mit-6

The Non-Union Complaint System at MIT: An Upward-Feedback, Mediation Model

2026-05-05T03:00:59Z

The Non-Union Complaint System at MIT: An Upward-Feedback, Mediation Model Rowe, Mary P. Many corporations, universities, and other institutions have developed explicit, non-union complaint systems. The non-union complaint system at MIT is similar to those of several hundred other high-technology, white collar, and research oriented employers. The similarity is perhaps strongest with respect to what at MIT is called "redundancy": provision of enough, different, responsible channels for complaint that most people will find such a channel easily. The MIT complaint system is a mediation-oriented service, available with many other helping services, to all students and to all non-union employees including faculty and managers. It is meant to serve any member of the MIT community who has a concern. The emphasis is on communications, counselling, fact-finding, conciliation, and mediation, with adjudication where necessary. The complaints structure also serves as an explicit “upward-feedback™ channel, designed to help bring information to line managers in an orderly, timely and supportive fashion. This paper sets forth the policy (please see appendix) and a discussion of functions, structure and performance of the MIT complaint system. This is a very early paper about intra-organizational complaint systems. This is a draft version, and the revised article was subsequently published as: Mary P. Rowe, "The Non-Union Complaint System at M.I.T.: An Upward-Feedback, Mediation Model," Alternatives to the High Cost of Litigation Vol. 2, No. 4 (April 1984): 10-18. It was then reprinted in Riskin, Leonard L. and James E. Westbrook, Dispute Resolution and Lawyers, 392-404. St. Paul, MN: West Publishing, 1987. Also found in Westin, Alan F. and Alfred G. Feliu, Resolving Employment Disputes Without Litigation. BNA, 1988.

ENSO and West Pacific Seasonality Driven by the South Asian Monsoon

2026-05-05T03:06:49Z

ENSO and West Pacific Seasonality Driven by the South Asian Monsoon Tuckman, PJ; Smyth, Jane E; Li, Jingyuan; Lutsko, Nicholas J; Marshall, John The equatorial Pacific exhibits a clear seasonal cycle, with West Pacific SSTs being highest during boreal autumn and El Niño/Southern Oscillation (ENSO) events tending to peak during boreal winter. In this work, we use the concept of a monsoonal mode and idealized coupled simulations to show that the presence of a large land mass in the Northern Hemisphere can lead to these seasonal asymmetries. Specifically, warm air moving east from the Asian summer monsoon suppresses surface fluxes in the West Pacific, leading to increased temperature there during the following months. The warmth of the West Pacific in boreal autumn strengthens the Walker circulation and the zonal temperature gradient across the Pacific, leading to the growth of El Niño events during that season. In summary, the presence of the Asian monsoon north of the equator results in ENSO events preferentially growing during boreal autumn and peaking during boreal winter.

Diurnal Vegetation Moisture Cycle in the Amazon and Response to Water Stress

2026-05-05T03:05:07Z

Diurnal Vegetation Moisture Cycle in the Amazon and Response to Water Stress Asgarimehr, Milad; Entekhabi, Dara; Camps, Adriano Water stress in the Amazon is exacerbated by rising temperatures and reduced moisture levels. However, understanding forest responses to increased aridity is hindered by limited in situ water potential observations in the Amazon. Remote sensing of water content has emerged as a promising metric. Vegetation Water Content (VWC) diurnal dynamics is hypothesized to reflect water stress responses. Conventional sensors' low sampling rates impede capturing and studying sub‐daily VWC dynamics. Leveraging Global Navigation Satellite System Reflectometry (GNSS‐R) with unprecedented sampling rates, this study reveals significant disparities in morning and evening VWCs in the Amazon, for example, by 1.1 and 1.0 kg/ during the wet and dry seasons of 2019. A strong correlation between VWC (the difference between evening and morning VWCs) and vapor pressure deficit is observed in Amazonian peatland. This highlights the potential of VWC from innovative remote sensing techniques in elucidating water stress dynamics in critical ecosystems.

Dust Source Areas and Their Plume Extent Derived From Satellite Data Fields

2026-05-05T03:07:01Z

Dust Source Areas and Their Plume Extent Derived From Satellite Data Fields AlNasser, Faisal; Chehbouni, Abdelghani; Entekhabi, Dara In this study, prominent dust source areas are identified along with their plume extent using high temporal frequency satellite observations. Hourly dust plume observations of the Dust Belt from geostationary‐orbit satellites are analyzed for the 2017‐12–2022‐11 period. To identify dust source areas and their extents, we back‐track plumes to their source, assessing source areas in terms of emission frequency, contribution, and plume extent patterns. This method advances over traditional source allocation techniques that rely on polar‐orbiting satellites based on a few daily passes and meteorological wind fields for backtracking. Our findings indicate that Boreal summer is the most intense season for most sources, except in the Southern Sahara, which experiences winterly winds. Our analysis also reveals significant contributions from regions within the Sahara that experience expansive but infrequent dust storms, highlighting the importance of considering both frequency and magnitude in understanding dust emissions.

Contrasting Chlorine Chemistry on Volcanic and Wildfire Aerosols in the Southern Mid‐Latitude Lower Stratosphere

2026-05-05T03:07:20Z

Contrasting Chlorine Chemistry on Volcanic and Wildfire Aerosols in the Southern Mid‐Latitude Lower Stratosphere Wang, Peidong; Solomon, Susan Volcanic eruptions and wildfires can impact stratospheric chemistry. We apply tracer‐tracer correlations to satellite data from Atmospheric Chemistry Experiment—Fourier Transform Spectrometer and the Halogen Occultation Experiment at 68 hPa to consistently compare the chemical impact on HCl after multiple wildfires and volcanic eruptions of different magnitudes. The 2020 Australian New Year (ANY) fire displayed an order of magnitude less stratospheric aerosol extinction than the 1991 Pinatubo eruption, but showed similar large changes in mid‐latitude lower stratosphere HCl. While the mid‐latitude aerosol loadings from the 2015 Calbuco and 2022 Hunga volcanic eruptions were similar to the ANY fire, little impact on HCl occurred. The 2009 Australian Black Saturday fire and 2021 smoke remaining from 2020 yield small HCl changes, at the edge of the detection method. These observed contrasts across events highlight greater reactivity for smoke versus volcanic aerosols at warm temperatures.

The South Pole‐Aitken Basin: Constraints on Impact Excavation, Melt, and Ejecta

2026-05-05T03:06:47Z

The South Pole‐Aitken Basin: Constraints on Impact Excavation, Melt, and Ejecta Citron, RI; Smith, DE; Stewart, ST; Hood, LL; Zuber, MT The formation and evolution of the South Pole‐Aitken (SPA) basin is critical to relating large impact basin formation and modification to lunar geophysical evolution. Most prior models of the SPA impact were conducted in 2D, making it difficult to compare model output to the 3D crustal structure and ejecta distribution. In order to better constrain the parameters of the SPA impactor and the expected post impact distribution of crust and ejecta, we conducted numerical simulations of the SPA impact in 3D. We tested a wide range of impact parameters and constrained model results with recent geophysical data. We found the crustal structure of the SPA basin is best fit by an oblique impact (30–45°) of a 350–400 km diameter projectile impacting at 12–16 km/s. The impact excavated material from as deep as 80–120 km, and ejecta was deposited in a butterfly pattern with a forbidden region uprange of the impact.

Vegetation‐Generated Turbulence Does Not Impact the Erosion of Natural Cohesive Sediment

2026-05-05T03:06:21Z

Vegetation‐Generated Turbulence Does Not Impact the Erosion of Natural Cohesive Sediment Deitrick, Autumn R; Ralston, David K; Esposito, Christopher R; Baustian, Melissa M; Burgos, Maricel Beltrán; Courtois, Andrew J; Nepf, Heidi Previous studies have demonstrated that vegetation‐generated turbulence can enhance erosion rate and reduce the velocity threshold for erosion of non‐cohesive sediment. This study considered whether vegetation‐generated turbulence had a similar influence on natural cohesive sediment. Cores were collected from a black mangrove forest with aboveground biomass and exposed to stepwise increases in velocity. Erosion was recorded through suspended sediment concentration. For the same velocity, cores with pneumatophores had elevated turbulent kinetic energy compared to bare cores without pneumatophores. However, the vegetation‐generated turbulence did not increase bed stress or the rate of resuspension, relative to bare cores. It was hypothesized that the short time‐scale fluctuations associated with vegetation‐generated turbulence were not of sufficient duration to break cohesion between grains, explaining why elevated levels of turbulence associated with the pneumatophores had no impact on the erosion threshold or rate.

Local and General Patterns of Terrestrial Water‐Carbon Coupling

2026-05-05T03:05:35Z

Local and General Patterns of Terrestrial Water‐Carbon Coupling Short Gianotti, Daniel J; Entekhabi, Dara Terrestrial carbon uptake and water availability have coupled feedbacks; specifically water uptake for plant growth and soil drying via transpiration. While we might expect this coupling over time at arid sites, climatic water availability also widely covaries geographically with biomass variables that control photosynthetic rates. Using eddy covariance data globally, we find convex, positively-covarying relations between carbon uptake and a turbulent flux metric controlled by land surface moisture (r = 0.73 monthly across sites) at the site level. We estimate a general, empirical relationship based on site-wise water-carbon dynamics. Most sites, and the general relationship, show strong power-law dependence, implicating the role of sub-seasonal land-cover dynamics. We also find that long-term mean carbon/water states follow a similar convex relationship to the site-specific temporal dynamics. We discuss opportunities and caveats for space-for-time frameworks of carbon/water feedback processes globally.

The Transition to Double‐Celled Circulations in Mock‐Walker Simulations

2026-05-05T03:04:30Z

The Transition to Double‐Celled Circulations in Mock‐Walker Simulations Lutsko, Nicholas J; Cronin, Timothy W Mock‐Walker simulations have the potential to play a key role in a tropical model hierarchy, bridging small‐scale Radiative‐Convective Equilibrium simulations and global models of tropical circulations. We demonstrate that mock‐Walker simulations transition from single‐ to double‐celled overturning circulations as mean Sea Surface Temperature (SST) is increased, with the transition occurring near 300 K. The transition is robust to domain geometry and microphysical scheme, and is favored by larger SST gradients. The transition is associated with the development of a mid‐tropospheric minimum in the radiative‐subsidence velocity over the cold pool of the simulations, and is likely reinforced by zonal moisture and temperature fluxes between the warm and cold pools. Several methods of suppressing the transition are investigated, but all set‐ups produce a double‐cell at sufficiently warm mean SSTs. The striking dynamical transition of mock‐Walker simulations dominates their response to warming, though its relevance for observed tropical climate change is unclear.

Reply to Comment by Poppema and Wüthrich on “Momentum and Energy Predict the Backwater Rise Generated by a Large Wood Jam”

2026-05-05T03:07:18Z

Reply to Comment by Poppema and Wüthrich on “Momentum and Energy Predict the Backwater Rise Generated by a Large Wood Jam” Follett, Elizabeth; Schalko, Isabella; Nepf, Heidi Follett et al. (2020a, https://doi.org/10.1029/2020gl089346) developed an analytical model to predict backwater rise by log jams, using the size and packing density of logs and the jam length, as well as river slope and bed roughness. We show that the model formulas can be rewritten using the Froude number instead of river slope and roughness, thus improving their applicability in engineering practice. The equation terms and results of Follett et al. (2020a, https://doi.org/10.1029/2020gl089346) are found to be similar to those of the empirically derived formula by Schalko et al. (2018, https://doi.org/10.1061/(asce)hy.1943-7900.0001501). However, some differences are identified, calling for further study. Most notably, these distinctions pertain to the effect of accumulation porosity, with additional minor differences in the exponent of the Froude number. Lastly, model implications for some broader applications are explored, showing a methodology to calculate the representative log size for log mixtures, and the expected effect of log orientation on backwater rise. This article relates to: Follett, E., Schalko, I., & Nepf, H. (2020). Momentum and energy predict the backwater rise generated by a large wood jam. Geophysical Research Letters, 47, e2020GL089346. https://doi.org/10.1029/2020GL089346

Plant Morphology Impacts Bedload Sediment Transport

2026-05-05T03:04:37Z

Plant Morphology Impacts Bedload Sediment Transport Liu, Chao; Shan, Yuqi; He, Li; Li, Fujian; Liu, Xingnian; Nepf, Heidi Bedload sediment transport plays an important role in the evolution of rivers, marshes and deltas. In these aquatic environments, vegetation is widespread, and plant species have unique morphology. However, the impact of real plant morphology on flow and sediment transport has not been quantified. This study used model plants with real plant morphology, based on the aquatic species Phragmites australis, Acorus calamus and Typha latifolia. The frontal area of these species increases away from the bed, which leads to higher near-bed velocity than would be predicted from depth-average frontal area. A plant morphology coefficient was defined to quantify the impact of vertically-varied plant frontal area. Laboratory experiments confirmed that the plant morphology coefficient improved the prediction of near-bed velocity, near-bed turbulent kinetic energy and bedload transport rate in canopies with realistic morphology. Plant morphology can alter transport rates by up to an order of magnitude, relative to the assumption of uniform morphology.

Negotiation 101: Theory and Practice

2026-05-01T03:01:00Z

Negotiation 101: Theory and Practice Rowe, Mary

A Bit More About Paul and Priscilla Gray

2026-05-01T03:00:53Z

A Bit More About Paul and Priscilla Gray Williams, Clarence; Rowe, Mary Two former MIT organizational ombuds describe the contributions the late MIT President Paul Gray made to making MIT more welcoming to people of color and women.

Spatially Resolved Temperature Response Functions to CO2 Emissions

2026-05-01T03:04:48Z

Spatially Resolved Temperature Response Functions to CO2 Emissions Freese, Lyssa M; Giani, Paolo; Fiore, Arlene M; Selin, Noelle E Carbon dioxide (CO2) emissions affect local temperature; quantifying that local response is important for learning about the earth system, the impacts of mitigation, and adaptation needs. We assume the climate system can be represented as a time-dependent linear system, diagnosing Green's Functions for the spatial temperature response to CO2 emissions based on CMIP6 earth system models. This allows us to emulate the linear component of the temperature response to CO2. This approach is sufficient to capture the spatial temperature response of CMIP6 experiments within one standard deviation of the multimodel spread across most regions, though accuracy is lower in the Southern Ocean and the Arctic. Our approach reveals where nonlinear feedbacks are important in current CMIP6 models, and where the local system response is well represented by a time-dependent linear differential operator. It incorporates emissions path dependency and may be useful for evaluating large ensembles of emission scenarios. Article relates to: Winkler, A. J., & Sierra, C. A. (2025). Towards a new generation of impulse-response functions for integrated Earth system understanding and climate change attribution. Geophysical Research Letters, 52, e2024GL112295. https://doi.org/10.1029/2024GL112295

Stratospheric Chlorine Processing After the Unprecedented Hunga Tonga Eruption

2026-05-01T03:04:32Z

Stratospheric Chlorine Processing After the Unprecedented Hunga Tonga Eruption Zhang, Jun; Wang, Peidong; Kinnison, Douglas; Solomon, Susan; Guan, Jian; Stone, Kane; Zhu, Yunqian Following the Hunga Tonga–Hunga Ha'apai (HTHH) eruption in January 2022, significant reductions in stratospheric hydrochloric acid (HCl) were observed in the Southern Hemisphere mid‐latitudes during the latter half of 2022, suggesting potential chlorine activation. The objective of this study is to comprehensively understand the loss of HCl in the aftermath of HTHH. Satellite measurements and a global chemistry‐climate model are employed for the analysis. We find strong agreement of 2022 anomalies between the modeled and the measured data. The observed tracer‐tracer relations between nitrous oxide (N2O) and HCl indicate a significant role of chemical processing in the observed HCl reduction, especially during the austral winter of 2022. Further examining the roles of chlorine gas‐phase and heterogeneous chemistry, we find that heterogeneous chemistry emerges as the primary driver for the chemical loss of HCl, and the reaction between hypobromous acid (HOBr) and HCl on sulfate aerosols is the dominant loss process.

Environment-assisted quantum transport of excitons in perovskite nanocrystal superlattices

2026-05-01T03:05:47Z

Environment-assisted quantum transport of excitons in perovskite nanocrystal superlattices Blach, Daria D; Lumsargis-Roth, Victoria A; Chuang, Chern; Clark, Daniel E; Deng, Shibin; Williams, Olivia F; Li, Christina W; Cao, Jianshu; Huang, Libai Transport of energy carriers in solid-state materials is determined by their wavefunctions and interactions with the environment. While quantum transport theory has predicted distinct transport in the intermediate coupling regime resulting from the intricate interplay between coherent wave-like and incoherent particle-like mechanisms, these predictions are awaiting experimental evidence. Here we demonstrate quantum transport signatures in perovskite nanocrystal superlattices by imaging exciton propagation with high spatial and temporal resolutions over 7-298 K. At 7 K, coherent propagation of the excitons dominates, with transient ballistic motion within a coherence length of up to 40 nanocrystal sites. The interference of the wave-like motion leads to Anderson Localization in the long-time limit. As temperature increases, a peak in the long-time diffusion constant is observed at a temperature where static disorder and dephasing are balanced, which substantiates evidence for environment-assisted quantum transport. Our results connect theoretical predictions and experiments using a stochastic Anderson localization model, highlighting perovskite nanocrystals as promising building blocks for quantum materials.

Dynamical generation and transfer of nonclassical states in strongly interacting light-matter systems in cavities

2026-05-01T03:04:38Z

Dynamical generation and transfer of nonclassical states in strongly interacting light-matter systems in cavities Tutunnikov, Ilia; Rokaj, Vasil; Cao, Jianshu; Sadeghpour, HR We propose leveraging strong and ultrastrong light-matter coupling to efficiently generate and exchange nonclassical light and quantum matter states. Two initial conditions are considered: (a) a displaced quadrature-squeezed matter state, and (b) a coherent state in a cavity. In both scenarios, polaritons mediate the dynamical generation and transfer of nonclassical states between light and matter. By monitoring the dynamics of both subsystems, we uncover the emergence of cavity-induced beatings in the collective matter oscillations. The beating period depends on the particle density through the vacuum Rabi splitting and peaks sharply under light-matter resonance conditions. For initial condition (a), nonclassicality is efficiently transferred from matter to photons under strong and ultrastrong coupling. However, for initial condition (b), nonclassical photonic states are generated only in the ultrastrong coupling regime due to the counter-rotating terms, highlighting the advantages of ultrastrong coupling. Furthermore, in the ultrastrong coupling regime, distinctive asymmetries relative to cavity detuning emerge in dynamical observables of both light and matter. The nonclassical photons can be extracted through a semi-transparent cavity mirror, while nonclassical matter states can be detected via time-resolved spectroscopy. This work highlights that polariton states may serve as a tool for dynamically generating and transferring nonclassical states, with potential applications in quantum technology.

The effect of an optical cavity on diabatic tunneling in an ensemble of symmetric double-well systems

2026-05-01T03:04:42Z

The effect of an optical cavity on diabatic tunneling in an ensemble of symmetric double-well systems Pollak, Eli; Cao, Jianshu The vacuum field of an optical cavity can potentially modify chemical reactivity and other dynamical properties via vibrational strong coupling (VSC). This intriguing finding has inspired numerous studies, but the underlying mechanisms remain unresolved. While many theoretical efforts focus on solvent or nuclear fluctuations, the tunneling overlap in non-adiabatic processes is usually assumed unperturbed by the cavity field. This paper presents a rigorous calculation of the tunneling splitting and associated ground-state shift resulting from the non-adiabatic coupling between two degenerate, harmonic diabatic surfaces in the ground vibrational state manifold under VSC. Based on this calculation, the tunneling splitting is suppressed by the cavity field for a single-molecule or a few-molecule system, but this cavity-induced effect is neither resonant nor cooperative and vanishes in the thermodynamic limit. This prediction demonstrates the many facets of VSC-induced phenomena and sheds new light on cavity-modified non-adiabatic processes, including charge transfer, Förster resonance energy transfer, energy relaxation, and conical intersection.

Insights Into Changing Interglacial Conditions in Subarctic Canada From MIS 11 Through MIS 5e From Seasonally Resolved Speleothem Records

2026-05-01T03:04:44Z

Insights Into Changing Interglacial Conditions in Subarctic Canada From MIS 11 Through MIS 5e From Seasonally Resolved Speleothem Records Batchelor, Cameron J; McGee, David; Shakun, Jeremy D; Woodhead, Jon; Jost, Adam B; Arnold, Sarah; Horne, Greg; Kinsley, Christopher W; Freudenburg‐Puricelli, Markey High‐resolution records from past interglacial climates help constrain future responses to global warming, yet are rare. Here, we produce seasonally resolved climate records from subarctic‐Canada using micron‐scale measurements of oxygen isotopes (δ18O) in speleothems with apparent annual growth bands from three interglacial periods—Marine Isotope Stages (MIS) 11, 9, and 5e. We find 3‰ lower δ18O values during MIS 11 than MIS 5e, despite MIS 11 likely being warmer. We explore controls on high‐latitude speleothem δ18O and suggest low MIS 11 δ18O values reflect greater contribution of cold‐season precipitation to dripwater from longer annual ground thaw durations. Other potential influences include changes in precipitation source and/or increased fraction of cold‐season precipitation from diminished sea ice in MIS 11. Our study highlights the potential for high‐latitude speleothems to yield detailed isotopic records of Northern Hemisphere interglacial climates beyond the reach of Greenland ice cores and offers a framework for interpreting them.

Spatiotemporal Facility‐Level Patterns of Summer Heat Exposure, Vulnerability, and Risk in United States Prison Landscapes

2026-05-01T03:06:40Z

Spatiotemporal Facility‐Level Patterns of Summer Heat Exposure, Vulnerability, and Risk in United States Prison Landscapes Ovienmhada, Ufuoma; Hines, Mia; Krisch, Michael; Diongue, Ahmed T; Minchew, Brent; Wood, Danielle R Heat is associated with increased risk of morbidity and mortality. People who are incarcerated are especially vulnerable to heat exposure due to demographic characteristics and their conditions of confinement. Evaluating heat exposure in prisons, and the characteristics of exposed populations and prisons, can elucidate prison‐level risk to heat exposure. We leveraged a high‐resolution air temperature data set to evaluate short and long‐term patterns of heat metrics for 1,614 prisons in the United States from 1990 to 2023. We found that the most heat‐exposed facilities and states were mostly in the Southwestern United States, while the prisons with the highest temperature anomalies from the historical record were in the Pacific Northwest, the Northeast, Texas, and parts of the Midwest. Prisons in the Pacific Northwest, the Northeast, and upper Midwest had the highest occurrences of days associated with an increased risk of heat‐related mortality. We also estimated differences in heat exposure at prisons by facility and individual‐level characteristics. We found higher proportions of non‐white and Hispanic populations in the prisons with higher heat exposure. Lastly, we found that heat exposure was higher in prisons with any of nine facility‐level characteristics that may modify risk to heat. This study brings together distinct measures of exposure, vulnerability, and risk, which would each inform unique strategies for heat‐interventions. Community leaders and policymakers should carefully consider which measures they want to apply, and include the voices of directly impacted people, as the differing metrics and perspectives will have implications for who is included in fights for environmental justice.

Obtaining High‐Resolution Magnetic Records From Speleothems Using Magnetic Microscopy

2026-05-01T03:07:11Z

Obtaining High‐Resolution Magnetic Records From Speleothems Using Magnetic Microscopy Borlina, Cauê S; Lima, Eduardo A; Feinberg, Joshua M; Jaqueto, Plinio; Lascu, Ioan; Trindade, Ricardo IF; Font, Eric; Sánchez‐Moreno, Elisa M; Dimuccio, Luca Antonio; Yokoyama, Yusuke; Parés, Josep M; Weiss, Benjamin P; Dorale, Jeffrey A Speleothems are mineral deposits capable of recording detrital and/or chemical remanent magnetization at annual timescales. They can offer high-resolution paleomagnetic records of short-term variations in Earth's magnetic field, crucial for understanding the evolution of the dynamo. Owing to limitations on the magnetic moment sensitivity of commercial cryogenic rock magnetometers (∼10−11 Am2), paleomagnetic studies of speleothems have been limited to samples with volumes of several hundreds of mm3, averaging tens to hundreds of years of magnetic variation. Nonetheless, smaller samples (∼1–10 mm3) can be measured using superconducting quantum interference device (SQUID) microscopy, with a sensitivity better than ∼10−15 Am2. To determine the application of SQUID microscopy for obtaining robust high-resolution records from small-volume speleothem samples, we analyzed three different stalagmites collected from Lapa dos Morcegos Cave (Portugal), Pau d'Alho Cave (Brazil), and Crevice Cave (United States). These stalagmites are representative of a range of magnetic properties and have been previously studied with conventional rock magnetometers. We show that by using SQUID microscopy we can achieve a five-fold improvement in temporal resolution for samples with higher abundances of magnetic carriers (e.g., Pau d'Alho Cave and Lapa dos Morcegos Cave). In contrast, speleothems with low abundances of magnetic carriers (e.g., Crevice Cave) do not benefit from higher resolution analysis and are best analyzed using conventional rock magnetometers. Overall, by targeting speleothem samples with high concentrations of magnetic carriers we can increase the temporal resolution of magnetic records, setting the stage for resolving geomagnetic variations at short time scales.

Evidence for Magnetically‐Driven Accretion in the Distal Solar System

2026-05-01T03:06:13Z

Evidence for Magnetically‐Driven Accretion in the Distal Solar System Mansbach, Elias N; Weiss, Benjamin P; Lima, Eduardo A; Sowell, Michael; Kirschvink, Joseph L; Fu, Roger R; Cambioni, Saverio; Bai, Xue‐Ning; Ream, Jodie B; Anai, Chisato; Kobayashi, Atsuko; Hidaka, Hironori Paleomagnetic measurements of meteorites indicate that magnetic fields existed in the inner solar nebula capable of driving accretion at rates similar to those observed for young stellar objects with protoplanetary disks. However, the field strength in the solar system beyond ∼7 astronomical units (AU) and its role in accretion remain poorly constrained. Returned samples from asteroid (162173) Ryugu offer the possibility of determining the nebular field intensity in this distal region. Here, we report paleomagnetic studies of three Ryugu particles which reveal that alteration occurred in the presence of a null or relatively weak (<15.8 μT) field within 3 million years (Ma) after solar system formation. This resolves previously contrasting reports that Ryugu's parent body experienced alteration in the presence of a strong (>80 μT) magnetic field and weak or null field (<3 μT). In addition, we re‐examine previous paleomagnetic and Mn‐Cr chronometry studies of three other distally‐sourced meteorites, Tagish Lake, Tarda, and Wisconsin Range 91600, which measured paleointensities of <0.9, <1.7 and 5.1 ± 4.5 μT respectively. While it was previously unclear whether these records were acquired while the nebula was present, our re‐analysis suggests that their records are sufficiently old (i.e., <3.5 Ma after solar system formation) to be nebular in origin. Collectively, these data demonstrate that the distal solar system nebular field, while faint, was likely still strong enough to drive accretion at rates like those observed in the inner solar system.

Astrobiological Potential of Rocks Acquired by the Perseverance Rover at a Sedimentary Fan Front in Jezero Crater, Mars

2026-05-01T03:07:04Z

Astrobiological Potential of Rocks Acquired by the Perseverance Rover at a Sedimentary Fan Front in Jezero Crater, Mars The Perseverance rover has collected seven oriented samples of sedimentary rocks, all likely older than the oldest signs of widespread life on Earth, at the exposed base of the western fan in Jezero crater, Mars. The samples include a sulfate‐ and clay‐bearing mudstone and sandstone, a fluvial sandstone from a stratigraphically low position at the fan front, and a carbonate‐bearing sandstone deposited above the sulfate‐bearing strata. All samples contain aqueously precipitated materials and most or all were aqueously deposited. Although the rover instruments have not confidently detected organic matter in the rocks from the fan front, the much more sensitive terrestrial instruments will still be able to search for remnants of prebiotic chemistries and past life, and study Mars's past habitability in the samples returned to Earth. The hydrated, sulfate‐bearing mudstone has the highest potential to preserve organic matter and biosignatures, whereas the carbonate‐bearing sandstones can be used to constrain when and for how long Jezero crater contained liquid water. Returned sample science analyses of sulfate, carbonate, clay, phosphate and igneous minerals as well as trace metals and volatiles that are present in the samples acquired at the fan front would provide transformative insights into past habitable environments on Mars, the evolution of its magnetic field, atmosphere and climate and the past and present cycling of atmospheric and crustal water, sulfur and carbon.

Responses of surface ozone to future agricultural ammonia emissions and subsequent nitrogen deposition through terrestrial ecosystem changes

2026-05-01T03:05:03Z

Responses of surface ozone to future agricultural ammonia emissions and subsequent nitrogen deposition through terrestrial ecosystem changes Liu, Xueying; Tai, Amos PK; Fung, Ka Ming With the rising food demands from the future world population, more intense agricultural activities are expected to cause substantial perturbations to the global nitrogen cycle, aggravating surface air pollution and imposing stress on terrestrial ecosystems. Much less studied, however, is how the terrestrial ecosystem changes induced by agricultural nitrogen deposition may modify biosphere–atmosphere exchange and further exert secondary feedback effects on global air quality. Here we examined the responses of surface ozone air quality to terrestrial ecosystem changes caused by year 2000 to year 2050 changes in agricultural ammonia emissions and the subsequent nitrogen deposition by asynchronously coupling between the land and atmosphere components within the Community Earth System Model framework. We found that global gross primary production is enhanced by 2.1 Pg C yr−1, following a 20 % (20 Tg N yr−1) increase in global nitrogen deposition by the end of the year 2050 in response to rising agricultural ammonia emissions. Leaf area index was simulated to be higher by up to 0.3–0.4 m2 m−2 over most tropical grasslands and croplands and 0.1–0.2 m2 m−2 across boreal and temperate forests at midlatitudes. Around 0.1–0.4 m increases in canopy height were found in boreal and temperate forests, and there were ∼0.1 m increases in tropical grasslands and croplands. We found that these vegetation changes could lead to surface ozone changes by ∼0.5 ppbv (part per billion by volume) when prescribed meteorology was used (i.e., large-scale meteorological responses to terrestrial changes were not allowed), while surface ozone could typically be modified by 2–3 ppbv when meteorology was dynamically simulated in response to vegetation changes. Rising soil NOx emissions, from 7.9 to 8.7 Tg N yr−1, could enhance surface ozone by 2–3 ppbv with both prescribed and dynamic meteorology. We, thus, conclude that, following enhanced nitrogen deposition, the modification of the meteorological environment induced by vegetation changes and soil biogeochemical changes are the more important pathways that can modulate future ozone pollution, representing a novel linkage between agricultural activities and ozone air quality.

Global tropospheric halogen (Cl, Br, I) chemistry and its impact on oxidants

2026-05-01T03:05:53Z

Global tropospheric halogen (Cl, Br, I) chemistry and its impact on oxidants Wang, Xuan; Jacob, Daniel J; Downs, William; Zhai, Shuting; Zhu, Lei; Shah, Viral; Holmes, Christopher D; Sherwen, Tomás; Alexander, Becky; Evans, Mathew J; Eastham, Sebastian D; Neuman, J Andrew; Veres, Patrick R; Koenig, Theodore K; Volkamer, Rainer; Huey, L Gregory; Bannan, Thomas J; Percival, Carl J; Lee, Ben H; Thornton, Joel A We present an updated mechanism for tropospheric halogen (Cl + Br + I) chemistry in the GEOS-Chem global atmospheric chemical transport model and apply it to investigate halogen radical cycling and implications for tropospheric oxidants. Improved representation of HOBr heterogeneous chemistry and its pH dependence in our simulation leads to less efficient recycling and mobilization of bromine radicals and enables the model to include mechanistic sea salt aerosol debromination without generating excessive BrO. The resulting global mean tropospheric BrO mixing ratio is 0.19 ppt (parts per trillion), lower than previous versions of GEOS-Chem. Model BrO shows variable consistency and biases in comparison to surface and aircraft observations in marine air, which are often near or below the detection limit. The model underestimates the daytime measurements of Cl2 and BrCl from the ATom aircraft campaign over the Pacific and Atlantic, which if correct would imply a very large missing primary source of chlorine radicals. Model IO is highest in the marine boundary layer and uniform in the free troposphere, with a global mean tropospheric mixing ratio of 0.08 ppt, and shows consistency with surface and aircraft observations. The modeled global mean tropospheric concentration of Cl atoms is 630 cm−3, contributing 0.8 % of the global oxidation of methane, 14 % of ethane, 8 % of propane, and 7 % of higher alkanes. Halogen chemistry decreases the global tropospheric burden of ozone by 11 %, NOx by 6 %, and OH by 4 %. Most of the ozone decrease is driven by iodine-catalyzed loss. The resulting GEOS-Chem ozone simulation is unbiased in the Southern Hemisphere but too low in the Northern Hemisphere.

Nonmonotonic Band Flattening near the Magic Angle of Twisted Bilayer MoTe2

2026-05-01T03:05:16Z

Nonmonotonic Band Flattening near the Magic Angle of Twisted Bilayer MoTe2 Deng, Yujun; Holtzmann, William; Zhu, Ziyan; Zaklama, Timothy; Majchrzak, Paulina; Taniguchi, Takashi; Watanabe, Kenji; Hashimoto, Makoto; Lu, Donghui; Jozwiak, Chris; Bostwick, Aaron; Rotenberg, Eli; Fu, Liang; Devereaux, Thomas P; Xu, Xiaodong; Shen, Zhi-Xun Twisted bilayer MoTe2 (tMoTe2) is an emergent platform for exploring exotic quantum phases driven by the interplay between nontrivial band topology and strong electron correlations. Direct experimental access to its momentum-resolved electronic structure is essential for uncovering the microscopic origins of the correlated topological phases therein. Here, we report angle-resolved photoemission spectroscopy measurements of tMoTe2, revealing pronounced twist-angle-dependent band reconstruction shaped by orbital character, interlayer coupling, and moiré potential modulation. Density functional theory captures the qualitative evolution, yet underestimates key energy scales across twist angles, highlighting the importance of electronic correlations. Notably, the hole effective mass at the 𝐾 point exhibits a nonmonotonic dependence on twist angle, peaking near 2°, consistent with band flattening at the magic angle predicted by continuum models. Via electrostatic gating and surface dosing, we further visualize the evolution of electronic structure versus doping, enabling direct observation of the conduction band minimum and confirm tMoTe2 as a direct band gap semiconductor. These results establish a spectroscopic foundation for modeling and engineering emergent quantum phases in this moiré platform.

Tensor Networks for Noninvertible Symmetries in 3+1⁢D and Beyond

2026-05-01T03:05:00Z

Tensor Networks for Noninvertible Symmetries in 3+1⁢D and Beyond Gorantla, Pranay; Shao, Shu-Heng; Tantivasadakarn, Nathanan Tensor networks provide a natural language for noninvertible symmetries in general Hamiltonian lattice models. We use ZX-diagrams, which are tensor network presentations of quantum circuits, to define a noninvertible operator implementing the Wegner duality in 3+1⁢D lattice ℤ2 gauge theory. The noninvertible algebra, which mixes with lattice translations, can be efficiently computed using ZX-calculus. We further deform the ℤ2 gauge theory while preserving the duality and find a model with nine exactly degenerate ground states on a torus, consistent with the Lieb-Schultz-Mattis-type constraint imposed by the symmetry. Finally, we provide a ZX-diagram presentation of the noninvertible duality operators (including noninvertible parity and reflection symmetries) of generalized Ising models based on graphs, encompassing the 1+1⁢D Ising model, the three-spin Ising model, the Ashkin-Teller model, and the 2+1⁢D plaquette Ising model. The mixing (or lack thereof) with spatial symmetries is understood from a unifying perspective based on graph theory.

