major problem faced by Argo floats operating in polar regions. In these areas, the presence of sea ice threatens to damage or destroy floats in the event of an impact at the surface. While methods have been proposed and implemented to combat this danger, the most successful of which is the Ice Sensing Algorithm (ISA), further work is necessary to fully mitigate the risks, particularly in the Arctic. In this analysis, past CTD profiles from the Arctic are compiled and matched with sea ice data to examine the performance of the ISA and recommend potential changes and new methods to further improve its accuracy. This is accomplished by fitting the data to statistical and machine learning models to predict the presence of ice and analyzing the results. Results show that both modifications to current methods and the inclusion of new variables may increase the predictive power of the ISA. Specifically, the analysis shows that the use of point measurements (as opposed to a metric over a pressure range) at the shallowest allowable depth provides the best performance. The additional inclusion of practical salinity and time of year as predictive variables also increases the performance of the algorithm. Results and statistics on the performance of the algorithm are provided and analyzed in various regions.

Enabling Robotic Manipulation in Remote Environments with Shared Autonomy

2025-04-07T09:18:29Z

Enabling Robotic Manipulation in Remote Environments with Shared Autonomy Phung, Amy The evolution of robotics technology continues to facilitate exploration and scientific study in remote environments, enabling research in areas that were previously impossible to reach. Robots operating in space and marine environments encounter similar operational challenges, as both face high operational costs, bandwidth-limited conditions, and natural, unstructured environments where dynamic obstacles might be present. Within the oceanographic domain, conventional deep-sea sampling operations involve remotely operated vehicles (ROVs) equipped with robotic manipulator arms to complete dexterous tasks at depth. While effective, deep-sea ROV operations require specialized instrumentation, highly trained shipboard personnel, and large oceanographic vessels, which make deep-sea samples inaccessible to most. This thesis presents the SHared Autonomy for Remote Collaboration (SHARC) framework, and evaluates its utility within an oceanographic context. By leveraging shared autonomy, SHARC enables shore-side operators to collaboratively carry out underwater sampling and manipulation tasks, regardless of their prior manipulator operations experience. With SHARC, operators can conduct manipulation tasks using natural language and hand gestures through a virtual reality (VR) interface. The interface provides remote operators with a contextual 3D scene understanding that is updated according to bandwidth availability. Evaluation of the SHARC framework through controlled lab experiments indicates that SHARC’s VR interface enables novice operators to complete manipulation tasks in framerate-limited conditions (i.e., <0.5 frames per second) faster than expert pilots using the conventional topside controller. For both novice and expert users, the VR interface also increased the task completion rate and improved sampling precision. During sea trials, SHARC enabled collection of an underwater in-situ X-ray fluorescence (XRF) measurement at more than 1000 meters water depth in the Eastern Pacific with centimeter-level precision by remote scientists with no prior piloting experience. This demonstration provides compelling evidence of SHARC’s utility for conducting delicate operations in unstructured environments across bandwidth-limited communications, which holds relevance for improving operations in other sensitive domains where dexterity is required. SHARC’s ability to relax infrastructure requirements and engage novice shore-side users provides a promising avenue for democratizing access to deep-sea research.

Comparison of compensators for double integrator plants

2025-10-30T17:03:41Z

Comparison of compensators for double integrator plants Schwartz, Adam L. Thesis: M.S., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 1989; Includes bibliographical references (leaves 186-189).

Issues in new product development--the introduction of tape automated bonding technology

2025-10-30T17:51:28Z

Issues in new product development--the introduction of tape automated bonding technology Maggs, Virginia Loop. Thesis: M.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1990; Includes bibliographical references (leaves 142-144).

Characteristics of electric strain gages at low temperatures

2025-10-30T17:51:29Z

Characteristics of electric strain gages at low temperatures Sevand, Ali H.; Day, Emmett E. Thesis: M.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1946; Bibliography: leaf 21.

An investigation of the reaction of sulfur vapor with a metallic oxide

2025-10-30T15:50:05Z

An investigation of the reaction of sulfur vapor with a metallic oxide Hard, Robert A. Thesis: M.S., Massachusetts Institute of Technology, Department of Chemical Engineering, 1949; Bibliography: leaf 59.

A compressible, high frequency numerical model of helicopter noise due to blade/vortex interaction

2025-10-30T18:06:09Z

A compressible, high frequency numerical model of helicopter noise due to blade/vortex interaction Lima, Luiz Hamilton de Resende. Thesis: M.S., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 1980; Includes bibliographical references.

A study of production smoothing in a job shop environment

2025-10-30T17:51:28Z

A study of production smoothing in a job shop environment Cruickshanks, Allan Benjamin.; Drescher, Robert D. Thesis: M.S., Massachusetts Institute of Technology, Sloan School of Management, 1982; Vitae.; Includes bibliographical references.

A study of multi position letter sorting machine operation in the United States Postal Service

2025-10-30T17:51:28Z

A study of multi position letter sorting machine operation in the United States Postal Service Cruce, A. C., 1858-1919.; Lee, Jerry Kenneth. Thesis: M.S., Massachusetts Institute of Technology, Sloan School of Management, 1982; Includes bibliographical references.

Analysis of the air bleeds and several typical idling systems of the carburetors

2025-10-30T15:50:01Z

Analysis of the air bleeds and several typical idling systems of the carburetors Ding, Qinghua. Thesis: M.S., Massachusetts Institute of Technology, Department of Aeronautical Engineering, 1946; Bibliography: leaf 59.

Double trouble: Predicting new variant counts across two heterogeneous populations

2025-04-07T08:53:12Z

Double trouble: Predicting new variant counts across two heterogeneous populations Shen, Yunyi Collecting genomics data across multiple heterogeneous populations (e.g., across different cancer types) has the potential to improve our understanding of disease. Despite sequencing advances, though, resources often remain a constraint when gathering data. So it would be useful for experimental design if experimenters with access to a pilot study could predict the number of new variants they might expect to find in a follow-up study: both the number of new variants shared between the populations and the total across the populations. While many authors have developed prediction methods for the single-population case, we show that these predictions can fare poorly across multiple populations that are heterogeneous. We prove that, surprisingly, a natural extension of a state-of-the-art single-population predictor to multiple populations fails for fundamental reasons. We provide the first predictor for the number of new shared variants and new total variants that can handle heterogeneity in multiple populations. We show that our proposed method works well empirically using real cancer and population genetics data.

The Influence of Root Geometry on Soil Cohesion and Anchoring Ability through Geologic Time

2025-04-07T08:33:47Z

The Influence of Root Geometry on Soil Cohesion and Anchoring Ability through Geologic Time Colicci, Vittorio Vegetation has become ubiquitous among most modern landscapes. However, for much of the Earth’s history, land plants were absent. Their rapid diversification throughout the Devonian and Carboniferous brought about a massive shift in geomorphology and landscape evolution. Complex rooting structures were the principal agents of change, mechanically reinforcing their substrates and generating cohesive sediments through weathering. This work examines the root systems of three major tree genera from these periods: Calamophyton, Lepidodendron, and Calamites. Simplified reconstructions were designed, 3D printed, and uprooted from a sand testbed to explore the effects of root geometry on anchoring ability. Force and displacement data were gathered for each model and used to calculate anchoring strength and uprooting work. Force laws were then derived to approximate the anchoring contributions of root weight, sediment weight, static friction, and shear strength. This analysis revealed a strong dependence on the span, surface area, and volume of the root system, which were used to normalize values across different geometries. The Calamophyton model required the greatest uprooting force per unit length, whereas the Lepidodendron model required the greatest uprooting force per unit area and volume. These results were interpreted within the environmental context of each genus alongside particular features of root geometry. Calamophyton contributed less to soil cohesion due to its simple unbranched architecture, however it likely increased wetland habitability for subsequent species. Meanwhile, Lepidodendron would have bolstered cohesion on account of its densely-packed dichotomous rootlets. Calamites is unique in its clonal reproductive habit and nodal branching architecture, which could have helped it colonize particularly unstable environments. We maintain that the earliest trees played a key role in surface stabilization within their ecosystems and likely paved the way for species that followed.

One-Pass Learning via Bridging Orthogonal Gradient Descent and Recursive Least-Squares

2025-04-07T09:26:58Z

One-Pass Learning via Bridging Orthogonal Gradient Descent and Recursive Least-Squares Min, Youngjae While deep neural networks are capable of achieving state-of-the-art performance in various domains, their training typically requires iterating for many passes over the dataset. However, due to computational and memory constraints and potential privacy concerns, storing and accessing all the data is impractical in many real-world scenarios where the data arrives in a stream. In this thesis, we investigate the problem of one-pass learning, in which a model is trained on sequentially arriving data without retraining on previous datapoints. Motivated by the increasing use of overparameterized models, we develop Orthogonal Recursive Fitting (ORFit), an algorithm for one-pass learning which seeks to perfectly fit every new datapoint while changing the parameters in a direction that causes the least change to the predictions on previous datapoints. By doing so, we bridge two seemingly distinct algorithms in adaptive filtering and machine learning, namely the recursive least-squares (RLS) algorithm and orthogonal gradient descent (OGD). Our algorithm uses the memory efficiently by exploiting the structure of the streaming data via an incremental principal component analysis (IPCA). Further, we show that, for overparameterized linear models, the parameter vector obtained by our algorithm is what stochastic gradient descent (SGD) would converge to in the standard multi-pass setting. Finally, we generalize the results to the nonlinear setting for highly overparameterized models, relevant for deep learning. Our experiments show the effectiveness of the proposed method compared to the baselines.

Design of a Precision Needle for Injection of Fluid into the Suprachoroidal Space of the Eye for the Treatment of Retinal Detachment

2025-04-08T04:40:34Z

Design of a Precision Needle for Injection of Fluid into the Suprachoroidal Space of the Eye for the Treatment of Retinal Detachment Rutherford, Emma Rhegmatogenous retinal detachment (RRD) is a vision-threatening condition that affects 10 to 18 per 100,000 people in the United States annually [1]. The current standard for treatment is pars plana vitrectomy (PPV), which is an invasive and expensive surgical procedure that leaves patients unable to perform usual activities for four to six weeks. In addition, current methods tend to produce distorted vision upon recovery. In-office Suprachoroidal Viscopexy™ (SCVEXY™) is a minimally invasive technique recently developed by Dr. Rajeev Muni for treating rhegmatogenous retinal detachment (RRD) which has been performed on a handful of people [2]. This procedure has the potential to greatly reduce the cost and recovery time of RRD while also improving the quality of the repair. It can be performed with no incision, no tamponade agent, and no patient post-op positioning requirements [2]. SCVEXY works by injecting viscous fluid into the suprachoroidal space, a “potential space” between the sclera and choroid, creating a “bleb” of fluid underneath the tear that pushes the choroid towards the retina and allows it to reattach. However, difficulty in safely injecting into this space at the location of the retinal tear currently limits the widespread utilization of the technique. If this procedure was made reliably safe, it could greatly change how retinal detachments are treated and improve patient outcomes. The primary difficulty arises in precisely locating the suprachoroidal space in order to inject the viscous fluid. The thickness of the sclera varies from patient to patient and between locations on the eye. Additionally, the scleral and choroidal tissues are very thin, leaving little room for positional error. Hemorrhage may occur if the needle punctures through the choroid and into the subretinal space, which could lead to bad outcomes. This work presents a device developed to minimally invasively reach posterior segments of the eye, deploy an injection needle in-situ with high resolution, sense when the needle tip has passed into the suprachoroidal space (SCS), and inject a viscous fluid. Not only will this device be used to treat retinal detachment in a minimally invasive manner, but it could also be used for drug injection or fluid aspiration via the suprachoroidal and subretinal spaces for treatment of a variety of posterior ocular diseases.

Nonrigid single-axis space integrator dynamics

2025-10-30T17:51:29Z

Nonrigid single-axis space integrator dynamics Shaw, Edward Eugene. Thesis: M.S., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 1964; Includes bibliographical references (leaves 63-64).

Stratospheric radiance

2025-10-30T17:03:42Z

Stratospheric radiance Schweickart, Rusty. Thesis: M.S., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 1963; Includes bibliographical references (leaves 68-70).

A comparison of the existing methods of studying the stability of earth slopes

2025-10-30T17:51:27Z

A comparison of the existing methods of studying the stability of earth slopes La Casta-Sanchez, Salvador. Thesis: M.S., Massachusetts Institute of Technology, Department of Civil and Sanitary Engineering, 1959; Includes bibliographical references (leaf 15).

Planning models

2025-10-30T17:03:43Z

Planning models Crooks, Lawrence. Thesis: M.S., Massachusetts Institute of Technology, Sloan School of Management, 1982; Bibliography: leaves 121-127.

An econometric/engineering model of United States demand for semi-fabricated copper products disaggregated by shape and end-use sector : and an econometric/engineering model of world demand for semi-fabricated copper products disaggregated by major consuming area

2025-10-30T18:06:08Z

An econometric/engineering model of United States demand for semi-fabricated copper products disaggregated by shape and end-use sector : and an econometric/engineering model of world demand for semi-fabricated copper products disaggregated by major consuming area Cummings, Mary Rowena. Thesis: M.S., Massachusetts Institute of Technology, Department of Materials Science and Engineering, 1982; Bibliography: leaves 174-177.

Levels, layers, and planes : the framework of a system of knowledge representation semantics

2025-10-30T18:06:08Z

Levels, layers, and planes : the framework of a system of knowledge representation semantics Smith, Brian Cantwell. Thesis: M.S., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 1978; Bibliography: leaves 199-203.

Decision making for energy conservation in existing commercial buildings.

2025-10-30T18:06:08Z

Decision making for energy conservation in existing commercial buildings. Chertow, Richard Philip. Thesis: M.S., Massachusetts Institute of Technology, Department of Civil Engineering, 1978; Includes bibliograhical references.

Conical flow modeling for polygonal cross section bodies at off design conditions.

2025-10-30T18:06:08Z

Conical flow modeling for polygonal cross section bodies at off design conditions. Kamkar, Hamid. Thesis: M.S., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 1977; Includes bibliographical references.

Clinical Cost-Effectiveness as a Novel Metric for Steering Emerging Medical Technology

2025-04-08T04:08:59Z

Clinical Cost-Effectiveness as a Novel Metric for Steering Emerging Medical Technology Richards, Daniel Herndon Background: Steering an emerging medical technology involves making decisions under uncertainty. Localized drug delivery (LDD) is an emerging medical technology that may be useful in treating epilepsy, which is burdensome and difficult to clinically manage. Costeffectiveness analysis (CEA) is a model-based, problem-oriented framework for determining whether a treatment should be prescribed and reimbursed, though it is typically used to compare treatment alternatives that are already clinically available. Two research questions were posed: How can a clinical CEA be constructed for an emerging medical technology to enhance its steering? And, under what conditions would an emerging technology, LDD, be prescribed in place of resective surgery for drug-resistant epilepsy? Methods: A CEA was constructed with the clinical decision point defined as pediatric patients with drug-resistant epilepsy of focal origin. A new treatment alternative, LDD, was proposed as a solution-neutral, generalized concept, and technological factors were posited that influence parameters in the CEA. A one-way sensitivity analysis was conducted to verify the model and observe its most sensitive parameters. A probabilistic sensitivity analysis was conducted to observe P10 and P90 values for clinical effectiveness. Results: The most sensitive driver of incremental effectiveness of LDD over surgery was, per the model, the potential of LDD to reduce systemic side effects. The potential clinical benefit of LDD over surgery was estimated, probabilistically, as between P10 and P90 values of 0.081 and 0.339 QALYs, respectively. Limitations of the model were discussed. A ‘utopia point’ was calculated. The relationship of the CEA to a total addressable market (TAM) calculation was discussed. The CEA modeling process enhanced learning about the problem and solution spaces. Conclusions: Despite its limitations, CEA modeling can enhance steering activities for emerging medical technologies. Insights from CEA may also help to assess trade-offs in capabilities and cost, as well as observe trends in clinical performance.

Expanding the structural diversity of discrete polymers accessible through iterative exponential growth

2025-04-08T04:07:46Z

Expanding the structural diversity of discrete polymers accessible through iterative exponential growth Khokhlov, Khrystofor Iterative exponential growth is a powerful method for the synthesis of atomically defined macromolecules. However, preparation of enantiopure IEG-ready monomers can be challenging, which may limit the attractiveness of IEG as a tool for the study of structurerelationship properties in discrete macromolecules, both in materials and in biological systems. Here, we present a new strategy for the synthesis of orthogonally protected monomers, suitable for IEG through cycles of azidation, alkyne deprotection, and CuAAC, in fewer steps and from readily available and affordable building blocks. This monomer synthesis was achieved through the development of a novel allylation methodology. Using alkynylation of epichlorohydrin, LiBr Finkelstein, and TfOH-promoted allylation, we have been able to prepare a monomer for 3A (number of carbons in each polymer repeat unit, excluding alkyne) IEG in just three steps. Furthermore, the same reactions can be integrated in the synthesis of other IEG architectures (2A/4A/5A), thus expanding the structural diversity and readily accessible substrate scope for atomically defined macromolecules. The configurations of stereogenic centers in IEG-mer backbones are defined by the starting material (R or S epichlorohydrin) and can be further controlled by combining different stereoisomers in desired fashion. This work outlines a conceptual strategy to diversify and expand the chemical space of discrete macromolecules and enable efficient and quick access to a variety of IEG-mer scaffolds.

Abrupt change of load on a synchronous machine

2025-10-30T17:51:29Z

Abrupt change of load on a synchronous machine Edgerton, Harold E. (Harold Eugene), 1903-1990. Thesis: M.S., Massachusetts Institute of Technology, Department of Electrical Engineering, 1927; Includes bibliographical references (leaves [102]-[103]).

Void formation in copper and selenium ion irradiated molybdenum.

2025-10-30T18:06:08Z

Void formation in copper and selenium ion irradiated molybdenum. Chernock, Richard Steven. Thesis: M.S., Massachusetts Institute of Technology, Department of Materials Science and Engineering, 1978; Includes bibliographical references.

Consolidation circuit for an MHD channel.

2025-10-30T18:06:08Z

Consolidation circuit for an MHD channel. Cheng, Rowley Lop Wah. Thesis: M.S., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 1978; Includes bibliographical references.

A fast approximate solution to the electrical power generation rescheduling and load shedding problem

2025-10-30T18:06:07Z

A fast approximate solution to the electrical power generation rescheduling and load shedding problem Chan, Sherman Man. Thesis: M.S., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 1978; Includes bibliographical references.

Coal nitrogen conversion to NO [subscript x] during simultaneous oxidation and pyrolysis.

2025-10-30T18:06:07Z

Coal nitrogen conversion to NO [subscript x] during simultaneous oxidation and pyrolysis. Cheng, Irene Teresa. Thesis: M.S., Massachusetts Institute of Technology, Department of Chemical Engineering, 1978; Bibliography: leaves 127-129.

Figuring the Middle Ground: A Search for Authorship in Perceiving China's COVID-19 Lockdowns

2024-12-19T03:33:14Z

Figuring the Middle Ground: A Search for Authorship in Perceiving China's COVID-19 Lockdowns Zhang, San Witnessing and attempting to comprehend China’s controversial response to COVID-19 over the past three years from a geographically distant yet culturally and emotionally intimate standpoint, I have grappled with multiple perspectives, sometimes as an insider, sometimes as an outsider, and most of the time as an impostor to both. As I continually query the incoherence of my positionality, I find myself in an obscure middle ground where my voice is filtered as inauthentic and unheeded. I ask myself: What should I do? What can I do? This project is an effort to give myself a voice in the process of figuring out the “middle ground”—a gradient of unsettled propositions stretching between cultural identities, negotiating with constructed collective memories, and discursively evolving over a three-year-long uncanny journey trying to perceive the COVID-19 lockdowns in China. By accepting the “middle ground” as a valid stance, I was able to devise a set of methods for navigating the complexity of materials gathered at various times and locations. In addition, utilizing architectural representation tools, I curated a collection of works that reproduce the research process and exhibit the processed information. This endeavor is not intended to rationalize pandemic control. Rather, it cultivates a ground for reflection that deconstructs a dichotomous perception of right or wrong, drawing attention to individual lived experiences that provide a nuanced interpretation of the COVID-19 pandemic as an international health emergency that affected everyone. Although somewhat fuzzy and uneasy, the “middle ground” position indicates the possibility that a personal desire to develop one’s authorship can lead to a means of making sense of a global crisis.