Generalized Statistics on Lattices

2026-05-01T03:07:40Z

Generalized Statistics on Lattices Kobayashi, Ryohei; Li, Yuyang; Xue, Hanyu; Hsin, Po-Shen; Chen, Yu-An The statistics of particles and extended excitations, such as loops and membranes, are fundamental to modern condensed matter physics, high-energy physics, and quantum information science, yet a comprehensive lattice-level framework for computing them remains elusive. In this work, we develop a universal microscopic method to determine the generalized statistics of Abelian excitations on lattices of arbitrary dimension and demonstrate it by deriving the statistics of particles, loops, and membranes in up to three spatial dimensions. Our approach constructs a sequence of local unitary operators whose many-body Berry phase encodes the desired statistical invariant. The required sequence is generated automatically from the Smith normal form of locality constraints and therefore needs no extra physical input. We prove that the resulting invariants are quantized, provide an algorithm that computes them efficiently, and show how they unify familiar braiding and fusion data of particles while also uncovering new self- and mutual statistics of loop and membrane excitations. We further demonstrate that each statistical invariant corresponds to an ’t Hooft anomaly of a generalized symmetry; we show that a nontrivial invariant both (i) obstructs gauging that symmetry and (ii) forbids any short-range-entangled (symmetry-preserving) ground state. This establishes a precise connection between microscopic lattice anomalies and many-body dynamics, providing a generalization of the Lieb-Schultz-Mattis theorem that constrains a wide class of quantum lattice systems.

Symmetry-based efficient simulation of higher-order coherences in quantum many-body superradiance

2026-05-01T03:06:22Z

Symmetry-based efficient simulation of higher-order coherences in quantum many-body superradiance Holzinger, Raphael; Rubies-Bigorda, Oriol; Yelin, Susanne F.; Ritsch, Helmut We propose an efficient method to numerically simulate the superradiant emission dynamics of large numbers of quantum emitters in ordered arrays in the presence of long-range dipole-dipole interactions mediated by the vacuum electromagnetic field. Using the spatial symmetries of the system, we rewrite the equations of motion in a collective spin basis and subsequently apply a higher-order cumulant expansion for the collective operators. By truncating the subradiant collective modes with a heavily suppressed decay rate and keeping only the effect from the radiating collective modes, we reduce the numerical complexity significantly. This allows to efficiently compute the dissipative dynamics of the observables of interest for linear and ring-shaped arrays of quantum emitters. In particular, we characterize the second-order intensity correlation function 𝑔(2)⁡(𝜏=0), which is challenging to compute for extended systems with traditional cumulant expansion methods.

Toward structure-preserving quantum encodings

2026-05-01T03:06:48Z

Toward structure-preserving quantum encodings Parzygnat, Arthur J.; Bradley, Tai-Danae; Vlasic, Andrew; Pham, Anh Harnessing the potential computational advantage of quantum computers for machine learning tasks relies on the uploading of classical data onto quantum computers through what are commonly referred to as quantum encodings. The choice of such encodings may vary substantially from one task to another, and there exist only a few cases where structure has provided insight into their design and implementation, such as symmetry in geometric quantum learning. Here, we propose the perspective that category theory offers a natural mathematical framework for analyzing encodings that respect structure inherent in datasets and learning tasks. We illustrate this with pedagogical examples, which include geometric quantum machine learning, quantum metric learning, topological data analysis, and more. Moreover, such a perspective provides a language in which to ask meaningful and mathematically precise questions for the design of quantum encodings and circuits for quantum machine learning tasks.

Perception of risk : disaster scenarios at Brookhaven

2026-04-30T03:06:47Z

Perception of risk : disaster scenarios at Brookhaven Shah, Sameer J. (Sameer Jay), 1981- Thesis: S.B., Massachusetts Institute of Technology, Program in Science, Technology, and Society, 2003; Includes bibliographical references.

A study of a direct current electrification of the Boston to Providence division of the N.Y., N.H. & H.R.R.

2026-04-30T03:06:43Z

A study of a direct current electrification of the Boston to Providence division of the N.Y., N.H. & H.R.R. Ramsbottom, J. Raymond.; Tierney, Harold J. Thesis: B.S., Massachusetts Institute of Technology, Department of Electrical Engineering, 1917

A relocation of a portion of the Mattapan-Brockton line of the Bay State Street Railway in relation to a study of the suburban transportation facilities of Boston

2026-04-30T03:06:38Z

A relocation of a portion of the Mattapan-Brockton line of the Bay State Street Railway in relation to a study of the suburban transportation facilities of Boston Miller, Henry L. Thesis: B.S., Massachusetts Institute of Technology, Department of Electrical Engineering, 1917

Variational principles in irreversible thermodynamics

2026-04-30T03:02:45Z

Variational principles in irreversible thermodynamics Manning, Irwin. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 1955; Vita.; Bibliography: leaves 93-95.

Framework and models for the provision of real-time driver information

2026-04-30T03:03:21Z

Framework and models for the provision of real-time driver information Kaysi, Isam Adnan. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Civil Engineering, 1992; Includes bibliographical references (leaves 205-212).

Toroidal plasma confinement configurations with noncircular cross sections.

2026-04-30T03:02:04Z

Toroidal plasma confinement configurations with noncircular cross sections. Dagazian, Rostom Yeya Serkos. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Nuclear Engineering, 1972; 4 unnumbered blank leaves inserted. Vita.; Includes bibliographical references.

An examination of esterification of sebacic acid and iso-octyl alcohol

2026-04-30T03:06:33Z

An examination of esterification of sebacic acid and iso-octyl alcohol Bhadani, Surendra Kumar. Thesis: B.S., Massachusetts Institute of Technology, Department of Chemical Engineering, 1955; Bibliography: leaf 43.

Analogic part programming with interactive graphics

2026-04-30T03:03:03Z

Analogic part programming with interactive graphics Gossard, David Clair. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1975; Vita.; Includes bibliographical references.

A study of the organic compounds in the lunar crust and in terrestrial model systems.

2026-04-30T03:02:21Z

A study of the organic compounds in the lunar crust and in terrestrial model systems. Preti, George. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemistry, 1971; Vita.; Includes bibliographical references.

Sequential decisionmaking and the theory of the firm.

2026-04-30T03:02:31Z

Sequential decisionmaking and the theory of the firm. Kohn, Meir G. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, 1973; Vita.; Bibliography: leaves 149-155.

High frequency vacuum pressure fluctuations

2026-04-30T03:06:30Z

High frequency vacuum pressure fluctuations Tucker, Gregory Sheridan. Thesis: B.S., Massachusetts Institute of Technology, Department of Physics, 1985; Bibliography: leaf 40.

An investigation of water hammer in the drive pipe of a rife hydraulic ram

2026-04-30T03:05:58Z

An investigation of water hammer in the drive pipe of a rife hydraulic ram Rollins, Harry T. Thesis: B.S., Massachusetts Institute of Technology, Department of Architecture, 1904

Impact of Lateral Gap on Flow Distribution, Backwater Rise, and Turbulence Generated by a Logjam

2026-04-30T03:10:17Z

Impact of Lateral Gap on Flow Distribution, Backwater Rise, and Turbulence Generated by a Logjam Schalko, Isabella; Follett, Elizabeth; Nepf, Heidi Logjams may form at natural obstructions and are also used as nature‐based solutions for river restoration and natural flood management. Previous research has described backwater rise due to logjams that span the full channel cross‐section and logjams with a gap between the lower edge of the logjam and the bed. Logjams that fill the channel depth, but not its width, leaving a lateral gap between the logjam and the channel bank, are also common natural formations and the focus of this study. The flow distribution between the logjam and the lateral gap, backwater rise, and wake turbulence are key factors in determining the ecologic and flood risk impact of a logjam. Specifically, relative to a channel‐spanning logjam, the introduction of a lateral gap can reduce backwater rise and increase the potential for trapping particles, such as nutrients or microplastics, within the wake region, but may also promote erosion in the gap. The choice of logjam and gap widths can be used to maximize flow and habitat diversity in rivers, while reducing erosion risk. We present experimental results demonstrating that the flow distribution between the logjam and the lateral gap can be predicted by assuming equal resistance through the logjam and gap sections. Further, we show that backwater rise can be determined from the predicted discharge through the logjam using a momentum balance developed for channel‐spanning logjams. Finally, turbulence generated within the jam was observed directly downstream of the logjam, and, for the densities considered, increased with jam density.

Influences of Space Weather Forecasting Uncertainty on Satellite Conjunction Assessment

2026-04-30T03:07:42Z

Influences of Space Weather Forecasting Uncertainty on Satellite Conjunction Assessment Parker, William E; Freeman, Mervyn; Chisham, Gareth; Kavanagh, Andrew; Mun Siew, Peng; Rodriguez‐Fernandez, Victor; Linares, Richard A significant increase in the number of anthropogenic objects in Earth orbit has necessitated the development ofsatellite conjunction assessment and collision avoidance capabilitiesfor new spacecraft. Neutral mass density variability in the thermosphere, driven by enhanced geomagnetic activity and solar EUV absorption, is a major source of satellite propagation error. This work investigates the impacts of space weather driver forecasting uncertainty on satellite drag and collision avoidance maneuver decision‐making. Since most operational space weather driver forecasts do not offer an uncertainty assessment, the satellite operator community is left to make dangerous assumptions about the trustworthiness of the forecast models they use to perform satellite state propagation. Climatological persistence‐based forecast models are developed for F10.7 and Kp. These models accurately capture the heteroscedastic and, at times, highly non‐Gaussian uncertainty distribution on forecasts of the drivers of interest. A set of realistic satellite conjunction scenarios is simulated to demonstrate the contributions of space weather driver forecast uncertainty on the probability of collision and maneuver decisions. Improved driver forecasts, especially forecasts of F10.7, are demonstrated to be very useful for enabling durable maneuver decisions with additional lead time (up to 24 hr for the period examined), though the improvement depends on the specific conjunction scenario of interest.

Constraining Plio‐Pleistocene Shifts in Northwest African Hydroclimate, Ecosystem Distributions, and Marine Productivity: New Paleo‐Records Across the Mid‐Pleistocene Transition

2026-04-30T03:08:35Z

Constraining Plio‐Pleistocene Shifts in Northwest African Hydroclimate, Ecosystem Distributions, and Marine Productivity: New Paleo‐Records Across the Mid‐Pleistocene Transition O’Mara, Nicholas A; Skonieczny, Charlotte; McGee, David; Winckler, Gisela; Bory, Aloys J‐M; Bradtmiller, Louisa I; Malaizé, Bruno; Polissar, Pratigya J Northwest Africa transitioned from a wet/vegetated landscape toward drier/sparser conditions sometime between the late‐Pliocene and the late‐Pleistocene. However, our understanding of the precise timing and nature of this transition is hampered by a paucity of paleo‐records which bridge these two intervals. Here we report new plant‐wax isotope as well as dust and opal flux records from the relatively brief interval ∼1.1–1.0 million years ago (Ma) to evaluate the astronomical timescale controls of Northwest African hydroclimate and vegetation during the Mid‐Pleistocene Transition (MPT) and, in context with published records, the drivers of long‐term climate and ecological trends over the Plio‐Pleistocene. The tempo and amplitude of the Northwest African monsoon rainfall swings closely track low latitude insolation forcings over the last 5 Ma. However, we demonstrate that a pronounced mean state decline in monsoon strength likely occurred following the MPT most likely instigated by increasing Atlantic meridional sea surface temperature gradients or declines in the strength of the meridional overturning circulation. The northward extent of vegetation does not track changes in monsoon strength over the Plio‐Pleistocene and thus may be more strongly influenced by changes in monsoon rainfall extent or ecosystem disturbances. Progressively diminished dust fluxes following a decline in monsoon strength after 1.0 Ma is consistent with reduced production and subsequent depletion of fine‐grained sediments in the Sahara. Synchroneity between dust and opal fluxes across timescales suggests nutrient delivery to the surface ocean via dust plays a key role in marine primary productivity off the coast of Northwest Africa.

Zonal Indian Ocean Variability Drives Millennial‐Scale Precipitation Changes in Northern Madagascar

2026-04-30T03:10:15Z

Zonal Indian Ocean Variability Drives Millennial‐Scale Precipitation Changes in Northern Madagascar Tiger, Benjamin H; Burns, Stephen; Dawson, Robin R; Scroxton, Nick; Godfrey, Laurie R; Ranivoharimanana, Lovasoa; Faina, Peterson; McGee, David The low latitude Indian Ocean is warming faster than other tropical basins, and its interannual climate variability is projected to become more extreme under future emissions scenarios with substantial impacts on developing Indian Ocean rim countries. Therefore, it has become increasingly important to understand the drivers of regional precipitation in a changing climate. Here we present a new speleothem record from Anjohibe, a cave in northwest (NW) Madagascar well situated to record past changes in the Intertropical Convergence Zone (ITCZ). U‐Th ages date speleothem growth from 27 to 14 ka. δ18O, δ13C, and trace metal proxies reconstruct drier conditions during Heinrich Stadials 1 and 2, and wetter conditions during the Last Glacial Maximum and Bølling–Allerød. This is surprising considering hypotheses arguing for southward (northward) ITCZ shifts during North Atlantic cooling (warming) events, which would be expected to result in wetter (drier) conditions at Anjohibe in the Southern Hemisphere tropics. The reconstructed Indian Ocean zonal (west‐east) sea surface temperature (SST) gradient is in close agreement with hydroclimate proxies in NW Madagascar, with periods of increased precipitation correlating with relatively warmer conditions in the western Indian Ocean and cooler conditions in the eastern Indian Ocean. Such gradients could drive long‐term shifts in the strength of the Walker circulation with widespread effects on hydroclimate across East Africa. These results suggest that during abrupt millennial‐scale climate changes, it is not meridional ITCZ shifts, but the tropical Indian Ocean SST gradient and Walker circulation driving East African hydroclimate variability.

A Machine Learning Bias Correction on Large‐Scale Environment of High‐Impact Weather Systems in E3SM Atmosphere Model

2026-04-30T03:10:09Z

A Machine Learning Bias Correction on Large‐Scale Environment of High‐Impact Weather Systems in E3SM Atmosphere Model Zhang, Shixuan; Harrop, Bryce; Leung, L Ruby; Charalampopoulos, Alexis‐Tzianni; Barthel Sorensen, Benedikt; Xu, Wenwei; Sapsis, Themistoklis Large‐scale dynamical and thermodynamical processes are common environmental drivers of high‐impact weather systems causing extreme weather events. However, such large‐scale environmental conditions often display systematic biases in climate simulations, posing challenges to evaluating high‐impact weather systems and extreme weather events. In this paper, a machine learning (ML) approach was employed to bias correct the large‐scale wind, temperature, and humidity simulated by the atmospheric component of the Energy Exascale Earth System Model (E3SM) at ∼1° resolution. The usefulness of the ML approach for extreme weather analysis was demonstrated with a focus on three high‐impact weather systems, including tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs). We show that the ML model can effectively reduce climate bias in large‐scale wind, temperature, and humidity while preserving their responses to imposed climate change perturbations. The bias correction is found to directly improve water vapor transport associated with ARs, and representations of thermodynamical flows associated with ETCs. When the bias‐corrected large‐scale winds are used to drive a synthetic TC track forecast model over the Atlantic basin, the resulting TC track density agrees better with that of the TC track model driven by observed winds. In addition, the ML model insignificantly interferes with the mean climate change signals of large‐scale storm environments as well as the occurrence and intensity of three weather systems. This study suggests that the proposed ML approach can be used to improve the downscaling of extreme weather events by providing more realistic large‐scale storm environments simulated by low‐resolution climate models.

A New WENO‐Based Momentum Advection Scheme for Simulations of Ocean Mesoscale Turbulence

2026-04-30T03:09:49Z

A New WENO‐Based Momentum Advection Scheme for Simulations of Ocean Mesoscale Turbulence Silvestri, Simone; Wagner, Gregory L; Campin, Jean‐Michel; Constantinou, Navid C; Hill, Christopher N; Souza, Andre; Ferrari, Raffaele Current eddy‐permitting and eddy‐resolving ocean models require dissipation to prevent a spurious accumulation of enstrophy at the grid scale. We introduce a new numerical scheme for momentum advection in large‐scale ocean models that involves upwinding through a weighted essentially non‐oscillatory (WENO) reconstruction. The new scheme provides implicit dissipation and thereby avoids the need for an additional explicit dissipation that may require calibration of unknown parameters. This approach uses the rotational, “vector invariant” formulation of the momentum advection operator that is widely employed by global general circulation models. A novel formulation of the WENO “smoothness indicators” is key for avoiding excessive numerical dissipation of kinetic energy and enstrophy at grid‐resolved scales. We test the new advection scheme against a standard approach that combines explicit dissipation with a dispersive discretization of the rotational advection operator in two scenarios: (a) two‐dimensional turbulence and (b) three‐dimensional baroclinic equilibration. In both cases, the solutions are stable, free from dispersive artifacts, and achieve increased “effective” resolution compared to other approaches commonly used in ocean models.

How Well do We Understand the Planck Feedback?

2026-04-30T03:09:17Z

How Well do We Understand the Planck Feedback? Cronin, Timothy W; Dutta, Ishir A reference or “no-feedback” radiative response to warming is fundamental to understanding how much global warming will occur for a given change in greenhouse gases or solar radiation incident on the Earth. The simplest estimate of this radiative response is given by the Stefan-Boltzmann law as 𝐴𝐴 −4𝜎𝜎𝑇𝑇𝑒𝑒 3 ≈ −3.8 W m−2 K−1 for Earth's present climate, where 𝐴𝐴 𝑇𝑇𝑒𝑒 is a global effective emission temperature. The comparable radiative response in climate models, widely called the “Planck feedback,” averages −3.3 W m−2 K−1. This difference of 0.5 W m−2 K−1 is large compared to the uncertainty in the net climate feedback, yet it has not been studied carefully. We use radiative transfer models to analyze these two radiative feedbacks to warming, and find that the difference arises primarily from the lack of stratospheric warming assumed in calculations of the Planck feedback (traditionally justified by differing constraints on and time scales of stratospheric adjustment relative to surface and tropospheric warming). The Planck feedback is thus masked for wavelengths with non-negligible stratospheric opacity, and this effect implicitly acts to amplify warming in current feedback analysis of climate change. Other differences between Planck and Stefan-Boltzmann feedbacks arise from temperature-dependent gas opacities, and several artifacts of nonlinear averaging across wavelengths, heights, and different locations; these effects partly cancel but as a whole slightly destabilize the Planck feedback. Our results point to an important role played by stratospheric opacity in Earth's climate sensitivity, and clarify a long-overlooked but notable gap in our understanding of Earth's reference radiative response to warming.

Multiple Equilibria and Soil Moisture‐Precipitation Feedbacks in Idealized Convection‐Permitting Simulations With an Open Hydrological Cycle

2026-04-30T03:09:02Z

Multiple Equilibria and Soil Moisture‐Precipitation Feedbacks in Idealized Convection‐Permitting Simulations With an Open Hydrological Cycle Abbott, Tristan H; Cronin, Timothy W Soil moisture‐precipitation feedbacks are influenced by both small‐scale land‐atmosphere coupling and large‐scale atmospheric circulations, and their sign has important implications for the stability of regional hydroclimate. However, the importance of both local and non‐local processes makes it difficult to model soil moisture‐precipitation feedbacks with high fidelity, limiting our ability to use models to understand controls on their sign. Here, we address this challenge by exploring a promising but seldom‐used approach to studying soil moisture‐precipitation feedbacks over tropical land: coupling small‐domain convection‐permitting simulations to a land‐like surface and a parameterization of large‐scale dynamics. The large‐scale dynamics parameterization, based on the weak temperature gradient (WTG) approximation, is a key component that produces an open hydrological cycle with interactive moisture convergence. We first show that WTG‐constrained simulations coupled to a freely‐evaporating land surface support both precipitating and non‐precipitating equilibria across a wide range of insolation. We then leverage this bistability to probe the influence of soil moisture feedbacks on dry spells by asking whether non‐precipitating equilibria remain stable as the underlying surface dries out. We find that surface drying can trigger transitions from dry equilibria back to precipitating equilibria—a negative soil moisture‐precipitation feedback—and attribute this transition to increasingly inefficient boundary layer ventilation by the parameterized large‐scale circulation. In sensitivity experiments, alternative versions of the WTG scheme modify the parameter space where the negative feedback occurs, but none eliminate it entirely. Our results provide a foundation for leveraging the rich behavior of WTG‐constrained simulations to probe controls on soil moisture‐precipitation feedbacks over tropical land.

Neural‐Network Parameterization of Subgrid Momentum Transport in the Atmosphere

2026-04-30T03:09:25Z

Neural‐Network Parameterization of Subgrid Momentum Transport in the Atmosphere Yuval, Janni; O’Gorman, Paul A Attempts to use machine learning to develop atmospheric parameterizations have mainly focused on subgrid effects on temperature and moisture, but subgrid momentum transport is also important in simulations of the atmospheric circulation. Here, we use neural networks to develop a subgrid momentum transport parameterization that learns from coarse‐grained output of a high‐resolution atmospheric simulation in an idealized aquaplanet domain. We show that substantial subgrid momentum transport occurs due to convection. The neural‐network parameterization has skill in predicting momentum fluxes associated with convection, although its skill for subgrid momentum fluxes is lower compared to subgrid energy and moisture fluxes. The parameterization conserves momentum, and when implemented in the same atmospheric model at coarse resolution it leads to stable simulations and tends to reduce wind biases, although it over‐corrects for one configuration tested. Overall, our results show that it is challenging to predict subgrid momentum fluxes and that machine‐learning momentum parameterization gives promising results.

Predicting Characteristic Length Scales of Barrier Island Segmentation in Microtidal Environments

2026-04-30T03:09:53Z

Predicting Characteristic Length Scales of Barrier Island Segmentation in Microtidal Environments Palermo, RV; Ashton, AD; Nepf, H; Kule, M; Swanson, T Segmented barrier islands can be found in regions with small tidal ranges. In contrast to tidally dominated barriers, where inlet dynamics are thought to control island length scales, the controls on barrier island length scales in wave‐dominated environments have not been quantified. These microtidal barriers typically have a curved shoreline, suggesting the influence of wave‐driven alongshore sediment transport. Microtidal barriers are also typically hydrodynamically isolated from one another, as weak tidal flows limit interactions between adjoining barriers. To better understand the controls on and scales of barrier segmentation in the relative absence of tides, here we develop a theoretical framework to estimate the alongshore length scales at which a barrier will either breach or heal following a disturbance in the barrier morphology. The non‐dimensional framework compares the timescales of overwash (advective) and alongshore sediment transport (diffusive) processes along barrier island chains. We then apply this framework to modern barrier islands in the microtidal Gulf of Mexico using wave hindcast data and the lengths, widths, heights, and lagoon depths measured from remotely sensed geospatial data and topobathymetric data. We find that most of these barriers are currently longer than their critical length scale, often as a result of coastal restoration efforts. Our critical length scale analysis suggests that most of the Gulf of Mexico barriers are vulnerable to segmentation despite coastal restoration efforts intended to protect fisheries and the mainland coasts.

DATA-DRIVEN DETECTION OF EN-ROUTE CONVECTIVE WEATHER AVOIDANCE AND DEVELOPMENT OF A WEATHER ASSESSMENT MODEL

2026-04-30T03:00:49Z

DATA-DRIVEN DETECTION OF EN-ROUTE CONVECTIVE WEATHER AVOIDANCE AND DEVELOPMENT OF A WEATHER ASSESSMENT MODEL Price, Rachel; Hansman, R. John

North–South Disparity in Impact of Climate Change on “Outdoor Days”

2026-04-30T03:08:42Z

North–South Disparity in Impact of Climate Change on “Outdoor Days” Choi, Yeon-Woo; Khalifa, Muhammad; Eltahir, Elfatih AB Here, we introduce the concept of “outdoor days” to describe how climate change can affect quality of life for different communities and individuals. An outdoor day is characterized by moderate temperature, neither too cold nor too hot, allowing most people to enjoy outdoor activities. The number of “outdoor days” is a nonlinear function of the daily surface air temperature. If the latter falls within a specific range describing assumed thermal comfort conditions, then we assign that day as an “outdoor day.” Using this function, we describe climate change impacts on temperature differently, compared to other studies which often describe these impacts in terms of the linear averaging of daily surface air temperature. The introduction of this new concept offers another way for communicating how climate change may impact the quality of life for individuals who usually plan their outdoor activities based on how local weather conditions compare to their preferred levels of thermal comfort. Based on our analysis of regional variations in “outdoor days,” we present observational and modeling evidence of a north–south disparity in climate change impacts. Under high-emission scenarios, CMIP5 and CMIP6 models project fewer “outdoor days” for people living in developing countries, primarily located in low-latitude regions. Meanwhile, developed countries in mid- and high-latitude regions could gain more “outdoor days,” redistributed across seasons.

The Zonal Seasonal Cycle of Tropical Precipitation: Introducing the Indo-Pacific Monsoonal Mode

2026-04-30T03:10:39Z

The Zonal Seasonal Cycle of Tropical Precipitation: Introducing the Indo-Pacific Monsoonal Mode Tuckman, PJ; Smyth, Jane; Lutsko, Nicholas J; Marshall, John The intertropical convergence zone (ITCZ) is associated with a zonal band of strong precipitation that migrates meridionally over the seasonal cycle. Tropical precipitation also migrates zonally, such as from the South Asian monsoon in Northern Hemisphere summer (JJA) to the precipitation maximum of the west Pacific in Northern Hemisphere winter (DJF). To explore this zonal movement in the Indo-Pacific sector, we analyze the seasonal cycle of tropical precipitation using a 2D energetic framework and study idealized atmosphere–ocean simulations with and without ocean dynamics. In the observed seasonal cycle, an atmospheric energy and precipitation anomaly forms over South Asia in northern spring and summer due to heating over land. It is then advected eastward into the west Pacific in northern autumn and remains there due to interactions with the Pacific cold tongue and equatorial easterlies. We interpret this phenomenon as a “monsoonal mode,” a zonally propagating moist energy anomaly of continental and seasonal scale. To understand the behavior of the monsoonal mode, we develop and explore an analytical model in which the monsoonal mode is advected by low-level winds, is sustained by interaction with the ocean, and decays due to the free tropospheric mixing of energy.

The QBO–MJO Connection: A Possible Role for the SST and ENSO

2026-04-30T03:10:31Z

The QBO–MJO Connection: A Possible Role for the SST and ENSO Randall, David A; Tziperman, Eli; Branson, Mark D; Richter, Jadwiga H; Kang, Wanying We examine the hypothesis that the observed connection between the stratospheric quasi-biennial oscillation (QBO) and the strength of the Madden–Julian oscillation (MJO) is modulated by the sea surface temperature (SST)—for example, by El Niño–Southern Oscillation (ENSO). A composite analysis shows that, globally, La Niña SSTs are remarkably similar to those that occur during the easterly phase of the QBO. A maximum covariance analysis suggests that MJO power and SST are strongly linked on both the ENSO time scale and the QBO time scale. We analyze simulations with a modified configuration of version 2 of the Community Earth System Model, with a high top and fine vertical resolution. The model is able to simulate ENSO, the QBO, and the MJO. The ocean-coupled version of the model simulates the QBO, ENSO, and MJO, but does not simulate the observed QBO–MJO connection. When driven with prescribed observed SST anomalies based on composites for QBO east and QBO west (QBOE and QBOW), however, the same atmospheric model produces a modest enhancement of MJO power during QBOE relative to QBOW, as observed. We explore the possibility that the SST anomalies are forced by the QBO itself. Indeed, composite Hovmöller diagrams based on observations show the propagation of QBO zonal wind anomalies all the way from the upper stratosphere to the surface. Also, subsurface ocean temperature composites reveal a similarity between the western Pacific and Indian Ocean subsurface signal between La Niña and QBOE.

Different Propagation Mechanisms of Deep and Shallow Wintertime Extratropical Cyclones over the North Pacific

2026-04-30T03:07:36Z

Different Propagation Mechanisms of Deep and Shallow Wintertime Extratropical Cyclones over the North Pacific Yao, Yuling; Zhang, Yang; Hodges, Kevin I; Tamarin-Brodsky, Talia Extratropical cyclones (ETCs) are three-dimensional synoptic systems in the middle and high latitudes. Previous studies on ETC propagation have typically focused on cyclones identified at a single level. However, more recent studies have found that ETCs have diverse vertical structures and cyclones with different vertical extents always exhibit distinct characteristics and surface impacts. In this work, we study the movement of wintertime (December–February) extratropical cyclones by classifying North Pacific ETCs into deep cyclones, shallow low-level cyclones, and shallow upper-level cyclones, based on reanalysis data from 1979 to 2019. Applying a Lagrangian perspective, we track the cyclones at different vertical levels to investigate the different characteristics and mechanisms for the propagation of deep and shallow ETCs. A potential vorticity (PV) tendency analysis of cyclone-tracking composites reveals that, for deep cyclones, the diabatic heating at 850 hPa and the horizontal advection by the stationary flow at 500 hPa are the main contributors to the poleward movement. For shallow cyclones, the nonlinear advection terms play a dominant role in their meridional motion, advecting shallow low-level cyclones poleward but shallow upper-level cyclones equatorward. A piecewise PV inversion analysis suggests that the nonlinear advection by winds induced from upper-level PV anomalies is responsible for the different performance of nonlinear advection terms for shallow low-level and upper-level cyclones. These findings further our understanding of the mechanisms and variations of cyclone propagation.

Global Climate Impacts of Greenland and Antarctic Meltwater: A Comparative Study

2026-04-30T03:07:30Z

Global Climate Impacts of Greenland and Antarctic Meltwater: A Comparative Study Li, Qian; Marshall, John; Rye, Craig D; Romanou, Anastasia; Rind, David; Kelley, Maxwell Both the Greenland and Antarctic ice sheets have been melting at an accelerating rate over recent decades. Meltwater from Greenland might be expected to initiate a climate response that is distinct, and perhaps different from, that associated with Antarctic meltwater. Which one might elicit a greater climate response, and what mechanisms are involved? To explore these questions, we apply climate response functions (CRFs) to guide a series of meltwater-perturbation experiments using a fully coupled climate model. In both hemispheres, meltwater drives atmospheric cooling, sea ice expansion, and strengthened Hadley and Ferrel cells. Greenland meltwater induces a slowdown of the Atlantic meridional overturning circulation (AMOC) and a cooling of the subsurface ocean in the northern high latitudes. Antarctic meltwater, instead, induces a slowdown of the Antarctic Bottom Water formation and a warming of the subsurface ocean around Antarctica. For melt rates up to 2000 Gt yr−1, the climate response is rather linear. However, as melt rates increase to 5000 Gt yr−1, the climate response becomes nonlinear. Due to a collapsed AMOC, the climate response is superlinear at high Greenland melt rates. Instead, the climate response is sublinear at high Antarctic melt rates, due to the halting of the northward expansion of Antarctic sea ice by warm surface waters. Finally, in the linear limit, we use CRFs and linear convolution theory to make projections of important climate parameters in response to meltwater scenarios, which suggest that Antarctic meltwater will become a major driver of climate change, dominating that of Greenland meltwater, as the current century proceeds.

Impact of x rays on the sensitivity of CR-39 detectors to 2.4-MeV protons

2026-04-30T03:07:57Z

Impact of x rays on the sensitivity of CR-39 detectors to 2.4-MeV protons Kishimori, R; Cufari, M; Zhong-Johnson, EZL; Buschmann, BI; Sinskey, AJ; Johnson, TM; Vanderloo, N; DeVault, A; Foo, BC; Vargas, J; Dannhoff, SG; Evans, TE; Kunimune, J; Lawrence, Y; Pearcy, JA; Reichelt, BL; Wink, CW; Petrasso, RD; Johnson, M Gatu; Frenje, JA Solid-state nuclear track detectors, such as CR-39, lose sensitivity when subjected to doses of x rays on the order of 1 Gy in nuclear-fusion relevant experiments. As a result, the formed tracks have, in general, smaller maximum radii and shallower maximum depths. Presented here are experiments using atomic force microscopy to measure the CR-39 sensitivity parameter Vt/Vb, the ratio of track to bulk etch rates, for ∼2.4 MeV protons. The measurements revealed that absorbed x-ray doses of the order of 1 Gy reduced the CR-39 track diameters by a factor of 3 from ∼1200 to ∼400 nm and reduced the track depths by an order of magnitude from ∼550 to ∼50 nm. The corresponding change in the sensitivity parameter was inferred and found to have substantially decreased from ∼1.1 to ≲ 1.01. Doses in excess of 0.5 Gy reduced the track diameter, at an etch time of 2 h, below the 1.0 μm limit typical of optical microscopy methods used when scanning large, i.e., ≳ 25 cm2 , samples of CR-39. The findings suggest that the mechanism of sensitivity loss is driven by x-ray induced cross-linking of the bulk CR-39. The enhanced cross-linking reduces the solubility of the material along charged-particle trajectories compared with that of the bulk material. Representative absorbed doses of between 0.01 and 1 Gy in CR-39 on OMEGA and NIF in typical experimental configurations are then compared with the results.

Wake turbulence modeling in stratified atmospheric flows using a novel k−ℓ model

2026-04-30T03:06:59Z

Wake turbulence modeling in stratified atmospheric flows using a novel k−ℓ model Klemmer, Kerry S; Howland, Michael F As turbines continue to grow in hub height and rotor diameter and wind farms grow larger, consideration of stratified atmospheric boundary layer (ABL) processes in wind power models becomes increasingly important. Atmospheric stratification can considerably alter the boundary layer structure and flow characteristics through buoyant forcing. Variations in buoyancy, and corresponding ABL stability, in both space and time impact ABL wind speed shear, wind direction shear, boundary layer height, turbulence kinetic energy, and turbulence intensity. In addition, the presence of stratification will result in a direct buoyant forcing within the wake region. These ABL mechanisms affect turbine power production, the momentum and kinetic energy deficit wakes generated by turbines, and the turbulent mixing and kinetic energy entrainment in wind farms. Presently, state-of-practice engineering models of mean wake momentum utilize highly empirical turbulence models that do not explicitly account for ABL stability. Models also often neglect the interaction between the wake momentum deficit and the turbulence kinetic energy added by the wake, which depends on stratification. In this work, we develop a turbulence model that models the wake-added turbulence kinetic energy, and we couple it with a wake model based on the parabolized Reynolds-averaged Navier–Stokes equations. Comparing the model predictions to large eddy simulations across stabilities (Obukhov lengths) and surface roughness lengths, we find lower prediction error in both power production and the wake velocity field across the ABL conditions and error metrics investigated.

Active search for bifurcations

2026-04-30T03:09:10Z

Active search for bifurcations Psarellis, Yorgos M; Sapsis, Themistoklis P; Kevrekidis, Ioannis G Bifurcations mark qualitative changes of long-term behavior in dynamical systems and can often signal sudden (“hard”) transitions or catastrophic events (divergences). Accurately locating them is critical not just for deeper understanding of observed dynamic behavior, but also for designing efficient interventions. When the dynamical system at hand is complex, possibly noisy, and expensive to sample, standard (e.g., continuation based) numerical methods may become impractical. We propose an active learning framework, where Bayesian Optimization is leveraged to discover saddle-node or Hopf bifurcations, from a judiciously chosen small number of vector field observations. Such an approach becomes especially attractive in systems whose state × parameter space exploration is resource-limited. It also naturally provides a framework for uncertainty quantification (aleatoric and epistemic), useful in systems with inherent stochasticity.

A grain boundary embrittlement genome for substitutional cubic alloys

2026-04-30T03:10:12Z

A grain boundary embrittlement genome for substitutional cubic alloys Tuchinda, Nutth; Olson, Gregory B; Schuh, Christopher A Grain boundary chemistry plays a critical role for the properties of metals and alloys, yet there is a lack of consistent datasets for alloy design and development. With the advent of artificial intelligence and machine learning in materials science, open materials models and datasets can be used to overcome such challenges. Here, we use a universal interatomic potential to compute a grain boundary segregation and embrittlement genome for the Σ5[001](210) grain boundary for FCC and BCC binary alloys. The grain boundary database calculated here serves as a design tool for the embrittlement of high-angle grain boundaries for alloys across 30 base metals of Ag, Al, Au, Ba, Ca, Ce, Co, Cr, Cs, Cu, Fe (both BCC and FCC), Ir, K, Li, Mo, Na, Nb, Ni, Pb, Pd, Pt, Rb, Rh, Sr, Ta, Ti, V, W, Yb, and Zr with 75 solute elements for each.

An accessible instrument for measuring soft material mechanical properties

2026-04-30T03:08:30Z

An accessible instrument for measuring soft material mechanical properties Unikewicz, BM; Pincot, AM; Cohen, T Soft material research has seen significant growth in recent years, with emerging applications in robotics, electronics, and healthcare diagnostics where understanding the 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 that 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, benchtop 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 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.

Modeling Cosmogenic Nuclides in Transiently Evolving Topography and Chemically Weathering Soils

2026-04-29T04:49:36Z

Modeling Cosmogenic Nuclides in Transiently Evolving Topography and Chemically Weathering Soils Reed, Miles M; Ferrier, Ken L; Perron, J Taylor Terrestrial cosmogenic nuclides (TCN) are widely employed to infer denudation rates in mountainous landscapes. The calculation of an inferred denudation rate (Dinf) from TCN concentrations is typically performed under the assumptions that denudation rates were steady during TCN accumulation and that soil chemical weathering negligibly impacted soil mineral abundances. In many landscapes, however, denudation rates were not steady and soil composition was significantly impacted by chemical weathering, which complicates interpretation of TCN concentrations. We present a landscape evolution model that computes transient changes in topography, soil thickness, soil mineralogy, and soil TCN concentrations. We used this model to investigate TCN responses in transient landscapes by imposing idealized perturbations in tectonically (rock uplift rate) and climatically sensitive parameters (soil production efficiency, hillslope transport efficiency, and mineral dissolution rate) on initially steady-state landscapes. These experiments revealed key insights about TCN responses in transient landscapes. (a) Accounting for soil chemical erosion is necessary to accurately calculate Dinf. (b) Responses of Dinf to tectonic perturbations differ from those to climatic perturbations, suggesting that spatial and temporal patterns in Dinf are signatures of perturbation type and magnitude. (c) If soil chemical erosion is accounted for, basin-averaged Dinf inferred from TCN in stream sediment closely tracks actual basin-averaged denudation rate, showing that Dinf is a reasonable proxy for actual denudation rate, even in many transient landscapes. (d) Response times of Dinf to perturbations increase with hillslope length, implying that response times should be sensitive to the climatic, biological, and lithologic processes that control hillslope length.

Architecture of Fluvial and Deltaic Deposits Exposed Along the Eastern Edge of the Western Fan of Jezero Crater, Mars

2026-04-29T04:50:16Z

Architecture of Fluvial and Deltaic Deposits Exposed Along the Eastern Edge of the Western Fan of Jezero Crater, Mars Mangold, N; Caravaca, G; Gupta, S; Williams, RME; Dromart, G; Gasnault, O; Le Mouélic, S; Paar, G; Bell, J; Beyssac, O; Carlot, N; Cousin, A; Dehouck, E; Horgan, B; Kah, LC; Lasue, J; Maurice, S; Núñez, JI; Shuster, D; Stack, KM; Weiss, BP; Wiens, RC Early observations from the Perseverance rover suggested a deltaic origin for the western fan of Jezero crater only from images of the Kodiak butte. Here, we use images from the SuperCam Remote Micro‐ Imager and the Mastcam‐Z camera to analyze the western fan front along the rover traverse, and further assess its depositional origin. Outcrops in the middle to lower half of the hillslopes comprise planar and inclined beds of sandstone that are interpreted as foresets of deltaic deposits. Foresets are locally structured in ∼20–25 m thick, ∼80–100 m long, antiformal structures interpreted as deltaic mouth bars. Above these foresets, interbedded sandstones and boulder conglomerates are interpreted as fluvial topset beds. One well‐preserved lens of boulder conglomerate displays rounded clasts within well‐sorted sediment deposited in overall fining upward beds. We interpret these deposits as resulting from lateral accretion within fluvial channels. Estimations of peak discharge rates give a range between ∼100 and ∼500 m3 s − 1 . By contrast, boulder conglomerates exposed in the uppermost part of hillslopes are poorly sorted and truncate the underlying beds. The presence of these boulder deposits suggests that intense sediment‐laden flood episodes occurred after the deltaic foreset and topset beds were deposited, although the origin, timing, and relationship of these boulder deposits to the ancient lake that once filled Jezero crater remains undetermined. Overall, these observations confirm the deltaic nature of the fan front, and suggest a highly variable fluvial input.

Sedimentology and Stratigraphy of the Shenandoah Formation, Western Fan, Jezero Crater, Mars

2026-04-29T04:52:18Z

Sedimentology and Stratigraphy of the Shenandoah Formation, Western Fan, Jezero Crater, Mars Sedimentary fans are key targets of exploration on Mars because they record the history of surface aqueous activity and habitability. The sedimentary fan extending from the Neretva Vallis breach of Jezero crater's western rim is one of the Mars 2020 Perseverance rover's main exploration targets. Perseverance spent ∼250 sols exploring and collecting seven rock cores from the lower ∼25 m of sedimentary rock exposed within the fan's eastern scarp, a sequence informally named the “Shenandoah” formation. This study describes the sedimentology and stratigraphy of the Shenandoah formation at two areas, “Cape Nukshak” and “Hawksbill Gap,” including a characterization, interpretation, and depositional framework for the facies that comprise it. The five main facies of the Shenandoah formation include: laminated mudstone, laminated sandstone, low‐angle cross stratified sandstone, thin‐bedded granule sandstone, and thick‐bedded granule‐pebble sandstone and conglomerate. These facies are organized into three facies associations (FA): FA1, comprised of laminated and soft sediment‐deformed sandstone interbedded with broad, unconfined coarser‐grained granule and pebbly sandstone intervals; FA2, comprised predominantly of laterally extensive, soft‐sediment deformed laminated, sulfate‐bearing mudstone with lenses of low‐angle cross‐stratified and scoured sandstone; and FA3, comprised of dipping planar, thin‐bedded sand‐gravel couplets. The depositional model favored for the Shenandoah formation involves the transition from a sand‐dominated distal alluvial fan setting (FA1) to a stable, widespread saline lake (FA2), followed by the progradation of a river delta system (FA3) into the lake basin. This sequence records the initiation of a relatively long‐lived, habitable lacustrine and deltaic environment within Jezero crater.

Depositional and Diagenetic Sulfates of Hogwallow Flats and Yori Pass, Jezero Crater: Evaluating Preservation Potential of Environmental Indicators and Possible Biosignatures From Past Martian Surface Waters and Groundwaters

2026-04-29T04:55:19Z

Depositional and Diagenetic Sulfates of Hogwallow Flats and Yori Pass, Jezero Crater: Evaluating Preservation Potential of Environmental Indicators and Possible Biosignatures From Past Martian Surface Waters and Groundwaters The Mars 2020 Perseverance rover has examined and sampled sulfate‐rich clastic rocks from the Hogwallow Flats member at Hawksbill Gap and the Yori Pass member at Cape Nukshak. Both strata are located on the Jezero crater western fan front, are lithologically and stratigraphically similar, and have been assigned to the Shenandoah formation. In situ analyses demonstrate that these are fine‐grained sandstones composed of phyllosilicates, hematite, Ca‐sulfates, Fe‐Mg‐sulfates, ferric sulfates, and possibly chloride salts. Sulfate minerals are found both as depositional grains and diagenetic features, including intergranular cement and vein‐ and vug‐cements. Here, we describe the possibility of various sulfate phases to preserve potential biosignatures and the record of paleoenvironmental conditions in fluid and solid inclusions, based on findings from analog sulfate‐rich rocks on Earth. The samples collected from these outcrops, Hazeltop and Bearwallow from Hogwallow Flats, and Kukaklek from Yori Pass, should be examined for such potential biosignatures and environmental indicators upon return to Earth.

Magnetism of the Acapulco Primitive Achondrite and Implications for the Evolution of Partially Differentiated Bodies

2026-04-29T04:53:38Z

Magnetism of the Acapulco Primitive Achondrite and Implications for the Evolution of Partially Differentiated Bodies Mansbach, Elias N; Weiss, Benjamin P; Schnepf, Neesha R; Lima, Eduardo A; Borlina, Cauê S; Chatterjee, Nilanjan; Gattacceca, Jérôme; Uehara, Minoru; Wang, Huapei Primitive achondrites like the acapulcoites-lodranites (AL) clan are meteorites that formed on bodies in the process of forming a metallic core, providing a unique window into how early solar system processes transformed unmelted material into differentiated bodies. However, the size and structure of the parent body of ALs and other primitive achondrites are largely unknown. Paleomagnetism can establish the presence or absence of a metallic core by looking for evidence of a dynamo field. We conducted a magnetic study of the Acapulco acapulcoite to determine its ferromagnetic minerals and their recording properties. This is the first detailed rock magnetic and first paleomagnetic study of a primitive achondrite group. We determined that metal inclusions inside silicate grains consist of two magnetic minerals, kamacite and tetrataenite, which have robust recording properties. However, the mechanisms and timing by which these minerals acquired any natural remanent magnetization are unknown. Despite this, Acapulco has not been substantially remagnetized since arriving on Earth and therefore should retain a record dating to 4.55 billion years ago. Future studies could characterize this record by using high-resolution magnetometry measurements of individual populations of grains and developing an understanding of how and when they became magnetized. Our discovery of tetrataenite in ALs provides the first mineralogical evidence for slow cooling [<∼5–10 × 103°C per million years (Ma−1)] of the AL parent body at low temperatures (∼320°C). Its presence suggests the AL parent body is unlikely to have been catastrophically disrupted at AL peak temperatures (∼1,200°C) without subsequent reaccretion.

Collection and In Situ Analyses of Regolith Samples by the Mars 2020 Rover: Implications for Their Formation and Alteration History

2026-04-29T04:49:56Z

Collection and In Situ Analyses of Regolith Samples by the Mars 2020 Rover: Implications for Their Formation and Alteration History The Perseverance rover has sampled mm-size lithic fragments containing olivine likely from at least two source regions from the surface of an inactive megaripple surface, and fine-grained material from the surface and to a depth of ∼4–6 cm. Some of the mm-size grains lack a coherent diffraction pattern measured by PIXL, consistent with the presence of poorly ordered secondary phases that have been altered. Analysis of these materials on Earth will allow examination of materials that have experienced aqueous, potentially habitable environments that could contain biosignatures. Fluorescence of three different patterns was detected, consistent with inorganic emissions from silica defects or rare earth elements in certain mineral phases, although organic origin cannot be excluded. Analysis of Autofocus Context Imager and Wide Angle Topographic Sensor for Operations and eNgineering images of the subsurface material and MEDA thermal inertia measurements indicate average grain sizes of ∼125 and ∼150 μm, respectively, for the bulk material within the megaripple. The fine-grained material in the sampling location indicates chemical compositions similar to previously proposed global components as well as airfall dust. In situ and associated atmospheric measurements provide evidence of recent processes likely including water vapor in soil crust formation. The sampled material will therefore help elucidate the formation of Martian soils; current surface-atmosphere interactions; the composition, shape, and size distribution of dust grains valuable for studies of past and present Martian climate and for assessing potential health and other risks to human missions; and ancient, aqueously altered environments that could have been habitable, and, if Mars contained life, possibly contain biosignatures.

Divergent Behavior of Hydrothermal Plumes in Fresh Versus Salty Icy Ocean Worlds

2026-04-29T04:55:15Z

Divergent Behavior of Hydrothermal Plumes in Fresh Versus Salty Icy Ocean Worlds Bire, Suyash; Mittal, Tushar; Kang, Wanying; Ramadhan, Ali; Tuckman, Philip J; German, Christopher R; Thurnherr, Andreas M; Marshall, John Water parcels close to their freezing point contract and become heavy on warming if they are sufficiently fresh (salinity less than 22g kg−1 for earth's ocean), but expand and become buoyant when salty (salinity greater than 22g kg−1). We explore the resulting divergent behavior of hydrothermal plumes in fresh versus salty icy ocean worlds, with particular emphasis on Europa and Enceladus. Large, salty, putative Europa-like oceans, develop buoyant plumes which rise upwards in the water column when energized by localized hydrothermal vents. Instead, small, fresher, putative Enceladus-like oceans, can develop bottom-hugging gravity currents when heated near the freezing point, due to the anomalous contraction of fluid parcels on warming. Such a bottom-filling regime would most likely be a transient stage in the evolution of an icy moon over geological time. The contrasting dynamics are highlighted and rationalized in terms of key non-dimensional numbers with a focus on the ability of ocean to carry bio-markers from the hydrothermal activity at the bottom to the ice shell at the top. Finally, the implications of our study for prioritizing future missions to icy moons are discussed. An advantage of a mission to a large icy moon (e.g., Europa), rather than a smaller target (e.g., Enceladus), is that a larger moon's ocean would likely support buoyant convection, which could bring signatures of seafloor venting to the outer ice-shell regardless of that ocean's salinity. For smaller icy moons, the nature of convection would hinge on its assumed salinity.

A Geostatistics‐Based Tool to Characterize Spatio‐Temporal Patterns of Remotely Sensed Land Surface Temperature Fields Over the Contiguous United States

2026-04-29T04:53:26Z

A Geostatistics‐Based Tool to Characterize Spatio‐Temporal Patterns of Remotely Sensed Land Surface Temperature Fields Over the Contiguous United States Torres‐Rojas, L; Waterman, T; Cai, J; Zorzetto, E; Wainwright, HM; Chaney, NW Surface fluxes and states can recur and remain consistent across various spatial and temporal scales, forming space-time patterns. Quantifying and understanding the observed patterns is desirable, as they provide information about the dynamics of the processes involved. This study introduces the empirical spatio-temporal covariance function and a corresponding parametric covariance function as tools to identify and characterize spatio-temporal patterns in remotely sensed fields. The method is demonstrated using 2 km hourly GOES-16/17 land surface temperature (LST) data over the Contiguous United States by splitting the area into 1.0° × 1.0° domains. The summer day-time LST ESTCFs for 2018 to 2022 are derived for each domain, and a parametric covariance model is fitted. Clustering analysis is applied to detect areas with similar spatio-temporal LST patterns. Six main zones within CONUS are identified and characterized based on their variance and temporal and spatial characteristic length scales (i.e., scales for which the temperature variations are temporally and spatially related), respectively: (a) Eastern plains with 3 K2, ∼6 hr, and 0.15°, (b) Gulf of California with 60 K2, ∼8 hr, and 0.34°, (c) mountains and coasts transition 1 with 16 K2, ∼11 hr, and 0.25°, (d) central US, Midwest, and South cities with 5.5 K2, ∼8 hr, and ∼0.2°, (e) mountains and coasts transition 2 with ∼10 K2, ∼8 hr, and 0.2°, and (f) largest mountains and coastlines with ∼19 K2, ∼13 hr, and 0.3°. The tools introduced provide a pathway to formally identify and summarize the spatio-temporal patterns observed in remotely sensed fields and relate those to more complex processes within the Soil-Vegetation-Atmosphere System.

Wind‐Induced Quasi‐Seasonal and Quasi‐Monthly Variations of Near‐Bottom Temperature on the Chukchi Slope of the Southwestern Canada Basin

2026-04-29T04:54:50Z

Wind‐Induced Quasi‐Seasonal and Quasi‐Monthly Variations of Near‐Bottom Temperature on the Chukchi Slope of the Southwestern Canada Basin Ku, Ahyoung; Jeon, Chanhyung; Peacock, Thomas; Chae, Jeong‐Yeob; Park, Taewook; Cho, Kyoung‐Ho; Park, Jae‐Hun The time series of near‐bottom temperatures collected from September 2018 until August 2020 from an array of three current‐ and pressure‐recording inverted echo sounders showed quasi‐seasonal and quasi‐monthly (∼28 days) variations at a depth of ∼1,300 m near the Chukchi slope in the western Arctic Ocean. They revealed an increase of ∼0.1°C during the winter‐spring period compared with the summer‐fall period. These variations were observed in the data‐assimilated Hybrid Coordinate Ocean Model (HYCOM) outputs near the observation site (correlation coefficient >0.7). They confirmed that variations in near‐bottom temperature are related to changes in the intensity of the Atlantic Water (AW) boundary current, concurrent with the deepening of the lower AW layer by approximately 50 m. The difference in sea surface height (SSH) between the Canada Basin and the Chukchi Shelf increased because of the negative wind stress curl (WSC) and retarded the AW boundary current according to the geostrophic effect. When the near‐bottom temperature increased during the winter‐spring period, the SSH in the Chukchi Shelf was lower than that in the summer‐fall period because of the less negative WSC. Quasi‐monthly variations were related to SSH on the Chukchi Shelf owing to the negative WSC. HYCOM outputs from 1994 to 2015 showed that the AW boundary current weakened more recently than in the past due to the increased melting of sea ice. The results imply that a longer sea‐ice‐free season in the Arctic amplifies changes in the AW boundary current and deep ocean temperature owing to increased atmospheric forcing.

Low‐Frequency Earthquakes Downdip of Deep Slow Slip Beneath the North Island of New Zealand

2026-04-29T04:50:14Z

Low‐Frequency Earthquakes Downdip of Deep Slow Slip Beneath the North Island of New Zealand Aden‐Antoniów, F; Frank, WB; Chamberlain, CJ; Townend, J; Wallace, LM; Bannister, S We report the first catalog of low‐frequency earthquakes in the Hikurangi subduction zone, located beneath the Kaimanawa Range of the North Island at 50 km depth, downdip of regularly recurring (every 4–5 years) deep M7 slow slip events. To systematically detect low‐frequency earthquakes within the regional continuous seismic data, we utilized a matched‐filter approach with template waveforms derived from previous observations of tectonic tremor. We built our catalog of 36 low‐frequency earthquake sources, that produced almost 21,000 events over more than a decade, with two matched‐filter search iterations. In each iteration, the detections were gathered into families and their coherent waveforms processed and stacked to extract high‐quality waveforms, allowing us to pick seismic phase arrivals to locate the low‐frequency earthquakes. We highlight three characteristic features to validate that our detected events are indeed low‐frequency earthquakes: the eponymous deficit of high frequencies in their seismic waveforms, the episodic swarms of activity that define their activity through time, and their location at the plate boundary with a double‐couple source mechanism and geometry consistent with the subduction interface. Considering the observed low‐frequency earthquakes' relationship to neighboring slow slip, we observe the event swarms to occur much more frequently than the M7 slow slip events located just updip. Similar to other deep low‐frequency earthquakes in other subduction zones, we suggest that this characteristic clustering in time is driven by more frequent, smaller slow slip events that are not clearly observable at the surface.

Partial Ruptures Cannot Explain the Long Recurrence Intervals of Repeating Earthquakes

2026-04-29T04:50:45Z

Partial Ruptures Cannot Explain the Long Recurrence Intervals of Repeating Earthquakes Turner, AR; Hawthorne, JC; Cattania, C Repeating earthquakes repeatedly rupture the same fault asperities, which are likely loaded to failure by surrounding aseismic slip. However, repeaters occur less often than would be expected if these earthquakes accommodate all of the long‐term slip on the asperities. Here, we assess a possible explanation for this slip discrepancy: partial ruptures. On asperities that are much larger than the nucleation radius, a fraction of the slip could be accommodated by smaller ruptures on the same asperities. We search for partial ruptures of repeating earthquakes in Parkfield using the Northern California earthquakes catalog. We find 3991 individual repeaters which have 4468 partial ruptures. The presence of partial ruptures suggests that the asperities of repeating earthquakes are much larger than the nucleation radius. However, we find that partial ruptures could accommodate only around 25% of the slip on repeating earthquake patches. A 25% increase in the slip budget can explain only a small portion of the long recurrence intervals of repeating earthquakes.

Possible Eoarchean Records of the Geomagnetic Field Preserved in the Isua Supracrustal Belt, Southern West Greenland

2026-04-29T04:54:19Z

Possible Eoarchean Records of the Geomagnetic Field Preserved in the Isua Supracrustal Belt, Southern West Greenland Nichols, Claire IO; Weiss, Benjamin P; Eyster, Athena; Martin, Craig R; Maloof, Adam C; Kelly, Nigel M; Zawaski, Mike J; Mojzsis, Stephen J; Watson, E Bruce; Cherniak, Daniele J Recovering ancient records of Earth's magnetic field is essential for determining the role of the magnetosphere in protecting early Earth from cosmic radiation and atmospheric escape. We present paleomagnetic field tests hinting that a record of Earth's 3.7‐billion‐year (Ga) old magnetic field may be preserved in the northeastern Isua Supracrustal Belt as a chemical remanent magnetization acquired during amphibolite‐grade metamorphism in the banded iron formation. Multiple petrological and geochronological lines of evidence indicate that the northernmost part of Isua has not experienced metamorphic temperatures exceeding 380°C since the Eoarchean, suggesting the rocks have not been significantly heated since magnetization was acquired. We use “pseudo” baked contact tests (intrusions emplaced 3.26–3.5 Ga ago) and a fold test (folding 3.6 Ga ago) to demonstrate that some samples preserve a ca. 3.7 Ga record of the magnetic field. We recover a field strength of >15 µT. This suggests that Earth's magnetic field may have been weak enough to enhance atmospheric escape during the Archean.

Automated polarization rotation for multi-axis rotational-anisotropy second harmonic generation experiments

2026-04-29T04:52:14Z

Automated polarization rotation for multi-axis rotational-anisotropy second harmonic generation experiments Morey, Karna A; Fichera, Bryan T; Lv, Baiqing; Shen, Zongqi; Gedik, Nuh Rotational anisotropy second harmonic generation (RA-SHG) is a nonlinear optical technique used to probe the symmetry of condensed matter systems. Measuring the dependence of the SHG susceptibility on one or more external parameters, notably strain, field, temperature, or time delay, is an extremely powerful way to probe complex phases of quantum materials. Experimentally, extracting maximal information about the SHG susceptibility tensor requires measurements of S and P polarized input and output combinations, which naturally involves the rotation of the polarizers during data collection. For multi-axis experiments, this has proved challenging since polarization rotation is typically done manually. Automating this process eliminates labor constraints, reduces uncertainty due to low-frequency noise, and expands the type of multi-axis datasets that can be collected; however, it is difficult due to geometrical constraints within the setup. In this work, we design and implement low-cost, high-fidelity automated polarization rotators for use in multi-axis RA-SHG. These polarization rotators utilize an electrical slip ring to transfer power to the rotating RA-SHG optical setup as well as a miniature stepper motor to perform the polarization rotation. We demonstrate this automated system in time-resolved RA-SHG measurements in the non-centrosymmetric semiconductor GaAs. For the multi-axis measurements described above, this automated system permits data averaging over longer periods, vastly expedites data collection, and expands the setup measurement capability. This ultimately opens new frontiers in probing quantum materials using multiple tunable external parameters.

Design of a correlation reflectometer radiometer diagnostic and measurements of the electron density–temperature cross-phase angle in the H-mode pedestal with small edge localized modes at ASDEX Upgrade

2026-04-29T04:53:16Z

Design of a correlation reflectometer radiometer diagnostic and measurements of the electron density–temperature cross-phase angle in the H-mode pedestal with small edge localized modes at ASDEX Upgrade Yoo, C; Conway, GD; Burke, W; Cabrera, PA Molina; Vanovac, B; Bielajew, R; Cruz-Zabala, DJ; Silva, A; White, AE This work presents the hardware design and first results from a newly commissioned correlation reflectometer radiometer diagnostic that measures the cross-phase angle between electron density and temperature fluctuations in ASDEX Upgrade plasmas. This diagnostic employs cross correlations between signals measured by a tunable, continuous wave, perpendicular incidence, fluctuation reflectometer, and a 24-channel radiometer sharing the same line of sight. Novel measurements in the pedestal of a helium H-mode plasma with small edge localized modes show changes in the cross-phase angle between the electron density and temperature fluctuations from ∼90° to 120°, suggesting changes in the properties of the turbulence driving transport in the plasma edge.

Seismic Anisotropy of Mafic Blueschists: EBSD‐Based Constraints From the Exhumed Rock Record

2026-04-29T04:55:04Z

Seismic Anisotropy of Mafic Blueschists: EBSD‐Based Constraints From the Exhumed Rock Record Ott, Jason N; Condit, Cailey B; Schulte‐Pelkum, Vera; Bernard, Rachel; Pec, Matej Seismic anisotropy constitutes a useful tool for imaging the structure along the plate interface in subduction zones, but the seismic properties of mafic blueschists, a common rock type in subduction zones, remain poorly constrained. We applied the technique of electron backscatter diffraction (EBSD) based petrofabric analysis to calculate the seismic anisotropies of 14 naturally deformed mafic blueschists at dry, ambient conditions. The ductilely deformed blueschists were collected from terranes with inferred peak P‐T conditions applicable to subducting slabs at or near the plate interface in active subduction zones. Epidote blueschists display the greatest P wave anisotropy range (AVp ∼7%–20%), while lawsonite blueschist AVp ranges from ∼2% to 10%. S wave anisotropies generate shear wave splitting delay times up to ∼0.1 s over a thickness of 5 km. AVp magnitude increases with glaucophane abundance (from areal EBSD measurements), decreases with increasing epidote or lawsonite abundance, and is enhanced by glaucophane crystallographic preferred orientation (CPO) strength. Two‐phase rock recipe models provide further evidence of the primary role of glaucophane, epidote, and lawsonite in generating blueschist seismic anisotropy. The symmetry of P wave velocity patterns reflects the deformation‐induced CPO type in glaucophane—an effect previously observed for hornblende on amphibolite P wave anisotropy. The distinctive seismic properties that distinguish blueschist from other subduction zone rock types and the strong correlation between anisotropy magnitude/symmetry and glaucophane CPO suggest that seismic anisotropy may be a useful tool in mapping the extent and deformation of blueschists along the interface, and the blueschist‐eclogite transition in active subduction zones.

A compact, asymmetric probe, planar transient grating spectroscopy system

2026-04-29T04:52:07Z

A compact, asymmetric probe, planar transient grating spectroscopy system Rajagopal, J; Wylie, APC; Dacus, B; St. Julian, T; Short, MP Transient grating spectroscopy (TGS) is a rapid and non-destructive technique for measuring thermal, acoustic, and elastic properties of solid materials with a multitude of uses across many areas of materials research. Current TGS systems require optics tables and cumbersome amounts of space for an entire setup, restricting TGS to being a lab-based method. This paper presents a new design for TGS systems that rotates the probe laser beams around the axis of the pump beam, allowing for an asymmetric probe, planar, optically 2D setup. This, in turn, allows the setup to be significantly simplified, which enables the setup presented in this paper to be roughly nine times smaller in volume than contemporary setups while being much easier to build, align, and operate. Part of the size reduction was enabled by a mono-homodyne system and the removal of the chopper. This system was benchmarked against an existing TGS system using a single-crystal tungsten sample. This showed that it can produce the same surface acoustic wave frequency data as the existing system. This design enables TGS to be more widely adopted for use in more varied and compact environments because of its smaller size and simplicity.

Basal Mantle Flow Over LLSVPs Explains Differences in Pacific and Indo‐Atlantic Hotspot Motions

2026-04-29T04:53:07Z

Basal Mantle Flow Over LLSVPs Explains Differences in Pacific and Indo‐Atlantic Hotspot Motions Bellas‐Manley, A; Royden, L Surface hotspot motions are approximately a factor of two faster in the Pacific than the Indo‐Atlantic, and the Indo‐Atlantic large low shear velocity province (LLSVP) appears to be significantly taller than the Pacific LLSVP. Hypothesizing that surface hotspot motions are correlated with the motion of plume sources on the upper surface of chemically distinct, intrinsically dense LLSVPs, we use 3D spherical mantle convection models to compute the velocity of plume sources and compare with observed surface hotspot motions. No contrast in the mean speed of Pacific and Indo‐Atlantic hotspots is predicted if the LLSVPs are treated as purely thermal anomalies and plume sources move laterally across the core‐mantle boundary. However, when LLSVP topography is included in the model, the predicted hotspot speeds are, on average, faster in the Pacific than the Indo‐Atlantic, even when modest topography is assigned to both LLSVPs (e.g., 100–300 km). The difference in mean hotspot speed increases to a factor of two for larger and laterally variable LLSVP topography estimated from seismic tomographic model S40RTS (up to 1,100–1,500 km for the Indo‐Atlantic region vs. 700–1,400 km for the Pacific region) and our results also broadly reproduce the convergence of Pacific hotspots toward the center of the Pacific LLSVP. These largescale features of global hotspot motions are only reproduced when ambient mantle material flows over large, relatively stable topographical features, suggesting that LLSVPs are chemically distinct and intrinsically dense relative to ambient mantle material.

The Pompeiian ‘Blue Room’: in situ detection and economic estimation of Egyptian blue pigment in an ancient domestic sacrarium

2026-04-29T04:52:37Z

The Pompeiian ‘Blue Room’: in situ detection and economic estimation of Egyptian blue pigment in an ancient domestic sacrarium Quraishi, Mishael A; Nicola, Marco; Weaver, James C; Grifa, Celestino; Amoretti, Valeria; Russo, Antonino; Zuchtriegel, Gabriel; Tarkanian, Michael J; Masic, Admir Egyptian blue (EB) was a prized, intense blue pigment in antiquity, its color commonly associated with wealth, status, and divinity. While frequently used in small quantities for decorative enhancements, Pompeii’s newly excavated “Blue Room”, a shrine in Regio IX Insula 10, is remarkable in that the entire room is covered with this historically famous pigment. Using a combination of visible (white light)-induced luminescence, SEM-EDS, and Raman spectroscopy, we were able to spatially map the large-scale distribution of EB in situ, identify the method of application, and calculate the total quantity of EB used. We estimate that between 2.7 and 4.9 kg of EB was applied in the fresco technique, corresponding to a total cost of between 93 and 168 denarii. These findings demonstrate how modern multi-scale characterization tools can be employed to provide valuable insights into material investment, artistic practice, and social status in an ancient Roman domestic context.

Along‐Strike Segmentation of Seismic Tremor and Its Relationship With the Hydraulic Structure of the Subduction Fault Zone

2026-04-29T04:49:11Z

Along‐Strike Segmentation of Seismic Tremor and Its Relationship With the Hydraulic Structure of the Subduction Fault Zone Farge, Gaspard; Jaupart, Claude; Frank, William B; Shapiro, Nikolai M Along the strike of subduction zones, tectonic tremor episodicity is segmented on a geologic scale. Here, we study how this segmentation reflects large‐scale variations of the structure and conditions of the fault interface where tremor is generated. We try to understand which properties of the hydraulic system of the fault allow elementary tremor sources to synchronize, leading to the emergence of long‐period, large‐scale episodic activity. We model tremor sources as being associated with rapid openings of low‐permeability valves in the fault zone, which channels the upward flow of metamorphic fluids. Valve openings cause pressure transients that allow interaction between sources. In such a system, tremor activity is thus controlled by unsteady fluid circulation. Using numerical simulations of fluid flow, we explore the impact of valve spatial distribution and fluid flux on the emergence of large‐scale patterns of tremor activity. We show that when valves are densely distributed and submitted to near‐critical input flux, they synchronize and generate more episodic activity. Based on our model, the most periodic and spatially coherent tremor bursts should thus be emitted from segments densely populated with valves, and therefore of lower permeability than less synchronized segments. The collective activity of their valve population is responsible for fluid‐pressure cycling at the subduction scale. In the tremor zone of Shikoku, Japan, the most temporally clustered segment coincides with a downgoing seamount chain, suggesting that the segmentation of the fault zone permeability, and hence of tremor activity, could be inherited from the topography of the subducting oceanic plate.

Systematic perturbation of cultures of K. phaffii by carbon co-feeding show gene signatures associated with production of recombinant proteins

2026-04-29T04:55:09Z

Systematic perturbation of cultures of K. phaffii by carbon co-feeding show gene signatures associated with production of recombinant proteins Acharya, Raghav; Hinckley, Joshua; Barry, Rachel; Cha, Eugenie; Narayanan, Harini; Sunday, Brittney C; Ford, Hayley; Whittaker, Charles A; Love, J Christopher Background Demand for recombinant proteins is rapidly growing, driven by their use as biotherapeutics, vaccine components, industrial enzymes, and food ingredients. The growing market requires novel strategies for increasing protein production in cellular hosts. Systems-level frameworks have been used to improve production, but have had difficulty relating complex cellular pathways with protein expression. Here, we demonstrate a method for mapping relationships between gene expression signatures and carbon source-related phenotypes related to recombinant protein production. Results Our approach induces systematic perturbations in cultures of K. phaffii using varied co-feeds of carbon sources. The different carbon sources significantly impacted cell growth, specific productivity, and transcriptional states. With these data, we identified metagenes for both immunoglobulin G1 monoclonal antibody (IgG1) and Variable domain on a heavy chain (VHH) antibody that explained significant transcriptomic variance. These metagenes strongly associated with two phenotypes: production of recombinant protein-to-biomass ratio, and response to methanol induction. We used these results to identify and knockout 31 novel gene targets for which expression inversely correlated with productivity. Nine of these genes improved productivity of IgG1 by up to 3x and ten genes increased productivity of VHH by up to 1.7x. Many of these genes are involved in the modulation and progression of the cell cycle but interestingly, disruption had little to no impact on cell growth. Conclusion This study establishes a framework for relating gene signatures to complex cellular phenotypes, providing a robust methodology for assessing production processes and identifying new targets for cellular engineering. While the identified specific metagenes depend on the complexity and structure of the recombinant protein produced, this framework is extensible across diverse proteins and potentially other host organisms. These signatures may serve as scale-independent, cellular-level metrics for traits like efficiency of production of recombinant proteins, facilitating the translation of findings across different scales and cultivation modes. Furthermore, this framework enables the identification of novel targets for genomic modifications that can improve strain performance, offering a predictive tool for the rational design of high-performing microbial cell factories.

Taurine intake ameliorates lactic acidosis and hyperferritinemia occurring after mRNA SARS-CoV-2 vaccination in a patient with β-thalassemia trait: a case report and review of literature

2026-04-29T04:49:32Z

Taurine intake ameliorates lactic acidosis and hyperferritinemia occurring after mRNA SARS-CoV-2 vaccination in a patient with β-thalassemia trait: a case report and review of literature Kyriakopoulos, Anthony M.; McCullough, Peter A.; Seneff, Stephanie Background Taurine is a powerful antioxidant necessary for mitochondrial function. Lactic acidosis is a complication encountered in the condition mitochondrial myopathy, encephalopathy, lactic acidosis and stroke-like episodes (MELAS), which can be successfully treated with supplemental taurine. Furthermore, taurine regulates the production of iron-dependent proteins such as ferritin that can act as chelating agents to sequester labile iron. Case presentation A 38-year-old Greek male with a β-zero thalassemia trait developed multiple severe symptoms soon after his first and only mRNA (Pfizer) SARS-CoV-2 vaccination that included hematological stress to be a candidate for blood transfusion. Amongst the hematological readings, the patient had lactate levels > 4 mmol/ml, indicating lactic acidosis, and ferritin levels > 820 ng/ml, representing hyperferritinemia. Moreover, the patient has organic acid and plasma metabolite levels in the urine that are indicative of mitochondrial dysfunction. Regular taurine intake (500 mg/day) for years helped the patient control lactate and ferritin levels and avoid more serious clinical decompensation. Conclusion Regular taurine intake helps to avoid lactic acidosis and reverse hyperferritinemia after mRNA SARS-CoV-2 vaccination in a patient with β-zero thalassemia trait with no obvious genetic trait linked to mitochondrial myopathy, encephalopathy, lactic acidosis and stroke-like episodes. Taurine seemed to be protective for mitochondria.