Topics in Marma (မာရမာ)

2024-12-19T03:32:34Z

Topics in Marma (မာရမာ) Marma, Rani Ukhengching (ဦး ချမ်း စိန် မာရမာ) Marma¹ an endangered indigenous language of Bangladesh, is spoken by approximately 200,000 Marma individuals residing in Bangladesh’s southern region called the Chittagong Hill Tracts (CHT). Marma language is closely related to Rakhine and Burmese, and many lexical items are almost identical to those in Burmese and Rakhine, “although Marma exhibits a more conservative phonological profile than Burmese in the grammatical particles” Keisuke (2011). This research study analyzed several morphemes and their roles in shaping discourse structures in Marma information structure (topic-focus articulation). Marma has “agglutinative morphology”, meaning words are formed by stringing together morphemes in specific sequences. We observed prefixation, suffixation, and infixation in Marma. We analyzed the multifunctionality of these selective morphemes [“က=ga/ka, ကိ ု =go/ko, စာ=cha,ရာ=ra, ယည်=yi”] within Marma discourse and explored their implications for a better understanding of information structure in Marma language. At the end of this paper, through instrumental analysis, we proposed three tones in Marma (i.High and creaky, ii. low, and iii. falling). Key words: Marma, indigenous language, information structure, topic and focus,morphology and tone. ¹“According to Bradley (1985:180), the Marma group would have first migrated from Arakan to the Chittagong Hill Tracts by the early sixteenth century and then after the Burmese conquest in 1785. They live mainly in the Chittagong Hill Tracts where they form one of the main Indigenous groups ( Htin, 2015) ”

Not Function but Function Conquered: Against a Functionalist Theory of Directives

2024-12-19T03:05:45Z

Not Function but Function Conquered: Against a Functionalist Theory of Directives Hill, John Ordering, requesting, and inviting are examples of directive speech acts. Philosophers have offered different accounts of what it is to perform a directive, which they have developed using different theoretical resources. Attitudinal theories of speech acts try to explain what it is to perform a directive in terms of a speaker’s beliefs, desires, and intentions. Nonattitudinal theories of speech acts try to explain directives in terms of something else. This thesis is concerned with functionalism, a nonattitudinal theory of speech acts. According to functionalism, performing a directive is making an utterance with the etiological function of causing hearers to act in response to one’s utterance. I argue that functionalism is false. I develop counterexamples that show functionalism is too permissive about the kinds of causation suitable for generating directives. I argue further that the most plausible way to address these counterexamples is to become more attitudinal: rather than be permissive, functionalism should hold that directives and hearers’ responses to them are caused by specific internal processes.

Know Thy Cell-Free DNA: Early Detection of Microsatellite Instability Using Ultra-Low-Pass Cell-Free DNA Sequences

2024-12-19T04:35:16Z

Know Thy Cell-Free DNA: Early Detection of Microsatellite Instability Using Ultra-Low-Pass Cell-Free DNA Sequences Lu, Nicole Microsatellites are short segments of repeated DNA motifs (i.e., base pair patterns) that are widespread in our genomes. Microsatellites are inherently more mutable than other genomic locations, and since cancer cells undergo many more cell divisions, microsatellites are useful for distinguishing tumor DNA from normal (non-cancerous) DNA. Microsatellite instability (MSI) arises as a result of mismatch repair deficiency (MMRD), wherein a patient loses function of both copies of certain genes related to mismatch repair. Current MMRD diagnostics rely on deep sequencing of tumor tissue samples, which can be expensive and overly-invasive to perform for early or routine screening. Less expensive sequencing methods such as ultra-low pass (ULP) sequencing exist, but thus far have not been utilized for detection of microsatellite instability. In this thesis, we focus on 0.1× ULP sequences, in which about 10% of the genomic locations have one read in expectation. Having so few reads makes it difficult to differentiate experimental noise from true mutations. Similarly, cell-free DNA (cfDNA) are DNA fragments from cells all over the body, which circulate in the blood. Collecting and sequencing cfDNA is much less invasive than collecting tissue samples, but presents another challenge in that the fraction of DNA fragments from any particular cell (or group of cells) is low. Thus, if cancerous cells exist within the body, its representation in a given cfDNA sample is likely low. Together, these challenges present a obvious trade-off between signal strength and cost/invasiveness for screening and detection of MSI. This thesis focuses on the implementation, validation, and additional research of a computational tool to detect microsatellite instability in ultra-low pass cell-free DNA samples.

Acoustically Controlled Remotely Operated Undersea Vehicles: A Quantitative Analysis

2024-12-19T03:01:45Z

Acoustically Controlled Remotely Operated Undersea Vehicles: A Quantitative Analysis Stites, Corwin Wesley This thesis topic stems from a U.S. Navy effort to alter an existing remotely operated vehicle (ROV) system. A vehicle reliant on a tethered connection to an operator requires adaptation to an untethered acoustically controlled vehicle. This project provides a tradespace exploration based in simulation of factors which limit untethered ROV performance. Factors which promote the use of an untethered system over a tethered system are also explored. A MATLAB simulation has been constructed to analyze a hypothetical ROV grid search mission across multiple parameters relating to the vehicle specifications, the mission layout, the acoustic communication system, and the operating environment. This simulation can then be used to generate a wide range of data regarding ROV performance by use of the Monte Carlo method. The performance metrics output by the simulation, along with an automated analytical tool created to process simulation data, provide quantitative insight into the viability of an ROV utilizing an acoustic communication system across a variety of scenarios.

Revealing SEI Formation and Evolution at the Li Anode/Liquid Electrolyte Interface in Li-ion Batteries by in situ Fourier Transform Infrared Spectroscopy

2024-12-19T04:14:53Z

Revealing SEI Formation and Evolution at the Li Anode/Liquid Electrolyte Interface in Li-ion Batteries by in situ Fourier Transform Infrared Spectroscopy Wang, Daniel A novel in-situ FTIR method is developed to probe the Li anode/liquid electrolyte interface. Three different conventional electrolyte systems were tested: 1.2 M LiPF₆ in EC, 1.0 M LiPF₆ in EMC, and LP57 (1.0 M LiPF₆ in EC:EMC (3/7 vol %)). Using the spectroelectrochemical cell, FTIR measurements for first plating step and cycled cells (up to 50 cycles) were collected to look for new species formation. In the case of 1.2 M LiPF₆ in EC, LEMC formation was observed when the potential was brought below 1.50 VLi. LEMC growth accelerated when the potential was reduced below 0.0 VLi, upon contact with freshly plated Li metal. When 1.0 M LiPF₆ in EMC was used for the same study, either lithium methyl carbonate or lithium ethyl carbonate were formed. Upon switching to LP57, Li₂CO₃ became the dominant SEI component. When the three electrolytes were cycled in the spectroelectrochemical cell, the SEI peaks continued to grow for the first 10 cycles. After the first 10 cycles, LEMC and Li₂CO₃ growth plateaued, indicating SEI stabilization. On the other hand, LRC signal diminished, indicating an unstable SEI formed by EMC. Additionally, anion decomposition was observed to be more pronounced under high concentrations of EC. Since anion decomposition can be used as a proxy for LiF formation, high concentration electrolytes perform better possibly due to larger amounts of LiF formation.

Causal Machine Learning to Discover Biochemical Determinants of Physical Fitness

2024-12-19T03:34:21Z

Causal Machine Learning to Discover Biochemical Determinants of Physical Fitness Nawaz, Hesham Identifying the key pathways relevant to cardiorespiratory fitness is of great importance for both predicting exercise responsiveness and potentially finding which interventions are likely to affect it. While contemporary deep learning models have demonstrated great success in pattern recognition and generation for various data modalities, their ability to decipher the causal mechanisms underlying these patterns is limited. This work proposes and evaluates a methodology using state-of-the-art causal discovery and causal inference methods to uncover the relationships between different proteins and their impact on changes in individuals’ maximal oxygen consumption (a proxy for physical fitness).

Tracing the Precursors and Amplifiers of Conflict in the Information Age: An NLP Inquiry of Tensions, Political Communication, and Misinformation

2024-12-19T03:24:05Z

Tracing the Precursors and Amplifiers of Conflict in the Information Age: An NLP Inquiry of Tensions, Political Communication, and Misinformation Zimmer, Philipp Violent conflicts, in their varied and complex forms, have long been a subject of research and political discourse. Despite increased attention for the field, various nuances and dynamics are yet to be explored. This thesis seeks to study three aspects of the multifaceted nature of conflicts through the lens of natural language processing (NLP), thereby not only offering new insights but also advancing the field's methodological landscape. First, the study delves into the identification of causal predictors of conflicts. By showcasing the potential of a frame-semantic parser, I am able to quantify the precursors that contribute to conflict and examine the potential for enhancing prediction models with greater qualitative depth. This chapter utilizes a rich but under-examined data source, news articles, which can aid closing the data gap in conflict studies. In the second chapter, the communication strategies of political leaders during crises are scrutinized to understand the rationale behind their messaging and the impact thereof. I argue that leaders' engagement frequency and style with their citizens is dependent on the political systems' characteristics and that it matters for societal conceptions. The final chapter addresses the spread of misinformation, such as in times of crisis, investigating which themes are prone to the widespread propagation on social media and presenting a novel ensemble method for the detection of misleading and false content. By integrating computational techniques with political theory, this work contributes to a nuanced understanding of conflict dynamics and offers rich potential for anticipatory actions of policymakers.

Coevolving Cybersecurity Adversaries for Industrial Control Systems in Failure-Prone Environments

2024-12-19T04:14:48Z

Coevolving Cybersecurity Adversaries for Industrial Control Systems in Failure-Prone Environments Wicks, Kathryn As industrial control systems become universally integrated with software and connected to the internet, they have become targets for cyberattacks and sabotage. Detecting cyberattacks on these networks is difficult because existing datasets on attacks is minimal and the bulk of intrusion detection systems are designed for enterprise environments rather than industrial environments. In industrial environments, mechanical failures, stress states, and electrical problems are expected, with repairs included in daily operations. In enterprise environments, such failures are rarer and more high-impact as a result. We investigate the extent to which this mismatch in the impact of physical stressors failures degrades the ability of traditional intrusion detection algorithms to perform in the industrial environment. In the sub-area that this thesis focuses on, power microgrids, such disturbances can come in the form of line-line faults, line-ground faults, lack of generation capacity to meet demand, and unintentional islanding, among many others. Microgrids must be resilient to these events, and this thesis investigates to what extent they are currently and if they can be improved. Specifically, this thesis asks: do traditional IDSs cause false alarms when placed in a failure-prone environment? How do these intrusion detectors perform overall? Can they be improved with additional training? And finally, can intrusion detection systems be tricked by attacks which appear to be "benign" failure modes? This thesis answers these questions by comparing the performance of different anomaly detection methods on cyberattack datasets with varying levels of stressor complexity and severity, and finds that stress on an industrial system can degrade anomaly-based intrusion detector performance. Expanding on this idea, an attacker is then trained to adversarially mask a dataset, and a detector is co-evolved alongside it to detect the attacks. Finally, the coevolution is brought into the hardware-in-theloop simulation environment, where attackers and defenders act in real time to change the state of a realistic microgrid simulation. From these experiments, it is found that attackers can leverage grid disturbances to hide their actions, and that accurate realtime simulations are highly useful for identifying vulnerabilities in a cyberphysical system.

Existence and Analysis of a Rotating Stall Inception Continuum & Development of Concept Questions in Fluid Dynamics

2024-12-12T03:16:06Z

Existence and Analysis of a Rotating Stall Inception Continuum & Development of Concept Questions in Fluid Dynamics Cherry, Maranda F. This thesis presents two projects, an analysis of rotating stall inception for axial compressors in turbomachinery, and a description of the creation of Concept Questions for a text on internal flows. The first part of this thesis identifies flow behavior that defines two routes to rotating stall, known as modal and spike type rotating stall inception. It continues previous studies by MIT and the University of Cambridge surrounding unification of these two stall types under a dynamical system framework. Calculations were carried out for an isolated rotor, with a high hub to tip radius ratio, using TBLOCK, a Reynolds Averaged Navier Stokes solver. The results show (i) the dependence of stall inception on the compressor axisymmetric pressure rise characteristic and the characterization of mode and spike stall inception as two paths, located at the ends of a continuum of possible paths to stall. (ii) the effect of blade passage accelerations and asymmetry in the onset process, and (iii) the divergence of stall inception from two-dimensionality as a function of the slope of the total-to-static compressor pressure rise characteristic. The calculations show that compressor pressure rise characteristic slopes, dψ/dϕ, less than 0.3 have a stall cell growth rate, σ, that agrees with two-dimensional theory. The divergence of stall inception from two-dimensionality is suggested as a distinguishing feature of spike type stall inception compared to modal type stall inception. The second part of this thesis encompasses the creation, editing and compilation of Concept Questions for seven book chapters in a new text that describes the use of Concept Questions in teaching (and learning) fluid mechanics. The composition and qualities of a good concept question are defined, and the process of generating and editing questions for the intended audience is discussed.

Tools for Mapping the Links Between Stimuli, AffectiveStates, and Behavior through Whole-Brain Imaging inZebrafish Larvae

2024-12-12T03:52:22Z

Tools for Mapping the Links Between Stimuli, AffectiveStates, and Behavior through Whole-Brain Imaging inZebrafish Larvae Zhang, Caroline Lige Affective states, often referred to as emotional states, exert substantial influence on behavior and decision-making processes. Traditionally, researchers have turned to functional imaging to delve into the neural mechanisms that drive both behavior and decision making. However, functional imaging of behaving animals often focuses on a singular brain region. Whole-brain imaging, on the other hand, has the capacity to significantly advance our understanding of the brain's functional architecture. In this pursuit, zebrafish larvae emerge as an ideal model for whole-brain imaging due to their transparency, small size, genetic manipulability, rapid development, high reproducibility, Recent advances in protein engineering and fluorescence microscopy have empowered researchers to observe neural activity across extensive neuronal populations. Genetically Encoded Calcium Indicators (GECIs) and Genetically Encoded Voltage Indicators (GEVIs) provide the means to probe brain dynamics with single-cell precision. The advent of lightsheet microscopy technologies has further enriched our capabilities, enabling the recording of brain activity at remarkable frame rates, ranging from several hundred to several thousand frames per second, all while the animal is exposed to precise visual, auditory, and/or olfactory stimulation. Leveraging these experimental advancements in conjunction with machine learning and computer vision techniques, our study aims to forge connections between stimulation, neural activity, and behavior through a larval zebrafish model.

Effects of Tip Clearance and Surface Roughness on Small-Scale Turbopump Impeller Performance

2024-12-12T03:43:09Z

Effects of Tip Clearance and Surface Roughness on Small-Scale Turbopump Impeller Performance Ruecker, Kinjal A. L. Centimeter-scale turbopump impellers typically used in liquid rocket engines of small launch vehicles suffer from reduced performance due to manufacturing challenges and nonuniform geometric scaling. This thesis aims to characterize the impact of impeller blade tip clearance and surface roughness on the performance of small-scale turbopump impellers by assessing the dominant flow features, quantifying the underlying loss mechanisms, and determining the sensitivity of performance losses to changes in tip clearance and surface roughness. The study identifies the primary flow features governing impeller performance to be blade tip leakage flow and secondary flow. The analysis identified two distinct flow regimes based on tip clearance: above 5% of tip clearance, the losses are predominantly due to blade tip leakage flow, whereas below this threshold, losses are governed by both secondary flow and blade tip leakage flow. For tip clearances above 5% of the blade span, blade tip leakage flow is estimated to contribute more than 80% of total impeller loss. A 1% change in tip clearance is estimated to result in a 0.8% loss in efficiency. The calculations suggest increasing surface roughness reduces the effective tip clearance due to increased viscous effects in the tip gap, but strengthens the secondary flow. This lowers the effective tip clearance that separates the flow regimes. The contribution of blade tip leakage loss to total impeller loss decreases by up to 22% for surface roughness increased from an Rₐ value of 1 µm to 10 µm. The strengthened secondary flow at higher surface roughness increases mixing of the blade tip leakage flow with the blade passage flow, leading to larger regions of blockage. Increasing the surface roughness from an Rₐ value of 1 µm to 10 µm results in a 4% loss in impeller efficiency. This study demonstrates that surface roughness is more impactful on small-scale impeller performance than blade tip clearance, and so manufacturing for smooth surfaces should be prioritized over reducing the blade tip clearance gap.

Authenticity in the Workplace: What does it really mean?

2024-12-12T03:09:01Z

Authenticity in the Workplace: What does it really mean? Pervaaz, Viquar A. Recently, the word authenticity has been used quite prevalently in organizations, specifically as an attribute needed in leaders. However, during the pandemic, the use of the word authenticity became more prominent and organizationally universal. While the term is great in concept, the power of the word “authenticity” remains nebulous. This poses a potential problem for organizations and teams as it presents the risk of not delivering on this commitment if the elements of authenticity are not defined and understood. Making a promise of authenticity without delivering on it may have a negative impact on the individual and organizational morale/culture and a longer-ranging impact in terms of employee engagement and retention. Using the lens of the cognitive dissonance theory as a construct to view authenticity as a “product” from a marketing perspective, one has a framework to postulate that if expectations are not clear and the perceived performance (delivery on the promise of specific elements of authenticity) is not optimal, then there will be ramifications of this in terms of satisfaction (e.g. employee engagement). This paper will explore why defining this word in an organizational context is important, what are the macro dimensions of authenticity to help frame and define it, and what variables contribute to bringing authenticity to life.

Limits to extreme event forecasting in chaotic systems

2024-12-12T03:24:28Z

Limits to extreme event forecasting in chaotic systems Yuan, Yuan Predicting extreme events in chaotic systems, characterized by rare but intensely fluctuating properties, is of great importance due to their impact on the performance and reliability of a wide range of systems. Some examples include weather forecasting, traffic management, power grid operations, and financial market analysis, to name a few. Methods of increasing sophistication have been developed to forecast events in these systems. However, the boundaries that define the maximum accuracy of forecasting tools are still largely unexplored from a theoretical standpoint. Here, we address the question: What is the minimum possible error in the prediction of extreme events in complex, chaotic systems? We derive the minimum probability of error in extreme event forecasting along with its information-theoretic lower and upper bounds. These bounds are universal for a given problem, in that they hold regardless of the modeling approach for extreme event prediction: from traditional linear regressions to sophisticated neural network models. The limits in predictability are obtained from the cost-sensitive Fano’s and Hellman’s inequalities using the Rényi entropy. The results are also connected to Takens’ embedding theorem using the information can’t hurt inequality. Finally, the probability of error for a forecasting model is decomposed into three sources: uncertainty in the initial conditions, hidden variables, and suboptimal modeling assumptions. The latter allows us to assess whether prediction models are operating near their maximum theoretical performance or if further improvements are possible. The bounds are applied to the prediction of extreme events in the Rössler system and the Kolmogorov flow.

Accelerating Urban Building Energy Modeling

2024-12-12T03:55:43Z

Accelerating Urban Building Energy Modeling Le Hong, Zoe; Wolk, Samuel Enabling data-driven decision-making in the built environment is critical to achieving ambitious and urgent decarbonization goals. In the building sector, urban building energy models (UBEMs) have become a valuable tool for jurisdictions to develop evidence-based retrofitting policies, but dynamically exploring solutions is hampered by the computational expense and organizational overhead of physics-based building energy models. In order to address these challenges, we present a fast, flexible, and comprehensive UBEM methodology which can be used to reduce identified barriers to time-sensitive decision-making in building stock decarbonization spheres. The methodology combines the speed of current data-driven approaches with the flexibility of computationally intensive, but accurate, engineering models. Identifying machine learning methods as a viable approach, we implement convolutional neural networks (CNNs) which embed timeseries from hourly weather data and building schedules; the embeddings are then combined with static building characteristics and projected to monthly heating and cooling loads. The proposed approach allows for programmatic flexibility and robustness to unique hourly weather conditions globally, while contextual abstraction enables geometric independence. A dataset of over 1 million detailed thermodynamics-based simulations was constructed to train and validate the surrogate model. Model results at the individual shoebox, building, and urban scales compare favorably to traditional numerical methods and meet accepted error bounds under national energy simulation standards. Additional validation at the urban- and national-scales are performed using public building simulation datasets. We then demonstrate expanded applications, which leverage the reduced computational cost of the framework to make traditionally infeasible analysis modes tractable and deployable. The methodology presented is intended to be utilized for both very-large-scale systematic analysis and near-real-time interactive explorations. In developing this framework, we aim to provide new mechanisms for key stakeholders in the decarbonization effort to quickly generate actionable insights and engage in iterative discussions to develop evidence-based policy across global building stocks.

Relative Robot Localization and Frame Alignment for Multi-Robot Collaboration

2024-12-12T04:11:26Z

Relative Robot Localization and Frame Alignment for Multi-Robot Collaboration Peterson, Mason B. The growing field of collaborative robotics has the potential to enable and improve the execution of many challenging robot applications. For instance, with teamwork between multiple agents, dynamic object tracking can more completely cover an environment and trajectory planning becomes safer. However, for robots to share the quickly changing spatial information involved in these tasks, robots need to be able to express information originally sensed or planned in their own frame into the frame of neighboring agents. This can be challenging in cases where robots have no global pose information resulting in steady accumulation of error, or drift, in their local pose estimates. To mitigate the effects of drift, neighboring agents must make up-to-date estimates of the alignment between their frames, which can be difficult due to ambiguous alignments and the presence of outlier measurements. To address these issues, the first contribution of this thesis is a method for performing fast incremental frame alignment between pairs of robots, enabling collaborative multiple object tracking (MOT), the task of monitoring the locations of dynamic objects in an environment. To perform frame alignment, robots build up maps of recently seen static objects and use these maps and the detections of tracked dynamic objects to correct for frame drift. Using frame alignment estimates, agents share object detection information and account for additional uncertainty associated with the alignment estimate. The second contribution of this thesis presents a method to perform frame alignment with no initial guess. Many potential frame alignments are computed and we develop a filter that uses temporal consistency to reject outlier alignments and only accept a series of alignments that are consistent over time. We demonstrate in hardware experiments our ability to perform frame alignment in difficult scenarios and improve the quality of collaborative object tracking onboard real robots.