Synonymous codon usage defines functional gene families

2026-04-29T04:50:36Z

Synonymous codon usage defines functional gene families Ghanegolmohammadi, Farzan; Ohnuki, Shinsuke; Byrne, Shane; Raman, Rahul; Begley, Thomas J; Dedon, Peter C The degeneracy of the genetic code is increasingly recognized for roles in regulating translation rate, protein folding, and cell response. However, the functional genomics of codon usage patterns remains poorly defined. We previously showed that prokaryotic and eukaryotic cells respond to individual stresses by uniquely reprogramming the tRNA pool and the dozens of tRNA modifications comprising the tRNA epitranscriptome to cause selective translation of mRNAs from codon-biased stress response genes. Here, we tested the hypothesis that functional gene families have distinct values of codon bias in the Saccharomyces cerevisiae genome by modeling isoacceptor codon distributions using a new approach—analysis of synonymous codon signatures (ASCS).

Protocol for development of a reporting guideline (TRIPOD-Code) for code repositories associated with diagnostic and prognostic prediction model studies

2026-04-29T04:53:32Z

Protocol for development of a reporting guideline (TRIPOD-Code) for code repositories associated with diagnostic and prognostic prediction model studies Pollard, Tom; Sounack, Thomas; Gao, Catherine A; Celi, Leo Anthony; Lindvall, Charlotta; Lee, Hyeonhoon; Lee, Hyung-Chul; Moons, Karel GM; Collins, Gary S The Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement was published to improve the reporting and critical appraisal of prediction model studies for diagnosis and prognosis. This paper describes the processes and methods that will be used to develop an extension to the TRIPOD statement (TRIPOD-Code) for the management of code associated with prediction model studies. TRIPOD-Code focuses specifically on the transparent reporting of analytical code used in prediction model studies, including code for data preprocessing, model development, and model evaluation.

SubMIT: A Physics Analysis Facility at MIT

2026-04-29T04:50:25Z

SubMIT: A Physics Analysis Facility at MIT Acosta, P.; Bendavid, J.; D’Alfonso, M.; Eysermans, J.; Freer, C.; Goncharov, M.; Heine, M.; Lavezzo, L.; Newman, D.; Moore, M.; Paus, C.; Shen, X.; Walter, D.; Wang, Z. The recently completed SubMIT platform is a small set of servers that provide interactive access to substantial data samples at high speeds, enabling sophisticated data analyses with very fast turnaround times. Additionally, it seamlessly integrates massive processing resources for large-scale tasks by connecting to a set of powerful batch processing systems. It serves as an ideal prototype for an Analysis Facility tailored to meet the demanding data and computational requirements anticipated during the High-Luminosity phase of the Large Hadron Collider. The key features that make this facility so powerful include highly optimized data access with a minimum of 100 Gbps networking per server, a large managed NVMe storage system, and a substantial spinning-disk Ceph file system. The platform integrates a diverse set of high multicore CPU machines for tasks benefiting from the multithreading and GPU resources for example for neural network training. SubMIT also provides and supports a flexible environment for users to manage their own software needs for example by using containers. This article describes the facility, its users, and a few complementary, generic and real-life analyses that are used to benchmark its various capabilities.

Geophysical applications to geothermal energy — Introduction

2026-04-29T04:51:19Z

Geophysical applications to geothermal energy — Introduction Gasperikova, Erika; Fehler, Michael; Asanuma, Hiroshi; Trainor-Guitton, Whitney; Tezkan, Bülent; Bertrand, Ted Geothermal energy is a renewable resource that has the potential to provide much-needed energy in many regions around the globe. Electric power generation from hydrothermal and geopressured geothermal systems has been successful worldwide for several decades. Direct use of shallow geothermal energy can address present needs for heating and cooling of buildings or hot water generation. Other potentially much larger resources, such as permeable strata, magmatic, and enhanced geothermal systems (EGS), need to be better understood and currently need help producing energy on an economically attractive scale.

Reply to the Discussion

2026-04-29T04:53:12Z

Reply to the Discussion Alkhimenkov, Yury Gassmann’s equations have been known for several decades and are widely used in geophysics. These equations are treated as exact if all the assumptions used in their derivation are fulfilled. However, a recent theoretical study claimed that Gassmann’s equations contain an error. Shortly after that, a 3D numerical calculation was performed on a simple pore geometry that verifies the validity of Gassmann’s equations. This pore geometry was simpler than those in real rocks but arbitrary. Furthermore, the pore geometry that was used did not contain any special features (among all possible geometries) that were tailored to make it consistent with Gassmann’s equations. In other recent studies, I also performed numerical calculations on several other more complex pore geometries that supported the validity of Gassmann’s equations. To further support the validity of these equations, I provide here one more convergence study using a more realistic geometry of the pore space. Given that there are several studies that rederive Gassmann’s equations using different methods and numerical studies that verify them for different pore geometries, it can be concluded that Gassmann’s equations can be used in geophysics without concern if their assumptions are fulfilled. MATLAB routines to reproduce the presented results are provided.

A simple and accurate model for attenuation and dispersion caused by squirt flow in isotropic porous rocks

2026-04-29T04:51:08Z

A simple and accurate model for attenuation and dispersion caused by squirt flow in isotropic porous rocks Alkhimenkov, Yury; Quintal, Beatriz Seismic waves propagating in fluid-saturated porous rocks exhibit attenuation and velocity dispersion in a broad range of frequencies. At sonic and ultrasonic frequencies, the attenuation is predominantly caused by fluid flow in cracks and grain contacts, so-called squirt flow. This physical mechanism for attenuation also may be relevant at seismic frequencies. We develop a simple and accurate analytical model for attenuation and dispersion caused by squirt flow in isotropic porous rocks. The input material properties for a specific rock model can be directly measured in a laboratory or calculated using analytical and numerical approaches. The results from our squirt flow model are compared with inherently accurate 3D numerical solutions for the same pore geometries. The analytical and numerical results are in good agreement. Furthermore, we observe that our analytical model is more accurate than the currently available analytical solution for squirt flow in isotropic porous rocks. MATLAB routines to reproduce the presented results are made available.

HDF5eis: A storage and input/output solution for big multidimensional time series data from environmental sensors

2026-04-29T04:51:06Z

HDF5eis: A storage and input/output solution for big multidimensional time series data from environmental sensors White, Malcolm CA; Zhang, Zhendong; Bai, Tong; Qiu, Hongrui; Chang, Hilary; Nakata, Nori Modern high-performance computing (HPC) tasks overwhelm conventional geophysical data formats. We describe a new data schema called HDF5eis (read H-D-F-size) for handling big multidimensional time series data from environmental sensors in HPC applications and implement a freely available Python application programming interface (API) for building and processing HDF5eis files. HDF5eis augments the popular Hierarchical Data Format 5 with a minimal set of additional conventions that facilitate fast and flexible data input and output protocols for regularly sampled (in time) data with any number of dimensions. HDF5eis supports arbitrary ancillary data (e.g., metadata) storage in columnar format or as UTF-8 encoded byte streams alongside time series data. Our HDF5eis API enables simple and efficient access to big data sets distributed across a potentially large number of small heterogeneous files through a single point of access. HDF5eis outperforms conventional seismic data formats by up to two orders of magnitude in terms of random read access times. We contribute HDF5eis as an operational tool and an experimental draft proposal that will help establish the next generation of data standards in the earth sciences.

RES.HST-001 MIT Little Devices Lab, Fall 2021

2026-04-28T17:35:58Z

RES.HST-001 MIT Little Devices Lab, Fall 2021 Gomez-Marquez, Jose; Young, Anna The MIT Little Devices Lab collaborates with healthcare professionals in developing countries to create affordable health and medical technologies. A large number of these healthcare professionals are nurses, and have been described as “stealth innovators,” “NurseMakers,” and “MacGyver Nurses.” (Rice, S. "Nurses Devise Their Own Innovations." Modern Healthcare, 17 Oct., 2015). The Little Devices Lab helps support these inventors by sending them kits with the modular parts and materials to invent and build their own customized, cost-effective medical devices. They can then solve challenges specific to their patients and work environments, for a range of applications from diagnostics to microfluidics to drug delivery. Similar to how breadboards enabled people to more easily build their own electronics, one of the lab’s projects involved creating a biochemical breadboard with plug-and-play sets of blocks for building paper analytical devices, which healthcare workers can use to make diagnostic tests that meet their needs. On the Little Devices Lab’s site, users will find more details about the lab's ongoing projects and research, video presentations about its work, and several of its members' publications.

Ombudsman Dilemmas: Confidentiality, Neutrality, Testifying, Record-Keeping

2026-04-28T03:01:03Z

Ombudsman Dilemmas: Confidentiality, Neutrality, Testifying, Record-Keeping Rowe, Mary P.; Simon, Mary G.; Bensinger, Ann Note: This article was initially published in the Proceedings of the Annual Conference of the Society of Professionals in Dispute Resolution (SPIDR) 1989, 282-293. SPIDR, 1990.

Corporate Ombudsmen: Functions, Caseloads, Approaches and Outcomes

2026-04-28T03:00:57Z

Corporate Ombudsmen: Functions, Caseloads, Approaches and Outcomes Ziegenfuss, James T. Jr.; Rowe, Mary; Munzenrider, Robert F. Note: Includes results of a 1989 survey of 55 corporate ombudsmen.

Micro-inequities in Medicine

2026-04-28T03:01:04Z

Micro-inequities in Medicine Silver, Julie K.; Rowe, Mary; Sinha, Michael S.; Molinares, Diana M.; Spector, Nancy D.; Mukherjee, Debjani

Shallow Slow Slip Events in the Imperial Valley With Along‐Strike Propagation

2026-04-28T03:19:48Z

Shallow Slow Slip Events in the Imperial Valley With Along‐Strike Propagation Materna, Kathryn; Bürgmann, Roland; Lindsay, Danielle; Bilham, Roger; Herring, Thomas; Crowell, Brendan; Szeliga, Walter Shallow creep events provide opportunities to understand the mechanical properties and behavior of faults. However, due to physical limitations observing creep events, the precise spatio‐temporal evolution of slip during creep events is not well understood. In 2023, the Superstition Hills and Imperial faults in California each experienced centimeter‐scale slip events that were captured in unprecedented detail by satellite radar, sub‐daily Global Navigation Satellite Systems, and creepmeters. In both cases, the slip propagated along the fault over 2–3 weeks. The Superstition Hills event propagated bilaterally away from its initiation point at average velocities of ∼9 km/day, but propagation velocities were locally much higher. The ruptures were consistent with slip from tens of meters to ∼2 km depths. These slowly propagating events reveal that the shallow crust of the Imperial Valley does not obey purely velocity‐strengthening or velocity‐weakening rate‐and‐state friction, but instead requires the consideration of fault heterogeneity or fault‐frictional behaviors such as dilatant strengthening. Article relates to: Vavra, E. J., Fialko, Y., Rockwell, T., Bilham, R., Štěpančíková, P., Stemberk, J., et al. (2024). Characteristic slow-slip events on the Superstition Hills Fault, Southern California. Geophysical Research Letters, 51, e2023GL107244. https://doi.org/10.1029/2023GL107244

Flow and Turbulence Due To Wood Contribute to Declogging of Gravel Bed

2026-04-28T03:19:56Z

Flow and Turbulence Due To Wood Contribute to Declogging of Gravel Bed Schalko, I; Ponce, M; Lassar, S; Schwindt, S; Haun, S; Nepf, H The placement of wood in rivers is a common restoration method used to locally affect hydraulic and morphologic conditions to create habitat. Laboratory experiments demonstrated that wood placements can also promote surface declogging, that is, removal of fine sediment from a gravel bed, thereby restoring spawning grounds for fish. Logs of different size and submergence level were placed on a gravel bed clogged with fines. Surface declogging was observed in regions of elevated turbulence in the log wake and elevated velocity adjacent to the log. A criteria for declogging was identified based on a modified non‐dimensional Shields parameter combining mean and turbulent velocity at the bed. The footprint of declogged bed scaled with log dimensions. Emergent logs produced a larger declogging footprint compared to submerged logs of the same length, due to their stronger influence on the flow field. Logs were also shown to prevent clogging over similar areas.

The Response of Tropical Rainfall to Idealized Small‐Scale Thermal and Mechanical Forcing

2026-04-28T03:19:35Z

The Response of Tropical Rainfall to Idealized Small‐Scale Thermal and Mechanical Forcing Velez‐Pardo, Martin; Cronin, Timothy W Predicting the spatiotemporal distribution of rainfall remains a key challenge in Tropical Meteorology, partly due to an incomplete understanding of the effects of different environmental factors on atmospheric convection. In this work, we use numerical simulations of tropical ocean domains to study how rainfall responds to imposed localized thermal and mechanical forcings to the atmosphere. We use the Normalized Gross Moist Stability—NGMS—to quantify the net precipitation response associated with a given net atmospheric heating. We find that NGMS values differ considerably for different forcings, but show that the relationship between precipitation and column relative humidity collapses along a universal curve across all of them. We also show that the contributions from mean vertical advection of moist and dry static energy only approximate the NGMS well at scales larger than a couple hundred kilometers, indicating that general horizontal mixing processes are not negligible at smaller scales.

Magnetic Field Signatures of Craters on Mars

2026-04-28T03:20:35Z

Magnetic Field Signatures of Craters on Mars Mittelholz, A; Steele, SC; Fu, RR; Johnson, CL; Lillis, RJ; Stucky de Quay, G Craters on Mars are a window into Mars' past and the time they were emplaced. Because the crust is heated and shocked during impact, craters can demagnetize or magnetize the crust depending on the presence or absence of a dynamo field at the time of impact. This concept has been used to constrain dynamo timing. Here, we investigate magnetic anomalies associated with craters larger than 150 km. We find that most of those craters, independent of age, exhibit demagnetization signatures in the form of a central magnetic low. We demonstrate a statistically significant association between such signatures and craters, and hypothesize that the excavation of strongly magnetic crustal material may be an important contribution to the dominance of demagnetized craters. This finding implies that the simple presence or absence of crater demagnetization signatures is not a reliable indicator for the activity of the Martian dynamo during or after crater formation.

Anomalous Meltwater From Ice Sheets and Ice Shelves Is a Historical Forcing

2026-04-28T03:20:59Z

Anomalous Meltwater From Ice Sheets and Ice Shelves Is a Historical Forcing Schmidt, Gavin A; Romanou, Anastasia; Roach, Lettie A; Mankoff, Kenneth D; Li, Qian; Rye, Craig D; Kelley, Maxwell; Marshall, John C; Busecke, Julius JM Recent mass loss from ice sheets and ice shelves is now persistent and prolonged enough that it impacts downstream oceanographic conditions. To demonstrate this, we use an ensemble of coupled GISS‐E2.1‐G simulations forced with historical estimates of anomalous freshwater, in addition to other climate forcings, from 1990 through 2019. There are detectable differences in zonal‐mean sea surface temperatures (SST) and sea ice in the Southern Ocean, and in regional sea level around Antarctica and in the western North Atlantic. These impacts mostly improve the model's representation of historical changes, including reversing the forced trends in Antarctic sea ice. The changes in SST may have implications for estimates of the SST pattern effect on climate sensitivity and for cloud feedbacks. We conclude that the changes are sufficiently large that model groups should strive to include more accurate estimates of these drivers in all‐forcing historical simulations in future coupled model intercomparisons.

When data systems meet ecosystems: Tensions between AI platforms and vitality in corporate sustainability

2026-04-28T03:19:01Z

When data systems meet ecosystems: Tensions between AI platforms and vitality in corporate sustainability Westerlaken, Michelle; Rattay, Sonja Ecosystems are not computers. They are alive, endlessly complex, and deeply intriguing. What if the data systems used to better understand living worlds could embed this more-than-human vitality? This paper examines ecosystem data tools in corporate sustainability, where AI tools have become key mediators in sustainable transitions. Drawing on desk research and empirical data with biodiversity specialists at 13 large corporations, it identifies four tensions that reveal deeper mismatches between computational systems and ecological worlds. These arise between demands for certainty and the unstable dynamics of ecosystems; between the need for data as neutral evidence and as arguments towards managing corporate trade-offs; between LLMs as tools for synthesis and the continued necessity of human interpretation; and between functional tool aesthetics and the vital attachments needed for regenerative change. From these findings, the paper articulates a more-than-human design space aligned with ecological dynamics, meaning-making, technosymbiotic relations, and transformative corporate change. DRS 26 Edinburgh, UK 8-12 June 2026

Isomer-driven polymerization, depolymerization, and reconstruction

2026-04-25T03:06:47Z

Isomer-driven polymerization, depolymerization, and reconstruction Wakefield IV, Herbert; Fromel, Nicholas J; Jiang, Jennifer; Kevlishvili, Ilia; Yao, Yunxin; Craig, Stephen L; Kulik, Heather J; Klausen, Rebekka S We report that differences in ring strain enthalpy between cis and trans isomers of sila-cycloheptene provide a driving force for both polymerization and depolymerization via olefin metathesis. A need for new methods to reintroduce the low-strain isomer into the plastic economy inspired the development of a polymerization based on ring-opening/cross-metathesis step polymerization, which afforded perfect sequence control for an alternating copolymer. The chemical principles are a platform for achieving both efficient polymerization and depolymerization with high mass recovery in functional polymers.

Designing membranes with specific binding sites for selective ion separations

2026-04-25T03:07:11Z

Designing membranes with specific binding sites for selective ion separations Violet, Camille; Ball, Akash; Heiranian, Mohammad; Villalobos, Luis Francisco; Zhang, Junwei; Uralcan, Betul; Kulik, Heather; Haji-Akbari, Amir; Elimelech, Menachem A new class of membranes that can separate ions of similar size and charge is highly desired for resource recovery, water reuse and energy storage technologies. These separations require membrane nanochannels with simultaneous ångström-scale confinement and ion-selective binding sites. Conventional membrane material design uses continuous, volume-averaged properties that cannot account for discrete chemical interactions between ions and binding sites. In this Perspective, we present a design framework for ultraselective membranes by describing how to select and incorporate ion-specific binding sites into membrane nanochannels. We begin by discussing how the chemical features of ions, functional groups and solvents impact ion-binding energy. We then describe the role of binding energy in selective ion transport through nanochannels and discuss the critical importance of intersite spacing. Subsequently, we draw inspiration from machine learning methods used for drug discovery and suggest a similar approach to identify functional groups with optimal ion-binding affinity. We conclude by outlining synthetic methods to incorporate ion-specific binding sites into prevalent nanostructured materials such as covalent organic frameworks, metal–organic frameworks, two-dimensional materials and polymers.

Machine Learning Prediction of the Experimental Transition Temperature of Fe(II) Spin-Crossover Complexes

2026-04-25T03:05:15Z

Machine Learning Prediction of the Experimental Transition Temperature of Fe(II) Spin-Crossover Complexes Vennelakanti, Vyshnavi; Kilic, Irem B; Terrones, Gianmarco G; Duan, Chenru; Kulik, Heather J Spin-crossover (SCO) complexes are materials that exhibit changes in the spin state in response to external stimuli, with potential applications in molecular electronics. It is challenging to know a priori how to design ligands to achieve the delicate balance of entropic and enthalpic contributions needed to tailor a transition temperature close to room temperature. We leverage the SCO complexes from the previously curated SCO-95 data set [Vennelakanti et al. J. Chem. Phys. 159, 024120 (2023)] to train three machine learning (ML) models for transition temperature (T1/2) prediction using graph-based revised autocorrelations as features. We perform feature selection using random forest-ranked recursive feature addition (RF-RFA) to identify the features essential to model transferability. Of the ML models considered, the full feature set RF and recursive feature addition RF models perform best, achieving moderate correlation to experimental T1/2 values. We then compare ML T1/2 predictions to those from three previously identified best-performing density functional approximations (DFAs) which accurately predict SCO behavior across SCO-95, finding that the ML models predict T1/2 more accurately than the best-performing DFAs. In addition, we study ML model predictions for a set of 18 SCO complexes for which only estimated T1/2 values are available. Upon excluding outliers from this set, the RF-RFA RF model shows a strong correlation to estimated T1/2 values with a Pearson’s r of 0.82. In contrast, DFA-predicted T1/2 values have large errors and show no correlation to estimated T1/2 values over the same set of complexes. Overall, our study demonstrates slightly superior performance of ML models in comparison with some of the best-performing DFAs, and we expect ML models to improve further as larger data sets of SCO complexes are curated and become available for model training.

Computational Investigation of the Role of Metal Center Identity in Cytochrome P450 Enzyme Model Reactivity

2026-04-25T03:04:10Z

Computational Investigation of the Role of Metal Center Identity in Cytochrome P450 Enzyme Model Reactivity Vennelakanti, Vyshnavi; Jeon, Mugyeom; Kulik, Heather J Mononuclear Fe enzymes such as heme-containing cytochrome P450 enzymes catalyze a variety of C-H activation reactions under ambient conditions, and they represent an attractive platform for engineering reactivity through changes to the native enzyme. Using density functional theory, we study both native Fe and non-native group 8 (Ru, Os) and group 9 (Ir) metal centers in an active site model of P450. We quantify how changing the metal changes spin state preferences throughout the catalytic cycle. Our calculations reveal an intermediate-spin ground state for all Fe intermediates while the heavier metals prefer low-spin ground states across most intermediates in the reaction cycle. We also study the rate-determining hydrogen atom transfer (HAT) step and the subsequent rebound step. We observe comparable HAT barriers for Fe and Ru, a much higher barrier for Os, and the lowest HAT barrier for Ir. Rebound steps are barrierless for all metals, and the rebound intermediate for Fe is most significantly stabilized. Examination of ground spin states of all intermediates in the reaction cycle reveals spin-allowed pathways for the group 8 metals and spin-forbidden energetics for the group 9 Ir with potential two-state reactivity. Our work highlights the differences between the group 8 metals and the group 9 Ir, and it suggests that engineered P450 enzymes with Ru in particular result in improved enzyme reactivity toward C-H hydroxylation.

Elastic geometric-mean reverse time migration for source imaging

2026-04-25T03:06:53Z

Elastic geometric-mean reverse time migration for source imaging Bai, Tong; Lyu, Bin; Li, Fangyu; Williamson, Paul; Nakata, Nori Passive source location is a fundamental problem in earthquake seismology and reservoir characterization with fluid injection. It has been shown that geometric-mean reverse time migration (GmRTM) can produce source images with improved resolution and fewer migration artifacts than conventional time-reversal imaging in acoustic media. However, the acoustic assumption often requires data windowing and polarity correction when we apply it to the elastic data, which involves manual efforts and introduces uncertainties. Therefore, we have extended this crosscorrelation-based GmRTM imaging method to elastic media. Back-propagation and Helmholtz decomposition are conducted individually for each receiver wavefield, after which we apply zero-lag crosscorrelations over the decoupled P or/and S wavefields to obtain the corresponding P-, S-, and PS-source images. Considering the influences of random noise, anisotropy, and interferences from multiple sources, we test the proposed elastic GmRTM method on a synthetic example with a layered model and determine its advantages over other imaging conditions (e.g., time-reversal imaging). The method is further validated using another synthetic test based on the elastic Marmousi model and a field-data example from Oklahoma.

Receiver grouping strategies for hybrid geometric-mean reverse time migration

2026-04-25T03:06:58Z

Receiver grouping strategies for hybrid geometric-mean reverse time migration Bai, Tong; Lyu, Bin; Williamson, Paul; Nakata, Nori Geometric-mean reverse time migration (GmRTM), a powerful crosscorrelation-based imaging method, generates higher resolution source images and is more robust to noise compared with conventional time-reversal imaging. The price to pay is the higher computational costs. Alternatively, we can adopt hybrid strategies by dividing the receivers into different groups. Conventional time reversal (i.e., wavefield summation) is performed inside each group, followed by the application of crosscorrelation imaging condition among different groups. Such hybrid strategies can retain the advantages of GmRTM and time reversal and are often more practical than pure GmRTM. Yet, designing appropriate grouping strategy is not trivial. Here, we have developed two grouping strategies (adjacent and scattered) and used synthetic and field-data examples to evaluate their performance with various group numbers. In addition to the spatial resolution of the source image, robustness to random noise is another important assessment criterion, for which we consider two distribution patterns, such as concentrated and scattered, of traces contaminated with strong random noise. We also evaluated their effectiveness to visualize events (in the image domain) that are not completely recorded by all receivers. Our comprehensive tests illustrate the respective advantages of the two grouping strategies.

Neuron–Glial Interactions: Implications for Plasticity, Behavior, and Cognition

2026-04-25T03:04:33Z

Neuron–Glial Interactions: Implications for Plasticity, Behavior, and Cognition Rangel-Gomez, Mauricio; Alberini, Cristina M; Deneen, Benjamin; Drummond, Gabrielle T; Manninen, Tiina; Sur, Mriganka; Vicentic, Aleksandra The traditional view of glial cells as mere supportive tissue has shifted, due to advances in technology and theoretical conceptualization, to include a diversity of other functions, such as regulation of complex behaviors. Astrocytes, the most abundant glial cells in the central nervous system (CNS), have been shown to modulate synaptic functions through gliotransmitter-mediated neurotransmitter reuptake, influencing neuronal signaling and behavioral functions. Contemporary studies further highlight astrocytes’ involvement in complex cognitive functions. For instance, inhibiting astrocytes in the hippocampus can lead to memory deficits, suggesting their integral role in memory processes. Moreover, astrocytic calcium activity and astrocyte–neuron metabolic coupling have been linked to changes in synaptic strength and learning. Microglia, another type of glial cell, also extend beyond their supportive roles, contributing to learning and memory processes, with microglial reductions impacting these functions in a developmentally dependent manner. Oligodendrocytes, traditionally thought to have limited roles postdevelopment, are now recognized for their activity-dependent modulation of myelination and plasticity, thus influencing behavioral responses. Recent advancements in technology and computational modeling have expanded our understanding of glial functions, particularly how astrocytes influence neuronal circuits and behaviors. This review underscores the importance of glial cells in CNS functions and the need for further research to unravel the complexities of neuron–glia interactions, the impact of these interactions on brain functions, and potential implications for neurological diseases.

Active Zone Maturation Controls Presynaptic Output and Release Mode and Is Regulated by Neuronal Activity

2026-04-25T03:06:08Z

Active Zone Maturation Controls Presynaptic Output and Release Mode and Is Regulated by Neuronal Activity Akbergenova, Yulia; Matthias, Jessica; Makeyeva, Sofya; Littleton, J Troy Synapse formation requires the accumulation of cytomatrix proteins and voltage-gated Ca2+ channels (VGCCs) at presynaptic active zones (AZs). At Drosophila melanogaster larval neuromuscular junctions, a sequential process of AZ maturation is observed, with initial incorporation of early scaffolds followed by arrival of late scaffolds and VGCCs. To examine how AZ maturation regulates presynaptic output, serial imaging of AZ formation and function was performed at time-stamped synapses of male larvae expressing glutamate receptors linked to the photoconvertible protein mMaple. Quantal imaging demonstrated older synapses have higher synaptic efficacy and sustain greater release across development, while immature sites lacking VGCC accumulation supported spontaneous fusion. To examine how activity regulates AZ maturation, the effects of cell autonomous disruptions to neurotransmitter release were analyzed. Decreased synaptic transmission reduced AZ seeding and caused hyperaccumulation of material at existing AZs. Generation of an endogenous photoconvertible version of the AZ scaffold protein BRP revealed neuronal silencing decreased the protein's turnover. Although enlarged AZs are also observed in rab3 mutants, activity reduction acted through an independent mechanism that required postsynaptic glutamate receptor-dependent signaling. Endogenous tagging of the Unc13B early AZ scaffold and the Unc13A late AZ scaffold revealed activity reduction decreased seeding of both early and late scaffolds, in contrast to rab3 mutants. Together, these data indicate AZ maturation regulates presynaptic release mode and output strength, with neuronal activity shaping both AZ number and size across development.

Evidence for an Active Handoff between Hemispheres during Target Tracking

2026-04-25T03:04:48Z

Evidence for an Active Handoff between Hemispheres during Target Tracking Broschard, Matthew B; Roy, Jefferson E; Brincat, Scott L; Mahnke, Meredith K; Miller, Earl K The brain has somewhat separate cognitive resources for the left and right sides of our visual field. Despite this lateralization, we have a smooth and unified perception of our environment. This raises the question of how the cerebral hemispheres are coordinated to transfer information between them. We recorded neural activity in the lateral prefrontal cortex, bilaterally, as two male nonhuman primates covertly tracked a target that moved from one visual hemifield (i.e., from one hemisphere) to the other. Beta (15–30 Hz) power, gamma (30–80 Hz) power, and spiking information reflected sensory processing of the target. In contrast, alpha (10–15 Hz) power, theta (4–10 Hz) power, and spiking information seemed to reflect an active handoff of attention as target information was transferred between hemispheres. Specifically, alpha power and spiking information ramped up in anticipation of the hemifield cross. Theta power peaked after the cross, signaling its completion. Our results support an active handoff of information between hemispheres. This “handshaking” operation may be critical for minimizing information loss, much like how mobile towers handshake when transferring calls between them.

Metal–Organic Framework Stability in Water and Harsh Environments from Data-Driven Models Trained on the Diverse WS24 Data Set

2026-04-25T03:05:38Z

Metal–Organic Framework Stability in Water and Harsh Environments from Data-Driven Models Trained on the Diverse WS24 Data Set Terrones, Gianmarco G; Huang, Shih-Peng; Rivera, Matthew P; Yue, Shuwen; Hernandez, Alondra; Kulik, Heather J Metal-organic frameworks (MOFs) are porous materials with applications in gas separations and catalysis, but a lack of water stability often limits their practical use given the ubiquity of water. Consequently, it is useful to predict whether a MOF is water-stable before investing time and resources into synthesis. Existing heuristics for designing water-stable MOFs lack generality and limit the diversity of explored chemistry due to narrowly defined criteria. Machine learning (ML) models offer the promise to improve the generality of predictions but require data. In an improvement on previous efforts, we enlarge the available training data for MOF water stability prediction by over 400%, adding 911 MOFs with water stability labels assigned through semiautomated manuscript analysis to curate the new data set WS24. The additional data are shown to improve ML model performance (test ROC-AUC > 0.8) over diverse chemistry for the prediction of both water stability and stability in harsher acidic conditions. We illustrate how the expanded data set and models can be used with a previously developed activation stability model in combination with genetic algorithms to quickly screen ∼10,000 MOFs from a space of hundreds of thousands for candidates with multivariate stability (upon activation, in water, and in acid). We uncover metal- and geometry-specific design rules for robust MOFs. The data set and ML models developed in this work, which we disseminate through an easy-to-use web interface, are expected to contribute toward the accelerated discovery of novel, water-stable MOFs for applications such as direct air gas capture and water treatment.

Diminished Repetition Suppression Reveals Selective and Systems-Level Face Processing Differences in ASD

2026-04-25T03:05:05Z

Diminished Repetition Suppression Reveals Selective and Systems-Level Face Processing Differences in ASD D'Mello, Anila M; Frosch, Isabelle R; Meisler, Steven L; Grotzinger, Hannah; Perrachione, Tyler K; Gabrieli, John DE Repeated exposure to a stimulus results in reduced neural response, or repetition suppression, in brain regions responsible for processing that stimulus. This rapid accommodation to repetition is thought to underlie learning, stimulus selectivity, and strengthening of perceptual expectations. Importantly, reduced sensitivity to repetition has been identified in several neurodevelopmental, learning, and psychiatric disorders, including autism spectrum disorder (ASD), a neurodevelopmental disorder characterized by challenges in social communication and repetitive behaviors and restricted interests. Reduced ability to exploit or learn from repetition in ASD is hypothesized to contribute to sensory hypersensitivities, and parallels several theoretical frameworks claiming that ASD individuals show difficulty using regularities in the environment to facilitate behavior. Using fMRI in autistic and neurotypical human adults (females and males), we assessed the status of repetition suppression across two modalities (vision, audition) and with four stimulus categories (faces, objects, printed words, and spoken words). ASD individuals showed domain-specific reductions in repetition suppression for face stimuli only, but not for objects, printed words, or spoken words. Reduced repetition suppression for faces was associated with greater challenges in social communication in ASD. We also found altered functional connectivity between atypically adapting cortical regions and higher-order face recognition regions, and microstructural differences in related white matter tracts in ASD. These results suggest that fundamental neural mechanisms and system-wide circuits are selectively altered for face processing in ASD and enhance our understanding of how disruptions in the formation of stable face representations may relate to higher-order social communication processes.

Caged AIEgens: Multicolor and White Emission Triggered by Mechanical Activation

2026-04-25T03:04:21Z

Caged AIEgens: Multicolor and White Emission Triggered by Mechanical Activation Sun, Yunyan; Wang, Kecheng; Huang, Xiao; Wei, Shixuan; Contreras, Enrique; Jain, Prashant K; Campos, Luis M; Kulik, Heather J; Moore, Jeffrey S Aggregation-induced emission luminogens (AIEgens) that respond to mechanical force are increasingly used as force probes, memory devices, and advanced security systems. Most of the known mechanisms to modulate mechanoresponsive AIEgens have been based on changes in aggregation states, involving only physical alterations. Instances that employ covalent bond cleavage are still rare. We have developed a novel mechanochemical uncaging strategy to unveil AIEgens with diverse emission characteristics using engineered norborn-2-en-7-one (NEO) mechanophores. These NEO mechanophores were covalently integrated into polymer molecules and activated in both the solution and solid states. This activation resulted in highly tunable fluorescence upon immobilization through solidification or aggregation, producing blue, green, yellow, and orange-red emissions. By designing the caged and uncaged forms as donor-acceptor pairs for Förster resonance energy transfer (FRET), we achieved multicolor mechanofluorescence, effectively broadening the color spectrum to include white emission. Additionally, we computationally explored the electronic structures of activated NEOs, providing insights into the observed regiochemical effects of the substituents. This understanding, together with the novel luminogenic characteristics of the caged and activated species, provides a highly tunable reporter that traces progress with continuous color evolution. This advancement paves the way for future applications of mechanoresponsive materials in areas like damage detection and bioimaging.

Electrophysiological Signatures of Visual Recognition Memory across All Layers of Mouse V1

2026-04-25T03:05:35Z

Electrophysiological Signatures of Visual Recognition Memory across All Layers of Mouse V1 Hayden, Dustin J; Finnie, Peter SB; Thomazeau, Aurore; Li, Alyssa Y; Cooke, Samuel F; Bear, Mark F In mouse primary visual cortex (V1), familiar stimuli evoke significantly altered responses when compared with novel stimuli. This stimulus-selective response plasticity (SRP) was described originally as an increase in the magnitude of visual evoked potentials (VEPs) elicited in layer 4 (L4) by familiar phase-reversing grating stimuli. SRP is dependent on NMDA receptors (NMDARs) and has been hypothesized to reflect potentiation of thalamocortical (TC) synapses in L4. However, recent evidence indicates that the synaptic modifications that manifest as SRP do not occur on L4 principal cells. To shed light on where and how SRP is induced and expressed in male and female mice, the present study had three related aims: (1) to confirm that NMDAR are required specifically in glutamatergic principal neurons of V1, (2) to investigate the consequences of deleting NMDAR specifically in L6, and (3) to use translaminar electrophysiological recordings to characterize SRP expression in different layers of V1. We find that knock-out (KO) of NMDAR in L6 principal neurons disrupts SRP. Current-source density (CSD) analysis of the VEP depth profile shows augmentation of short latency current sinks in layers 3, 4, and 6 in response to phase reversals of familiar stimuli. Multiunit recordings demonstrate that increased peak firing occurs in response to phase reversals of familiar stimuli across all layers, but that activity between phase reversals is suppressed. Together, these data reveal important aspects of the underlying phenomenology of SRP and generate new hypotheses for the expression of experience-dependent plasticity in V1.

The tension-activated carbon–carbon bond

2026-04-25T03:04:46Z

The tension-activated carbon–carbon bond Sun, Yunyan; Kevlishvili, Ilia; Kouznetsova, Tatiana B; Burke, Zach P; Craig, Stephen L; Kulik, Heather J; Moore, Jeffrey S Mechanical force drives distinct chemical reactions; yet, its vectoral nature results in complicated coupling with reaction trajectories. Here, we utilize a physical organic model inspired by the classical Morse potential and its differential forms to identify effective force constant (k eff) and reaction energy (ΔE) as key molecular features that govern mechanochemical kinetics. Through a comprehensive experimental and computational investigation with four norborn-2-en-7-one (NEO) mechanophores, we establish the relationship between these features and the force-dependent energetic changes along the reaction pathways. We show that the complex kinetic behavior of the tensioned bonds is generally and quantitatively predicted by a simple multivariate linear regression based on the two easily computed features with a straightforward workflow. These results demonstrate a general mechanistic framework for mechanochemical reactions under tensile force and provide a highly accessible tool for the large-scale computational screening in the design of mechanophores.

Transgenic Targeting of Fcrls Creates a Highly Efficient Constitutively Active Microglia Cre Line with Differentiated Specificity

2026-04-25T03:05:11Z

Transgenic Targeting of Fcrls Creates a Highly Efficient Constitutively Active Microglia Cre Line with Differentiated Specificity Kaiser, Tobias; Dattero, Jordan; Li, Liang; Chen, Mandy; Jiang, Minqing; Harrahill, Andrew; Butovsky, Oleg; Feng, Guoping Microglia carry out important functions as the resident macrophages of the brain. To study their role in health and disease, the research community needs tools to genetically modify them with maximum completeness in a manner that distinguishes them from closely related cell types, such as monocytes. While currently available tamoxifen-inducible CreERT2 lines can achieve the differentiation from other cells, the field needs improved and publicly available constitutively active Cre lines, especially ones with favorable efficiency and specificity profiles for studies where high recombination efficiency is imperative and where tamoxifen administration is contraindicated. Here, we leverage the microglia-specific Fcrls gene to generate mice expressing Cre. Using genomic methods, we show correct positioning of the transgene and intact microglia homeostasis in Fcrls-2A-Cre mice. Crossing Fcrls-2A-Cre mice to four different reporters, we demonstrate highly efficient recombination in microglia across differentially sensitive loxP alleles in different genomic contexts, indicating robust applicability of the line. Further, we show that microglia recombine a loxP reporter during early embryonic development, supporting the use of the line for developmental studies. Finally, using immunofluorescence and flow cytometry, we reveal that most border-associated macrophages are also targeted whereas only few liver and spleen macrophages and virtually no white blood cell subsets exhibit Cre activity, distinguishing this line from another publicly available Cre line, Cx3cr1-CreM. Fcrls-2A-Cre mice are immediately available (JAX #036591) and serve as a valuable addition to the community's microglia toolbox by providing highly efficient constitutive Cre activity with excellent specificity, particularly for studies where tamoxifen administration is undesirable.

𝑘-Variance: A Clustered Notion of Variance

2026-04-25T03:06:02Z

𝑘-Variance: A Clustered Notion of Variance Solomon, Justin; Greenewald, Kristjan; Nagaraja, Haikady We introduce 𝑘-variance, a generalization of variance built on the machinery of random bipartite matchings. 𝑘-variance measures the expected cost of matching two sets of 𝑘 samples from a distribution to each other, capturing local rather than global information about a measure as 𝑘 increases; it is easily approximated stochastically using sampling and linear programming. In addition to defining 𝑘-variance and proving its basic properties, we provide in-depth analysis of this quantity in several key cases, including one-dimensional measures, clustered measures, and measures concentrated on low-dimensional subsets of ℝ𝑛. We conclude with experiments and open problems motivated by this new way to summarize distributional shape.

Data-Driven Screening and Discovery of Metal–Organic Frameworks as C2 Adsorbents from over 900 Experimental Isotherms

2026-04-25T03:05:41Z

Data-Driven Screening and Discovery of Metal–Organic Frameworks as C2 Adsorbents from over 900 Experimental Isotherms Rivera, Matthew P; Terrones, Gianmarco G; Lee, Tae Hoon; Smith, Zachary P; Kulik, Heather J The separation of ethylene from ethane accounts for almost 100 million tons of CO2 emissions annually and 0.3% of global primary energy usage. Replacing current cryogenic distillation units with adsorption separation units, especially for the minor component of ethane, would enable significant efficiency gains. Metal–organic frameworks (MOFs) are well-suited for adsorption separation due to their high surface areas and tunable chemical properties. Exploring all possible MOFs is a daunting experimental challenge, motivating in silico screening with machine learning models. We present a database of 948 experimentally measured pure-component C2 isotherms from 192 MOFs gathered from the literature and use it to train machine learning models to predict MOF ethane and ethylene uptake across a range of temperature and pressure conditions. The models have high accuracy in interpolative tasks (mean absolute error ∼0.05 mmol/g) when trained on only 20% of available data. Performance on unseen structures was also reasonably accurate with a mean absolute error (MAE) ∼0.7 mmol/g. We apply the models to screen the CoRE MOF2019 ASR database and identify the most promising candidates. Several MOFs containing lanthanide metals were predicted to have high ethane selectivity, suggesting that this class of MOFs may merit further investigation. Feature importance analysis suggests that both optimizing MOF secondary building unit chemistry and the process conditions at which the sorbent will operate are critical for enabling ethane-selective separation. We synthesize a MOF predicted to exhibit high ethane selectivity and experimentally validate qualitative agreement with model predictions, highlighting the utility of both the data set and model in discovering unexplored C2 adsorbents.

Wide-Band Butterfly Network: Stable and Efficient Inversion Via Multi-Frequency Neural Networks

2026-04-25T03:07:39Z

Wide-Band Butterfly Network: Stable and Efficient Inversion Via Multi-Frequency Neural Networks Li, Matthew; Demanet, Laurent; Zepeda-Núñez, Leonardo We introduce an end-to-end deep learning architecture called the wide-band butterfly network (WideBNet) for approximating the inverse scattering map from wide-band scattering data. This architecture incorporates tools from computational harmonic analysis, such as the butterfly factorization, and traditional multi-scale methods, such as the Cooley–Tukey FFT algorithm, to drastically reduce the number of trainable parameters to match the inherent complexity of the problem. As a result, WideBNet is efficient: it requires fewer training points than off-the-shelf architectures and has stable training dynamics which are compatible with standard weight initialization strategies. The architecture automatically adapts to the dimensions of the data with only a few hyper-parameters that the user must specify. WideBNet is able to produce images that are competitive with optimization-based approaches, but at a fraction of the cost, and we also demonstrate numerically that it learns to super-resolve scatterers with a full aperture configuration.

Optimal (Euclidean) Metric Compression

2026-04-25T03:05:48Z

Optimal (Euclidean) Metric Compression Indyk, Piotr; Wagner, Tal We study the problem of representing all distances between 𝑛 points in ℝ𝑑, with arbitrarily small distortion, using as few bits as possible. We give asymptotically tight bounds for this problem, for Euclidean metrics, for ℓ1 (also known as Manhattan)-metrics, and for general metrics. Our bounds for Euclidean metrics mark the first improvement over compression schemes based on discretizing the classical dimensionality reduction theorem of Johnson and Lindenstrauss [Contemp. Math. 26 (1984), pp. 189--206]. Since it is known that no better dimension reduction is possible, our results establish that Euclidean metric compression is possible beyond dimension reduction.

No Bridge between Us: EXAFS and Computations Confirm Two Distant Iron Ions Comprise the Active Site of Alkane Monooxygenase (AlkB)

2026-04-25T03:06:10Z

No Bridge between Us: EXAFS and Computations Confirm Two Distant Iron Ions Comprise the Active Site of Alkane Monooxygenase (AlkB) Reinhardt, Clorice R; Lee, Juliet A; Hendricks, Lauren; Green, Tierani; Kunczynski, Lily; Roberts, August Jaunzarins; Miller, Naomi; Rafalin, Noga; Kulik, Heather J; Pollock, Christopher J; Austin, Rachel N Alkane monooxygenase (AlkB) is the dominant enzyme that catalyzes the oxidation of liquid alkanes in the environment. Two recent structural models derived from cryo-electron microscopy (cryo-EM) reveal an unusual active site: a histidine-rich center that binds two iron ions without a bridging ligand. To ensure that potential photoreduction and radiation damage are not responsible for the absence of a bridging ligand in the cryo-EM structures, spectroscopic methods are needed. We present the results of extended X-ray absorption fine structure (EXAFS) experiments collected under conditions where photodamage was avoided. Careful data analysis reveals an active site structure consistent with the cryo-EM structures in which the two iron ions are ligated by nine histidines and separated by at least 5 Å. The EXAFS data were used to inform structural models for molecular dynamics (MD) simulations. The MD simulations corroborate EXAFS observations that neither of the two conserved carboxylate-containing residues (E281 and D190) near the active site are likely candidates for metal ion bridging. Mutagenesis experiments, spectroscopy, and additional MD simulations were used to further explore the role of these carboxylate residues. A variant in which a carboxylate containing residue (E281) was changed to a methyl residue (E281A) showed little change in pre-edge features, consistent with the observation that it is not essential for activity and hence unlikely to serve as a bridging ligand at any point in the catalytic cycle. D190 variants had substantially diminished activity, suggesting an important role in catalysis not yet fully understood.

Computational Screening of Putative Catalyst Transition Metal Complexes as Guests in a Ga4L612– Nanocage

2026-04-25T03:05:29Z

Computational Screening of Putative Catalyst Transition Metal Complexes as Guests in a Ga4L612– Nanocage Reinhardt, Clorice R; Manetsch, Melissa T; Li, Wan-Lu; Román-Leshkov, Yuriy; Head-Gordon, Teresa; Kulik, Heather J Metal–organic cages form well-defined microenvironments that can enhance the catalytic proficiency of encapsulated transition metal complexes (TMCs). We introduce a screening protocol to efficiently identify TMCs that are promising candidates for encapsulation in the Ga4L612– nanocage. We obtain TMCs from the Cambridge Structural Database with geometric and electronic characteristics amenable to encapsulation and mine the text of associated manuscripts to curate TMCs with documented catalytic functionality. By docking candidate TMCs inside the nanocage cavity and carrying out electronic structure calculations, we identify a subset of successfully optimized candidates (TMC-34) and observe that encapsulated guests occupy an average of 60% of the cavity volume, in line with previous observations. Notably, some guests occupy as much as 72% of the cavity as a result of linker rotation. Encapsulation has a universal effect on the electrostatic potential (ESP), systematically decreasing the ESP at the metal center of each TMC in the TMC-34 data set, while minimally altering TMC metal partial charges. Collectively these observations support geometry-based screening of potential guests and suggest that encapsulation in Ga4L612- cages could electrostatically stabilize diverse cationic or electropositive intermediates. We highlight candidate guests with associated known reactivity and solubility most amenable for encapsulation in experimental follow-up studies.

Role of Active Site Residues and Weak Noncovalent Interactions in Substrate Positioning in N,N-Dimethylformamidase

2026-04-25T03:07:18Z

Role of Active Site Residues and Weak Noncovalent Interactions in Substrate Positioning in N,N-Dimethylformamidase Reinhardt, Clorice R; Kastner, David W; Kulik, Heather J N,N-Dimethylformamide (DMF) is a solvent that can be metabolized naturally by DMF-utilizing microorganisms via a nonheme iron enzyme N,N-dimethylformamidase (DMFase). DMF is a small molecule with very few hydrogen bond donors or acceptors, and thus must be bound in the active site through other noncovalent interactions. We investigated the unusual protein fold, role of active site residues, and substrate positioning by performing molecular dynamics (MD) simulations and studying DMF binding. Our docking studies support idea that the DMF substrate directly coordinates the iron center through its carbonyl group, with Fe–DMF distances consistent with structures of inorganic complexes. DMF binding is predominantly stabilized by weak noncovalent interactions with nearby phenylalanine residues, which also serve to control access of solvent to the active site according to cavity analysis of crystal structures and MD snapshots. Based on noncovalent interactions sampled in our simulations and on sequence conservation, we ascribe roles to active site residues E657β, H519β, N547β, F611β, and F693β′. We perform sequence and structural alignments to identify putative DMFases and active site geometries in protein structures predicted from metagenomic DNA. These analyses suggest common conserved residues among putative DMFases and relate them to catalytic function, providing guidance for future experimental studies or characterization of new DMFases for DMF bioremediation.

Benchmarking nitrous oxide adsorption and activation in metal–organic frameworks bearing coordinatively unsaturated metal centers

2026-04-25T03:05:59Z

Benchmarking nitrous oxide adsorption and activation in metal–organic frameworks bearing coordinatively unsaturated metal centers Pitt, Tristan A; Jia, Haojun; Azbell, Tyler J; Zick, Mary E; Nandy, Aditya; Kulik, Heather J; Milner, Phillip J Anthropogenic emissions of N2O, the third most abundant greenhouse gas after CO2 and CH4, are contributing to global climate change. Although metal–organic frameworks (MOFs) have been widely studied as adsorbents for CO2 and CH4, less effort has focused on the use of MOFs to remove N2O from emission streams or from air. Further, N2O activation would enable its use as an inexpensive oxidant for fine chemical synthesis. Herein, we identify features that contribute to strong binding and high uptake of N2O at coordinatively unsaturated metal sites in the M2Cl2(btdd) (M = Mn, Co, Ni, Cu; btdd2− = bis(1,2,3-triazolo[4,5-b],[4′,5′-i])dibenzo[1,4]dioxin) and M2(dobdc) (M = Mg, Mn, Fe, Co, Ni, Cu, Zn; dobdc4− = 2,5-dioxido-1,4-benzenedicarboxylate) series of MOFs. Combined experimental and computational studies suggest that N2O adsorption at open-metal-sites is primarily based on electrostatic interactions, rather than π-backbonding, causing MOFs with more Lewis acidic metal centers to be superior N2O adsorbents. As a result, Mg2(dobdc) demonstrates strong binding and record-setting N2O uptake (8.75 mmol g−1 at 1 bar and 298 K). Using density functional theory (DFT) to characterize reactive intermediates and transition states, we demonstrate that N2O activation to form a M(IV)–oxo species and N2 is thermodynamically favorable in Mn2(dobdc) and Fe2(dobdc) but appears to be kinetically limited in Mn2(dobdc). Our work lays a foundation for understanding N2O adsorption and activation in MOFs, paving the way for the design of promising next-generation materials for N2O capture and utilization.

MOFs with the Stability for Practical Gas Adsorption Applications Require New Design Rules

2026-04-25T03:06:41Z

MOFs with the Stability for Practical Gas Adsorption Applications Require New Design Rules Oh, Changhwan; Nandy, Aditya; Yue, Shuwen; Kulik, Heather J Metal-organic frameworks (MOFs) have been widely studied for their ability to capture and store greenhouse gases. However, most computational discovery efforts study hypothetical MOFs without consideration of their stability, limiting the practical application of novel materials. We overcome this limitation by screening hypothetical ultrastable MOFs that have predicted high thermal and activation stability, as judged by machine learning (ML) models trained on experimental measures of stability. We enhance this set by computing the bulk modulus as a measure of mechanical stability and filter 1102 mechanically robust hypothetical MOFs from a database of ultrastable MOFs (USMOF DB). Grand Canonical Monte Carlo simulations are then employed to predict the gas adsorption properties of these hypothetical MOFs, alongside a database of experimental MOFs. We identify privileged building blocks that lead MOFs in USMOF DB to show exceptional working capacities compared to the experimental MOFs. We interpret these differences by training ML models on CO₂ and CH₄ adsorption in these databases, showing how poor model transferability between data sets indicates that novel design rules can be derived from USMOF DB that would not have been gathered through assessment of structurally characterized MOFs. We identify geometric features and node chemistry that will enable the rational design of MOFs with enhanced gas adsorption properties in synthetically realizable MOFs.

Incorporating Anionic Ligands in Chemical Space Exploration with New Ligand Additivity Relationships

2026-04-25T03:07:34Z

Incorporating Anionic Ligands in Chemical Space Exploration with New Ligand Additivity Relationships Kulik, Heather J Chemical space exploration motivates the development of data-driven models that bypass explicit computation or experiment. Cost-efficient strategies include the concept of additivity via the many-body expansion that treats a molecule as the sum of its parts. In the context of transition metal chemistry, ligand-wise additivity has been established as a powerful tool to infer the properties of heteroleptic transition metal complexes (TMCs) from homoleptic TMCs to excellent accuracy, including spin-splitting, orbital energies, and reaction energies. Nevertheless, this framework is incompatible with anionic ligands because a stable homoleptic, and thus polyanionic, parent complex cannot be simulated readily. Here, I explore alternative approaches, first identifying the limits of stability of heteroleptic TMCs when successive Cl^- anions are added in representative complexes formed with neutral H₂O and CO ligands. I establish that expected linear relationships are preserved, albeit not as strongly as in complexes with neutral ligands. I propose data-efficient interpolation and extrapolation schemes for TMCs that achieve root-mean-square errors as low as 0.15-0.36 eV on HOMO/LUMO levels and gaps or ionization potentials and electron affinities and 4 kcal/mol on adiabatic spin-splitting energies for Fe(II) complexes. I show that this approach generalizes well across TMCs with 14 other 3d, 4d, and 5d metals. Finally, I extend this approach to predict properties of thousands of binary and ternary Fe(II) or Zn(II) complexes involving a single neutral ligand and up to two unique anionic ligands by leveraging a handful of calculations. I show how this interpolated space can be used to infer the limits of stable and valid complexes and to discover complexes with novel properties.

Roadmap on Machine learning in electronic structure

2026-04-24T03:06:42Z

Roadmap on Machine learning in electronic structure In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century.

Reaction: The challenge of open-shell transition metal catalysis in “systems chemistry”

2026-04-24T03:06:11Z

Reaction: The challenge of open-shell transition metal catalysis in “systems chemistry” Kulik, Heather J Data-driven methods have transformed the scale at which chemical transformations are being explored. This includes novel machine learning models for retrosynthesis, reaction prediction, and small-molecule generation, to name a few. Novel datasets from high-throughput computation (e.g., with first-principles density functional theory) as well as high-throughput experimentation or extraction from the literature are dramatically increasing the scale at which new compounds are discovered as well as the benefits that can be reaped from deep learning models.

Heterocyclic Modification Leading to Luminescent 0D Metal Organochalcogenide with Stable X-ray Scintillating Properties

2026-04-24T03:07:25Z

Heterocyclic Modification Leading to Luminescent 0D Metal Organochalcogenide with Stable X-ray Scintillating Properties Khamlue, Rattapon; Sakurada, Tomoaki; Cho, Yeongsu; Lee, Woo Seok; Leangtanom, Pimpan; Taylor, Michael G; Naewthong, Worakit; Sripetch, Pongsakun; Na Ranong, Busayakorn; Autila, Tossawat; Rungseesumran, Thiti; Kaewkhao, Jakrapong; Sudyoadsuk, Taweesak; Kopwitthaya, Atcha; Müller, Peter; Promarak, Vinich; Kulik, Heather J; Tisdale, William A; Paritmongkol, Watcharaphol Metal organochalcogenides (MOCs) are an emerging class of luminescent hybrid organic–inorganic semiconductors, whose structures and properties can be tuned by organic functionalization and substitutions of their metal and chalcogen elements. Herein, we present a new design strategy by heterocyclic modification, resulting in the transformation of prototypical two-dimensional (2D) silver phenylselenide (AgSePh) to a zero-dimensional (0D) silver pyridinylselenide (AgSePy) via the formation of Ag–N bonds. At room temperature, AgSePy shows strong and broad orange photoluminescence (PL; λmax = 636 nm, full-width-at-half-maximum = 111 nm, quantum yield = 64%) with a large 259 nm Stoke’s shift and a 3.4 μs lifetime. Using steady-state and time-resolved PL spectroscopy under varying temperature and oxygen conditions, we found AgSePy to exhibit air-stable luminescence and maintain a high PL quantum yield and a single exponential PL lifetime down to 4 K. Furthermore, AgSePy shows excellent thermal stability up to ∼250 °C and chemical stability against polar, nonpolar, and aqueous solvents at pH 3–14. Density functional theory calculations further confirm the 0D electronic structure. Finally, we successfully demonstrated the performance of AgSePy as an X-ray scintillator with an estimated light yield of ∼8,000 phe/MeV and a spatial resolution down to 0.080 ± 0.005 mm. Overall, this work provides a novel tactic to modify the structures and properties of MOCs, highlighting their structural richness and structure–property relationship, and introduces their new use as X-ray scintillators, encouraging further development in radiation detection and medical imaging.

The Role of the Unusual 2-Tyr-1-carboxylate Nonheme Iron Motif in the Mechanism of N,N-Dimethylformamidase

2026-04-24T03:06:05Z

The Role of the Unusual 2-Tyr-1-carboxylate Nonheme Iron Motif in the Mechanism of N,N-Dimethylformamidase Kastner, David W; Reinhardt, Clorice R; Adamji, Husain; Oscar-Okpala, Terence S; Kevlishvili, Ilia; Román-Leshkov, Yuriy; Kulik, Heather J N,N-Dimethylformamidase (DMFase) is a nonheme iron enzyme that catalyzes the hydrolysis of N,N-dimethylformamide (DMF) using a noncanonical Fe(III)-2Tyr-1Glu coordination motif. The precise role that this nonconventional active site plays in catalysis remains poorly understood. We performed an extensive computational investigation of DMFase catalysis, combining reaction pathway analysis with quantum mechanical cluster models, charge shift analysis, and energy decomposition analysis to identify the mechanistic role of the coordinating tyrosines/glutamate and second coordination sphere residues. We compared two mechanisms initiated by the key second coordination sphere residues Glu657 and His519. While both mechanisms generate a ferric hydroxide intermediate, the Glu657-initiated mechanism exhibits more favorable barriers and thermodynamics. These calculations reveal distinct catalytic roles for the second-sphere residues: Glu657 facilitates direct proton transfers, His519 and Asn547 stabilize the rate-determining transition state, and Lys567 stabilizes the anionic tyrosinate state of Tyr440. Mechanistic comparisons to canonical Fe(II)/Fe(III)-2His-1Glu variants reveal that coordination of Fe by tyrosine residues lowers the barrier for deprotonation of a water ligand and subsequent nucleophilic attack on DMF. Attempts to tune the active site through fluorination of coordinating tyrosinate residues yield minimal additional benefits, indicating that the native motif has finely tuned electronic characteristics. These results demonstrate how the 2Tyr-1Glu motif and its second coordination sphere context enable hydrolytic reactivity in DMFase and suggest Glu657 and Lys567 as targets of future mutagenesis to validate their mechanistic roles.

Computational exploration of codoped Fe and Ru single-atom catalysts for the oxygen reduction reaction

2026-04-24T03:05:50Z

Computational exploration of codoped Fe and Ru single-atom catalysts for the oxygen reduction reaction Jia, Haojun; Duan, Chenru; Terrones, Gianmarco G; Kevlishvili, Ilia; Kulik, Heather J The oxygen reduction reaction (ORR) is essential in a range of energy conversion and storage technologies, including fuel cells and metal–air batteries. Single-atom catalysts (SACs), characterized by isolated metal atoms especially in doped graphitic substrates, have emerged as promising ORR catalysts due to their unique electronic and geometric properties. We employ virtual high-throughput screening (VHTS) with density functional theory and machine learning (ML) to explore the potential of codoped SACs with Fe and Ru centers for optimizing ORR reaction energetics. We also develop ML models, trained on VHTS data, that offer increased predictive accuracy of reaction energetics, surpassing the capabilities of conventional linear free energy relationship approaches. The results underscore codoping as an effective strategy for tuning SAC properties, enabling the rational design of high-performance ORR catalysts.

Computational Discovery of Codoped Single-Atom Catalysts for Methane-to-Methanol Conversion

2026-04-24T03:07:44Z

Computational Discovery of Codoped Single-Atom Catalysts for Methane-to-Methanol Conversion Jia, Haojun; Duan, Chenru; Kevlishvili, Ilia; Nandy, Aditya; Liu, Mingjie; Kulik, Heather J The absence of a synthetic catalyst that can selectively oxidize methane to methanol motivates extensive study of single-site catalysts that possess a high degree of tunability in their coordination environments and share similarities with natural enzymes that can catalyze this reaction. Single-atom catalysts (SACs), in particular doped graphitic SACs, have emerged as a promising family of materials due to their high atom economy and scalability, but SACs are yet to be exhaustively screened for methane-to-methanol conversion. Modulating the coordination environment near single metal sites by means of codopants, we carry out a large-scale high-throughput virtual screen of 2048 transition metal (i.e., Mn, Fe, Co, and Ru) SACs codoped with various elements (i.e., N, O, P, and S) in numerous spin and oxidation (i.e., M(II)/M(III)) states for the challenging conversion of methane to methanol. We identify that the ground-state preference is metal- and oxidation-state-dependent. We observe a weak negative correlation between the oxo formation energy (ΔE(oxo)) and the energy of hydrogen atom transfer (ΔE(HAT)), thanks to the high variability in the coordination environment. Therefore, codoped SACs demonstrate flexible tunability that disrupts linear free energy relationships in a manner similar to that of homogeneous catalysts without losing the scalability of heterogeneous catalysts. We identify energetically favorable catalyst candidates along the Pareto frontier of ΔE(oxo) and ΔE(HAT). Further kinetic analysis reveals an intermediate-spin Fe(II) SAC and a low-spin Ru(II) SAC as promising candidates that merit further experimental exploration.

Force-Activated Spin-Crossover in Fe2+ and Co2+ Transition Metal Mechanophores

2026-04-24T03:05:47Z

Force-Activated Spin-Crossover in Fe2+ and Co2+ Transition Metal Mechanophores Huang, Xiao; Kevlishvili, Ilia; Craig, Stephen L; Kulik, Heather J Transition metal mechanophores exhibiting force-activated spin-crossover are attractive design targets, yet large-scale discovery of them has not been pursued due in large part to the time-consuming nature of trial-and-error experiments. Instead, we leverage density functional theory (DFT) and external force explicitly included (EFEI) modeling to study a set of 395 feasible Fe2+ and Co2+ mechanophore candidates with tridentate ligands that we curate from the Cambridge Structural Database. Among nitrogen-coordinating low-spin complexes, we observe the prevalence of spin crossover at moderate force, and we identify 155 Fe2+ and Co2+ spin-crossover mechanophores and derive their threshold force for low-spin to high-spin transition (FSCO). The calculations reveal strong correlations of FSCO with spin-splitting energies and coordination bond lengths, facilitating rapid prediction of FSCO using force-free DFT calculations. Then, among all Fe2+ and Co2+ spin-crossover mechanophores, we further identity 11 mechanophores that combine labile spin-crossover and good mechanical robustness that are thus predicted to be the most versatile for force-probing applications. We discover two classes of mer-symmetric complexes comprising specific heteroaromatic rings within extended π-conjugation that give rise to Fe2+ mechanophores with these characteristics. We expect the set of spin-crossover mechanophores, the design principles, and the computational approach to be useful in guiding the high-throughput discovery of transition metal mechanophores with diverse functionalities and broad applications, including mechanically activated catalysis.

Fontimonas thermophila Alkane Monooxygenase (FtAlkB) Is an Alkyl Fluoride Dehalogenase

2026-04-24T03:05:06Z

Fontimonas thermophila Alkane Monooxygenase (FtAlkB) Is an Alkyl Fluoride Dehalogenase Hendricks, Lauren; Reinhardt, Clorice R; Green, Tierani; Kunczynski, Lily; Roberts, August Jaunzarins; Miller, Naomi; Rafalin, Noga; Kulik, Heather J; Groves, John T; Austin, Rachel N Purified alkane monooxygenase (AlkB) from Fontimonas thermophila (FtAlkB) catalyzes the defluorination of 1- fluorooctane, producing octanal, which is partially reduced under the reaction conditions to generate 1-octanol. This reaction occurs preferentially at the monofluorinated methyl group, with only a minor amount of oxidation at the nonfluorinated end of the molecule. The dehalogenation chemistry is specific to 1-fluorooctane, as neither 1-chlorooctane or 1-bromooctane are dehalogenated to an appreciable extent. Furthermore, P. putida cells containing the structurally related AlkB (PpAlkB) along with the full set of genes required for alkane metabolism, utilize 1-fluorooctane as their sole source of carbon with growth rates comparable to those for cells grown with octane.

Ligand Many-Body Expansion as a General Approach for Accelerating Transition Metal Complex Discovery

2026-04-24T03:05:24Z

Ligand Many-Body Expansion as a General Approach for Accelerating Transition Metal Complex Discovery Chu, Daniel BK; González-Narváez, David A; Meyer, Ralf; Nandy, Aditya; Kulik, Heather J Methods that accelerate the evaluation of molecular properties are essential for chemical discovery. While some degree of ligand additivity has been established for transition metal complexes, it is underutilized in asymmetric complexes, such as the square pyramidal coordination geometries highly relevant to catalysis. To develop predictive methods beyond simple additivity, we apply a many-body expansion to octahedral and square pyramidal complexes and introduce a correction based on adjacent ligands (i.e., the cis interaction model). We first test the cis interaction model on adiabatic spin-splitting energies of octahedral Fe(II) complexes, predicting DFT-calculated values of unseen binary complexes to within an average of 1.4 kcal/mol. Uncertainty analysis reveals the optimal basis, comprising the homoleptic and mer symmetric complexes. We next show that the cis model (i.e., the cis interaction model solved for the optimal basis) infers both DFT- and CCSD(T)-calculated model catalytic reaction energies to within 1 kcal/mol on average. The cis model predicts low-symmetry complexes with reaction energies outside the range of binary complex reaction energies. We observe that trans interactions are unnecessary for most monodentate systems but can be important for some combinations of ligands, such as complexes containing a mixture of bidentate and monodentate ligands. Finally, we demonstrate that the cis model may be combined with D-learning to predict CCSD(T) reaction energies from exhaustively calculated DFT reaction energies and the same fraction of CCSD(T) reaction energies needed for the cis model, achieving around 30% of the error from using the CCSD(T) reaction energies in the cis model alone.

Assessing UFF and DFT-Tuned Force Fields for Predicting Experimental Isotherms of MOFs

2026-04-24T03:07:23Z

Assessing UFF and DFT-Tuned Force Fields for Predicting Experimental Isotherms of MOFs Cho, Yeongsu; Teetz, Jakob; Kulik, Heather J Metal-organic frameworks (MOFs) are promising materials for gas storage and separation applications due to their high tunability and porosity. The rational design of MOFs relies on accurate computational modeling, with grand canonical Monte Carlo (GCMC) simulations frequently employed to model gas uptake. However, GCMC predictions often deviate from experimental observations, limiting their utility in MOF screening. These discrepancies primarily arise from three factors: inaccuracies in the force field, neglect of atomic motions, and neglect of structural imperfections in MOFs. In this study, we systematically evaluate the impact of the first factor on the predictive accuracy of GCMC simulations. We evaluate the widely used Universal Force Field (UFF) by comparing its predictions with experimental isotherms for four representative adsorbates, H2, CO2, C2H4, and C2H6, across 379 isotherms from 142 MOFs. The results show that UFF consistently overestimates gas uptake in GCMC simulations. To isolate the contribution of force field inaccuracies to errors in GCMC, we develop a practical scheme for fitting force field parameters to DFT-calculated energies for a large set of MOFs. While the refined force field improves the accuracy of interatomic interaction energies, its reduction of repulsion, combined with UFF’s tendency to overestimate gas uptake, ultimately amplifies the overestimation. Our analysis suggests that improving agreement of gas adsorption prediction with experiments requires addressing atomic motion and structural defects in MOFs alongside force field refinements.

Data-Driven Discovery of Water-Stable Metal–Organic Frameworks with High Water Uptake Capacity

2026-04-24T03:06:37Z

Data-Driven Discovery of Water-Stable Metal–Organic Frameworks with High Water Uptake Capacity Ball, Akash K; Terrones, Gianmarco G; Yue, Shuwen; Kulik, Heather J Metal-organic frameworks (MOFs) are promising candidate materials for applications that would benefit from precise chemical patterning, such as desalination, but many MOFs suffer from poor stability in water. In addition to water stability, high water uptake capacity in ambient conditions is expected to be necessary for water-related practical applications of MOFs, motivating large-scale search that can only be achieved computationally. Here, we take a combined machine learning and high-throughput screening approach to identify water-stable MOFs with high water uptake capacities. Starting from a subset of previously curated MOFs with experimentally known exceptionally high stability in water, we explore the effect of linker functionalization with 12 diverse hydrophilic functional groups expected to further tune water uptake. For these 736 MOFs, we use grand canonical Monte Carlo (GCMC) simulations to compute their water uptake capacity. We observe strong positive correlations between MOF pore features (e.g., the largest cavity diameter and volumetric pore volume) and water uptake capacity, although we notice breakdowns of such correlations in MOFs with extremely hydrophobic linkers that repel water molecules despite having large pores. Finally, we develop machine learning models to screen new MOFs simultaneously for water stability and water uptake capacity. From a pool of hypothetical and experimental MOFs, we identify 74 promising materials within the domain of applicability of the machine learning models that are predicted to both be water-stable and have high water uptake.

Spectral Independence in High-Dimensional Expanders and Applications to the Hardcore Model

2026-04-24T03:06:51Z

Spectral Independence in High-Dimensional Expanders and Applications to the Hardcore Model Anari, Nima; Liu, Kuikui; Gharan, Shayan Oveis We say a probability distribution 𝜇 is spectrally independent if an associated pairwise influence matrix has a bounded largest eigenvalue for the distribution and all of its conditional distributions. We prove that if 𝜇 is spectrally independent, then the corresponding high-dimensional simplicial complex is a local spectral expander. Using a line of recent works on mixing time of high-dimensional walks on simplicial complexes [T. Kaufman and D. Mass, Proceedings of ITCS, 2017, pp. 4:1–4:27; I. Dinur and T. Kaufman, Proceedings of the IEEE 58th Annual Symposium on Foundations of Computer Science, 2017, pp. 974–985; T. Kaufman and I. Oppenheim, Proceedings of APPROX/RANDOM, 2018, pp. 47:1–47:17; V. L. Alev and L. C. Lau, Proceedings of the 52nd Annual ACM Symposium on Theory of Computing, 2020], this implies that the corresponding Glauber dynamics mixes rapidly and generates (approximate) samples from 𝜇. As an application, we show that natural Glauber dynamics mixes rapidly (in polynomial time) to generate a random independent set from the hardcore model up to the uniqueness threshold. This improves the quasi-polynomial running time of Weitz's deterministic correlation decay algorithm [D. Weitz, Proceedings of the 38th Annual ACM Symposium on Theory of Computing, 2006, pp. 140–149] for estimating the hardcore partition function, also answering a long-standing open problem of mixing time of Glauber dynamics [M. Luby and E. Vigoda, Proceedings of the 29th Annual ACM Symposium on Theory of Computing, 1997, pp. 682–687; M. Luby and E. Vigoda, Random Structures Algorithms, 15 (1999), pp. 229–241; M. Dyer and C. Greenhill, J. Algorithms, 35 (2000), pp. 17–49; E. Vigoda, Electron. J. Combin., 8 (2001); C. Efthymiou et al., Proceedings of FOCS, 2016, pp. 704–713].