Database and Application Programming Interface Development for Rotational Spectroscopy

2024-12-12T03:38:23Z

Database and Application Programming Interface Development for Rotational Spectroscopy Cheung, Jasmine So Yee The Species-agnostic Automated Gas Analyzer (SAAGA) project aims to automate the detection and characterization of chemical compounds in a complex chemical mixture in the gas phase through experimental rotational spectroscopy and computational tools. A database of spectroscopic data serves as the foundation of the automation pipeline for assigning spectral lines to species. While there are existing databases available for use, we developed our custom database, named SAAGAdb, and an application programming interface (API) to access the database to fulfill the needs of SAAGA. SAAGAdb is designed to store structured, high quality spectroscopic data of all species not limited to astrochemically relevant ones, enabling convenient data manipulation, integration into future automation pipelines, deployment, and maintenance. We implemented software development best practices, including software development life cycle, continuous integration/continuous delivery, and version control, to develop a PostgreSQL database with a Python API built on Django with RDKit integration. The product passed all unit tests and was successfully seeded with data. With the flexibility provided by the Django framework as well as detailed documentation of the software, SAAGAdb and its API can be easily improved and expanded in the future to suit the needs of the SAAGA project.

A GPU-Enabled Building Block Flow Model for Computational Fluid Dynamics

2024-12-12T03:04:11Z

A GPU-Enabled Building Block Flow Model for Computational Fluid Dynamics Costa, Samuel Thomas Computational Fluid Dynamics (CFD) is an key tool in the design of aircraft, allowing engineers to predict the performance of a configuration without having to conduct expensive physical tests. However, in order to move to a greater reliance on CFD, the industry requires a high level of accuracy and fast turnaround time, which current methods cannot deliver. In recent years, the rapid development of the GPU industry has led to an explosion of computational power with the GPU architecture. This has allowed wall-modeled large eddy simulation (WMLES), a higher fidelity simulation technique, to become practical for industry use. WMLES requires the use of both a sub-grid scale (SGS) model and a wall model in order to close the system of equations for integration. Although WMLES delivers an improvement over previous methods, classical SGS and wall models do not deliver the accuracy required by the aviation industry. To help close this gap, we introduce a GPU-compatible version of the Building-Block Flow Model (BFM), a machine learning based unified sub-grid scale and wall model for LES introduced in [1]. In this thesis, we discuss the implementation of the BFM for GPU, timing of the BFM versus other closure models for WMLES, and a variety of tests with the BFM designed to evaluate its performance, and possible avenues of improvement.

Relationship between synoptic scale meteorology, aircraft parameters, and observable contrails

2024-12-12T03:44:27Z

Relationship between synoptic scale meteorology, aircraft parameters, and observable contrails Barbosa, Maria Paula Long-lasting or "persistent" contrails are line-shaped clouds that form when airplanes fly through cold and humid parts of the atmosphere that are ice-supersaturated. Various studies have shown that persistent contrails may be responsible for more than half of aviation’s radiative forcing [1]. Efforts to mitigate persistent contrail formation include operational contrail avoidance. Current research suggests that minor (∼ 2000 ft) deviations in altitude of flights during cruise, in conjunction with advancing engine technologies, have the potential to reduce contrail climate forcing by approximately 90% [2]. Identifying and attributing observed contrails to specific individual flights is necessary to demonstrate the success of flight deviations. Reliable flight attribution, therefore, is critical in verifying large-scale implementation of contrail avoidance strategies. Flight attribution leverages both Earth-observation methods, such as satellite images and weather data, and flight data. However, temporal and spatial "blindspots" in satellite instruments, coupled with uncertainties in wind fields, have hindered reliable flight attribution. In this work, we consider eight different probabilistic flight attribution algorithms. All algorithms rely on the use of "similarity measures" which we define as the differences in distance, heading, and altitude between a contrail and flight line segment candidates. We define two-dimensional (2D) algorithms as those that use only distance and heading difference measures and the ones that additionally include altitude as three-dimensional (3D) algorithms. The probabilistic aspect of all eight algorithms is intended to account for errors in wind data and relies on the calculation of a Gaussian probability density function for each similarity measure. In an attempt to mitigate wind and positional errors that compound over time, four of the algorithms feature the inclusion of contrails from previous timestamps as potential match candidates. To account for the changes in flight path due to temporal factors, four of the algorithms include the use of time-dependent Gaussian parameters. The inputs to all algorithms include contrail detections, weather data, and flight data. To perform this analysis, a dataset of 180 manually-attributed, unique contrails was created that captures regional (across the continental United States) and diurnal variation. Each contrail was tracked for part of its lifetime, which results in the generation of 1980 total attributions. These attributions were created by seven labelers, with some overlapping scenes. A parameter sweep was performed on the four 2D algorithms to determine locally optimal Gaussian parameters. This sweep was performed on a reduced dataset that consists of 32 unique contrails and 218 total labels. The results of this sweep show that the performance of the algorithms, when using optimal Gaussian parameters, range from 79.7% to 83.6% accuracy. Accuracy is defined as the percentage of contrails that were attributed to the correct flights. These results are solely for the 2D algorithms that were analyzed on the reduced dataset. We then applied the "locally" optimal Gaussian parameters from the four 2D algorithms to the respective 3D algorithms and ran all eight algorithms on the remaining 148 contrails (1762 labels). We find that the optimal performance for all eight algorithms ranges from 68.2% to 76.2%. A deeper analysis is also conducted to evaluate the scene conditions that affect algorithm performance.

Site-Selective Anion Exchange in a Palladophosphorane

2024-12-12T03:30:54Z

Site-Selective Anion Exchange in a Palladophosphorane Khuichad, Nichakan Reported here are studies on the chemoselective ligand substitution at a palladophosphorane possessing two potential sites of chloride substitution. Ligation of palladium(II) chloride with a tridentate chelating ligand (L, P(N(o-N(2-pyridyl)C₆H₄)₂) results in formation of a complex comprising a d⁸ square planar palladium center supported by a geometrically constrained chlorophosphorane (PdClL superscript Cl). The complex thus formed was studied for ligand substitution reactions of the chloro ligand at Pd and P, respectively. Treatment with phenol resulted in substitution of the chloride at the P center while the chloride of the Pd stayed intact, giving complex PdClL superscript OPh. Relatedly, treatment with AgF provided a compound whose NMR spectra are consistent with formation a P–F containing pallado-phosphorane PdClL superscript F. However, attempt to recrystallize the fluoride complex resulted in a formation of a cationic complex with a fluoride-bridged species instead although the fluoride still resided between the two phosphorus centers. Overall, substitution experiments of this palladophosphorane indicated a preference for P–Cl substitution over Pd–Cl. The driving force for the favor toward the exchange at phosphorus has not been extensively explored, but hypotheses have been made which may entail the concept of hard-soft acid-base chemistry and the strength of the bonds involved.

The development of the side-rod locomotive

2025-10-30T17:51:29Z

The development of the side-rod locomotive Voelcker, J. Westgarth. Thesis: M.S., Massachusetts Institute of Technology, Department of Electrical Engineering, 1923; Includes bibliographical references (leaf [86]).

Critical evaluation and correlation of tool-life data

2025-10-30T17:03:45Z

Critical evaluation and correlation of tool-life data Colding, Bertil N. Thesis: M.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1957; Bibliography: leaves 46-47.

A case study of two autopilot design methodologies : linear quadratic and H-infinity for a tail controlled missile

2025-10-30T17:03:46Z

A case study of two autopilot design methodologies : linear quadratic and H-infinity for a tail controlled missile Edeburn, Mark Anthony. Thesis: M.S., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 1993; Includes bibliographical references.

Optimal pricing for peak loads and joint production : theory and applications to diverse conditions.

2025-10-30T18:06:08Z

Optimal pricing for peak loads and joint production : theory and applications to diverse conditions. Chernick, Paul Lee. Thesis: M.S., Massachusetts Institute of Technology, Department of Civil Engineering, 1978; Bibliography: leaves 222-234.

Toeplitz operators

2025-10-30T18:06:09Z

Toeplitz operators Gencarelli, Frank Thomas. Thesis: M.S., Massachusetts Institute of Technology, Department of Mathematics, 1977; Bibliography : leaf 45.

Tomorrow's Typography

2024-12-03T03:50:41Z

Tomorrow's Typography van de Seyp, Vera This thesis is an exploration for new tools for typography that investigates how emerging (AI) technologies can contribute to the type design practice in a meaningful way. I created computational design experiments focusing on three areas: (A) design automation, (B) interfacing, and (C) creative exploration. A lot of care has been put in understanding the current scene through expert interviews, workshops, talks and surveys. With pose estimation, generative visual AI, and large language models that operate on text, I explore whether typographic shapes can be created and manipulated with different modes of expression, in a playful, intuitive and collaborative way.

Towards a Single Bio-molecule Detector Based on CMOS Nanofluidic Platform

2024-12-03T03:46:49Z

Towards a Single Bio-molecule Detector Based on CMOS Nanofluidic Platform Zikrallah, Ahmed S. Cytokines secretion is a core component of the function of many cell therapy products: It affects the tissue repair capacity of induced Pluripotent Stem Cells (iPSCs) and Mesenchymal Stem cells (MSCs) and the tumorigenicity of Chimeric Antigen Receptor (CAR) T-cell therapies. Ideally, we would be able to continuously monitor the secretome of these cell therapies as they are transformed and expanded in manufacturing.However, state-of-theart techniques for monitoring typically low concentrations of cytokines require either Mass Spectroscopy (MS) or immunoassays like Enzyme-linked Immunosorbent Assay (ELISA). We propose the use of CMOS technology to build a proteomic platform with a single biomolecule resolution. A prototype chip has been designed and fabricated using standard foundary process incorporating a new implementation of a Solid State Nanopore (SSN) of size 55nm×162nm×100nm (w×l×h) with nanofluidic access channels that bridge the buffer solution between the assay space in the packaging structure – a poly carbonate/Polydimethylsiloxane (PDMS) package- and the nanopore on the chip. A silicon Single Photon Avalanche Detectors (SPADs) was also implemented and placed near the nanochannels to utilize fluorescence labeling imaging techniques. In addition, a read-out amplifier that achieves a midband gain of 36.2 dB at a 3 dB bandwidth of 0.1-3.6 MHz is also implemented on the same silicon die, paving the way to superior performance compared to ionic current read-out systems used earlier for electrical biomolecule detection, thanks to low parasitics as a result of integration. The aforementioned modalities integrated on a single chip open the space for the use of CMOS platforms in the electrical and optical interrogation of biomolecules, opening a new horizon for near real-time biomarker assays. The following thesis builds on earlier work that was performed in [1][2] with the objective of expanding on different techniques to interface and characterize the performance of these modalities, especially after post-processing the chips with the aid of tools at MIT.nano. The thesis explores the further deployment of integrated SPAD in a Fluorescence Lifetime Imaging (FLIM) system to image fluorescence-labeled molecules, showcasing the capabilities of the CMOS nanofluidic platform to detect biomarkers such as cytokines.

Investigating cofactor transfer for a B₁₂-dependent enzyme

2024-12-03T03:07:14Z

Investigating cofactor transfer for a B₁₂-dependent enzyme Duong, Alexander T. The metallocofactors utilized by enzymes can range in complexity from single metal ions to organometallic cofactors well over 1000 Da. These cofactors enable these metalloenzymes to accomplish a diverse set of unique and challenging chemistry that are critical to core life functions. One of these metallocofactors, adenosylcobalamin (AdoCbl), has only one cognate enzyme in humans: methylmalonyl-CoA mutase (MCM), which is involved in the catabolism of several amino acids, cholesterol, and odd-chain fatty acids. MCM relies on two other proteins, a G-protein metallochaperone called methylmalonic aciduria type A protein (MMAA) and a protein called adenosyltransferase (ATR), to load and off-load cofactor. Mutations or deletions of the gene for MCM, or in any of the genes corresponding to accessory proteins which interfere with cofactor delivery and removal, can lead to a potentially lethal inborn error in metabolism. If the cofactor becomes damaged in the active site of MCM, ATR unloads the cofactor, repairs it, and reloads the regenerated AdoCbl onto the mutase. A molecular understanding of this process has been challenging to obtain due to the difficulty of structurally characterizing a three-protein MCM-MMAA-ATR complex that is transient in nature. An orthologous protein from C. metallidurans in which the G-protein metallochaperone is naturally fused to its target mutase isobutyryl-CoA mutase (IcmF) provides an alternative two-protein IcmF-ATR system for structural and biochemical characterization. Recent work has shown that the IcmF system utilizes a mechanism of active site opening similar to non-fused systems like that of humans. However, the mechanisms by which ATR recognizes the presence of damaged cofactor and then removes it remains unclear. In this thesis, we discuss the development of an assay based on UV-Vis spectroscopy to monitor cofactor transfer between IcmF and ATR. We also discuss efforts to substitute histidine residues in IcmF suspected of serving as intermediate binding sites during cofactor transfer, with the goal of using the developed assay as a means of observing potential changes in transfer efficiency by perturbing these histidine residues. This work seeks to improve our understanding of AdoCbl-dependent enzyme maturation, and inform our ability to harness their unique reactivity.

Listening by Synthesizing

2024-12-03T03:31:08Z

Listening by Synthesizing Cherep, Manuel Generative audio models offer a scalable solution for producing a rich variety of sounds. This can be useful for practical tasks, like sound design in music, film, and other media. However, these models overwhelmingly rely on deep neural networks, and their massive complexity hinders our ability to fully leverage them in many scenarios, as they are not easily controllable or interpretable. In this thesis, I propose an alternate approach that relies on a virtual modular synthesizer; a computational model with modules for controlling, generating, and processing sound that connect together to produce diverse sounds. This approach has the advantage of using only a small number of physically-motivated parameters, each of which is intuitively controllable and causally interpretable in terms of its influence on the output sound. This design takes inspiration from devices long used in sound design and combines it with state-of-the-art machine learning techniques. In this thesis, I present three projects that use this formulation. The first is SynthAX, an accelerated virtual modular synthesizer that implements the core computational elements in an accelerated framework. The second, CTAG, combines the synthesizer with an audio-language model into a novel method for text-to-audio synthesis via parameter inference. This method produces more abstract sketch-like sounds that are distinctive, perceived as artistic, and yet similarly identifiable to recent neural audio synthesis models. The third is audio doppelgängers, sounds generated by randomly perturbing the parameters of the synthesizer to create positive pairs for contrastive learning, encompassing more of the variety found in real-world recordings, with controlled variations in timbre, pitch, and temporal envelopes. This method offers an efficient alternative to collecting real-world data, producing robust audio representations that compete with real data on established audio classification benchmarks. This thesis contributes tools for understandably generating rich and diverse sounds, using them and their parameters for sound design and understanding at scale.

Piezoelectric single crystal based one-dimensional phased array for breast tissue imaging

2024-12-03T03:14:20Z

Piezoelectric single crystal based one-dimensional phased array for breast tissue imaging Du, Wenya Ultrasound is widely used in clinical practice because it is safe, non-invasive, non-ionizing, low-cost, and provides real-time imaging, monitoring, and therapy. However, conventional ultrasound probes are rigid, pressure-required, and operator-dependent. Replacing rigid transducers with conformable ultrasound transducer arrays can allow image acquisition on curved body parts, improve image quality, and enable functions such as long-term monitoring. In this thesis, I propose a conformable ultrasound breast patch (cUSBr-Patch) consisting of a one-dimensional (1D) phased array and a nature-inspired patch design, which offers large-area, deep tissue scanning and multi-angle, repeatable breast imaging while avoiding the drawbacks of conventional ultrasound imaging technologies. I used a Yb/Bi-doped PIN-PMN-PT single crystal as the active element due to its superior piezoelectric properties (d33 = 2,800 pC/N, εr = 7,000, k33 = 0.93). I then fabricated a 1D phased array transducer consisting of 64 elements with an operational frequency of 7.0 MHz. The 1D array exhibits promising acoustic performance with i) a maximum imaging depth of 80 mm, ii) contrast sensitivity of 3 dB, iii) axial/lateral resolutions of 0.25/1.0 mm at 30 mm depth, and iv) a larger field of view than the commercial handheld linear probe at depths of approximately 30 mm or deeper, indicating a potential reliable capability to detect early-stage breast tumors. Beyond this, comprehensive in vitro experimental studies establish that the cUSBr-Patch can provide accurate and reproducible imaging of different phantoms. The clinical trials reveal that the patch exhibits a sufficient contrast resolution (~3 dB) and axial/lateral resolutions of 0.25/1.0 mm at 30 mm depth, allowing the observation of small cysts (~ 0.3 cm) in the breast. This research develops a first-of-its-kind ultrasound technology for breast tissue scanning and imaging which offers a non-invasive method for tracking real-time dynamic changes of soft tissue.

Last-Meter Delivery: Solving the Unattended Delivery Challenge from Streets to Doorsteps

2024-12-03T03:04:28Z

Last-Meter Delivery: Solving the Unattended Delivery Challenge from Streets to Doorsteps Xiao, Wen-Xin The rise of e-commerce has led to a surge in package deliveries, resulting in the proliferation of unattended delivery methods to address the "last-meter" problem – the challenge of delivering packages from the roadside or sidewalk to the customer's front door. This thesis proposes a methodology for implementing Large Language Model (LLM), and Vision Language Model (VLM) to enable delivery robots to identify the final delivery target and navigate the complex terrain from the curb to the front door. The proposed solution aims to enhance the autonomy and safety of last-mile delivery systems, addressing the "last-meter" challenge and improving the customer experience. This thesis presents a comprehensive overview of the last-meter delivery concept, aiming to bridge the gap between the roadside/sidewalk and the customer's front door. It begins by introducing the significance of last-meter delivery in the growing e-commerce industry and the challenges posed by unattended deliveries. The thesis then reviews the existing literature on autonomous and unmanned delivery systems, multimodal delivery approaches, and the application of large language models and vision language models in robotics. This research identifies the advancements and gaps in the field that the proposed methodology aims to address. The thesis primarily focuses on leveraging Large Language Models, the Segment Anything Model, and the open-source Florence-2 vision foundation model to enable the transmission of customers' delivery instructions to the final delivery target in the context of last-meter delivery. It outlines the methodology for data preparation, object detection and labeling, as well as the integration of Large Language Models to handle customer instructions and coordinate delivery target. It also describes the experimental design and methodologies employed to validate the effectiveness of the proposed system. This includes the use of a last-meter dataset and the evaluation of last-meter scene and target coordinate identification. The thesis concludes by summarizing the key findings and contributions, discussing the broader implications of the proposed methodology, and suggesting directions for future work, such as enhancing system robustness and scalability. KEYWORDS: Last-Mile Delivery, last-meter Delivery, Large Language Models (LLM), Vision Language Models (VLM), Robotics, Segment Anything Model (SAM), Open-Vocabulary Object Detection (OVD).

Imagine Yourself: Explorations in Fostering Personal Expression with Generative AI

2024-12-03T03:13:42Z

Imagine Yourself: Explorations in Fostering Personal Expression with Generative AI Chadha, Karishma Generative Artificial Intelligence (AI) technology has been promoted with many exciting promises to enhance human creativity. However, it has also been shown to amplify human bias and perpetuate harmful stereotypes. In the new age being ushered in by this technology, this thesis explores how educators and designers can use this technology to support young people in exploring and expressing aspects of their unique identities. In particular, I use a design based research methodology to iteratively create Imagine Yourself, a new digital experience adapting off-the-shelf text-to-image generation technology to support young people creating personal representations and stories. Imagine Yourself combines OpenAI’s Dall-E 3 image generation technology with Scratch, a rich environment for young people to imagine and create interactive multimedia stories, animations, and more. Guided by a core value of designing for belonging, this project explores how experiences with generative AI can be designed to foster young people’s creative process in creating personally meaningful stories reflecting their own unique identities, experiences, and cultures. I discuss the iterative design process of creating Imagine Yourself in tandem with creative workshops, aiming to support more diverse representation within the image generation output and invite a tinkerable and iterative process of creating. I discuss observations and feedback from creative workshops with young people and adults, creating with Imagine Yourself. Finally, I conclude with reflections on the design process as well as a discussion of challenges, limitations, opportunities, and open questions for future work incorporating generative AI into young people’s creative learning experiences.

Comparison of Finite Element Methods and Satellite InSAR for Monitoring Deformations of a Large Tailings Dam

2024-12-03T03:48:14Z

Comparison of Finite Element Methods and Satellite InSAR for Monitoring Deformations of a Large Tailings Dam Fetell, Robert Henry Following the recent catastrophic failure of several mine tailings dams there has been much interest in the use of numerical modeling and remote sensing for monitoring the safety and stability of these structures. This thesis presents a case study that investigates the accuracy of InSAR measurements and the predictive capabilities of finite element models using ground truth surface and sub-surface monitoring data applied to the Zelazny Most (SW Poland) copper tailings storage facility. This site has a well-documented history of lateral deformations in a critical section (XVIE) of the East dam that have been attributed to a deep-seated translation mechanism of shearing through the underlying Pliocene, glacial clays. Since 2014, operators of the facility have constructed a series of stabilizing berms at this critical section. We investigated the accuracy of InSAR over this period, ending in 2019, by analyzing 186 ascending Sentinel-1 C-band images and 219 descending images using Persistent Scatterer Interferometry and SARProzTM software, comparing results with two surface geodetic benchmarks. Finite element analyses of the structure required a 2D model of section XVIE. We developed and integrated a stratigraphic model for the foundation soils, the complete construction history of the dam (since 1975), and selected input parameters for constitutive models to represent the soil behavior (foundation soils, tailings, dyke and berm materials) using PlaxisTM software. Our results show that InSAR achieves very consistent agreement with geodetic measurements for vertical (Up-Down) and lateral (E-W) surface deformations, over a time period where construction was limited to raising of the dyke near the crest of the dam and berm construction at the toe. The InSAR data are also insightful in showing relatively uniform lateral deformations occurring over the face of the dam, consistent with the interpreted translational failure mechanism. In contrast, it has proved much more challenging to predict subsurface deformations by FE analyses. The computed movements reflect accumulation of deformations over multiple stages of construction and involve shearing through the complex foundation stratigraphy. We were able to achieve credible estimates of lateral deformations within the range of laboratory shear strength properties published in the literature and using the Hardening Soil (HS) model for non-linear shear stress-strain properties. However, the predictions of surface settlements and lateral deformation are much less reliable and depend on undocumented properties of the tailings, phreatic conditions in the tailings and details of the construction history.

Designing for Connection with Inner Processes

2024-12-03T03:44:33Z

Designing for Connection with Inner Processes Mindel, Jessica Rachel At a time of division, it is more important than ever that we help each other feel truly understood. Today's online ecosystems offer us many new ways to communicate personal stories, often through fast-paced, reactive channels, but few if any technologies enable us to share what I posit to be a crucial component of how we implicitly understand each other: our inner processes, e.g., how we form our values and identities, navigate unspoken tensions in a community, or feel that something resonates with us. This thesis explores inner processes as a resource for the design of systems that support human connection, interpersonal understanding, and reflection. Through a series of design iterations, I weigh approaches to eliciting inner processes, choosing media to externally, evocatively represent them, and encouraging perspective-taking behavior by guiding users through each other's inner processes. I approach this topic through three streams of projects, grounded in literatures that outline guidelines for successful perspective-taking and the development of interpersonal closeness, and that assert the value of creative play in surfacing and communicating inner processes, supporting perspective-taking, making room for new social norms, and enabling reframing. First, I present our collaborative work on Closer Worlds, a two-player, AI-assisted game in which players generate a world they might both want to live in in order to scaffold an emotionally intimate conversation about their memories and shared values. Next, to better understand inner processes entangled with creative practice, I conduct interviews with creative practitioners about the relationships they build through their practice, and design and develop prototypes for implicitly retracing inferred versions of one's own or another person's creative process, capitalizing on room for interpretation. Prototypes include Sjuzet, a compass that anchors the latent space of a user's creative writing to a local map in order to prompt reflection as a user physically wanders through memories, and Pull It Together, a material speculation on textile swatches whose wear and tear modulates to correspond to invisible sociocultural tensions. Finally, I shift my focus to explicitly, informatively trading inner processes in my design of Metaswap, an asynchronous, written activity in which strangers compare annotations about inner processes that arise as they tell personal stories about an uncertainty they are working to resolve in their lives. Making inner processes explicit and prompting revisitation of them offered both benefits and drawbacks for connection and reflection, but revealed important questions. A mixed-methods analysis across this work presents tensions in the human and machine instinct to make inferences and assumptions about others, and offers opportunities for interpersonally insightful, vulnerable, and trusting conversation when computer-mediated communication and sense-making systems produce deep content rather than deep interactions. Through this work, I hope to lay the foundation for future research on technology's role in supporting interpersonal understanding at a time when so many subjectivities collide and are summarized at the speed of data.

Matters of Illuminance - Transforming Light into Material Artifacts

2024-12-03T03:37:51Z

Matters of Illuminance - Transforming Light into Material Artifacts Callender III, Dexter This research explores a process to transform light into physical artifacts. It develops a series of custom software systems to capture images of sunlight moving through a building and transform them into three-dimensional forms. It uses digital manufacturing methods to create the three-dimensional forms out of glass. The aim of this work is 1) to construct a methodology for recording light’s interaction with architecture as three-dimensional forms 2) to produce glass sculptures that exist in a fine art setting and contribute to the lineage of 21st century light artists. The academic contribution of this research builds upon the autographic design framework defined by Dietmar Offenhuber. Offenhuber describes the autographic design process as “the practice of shaping the conditions that allow traces to emerge and guiding their interpretation to demonstrate causality and evidence”.1 The technique I use to transform light into three-dimensional forms follows the four steps of the autographic design process. The goal of this technique is to provide a repeatable process and data format that captures information about light’s interaction with architecture at specific locations. The process produces three-dimensional forms, physical glass sculptures, and media that guide their interpretation, which can be interpreted to provide insight on the design and history of the building. The artistic contribution of this research produces glass sculptures that physicalize the shapes of light I observed and recorded at the location. The goal of these sculptures is to create meaningful physical artworks that reflect the nuanced shapes and subtle aesthetic qualities of natural light. Exhibiting the sculptures in spaces that are abundant with natural light creates new interactions between the glass and the light, offering unique visual experiences that change over time. I bolster these artworks with experiential accounts of my time spent in the building. The artwork I produced as part of this research was exhibited at the Wiesner Gallery at MIT and aims to exist in a fine arts setting, contributing to the lineage of Light & Space artists such as Larry Bell and Robert Irwin.

Beyond-the-Ice: Designing Games for Facilitating Deeper Conversations

2024-12-03T04:01:23Z

Beyond-the-Ice: Designing Games for Facilitating Deeper Conversations Lee, Cassandra In this age of constant communication, we’ve never been more connected, yet all of our numerous, fast, and convenient connections lack the depth and intimacy we truly crave. The desire for more authentic social experiences necessitates vulnerability, honesty, and risk; but introducing such dynamics presents a great challenge in the context of the wider landscape of public discourse. Designers across disciplines have suggested using games to facilitate stronger social connection, since the structures within games can expose players to alternate social norms and encourage risk-taking. However, few have designed games that specifically foster more intimate forms of dialogue or offer scaffolding for players to see the act of sharing authentically and listening deeply as ways to play. In this thesis, I explore the novel intersection between play, intimate conversation, and technology by presenting a variety of prototypes and fully developed games that employ innovative mechanics designed to facilitate authenticity, vulnerability, complexity, and subjectivity. This work builds on formal knowledge from the social sciences, HCI, and game design, as well as informal knowledge from facilitation, gathering practices, party games, and Tarot, by presenting five distinct design principles aligned with theories grounded in past work: 1) Make emotional disclosure special; 2) Scaffold responsiveness; 3) Approach depth through fun; 4) Empower “the work” through constraints and permissions; 5) Center objects to feel with. Following a thorough Research through Design (RfD) method, I designed 15 unique prototypes and proof-of-concepts which explore various aspects of the five principles. Two of the games were designed, developed, playtested, and evaluated – Analogia, a card game that uses generative images to inspire emotion-rich conversations and Crossroads, a digital game where players are guided to unlock a secret insight by co-creating generative images inspired one another’s real experiences. This work contributes two well-tested games that evoke five compelling principles; a series of mechanics for stimulating dialogue (dual-stimulus, bridge-and-tunnel, image scrying, listener roles); and pilot data from playtests that demonstrate the ability and challenge of these mechanics to create conversational outcomes. Additionally, both spotlighted games creatively employ generative artificial intelligence (AI) to help mediate player interactions through image interpretation and co-creation. Although this is a thesis about conversation games, it critically engages with the current social zeitgeist, provides widely applicable insights and presents nuanced ways to think about the future of social-technical systems that seek to encourage deeper, more authentic ways of connecting.

Towards Perceptual Augmentation

2024-12-03T03:01:24Z

Towards Perceptual Augmentation Chin, Sam This thesis explores the concept of perceptual augmentation, focusing on expanding human sensory capabilities beyond their biological limitations. It challenges traditional approaches to sensory enhancement by emphasizing the importance of perception over mere sensory input. Drawing inspiration from the diverse sensory abilities found in nature, the research aims to develop methods for meaningful augmentation of human perception that can impact daily life. The study adopts an ecological approach to perceptual augmentation, grounded in Gibsonian ecological psychology. Key principles include providing correct mental models of augmentation devices, leveraging environmental training and natural tasks, emphasizing multisensory interfaces with sensorimotor feedback, and creating affordances that mimic the natural world. This approach seeks to facilitate perceptual learning through natural interaction with the environment, rather than relying on extensive explicit training. The thesis presents early work in exploring and evaluating individual principles of this ecological framework for perceptual augmentation. While acknowledging the gap between the proposed theoretical approach and current research outcomes, the studies conducted focus on augmenting perception for specific tasks such as pitch interval perception, pilot situation awareness, and sleep staging. The research does not yet demonstrate a generalized, "all-purpose" augmented sense, but lays groundwork for future investigations, including a proposed experiment to mitigate age-related hearing loss using the developed principles.

Temporal Telepresence: Immersive Interfaces for TeleAbsence

2024-12-03T03:30:31Z

Temporal Telepresence: Immersive Interfaces for TeleAbsence Pillis, D. To store the past in a simulation may enable greater understanding of ourselves, our stories, and our histories. The urge to capture our past into networks of photographic, written, filmed, and object-based narratives has long been a means for individuals to identify change, growth, and gain perspective on themselves. Using a dataset of human narratives derived from records and ephemera, this thesis explores a novel approach to preserving and interacting with memories. We present an interactive system of objects and applications that supports intergenerational memory preservation by enabling individuals to actively explore the relationship between personal artifacts, photographs, the spaces of their past, and their memories. This system integrates personal digital twins, photogrammetry, Gaussian splatting, and tangible interfaces to create a new way of experiencing the past, based on interactivity with architectural artifacts and simulations from an individual’s life. Using an iterative participatory design process, we developed a set of multisensory interaction experiences that allow individuals to explore their relationship to autobiographical memory. The system dynamically links autobiographical memories with the environments where they took place, responding to text, photo, and object-based interactions. This experience invites individuals to modify their recollections by exploring how photo, video, and 3D space relate to the experience of revisiting narratives from the past. Applications of this system include assisting with dementia, aging, memory loss, and Alzheimer’s. Our initial studies were promising. When using the simulation system, individuals spent more time reminiscing, discussing more memories, and experiencing greater presence in their recollections than without the interactive paradigm. The system also encouraged family members to reinforce their memories by actively re-encoding them through the simulation interfaces. Results demonstrated that presence in memories seemed more vivid, detailed, and spatially accurate than before the intervention. The result is a new memory-sharing experience that benefits individuals and families by allowing them to understand how their interactions with the past can be enriched through the integration of artifacts and simulations that impact the development of autobiographical memory.

Risk-Benefit Assessment of Pandemic Virus Identification

2024-12-03T03:36:38Z

Risk-Benefit Assessment of Pandemic Virus Identification Jeyapragasan, Geetha Pandemic Virus Identification (PVI) aims to assess unknown viruses for their pandemic potential in immunologically naive human populations. While proponents argue that PVI could facilitate targeted spillover prevention and accelerate medical countermeasure development, critics raise concerns about biosafety and biosecurity risks. This thesis presents a comprehensive mathematical framework to evaluate the benefits, biosafety risks, and biosecurity risks associated with PVI research. Using a combination of mathematical modeling and expert elicitation, we developed a structured approach to estimate the potential impacts of PVI. Our framework suggests that identifying a single pandemic-capable virus through PVI could potentially save lives by reducing natural pandemic risks. However, this benefit is substantially outweighed by the estimated anthropogenic risks from potential accidental pandemic events and deliberate misuse scenarios. The overall expected value of identifying a single pandemic-capable pathogen was estimated to be strongly negative. Significant uncertainty exists in many key parameters estimated through surveys, with wide confidence intervals reflecting the lack of consensus among experts. Expert opinions varied considerably on topics such as the likelihood of funding for medical countermeasures and the potential for deliberate misuse of pandemic agents. This modeling work primarily aims to provide exploratory estimates to guide future work. Our findings underscore the urgent need for improved governance of research involving potential pandemic pathogens. This study provides a quantitative basis for ongoing discussions about the balance between scientific advancement and public safety in high-risk areas of life sciences research.

Multi-bounce Returns for Specular Surface Mapping from Consumer-grade Flash LiDAR

2024-12-03T03:01:53Z

Multi-bounce Returns for Specular Surface Mapping from Consumer-grade Flash LiDAR Lin, Tsung-Han This thesis proposes an approach to leverage multi-bounce returns of a flash LiDAR on portable smartphones for 3D specular surface reconstruction. This is an important research problem as most traditional LiDAR systems fail to detect specular surfaces. As mirror and glass are everywhere, vision systems failing to detect specular surface can be detrimental. Applications like mapping may become inaccurate, and more critically, robots could crash into undetected windows during navigation, leading to potentially fatal outcomes. We perceive this work can impactfully enhance the robustness of specular surface detection, with LiDAR complementing any kind of vision system, particularly image-based. Traditional LiDAR systems typically assume that all returns are single-bounce, which can lead to inaccurate representations of specular surfaces like mirrors or glass, often causing them to appear as though there is a hole. In contrast, this approach models the multi-bounce paths, providing a more accurate reconstruction of these specular surfaces. We operate with a consumer-grade LiDAR that does not require manual calibration and can be operated in real-time on an affordable and portable smartphone. Consumer-grade LiDAR multi-beam flash LiDAR is challenging with its coarse resolution, co-located sensors, and multiplexing setup. In face of these challenges, we propose to solve the association problem with the `reciprocal pair’ algorithm, which can discern different types of bounces from the multi-bounce returns. The algorithm is shown to detect over multiple consecutive frames for dense mirror mapping. In addition to 3D reconstruction, we show multi-bounce returns help to enhance performances on applications such as segmentation and novel view synthesis. Our method can be combined with these state-of-the-art learned-based model, enhancing its robustness by discerning ambiguous scenarios. In general, this approach can map various specular surfaces like mirrors and glasses, without making assumption about particular specular surface shapes, and can operate on non-perpendicular specular-diffuse surface pairs.

Timbral Transformations

2024-12-03T03:23:31Z

Timbral Transformations Shand, Jessica From folk songs to festivals, cafes to concert halls, and religious rituals to recording studios, the flute has long had a shapeshifting, cross-cultural presence. This thesis leverages 21stcentury technologies not only to explore and extend the timbral versatility of flutes, but also to underscore the performative, fluid, and ever-evolving nature of timbre more generally. At the core of the project is the creation of sequences of discrete sounds that interpolate between semantic categories and a collection of fixed media compositions based on those sequences, both of which consist entirely of flute sounds that have undergone varying degrees of electronic manipulation. By means of digital signal processing techniques, the flute wavers in and out of a multitude of sonic identities. Sometimes, it masquerades as another familiar object or interface (e.g., a ticking clock) or abstractly evokes a concept or phenomenon (e.g., a storm); at other times, it beckons toward the ethereal or ineffable, resisting indexical identification altogether. With source materials warped, layered, and splayed across the frequency spectrum, such concerns as “the real” and “the true” begin to move out of focus, making way for attention to embodied phenomenological experiences of sound. As this thesis positions compositional practice as a form of research, its outputs range from the conceptual to the creative and the computational. In addition to the music at its core, the project interfaces with gender studies in its original exposition on timbre and timbral identity, includes a rigorous set of experiments with human and machine listeners, and makes original applications of multimodal language models not before seen in musicology or music theory. A live performance incorporating each of these project vectors and an audience discussion following the event offer further opportunities for reflection and critique.

Practice of consulting firms in corporate strategic planning.

2025-10-30T18:06:08Z

Practice of consulting firms in corporate strategic planning. Chapman, Beverly Jean. Thesis: M.S., Massachusetts Institute of Technology, Sloan School of Management, 1978; Bibliography: leaves 82-84.

Derived distribution of water volume above a given threshold discharge.

2025-10-30T18:06:08Z

Derived distribution of water volume above a given threshold discharge. Chan, Siu-On. Thesis: M.S., Massachusetts Institute of Technology, Department of Civil Engineering, 1978; Bibliography : leaves 138-139.

Wear studies of abrasive particles

2025-10-30T17:51:25Z

Wear studies of abrasive particles Distel, Joseph William. Thesis: M.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1956; Bibliography: leaf 50.

Wear studies of single aluminum oxide grains during grinding

2025-10-30T17:51:25Z

Wear studies of single aluminum oxide grains during grinding Cole, John M. (John Martin) Thesis: M.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1955; Includes bibliographical references (leaf 48).

Forces in internal grinding

2024-11-22T03:52:12Z

Forces in internal grinding Reichenbach, George S. (George Sheridan) Thesis: M.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1952; Includes bibliographical references (leaves 28-29).

The mechanics of dry surface grinding

2024-11-22T03:42:16Z

The mechanics of dry surface grinding Marshall, Earle Robert, 1919- Thesis: M.S., Massachusetts Institute of Technology, Department of Metallurgy, 1949

An investigation of adhesion mechanisms

2025-10-30T18:06:08Z

An investigation of adhesion mechanisms Yee, Geary Yee. Thesis: M.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1976; Includes bibliographical references.

3-D Topology Optimization of Spatially Averaged Surface-Enhanced Raman Devices

2024-11-19T03:16:47Z

3-D Topology Optimization of Spatially Averaged Surface-Enhanced Raman Devices Hammond, Ian M. Numerous nanophotonics applications necessitate designs that enhance distributed incoherent emission. Representative applications include light-emitting diodes, thermal emitters, and Raman sensing. Previous efforts in full-scale topology optimization for Surface Enhanced Raman Sensing (SERS) have predominantly focused on single particle emissions or two-dimensional systems, which are impractical for actual fabrication. An objective function represented by ട|E|⁴dV effectively approximates Raman enhancement. This function tends to diverge near sharp tips and other singular geometries in three-dimensional spaces for relevent materials. This thesis delves into methodologies for regularizing the optimization process to preclude the formation of such problematic geometries. Additionally, it integrates lithography constraints to ensure that the optimized SERS substrates are viable for fabrication. To align with computational limits, various strategies are employed to make the system manageable. The techniques developed in this study facilitate the practical design of 3-D systems that enhance incoherent emission through topology optimization.

Natural Language Control for for Visually Interactive Decision Support Tools in Supply Chain Management

2024-11-19T03:55:35Z

Natural Language Control for for Visually Interactive Decision Support Tools in Supply Chain Management Guter, Willem J. Supply chains are complex networks where changing one variable can have unforeseen effects on the entire chain. Interactive supply chain visualizations are useful for understanding these effects, and can lead to decreased cost. However, these interactive visualizations can require technical and domain expertise to operate and understand. A solution for this is natural language interfaces, allowing users to use natural language commands to control the visualization. Additionally, natural language interfaces can be difficult to implement, and require applications specific programming or training. This thesis proposes integrating a pre-trained large language model as the natural language interface. An example application is created using an existing supply chain network visualization application. Various large language models are then evaluated for usability, functionality, and accuracy. We find that a state of the art commercial model is able to practically fulfill the role of a natural language interface, but that open-source large language models are not currently capable of functioning in this way.

Investigating Fine-Tuning of Language Models for Multiple-Choice Questions

2024-11-19T03:46:08Z

Investigating Fine-Tuning of Language Models for Multiple-Choice Questions Wang, Ivy A. This thesis investigates the positional and contextual bias of large language models (LLMs) when used to answer multiple-choice questions (MCQs). Given the increasing use of generative language models in fields ranging from cybersecurity to biomedical research, it is important to understand the causes of their behavior in order to mitigate biases and prevent errors. One known method of improving the performance of LLMs is fine-tuning, wherein a model is additionally trained on data from a specified distribution or subject area. We specifically investigate training data properties related to positional bias in fine-tuned language model performance on correctly answering MCQs. To improve model efficiency, we used parameter-efficient fine-tuning, specifically LoRA (Low-Rank Adaptation), which reduces the dimensionality of weight matrices used in the model’s layers. We verify that if the training data for the model possesses the same qualities and distributions as the test data, the LLM will achieve the best performance. In our experiments, we scaled and balanced our fine-tuning datasets and learned that both processes improve the accuracy on test sets of MCQs.