Rapid Mixing of Glauber Dynamics up to Uniqueness via Contraction

2026-04-24T03:07:16Z

Rapid Mixing of Glauber Dynamics up to Uniqueness via Contraction Chen, Zongchen; Liu, Kuikui; Vigoda, Eric For general antiferromagnetic 2-spin systems, including the hardcore model on weighted independent sets and the antiferromagnetic Ising model, there is an 𝖥𝖯𝖳𝖠𝖲 for the partition function on graphs of maximum degree Δ when the infinite regular tree lies in the uniqueness region by Li, Lu, and Yin [Correlation Decay up to Uniqueness in Spin Systems, preprint, https://arxiv.org/abs/1111.7064, 2021]. Moreover, in the tree nonuniqueness region, Sly in [Computational transition at the uniqueness threshold, in Proceedings of the 51st Annual IEEE Symposium on Foundations of Computer Science, 2010, pp. 287–296] showed that there is no 𝖥𝖯𝖱𝖠𝖲 to estimate the partition function unless 𝖭𝖯 =𝖱𝖯. The algorithmic results follow from the correlation decay approach due to Weitz [Counting independent sets up to the tree threshold, in Proceedings of the 38th Annual ACM Symposium on Theory of Computing, 2006, pp. 140–149] or the polynomial interpolation approach developed by Barvinok [Combinatorics and Complexity of Partition Functions, Springer, 2016]. However, the running time is only polynomial for constant Δ. For the hardcore model, recent work of Anari, Liu, and Oveis Gharan [Spectral independence in high-dimensional expanders and applications to the hardcore model, in Proceedings of the 61st Annual IEEE Symposium on Foundations of Computer Science, 2020, pp. 1319–1330] establishes rapid mixing of the simple single-site Markov chain, known as the Glauber dynamics, in the tree uniqueness region. Our work simplifies their analysis of the Glauber dynamics by considering the total pairwise influence of a fixed vertex 𝑣 on other vertices, as opposed to the total influence of other vertices on 𝑣, thereby extending their work to all 2-spin models and improving the mixing time. More important, our proof ties together the three disparate algorithmic approaches: we show that contraction of the so-called tree recursions with a suitable potential function, which is the primary technique for establishing efficiency of Weitz’s correlation decay approach and Barvinok’s polynomial interpolation approach, also establishes rapid mixing of the Glauber dynamics. We emphasize that this connection holds for all 2-spin models (both antiferromagnetic and ferromagnetic), and existing proofs for the correlation decay and polynomial interpolation approaches immediately imply rapid mixing of the Glauber dynamics. Our proof utilizes the fact that the graph partition function is a divisor of the partition function for Weitz’s self-avoiding walk tree. This fact leads to new tools for the analysis of the influence of vertices and may be of independent interest for the study of complex zeros.

Convergence of Anisotropic Mesh Adaptation via Metric Optimization

2026-04-24T03:07:32Z

Convergence of Anisotropic Mesh Adaptation via Metric Optimization Carson, Hugh A; Allmaras, Steven; Galbraith, Marshall; Darmofal, David Adaptive finite element methods (AFEMs) are an increasingly common means of automatically controlling error in numerical simulations. Proofs of convergence and rate of convergence exist for AFEMs; however, these proofs typically rely upon a nested structure for the sequence of meshes. A metric adaptive finite element method (MAFEM) utilizes the continuous mesh model and instead seeks to optimize a Riemannian metric field for a given cost, from which a mesh is generated. This meshing process results in a sequence of nonnested meshes. In this paper we introduce a proof of convergence for a class of MAFEM, utilizing an optimization statement to relate the error on the sequence of meshes. In addition, we prove that such a sequence of meshes will demonstrate the optimal asymptotic rate of convergence for a given polynomial order. Finally some numerical results demonstrate the performance of the algorithm for a singularly perturbed linear advection diffusion problem.

PD-1 regulates tumor-infiltrating CD8+ T cells in both a cell-intrinsic and a cell-extrinsic fashion

2026-04-24T03:08:01Z

PD-1 regulates tumor-infiltrating CD8+ T cells in both a cell-intrinsic and a cell-extrinsic fashion Pauken, Kristen E; Markson, Samuel C; Conway, Thomas S; Juneja, Vikram R; Shahid, Osmaan; Burke, Kelly P; Rowe, Jared H; Nguyen, Thao H; Collier, Jenna L; Walsh, Jaclyn ML; Fung, Megan E; Luber, Jacob M; Ringel, Alison E; Schenkel, Jason M; Freeman, Gordon J; Haigis, Marcia C; Singer, Meromit; Sharpe, Arlene H Although PD-1 inhibitors are FDA-approved for over 25 different cancers, the mechanisms contributing to response remain incompletely understood. To investigate how PD-1–deleted CD8+ T cells influence PD-1–expressing CD8+ T cells in the same tumor microenvironment, we developed an inducible PD-1 knockout (KO) model in which PD-1 is deleted on ∼50% of cells. PD-1 deletion beginning at day 7 after implantation of MC38 tumor cells led to robust tumor control. Remarkably, PD-1–expressing CD8+ T cells in the tumor had increased functionality similar to PD-1 KO CD8+ T cells. Using single-cell RNA-seq and TCR-seq, we found that the major transcriptional changes following PD-1 deletion were shared by PD-1 KO and PD-1–expressing CD8+ T cells, although PD-1 KO clones preferentially expanded. These data suggest PD-1 inhibitors not only exert cell-intrinsic effects but also may promote increased T cell function through non–cell-autonomous mechanisms, which has important implications for design of PD-1–based cancer immunotherapies.

A novel molecular class that recruits HDAC/MECP2 complexes to PU.1 motifs reduces neuroinflammation

2026-04-24T03:06:31Z

A novel molecular class that recruits HDAC/MECP2 complexes to PU.1 motifs reduces neuroinflammation Ralvenius, William T; Mungenast, Alison E; Woolf, Hannah; Huston, Margaret M; Gillingham, Tyler Z; Godin, Stephen K; Penney, Jay; Cam, Hugh P; Gao, Fan; Fernandez, Celia G; Czako, Barbara; Lightfoot, Yaima; Ray, William J; Beckmann, Adrian; Goate, Alison M; Marcora, Edoardo; Romero-Molina, Carmen; Ayata, Pinar; Schaefer, Anne; Gjoneska, Elizabeta; Tsai, Li-Huei Pervasive neuroinflammation occurs in many neurodegenerative diseases, including Alzheimer’s disease (AD). SPI1/PU.1 is a transcription factor located at a genome-wide significant AD-risk locus and its reduced expression is associated with delayed onset of AD. We analyzed single-cell transcriptomic datasets from microglia of human AD patients and found an enrichment of PU.1-binding motifs in the differentially expressed genes. In hippocampal tissues from transgenic mice with neurodegeneration, we found vastly increased genomic PU.1 binding. We then screened for PU.1 inhibitors using a PU.1 reporter cell line and discovered A11, a molecule with anti-inflammatory efficacy and nanomolar potency. A11 regulated genes putatively by recruiting a repressive complex containing MECP2, HDAC1, SIN3A, and DNMT3A to PU.1 motifs, thus representing a novel mechanism and class of molecules. In mouse models of AD, A11 ameliorated neuroinflammation, loss of neuronal integrity, AD pathology, and improved cognitive performance. This study uncovers a novel class of anti-inflammatory molecules with therapeutic potential for neurodegenerative disorders.

Remodeling of colon plasma cell repertoire within ulcerative colitis patients

2026-04-24T03:07:07Z

Remodeling of colon plasma cell repertoire within ulcerative colitis patients Scheid, Johannes F; Eraslan, Basak; Hudak, Andrew; Brown, Eric M; Sergio, Dallis; Delorey, Toni M; Phillips, Devan; Lefkovith, Ariel; Jess, Alison T; Duck, Lennard W; Elson, Charles O; Vlamakis, Hera; Plichta, Damian R; Deguine, Jacques; Ananthakrishnan, Ashwin N; Graham, Daniel B; Regev, Aviv; Xavier, Ramnik J Plasma cells (PCs) constitute a significant fraction of colonic mucosal cells and contribute to inflammatory infiltrates in ulcerative colitis (UC). While gut PCs secrete bacteria-targeting IgA antibodies, their role in UC pathogenesis is unknown. We performed single-cell V(D)J- and RNA-seq on sorted B cells from the colon of healthy individuals and patients with UC. A large fraction of B cell clones is shared between different colon regions, but inflammation in UC broadly disrupts this landscape, causing transcriptomic changes characterized by an increase in the unfolded protein response (UPR) and antigen presentation genes, clonal expansion, and isotype skewing from IgA1 and IgA2 to IgG1. We also directly expressed and assessed the specificity of 152 mAbs from expanded PC clones. These mAbs show low polyreactivity and autoreactivity and instead target both shared bacterial antigens and specific bacterial strains. Altogether, our results characterize the microbiome-specific colon PC response and how its disruption might contribute to inflammation in UC.

TCR signal strength defines distinct mechanisms of T cell dysfunction and cancer evasion

2026-04-24T03:05:17Z

TCR signal strength defines distinct mechanisms of T cell dysfunction and cancer evasion Shakiba, Mojdeh; Zumbo, Paul; Espinosa-Carrasco, Gabriel; Menocal, Laura; Dündar, Friederike; Carson, Sandra E; Bruno, Emmanuel M; Sanchez-Rivera, Francisco J; Lowe, Scott W; Camara, Steven; Koche, Richard P; Reuter, Vincent P; Socci, Nicholas D; Whitlock, Benjamin; Tamzalit, Fella; Huse, Morgan; Hellmann, Matthew D; Wells, Daniel K; Defranoux, Nadine A; Betel, Doron; Philip, Mary; Schietinger, Andrea T cell receptor (TCR) signal strength is a key determinant of T cell responses. We developed a cancer mouse model in which tumor-specific CD8 T cells (TST cells) encounter tumor antigens with varying TCR signal strength. High-signal-strength interactions caused TST cells to up-regulate inhibitory receptors (IRs), lose effector function, and establish a dysfunction-associated molecular program. TST cells undergoing low-signal-strength interactions also up-regulated IRs, including PD1, but retained a cell-intrinsic functional state. Surprisingly, neither high- nor low-signal-strength interactions led to tumor control in vivo, revealing two distinct mechanisms by which PD1hi TST cells permit tumor escape; high signal strength drives dysfunction, while low signal strength results in functional inertness, where the signal strength is too low to mediate effective cancer cell killing by functional TST cells. CRISPR-Cas9–mediated fine-tuning of signal strength to an intermediate range improved anti-tumor activity in vivo. Our study defines the role of TCR signal strength in TST cell function, with important implications for T cell–based cancer immunotherapies.

Allocation of resources among multiple daughter cells

2026-04-24T03:04:48Z

Allocation of resources among multiple daughter cells Wirshing, Alison CE; Alonso-Matilla, Roberto; Yan, Michelle; Khalid, Samra; Colarusso, Analeigha V; Odde, David; Lew, Daniel J Cell division commonly produces two daughter cells, but there are many exceptions where large cells produce multiple daughters. Multiple fission of some green algae and bacteria; cellularization during embryogenesis of plants and insects; and growth of Ichthyosporeans, Chytrids, and Apicomplexans all provide variations on this theme. In some yeast species, a large multinucleate mother cell grows multiple buds (daughters) simultaneously. Here, we address how mothers partition growth equally among their buds in the multi-budding yeast Aureobasidium pullulans. Bud growth is directed by actin cable networks that appear to be optimized for even partitioning despite complex cell geometries. Even partitioning does not rely on compensatory mechanisms to adjust bud volumes but rather stems directly from effective equalization of polarity sites. These results reveal how conserved cell polarity and cytoskeletal networks are adapted to build complex morphologies in fungi.

Mechanisms of nuclear segregation in a multinucleate multibudding yeast

2026-04-24T03:07:49Z

Mechanisms of nuclear segregation in a multinucleate multibudding yeast Petrucco, Claudia A; Crocker, Alex W; Wirshing, Alison CE; Colarusso, Analeigha V; Waarts, Maya; Gladfelter, Amy S; Lew, Daniel J Budding yeasts present an especially challenging geometry for segregation of chromosomes, which must be delivered across the narrow mother–bud neck into the bud. Studies in the model yeast Saccharomyces cerevisiae have revealed an elaborate set of mechanisms that selectively orient one mitotic spindle pole toward the bud and then drive spindle elongation along the mother–bud axis, ensuring nuclear segregation between mother and bud. It is unclear how these pathways might be adapted to yield similar precision in more complex cell geometries. Here, we provide the first description of the dynamics of mitosis in a multinucleate, multibudding yeast, Aureobasidium pullulans, and identify many unexpected differences from uninucleate yeasts. Mitotic spindles do not orient along the mother–bud axis prior to anaphase, and accurate nuclear segregation often occurs after spindle disassembly. Cortical Num1–dynein forces pull highly mobile nuclei into buds, and once a nucleus enters a bud, it discourages others from entering, ensuring that most daughters inherit only one nucleus.

A genome-wide arrayed CRISPR screen identifies PLSCR1 as an intrinsic barrier to SARS-CoV-2 entry that recent virus variants have evolved to resist

2026-04-24T03:05:45Z

A genome-wide arrayed CRISPR screen identifies PLSCR1 as an intrinsic barrier to SARS-CoV-2 entry that recent virus variants have evolved to resist Interferons (IFNs) play a crucial role in the regulation and evolution of host–virus interactions. Here, we conducted a genome-wide arrayed CRISPR knockout screen in the presence and absence of IFN to identify human genes that influence Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. We then performed an integrated analysis of genes interacting with SARS-CoV-2, drawing from a selection of 67 large-scale studies, including our own. We identified 28 genes of high relevance in both human genetic studies of Coronavirus Disease 2019 (COVID-19) patients and functional genetic screens in cell culture, with many related to the IFN pathway. Among these was the IFN-stimulated gene PLSCR1. PLSCR1 did not require IFN induction to restrict SARS-CoV-2 and did not contribute to IFN signaling. Instead, PLSCR1 specifically restricted spike-mediated SARS-CoV-2 entry. The PLSCR1-mediated restriction was alleviated by TMPRSS2 overexpression, suggesting that PLSCR1 primarily restricts the endocytic entry route. In addition, recent SARS-CoV-2 variants have adapted to circumvent the PLSCR1 barrier via currently undetermined mechanisms. Finally, we investigate the functional effects of PLSCR1 variants present in humans and discuss an association between PLSCR1 and severe COVID-19 reported recently.

Visual perception of highly memorable images is mediated by a distributed network of ventral visual regions that enable a late memorability response

2026-04-24T03:05:01Z

Visual perception of highly memorable images is mediated by a distributed network of ventral visual regions that enable a late memorability response Lahner, Benjamin; Mohsenzadeh, Yalda; Mullin, Caitlin; Oliva, Aude Behavioral and neuroscience studies in humans and primates have shown that memorability is an intrinsic property of an image that predicts its strength of encoding into and retrieval from memory. While previous work has independently probed when or where this memorability effect may occur in the human brain, a description of its spatiotemporal dynamics is missing. Here, we used representational similarity analysis (RSA) to combine functional magnetic resonance imaging (fMRI) with source-estimated magnetoencephalography (MEG) to simultaneously measure when and where the human cortex is sensitive to differences in image memorability. Results reveal that visual perception of High Memorable images, compared to Low Memorable images, recruits a set of regions of interest (ROIs) distributed throughout the ventral visual cortex: a late memorability response (from around 300 ms) in early visual cortex (EVC), inferior temporal cortex, lateral occipital cortex, fusiform gyrus, and banks of the superior temporal sulcus. Image memorability magnitude results are represented after high-level feature processing in visual regions and reflected in classical memory regions in the medial temporal lobe (MTL). Our results present, to our knowledge, the first unified spatiotemporal account of visual memorability effect across the human cortex, further supporting the levels-of-processing theory of perception and memory.

Recommendations for accelerating open preprint peer review to improve the culture of science

2026-04-24T03:04:28Z

Recommendations for accelerating open preprint peer review to improve the culture of science Peer review is an important part of the scientific process, but traditional peer review at journals is coming under increased scrutiny for its inefficiency and lack of transparency. As preprints become more widely used and accepted, they raise the possibility of rethinking the peer-review process. Preprints are enabling new forms of peer review that have the potential to be more thorough, inclusive, and collegial than traditional journal peer review, and to thus fundamentally shift the culture of peer review toward constructive collaboration. In this Consensus View, we make a call to action to stakeholders in the community to accelerate the growing momentum of preprint sharing and provide recommendations to empower researchers to provide open and constructive peer review for preprints.

Effects of Enhanced Legal Aid in Child Welfare: Evidence from a Randomized Trial of Mi Abogado

2026-04-24T03:06:48Z

Effects of Enhanced Legal Aid in Child Welfare: Evidence from a Randomized Trial of Mi Abogado Cooper, Ryan; Doyle, Joseph J; Hojman, Andrés Children spend years in foster care, and bureaucratic hurdles can unnecessarily prolong their stays. The Mi Abogado program was introduced in Chile to enhance legal aid for foster children and accelerate family reunification. In a novel approach, the Chilean government randomized the introduction of the program for children living in institutions to evaluate effects on child well-being. Using registry data, we find the program significantly reduced the duration of foster care without increasing subsequent maltreatment and placements. The exposure also decreased criminal justice involvement and improved school attendance. Results suggest that strengthening foster care services can cost-effectively improve child well-being. (JEL I21, I31, J13, K36, O15, O17)

The Debt-Inflation Channel of the German (Hyper)Inflation

2026-04-23T03:05:40Z

The Debt-Inflation Channel of the German (Hyper)Inflation Brunnermeier, Markus; Correia, Sergio; Luck, Stephan; Verner, Emil; Zimmermann, Tom This paper studies how a large increase in the price level is transmitted to the real economy through firm balance sheets. Using newly digitized macro- and micro-level data from the German inflation of 1919–1923, we show inflation led to a large reduction in real debt burdens and bankruptcies. Firms with higher nominal liabilities at the onset of inflation experienced a larger decline in interest expenses, a relative increase in their equity values, and higher employment during the inflation. The results are consistent with real effects of a debt-inflation channel that operates even when prices and wages are flexible. (JEL D22, E23, E31, G32, N14, N24)

Data, Competition, and Digital Platforms

2026-04-23T03:07:07Z

Data, Competition, and Digital Platforms Bergemann, Dirk; Bonatti, Alessandro A monopolist platform uses data to match heterogeneous consumers with multiproduct sellers. The consumers can purchase the products on the platform or search off the platform. The platform sells targeted ads to sellers that recommend their products to consumers and reveals information to consumers about their match values. The revenue-optimal mechanism is a managed advertising campaign that matches products and preferences efficiently. In equilibrium, sellers offer higher qualities at lower unit prices on than off platform. The platform exploits its information advantage to increase its bargaining power vis-à-vis the sellers. Finally, privacy-respecting data-governance rules can lead to welfare gains for consumers. (JEL D11, D42, D44, D82, D83, M37)

Dying or Lying? For-Profit Hospices and End-of-Life Care

2026-04-23T03:05:18Z

Dying or Lying? For-Profit Hospices and End-of-Life Care Gruber, Jonathan; Howard, David H; Leder-Luis, Jetson; Caputi, Theodore L The Medicare hospice program is intended to provide palliative care to terminal patients, but patients with long stays in hospice are highly profitable, motivating concerns about overuse among the Alzheimer's and Dementia (ADRD) population in the rapidly growing for-profit sector. We provide the first causal estimates of the effect of for-profit hospice on patient spending using the entry of for-profit hospices over 20 years. We find hospice has saved money for Medicare by offsetting other expensive care among ADRD patients. As a result, policies limiting hospice use including revenue caps and antifraud lawsuits are distortionary and deter potentially cost-saving admissions. (JEL H51, I11, I12, I18, J14, L84)

The Missing Intercept: A Demand Equivalence Approach

2026-04-23T03:06:36Z

The Missing Intercept: A Demand Equivalence Approach Wolf, Christian K I give conditions under which changes in private spending are accommodated in general equilibrium exactly like changes in aggregate fiscal expenditure. Under such demand equivalence, researchers can use time series evidence on fiscal multipliers to recover the general equilibrium “missing intercept” of shocks to private spending identified in the cross section. Through the lens of this theory, time series estimates of a fiscal multiplier around one suggest a missing intercept close to zero—an observation that I illustrate with an application to the 2008 tax rebates. I also discuss the robustness of this aggregation approach to plausible violations of demand equivalence. (JEL E12, E21, E24, E62, G51, H24, H31)

Opportunity Unraveled: Private Information and the Missing Markets for Financing Human Capital

2026-04-23T03:06:59Z

Opportunity Unraveled: Private Information and the Missing Markets for Financing Human Capital Herbst, Daniel; Hendren, Nathaniel We examine whether adverse selection has unraveled private markets for equity and state-contingent debt contracts for financing higher education. Using survey data on beliefs, we show a typical college-goer would have to repay $1.64 in present value for every $1 of financing to overcome adverse selection in an equity market. We find that risk-averse college-goers are not willing to accept these terms, so markets unravel. We discuss why moral hazard, biased beliefs, and outside credit options are less likely to explain the absence of these markets. We quantify the welfare gains for subsidizing equity-like contracts that mitigate college-going risks. (JEL D82, D83, G51, I22, I23, I26, J24)

Unobserved-Offers Bargaining

2026-04-23T03:05:33Z

Unobserved-Offers Bargaining Wolitzky, Alexander I study ultimatum bargaining with imperfectly observed offers. Imperfectly observed offers must be rejected with positive probability, even when the players’ preferences are common knowledge. Noisier observations imply a greater risk of rejection. In repeated ultimatum bargaining, the responding party can obtain a positive payoff if his signal of the opponent’s offer is also observed by the opponent herself, but not if his signal is private. In alternating-offers bargaining, a player is better off when her own offers are observed more precisely and her opponent’s offers are observed less precisely. Possible applications include international relations, regulation, principal-agency, and product quality provision. (JEL C73, C78, D82)

Conflict and Intergroup Trade: Evidence from the 2014 Russia-Ukraine Crisis

2026-04-23T03:06:42Z

Conflict and Intergroup Trade: Evidence from the 2014 Russia-Ukraine Crisis Korovkin, Vasily; Makarin, Alexey Does armed conflict reduce trade, even in noncombat areas, through the destruction of intergroup social capital? We analyze Ukrainian trade transactions before and after the 2014 Russia-Ukraine conflict. In a difference-in-differences framework, we find that Ukrainian firms from districts with fewer ethnic Russians experienced a deeper decline in trade with Russia. This decline is economically significant, persistent, and can be explained by erosion of intergroup trust. Affected Ukrainian firms suffered a decrease in performance and diverted trade to other countries. Our results suggest that, through social effects, conflict can be economically damaging even away from combat areas. (JEL D74, F14, F51, J15, P31, P33, Z13)

Heterogeneous Global Booms and Busts

2026-04-23T03:06:12Z

Heterogeneous Global Booms and Busts Farboodi, Maryam; Kondor, Péter We investigate the heterogeneous boom and bust patterns across countries that emerge as a result of global shocks. Our analysis sheds light on the emergence of core and periphery countries, and the joint determination of the depth of recessions and tightness of credit across countries. The model implies that interest rates are similar across core and periphery countries in booms, with larger credit and output growth in periphery countries. However, a common global shock that leads to a credit crunch across the globe gives rise to a sharper spike in interest rates and a deeper recession in periphery countries, while a credit flight to the core alleviates the adverse consequences in these countries. (JEL E21, E32, E43, E44, F44, G01, G15)

Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions

2026-04-23T03:07:36Z

Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions Tuckute, Greta; Feather, Jenelle; Boebinger, Dana; McDermott, Josh H Models that predict brain responses to stimuli provide one measure of understanding of a sensory system and have many potential applications in science and engineering. Deep artificial neural networks have emerged as the leading such predictive models of the visual system but are less explored in audition. Prior work provided examples of audio-trained neural networks that produced good predictions of auditory cortical fMRI responses and exhibited correspondence between model stages and brain regions, but left it unclear whether these results generalize to other neural network models and, thus, how to further improve models in this domain. We evaluated model-brain correspondence for publicly available audio neural network models along with in-house models trained on 4 different tasks. Most tested models outpredicted standard spectromporal filter-bank models of auditory cortex and exhibited systematic model-brain correspondence: Middle stages best predicted primary auditory cortex, while deep stages best predicted non-primary cortex. However, some state-of-the-art models produced substantially worse brain predictions. Models trained to recognize speech in background noise produced better brain predictions than models trained to recognize speech in quiet, potentially because hearing in noise imposes constraints on biological auditory representations. The training task influenced the prediction quality for specific cortical tuning properties, with best overall predictions resulting from models trained on multiple tasks. The results generally support the promise of deep neural networks as models of audition, though they also indicate that current models do not explain auditory cortical responses in their entirety.

Hypoxia extends lifespan and neurological function in a mouse model of aging

2026-04-23T03:07:04Z

Hypoxia extends lifespan and neurological function in a mouse model of aging Rogers, Robert S; Wang, Hong; Durham, Timothy J; Stefely, Jonathan A; Owiti, Norah A; Markhard, Andrew L; Sandler, Lev; To, Tsz-Leung; Mootha, Vamsi K There is widespread interest in identifying interventions that extend healthy lifespan. Chronic continuous hypoxia delays the onset of replicative senescence in cultured cells and extends lifespan in yeast, nematodes, and fruit flies. Here, we asked whether chronic continuous hypoxia is beneficial in mammalian aging. We utilized the Ercc1 Δ/- mouse model of accelerated aging given that these mice are born developmentally normal but exhibit anatomic, physiological, and biochemical features of aging across multiple organs. Importantly, they exhibit a shortened lifespan that is extended by dietary restriction, the most potent aging intervention across many organisms. We report that chronic continuous 11% oxygen commenced at 4 weeks of age extends lifespan by 50% and delays the onset of neurological debility in Ercc1 Δ/- mice. Chronic continuous hypoxia did not impact food intake and did not significantly affect markers of DNA damage or senescence, suggesting that hypoxia did not simply alleviate the proximal effects of the Ercc1 mutation, but rather acted downstream via unknown mechanisms. To the best of our knowledge, this is the first study to demonstrate that “oxygen restriction” can extend lifespan in a mammalian model of aging.

Shared biophysical mechanisms determine early biofilm architecture development across different bacterial species

2026-04-23T03:06:46Z

Shared biophysical mechanisms determine early biofilm architecture development across different bacterial species Jeckel, Hannah; Díaz-Pascual, Francisco; Skinner, Dominic J; Song, Boya; Jiménez-Siebert, Eva; Strenger, Kerstin; Jelli, Eric; Vaidya, Sanika; Dunkel, Jörn; Drescher, Knut Bacterial biofilms are among the most abundant multicellular structures on Earth and play essential roles in a wide range of ecological, medical, and industrial processes. However, general principles that govern the emergence of biofilm architecture across different species remain unknown. Here, we combine experiments, simulations, and statistical analysis to identify shared biophysical mechanisms that determine early biofilm architecture development at the single-cell level, for the species Vibrio cholerae, Escherichia coli, Salmonella enterica, and Pseudomonas aeruginosa grown as microcolonies in flow chambers. Our data-driven analysis reveals that despite the many molecular differences between these species, the biofilm architecture differences can be described by only 2 control parameters: cellular aspect ratio and cell density. Further experiments using single-species mutants for which the cell aspect ratio and the cell density are systematically varied, and mechanistic simulations show that tuning these 2 control parameters reproduces biofilm architectures of different species. Altogether, our results show that biofilm microcolony architecture is determined by mechanical cell–cell interactions, which are conserved across different species.

Editorial: Novel methods to advance diagnostic and treatment value of medical imaging for cardiovascular disease

2026-04-23T03:05:45Z

Editorial: Novel methods to advance diagnostic and treatment value of medical imaging for cardiovascular disease Keshavarz-Motamed, Zahra; Del Alamo, Juan C; Bluestein, Danny; Edelman, Elazer R; Wentzel, Jolanda J The use of medical imaging has substantially increased over the past decade, thanks to the technological advancements evident from the dramatic improvement in the sensitivity and spatial resolution of imaging modalities. Cardiovascular imaging has been at a crossroads regarding technological advances, with a shift in focus from single-modality diagnosis to an integrated multimodality approach that can provide comprehensive assessments of morphology, pathophysiology, and disease biology to stratify the patient risk and guide therapies. The wide inter-subject variability in cardiovascular anatomy and pathophysiology urges the design of personalized patient management, which can highly benefit from clinical imaging technologies. The remarkable advances in medical imaging have sparked the development of new image processing algorithms and image-based simulation tools. In addition to providing comprehensive diagnostic information, some tools can even predict intervention outcomes, thereby enabling personalized intervention planning. This Research Topic, Novel Methods to Advance Diagnostic and Treatment Value of Medical Imaging for Cardiovascular Disease, focuses on tools that augment the power of medical imaging to provide detailed quantification of cardiovascular disease. Here, we present a Research Topic of 21 research articles that provide the reader with information regarding recent advancements in medical imaging improving diagnosis, prediction, monitoring, and treatment of cardiovascular disease.

Light-regulated gene expression in Bacteria: Fundamentals, advances, and perspectives

2026-04-23T03:07:09Z

Light-regulated gene expression in Bacteria: Fundamentals, advances, and perspectives Ohlendorf, Robert; Möglich, Andreas Numerous photoreceptors and genetic circuits emerged over the past two decades and now enable the light-dependent i.e., optogenetic, regulation of gene expression in bacteria. Prompted by light cues in the near-ultraviolet to near-infrared region of the electromagnetic spectrum, gene expression can be up- or downregulated stringently, reversibly, non-invasively, and with precision in space and time. Here, we survey the underlying principles, available options, and prominent examples of optogenetically regulated gene expression in bacteria. While transcription initiation and elongation remain most important for optogenetic intervention, other processes e.g., translation and downstream events, were also rendered light-dependent. The optogenetic control of bacterial expression predominantly employs but three fundamental strategies: light-sensitive two-component systems, oligomerization reactions, and second-messenger signaling. Certain optogenetic circuits moved beyond the proof-of-principle and stood the test of practice. They enable unprecedented applications in three major areas. First, light-dependent expression underpins novel concepts and strategies for enhanced yields in microbial production processes. Second, light-responsive bacteria can be optogenetically stimulated while residing within the bodies of animals, thus prompting the secretion of compounds that grant health benefits to the animal host. Third, optogenetics allows the generation of precisely structured, novel biomaterials. These applications jointly testify to the maturity of the optogenetic approach and serve as blueprints bound to inspire and template innovative use cases of light-regulated gene expression in bacteria. Researchers pursuing these lines can choose from an ever-growing, versatile, and efficient toolkit of optogenetic circuits.

Intervertebral Disc-on-a-Chip as Advanced In Vitro Model for Mechanobiology Research and Drug Testing: A Review and Perspective

2026-04-23T03:06:10Z

Intervertebral Disc-on-a-Chip as Advanced In Vitro Model for Mechanobiology Research and Drug Testing: A Review and Perspective Mainardi, Andrea; Cambria, Elena; Occhetta, Paola; Martin, Ivan; Barbero, Andrea; Schären, Stefan; Mehrkens, Arne; Krupkova, Olga Discogenic back pain is one of the most diffused musculoskeletal pathologies and a hurdle to a good quality of life for millions of people. Existing therapeutic options are exclusively directed at reducing symptoms, not at targeting the underlying, still poorly understood, degenerative processes. Common intervertebral disc (IVD) disease models still do not fully replicate the course of degenerative IVD disease. Advanced disease models that incorporate mechanical loading are needed to investigate pathological causes and processes, as well as to identify therapeutic targets. Organs-on-chip (OoC) are microfluidic-based devices that aim at recapitulating tissue functions in vitro by introducing key features of the tissue microenvironment (e.g., 3D architecture, soluble signals and mechanical conditioning). In this review we analyze and depict existing OoC platforms used to investigate pathological alterations of IVD cells/tissues and discuss their benefits and limitations. Starting from the consideration that mechanobiology plays a pivotal role in both IVD homeostasis and degeneration, we then focus on OoC settings enabling to recapitulate physiological or aberrant mechanical loading, in conjunction with other relevant features (such as inflammation). Finally, we propose our view on design criteria for IVD-on-a-chip systems, offering a future perspective to model IVD mechanobiology.

Construction of an IS-Free Corynebacterium glutamicum ATCC 13 032 Chassis Strain and Random Mutagenesis Using the Endogenous ISCg1 Transposase

2026-04-23T03:05:57Z

Construction of an IS-Free Corynebacterium glutamicum ATCC 13 032 Chassis Strain and Random Mutagenesis Using the Endogenous ISCg1 Transposase Linder, Marten; Haak, Markus; Botes, Angela; Kalinowski, Jörn; Rückert, Christian Mobile genetic elements (MGEs) contribute to instability of the host genome and plasmids. Previously, removal of the prophages in the industrial amino acid producer Corynebacterium glutamicum ATCC 13 032 resulted in strain MB001 which showed better survival under stress conditions and increased transformability. Still, eight families of Insertion Sequence (IS) elements with 27 potentially active members remain in MB001, two of which were demonstrated to be detrimental in biotechnological processes. In this study, systematical deletion of all complete IS elements in MB001 resulted in the MGE-free strain CR101. CR101 shows growth characteristics identical to the wildtype and the increased transformability of MB001. Due to its improved genome stability, we consider this strain to be an optimal host for basic research and biotechnology. As a “zero-background” host, it is also an ideal basis to study C. glutamicum IS elements. Re-sequencing of CR101 revealed that only five spontaneous point mutations had occurred during the construction process, highlighting the low mutation rate of C. glutamicum on the nucleotide level. In a second step, we developed an easily applicable ISCg1-based transposon mutagenesis system to randomly transpose a selectable marker. For optimal plasmid stability during cloning in Escherichia coli, the system utilizes a genetic switch based on the phage integrase Bxb1. Use of this integrase revealed the presence of a functional attB site in the C. glutamicum genome. To avoid cross-talk with our system and increase ease-of-use, we removed the attB site and also inserted the Bxb1 encoding gene into the chromosome of CR101. Successful insertion of single markers was verified by sequencing randomly selected mutants. Sequencing pooled mutant libraries revealed only a weak target site specificity, seemingly random distribution of insertion sites and no general strand bias. The resulting strain, ML103, together with plasmid pML10 provides a easily customizable system for random mutagenesis in an otherwise genomically stable C. glutamicum. Taken together, the MGE-free C. glutamicum strain CR101, the derivative ML103, and the plasmid pML10 provide a useful set of tools to study C. glutamicum in the future.

Deep neural network enabled active metasurface embedded design

2026-04-23T03:05:12Z

Deep neural network enabled active metasurface embedded design An, Sensong; Zheng, Bowen; Julian, Matthew; Williams, Calum; Tang, Hong; Gu, Tian; Zhang, Hualiang; Kim, Hyun Jung; Hu, Juejun In this paper, we propose a deep learning approach for forward modeling and inverse design of photonic devices containing embedded active metasurface structures. In particular, we demonstrate that combining neural network design of metasurfaces with scattering matrix-based optimization significantly simplifies the computational overhead while facilitating accurate objective-driven design. As an example, we apply our approach to the design of a continuously tunable bandpass filter in the mid-wave infrared, featuring narrow passband (∼10 nm), high quality factors (Q-factors ∼ 102), and large out-of-band rejection (optical density ≥ 3). The design consists of an optical phase-change material Ge2Sb2Se4Te (GSST) metasurface atop a silicon heater sandwiched between two distributed Bragg reflectors (DBRs). The proposed design approach can be generalized to the modeling and inverse design of arbitrary response photonic devices incorporating active metasurfaces.

Microresonator frequency comb based high‐speed transmission of intensity modulated direct detection data

2026-04-23T03:06:22Z

Microresonator frequency comb based high‐speed transmission of intensity modulated direct detection data Xing, Peng; Chen, George Fengrong; Gao, Hongwei; Chia, Xavier; Agarwal, Anuradha M; Kimerling, Lionel C; Tan, Dawn TH Globally, the long-haul transmission of ultra-high bandwidth data is enabled through coherent communications. Driven by the rapid pace of growth in interconnectivity over the last decade, long-haul data transmission has reached capacities on the order of tens to hundreds of terabits per second, over fiber reaches which may span thousands of kilometers. Data center communications operate in regimes featuring shorter reaches and higher cost sensitivity. While integrated microresonator frequency combs are poised to revolutionize light sources used for high-speed data transmission over fiber, recent progress has focused largely on coherent detection schemes. Furthermore, though state-of-the-art intensity modulators are advancing in speed, it has not been demonstrated in the literature if microresonator-based comb lines can accommodate higher intensity modulated direction data (IMDD) line rates in tandem with these advancements. In this manuscript, we demonstrate the use of microresonator frequency combs pumped with a single laser for the transmission of high-speed IMDD data. We demonstrate error-free transmission of 30 Gbs−1 per comb non-return-to-zero data over fiber lengths of 6 km, as well as bit error rates under the forward error correction limit for propagation through 20 km of optical fiber. 60 Gbs−1 and 42 Gbs−1 pulse modulation amplitude 4 (PAM4) data modulated on each frequency comb line is further quantified to have a bit error rate under the forward error correction limit for fiber reaches of up to 6 km and 20 km respectively. The results showcase CMOS-compatible microresonator frequency comb modulated using IMDD formats as a promising technology for high-speed transmission in the data center transceiver industry.