Radiation Shielding Design and Radioactive Waste Assessment of Horizontal Compact High Temperature Gas-Cooled Reactor

2024-11-19T03:57:51Z

Radiation Shielding Design and Radioactive Waste Assessment of Horizontal Compact High Temperature Gas-Cooled Reactor Kudriavtseva, Anna With the objective that nuclear power plants utilizing small High Temperature Gas-Cooled Reactors (HTGRs) can provide economic, environmentally favorable and reliable electricity and heat for community and industrial purposes, Boston Atomics LLC initiated the design of Horizontal Compact HTGR (HC-HTGR). This work addresses shielding, activation analysis and the decommissioning cost assessment as an integrated part of the design process. Reinforced regular and borated concrete were considered as shielding materials for the reactor building and Reactor Cavity Cooling System (RCCS) tanks. It was found that for locations of the reactor building where the dose rates during normal operation were greater than the Nuclear Regulatory Commission (NRC) limit of 0.1 rem/hr, 175 cm of borated concrete is required. The shielding concerns motivated the decision to separate RCCS tanks from the reactor room with a 75 cm borated concrete wall to ensure that the radiation levels do not exceed the NRC limit. Additionally, several shielding options were proposed to protect steam generator modules from radiation-induced activation. The activation analysis was performed for the key equipment and graphite reflector components of the HC-HTGR design. The core barrel made of Incoloy 800H was characterized as a class C waste component after 40 years of reactor operation. It was proposed that 2.25Cr-1Mo alloy be considered as barrel material to decrease activity levels. The reactor pressure vessel (RPV) and RCCS tubes made of carbon steel were characterized as a class A waste component. The graphite reflector components are characterized as Class C level waste. Furthermore, this work discusses the neutron irradiation effects and their impact on the integrity of the barrel, RPV, and graphite reflector against material property changes. It was found that 2.25Cr-1Mo alloy has a higher radiation resistance due to the higher iron content in the composition. Based on the results, the reactor vessel is safe from radiation damage for 32 years of operation. The data evaluated for the graphite reflectors indicate that the components should be replaced after 20 years before they pass the turnaround point. The concentrations of radionuclides computed during activation analysis were used to predict the radiation levels from beta and gamma sources that could be encountered during the disposal of the core barrel and RPV. Based on the obtained data, it is clear that if the barrel is not replaced during operation, the radiation dose rate will remain above acceptable levels, requiring a more rigorous disposal approach. The radiation levels are reduced for the reactor vessel as it was exposed to a lower flux and radiation-induced activation. A similar analysis was performed to derive the exposure dose rate from gamma and beta rays that can be detected by a sensor of a refueling camera. Beta particles will deposit most of the energy in a graphite layer, and the camera will register negligible dose rates. The gamma ray estimates indicate that a more enduring refueling machine is required. The results of this work provide the disposal costs for HC-HTGR immediate dismantlement and after a given decay period. Overall, the disposal costs of core barrel, RPV and graphite reflector are $13 million for HC-HTGR design after 40 years of full operation if the billable charge limits are set on radioactivity levels. If this option is not considered, the total disposal costs grow up to $225 million. However, extending the storage up to 10 years would decrease the activity, reducing the cost of disposal.

Systematic Studies on the Chelating Ligand Effects of Novel Borafluoronium Ions

2024-11-19T04:14:46Z

Systematic Studies on the Chelating Ligand Effects of Novel Borafluoronium Ions Allen, Marissa D. This study explores the synthesis and characterization of borafluoronium ions via a ligand-based strategy using bidentate amine and phosphine bases as chelating agents to cationic boronium ions.The borafluoronium complexes A–C were synthesized in high yields (80%–95%) and characterized using NMR spectroscopy and single crystal X-ray diffraction. Further investigations into the coordination of other bisphosphine ligands, such as dppe, rac-BINAP, and Xantphos, resulted in the formation of Lewis adducts rather than the desired borafluoronium ions. The challenges in isolating these species are attributed to steric and chelate effects inherent of the ligands, with NMR analysis providing insights into the coordination chemistry and stability of these complexes.This work advances the understanding of borafluoroniumion formation and the impact of ligand structure on their properties.

Optimization under ecological realism reproduces signatures of human speech perception

2024-11-19T03:12:41Z

Optimization under ecological realism reproduces signatures of human speech perception Magaro, Annika K. Recent advances in machine learning have made real-world perception tasks feasible for computers, in many cases approaching levels of performance similar to those of humans. In particular, optimizing models for ecologically realistic training datasets has helped to yield more human-like model results. In the field of speech recognition, models trained under realistic conditions with simulated cochlear input reproduce some characteristics of human speech recognition. However, it is unclear how similar the behavior of these models is to that of humans across the many ways in which speech can be manipulated or degraded, since human and model behavior have not been extensively compared. In this paper, we address this question by comprehensively testing a neural network model trained in ecological conditions across a large set of speech manipulations, comparing its behavior to that of humans. We find that training in ecological conditions yields a fairly good overall match to human behavior, with some discrepancies that can be largely resolved by training specifically on these conditions. The results support the idea that the phenotype of human speech recognition can be understood as a consequence of having been optimized for the problem of speech recognition in natural conditions.

A study of competition in freight transportation to and from Boston, Massachusetts

2025-10-30T17:03:43Z

A study of competition in freight transportation to and from Boston, Massachusetts Luykx, H. M. C.; McHugh, G. E. Thesis: M.S., Massachusetts Institute of Technology, Department of Civil Engineering, 1931; Appendix contains numerous pamphlets.

The use of discriminators in the linear detection of F-M signals

2025-10-30T17:03:45Z

The use of discriminators in the linear detection of F-M signals Lu, Pao-Wei. Thesis: M.S., Massachusetts Institute of Technology, Department of Electrical Engineering, 1944; Includes bibliographical references (leaf 51).

Valuation model for Less Developed Countries' Debt in the secondary market

2025-10-30T17:51:27Z

Valuation model for Less Developed Countries' Debt in the secondary market Carballo, Carlos Federico. Thesis: M.S., Massachusetts Institute of Technology, Sloan School of Management, 1989; Includes bibliographical references (leaves 75-79).

Urban space heating with a heat pump-condenser temperature water system

2025-10-30T18:06:08Z

Urban space heating with a heat pump-condenser temperature water system Yee, Wee Tong. Thesis: M.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1976; Includes bibliographical references.

Average frequency trajectory control : normal mode.

2025-10-30T18:06:08Z

Average frequency trajectory control : normal mode. Yared, Khaled Ibrahim. Thesis: M.S., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 1976; Includes bibliographical references.

The implementation of a joint disaggregate demand model in an urban simulation

2025-10-30T18:06:09Z

The implementation of a joint disaggregate demand model in an urban simulation Worms, Vincent Robert. Thesis: M.S., Massachusetts Institute of Technology, Department of Civil Engineering, 1976; Bibliography: leaves 114-115.

Echoes From the Stone Reframing Preservation in Syria Through Haurani Folklore

2024-10-17T03:12:54Z

Echoes From the Stone Reframing Preservation in Syria Through Haurani Folklore Alrifai, Hajar Partially buried in the landscape of Hauran in southern Syria, my family’s 1500-year-old house, Alali—formerly a Byzantine church—further erodes with each passing year. Throughout the decades, the house has been subjected to various forms of destruction: from development, demolition, and rocket strikes to violent reconstruction. Its crumbling stones are laden with the memories of four generations and echo with a way of life that is disappearing. At the heart of Hauran are the fellahin, farmers who permanently settled in its villages in the late 19th century. As they settled, the fellahin reclaimed, inhabited, dismantled, and rebuilt the Byzantine structures, often rearranging or reimagining the original programs: chapels, houses, and cemeteries. In my family’s border village of Nasib—a place both liminal and at the margin—this rich local history lives not in formal archives but in scattered material like architectural ruins, oral poems, folk songs, diasporic transcripts, and 8mm video cassettes, many of which resonate as sonic artifacts. What began as a project of documenting the decay of our old house evolved into a meditation and manifesto on preservation outside the purview of top-down institutions. Through creative writing and cinematic intervention, Echoes from the Stone asks: what does it mean to preserve a place, and preservation for whom? In this proposed paradigm, ‘story’ becomes integral to architectural preservation. This story of Alali interweaves my journal entries with the encounters of my greatgreat grandfather, Hassan Ali, an oral poet who founded the village. I further draw from my grandfather Faisal’s diaries, our family’s archival videos, and interviews with Nasib’s elders, including my grandmother Um Ghazi, an olive farmer, and Um Saado, a Bedouin matriarch and shepherd who once lived in the old home with her family. By foraging for this counter-archive of living memories, I reveal intergenerational intersections which complicate and reimbue the colonial history of the village—and of Syria—with voices that echo from the stone, voices that persist and whisper from the ground, from across borders and oceans, and from within. This interdisciplinary chronicle draws from architecture, agriculture, literature, anthropology, and film, to reconstruct a social history of the village and speculate on alternate ways of dwelling, building, and preserving— reclaiming the archive, reinserting narrative, and reframing heritage through the folklore of Hauran.

Wave Mechanics in Constructed Oyster Reefs and the Design of Nature-Based Coastal Adaptation

2024-10-17T03:22:41Z

Wave Mechanics in Constructed Oyster Reefs and the Design of Nature-Based Coastal Adaptation Brice, James Vincent There has been great interest in the potential of constructed oyster reefs (CORs) to function as nature-based coastal protection infrastructure, but most projects to-date are designed primarily for wave attenuation and fail to consider both the environmental conditions necessary for long-term oyster reef sustainability as well as the importance of education and outreach in fostering environmental stewardship. Realizing the promise of nature-based coastal adaptation means building physical, ecological and social infrastructure simultaneously, requiring a design-research methodology that combines an understanding of biological design constraints, physical analysis and community engagement. Physical and numerical wave flume experiments were conducted to investigate mechanisms of wave energy loss in oyster shell gabion-type CORs that place oyster biology in the foreground— particularly, the influence of across-shore width, spacing and structure porosity on wave attenuation under non-breaking wave conditions. Gabion widths of O(1) wavelength were found to attenuate waves by 40%. These losses were driven primarily by internal drag which was characterized experimentally and accurately modeled with the modified Ergun Equations and the waves2Foam library of the open-source CFD software OpenFOAM. This research was then translated into a suite of interactive design activities, featuring a tabletop wave flume, scale models of coastal features, and a set of coastal community member cards. Through design and creative inquiry, these tools seek to communicate complex biophysical processes in coastal ecosystems while empowering communities to reimagine what it really means to "build with nature".

When the Earth Breathes: An Anthology of Volcanic Urbanism

2024-10-17T04:01:07Z

When the Earth Breathes: An Anthology of Volcanic Urbanism Carucci Alvarez, Maria Gabriela Malpaís. A Spanish word used in volcanically-active landscapes to refer to the new basalt terrain that solidifies after an eruption. It translates literally to “bad country”, and it is defined as a “sterile, arid surface”. This thesis looks at the Tajogaite volcano, the most recent eruption in La Palma, one of the youngest of eight islands in the oceanic volcanic arc formation of the Canary Islands. It positions this event not as a unique site but as a manifestation of a network of bureaucratic colonial imaginaries that still operate within a disaster relief framework that exists in volcanic landscapes throughout the world. Together, these imaginaries draw an unyielding binary narrative about volcanoes as purely destructive entities, and further dismiss the porosity that exists between the geos, the bios and the polis. Igneous landscapes, through the production of new basalt floors, rich soils and ocean intrusions, traverse and redefine property boundary lines and national coastlines, which extends beyond plan views and into sectional shifts. This project aspires to spatialize the temporal moments of one volcanic eruption, questioning, ultimately, how the ownership of materials in flux, along with their transformations, can reframe our imagination of a city-volcano production that frames both as ephemeral, ever changing entities. Through ten allegories, cities are positioned inside of the geological realm, and are de-centered to contextualize them within a volcano’s lifespan. The first five stories describe the current framework, while the other half become allegories through which architecture and urbanism are leveraged as tools through which to understand the earth’s movements at different scales, temperatures and states of matter, in order to provide an alternative imaginary to current answers to the question of volcanic urbanism.

Data-driven Home Workspace Design: Interactive DIY Platform Mediating the User and Expert Literature

2024-10-17T03:12:20Z

Data-driven Home Workspace Design: Interactive DIY Platform Mediating the User and Expert Literature Yi, Wangli After COVID-19, some employees have opted to continue working from home (WFH) or have chosen a hybrid working mode. Previous research has shown that satisfaction with the physical environment and characteristics of home workspaces are directly related to mental health, which can affect productivity and well-being. This underscores the need for better designed WFH environments. This study explores the use of data-driven tools in interior design to enhance WFH setups. It posits that these tools can transcend traditional design limitations by incorporating professional expertise and facilitating user- driven design processes. The tool's backend is built on a comprehensive collection and classification of research literature on WFH environments, creating an interactive platform where users can engage directly in the design process. This is achieved through real-time, machine-mediated suggestions that enhance well-being without the need for professional human designers. Employing a user-centered design framework, the study develops and tests a prototype to assess its effectiveness in empowering users to intentionally and sensitively redesign their home workspaces. Results show that participating graduate students became more aware of their WFH environment during the design process, but largely it did not change their existing workspace decisions. This observation indicates the potential benefit of this interactive machine-mediated system as a design education tool. Further test on other demographic groups, such as those who need to focus for long hours professionally at home as well as those who are specifically concerned with mental health issues, is anticipated as the next step for the evaluation of this platform.

From Waste to Structure: A Deep Reinforcement Learning Approach to Circular Design

2024-10-17T03:51:02Z

From Waste to Structure: A Deep Reinforcement Learning Approach to Circular Design Sørensen, Karl-Johan I. The design-to-construction process of buildings predominantly follows a top-down linear workflow, where a design is drawn and subsequently refined to determine the required materials and components. This approach assumes an infinite material supply or the capability to manufacture what is needed for the design. Constructing in this manner is resource-intensive and wasteful, making it incompatible with our global climate goals. One way to significantly reduce our material and environmental footprint is by extending the lifespan of building materials through circular design practices. In this approach, the available materials define the architecture, inverting the process from top-down to bottom-up. This method, known as Inventory-Constrained Design, enables the creation of new buildings using materials sourced from construction and demolition waste streams. These inventories, characterized by their non-standard and uniquely varied elements, are hard to design with due to the enormous quantity of possible combinations of even a few discrete elements. Identifying a feasible design that aligns with the designer's intent and meets functional requirements becomes an overwhelmingly time-consuming task, heavily reliant on manual trial and error. Computational optimization has been implemented to automate the process, but state-of-the-art algorithms still require manually pre-defining a parametric target design-space or take too long to compute when applied to larger problems. This thesis proposes a new method for circular design utilizing Deep Reinforcement Learning (RL) to design structures, requiring only a design gesture and the inventory as input. It works by training an artificial neural network to sequentially assemble a structure from inventory elements, following the gesture while meeting a structural goal. Hence, the design layout directly arises from available inventory. After training, the neural net can be employed instantaneously to design new structures with new inventories without any significant computational expense. To evaluate the effectiveness of the RL method, it is applied to the specific problem of inventory-constrained design of planar roof trusses and demonstrated in a realistic example of assembling a long-span roof from a disassembled transmission tower.

Exœrcising a Haunted City

2024-10-17T04:04:35Z

Exœrcising a Haunted City Wong, Bryan Hon Ting With the looming threat of cultural erasure posed by Hong Kong’s repatriation to China no later than 2047, rituals emerge as the last resource sustaining the collective identity of the city. This thesis documents, through the study of local Taoist-Buddhist practices, the choreographies of rituals as a reparative tool to resist the disap- pearance of local culture. It is linked to findings from everyday domestic offerings to ancestors, annual festive performances of traumatic cleansing, and the booming clientele businesses of precautionary rites, all of which demonstrate their spatial and temporal qualities as methods to resist modern state control. To retain the residue of pre-modern practices as a critique of socio-political turmoil, this thesis suggests an alternative design that preserves and promotes the annual ghost festival for public participation. By revising the festival’s pilgrimage route and ritual sheds, this thesis transforms the traditional nature of ephemeral scaffold- ings into permanent poles and follies. Situated along the city’s most haunted public estate, these structures are programmed as public facilities for fitness training and children’s playscapes. During the festival, they will be activated into ritual sheds, demonstrating a formal and functional contrast between the everyday and the ritu- al—from form to formlessness, exposure to closure, and lightness to heaviness. Designed to evade institutional surveillance, these clandestine transformations preserve solidarity and identity not by emphasizing the significance of priests exorcising in rituals, but by highlighting the quotidian motor memories developed from locals exercising within. The duality of ritual and everyday movements shall exercise the ghosts of a haunted city.

Common Grounds in Shared Waters Integrated Design for Negotiating Equitable Development in Gosabara-Mokarsagar

2024-10-17T03:39:32Z

Common Grounds in Shared Waters Integrated Design for Negotiating Equitable Development in Gosabara-Mokarsagar Mehta, Dhwani Along the west coast of India, in the waters of Gosabara-Mokarsagar, conflicting visions for the landscape mix and muddle. In 2016, Muslim fisherfolk of Gosabara, 100 families, already marginalized by religious, caste, and class distinctions, were banned from fishing, which was their sole traditional livelihood due to environmental protection claims. This led the community to file a petition for mass euthanasia to protest the loss of their rights. Despite their protests, the Government of India announced the Kerly Recharge Reservoir Ecotourism project in 2022 that overlooked their needs, threatened their cultural identity linked to fishing, and exacerbated their traumatic history of displacement that dates back to India and Pakistan’s 1947 partition. Although many groups’ contested visions map onto the shared waters of Gosabara-Mokarsagar, the fisherfolk are particularly excluded from decision-making processes. Finding a singular common ground among the contesting groups is challenging due to vast differences in power, position, and privilege. This thesis, therefore, aims to ensure equitable representation for all stakeholders, particularly disempowered fisherfolk, by an integrative design approach of forging a network of multiple ‘common grounds.’ The term ‘common grounds’ defines partnerships of two or three stakeholders, instead of all, based on mutual understanding and shared objectives like sustainable livelihoods, economic development, ecotourism, and avian conservation. First, I established a common ground with a local NGO, Mokarsagar Wetland Conservation Committee, by using photography, videography, and drawings to raise public awareness about this unique landscape. Initially intuitive and later strategic, I represented the lush waters as a shared home for both the fisherfolk and the birds. Second, I present a network of localized design strategies to enable partnerships that position the NGO as a mediator between the government and local communities, especially the fisherfolk, enabling it to foster alternative models of environmental stewardship. Through these partnerships, rooted in figurative ‘common grounds,’ the fisherfolk become primary, active collaborators in development processes. This thesis creates the conditions for a more equitable development model for this landscape by using design to enable grassroots partnerships that integrate communities into ecological conservation and economic growth projects.

Office of Back of House

2024-10-17T03:38:12Z

Office of Back of House Bilal, Ekin Office of Back of House (OoBoH, pronounced “ooh-boo”), is an architectural practice that operates at the intersection of ducts, conduits, scaffolding, custodial carts, mechanical rooms and sheds. OoBoH conducts design experiments in and around these maintenance objects and spaces typically separated from “architecture-proper.” By looking at the regulations, funding initiatives, zoning amendments and energy consumption routines that rule these spaces, OoBoH questions the boundaries that separate them from the “front of house” to begin with. These “back of house” spaces exist right inside the thick poché line that bounds what is thought to be the domain of design. Back of house (BoH) is dictated by an obscured regime of maintenance processes, and by leveraging these currently unexamined spaces, OoBoH believes that they can become the site for tactical design interventions and new visions of maintenance culture. OoBoH is an attempt at entering architecture from the back door, re-characterizing existing buildings as dependent on the spaces and labor often hidden behind pastiche and façade.

Tectonics of the semi-permanent: Reassembling fit-out architecture

2024-10-17T04:14:20Z

Tectonics of the semi-permanent: Reassembling fit-out architecture Schnitzler, Jenna In New York engineer Reginald Pelham Bolton’s 1911 obsolescence study “Building for Profit: Principles Governing the Economic Improvement of Real Estate”, he foretold a truth that remains today, that “the useful or economic existence of all classes of buildings, in the rapid march of modern conditions, is constantly shortening” (Bolton, 68). He details how the parts of buildings lose value at different rates—as they physically deteriorate, materials wear and things fall out of style, but even more quickly, he notes, do our structures become economically obsolete. Then and still today the durability of building materials is the least of our concerns when considering functional obsolescence. The physical is almost certain to exceed the economic durability of a building as a whole. Designers and developers recognize this gap between physical and economic obsolescence, and in response have called for a moratorium on new construction—opting instead to convert existing structures to meet changing programmatic demands. Yet in these conversions, we use the same extractive methods as new construction, filling existing frames and envelopes with non-structural light framing to differentiate the space inside. In this paradigm, to build inside an existing frame still relies first on the tool of demolition. The uneven wearing that Bolton wrote about in 1911, appears again in the iconic shearing layers diagram from Frank Duffy and Stewart Brand, who make a very similar economic argument, demonstrating that the economically fast-wearing interior layer accumulates the most investment over time, rebuilt on a cycle of every 5-10 years. We are facing a turning point in building; as of 2020, over 35% of total construction activity is renovation work, and we are making increasingly rapid changes to building function. This creates a paradigm of fit out architecture that answers unpredictability and shifting values with indeterminacy, perpetuating a cycle of repetitive building. This project takes the converted structure as its starting point, experimenting with disassembly, reassembly, and the boundaries between fit out and frame, sited within a larger material and economic framework that expands the definition of “value” beyond the monetary to include material resources embodied by a given structure.

Automated Engineering Design for Reusable Concrete Building Structures

2024-10-17T04:15:35Z

Automated Engineering Design for Reusable Concrete Building Structures Wongsittikan, Pitipat Concrete contributes to 8% of global CO2 emission through reinforced concrete (RC) structural system. Unlike steel and timber structures, RC components are rarely reused due to the inseparable phase between concrete and steel. This results in down cycling of the components into aggregates or landfill material. The Pixelframe structural system [1] was proposed to facilitate the reusability of concrete components by implementing the existing external post-tensioning system in bridge structures and fiber reinforced system to design building beams and columns. This work presents an automated engineering design workflow for Pixelframe, including a engineering mechanics of the system that conforms to ACI 318- 19 [2] and fib Model Code 2010 [3], half-scale tests to verify the preliminary behavior of the system, and a scalable design algorithm for minimum embodied carbon designs. The workflow also uncovers new insights on choosing ranges of concrete strengths based on the element lengths and potential carbon reduction from refining the number of different concrete strengths in a building. This work demonstrates the utilization of existing building systems in the context of reusability and the potential of automated computational structures in aiding the design decisions to facilitate the circular economy of concrete structures.

Salt to Scale: The Seasoning of Buildings

2024-10-17T03:02:49Z

Salt to Scale: The Seasoning of Buildings Battikha, Christina We exist in thick layers of ancient minerals and material formations that perform to shape human architectural practices. Yet, with a continuous desire to force materials into designs, humanity has never ceased to disregard the active strength of a material to perform with time. The next twenty years align with a future of salt in the form of a dynamic, preservative, and corrosive mineral that shall never expire from Earth’s crust. Nevertheless, aspiring to mine, build, maintain, and preserve, humanity remains in constant search of other more durable materials designed with the presumption to last forever. Salt is certainly not the neutral product of a chemical reaction. It actively performs to preserve, corrode, accumulate, or maintain humanity’s creations. Embracing its ability to expand and reduce timescales, I investigate salt as a material that provides both corrosive and preservative properties offering current architectural practices the choice and responsibility of building for eternity or for a finite moment. I explore ancient salt cycles shaping the last human activities remaining on the Eastern coast of the Mediterranean, in Anfeh, Lebanon. Molded into a series of geo-cultural objects, salt containers embrace their materiality and escape the dullness of a mold to acknowledge the continuous cultural cycles that exists between time, salt, and its people. This thesis invites current design and construction practices to think across new intervals of time that reflect the building and un-building capacities of salt as a scalable mineral contributing to a salty architectural ritual that passes from generation to the next; a source of luck amidst a time of ongoing crisis. Providing recipes from a salty kitchen, the work integrates seasonal practices to mine and craft salt into animate typologies embracing the forces of salt to challenge the standard architectural practice against one that thinks with the durations of salt.

In Tension: Computational exploration of the design space of tensile network structures

2024-10-17T03:43:06Z

In Tension: Computational exploration of the design space of tensile network structures Burke, Adam T. Cable and rope net structures are lightweight tensile systems and generally cannot resist compression or bending. Tensile network structures are often used to span long distances without intermediate supports and have found applications in art, architecture, and structural engineering due to their physical and visual lightness. However, the design of tensile net structures is generally challenging since their form cannot be arbitrarily defined. Instead a process of form-finding must be used to establish a geometry where all edges of the network carry only tensile forces. Physical models and computational methods can be used for the form-finding of tensile network structures; however the primary challenge in the design process is the adjustment of the network parameters to achieve a specific design. Recent work has shown that automatic differentiation software packages can be used to efficiently design funicular structures (that is, those that work in pure tension or pure compression) with additional designer driven objectives, but these techniques remain largely inaccessible to general designers, architects, and engineers due to the involved process of problem setup and limited interactivity of existing tools. To address this limitation, I introduce a new tool set consisting of two main components, Ariadne and Theseus. These components take advantage of automatic differentiation of objective functions for efficient tensile network simulation and provide a user interface for architects, engineers, and other designers as a plugin for a commonly used 3d modeling software. In this thesis, I outline the structure and features of this tool set, show results of networks optimized with different composable objectives, and show some fabricated examples. Next, I explore the the generation of more complex 3d network topologies through a procedural shape grammar. Finally, I explore the use of differentiable simulation in conjunction with machine learning techniques to optimize the geometry of tensile networks using semantic input and to develop an implicit representation of the space of equal edge length tensed network poses. Together, this new tool set and additional methods enable a more expansive exploration of the design space of tensile networks where design intent and practical constraints are respected.

Developing frameworks for an equitable future: from building decarbonization to generative modeling.

2024-10-17T03:20:49Z

Developing frameworks for an equitable future: from building decarbonization to generative modeling. De Simone, Zoe In this thesis I develop computational frameworks to understand equity under two perspectives: building decarbonization policy and generative modeling. Part 1 - Equitable building decarbonization Buildings significantly contribute to global carbon emissions, necessitating urgent decarbonization to meet 2050 climate targets. The U.S. strives for net-zero emissions by 2050, supported by federal incentives promoting building upgrades. However, financing deep retrofits for all U.S. homes exceeds available public funds. This chapter proposes a model that examines long-term carbon reduction trajectories under various incentive policies, focusing on fairness and equity. Using Oshkosh, WI, as a case study, it explores the philosophical, economic, political, and mathematical dimensions of creating just and effective decarbonization policies that ensure healthy, low-carbon homes for all. Part 2 - Equitable diffusion models Generative Text-to-Image (TTI) models, while capable of producing high-quality images, often replicate training data biases. Traditional fairness views in machine learning, which consider fairness as binary, are challenged. This section introduces DiffusionWorldViewer, a novel framework with a Web UI that enables users to analyze the underlying worldviews of diffusion models and edit model outputs to align with their personal fairness perspectives, thus promoting a diverse understanding of fairness in AI technologies.

Low-Cost Masonry for the Design of Barrel-Vaulted Flooring Systems

2024-10-17T03:09:26Z

Low-Cost Masonry for the Design of Barrel-Vaulted Flooring Systems Haile, Nebyu Samuel The world's population is projected to grow rapidly in urban areas, with a projected 2.5 billion more urban dwellers by 2050 (UN-DESA, 2019). This urban growth will notably concentrate in Less Economically Developed Countries (LEDCs), where 16 of the top 20 most populous cities are anticipated to be situated by 2100 (Hoornweg & Pope, 2017). LEDCs face a critical challenge in meeting the demand for affordable housing due to various factors, notably the high material costs, which can make up to 90% of residential construction expenses (Meikle, 2011). Most multi-story housing in LEDCs relies on reinforced concrete frames with flat slabs. This structurally inefficient system heavily depends on imported cement and steel for many locations. Compounding this issue, in LEDCs, the construction sector contributes significantly to their annual carbon emissions, sometimes doubling the global average and exacerbating the climate crisis (Yokoo et al., 2016). Addressing the pressing need for affordable housing requires alternative, more efficient structural systems that utilize affordable and environmentally conscious materials. This thesis aims to address the challenge of affordable housing by proposing the implementation of unreinforced barrel-vaulted earthen floor systems as an alternative to conventional concrete flat slabs, which are often cost-prohibitive in LEDCs. While existing research predominantly focuses on thin concrete shells for vaulted floors, this study emphasizes earthen vaulted floor systems, utilizing locally available and cost-effective materials. Specifically, it analyzes the maximum spanning capacity of three shallow unreinforced earthen barrel-vaulted floor typologies, examining their associated costs and carbon footprints. Furthermore, the thesis investigates the feasibility of one of these typologies by constructing and evaluating a physical 3m span prototype subjected to international building code loads. The outcomes highlight the structural integrity, cost-effectiveness, and reduced carbon footprint of earthen vaulted floor systems, offering insights into a more environmentally conscious and economically feasible floor system typology for building construction in LEDCs.

The Matter of the Hold: Housing futures and the paradigm of the ship

2024-10-17T03:44:06Z

The Matter of the Hold: Housing futures and the paradigm of the ship Donovan, Inge; Pankhurst, David Many of the port cities of North America are built upon ballast stones, discarded by ships after their transit across the Atlantic. Oftentimes, this material was sourced from waste, such as stone offcuts from quarrying, and transported across space and time, slipping through value systems; from waste, to weight, to commodity. In time, structures across the continent boasted chimneys or foundations that had begun their life in the distant granite quarries of Cornwall, and from bricks that had rounded Cape Horn - their material transience obscured by a perceived stability of form. Buildings are usually seen as the endpoint of material flows, where they remain in intractable, fused assemblies until they reach obsolescence. This familiar pattern is currently playing out in the phased demolition of the Bunker Hill Public Housing Development, the largest affordable housing community on the East Coast. The BHHD can be seen in contrast to the Charlestown Navy Yard, an adjacent shipyard where centuries of investment have established a robust infrastructure of maintenance. We ask: how could the paradigm of the ship, and the creation of material strategies for large, complex assemblages funded by public spending be applied to housing in a resource constrained world? In The Matter of the Hold, the demolition waste from Bunker Hill is inherited as ballast and transformed, a process made possible by the concept of the “building as hold.” In light of the increasing shift towards buildings as storehouses of material to be held for future reuse, and as vessels of carbon sequestration, our thesis explores how design for the uneven, yet cyclical ebbs and flows of renewable resources erodes architecture’s traditionally rigid temporal boundaries of planning, construction, and occupancy, and produces temporally dynamic regimes of figure and form. The collection, administration and reconfiguration of waste materials results in the creation of new, regenerative forms of collective living that challenge the boom-and-bust logic of investment in public infrastructures.

Stories of the Sky

2024-10-17T03:01:04Z

Stories of the Sky Chen, Zhanyi My art practice probes how soft science fiction provides intervals to contemplate the tension among the relentless advancement of infrastructural technologies, their environmental and psychological repercussions, and the metaphors and culture in weather and environments. In this thesis, I explore such tension with a specialized focus on the sky via a series of artworks that engage with clouds, weather satellites, and human feelings. My experience receiving image signals from the Russian weather satellite Meteor-M2 has led me to understand the pervasive presence of satellites and their silent integration into, and control over, various environments—similar to numerous other contemporary infrastructures. The sky has never been merely a smooth surface but is striated with all kinds of machines, politics, and power dynamics. My thesis can be seen as exploring methods of coping as responses from an individual caught in such an intermingled environment, and as an inquiry into how we perceive things that are distant from us. Referring to soft science fiction approaches, I strategically misuse technologies to prioritize human subjectivity over technological functionality. In moments where the misused technologies cease to function, but to obscure, to resist, to complicate, to affect, I put the current dynamics between the self and technologies into play. Parallel to my artistic practice, I also take inspiration from elemental media studies for their broader theoretical discourse on the interplay between the environment and media. Media historian John Durham Peters argues for a more encompassing definition of media that includes environmental elements, including the sky, challenging the traditional dichotomy between nature and culture and the previous academic emphasis on culture over nature. This perspective allows for the exploration and appreciation of the sky’s cultural, emotional, and historical values which are just as important, if not more so, than any other conventional media, resonating with the intentions behind my artworks. Thus, “media” becomes a term that is semantically richer than it already is and requires a nuanced interpretation embracing all its connotations, and my thesis provides ways to explore this materially. By focusing on the sky as a juncture where nature and culture collide, my thesis advocates for a synthesized view that recognize the multifaceted narratives woven through the sky—stories of technology, of culture, of grand dreams and of small melancholy.

Comparing Parameter Efficient Finetuning Techniques (PEFT) using Datamodels

2024-10-17T03:21:42Z

Comparing Parameter Efficient Finetuning Techniques (PEFT) using Datamodels Chamdal, Harshal Advances in machine learning, particularly through algorithmic innovations and large datasets, have led to models with hundreds of billions of parameters. Deploying these models is challenging and costly, especially due to the extensive finetuning required. Parameter-efficient finetuning techniques (PEFT) have been proposed to address this issue by significantly reducing the number of trainable parameters, achieving comparable results to full-parameter finetuning. Despite widespread adoption, PEFT methods are often used interchangeably without considering their qualitative differences and performance under various data distributions. This thesis extensively compares three PEFT methods: LoRA, BitFit, and (IA)³, using the ModelDiff framework to identify and apply data interventions. Our analysis reveals that the performance of these methods varies widely with different interventions, with BitFit showing the most variance, while LoRA and (IA)³ demonstrate greater resilience. This study informs the selection and optimization of PEFT techniques based on specific NLP task requirements, balancing performance, computational efficiency, and robustness to text variations.

Gender Glitch Matrix: Queer Aesthetics and the Politics of Error in Digital Media

2024-10-17T04:10:13Z

Gender Glitch Matrix: Queer Aesthetics and the Politics of Error in Digital Media Akdoğan, Merve Situated at the intersection of digital media studies, queer theory, and glitch art, this thesis critically examines the normative biases and centralization in artificial intelligence (AI) and, more specifically, machine learning systems as they relate to marginalized identities. Unlike conventional approaches that prioritize optimization and polishing of AI, this thesis introduces the notion of a glitch—a short-lived digital error—as both a metaphorical and an artistic technique that critically subverts societal norms. The thesis interrogates AI’s structure, dissecting it to reveal “black box” complexities to question the vulnerability of computational systems. It proposes an alternative approach that embraces error as a means of resistance, developing a critical commentary on technology production through artistic interventions. Grounded in Judith Butler’s “Matrix of Intelligibility,” the artistic interventions introduced in this thesis aim to craft a glitch aesthetic that integrates queer theoretical perspectives with practical machine learning applications. This thesis interrogates how AI models can potentially propagate entrenched societal norms about gender, what the political errors made by AI systems are and what can be the activist potential of technology in challenging these cisheteronormative renderings. Aiming to develop and test machine learning models for identifying bias in digital media, this research is organized into four sections, beginning with the development of a theoretical framework and a review of relevant literature on AI errors and glitch art. Subsequently, the thesis explores the design of glitch prototypes through training and testing machine learning models. Finally, through experiments using these methodologies, including archival work, media manipulation, and attribution studies with AI models, this thesis reveals the AI systems’ deficiencies as they relate to queer identities. This work underscores the transformative potential of integrating artistic techniques to subvert and reveal technological development. It envisions technology not merely as a mechanism for perfecting systems but as a powerful conduit for advocating a more inclusive future.

Between City and Self: Reading Beirut in Mohamed Soueid’s Tango of Yearning

2024-10-17T03:57:52Z

Between City and Self: Reading Beirut in Mohamed Soueid’s Tango of Yearning Anouti, Ghida Set in Beirut in the aftermath of the Lebanese Civil War (1975-1990), the pseudo-documentary film Tango of Yearning (1998) follows the lives of several subjects who speak of love, loss, dreams, and cinema as they navigate their fragmented postwar city. Directed by underground Lebanese filmmaker Mohamed Soueid (b. 1959) and shot purely on video, the film is saturated with cinematic references, images of urban sites, sensual and religious symbols, and sociopolitical intimations. Soueid sees Tango of Yearning – the first in a trilogy titled Civil War – as an ‘obituary’ of his life prior to making this film. Hence, for him, the film is rooted in the past, yet I argue that it is a significant augury of Beirut itself as a palimpsest of urban memories sublimated by Soueid. This argument is nestled between Soueid’s assessment of his film as a personal work of cinema, and my own reception of it as symptomatic of Beirut’s history in the periods prior to, during, and after the Civil War. Tango of Yearning is, at its core, a meditation on the city of Beirut as it transformed throughout various periods governed by the traumatic event of the Civil War. Through a close reading of the film, I reveal how an ostensibly private essay is also a medium for archiving memories either forgotten or suppressed by the nation’s contested amnesia of the war, while also investigating how the postwar city’s history intertwines with the filmmaker’s biography. A largely unrecognized yet significant contributor to the Arab world’s video and cinema scene, Soueid – an agent, actor, and narrator of the city – is one of the most sensitive chroniclers of life in Beirut during the 1990s and early 2000s. Weaving historical realism with fabulation to fill or distort representational lacuna, his film offers doubled lenses – one of his life and another of Beirut’s contemporary history. Through a chronological reading of an otherwise nonlinear film, I extract a history of Beirut in three stages: its cosmopolitan yet polarized 1960s with a brimming arts, film, and literature scene; its violent war characterized by sectarianism and fragmented nationalism; and its amnesic postwar era in which the film was created. Accordingly, I ask how Soueid’s private image-making apparatus draws an image of Beirut through his own autobiographical narration.

Damp Skin: Portraits of Taiwanese Domesticity, Resilience, and Otherness

2024-10-17T03:48:52Z

Damp Skin: Portraits of Taiwanese Domesticity, Resilience, and Otherness Chan, Cheng-Hsin This thesis is an intricate exploration of Taiwanese life under the constant dampness, weaving together the present with historical threads and personal memories of home and motherhood alongside broader socio-historical narratives. It examines Taiwanese domesticity through the dual prisms of “dampness” and “enclosure failure” to reveal how these elements influence or fail to meet Taiwanese people’s physical comfort and needs. Central to this research is exploring the historical marginalization of the Taiwanese body in domestic spatial development under the influence of external powers. Damp Skin unfolds through three intertwined registers that offer diverse materials and perspectives spanning time and space, providing a layered understanding of Taiwanese history and contemporary experiences: I. Home, Memory, and Motherhood, II. Planetary Climate and Body, and III. Domesticity and Architectural Enclosure in Taiwan. This thesis argues the continuous repositioning of our bodies (ourselves and family) in response to external factors — climate, society, and power. It serves to revisit the past, document the present, and speculate on the future, enhancing our understanding of everyday life in Taiwan and exploring potential cultural adaptations. Each thread collects materials and offers distinct perspectives on Taiwanese identity and space’s historical and contemporary shaping. Together, they form portraits of the complexities and nuances of Taiwanese domesticity, resilience, and otherness, framed through the intimate and expansive lens of dampness and enclosure.

The Cycles of aMaízing Things

2024-10-17T03:40:54Z

The Cycles of aMaízing Things del Busto, Juan Manuel Chávez Fernández Throughout this thesis, maíz becomes a trans-scalar agent of exchange across time, cultures, and territories. Maíz, as both a symbol and a subject, is intensely charged with tradition and disruption, operating within a jumbled feedback state that transcends myths and industry. The work situates my reading of the artwork, Río Revuelto by the Mexican artist José Chávez Morado (1949), as a guiding framework to approach a kaleidoscopic entanglement of different narratives. Considered under four different lenses: the cosmological, the national identity, the resistance, and the product, I argue for constant feedback between them for the re-transforming cycles of maíz. The crucial concern driving this exploration is how maíz and humans are ingrained into each other's systems — re-configuring methods, spaces, and forms of display. The display refers not only to maíz as a ‘product’ but as a continuous entity in transition, transforming and adapting to the social and cultural conditions where it circulates —whether through myth, ritual, portrayal, strategy of preservation, building typology, commodity, by-product, or history. The design approach is presented through performative artifacts that symbolize the systems through which maíz circulates. They are further represented in an essay film. Whether referencing myths, projections, displays, or products, the artifacts become mnemonic objects to think with—depicting the cycles of maíz as a world-building exercise. Maíz becomes the point that traces simultaneity in the history of humanity, representing a symbol eternally under construction. Acknowledging this monumental scale requires my work to be only a grain-sized glimpse of speculative potentials in design.

Alive Scene: Participatory Multimodal AI Framework for Collective Narratives in Dynamic 3D Scene

2024-10-17T03:17:10Z

Alive Scene: Participatory Multimodal AI Framework for Collective Narratives in Dynamic 3D Scene Cheng, Chi-Li This thesis introduces "Alive Scene," an online participatory platform for recording dynamic 3D environments and building collective interpretations of objects, events, and atmospheres within them. For instance, a user can browse a recording of a room and describe objects or events to locate them; or select a time frame, adjust the camera angle, and add a comment to share a new narrative of the scene with others. Unlike traditional digital formats such as simple videos or 3D models, this platform is both three-dimensional and temporal at the same time, and the views are searchable using natural language sentences and sorted by relevance. By building the platform and testing it with human subjects, this thesis demonstrates that such a new participatory media of dynamic 3D environments fosters communal knowledge and enhances the spatial understanding of individual users. Alive Scene produces rich, semantic-level communication among users, akin to the dynamic propagation of cultural memes. The Alive Scene System integrates two advanced techniques: 3D scene reconstruction using Gaussian splatting, and semantic linking of human perceptions through the Contrastive Language-Image Pretraining (CLIP) model. These methods are currently among the most popular and efficient. The platform continually enriches its collection of users' views and interpretations through interactions with this semantic AI system, enabling the archiving of user inputs and suggesting new avenues for exploring diverse perspectives. The streamlined interaction interface promotes user engagement and facilitates the discovery of related views and perceptions. The user test employs a dynamic 3D scene of a student lounge, recorded at four different times, and involves 20 participants generating a total of 235 inputs. Four types of interactive behaviors were observed regarding users' views and interpretations: Disagreement, Simple Agreement, Sharing Perception by adding comments, and Adjusting Views. The analysis indicates evolutionary trends: Initially, users’ express disagreements and provide objective, general comments. As the platform gathers these inputs, a transition occurs where users begin sharing more subjective information and reinterpreting others' views. Eventually, users adjust camera angles when the captions are agreeable. Visualizations of this analysis illustrate that these dynamic behavioral changes facilitate the development of collective perception. For further investigations, this study could benefit from incorporating more elaborate 3D scenes, additional recording times, and a larger number of participants.