Reduced Deep Convection and Bottom Water Formation Due To Antarctic Meltwater in a Multi‐Model Ensemble

2026-04-23T03:05:51Z

Reduced Deep Convection and Bottom Water Formation Due To Antarctic Meltwater in a Multi‐Model Ensemble Chen, Jia‐Jia; Swart, Neil C; Beadling, Rebecca; Cheng, Xuhua; Hattermann, Tore; Jüling, André; Li, Qian; Marshall, John; Martin, Torge; Muilwijk, Morven; Pauling, Andrew G; Purich, Ariaan; Smith, Inga J; Thomas, Max The additional water from the Antarctic ice sheet and ice shelves due to climate‐induced melt can impact ocean circulation and global climate. However, the major processes driving melt are not adequately represented in Coupled Model Intercomparison Project phase 6 (CMIP6) models. Here, we analyze a novel multi‐model ensemble of CMIP6 models with consistent meltwater addition to examine the robustness of the modeled response to meltwater, which has not been possible in previous single‐model studies. Antarctic meltwater addition induces a substantial weakening of open‐ocean deep convection. Additionally, Antarctic Bottom Water warms, its volume contracts, and the sea surface cools. However, the magnitude of the reduction varies greatly across models, with differing anomalies correlated with their respective mean‐state climatology, indicating the state‐dependency of the climate response to meltwater. A better representation of the Southern Ocean mean state is necessary for narrowing the inter‐model spread of response to Antarctic meltwater.

Bias formulas for violations of proximal identification assumptions in a linear structural equation model

2026-04-23T03:05:03Z

Bias formulas for violations of proximal identification assumptions in a linear structural equation model Cobzaru, Raluca; Welsch, Roy; Finkelstein, Stan; Ng, Kenney; Shahn, Zach Causal inference from observational data often rests on the unverifiable assumption of no unmeasured confounding. Recently, Tchetgen Tchetgen and colleagues have introduced proximal inference to leverage negative control outcomes and exposures as proxies to adjust for bias from unmeasured confounding. However, some of the key assumptions that proximal inference relies on are themselves empirically untestable. In addition, the impact of violations of proximal inference assumptions on the bias of effect estimates is not well understood. In this article, we derive bias formulas for proximal inference estimators under a linear structural equation model. These results are a first step toward sensitivity analysis and quantitative bias analysis of proximal inference estimators. While limited to a particular family of data generating processes, our results may offer some more general insight into the behavior of proximal inference estimators.

A Nobel Prize Winner’s Life of Mentoring

2026-04-22T03:01:01Z

A Nobel Prize Winner’s Life of Mentoring Rowe, Mary P. Former MIT ombuds Mary P. Rowe describes lessons learned about mentoring from a conversation with Dr. John Goodenough, who later won a Nobel Prize.

Using Operationally Useful Data to Communicate Ombuds Value and Effectiveness

2026-04-22T03:00:55Z

Using Operationally Useful Data to Communicate Ombuds Value and Effectiveness Rowe, Mary Why keep data? The answer may be different for every organizational ombuds professional (OO). When considering what data to collect about their work, each OO will wish to learn the interests of their constituents and then try to meet the needs of their visitors, organization, and leadership, as well as their own needs. The statistical data collected by organizational ombuds (OOs) represent a major source of informal power for OOs to help constituents. Data also can demonstrate the OO’s value for constituents—including the employer. Data can help every OO identify how they can be more effective. Data can also help to show OO expertise in many vital areas of management where AI faces limitations in comparison to humans.

Imaging the Spatiotemporal Evolution of Plate Coupling With Interferometric Radar (InSAR) in the Hikurangi Subduction Zone

2026-04-22T03:14:03Z

Imaging the Spatiotemporal Evolution of Plate Coupling With Interferometric Radar (InSAR) in the Hikurangi Subduction Zone Maubant, L; Frank, WB; Wallace, LM; Williams, CA; Hamling, I The coupling at the interface between tectonic plates is a key geophysical parameter to capture the frictional locking across plate boundaries and provides a means to estimate where tectonic strain is accumulating through time. Here, we use both interferometric radar (InSAR) and Global Navigation Satellite System (GNSS) data to investigate the plate coupling of the Hikurangi subduction zone beneath the North Island of New Zealand, where multiple slow slip cycles are superimposed on the long‐term loading. We estimate the plate coupling across the subduction zone over three multi‐year observational periods targeting different stages of the slow slip cycle. Our results highlight the importance of the observational time period when interpreting coupling maps, emphasizing the temporal variability of plate coupling. Leveraging multiple geodetic data sets, we demonstrate how InSAR provides powerful constraints on the spatial resolution of both plate coupling and slow fault slip, even in a region where a dense GNSS network exists.

Land and Atmosphere Precursors to Fuel Loading, Wildfire Ignition and Post‐Fire Recovery

2026-04-22T03:13:19Z

Land and Atmosphere Precursors to Fuel Loading, Wildfire Ignition and Post‐Fire Recovery Alizadeh, Mohammad Reza; Adamowski, Jan; Entekhabi, Dara Land surface‐atmosphere coupling and soil moisture memory are shown to combine into a distinct temporal pattern for wildfire incidents across the western United States. We investigate the dynamic interplay of observed soil moisture, vegetation water content, and atmospheric dryness in relation to fuel loading, fire ignition and post‐fire recovery. We find that positive soil moisture anomalies around 5 months before fire ignition increase biomass growth in the subsequent months, thereby shaping fire‐prone vegetation conditions. Then, concurrent decrease in soil moisture, vegetation dehydration, and atmospheric dryness collectively contribute to the occurrence of fire ignition events. This is followed by a rapid recovery in both soil and atmospheric moisture within several weeks after the fire incidents. Our findings provide insights into understanding of wildfire ignition dynamics, supporting fire modeling and enabling improved fire predictions, early warning systems, and mitigation strategies.

Estimating Resistance to Digital Technology Infusion: A Multi-Method Modeling Approach

2026-04-22T03:09:18Z

Estimating Resistance to Digital Technology Infusion: A Multi-Method Modeling Approach Marsh, Delaney C. As technology continues to evolve, it continuously alters how work is done, how people interact, and how decisions are made. However, these transformations commonly face a critical barrier to successful implementation: resistance. Resistance arises at both the individual and organizational levels, often resulting in delays or stagnation in technology initiatives. This thesis explores how resistance might be quantitatively modeled in order to facilitate more successful technology deployment. Building on insights from an extensive literature review, two methods are proposed that capture the common constructs present throughout the literature as well as how systems thinking can be applied. The first method leverages Large Language Models (LLMs) to perform a sentiment analysis on qualitative data, extracting sentiment related to key constructs that shape resistance attitude and behavior. The second method applies systems architecture tools and principles to analyze changes to workflows and interactions resulting from the proposed technology initiative, developing system-based proxies for resistance constructs. While both methods yield overall resistance estimates, they also generate valuable insights that can be used to strengthen change management and transition plans. A case study within a large global corporation, featuring the deployment of the same technology across two organizations, demonstrates the application of these methods. This case study not only shows the type of insights that might be gained from utilizing these models, but exposes the limitations of each technique and the practicality for use within an enterprise. These limitations emphasize the need for large datasets and extensive system knowledge to perform a thorough analysis of the anticipated resistance. Ultimately, this research highlights the complex technical, social, and psychological dynamics of resistance and the challenges of capturing these factors within a quantitative model. The proposed methods build upon theoretical models for change resistance and technology adoption 3 to demonstrate the value in leveraging systems thinking and cutting edge technology to generate unique resistance insights.

Generation of Structural-and-Automation Compliant Cost-Effective Product Designs: Application to Large-Scale Sheet Metal Manufacturing

2026-04-22T03:09:13Z

Generation of Structural-and-Automation Compliant Cost-Effective Product Designs: Application to Large-Scale Sheet Metal Manufacturing Salim, Stanley Sebastian Sheet metal manufacturing holds importance due to its vast applications in construction, automotive, and aerospace. Growth in the sheet metal manufacturing industry has pushed companies to automate manufacturing processes to increase competitiveness by reducing costs while maintaining quality. However, implementation of a new system comes with challenges across an organization, as different teams are subject to biases that result in push-back towards adoption. This thesis focuses on the generation of cost-effective product designs that are compliant with requirements provided by design, structural, and manufacturing teams through the development of a cost model to compare manufacturing value streams and a design generation tool that generates cost-effective designs given structural and automation constraints. The work resulted in cost reductions between 10% to 22%, with improvements in quality and safety metrics, when compared to legacy designs using a manual press brake (MPB). Across the series of designs generated in this work, cost reduction occurs in a distribution of part populations between 41-59% and 77-23% split across the automated panel bender (APB) and automated tube laser (TL) respectively. The implications of these results show that significant cost reductions can be achieved through minimizing material usage, which leads to lower material costs, lower fabrication or labor costs, and unchanged assembly costs.

Biochemical characterization of a family of sigma factor-associated TnpB proteins

2026-04-22T03:03:43Z

Biochemical characterization of a family of sigma factor-associated TnpB proteins Liu, Catherine C. The TnpB proteins are a widespread family of bacterial and archaeal RNA-guided enzymes thought to be the evolutionary ancestors of Cas12 endonucleases. Previous studies have identified tnpB genes associated with proteins of diverse functions, including bacterial transcriptional regulation [1-3]. In my doctoral thesis work, I characterize the biochemical properties of a family of sigma factor-associated TnpB proteins. I show that various sigma factor-associated tnpB loci encode a non-coding RNA species (ωRNA) downstream of the tnpB stop codon that binds to the purified TnpB protein. When co-expressed with their cognate ωRNAs, the sigma factor-associated TnpB proteins target and bind regions of the E. coli genome corresponding to a 4-8 nucleotide guide sequence found within the ωRNA and an additional 5’ 2-5 nucleotide target-adjacent motif (TAM). In vitro binding assays confirm that TnpB binds its double-stranded DNA target in an ωRNA- and TAM-dependent manner. Focusing on one ortholog with a well-defined TAM, I find that the TnpB protein can be partially retargeted to different regions of the E. coli genome by reprogramming its 8 nucleotide ωRNA guide sequence. I furthermore demonstrate that this TnpB co-immunoprecipitates with its associated sigma factor when both are heterologously expressed in E. coli. Collectively, these data suggest that this family of TnpBs are RNA-guided DNA-targeting proteins that may be functionally involved in transcriptional regulation through their association with bacterial sigma factors.

Verification of the ADDER Fuel Management Code for MITR-II Fuel Management Analysis

2026-04-22T03:07:17Z

Verification of the ADDER Fuel Management Code for MITR-II Fuel Management Analysis Ahuactzin-Garcia, Emilio The Massachusetts Institute of Technology Reactor (MITR-II) is a 6 MWth research reactor with highly enriched uranium fuel. Due to the proliferation concerns of civilian HEU use, the MIT Nuclear Reactor Laboratory is actively participating in the US High Performance Research Reactor (USHPRR) program, which seeks to transition the remaining HEU fueled research reactors to newly developed LEU fuel. As part of the USHPRR program, the Advanced Dimensional Depletion for Engineering of Reactors (ADDER) software is being developed at Argonne National Laboratory for research reactor fuel management, and is being analyzed here as a replacement for the MCODE-FM currently used for MITR-II fuel management. This work performed code-to code comparison of MCODE and ADDER for actually operated core configurations, expanding on previous work done on fresh HEU and LEU core configurations. This study showed satisfactory agreement between MCODE and ADDER results. While the predicted critical bank heights at each time step were subtly different between the codes, the differences are nearly all within the 200 pcm acceptance band set in the criticality search algorithms, and are less than the differences between modeled and actual bank heights, so are acceptable. All runs showed an end of cycle U-235 mass discrepancy between MCODE and ADDER less than 0.5 g per element, and no other isotope had a discrepancy greater than 3% per element. Neutron flux and fission density had similarly minor differences. This overall close agreement indicates that for actual fuel management tasks, ADDER is a suitable replacement for MCODE.

System's approach evaluation for decision-making in a lubricant additives package blending plant

2026-04-22T03:07:51Z

System's approach evaluation for decision-making in a lubricant additives package blending plant Pommier, Stephanie This thesis applies a systems thinking approach to evaluate decision-making in a lubricant additives package blending plant facing increasing product complexity and demand. The blending unit operates in a high-mix, multi-product environment, with partial automation and strong reliance on human expertise. Nowadays, capacity is more constrained by loading activities than mixing, but future growth and complexity may shift bottlenecks. Current scheduling practices, based on empirical “equivalent quantity” metrics, inadequately capture true capacity constraints or the impact of complexity, particularly when the in-line mixing facility becomes the critical path. The study combines literature review, on-site observation, data analysis, and system architecture modeling—using Object Process Methodology (OPM) and Design Structure Matrices (DSM)—to represent product families, process interdependencies, and stakeholder needs. Operational data are analyzed to compare actual facility utilization with theoretical conservative critical path metrics, revealing difficulty in adequately capturing data. The actual calculation, Equivalent Quantity, and the new proposed one, Theoretical Critical Path, are proposed to be utilized as complementary metrics to improve scheduling validation and bottleneck identification. Simulation models and metrics are developed to evaluate feasibility, anticipate facility saturation, and assess the impact of introducing new packages. These models consider package composition, number of process steps, facility type, and priority rules. The research concludes that integrating system-level representations with improved metrics and simulation can enhance daily scheduling robustness, support long-term investment planning, and maintain operational resilience. Recommendations include refining data capture, adopting dual-metric scheduling, keeping humans for complex decision-making in this moving and uncertain environment, and applying simulation for scenario testing to prepare the blending unit for future demand and complexity.

Shared Autonomous Micro-Mobility: Evaluating Fleet Performance, Operational Strategies, and Sustainability

2026-04-22T03:05:29Z

Shared Autonomous Micro-Mobility: Evaluating Fleet Performance, Operational Strategies, and Sustainability Coretti Sánchez, Naroa Autonomous vehicles (AVs) have long been anticipated to revolutionize transportation, offering promises of effortless, safe, and congestion-free travel. Yet, concerns remain that AVs may exacerbate the car-dependency that has dominated the transportation landscape for over six decades. In contrast, sustainability-driven concepts—such as walkable cities and the "15-minute city"—are gaining momentum, advocating for urban spaces designed around pedestrians, public transport, and micro-mobility modes like bicycles and e-scooters. This thesis investigates how shared autonomous micro-mobility (SAmM) systems—lightweight, shared autonomous vehicles—can reconcile the tension between AV innovation and the push for human-centric urban mobility. While much of the research on AVs has focused on cars and taxis, this thesis turns its attention to micro-mobility systems such as bicycles and scooters. It explores whether the efficiencies attributed to shared autonomous vehicles can also enhance the performance of shared micro-mobility systems. Early prototypes of SAmM vehicles, such as autonomous bicycles and tricycles, have mostly addressed technical challenges at the individual vehicle level. However, broader fleet-level impacts and operational strategies remained understudied. To address this gap, this thesis presents a number of studies that are structured as follows. First, it introduces an agent-based model to evaluate the fleet-level performance of SAmM with a case study focused on autonomous bicycles. Second, it investigates operational strategies, such as decentralized fleet management and multi-functional services that cater to both passengers and delivery needs. Lastly, the thesis presents a life-cycle environmental impact analysis and examines the potential mode shifts associated with these new vehicles, offering insights into their sustainability and broader societal impacts. By examining the fleet performance and sustainability of SAmM, this research contributes to the emerging field of lightweight autonomous vehicles and provides early-stage guidelines for their development and deployment. While the long-term impacts of these mobility modes remain uncertain, shaping their design from the outset is essential to prevent the unintended consequences that have accompanied previous waves of transportation innovation, such as the rise of car dependency or the initial deployments of shared micro-mobility systems. Ultimately, this thesis offers a vision for how autonomous mobility can evolve to support, rather than disrupt, the goals of walkable, human-centric urban spaces and environmentally sustainable transportation.

Performing Performance Spaces: Expressing Context in Live Music

2026-04-22T03:08:44Z

Performing Performance Spaces: Expressing Context in Live Music Schon, Ana From basements and living rooms, to bars and clubs, to stadiums and arenas, the spaces that become venues for music performances bring with them unique features that can present sound reinforcement challenges. This is especially the case with smaller, lower-budget community-run or pop-up performance spaces, which may display more non-ideal characteristics like a higher electrical noise floor or unique acoustics. Qualities like these, coupled with the people occupying the space, distinguish one performance context from another—to a band on a long tour or to an audience member attending many shows in the same city. This thesis is structured around a series of live concerts using a digital audio processing system that captures, exaggerates, and reproduces unique acoustic qualities of the spaces where the concerts are taking place. This allows performers to exaggerate these qualities expressively by modulating their prominence with a MIDI controller. The concerts feature performances by the author as well as local Boston-area musicians. They aim to encourage their participants, both musicians and audiences, to share a musical experience that fosters awareness of context, the here and now, and shows the significance (in music and beyond) of the places we occupy and the people we share them with.

Planetary Traction Drive for Submarine Application withTapered Rollers

2026-04-22T03:05:41Z

Planetary Traction Drive for Submarine Application withTapered Rollers Murphy, Trevor Fundamental contributions of this research include: • Demonstrate scaling potential of traction drives for larger power scales (on the order of 80MW) • Creation, modeling, and testing verification of a tapered traction drive that uses axial thrust to provide operational preload to maximize fatigue lifetime with variable preloading • Demonstrate water’s feasibility as a traction fluid Within demonstrating scaling potential, the stresses generated in a traction roller are modeled and applied to a case study of the Emma Maersk suggesting that a 3.5m hub inner diameter with a transmission ratio of 3 is within infinite lifetime operation of a highstrength bearing steel traction drive at 81MW and 100rpm output power and rotation speed respectively. Variable preloading can be produced from the thrust force of a propeller pushing into tapered roller planetary transmission and provide enough preload to sustain its entire operational range if a non-dimensional slope parameter is less than [formula]. Here θ is the taper angle of the sun roller, µ is the coefficient of traction between rolling surfaces, rₛ is the sun roller radius, ϕ is the transmission ratio of the drive, k_q is the torque coefficient of the propeller, kₜ is the thrust coefficient of the propeller, and D is the diameter of the propeller. If instead the slope parameter is greater than one, extra initial axial preload is needed for the propeller’s thrust to sustain its operation and can be calculated with [formula]. Water has the potential to be comparable to, and even surpass, existing traction fluids in traction performance because it can transform into Ice VI under pressures in the elastohydrodynamic layer leading to a potential 0.24 effective coefficient of traction between rolling surfaces compared to a maximum of 0.13 for traction oils. To confirm the theory, more testing is needed to measure actual rheological properties. A dynamic rheometer test would require sufficient surface speeds to entrain water into a traction drive’s contact zones against the large pressure gradient. An example system with a rotation element of 5in radius and a rotation speed of 2000rpm is theorized to work. For water to be effectively used in a traction drive system, faster roller surface speeds, larger system sizes, better surface roughness specifications, or some combination of these would be necessary.

Leaving the Legacy: Architecting Technical Centers in Energy Enterprises

2026-04-22T03:07:20Z

Leaving the Legacy: Architecting Technical Centers in Energy Enterprises Rucks, Alexa Beverly Metz As major oil and gas companies compete in a landscape of evolving regulations, growing demand for diverse energy sources, and rapid digitalization, they face increasing complexity in adapting to the changing world. While historically, the technical centers embedded within these enterprises have driven technological advancement, the traditional way of working may not be delivering the required value to the broader enterprise. This thesis examines enterprise architecture transformation for a technical center in the oil and gas sector, emphasizing the need for greater agility, resilience, and innovation in the face of intensifying regulatory, technological, and market pressures. In response to complex challenges such as decarbonization mandates, digitalization, and evolving stakeholder demands, the research explores how technical centers, as key hubs of expertise and innovation, can be strategically positioned to support effective organizational change. A qualitative, design-oriented methodology is employed, integrating literature review, stakeholder analysis, and application of the ARIES framework to a technical center. The study maps internal and external enterprise landscapes, assesses current architectural practices using structured models, and develops future-oriented concepts evaluated against criteria aligned with sector-specific transformation needs. Findings reveal the limitations of conventional enterprise architecture in addressing dynamic industry conditions and illustrate how a holistic, stakeholder-centered approach can improve alignment, innovation capacity, and operational effectiveness. The thesis concludes with future areas of research and exploration.

A New Lens on Life: Cross-Contextual Sensing Technologies from Human Insights to Wildlife Conservation

2026-04-22T03:03:05Z

A New Lens on Life: Cross-Contextual Sensing Technologies from Human Insights to Wildlife Conservation Chwalek, Patrick C. The growing complexity of human-centric and ecological systems demands new sensing technologies capable of capturing holistic, contextual insights in real-world environments. However, a critical gap exists in the availability of integrated, intelligent platforms that can be adapted across these diverse domains. This dissertation addresses this challenge by designing, engineering, and validating a series of novel, multi-modal sensing platforms to provide a new and more insightful lens on a broad range of life. The research spans two primary contexts. First, to explore the nuances of human well-being, the AirSpecs smart-eyeglass platform was developed and deployed. This system holistically measures an individual's proximate environment and physiological parameters, and was validated in a multi-site international study to investigate the dynamics of human comfort "in-the-wild". Second, to advance ecological monitoring, a progression of acoustic platforms was engineered. The SoundSHROOM system was created as a robust, multi-channel recorder for harsh environments and successfully deployed in the Arctic. Building on this, the BuzzCam system was developed for targeted pollinator monitoring, culminating in an end-to-end pipeline for on-device AI classification of endangered and invasive bee species in Patagonia. Finally, the CollarID platform was engineered and characterized as a versatile, low-power, multi-modal animal-borne sensor for wildlife tracking, integrating inertial, bioacoustic, and comprehensive environmental sensing to move beyond the limitations of location-only devices. Key contributions of this work include the validated hardware platforms themselves; several unique, publicly available datasets from urban, Arctic, and Patagonian deployments; and a demonstrated methodology for implementing on-device AI to address the data-to-insight bottleneck in ecological monitoring. Collectively, this research provides the scientific community with a new suite of powerful research tools and demonstrates a cross-contextual design philosophy, leveraging engineering principles across disparate fields to enable a deeper understanding of organisms and their complex interactions with their environments.

A System Approach To Architecting Multi-Agent Systems In Enterprises

2026-04-22T03:06:55Z

A System Approach To Architecting Multi-Agent Systems In Enterprises Ruscalleda-Escobar, Gabriel This thesis develops a systematic framework for the architecture, evaluation, and phased deployment of AI-powered multi-agent systems in enterprise environments. Beginning with a comprehensive review of agent capabilities, architectural patterns, coordination protocols, and governance models, the study identifies critical design criteria guiding effective MAS integration. Employing a structured systems engineering approach, combining architectural decision analysis, multi-attribute utility modeling, and Monte Carlo-based tradespace exploration, the research quantifies cost-performance trade-offs and evaluates MAS concepts against prioritized enterprise metrics. Prototype implementations targeting real-world workflow challenges validate key architectural choices and inform a detailed strategic and technical roadmap for MAS integration, grounded in an AI System Readiness Level framework. This thesis concludes with actionable recommendations and future research directions to accelerate the adoption and maturity of MAS in organizational practice.

Design and Applications of Piezoelectric Yarns with Olefin Block Copolymer (OBC) Elastomeric Cores

2026-04-22T03:10:54Z

Design and Applications of Piezoelectric Yarns with Olefin Block Copolymer (OBC) Elastomeric Cores Villarreal Beltran, Sahara C. This thesis explores the development of a helical auxetic yarn composed of two fibers: (1) a piezoelectric fiber with energy-harvesting abilities and (2) an olefin block copolymer core (OBC) yarn with an optimal combination of strength and elasticity. This yarn would be incorporated into a larger fabric or garment that would eventually take advantage of the mechanical forces exerted by the human body to harvest energy through the piezoelectric effect passively. For this to occur, several factors must be considered. The auxetic nature of the yarn is hypothesized to amplify the mechanical deformation of the piezoelectric fiber, thereby enhancing the overall electric output of the fabric; however, the efficiency and reporting of piezoelectric fiber performance remains unstandardized. A tabulated literature review will show commonalities and trends in presently developed piezoelectric fibers. Additionally, any fabric intended for human wear must be able to withstand the forces exerted by the human body. OBC, with its unique microstructure, may have the correct balance of elasticity and strength required for this task. One option to modify, and hopefully improve, the mechanical properties of OBC is through the use of ionizing particle radiation to cross-link the polymer chains. Considerations for how to mount OBC fibers for irradiation are also included in this thesis, along with an analysis of OBC’s existing mechanical properties.

Democratizing Smart Manufacturing: A Comprehensive Framework for FrED’s Production and Educational Enhancement

2026-04-22T03:10:05Z

Democratizing Smart Manufacturing: A Comprehensive Framework for FrED’s Production and Educational Enhancement Yu, Yiming The Fiber Extrusion Device (FrED) serves as an accessible desktop tool designed to emulate the industrial fiber drawing process, providing engineering students with hands-on experience in critical areas such as data acquisition, control systems, computer vision, data analytics, and smart manufacturing. Its primary objective is to deliver a practical laboratory experience within remote learning environments. Building upon previous iterations, the 2025 FrED team focused on refining the device’s design, scaling up manufacturing, optimizing assembly lines, managing inventory with Tulip, and developing comprehensive educational content. This thesis specifically details the author’s contributions to the implementation of Design for Manufacturability (DFM) and Design for Assembly (DFA) principles, significantly improving product quality and stability. Key contributions also include substantial upgrades to the image processing and Graphical User Interfaces (GUIs), alongside the development of Manufacturing Standard Operating Procedures (MSOPs) for 3D printable components. Through these efforts, the team successfully scaled production to 30 units, optimized inventory management systems using Tulip, and created valuable educational materials. While significant progress was made, certain challenges remain that impact FrED’s potential as a consumer product. These include inconsistencies in diameter readings from the USB camera, complexities associated with the hot glue preform’s physics, and suboptimal motor speed assumptions identified during closed-loop testing, where a necessary reduction in spool speed to increase diameter interfered with subsequent camera readings. Despite these hurdles, pilot production runs, user testing, and the ongoing development of educational content have yielded promising results, affirming FrED’s potential as a valuable educational tool.

A Systems Approach to Redesigning Human-AI Interactions in Gaslift Operations for Upstream Oil and Gas

2026-04-22T03:09:51Z

A Systems Approach to Redesigning Human-AI Interactions in Gaslift Operations for Upstream Oil and Gas Momoh, Victor I. Artificial intelligence tools are increasingly used to support engineering decisions, yet their value depends on whether humans trust and act on their recommendations. This thesis investigates how LLM response style and user expertise influence trust in a technical context. Gas‑lift troubleshooting was used as a domain case. One hundred thirty‑one participants across five expertise levels were randomly assigned to evaluate a diagnostic scenario answered by one of four curated LLM personas that varied in tone, confidence, and accuracy. After each of five steps, participants rated trust and explained their rationale. We analyzed trust ratings using one‑ and two‑way ANOVA with post‑hoc tests and complemented the analysis with text mining and exploratory factor analysis of perceived trust drivers. Trust varied by LLM persona and by participant expertise, with no evidence of interaction effects. Beyond accuracy, three attributes consistently associated with higher trust were clarity, source citation, and presentation of a confidence score. Text analysis showed that experts emphasized technical validity, while less‑experienced users prioritized clarity and information sufficiency. Based on these results, the thesis proposes a conceptual redesign for human-AI interaction that adapts explanations to expertise, discloses confidence, and links recommendations to supporting sources. The framework provides measures of operational effectiveness to evaluate adoption and impact in production settings. Findings suggest that tailoring communication rather than using a one‑size‑fits‑all style can improve trust and decision quality in gas‑lift operations and similar engineering workflows.

Impact of Artificial Intelligence in Cross-Cultural Knowledge Management for Multinational Enterprises

2026-04-22T03:07:38Z

Impact of Artificial Intelligence in Cross-Cultural Knowledge Management for Multinational Enterprises Del Mundo, Mel-jie Brent Multinational enterprises (MNEs) are faced more frequently with challenges in managing knowledge across diverse cultural environments, particularly when dealing with tacit knowledge transfer. Tacit knowledge, inherently rooted in personal experiences and social interactions, is significantly challenged from cultural differences, including relational, cognitive, and structural dimensions. Although well known models of Knowledge Management (KM), like Nonaka's SECI model, provide structured knowledge conversion and sharing approaches, they do not inherently address these cross-cultural complexities. As Artificial Intelligence (AI) rapidly evolves, it offers significant potential to bridge these cultural gaps, facilitating more effective and timely tacit knowledge sharing. Thus, this exploratory research addresses the intersection of AI and cross-cultural tacit knowledge sharing within MNEs. To systematically examine this intersection, a conceptual research model was developed based on a comprehensive review of existing literature on KM, cross-cultural social capital theory (relational, cognitive, and structural), and AI capabilities. The model positions AI tools as key facilitators, bridging specific cultural barriers. Relational dimension addresses interpersonal trust and group dynamics, cognitive dimension emphasizes common understanding and communication clarity, and structural dimension pertains to organizational hierarchies and communication channels. Additionally, AI understanding and trust were hypothesized as critical moderators, affecting user engagement and reliance on AI systems. This research employed a mixed-method approach centered on structural equation modeling (SEM) to validate the proposed conceptual model. Data were collected from 217 respondents across various industries and organizational departments, emphasizing individuals with recent cross-cultural collaboration and AI tool experience. The analysis proceeded through Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and finally Structural Equation Modeling (SEM), including Common Method Bias (CMB) assessments for transparency and validity. Results from SEM showed interesting findings. While specific direct paths, such as AI tools' immediate impact on effective tacit knowledge sharing, were not statistically significant, indirect effects were notably evident. Particularly, AI understanding significantly enhanced AI trust, subsequently facilitating AI tools' perceived effectiveness. Moreover, majority of the respondents consistently acknowledged the value of AI in bridging cultural barriers across relational, cognitive, and structural dimensions. Nevertheless, considerable number of respondents expressed uncertainty regarding the precise capabilities of AI, suggesting a threshold of trust and understanding yet to be fully surpassed for substantial impacts to be consistently observable. In summary, the thesis demonstrates that AI holds promise in addressing cultural barriers in tacit knowledge sharing within MNEs. However, the effectiveness strongly hinges on the users' understanding and trust in AI. These findings provide valuable insights for organizations intending to leverage AI strategically in multicultural contexts. Future research directions include more profound exploration of AI's subtle indirect impacts and longitudinal assessments of trust-building interventions.

Systems Engineering for Biopharmaceutical Manufacturing Processes with Phase Transitions

2026-04-22T03:03:25Z

Systems Engineering for Biopharmaceutical Manufacturing Processes with Phase Transitions Srisuma, Prakitr The biopharmaceutical industry has been growing significantly over the past few years. Various biotherapeutics have been developed and played a crucial role in advancing global health, e.g., monoclonal antibodies, cell and gene therapy, and messenger ribonucleic acid (mRNA) vaccines. Nevertheless, the current biopharmaceutical manufacturing is facing several challenges associated with production scalability, operational flexibility, and quality control. For example, many biopharmaceutical processes are still operated in batch mode, while continuous manufacturing has already been adopted in other major industrial sectors. As such, significant efforts have been recently dedicated to the development and integration of advanced manufacturing technology for biopharmaceutical processes. This thesis presents an array of novel computational tools and methods across process systems engineering (PSE) domains developed for biopharmaceutical manufacturing. Several PSE applications are discussed and demonstrated, including process modeling, monitoring and state estimation, model-based control and optimization, and uncertainty quantification. Two important biopharmaceutical processes that involve phase transitions are studied, namely (1) lyophilization and (2) cell thawing, serving as the two main parts of this thesis. The first part of this thesis centers on lyophilization. Lyophilization (also known as freeze drying) is a low-temperature, low-pressure dehydration process used for improving the stability of various biotherapeutics, including its recent application to mRNA vaccines. In this thesis, we first present a set of mechanistic models for various lyophilization designs, including conventional/batch lyophilization, microwave lyophilization, hybrid lyophilization, and the state-of-the-art lyophilization technology, namely continuous lyophilization of suspended vial. The validated models can accurately predict the evolution of the critical process parameters for the entire lyophilization cycle, including the product temperature, ice/water fraction, sublimation front position, and concentration of bound water (residual moisture). Second, we showcase a state observer that estimates the concentration of bound water by using temperature measurement as an input. This state observer allows for real-time tracking of the residual moisture, which is typically present in trace amounts and difficult to measure online. Third, we describe a highly efficient framework for incorporating the probabilistic uncertainty into our model via polynomial chaos theory (PCT). Our PCT-based lyophilization model is demonstrated for fast uncertainty quantification and stochastic control. Finally, we propose a new, efficient way of solving optimal control problems via reformulation and simulation of a hybrid discrete/continuous system of mixed-index differential-algebraic equations. The proposed algorithm is several orders of magnitude faster than the traditional optimization-based approaches while maintaining similar/better accuracy. The algorithm is also demonstrated for finding the optimal control policies for lyophilization under different conditions. The second part of this thesis focuses on cell thawing. Cell thawing a critical step in cell therapy before cells are introduced to the patients. Traditionally, the thawing process requires human intervention to ensure proper progression and completion before the cells can be administered to the patients. In this thesis, we build a thermal imaging-based system that facilitates the monitoring, control, and automation of cell thawing. First, a mechanistic model is proposed to guide the design of cell thawing systems. Then, a thermal imaging-based state observer is developed for real-time process monitoring, which keeps track of the phase transition (melting/thawing) and accurately identifies the endpoint of the process. Lastly, we present a novel optimal control architecture for accelerating the process and controlling the final product quality corresponding to the thawing protocols. Finally, with all the tools and methods established, we discuss the development of a digital twin for biopharmaceutical manufacturing. The digital twin receives and processes data from its physical counterpart, performs several complex computations in real time, and provides instructions/feedback to the physical system, serving as a virtual representation of the physical system of the manufacturing system. Ultimately, this thesis provides a complete PSE framework and case studies for advanced biopharmaceutical manufacturing that involves phase transitions.

A Self-Driving Autonomous Robotic Laboratory for High-Throughput Semiconductor Materials Discovery

2026-04-22T03:05:38Z

A Self-Driving Autonomous Robotic Laboratory for High-Throughput Semiconductor Materials Discovery Siemenn, Alexander E. Discovering new high-performance and functional materials is critical for the advancement of sustainable technologies. This thesis presents the design of a fully autonomous and self-driving laboratory for the discovery of optimized semiconductor materials used in solar photovoltaic applications. By leveraging sample miniaturization, parallelization, and machine learning-controlled robotics, the developed self-driving laboratory achieves a throughput of over 1,000 unique material compositions created, measured, and optimized per day. The challenge of discovering a new optimal material comes down to the number of potential candidates within a search space. Perovskites — a class of semiconductors commonly studied for their properties as solar cells — have millions of possible compositional candidates within only an eight-dimensional search due to their widely interchangeable chemical formula. However, only a small fraction of these candidates have promise as high-performance semiconductors. While computational and predictive approaches provide a means to quickly reduce this search space, these methods alone often fail to accurately generalize to such vast and complex spaces due to few quality training datasets existing. Conversely, experimental approaches have high single-candidate accuracy but suffer from low throughputs. The presented self-driving laboratory fuses both computational and experimental approaches to rapidly down-select candidates with improved accuracy by iteratively generating its own experimental training dataset. The developed self-driving laboratory is divided into four chapters: (1) synthesis, (2) characterization, (3) optimization, and (4) discovery. Each subsystem outlined in these chapters is engineered for speed and repeatability to maximize the cycles of learning when searching through vast and high-dimensional search spaces: (1) Synthesis: A high-throughput synthesis tool is developed to automate the printing and crystallization of semiconductors by combining up to ten unique precursor formulations into target compositions, requested by a machine learning algorithm. (2) Characterization: Two characterization systems are developed: one that rapidly parallelizes measurement of optical band gap properties using computer vision, and another that autonomously measures photoconductance electrical properties through contact-based measurement, controlled by neural network predictions. (3) Optimization: A machine learning algorithm employing bounded search techniques is developed to guide the discovery of exceptional material compositions from high-dimensional and non-convex search spaces using the characterization results to suggest new optimal target compositions for synthesis in each subsequent iteration. (4) Discovery: Lastly, control and multithreading communication protocols are developed to integrate all subsystems together with the goal of autonomously discovering optimal materials from a search space. Autonomous operation is achieved by coupling the subsystems with robotic arms and automated sample transporters, culminating in a fully self-driving laboratory, entitled DiSCO ([Di]scovery, [S]ynthesis, [C]haracterization, and [O]ptimization). DiSCO creates, measures, and decides which new materials to make with full autonomy. A 24-hour optimization campaign is run to demonstrate the autonomy and performance of DiSCO with the objective of finding semiconductor compositions that have optimal optoelectronic properties for use as perovskite solar cells. The convergence to the optimization target as well as the number of candidate compositions synthesized from the search space are compared against experimentally derived results from literature.