Beyond the Bioclimatic Chart: An Automated Simulation-Based Method for Assessing Natural Ventilation and Passive Design Potential

2024-10-17T04:09:36Z

Beyond the Bioclimatic Chart: An Automated Simulation-Based Method for Assessing Natural Ventilation and Passive Design Potential Herb, Svenja Technological advancements in the building industry have significantly transformed climate and comfort control in buildings. This allows for air conditioning in deserts and heating in the Arctic, ensuring occupant comfort. This innovation, however, has contributed to a homogenization in architectural designs globally, from the hot climates of Mumbai to the cold environments of Boston, and moderate settings like London. Such uniformity often overlooks local climatic conditions, resulting in increased energy consumption and elevated greenhouse gas emissions. Climate-responsive design on the other hand creates solutions that leverage local climates—such as through natural ventilation and optimal solar gain management—to reduce energy consumption. Depending on climate and program, the coordinated use of these passive design strategies may or may not lead to indoor thermal comfort conditions without the need for an air-conditioning system. There are two primary approaches to explore the passive design potential of a building during schematic design: Bioclimatic chart and building energy modeling (BEM). The former method is a key feature in building science textbooks and is solely based on widely available local weather data. It provides general design advice without requiring previous knowledge or the need to describe the building program. BEMs facilitate detailed testing of how a building is operated and how the above listed passive design techniques can be combined to obtain the highest possible comfort conditions and energy savings. However, the use of BEM has traditionally been more complex and time-consuming to use as it requires significant knowledge of the underlying building physics and numeric methods to mimic them. This thesis evaluates the bioclimatic chart's accuracy in predicting overheating hours associated with various passive design strategies, through comparison with BEM data. Furthermore, it introduces a new simulation-based approach called “ECOmpass”. ECOmpass automates early-stage design simulations and offers design recommendations for passive strategies with just one click.

Alternate Imaginaries for the Kinara: River Ravi’s Edge as a Threshold

2024-10-17T03:47:26Z

Alternate Imaginaries for the Kinara: River Ravi’s Edge as a Threshold Khalil, Mahwish In the lower riparian landscape of Punjab, Pakistan, various communities confront the challenges of living within the active floodplain of river Ravi as it flows alongside the city of Lahore. These communities navigate the dissonances of the river’s edge—its Kinara, marked and molded by persistent colonial (mis)representations rooted in practices of erasure and division. Stepping away from historical depictions that have reduced the river to a mere resource for acquisition, this thesis engages with design and the oral tradition of storytelling, known as Qissa Khwani, to propose new modes of knowing, witnessing, and ultimately, cultivating alternative imaginaries for Ravi. This thesis seeks to illuminate the overlooked narratives of a river and its communities by drawing inspiration from, and centering the voices and legacies of, those most impacted by regressive depictions of a linear floodplain. It stages newer encounters and engagements with Ravi and its communities by stitching together stories of numerous community members, the dwellers, the boatmen, and the civil defense divers, actively defying and transforming the seemingly static Kinara—their home—through cultural and economic production. These pluralistic alternatives serve as a deliberate departure from the current large-scale, mega-urban development projects planned for the riverfront, which not only overlook the communities living along its banks but also employ idealized depictions of Ravi to attract capital. Finally, this thesis questions how the river's edge can be remapped to allow for the dismantling of top-down visions while addressing an urgency embodied within the shallow, receding flows of a polluted river, whose uncertain future remains contingent on distinct lines.

Disrupting Monocultural Tendencies through Multimodal Montage

2024-11-12T18:32:17Z

Disrupting Monocultural Tendencies through Multimodal Montage Singha, Mrinalini This thesis contends with the pervasive impact of monocultural tendencies as manifested in the political, cultural, and media landscapes of contemporary India, particularly focusing on the unfolding context of 2024. Amidst an intensifying crisis marked by polarization, historical erasure, and the rise of hegemonic nationalism, this thesis posits art, particularly through the framework of `multi-modal montage,' as a agent of political disruption for `redistributing the sensible.' Tracing the aesthetic and political evolution of montage from its early 20th-century origins in Soviet cinema to its contemporary forms, the thesis outlines the transition from montage defined by collision and conflict to the soft, spatial, and interactive practices of figures such as Nam June Paik and Harun Farocki. It further investigates how `surface tension' and `unquiet objects' manifest within the multi-modal montage in the works of artists like Nalini Malani, Krzysztof Wodiczko, Shilpa Gupta and Nida Sinnokrot. As an Indian artist, the author situates her own practice within this discourse, highlighting projects such as `The Whistleblower' (2023), a tangible archive within an everyday object, and `A Mystery for You' (2023-24), a fact-checking game that merges a tangible interface with a large language model (LLM). These works exemplify the thesis's argument that artistic interventions can critically challenge and reframe dominant sociopolitical narratives, offering new perspectives and resistances against the monocultural hegemonies. Extending this analysis, the author discusses her exhibition 'Forensic Artifacts of a Democracy in Crisis' (2023) as an operative space. Through a curated assemblage of works, the exhibition provided a physical space for interaction, reflection, and conversation, enabling audiences to engage with the themes of the thesis viscerally. In all, this thesis argues for the critical role of art in challenging memory and forgetting, from fabricated histories to the fall and rise of monuments. From the polarization of media to the flattening of identities, of echo-chambers and absences and grand narratives.

Designing (with) Trees: Active Agents in Architectural Production

2024-10-17T03:37:17Z

Designing (with) Trees: Active Agents in Architectural Production Garinois, Laura-India This thesis embarks on a multifaceted exploration of the relationship between urban trees, architectural representation, and the legal framework governing their existence, with a particular focus on tree hearings in Boston as a platform for this study. Against the backdrop of capitalist influences shaping urban landscapes, standardized modes of representation often prioritize economic interests, relegating urban trees to two-dimensional depictions in architectural drawings. Such representations obscure the rich complexity and ecological significance of trees, thereby shaping design choices that threaten their vitality. Amidst these challenges, Massachusetts has initiated efforts towards granting public trees legal recognition, providing a foundation upon which this study builds on to advocate for further improvements in tree rights and protections. This encompasses tree hearings, where developers and residents seek permission for the removal of healthy public trees, involving municipal authorities, tree wardens, and local communities. Through extensive dialogue with experts and stakeholders dedicated to this cause, the thesis identifies loopholes within existing laws and institutional frameworks, leading to the development of a tree appraisal system that employs alternate representations of trees that encourage new ways of valuing their role within architectural thinking and production. The exploration examines how a more nuanced collaboration with trees in design processes can enhance the value of architecture, and how design can in turn contribute to the protection of trees. Ultimately, the goal is to enrich tree hearing conversations by recognizing them as reflections of a larger climate conversation around trees and nature. By intervening in their legal site and imagination, the thesis fosters a more inclusive dialogue that transcends the binary decision of whether to cut down a tree or not.

What is Ecology?

2024-10-17T03:03:46Z

What is Ecology? James, Aubrie R. M. There are many ways to try to make sense of that which is. Ecology, which deals with organisms in relation to their environments, makes sense of that which is through the study of relations among and between organisms and their environments. Modern ecology is predominantly understood as a scientific enterprise. However, science as a methodology is too often aligned and entangled with extractive, capitalist logics: the cycle of enclosure–dispossession-scientific practice-imperial expansion not only undergirds and defines the ecological crises of our times but forecloses our ability to conceive of the diverse ways in which life is configured. For ecology, this is a predicament of ethics, yes, but also of a cleareyed understanding of what is (and our relationship to it). The urgent question for ecologists given this predicament is to ask is how to break out of this cycle. This thesis explores the potential of building an artistic practice to question the forms of ecology: how it is conducted, how it is communicated, and what it produces. Drawing inspiration variously from feminist, postcolonial, and ecosocial art, media theory, and philosophy, this thesis probes the limits of ecology under the suspicion that the point of leverage for change is to differently enact how we think, make, and do in relation to the world in, around, and constituting us.

How Much Does It Really Cost? A Dynamic Approach to Building Retrofit Costs for Decarbonization Pathways

2024-10-17T03:36:42Z

How Much Does It Really Cost? A Dynamic Approach to Building Retrofit Costs for Decarbonization Pathways Kirkeby, Amanda Carbon emissions are driving the planet out of its delicate Goldilocks balance. Evidence and the call-to-action date back to 1896 with Swedish scientist Svante Arrhenius and his seminal paper that first predicted the effect of carbon dioxide on the global temperatures. With the Intergovernmental Panel on Climate Change (IPCC) goal of global net zero emissions by 2050, the urgency is stronger than ever. An ever-growing number of municipalities are setting pledges to do their part, often without a concrete plan. With buildings accounting for 40% of total global emissions, building retrofits are a key component to these pathways to zero carbon. Urban building energy modeling (UBEM) research efforts have developed physics-based decision-making tools to define city-scale technology pathways to reach climate goals. However, a crucial question in making these pathways actionable has been largely neglected: how much does it really cost? The scarcity of contemporary cost data and methods for cost prediction at the urban scale makes this question difficult, and further questions around equitable incentive programs nearly impossible to answer. This work demonstrates the concept and relevance of implementing a dynamic cost model in the UBEM context. Several cost models are applied to a case study of 13,000 residences in Oshkosh, WI to predict costs for homeowners to retrofit their homes with three different upgrade packages. A willingness to pay analysis is then performed with upfront cost predictions from different models, illustrating the impact a more robust cost model may have in providing more realistic predictions of an upgrade strategy’s techno-economic success. Through its compatibility with existing UBEM frameworks and local input costs, the dynamic building upgrade cost model hosts the potential to further support municipalities in developing economically feasible building retrofit strategies for decarbonization pathways.

Pathways to Net Zero: Financing Strategies For Low-Income Homeowners

2024-10-17T03:48:04Z

Pathways to Net Zero: Financing Strategies For Low-Income Homeowners Moore, Lauren Housing retrofits are crucial for accomplishing national housing sector decarbonization goals. Single measure retrofit improvements are not sufficient for low-income homes which are often in less-thanoptimal condition and are subsequently uncomfortable and expensive to operate. Comprehensive retrofit approaches are necessary to achieve the energy efficiency targets for the aging housing stock. Historic educational and economic barriers pose challenges for incentivizing low-income homeowners to retrofit their homes. Proactive strategizing that considers both educational and economic factors are needed to see increased retrofit adoption amongst these groups. Policy makers need an understanding of retrofit impact for more effective resource allocation and homeowners need better incentives, and tools to conceptualize the benefits, time commitment and cost associated with deep retrofits. To address this problem, we present a retrofit pathway modeling framework to accurately predict the time required to achieve comprehensive retrofits for the homeowner. Taking retrofit cost and annual energy saving into account, we are proposing a new Government sponsored and led financing program inspired by the successful 401(k) retirement plans and level 529 saving programs, which offers an either 2x or 3x match to the annual investment the homeowner commits to saving each year to ensure low-income homeowners are accounted for in the journey to building sector decarbonization by 2050 and beyond. For a case study home in the Grove Park neighborhood located in Atlanta, Georgia, hot water heat pump retrofits are the most impactful on building annual energy use but retrofits that have low cost and short payback periods such as installing LED light fixtures and low-flow showerheads are the recommended have the largest potential for shortening the years required to achieve comprehensive retrofits and therefore, are recommended for policy makers to incentivize in the community. Strategic financing can be used to ensure a financially feasible pathways for homeowners with varying annual budget amounts. For the example home, the program allows homeowners who invest only $50 annually to achieve comprehensive retrofits four times faster than if they only utilize existing incentive programs. Individual building energy simulation combined with socioeconomic analyses are needed to meet the needs of diverse low-income communities across the United States.

From Liquid to Stone: Reimagining the design of concrete structures for reuse

2024-10-17T03:01:14Z

From Liquid to Stone: Reimagining the design of concrete structures for reuse Donovan, Inge; Schnitzler, Jenna Every year, 360 million metric tons of concrete construction waste are sent to landfill in the United States, in large part originating from the demolition of economically obsolete buildings. Meanwhile, global demand for new concrete is accelerating – in 2021, the production of new concrete was responsible for up to 9% of global CO2e emissions, and our dependence on concrete is only expected to rise over the next 50 years. Concrete’s ubiquity is reinforced by its liquidity; it is simultaneously invisible and ever-present, undergirding global modernization through its cheap, local nature and its ability to take on any form in quick order. However, design with concrete has remained mostly unchanged, with inefficient, irreversibly fused structures cast in place to meet quickly changing programmatic needs, few of which survive longer than 30-50 years. Due to its careless application, concrete is perceived as a low-value material, and is therefore used wastefully, discarded quickly, and usually downcycled. The monolithic and inflexible nature of reinforced concrete structures perpetuate concrete’s culture of obsolescence and demolition. To meet emissions targets and demand for building, we need to close the loop by developing a circular economy of structural materials. Instead of reusing salvage materials that have already entered the waste stream, this thesis confronts the design of new concrete structures directly, presenting the design of and methodology behind Pixelframe, a precast kit of parts for reconfigurable concrete structures. In a future where buildings are increasingly seen as stockpiles for subsequent reuse, the reinvention of concrete structures is an imperative that presents an opportunity for a new tectonic – concrete is no longer a liquid poured once and cured on site, but instead is a material more akin to stone, retaining value across multiple lifespans.

Parametric PAINTOVER: Generating Design Models via Image Encoders and Latent Trajectories

2024-10-17T03:49:18Z

Parametric PAINTOVER: Generating Design Models via Image Encoders and Latent Trajectories Tas, Demircan Design is an iterative process where physical or virtual prototypes are created, rendered, evaluated and modified repeatedly. Sketches and direct manipulations are made on the rendered or fabricated mediums to create and communicate intended changes. Parametric design is a prominent paradigm in design and architecture where hand crafted functions map input parameters to a design space to rapidly generate samples. Direct modifications often lead to novel states outside the design space of a parametric model. Moreover, Parametric models are not cyclic, their input and output spaces are not interchangeable without human intervention. Models must be reconfigured to accommodate out-of-domain changes, preventing parametric design tools from being integrated into early phases of design where changes are commonplace. We propose latent spaces of large pre-trained auto-encoders as shared, design spaces for translating states of design among mediums and dimensions. We implement rendering and image encoding to use images as an interface among the outputs and inputs of the model, enabling users with direct modification via painting over. We use sketches, renderings, and 3d models for sampling latent spaces. We share experiment results acquired through linear interpolation and a custom spline implementation in latent spaces. We present samples from found latent trajectories matching to samples from ground truth parametric design models. We find that trajectories exist in latent spaces that approximate axes in parameter spaces. Using images and 3d models as input and output, we provide a cyclic, software agnostic tool for design generation with parameter approximation capabilities that generalize. We provide findings from experiments and present a software repository for parametric paintover including our sketch augmentation model Inverse Drawings and many-dimensional latent spline implementation L-NURBS.

Implementation of Machine Connectivity in Low-Volume High Variety Manufacturing Line

2024-10-17T03:09:15Z

Implementation of Machine Connectivity in Low-Volume High Variety Manufacturing Line Pal, Kanishk This thesis provides a comprehensive analysis and implementation plan for enhancing machine connectivity within a manufacturing facility at SLB. The study investigates the existing limitations of the facility's connectivity infrastructure and proposes an advanced connectivity software suite as a solution, presenting a compelling business case for its implementation. The software’s scope involved DNC (direct numerical control), allowing for line-by-line feeding of CNC code to machine controllers, as well as machine data collection for real-time shop floor monitoring. The research emphasizes the development and implementation of an advanced network infrastructure designed to improve efficiency, security, and data handling capabilities. There is discussion regarding cybersecurity practices, specifically those related to industrial control systems that leverage CNC machining processes. The software implementation process is detailed, highlighting the necessary steps and information required for successful integration. These include: 1) securing connection to critical CNC machine controllers, 2) acquisition of hardware including local server and network switch, 3) server bring-up through remote imaging and installation of standard monitoring tools and 4) implementation of software on edge devices for CNC file transfer and machining data collection. Additionally, the thesis discusses the limitations encountered during implementation and outlines future steps to address these challenges.

Capitalization of electric railways

2025-10-30T17:03:42Z

Capitalization of electric railways Zee, J. Zohn.; Zi, Su. Thesis: M.S., Massachusetts Institute of Technology, Department of Electrical Engineering, 1915

Dietary fatty acids, prostaglandins and infectious and endotoxin challenges in guinea pigs

2025-10-30T17:51:28Z

Dietary fatty acids, prostaglandins and infectious and endotoxin challenges in guinea pigs Mascioli, Edward A. Thesis: M.S., Massachusetts Institute of Technology, Department of Nutrition and Food Science, 1984; Vita.; Includes bibliographical references.

Telecommunication industry in Japan : comparative study of telephone market history between U.S. and Japan.

2025-10-30T18:06:07Z

Telecommunication industry in Japan : comparative study of telephone market history between U.S. and Japan. Yamanouchi, Ichiro. Thesis: M.S., Massachusetts Institute of Technology, Sloan School of Management, 1976; Bibliography: leaves 153-155.

Steady spinning of synthetic silk-like fibers and transient filament stretching of semi-dilute and concentrated polymeric fluids

2025-10-30T15:50:06Z

Steady spinning of synthetic silk-like fibers and transient filament stretching of semi-dilute and concentrated polymeric fluids Brauner, Octavia Flora, 1975- Thesis: S.M., Massachusetts Institute of Technology, Department of Chemical Engineering, 2001; Includes bibliographical references (p. 111-115).

Inosine-containing mRNA induces an innate immune response and is translated with lower efficiency

2024-10-10T03:01:08Z

Inosine-containing mRNA induces an innate immune response and is translated with lower efficiency Bao, Caroline Inosine is a nucleoside formed by deamination of adenosine by adenosine deaminases acting on RNA (ADAR). ADAR editing activity is known to play a key role in modulating the host cell’s immune response to RNA. Here, we specifically study the effect of the presence of inosine in RNA by generating an inosine-containing reporter mRNA sequence. We also generated mRNA sequences that contained pseudouridine, an RNA modification known to decrease immune response to in vitro transcribed (IVT) mRNA and elevate the expression of the encoded gene, to examine the interaction between pseudouridine and inosine modifications. While A-to-I editing activity is required for endogenous RNA to evade the innate immune response, our results show that inosine-containing IVT RNA induces an elevated immune response and is translated at a lower efficiency. This effect is dominant over pseudouridine modification, such that mRNAs containing both pseudouridine and inosine modifications still potently activate the innate immune response and exhibit a loss of translation. These results point to the potent immunostimulatory effects of inosine in transfected IVT mRNA. This elevated immune response is likely receptor-specific and we have demonstrated that it cannot be attributed to the sensors RIG-I, MDA5, TLR3, or PKR.

Improving Technology Adoption Process in Accounting and Finance Using Systems Thinking Methods

2024-10-10T04:07:24Z

Improving Technology Adoption Process in Accounting and Finance Using Systems Thinking Methods Chun, Albert Y. In the era of digital transformation, Accounting and Finance (A&F) functions face the challenge of making well-informed decisions about which technologies to adopt, which processes to prioritize, and why. These decisions require stakeholders to carefully evaluate available options, assess their implications and tradeoffs, and align diverse preferences to make well-supported investment choices. Conducting this process in a siloed and unstructured manner can lead to inefficiencies. This study explores the application of Systems Thinking (ST) and Systems Engineering (SE) methods, developing an integrated framework that combines Rich Picture, Object-Process Diagram (OPD), Design Structure Matrix (DSM), and Multi-attribute Tradespace Exploration (MATE) to enhance the technology adoption decision-making process within A&F functions. The focus is on Internal Audit (IA) as a case study for a simplified model and demonstration. While empirical data collection and hypothesis testing were not conducted due to data and time constraints, qualitative insights were gathered from industry practitioners. Key findings suggest that the integrated framework can potentially reduce the time and effort needed to reach technology adoption decisions. Providing a structured and comprehensive approach ensures that the decision-making process is more holistic, unbiased, and quantifiable. This can also offer post-implementation benefits, as the technologies adopted align better with the organization’s requirements and preferences, resulting in improved efficiency and effectiveness. This study extends the practical application of ST methodologies into A&F. By presenting this integrated framework, it contributes to the foundation for future research on applying ST to improve the technology adoption decision-making in A&F.

Red Teaming Language Conditioned Robotic Behavior

2024-10-10T04:00:05Z

Red Teaming Language Conditioned Robotic Behavior Abhangi, Nishant Natural language instruction following capabilities are important for robots to follow tasks specified by human commands. Hence, many language conditioned robots have been trained on a wide variety of datasets with tasks annotated by natural language instructions. However, these datasets are often limited in their size and hence the distribution and nature of the instructions given by real world users might be different from that in the datasets. This makes it unclear how these robots will perform in real world environments. Hence, a large scale evaluation with diverse instructions is needed to benchmark the performance of these robots. However, using humans to collect more annotations is prohibitively expensive. We show that recent large language models provide a scalable and inexpensive way to do such an evaluation. Moreover, there is a large performance drop in robots when evaluated on this larger set of instructions. We also show that we can use different prompts to LLMs to control properties such as diversity of the generated instructions.

Instrumenting Observability in a Decentralized Microservice Architecture

2024-10-10T03:01:35Z

Instrumenting Observability in a Decentralized Microservice Architecture Liu, Helen X. Software systems have increased in complexity over time, and with this increased complexity has come an increased need to keep these systems organized and functioning efficiently. Observability is closely attached to ensuring this correct and effective system function. Without system monitoring, it is difficult to pinpoint when errors occur and correct them at their sources. Monitoring systems also helps to understand a system from the outside by allowing developers to ask questions about the system’s state and function without needing to know the details of what comprises the system’s internal behavior. While there are existing solutions for observability frameworks, these solutions do not target microservice architectures, which are used more and more with expansive code bases, such as those likely to be employed in an industry environment. They also require extensive configuration to be fully integrated with a pre-existing system. As such, the challenge lies primarily in adapting observability solutions to a decentralized, microservice architecture found in an industry setting. The existing solutions also come with advantages and disadvantages for different situations, so they are often incomplete in addressing an entire system’s needs. The integrated system created here satisfies our system’s requirements of a consolidated observability platform while also enabling future customizations, thereby allowing problems to be identified more quickly and proactively.

SongGen: Framework for Controllable AI Song Generation through Interactive Songwriting and Artist Emulation

2024-10-10T03:36:05Z

SongGen: Framework for Controllable AI Song Generation through Interactive Songwriting and Artist Emulation Arora, Ajay We propose SongGen, an AI-based song-writing and song co-creation framework. Building upon existing AI tools like Suno.ai, SongGen features a chat interface with a trained AI songwriter assistant, emulating the traditional back-and-forth of human collaboration. The system offers enhanced capabilities for greater control over the songwriting process, including concept ideation, lyric generation and editing, real-time song generation, and granular instrumental specification. Comparative evaluations demonstrate SongGen’s superiority in key metrics such as steerability, expressiveness, personalization, and user satisfaction. We also present an extension of the SongGen framework for artist emulation and on-demand song generation. Future development aims to incorporate voice-based interaction and real-time voice conversion, enabling music artists to guide fans in creating personalized songs.

Hofstadter Physics and Composite Fermionic Phase in Moiré Systems

2024-10-10T04:07:10Z

Hofstadter Physics and Composite Fermionic Phase in Moiré Systems Ding, Shuhan This thesis explores the intricate electronic phenomena in Moiré systems, particularly focusing on twisted bilayer transition metal dichalcogenides (TMD). These systems, with their unique superlattice structures and strong electron correlations, provide fertile ground for investigating novel quantum states. A key focus is on understanding Hofstadter physics and the emergence of composite fermion phases in these materials. In this work, we first develop a continuum model to describe the low-energy electronic structure of twisted TMD bilayers, emphasizing the role of the Moiré superlattice in modifying the band structure and introducing non-trivial topological properties. We analyze the resulting Hofstadter spectrum under an external magnetic field, revealing the rich fractal pattern and the impact of valley polarization induced by the magnetic field. Building on this framework, we delve into the concept of composite fermions, particularly in the context of the fractional quantum Hall effect (FQHE). We extend Jain’s composite fermion theory and the Chern-Simons field theory to Moiré TMD systems, proposing the existence of an anomalous composite fermion liquid state at half-filling. Through a detailed mean-field analysis, we demonstrate that this state, characterized by a strong valley polarization and an effective magnetic field arising from Berry curvature, could be energetically favored under certain conditions. Our findings suggest that Moiré TMDs are promising candidates for realizing fractional Chern insulators and other exotic quantum phases, opening up new avenues for experimental exploration and potential applications in quantum technology.

Condensed Buck-Boost Switched Capacitor Converter for Efficient Voltage Distribution in Electrified Aircraft

2024-10-10T03:30:56Z

Condensed Buck-Boost Switched Capacitor Converter for Efficient Voltage Distribution in Electrified Aircraft Aron, Aklilu Switched capacitor converters are a category of power electronic converters that harness the significantly improved energy density of capacitors as opposed to that of their conventional, inductor-based counterparts to reap benefits in terms of efficiency, size, and utilization. This work presents the analysis, design, construction, and evaluation of one such converter, inspired by the flying capacitor multilevel topology and referred to as a condensed buck-boost converter. This converter is designed and built for an application as the interface between the battery voltage and DC bus on partially electrified aircraft, where the advantages of its ability to step up/down voltage in an efficient and lightweight fashion can be fully realized. In order to be implemented in hardware for the first time, this work utilize new monolithic, bidirectional GaN FETs, whose reverse voltage blocking capabilities open new possibilities for a converter design that wastes less power and occupies less board area. This converter is compared with others that perform similar functions to showcase the benefits that this topology has to offer.

Cellulose Nanofoams: 3D Printing and Characterization

2024-10-10T03:44:40Z

Cellulose Nanofoams: 3D Printing and Characterization Padia, Vineet In recent years, the advancement in cellulosic nanofoams has been considerable. Yet, their customization potential for diverse application requirements has been constrained by reproducibility challenges. Our research, therefore, focused on two primary objectives: enhancing the thermal regulation capabilities and mechanical properties of cellulose nanofibrils (CNF) nanofoams, and developing a reproducible methodology for printing customized three-dimensional (3D) structures using direct-ink-write (DIW) technology and molding. We developed composite nanofoams using TEMPO-modified cellulose nanofiber (TCNF). The resultant composite nanofoams showcased remarkable properties such as ultra-low thermal conductivity, low density, outstanding flexibility, and infrared shielding capabilities. In a bid to create robust and environmentally friendly nanofoams, we employed a crosslinking process with CaCl2. The crosslinked nanofoams were extraordinarily lightweight yet boasted superior mechanical properties, significantly amplified by the crosslinker. Remarkably, these freeze-dried T-CNF/CaCl2 nanofoams maintained their form and demonstrated admirable flexibility, even when subjected to weight exceeding thousands of times their own. Furthermore, transient characterization confirmed their excellent thermal insulation capabilities. In conclusion, our research has pioneered the fabrication of sustainable, high-stability cellulose nanofoams. We have significantly enhanced the thermal management capabilities and mechanical performance of these nanofoams, marking a remarkable advancement in the field. The demonstrated sustainability, biocompatibility, ultra-light weight, high porosity, and deformability of the resultant nanofoams suggest considerable potential for diverse applications, including thermal insulation, shock and vibration damping, as well as tissue engineering.

Development of Machine Connectivity Guidelines for Production Floor

2024-10-10T03:01:11Z

Development of Machine Connectivity Guidelines for Production Floor Sehnawi, Kenan Hayel This thesis introduces and uses a standardized method for assessing machine connectivity at manufacturing facilities and develops a roadmap for an organization looking to implement connectivity at its facilities. As technology rapidly advances and Industry 4.0 takes hold of manufacturing worldwide, it is essential for manufacturing companies to utilize the latest technology to maintain a competitive advantage by optimizing operations, improving productivity, and increasing throughput. In this work, an overview of machine connectivity and its benefits are presented, and technologies and security measures used for connectivity are explored. Upon compilation of this information, a comprehensive rubric was developed with six weighted connectivity criteria, each scored from 0 (no progress) to 4 (fully complete), from which a total connectivity score can be computed. The rubric serves as a guiding tool for gauging a manufacturing facility’s level of maturity with regards to connectivity, and helps identify areas of need both within a facility and within an organization as a whole. The connectivity levels of six different manufacturing facilities were assessed using the rubric. The results were compiled to understand the development of connectivity at different facilities across the organization. The learnings from this analysis are used to develop guidelines as the organization continues its push towards full connectivity across all of its facilities. The next steps in this initiative are to: 1) utilize the developed rubric to assess connectivity at all of its manufacturing facilities, 2) identify facilities in need of the most resources in order to plan and execute connectivity, and 3) encourage collaboration between facilities to expedite the connectivity implementation process.

Cycle Time Reduction for CNC Machining Workcells in High-Mix Low-Volume Manufacturing

2024-10-10T04:03:44Z

Cycle Time Reduction for CNC Machining Workcells in High-Mix Low-Volume Manufacturing Sun, Brandon Christopher The demand for the product under investigation exceeds the available manufacturing capacity, with the CNC milling workcell identified as the bottleneck operation. This research, conducted in an active, high-mix, low-volume production environment, focuses on evaluating and implementing improvements to CNC machining parameters to enhance the workcell's capacity. Key areas of investigation include machining speeds and feeds, depth of cut, machine settings, toolpath strategies, stepover percentages, and alternative tooling. The study specifically targeted the initial roughing operation, which uses a feed mill and is the longest milling process. Addressing the challenges of high mix and low volume, the research successfully optimized machining and CNC programming parameters, reducing total machining cycle times by 25% and resulting in a 33% increase in throughput. Additionally, the methodologies and findings from this work have provided a framework for implementing further milling process improvements outside of the roughing operation, demonstrating their applicability to similar production scenarios.

Development and Execution of a Testing Strategy for Omnidirectional Wheels

2024-10-10T03:10:42Z

Development and Execution of a Testing Strategy for Omnidirectional Wheels Donnellan, Michael J. Omnidirectional wheels enable robots to achieve holonomic motion; however, this often comes at the cost of increased rolling resistance compared to traditional caster wheels. The rolling resistance in omnidirectional wheels is higher than in many other wheels due to several factors including an irregular tread shape, material compliance, and friction in the bushinglike cross rollers during lateral motion. Testing standards exist for characterizing the rolling resistance, compressive strength, and other attributes of commonly used wheels such as caster wheels. However, there are no comprehensive testing standards or research that broadly characterize the performance of omnidirectional wheels. Here, test methods are described for characterizing the load relaxation, stiffness, and rolling resistance of omnidirectional wheels, and the results from these tests are presented. Test apparatuses for static loading and rolling resistance were created. Test results were analyzed to determine important factors for determining the ultimate compressive strength in static loading and the rolling resistance coefficient of an array of omnidirectional wheels, and results indicate wheel manufacturing methods and materials are the most important factors for determining these responses.

The Power of Perception in Human-AI Interaction: Investigating Psychological Factors and Cognitive Biases that Shape User Belief and Behavior

2024-10-10T04:08:46Z

The Power of Perception in Human-AI Interaction: Investigating Psychological Factors and Cognitive Biases that Shape User Belief and Behavior Lee, Eunhae This thesis investigates the psychological factors that influence belief in AI predictions, comparing them to belief in astrology- and personality-based predictions, and examines the "personal validation effect" in the context of AI, particularly with Large Language Models (LLMs). Through two interconnected studies involving 238 participants, the first study explores how cognitive style, paranormal beliefs, AI attitudes, and personality traits impact perceptions of the validity, reliability, usefulness, and personalization of predictions from different sources. The study finds a positive correlation between belief in AI predictions and belief in astrology- and personality-based predictions, highlighting a "rational superstition" phenomenon where belief is more influenced by mental heuristics and intuition than by critical evaluation. Interestingly, cognitive style did not significantly affect belief in predictions, while paranormal beliefs, positive AI attitudes, and conscientiousness played significant roles. The second study reveals that positive predictions are perceived as significantly more valid, personalized, reliable, and useful than negative ones, emphasizing the strong influence of prediction valence on user perceptions. This underscores the need for AI systems to manage user expectations and foster balanced trust. The thesis concludes with a proposal for future research on how belief in AI predictions influences actual user behavior, exploring it through the lens of self-fulfilling prophecy. Overall, this thesis enhances understanding of human-AI interaction and provides insights for developing AI systems across various applications.

Quantifying Emissions and Costs of Geologic Hydrogen: An Integrated Lifecycle Emissions and Techno-economic Approach

2024-10-10T04:10:06Z

Quantifying Emissions and Costs of Geologic Hydrogen: An Integrated Lifecycle Emissions and Techno-economic Approach Blackford, Timothy In the pursuit of sustainable energy solutions, this thesis explores the lifecycle emissions and economic feasibility of geologic hydrogen production. This research extends Brandt's 2023 study of 'prospective' lifecycle assessment (LCA), enhancing the underlying open-source LCA model used in this work and adding a preliminary techno-economic analysis (TEA). The findings demonstrate that geologic hydrogen developments should have emissions intensities that compare favourably to all other hydrogen production pathways. The value of lifetime emissions intensity for Brandt’s Baseline case is estimated at 0.40 kgCO2e/kgH2, representing an increase of ~6% over Brandt’s estimation. The study also highlights the potential for geologic hydrogen to achieve competitive levelized costs (estimated at $1.45/kg), making it a promising candidate in the hydrogen economy. It finds that to achieve the best possible emissions and economic results, proponents of geologic hydrogen developments should seek to maximise the productivity of each well. It also studies the impact of the United States regime of production tax credits for hydrogen, finding that the fivefold increase in the magnitude of credits for meeting employment conditions is generally more impactful than lowering emissions intensity. The thesis underscores the importance of continued refinement of LCA and TEA models to understand geologic hydrogen resources better and ensure they are developed appropriately.

From Shipyard to Sea: A Flexible System Design Approach to the Transition from Shipbuilding to Operations A Case Study Using the United States Coast Guard Offshore Patrol Cutter Program

2024-10-10T03:49:53Z

From Shipyard to Sea: A Flexible System Design Approach to the Transition from Shipbuilding to Operations A Case Study Using the United States Coast Guard Offshore Patrol Cutter Program Kime, Jeremy A. The United States Coast Guard faces significant challenges transitioning new ships from shipbuilding to operations. Historically the low volume and irregular pace of major ship deliveries, combined with diverse homeporting factors, have resulted in anomalous post-delivery requirements. Today, a growing fleet, personnel shortages, and sweeping technological advancements are amplifying the complexity of post-delivery activities. At the same time, the Coast Guard is engaged in its largest shipbuilding effort since World War II, with seven acquisition programs scheduled to deliver 134 new ships over the next 15 years. In light of these factors the current approach, which places significant strain on crews, escalates costs, and delays operational use of the Coast Guard’s newest assets, warrants thorough examination. This thesis examines the issue through case study analyses using the Offshore Patrol Cutter (OPC) Program. The Coast Guard’s challenges are driven by three primary factors: the inherent uncertainty in ship construction, sociotechnical system dynamics associated with organizational management of pre-commissioning crews, and the ongoing evolution of technology. To address these challenges, this analysis employs an integrated approach, synthesizing principles and techniques from Architecting Innovative Enterprise Strategy (ARIES), Flexible Engineering Design (FED), and System Design and Management (SDM). This systems thinking approach aims to develop opportunities to reduce costs, improve schedules, and optimize workforce outcomes. The analysis recommends a three-phased strategy that could yield cost savings on the order of $400 million over the OPC Program’s lifespan, significantly mitigate risks associated with unforeseen shipbuilding developments, and enhance organizational outcomes regarding workforce, operational availability, and life cycle sustainment. The staffing of pre-commissioning crews is pinpointed as a pivotal discretionary event that triggers an exponential increase in system complexity and a surge in scope by introducing interdependent yet organizationally disparate requirements. Consequently, major personnel activities are decoupled from highly variable ship construction milestones. This paves the way for a paradigm shift from fixed to flexible approaches, replacing fragmented, ad hoc approaches with a flexible system architecture capable of continuous enterprise learning and improvement. Dynamic post-delivery activities are reimagined as a continuous business line, to professionalize the transition of new ships from shipbuilding to operations.

The Intangible Reverberations Following Mergers & Acquisitions

2024-10-10T03:05:44Z

The Intangible Reverberations Following Mergers & Acquisitions Warren, Laura N. This study preliminarily investigates how merger and acquisition (M&A) activities affect employees as stakeholders of the company system - specifically in the areas of leadership, communication, company direction, project autonomy, and path for career growth. Interviews of 14 employees supporting the oil and gas industry were conducted to determine the effect (if any) that M&A activities had on their careers and any similarities in their experiences. This data was evaluated against research completed by Steigenberger & Mirc and Schweizer & Patzelt. While the hypotheses presented cannot be proven, recommendations for future research are provided to gain and evaluate additional information.

Experimental Evaluation of Underwater Semantic SLAM

2024-10-10T03:07:04Z

Experimental Evaluation of Underwater Semantic SLAM Song, Thomas Jeongho Autonomy is crucial for underwater vehicles due to the challenging and inaccessible nature of underwater environments. These environments pose significant difficulties for human-operated systems because of limited visibility, high pressure, and vast areas that are costly and risky to explore manually. Implementing autonomy in underwater vehicles presents unique challenges due to the marine environment's harsh and complex nature. Underwater communication is severely limited as water absorbs and scatters most electromagnetic signals used in terrestrial communications. This necessitates the use of acoustic communication, which has a lower bandwidth and is prone to delays and signal distortion. Similarly, GPS signals do not penetrate water, complicating navigation and creating dependence on inertial and sonar sensors, which suffer from noisy measurements that are guaranteed to drift over time. The unpredictable dynamics of underwater environments, including varying currents, lighting conditions and obstacles, further complicate autonomous navigation. As such, data collection while moving through a preplanned course is the traditional mission of the Autonomous Underwater Vehicle (AUV), defining the limitation of current technology. Higher-level missions such as search, surveillance, maintenance and manipulation require greater situational awareness, decision-making and navigation abilities, facilitated by processing semantic visual information and applying it to map generation and localization. To address the limited autonomy of current AUVs and enhance their capability for complex missions, this thesis presents the development and evaluation of a real-time, monocular visual-inertial semantic Simultaneous Localization and Mapping (SLAM) system for underwater environments, implemented on the cost-effective BlueROV2 platform. The research aims to enhance AUV autonomy and enable complex underwater missions through improved navigation and semantic mapping capabilities. Key contributions include the integration of a custom-trained object detector for underwater environments, adaptation of a hybrid SLAM algorithm combining Gaussian and Non-Gaussian landmarks for underwater operation, preliminary assessment of the SLAM system's accuracy using motion capture-based ground truth measurements, and comparative evaluation of the developed semantic SLAM system against state-of-the-art alternatives in an indoor pool experiment using the BlueROV2. This work addresses the challenges of underwater navigation and semantic mapping, offering a potential solution to extend the operational capabilities and mission complexity of affordable AUV platforms.

CAD-Based Geometry Representations for Monte Carlo Fusion Neutronics Methods and CSG vs. DAGMC Performance Tradeoffs in OpenMC

2024-10-10T03:02:13Z

CAD-Based Geometry Representations for Monte Carlo Fusion Neutronics Methods and CSG vs. DAGMC Performance Tradeoffs in OpenMC Du, Katelin Fusion reactors utilizing deuterium and tritium fuel produce high-energy 14.1 MeV neutrons, necessitating a thorough understanding of their behavior for effective reactor design. Neutron transport codes play a critical role in determining key parameters such as tritium breeding ratio, neutron wall loading, and heat deposition, vital for assessing operational considerations. Monte Carlo (MC) radiation transport methods have become standard in fusion neutronics due to their ability to handle energy and angular variables continuously. However, manual modeling of complex fusion geometries with traditional constructive solid geometry (CSG) methods remains labor-intensive, prompting the integration of computer-aided design (CAD) models into MC radiation transport. This thesis investigates the integration of CAD-based geometry representations into MC radiation transport, focusing on computational performance implications of the Direct Accelerated Geometry Monte Carlo (DAGMC) approach. This work examines different neutronics model representations, including CSG, Unstructured Mesh (UM), and DAGMC for the practical solutions they can provide for fusion neutronics needs. Tracking algorithms associated with each representation are explored, highlighting UM and DAGMC’s versatility in the way they integrate with CAD-based design processes. Performance comparison between CSG and DAGMC geometries in OpenMC is analyzed by evaluating particle simulation rates and memory usage across four progressively complex fusion-like models. Performance results reflect positively on DAGMC transport, but areas of future work are identified for more comprehensive results. From the lens of computational performance, this study contributes to determining the viability of CAD-based geometry representations for use in fusion-relevant MC radiation transport.

Feasibility of Vector Instruction-Set Semantics Using Abstract Monads

2024-10-10T03:53:10Z

Feasibility of Vector Instruction-Set Semantics Using Abstract Monads De Belen, Arthur Reiner Formalizations of instruction-set semantics help establish formal proofs of correctness of both hardware designed to implement these instruction sets and the software implemented against this specification. One such prior work1 formalizes a specification of a subset of the RISC-V instruction-set architecture using a general-purpose language, Haskell, using its monad and typeclass support to abstract over effects. Another member of the same family is the RISC-V V extension, which specifies instructions for operating on multiple data elements in a single instruction, which is useful for domains with high levels of data parallelism, such as graphics rendering and machine learning. In this work I examine the question of whether the same prior work can be extended to formalize the semantics of the vector extension. I answer this question with a tentative “yes”, backed by a partial specification in Haskell of a small but nontrivial subset of this vector extension, a translation of the same specification into Coq using hs-to-coq², and work towards demonstrating the utility of this specification.

Labeling Schemes for Improving Cilksan Performance

2024-10-10T03:21:55Z

Labeling Schemes for Improving Cilksan Performance Holla, Satya While race detection algorithms like SP-bags have provably good theoretical properties, large overheads exist in practice, which urges the need for performance optimization. In this thesis, I propose labeling schemes as a method of circumventing many of the expensive operations in Cilksan, an implementation of the SP-bags algorithm. The proposed labeling schemes give strands of a parallel program labels during the execution of Cilksan, allowing Cilksan to shortcut the processing of certain memory accesses if the label comparison allows. I describe and prove correctness for two labeling schemes, the procedure labeling scheme and the prefix labeling scheme, implement both in Cilksan, and measure their performance. While the results show that the overhead of maintaining labels is too high in my implementation, the labeling schemes manage to circumvent many of the memory access operations, suggesting the merit of a more performant implementation of the same schemes.