Designing the CthulhuCosmos: Toward a Speculative Cosmo-politics of Indigenous Epistemology in Space

2026-04-22T03:04:30Z

Designing the CthulhuCosmos: Toward a Speculative Cosmo-politics of Indigenous Epistemology in Space Muniyappa, Prathima For millennia, civilisations scattered across the earth have craned their necks up towards the cosmos to seed their origin mythologies into the blanket expanse of deep space. However the imbrication of imperial conquest, colonial expansion, and militarized competition transformed humanity’s seedbed of cosmology into a project of material incursion, casting outer space as a frontier primed for possession, surveillance, and extraction. The history of astronomy is a history of receding horizons, but the horizon is no monolithic constant. It remains a jagged line as it wrestles with issues of democratisation of space exploration and access. For some countries and corporations the apparent ‘frontier’ of space is a territory familiar with significant resources and infrastructure to have become veteran voyagers; others take nascent steps, developing strategies to strengthen their space programs; and yet others remain feet planted firmly, gazing at the horizon that never moved, exploring the deep expanse of the cosmos through myth, language, ritual and dreamscape. Indigenous people across the globe have remained explorers of this enigma through rich cultural cosmologies evolved over millennia of observation. Their knowledge represents diverse epistemologies that offer insight into radically different relationships that humans have evolved about space and its exploration, and is a fount of intangible heritage that rarely makes an appearance in the mainstream discourses. As humans become prominent actors in extraterrestrial realms, it stirs in its wake complex questions of identity politics. Whose identity becomes a blueprint for ‘humanity’? What cultures are represented and how? What cultures and narratives are silenced by deliberate obscuration or worse by ignorance and apathy? These questions emerge as a dialectic to the monolithic identities and monocultures of mind that underpin mainstream space discourse. Nested within storied cultural heritages are alternative cosmologies, attuned to other dimensions and extended voyages that offer the possibility of thickening the discourse towards a more inclusive mythology for future space exploration. For speculative design to become truly liberatory, it must create space for those rendered inarticulate by the architectures of structural design. This requires a reconfiguration of the very grammar of futurity, an effort to recruit voices we have not trained ourselves to hear. What forms of consultation and authorship emerge when the techno-poetics of the disenfranchised are taken seriously? What does epistemic hospitality look like when mediated through art and design? These questions shape the methodological horizon of this dissertation: Can voices from the margins thicken the discourse on space exploration? Can the subaltern speculate? And if so, what futures become possible when they do? The argument then is to transition from a colonial aerospace to a decolonial cosmos. This dissertation proposes the CthulhuCosmos as a site of possibility, a speculative cosmopolitics inspired by Donna Haraway’s Chthulucene, calling for tentacular forms of kinship that thread together the terrestrial and the extraterrestrial. Drawing from field-based engagements with the Likan Antai of the Atacama Desert, the Changpa of Ladakh, the Khasi of Meghalaya, the Maasai of Kenya, and other communities and sites in Central Asia, the dissertation extends epistemic hospitality to these cosmological traditions to enter into dialogue with modern techno-imaginaries in the realm of space exploration. Much has been written on dualisms in science subject/object, culture/nature but less attention has been paid to the void that undergirds these dialectics, and the mythologies of purity that enforce their boundaries. Rather than seeking synthesis or translation, this dissertation positions these cosmologies in deliberate adjacency, provoking the emergence of ontological multiplicity without collapsing difference into a universal frame. To navigate these entangled terrains, it proposes threshold craft as a methodological framework: a practice of designing and mediating epistemic encounters at the interstices of science, cosmology, and speculative design. Threshold craft emerges as a form of cosmopolitical calibration attuned to asymmetry, opacity, and plural rationalities through which the CthulhuCosmos can be articulated as a scaffold for relational intelligibility between divergent ways of knowing. The dissertation offers a speculative infrastructure for navigating the architectures of space exploration and other emergent technoscientific domains wherever futures are being imagined and ethics remain unsettled. As a methodological framework, it enables the design of epistemic encounters grounded in ethical entanglement and relational responsibility, allowing divergent cosmologies to co‑inhabit speculative terrains without erasure or collapse.

Scaling AI Across Retail: Leveraging NLP and Computer Vision to Enhance Customer Experience and Reduce Returns

2026-04-22T03:07:56Z

Scaling AI Across Retail: Leveraging NLP and Computer Vision to Enhance Customer Experience and Reduce Returns Pederson, Lisa Product returns in fashion e-commerce are a persistent and costly challenge, often signaling deeper misalignments in customer expectations, product representation, and operational processes. This thesis explores how Artificial Intelligence (AI), specifically Natural Language Processing (NLP) and Computer Vision (CV), can be applied to reduce returns and improve the customer experience across digital retail channels. Through a systems-based approach, the thesis evaluates technical applications for NLP and CV, and provides strategic frameworks for deploying AI in real-world contexts. Using fashion e-commerce as a representative use case, the research analyzes applications such as virtual try-on, size and fit prediction, visual search, defect detection, and customer sentiment analysis. The thesis introduces a strategic AI adoption roadmap tailored to omnichannel fashion retailers, incorporating AI maturity assessments, change management frameworks, and KPIs to guide successful implementation. These approaches can support retailers to assess readiness, prioritize use cases, and scale responsibly. The thesis integrates qualitative insights and quantitative modeling to estimate the range in potential financial impact, highlighting that AI-enabled interventions can both reduce costs and increase sales from brand loyalty. The findings offer a practical framework for fashion retailers seeking to embed AI into customer-facing systems and decision-making processes for an enterprise-wide AI transformation.

Desalinating Produced Water: A Framework for Brine Concentration and Valorization in the Permian Basin

2026-04-22T03:09:58Z

Desalinating Produced Water: A Framework for Brine Concentration and Valorization in the Permian Basin Dagnaw, Dawit Y. Oilfield produced water (PW) in the Permian Basin is projected to exceed 25 million barrels a day by 2030, continuing to pose challenges straining saltwater-disposal (SWD) wells and raising induced seismicity concerns that can constrain production. While recycling it to fracture new wells is progressing well (20% estimated), the vast volume is disposed of via SWD wells. The pore pressure build-up is increasing the risks of induced seismicity (mostly deep disposal) and potential communication to other wells. Simultaneously, West Texas and southeast New Mexico experience chronic drought and water scarcity, creating demand for alternative water sources such as treated produced water. This thesis develops a comprehensive framework grounded in non-ideal thermodynamic modeling, pilot performance data, and navigating evolving regulatory policy for desalinating produced water for beneficial reuse. Produced water volumes and chemistry are characterized, noting extreme salinities (often 100,00 – 200,000 mg/L TDS) and emphasizing the need for robust pre-treatment to remove organic compounds, contaminants, solids, and scale-forming ions. The thesis further evaluates desalination technologies, from advanced membrane processes (e.g., osmotically assisted and low-salt-rejection reverse osmosis) to thermal methods (mechanical vapor compression and humidification-dehumidification), in the context of the Permian Basin’s abundant water, heat, and natural gas resources. Case studies and pilot projects in the Basin demonstrate the viability of achieving high-quality treated water while reducing volume requiring disposal. An integrated framework is proposed for selecting treatment trains and desalination technologies and planning for concentrate management. While disposal by injection is currently cheaper, the strategic use of waste heat, technological innovations, and valorization can shrink the cost gap. Lastly, this thesis explores areas for improvement and recommends future research to further transform a waste challenge into a new water supply and alleviate disposal impacts.

Deep Learning Framework for Solving Geoacoustic Inversion Problems using Normal Mode Theory

2026-04-22T03:04:33Z

Deep Learning Framework for Solving Geoacoustic Inversion Problems using Normal Mode Theory Vardi, Ariel Geoacoustic inversion, the process of estimating seafloor properties from acoustic measurements, is crucial for applications ranging from sonar performance prediction to environmental monitoring. However, traditional inversion methods are often computationally intensive, limiting real-time analysis and large-scale spatial characterization. This thesis presents a deep learning framework to overcome these limitations in shallow-water environments. The core approach involves training one-dimensional convolutional neural networks (1D-CNN) on large synthetic datasets generated using the KRAKEN normal mode acoustic propagation model. By learning the complex mapping from the time series of acoustic pressure to environmental parameters, these networks enable near-instantaneous geoacoustic inversion. Three main contributions are presented. First, a fully automated end-to-end system is developed for use with a single hydrophone. This system detects impulsive acoustic events, locates the source, and inverts key geoacoustic parameters, including sediment sound speed, density, and layer thickness. Validation using data from the 2022 Seabed Characterization Experiment (SBCEX) in the New England Mud Patch shows results comparable to traditional methods but achieved in milliseconds. Second, the methodology is extended to use data from distributed hydrophone arrays to estimate the spatial variability of seabed properties. By combining hundreds of individual track inversions, detailed maps of sediment sound speed and sound speed ratio across the New England Mud Patch are constructed, revealing spatial trends consistent with independent geological surveys and localized inversion studies. Third, to facilitate sensitivity analysis and potential future physics-informed machine learning, a differentiable implementation of the KRAKEN normal mode solver is developed in the Julia programming language. This enables the efficient and exact calculation of the acoustic field derivatives with respect to environmental parameters using automatic differentiation, providing valuable insights into parameter sensitivities. The results demonstrate that this deep learning framework significantly accelerates geoacoustic inversion while maintaining accuracy. It enables real-time applications and comprehensive spatial mapping previously infeasible, opening new possibilities for underwater acoustic research and bridging data-driven methods with physics-based modeling.

Electroforming Klystron Cavities Using Machined and 3D Printed Mandrels

2026-04-22T03:07:27Z

Electroforming Klystron Cavities Using Machined and 3D Printed Mandrels Wright, William Klystron amplifiers are critical for delivering GHz range radio frequency (RF) power for plasma heating. However, only recently has there been significant interest in raising the efficiency of klystrons. Traditional manufacturing methods remain expensive and time-consuming, limiting the development of high-efficiency designs. This thesis explores electroplated sacrificial mandrels as a rapid fabrication method for klystron cavities. Two mandrel types were evaluated: machined aluminum and 3D-printed plastics. RF performance was assessed via measurements of resonant frequency and unloaded quality factor. Cavities from aluminum mandrels showed resonant frequencies within ~25 MHz of predictions and unloaded Q-factors above 5000. Despite plating challenges, 3D-printed mandrels produced cavities with resonant frequencies within ~50 MHz of the target frequency and Q-factors between 3000–5000. These results demonstrate the viability of additive manufacturing for producing functional klystron cavities with reduced fabrication time.

High-Fidelity Multiphase Modeling of Critical Heat Flux Phenomena in Pressurized Water Reactor Fuel

2026-04-22T03:09:00Z

High-Fidelity Multiphase Modeling of Critical Heat Flux Phenomena in Pressurized Water Reactor Fuel Moncuit, Anne The prediction of Critical Heat Flux (CHF), the limiting phenomenon of boiling heat transfer, has remained a major challenge since the early development of nuclear reactors. Although several mechanistic models have been proposed, current industry practices still rely heavily on empirical correlations and sub-channel codes, which are limited in scope and accuracy outside of their calibration domain. This thesis aims to improve CHF prediction methodology, focusing specifically on the Departure from Nucleate Boiling (DNB) under high-pressure conditions representative of Pressurized Water Reactors. This work is conducted using Multiphase Computational Fluid Dynamics in a Reynolds-Averaged Navier-Stokes Eulerian-Eulerian framework. Two sets of boiling closures, CASL-FY19 and MITB, were compared in the commerically available Siemens STAR-CCM+ software. An automated Java-based heat flux incrementation method was developed, and validation was performed against experimental data from both circular and square test sections at pressures between 2 and 13.79 MPa. The performance of a near-wall void fraction DNB criterion was evaluated and found to consistently underpredict experimental values, with an average error of 11.24 \%. This error was found to be strongly dependent on the mass flux of each case. Different modeling configurations were explored to improve results, revealing that accurate modeling of the void fraction distribution plays a central role in prediction quality. The technical readiness of a heat partitioning based DNB criterion was also assessed. Two key limitations were identified: first, numerical instabilities due to excessive vapor accumulation near the wall at high heat fluxes; second, a persistently low dry area fraction even under vigorous boiling, attributed to an incomplete modeling of merging dry spots and an inadequate contact angle specification. The findings underscore the importance of improving near-wall void fraction representation for both DNB detection models. While the heat partitioning based model demonstrates a stronger physical foundation and potential for greater accuracy, the near-wall void fraction model currently offers superior robustness and more consistent results within the framework.

Investigation of Compact Liquid Organic Scintillators for Neutron Spectrum Measurements in Tokamaks

2026-04-22T03:08:22Z

Investigation of Compact Liquid Organic Scintillators for Neutron Spectrum Measurements in Tokamaks Lanzrath, Andrew Thomas Neutron diagnostics play a key role in both magnetic confinement and inertial confinement fusion platforms and will be increasingly more important as those systems continue to improve with more devices achieving burning plasma performance. Many fuel cycles, such as deuterium-tritium, produce neutrons as a reaction product. Since neutrons are electrically charge-neutral, they leave the plasma carrying important information about ion temperature, macroscopic flow velocity, fuel ratios, and more. These neutrons are detected and analyzed with a variety of different methods including flux monitors, emission profile monitors, and spectrometers. Neutron spectrometers work based on different neutron interactions and come in different shapes and sizes. The liquid organic scintillator is one type of neutron spectrometer that has been around for many years and has the benefits of being cost effective and well-characterized. One downside to these detectors is the need for post-processing scripts, commonly referred to as unfolding algorithms, that take the raw energy spectrum comprised solely of Compton and recoil edges to convert it to an energy spectrum containing peaks, which is more convenient for analysis. There are many unfolding schemes being developed and in use, each with its own set of built-in assumptions and sources of error. A new method for liquid organic scintillator spectral analysis that eliminates the need for spectral unfolding is presented and applied to fusion-relevant measurements. It incorporates the AIMS Data AcQuisition (ADAQ) libraries with associated data acquisition and analysis user interfaces and the GEANT4 Monte Carlo particle transport simulation toolkit to produce synthetic detector spectra for EJ-301/NE-213 liquid organic scintillators. Changes to plasma properties cause variations in the synthetic spectra, which can be quantified to develop an analysis workflow that can be used with experimental spectra. The workflow is applied to measure neutron rate, ion temperature, and toroidal rotation velocity. Future work including applying the workflow to other types of organic scintillators and other potential plasma measurements are discussed.

Remote Sensing Architectures for Detecting Small, Non-Cooperative Maritime Vessels

2026-04-22T03:08:11Z

Remote Sensing Architectures for Detecting Small, Non-Cooperative Maritime Vessels Mohler, Matthew D. Despite recent advancements in remote sensing technologies, the United States Coast Guard (USCG) and other organizations face significant challenges in detecting, identifying, and monitoring small, non-cooperating vessels at sea. Recent increases in daily maritime activity, both legal and illegal, have only intensified the demand for technological solutions that provide instantaneous position, velocity, and time data (PVTD) in near-real time to support an adequate response. Environmental and operational conditions, such as adverse weather, cloud cover, sea state, signal interference, poor lighting, and extended data processing times, continue to limit sensor performance and reduce the timeliness of PVTD data across these use cases. Although significant research around remote sensing technologies currently exists, there exists limited public research investigating the challenges and potential solutions associated with these specific use cases. Furthermore, the recent trend of more cost-efficient space launch costs and the potential incorporation of AI/ML algorithms and other tools for processing high-volume data sets lend the possibility or opportunity of considering new, larger constellation architectures not previously thought of due to previously high costs. Therefore, this thesis explores the challenges associated with identifying and monitoring small objects at sea and proposes a set of potential system architectures and concepts robust to dynamic environmental and operational conditions through an argument of plausibility that adequately addresses stakeholder needs using preexisting and emerging technologies. An analysis of hundreds of design permutations using a modeling framework capable of evaluating architectures under uncertainty identified three unique architectures that supported sub-hour data collection while maintaining cost-effectiveness. Additionally, a framework for implementation outlines actionable steps such as the phased deployment of potential architecture constellations, integration of AI/ML-enabled functionality to reduce overall latency, leveraging commercial launch partnerships to reduce timelines and costs, and considerations for single- vs. multi-sensor payloads when considering cost performance under uncertainty. Notably, the performance-cost analysis revealed that multi-sensor architectures, despite their common use in modern, small constellation systems, failed to appear on any Pareto frontiers, with system complexity and cost penalties outweighing performance gains, thus reinforcing the value of smaller, simpler single-sensor designs.

Leveraging Innovation-Execution Dynamics to Optimize Portfolio Value

2026-04-22T03:07:12Z

Leveraging Innovation-Execution Dynamics to Optimize Portfolio Value Crane, James M. Conventional project portfolios often silo execution and capability enhancements, missing dynamic feedback loops that can reveal additional value. This thesis presents a system-aware framework, Innovation Enhanced Project Portfolio Optimization, for evaluating the value uplift of capability enhancements during Project Portfolio Selection. This evaluation includes integrating non-linear growth trajectories with combinatorial sequencing and Pareto-front analysis. Key elements include scenario-based Pareto envelope mapping under varied capital-allocation rules, a no-growth baseline benchmark, and delta-driven Pareto front evaluations to quantify uplift. Applied to a demonstration portfolio, we find that most NPV gains accrue early as growth-rates increase before capex efficiencies and production curves converge to a shared ceiling. Decomposing enhancement value reveals up to 65% NPV uplift from envelope expansion of original sequences plus up to an additional 30% from additional sequence optimization. The amount of uplift is highly dependent upon the evaluation timeframe, enhancement growth rate, business model sensitivity to growth, and capital allocation constraints. Pareto clustering uncovers a core set of projects that consistently dominate, guiding robust funding targets. Practitioners should deploy this method when capability-growth effects materially reshape portfolio priorities or when long-term transformation is at stake. Implementation hinges on reliable business model inputs, calibrated growth-curves, and robust cross-functional discussions that can be enabled by interactive dashboards to visualize envelope shifts and sequence clusters. Iterative “what-if” scenario testing empowers dynamic capital reallocation, transparent performance attribution, and timely investment in high-impact enhancements. Omitting this analysis does not always produce suboptimal outcomes. Organizations may “luck into” optimal sequences when enhancement effects align with intuition. However, without structured modeling, value remains unrecognized and unreproducible, execution contributions can be misattributed, and critical R&D underfunded, limiting organizational learning, adaptation, and the ability to replicate gains.

Feasibility and techno-economic analysis of combining water tower energy storage with geothermal energy systems

2026-04-22T03:08:52Z

Feasibility and techno-economic analysis of combining water tower energy storage with geothermal energy systems Brown, David A. In some enhanced geothermal systems (EGS), pumps must provide hydraulic pressure in excess of 2000 psi to re-inject the geothermal brine into the reservoir. The parasitic load used to power the injector pumps is significant. Total parasitic load in geothermal plants can range from 15% to 45% of the plant electricity output (Chagnon-Lessard et al., 2020). The thesis hypothesizes that integrating a system of water towers with geothermal systems could provide useful energy storage and reduce overall pumping power costs. This conceptual system would utilize an elevated tank or reservoir to store water during times of low power demand. The stored potential energy could be dispatched by discharging the water from the elevated tank to provide hydraulic pressure to help inject fluid into a geothermal system. Reducing the power required to run the injection pumps could significantly boost the power output of the system during discharge of the stored energy when electricity costs are highest.

Multiphase Soft Materials: Local Structuring Sets Global Mechanical Behavior

2026-04-22T03:03:21Z

Multiphase Soft Materials: Local Structuring Sets Global Mechanical Behavior Dellatolas, Ippolyti Multiphase materials are composed of two or more constituents of different phases whose characteristics can vary widely, leading to a multi-component system with properties that are greater than the sum of its parts. The mechanical behavior of multiphase materials depends on the mesoscale structure of the material, which itself can be affected by the microscopic interactions between the constituents. Multiphase materials are ubiquitous; examples include toothpaste, sandcastles, or photographic film. We here study the macroscopic behavior of multiphase soft materials, focusing on two systems: nanofiller-reinforced composite hydrogels and colloidal gels. Hydrogels and colloidal gels are promising functionalized materials in the biomedical field due to their high water content and biocompatibility. Predicting the behavior of these soft materials under deformation or load is key to fine-tuning their use to specific applications, and relies on elucidating the link between microscopic inter- and intra-phase interactions and macroscopic rheological properties. We present a comprehensive characterization of the mechanical properties of multiphase soft materials, from their linear elastic response to their yielding and failure mechanisms. We first show that the addition of nanofillers to hydrogels can greatly improve the material’s stiffness. We propose that this reinforcement stems from a local densification of the polymer around the nanofillers due to nanofiller-polymer attractive interactions. This densification enhances the stress coupling throughout the material, reduces the gel fluctuations, and ultimately leads to global stiffening of the composite. We then characterize the yielding and failure of the composite hydrogels. In these strain stiffening materials, we identify a transition to macroscopic irreversibility, beyond which the material cannot recover its full nonlinear viscoelastic response. This transition occurs at the strain at which the elastic modulus of the material is maximal. We reveal that the permanent damage is due to the breakage of bonds that are responsible for the material’s strain stiffening response. Finally, we relate the nonlinear mechanical response of two types of colloidal gels to the microscopic characteristics of their yielding, showing that colloidal gels of different interaction strengths exhibit distinct modes of failure. This work provides a comprehensive understanding of the behavior of multiphase soft materials under both small and large deformations. It establishes a framework that is critical to tailoring these materials for specific engineering applications and showcases the key effect of local mechanisms in the global mechanical response of materials.

Effects of Flow Obstacles on Single-Phase Heat Transfer and CHF in a Narrow Rectangular Channel

2026-04-22T03:09:07Z

Effects of Flow Obstacles on Single-Phase Heat Transfer and CHF in a Narrow Rectangular Channel Pisinger, Mateo Mixing vane geometries enhance the fuel-to-coolant heat transfer within nuclear reactors, which allows higher power ratings for reactors and improves their economics. At the same time, their presence may affect the critical heat flux (CHF), the upper limit to heat extraction rate of a reactor. Numerical simulations often do not accurately reflect the changes in CHF when mixing vanes are included in nuclear fuel assemblies, suggesting that the CHF models are not resolving the relevant boiling phenomena present with mixing vane geometries. Thus, there is a need for experimental data to inform future simulations for more accurate results. This thesis aims to address this need by modifying an existing experimental apparatus to reproduce and measure the momentum and thermal transport of flows when perturbed by flow obstacles. The capabilities of the apparatus were demonstrated through experiments comparing heat transfer characteristics of single-phase flow in an empty rectangular channel and in a channel with a flow obstacle. Comparisons with simulations of the experiment were done to highlight the ability of simulation tools to elucidate some of the hydrodynamic behavior of fluids near obstacles responsible for the enhancement of heat transfer. Initial two-phase experiments were also done to characterize the changes in CHF in the presence of a flow obstacle, which when compared to an empty flow channel show a degradation of CHF at low mass fluxes and an enhancement at high mass fluxes. However, future research is needed to understand the reason for these changes.

Interphase Momentum Closure Analysis for Multiphase Computational Fluid Dynamics of Horizontal Bubbly Flow

2026-04-22T03:10:21Z

Interphase Momentum Closure Analysis for Multiphase Computational Fluid Dynamics of Horizontal Bubbly Flow Henderson, Philip Multiphase Computational Fluid Dynamics (M-CFD) offers a high-fidelity method for analysis of safety and performance in helical coil steam generators (HCSGs). Of the M-CFD frameworks, Eulerian multiphase (EMP) is the industrial standard, its efficacy determined by the ability of implemented closures to represent physical phenomena. Research has overwhelmingly focused on the development and validation of EMP closures for vertical bubbly flow, rather than the near-horizontal orientations of HCSGs. Advancement of horizontal bubbly flow modeling would provide a foundation for modeling HCSGs, as well as bubbly flows more broadly. Evaluation of conventional closures for vertical flow indicates that a similar approach is generally inappropriate for horizontal configurations. Lift and drag-based turbulent dispersion closures control the radial void distribution in vertical flows, both of which are based on slip velocity and bubble shape due to deformation. Analyses demonstrate these are both negligible in horizontal flows, thus lift and drag-based turbulent dispersion play at most a minor role. Analysis also shows that the standard EMP momentum equations are erroneous, and an alternative stress term for the dispersed phase is recommended for inclusion. In the absence of established approaches, a rarely discussed closure, virtual mass stress (VMS), is explored. VMS is affected by a range of factors: virtual mass coefficient, turbulence response coefficient and turbulence anisotropy. The performance of VMS and the influence of these factors, along with the recommended particle-averaged stress term, were investigated by modeling flows in microgravity, horizontal and vertical configurations. Modeling results suggest implementation of VMS has potential to capture void fraction distributions, however it remains strongly influenced by the aforementioned factors. Of these, the turbulence response coefficient yields the largest effects, and cannot be reliably predicted by available models. Further work into the turbulence modeling of both phases is required to realize the potential of VMS and accurately model horizontal bubbly flow.

Fantasy IDEs: Explorations in the IDE

2026-04-22T03:07:25Z

Fantasy IDEs: Explorations in the IDE Stiles, Charlotte This thesis introduces "Fantasy IDEs," a new paradigm for integrated development environments (IDEs) that reimagines coding as a performative, gestural practice. While existing approaches to making programming more accessible focus on simplification or visual abstraction, this work transforms the IDE itself into a medium for creative expression. Through experimental interfaces that integrate conversational AI, real-time visual feedback, and audience engagement, I demonstrate how development environments can transcend their utilitarian origins to become dynamic spaces for artistic exploration. I do this by addressing the mismatch between artistic values and ubiquitous software engineering tools and the technical barriers that prevent creative practitioners from focusing on expression, while focusing on the need for immediate feedback in creative processes. Through live performance contexts and audience interaction, the Fantasy IDEs demonstrate how programming environments can become more wild, unpredictable, and creative while maintaining expressive power. This research contributes to the future of creative coding environments by showing how the interface itself can become a performative instrument, ultimately inspiring a more playful and accessible relationship with computation.

Learning to Teach: Models for Semantic and Adaptive Personalization of AI Tutors

2026-04-22T03:06:21Z

Learning to Teach: Models for Semantic and Adaptive Personalization of AI Tutors Grover, Ishaan AI tutoring systems have the potential to make education affordable, equitable, and effective. However, despite recent advances in large language models (LLMs), mainstream use of AI as tutors remains limited due to a lack of pedagogically grounded frameworks. Previous work has shown that effective tutoring involves not only linguistic fluency, but also the ability (i) to effectively engage in open-domain dialog to build rapport and (ii) adapt dynamically to feedback cues from a learner. This thesis addresses these limitations by providing two key contributions. First, we study the cognitive and psychological processes that humans use when engaging in conversations. We propose a novel probabilistic approach using Markov Random Fields (MRF) to augment existing closed-source and open-source LLMs (GPT-4o, Gemini, and LLaMa) for improved next-utterance generation. Using human evaluations, we show that our augmentation approach significantly improves the performance of existing state-of-the-art LLMs for open-domain conversational agents. Next, we explore the challenge of dynamic pedagogical adaptation. Learners provide feedback in different forms (facial expressions, gaze, questions, seeking help, etc.), and the interpretation of this feedback may differ from learner to learner. However, existing approaches like reward shaping, policy shaping, and preference-based RL assume a static interpretation of feedback labels. To this end, we present an adaptive reinforcement learning algorithm that generalizes to multiple labels and autonomously interprets the meaning of feedback cues during online deployment. We show that policy shaping is a special case of adaptive RL under certain conditions. In addition, through data from human subjects, we show that the algorithm can learn from feedback cues and outperform any static interpretation in a simulated environment. We provide rigorous studies showing that adaptive RL is invariant to noise and bias in feedback cues, as well as the choice of MDP. We further outline the conditions for employing such an algorithm. Finally, we integrate our adaptive RL algorithm with an existing AI tutor for Python programming to enable it to adapt to each user. We study the characteristics of our algorithm against baseline RL in real-world tutoring systems and present our findings and recommendations for future iterations. By integrating cognitive modeling and adaptive RL into AI tutors, this thesis contributes to the design of intelligent, personalized, and pedagogically grounded educational systems.

Characterization of the DIII-D High-Field Side Scrape-Off Layer and Implications for High-Field Side Lower Hybrid Current Drive

2026-04-22T03:05:25Z

Characterization of the DIII-D High-Field Side Scrape-Off Layer and Implications for High-Field Side Lower Hybrid Current Drive Leppink, Evan High-field side lower hybrid current drive (HFS LHCD) is a potential candidate to provide efficient, non-inductive, off-axis current drive in tokamaks for steady-state operation, stability control, and/or access to advanced tokamak scenarios. The first experimental test of HFS LHCD has begun on the DIII-D tokamak. LHCD is known to be sensitive to scrape-off layer (SOL) conditions local to the coupler, and to characterize the HFS SOL in preparation for the high-power HFS LHCD experiments, a high-field side reflectometer was installed on DIII-D. The reflectometer operates in the 6-19 GHz range in O-mode polarization, corresponding to a measured density of 4.5e17-4.5e18 per cubic meter. A HFS SOL measurement database of over 600 DIII-D discharges has been constructed and includes HFS LHCD target plasmas, such as high q-min discharges. Furthermore, the effect of ELMs and magnetic configuration has been studied in detail. Machine learning techniques have also been applied to this database, allowing for the prediction of HFS SOL density profiles from global discharge parameters such as plasma current, plasma density, and plasma shaping parameters. Simulation-based inference (SBI) techniques were used to infer HFS SOL turbulence characteristics from reflectometer density profile measurements. Finally, experimental data, turbulence inferences, and machine learning regression enable accurate prediction and optimization of HFS LHCD coupler loading, which is calculated using 3-D finite element full-wave codes.

Koopman Dynamic Modeling and Control for Robotic Systems Making and Breaking Contact

2026-04-22T03:06:27Z

Koopman Dynamic Modeling and Control for Robotic Systems Making and Breaking Contact O'Neill, Cormac Controlling robots that dynamically engage in contact with their environment is a pressing challenge. Whether a legged robot making-and-breaking contact during locomotion, or a manipulator grasping objects, contact is everywhere. Unfortunately, the switching of dynamics at contact boundaries makes practical control difficult. Applying predictive control techniques to systems with contact results in non-convex optimization problems, which are notoriously difficult to solve online. In this work, I show how the use of a compliant contact model can enable the application of Koopman linearization techniques to systems with contact. This enables the construction of fast and reactive controllers for online decision making. This approach is first demonstrated on the planar pushing task, which captures the key challenges of switching contact modes and non-prehensile manipulation. I show that a Koopman-based controller can discover dynamic control policies without any sequence of contacts being defined in advance, while still allowing for operation at real-time rates. I highlight the importance of compliance in enabling Koopman theory for contact dynamics, and also explore how mechanical compliance can be co-designed with the controller for optimal performance. Varying the compliance of a robotic manipulator results in a trade-off between the responsiveness of the system and the accuracy of the linearized model being used for control. I study the robustness of this controller to modeling errors in friction, and present a friction-aware extension that enables adaptation to online friction estimates. A novel framework for constructing Koopman approximations is also developed for systems with contact dynamics, and compared to existing approaches for a range of autonomous systems. By combining radial basis functions (RBFs) and Deep Koopman Networks (DKNs), we observe improved prediction accuracy for complex systems, while also highlighting the impressive capabilities of completely learning-based DKN approaches. Finally, we look at segmented dynamics more generally. A simple problem - inspired by a hot air balloon that must navigate alternating wind currents - is used to probe how Koopman models enable predictive control in the proximity of mode boundaries. We show that linear approximations of the global dynamics, as afforded by Koopman, can allow for Koopman MPC to outperform nonlinear (and non-convex) alternatives that explicitly represent the true segmented dynamics with a mixed-integer formulation.

Caresets and the Experimental Public Co-Design of Tomorrow

2026-04-22T03:08:39Z

Caresets and the Experimental Public Co-Design of Tomorrow Aydemir, Deniz Participatory planning can help cities make better policy and planning decisions. An effective participatory planning framework must represent complex urban systems (compositional), compute Pareto-optimal solutions (computational), and incorporate residents into the decision-making process (collaborative). We build a mathematical language using partially ordered sets to formally describe care in three forms: preference, ethics, and design. We then create a compositional, computational, and collaborative framework for participatory planning called the Experimental Public Co-Design of Tomorrow (EPCODOT). This framework adapts monotone co-design to work with our language of care and extends the approach with a collaborative interface. We demonstrate EPCODOT's capabilities first by modeling an MIT Senseable City Lab research project on trade-offs between data privacy and urban well-being, and then by modeling its potential application with a real public project in Durham, North Carolina. We provide a software prototype at epcodot.com. This work develops two interconnected contributions: a mathematical language of care connecting applied category theory, decision theory, and ethics; and EPCODOT, a software tool enabling participatory planning for researchers, communities, and cities. Future work should pursue four directions: exploring additional category-theoretic structures within the mathematical language, formalizing connections to social choice and decision theory, testing EPCODOT directly with communities, and enhancing the computational capabilities of the framework's monotone co-design adaptation.

A Sociotechnical Systems Analysis of Enterprise Robotics Innovation

2026-04-22T03:10:16Z

A Sociotechnical Systems Analysis of Enterprise Robotics Innovation Jones, William Pierce Mobile intelligent robotics offer the opportunity for integrated energy companies to remove personnel from hazardous operating environments and increase working efficiencies, yet many corporate innovation groups face significant headwinds resisting scaled robotics deployments. This thesis analyzes the sociotechnical systems that comprise an enterprise innovation group, using a newly established real-world robotics organization as a case study. Guided by the Architecting Innovative Enterprise Strategy (ARIES) framework, this study explores how an intentionally modified enterprise organizational architecture can enable large-scale and sustained robotics development and deployment activities across diverse operating environments and within a complex corporate context. System artifacts including stakeholder salience maps, value flow networks, design structure matrices, candidate architecture tradespaces, and epoch-based future proof tests were synthesized from a series of surveys, interviews, and virtual observations. Analysis revealed specific recommendations for the focus enterprise including: a need for an institutionalized vision; integration and incorporation of parallel and adjacent functions; an embedded agent model; standardized models supporting consistent customer experiences; and integral architectural reevaluation structures. No single proposed architectural transformation dominates for all scenarios and envisioned futures; accordingly, a broader approach of selecting architectural models tailored to a clarified vision and with embedded reconfiguration flexibility is recommended. This study demonstrates that enterprise architecture is a critical factor governing the pace and scale of mobile robotics adoption in industrial environments and offers a readily transferable systems-thinking approach for broader innovation groups operating in complex ecosystems.

Leveraging Temporal Fusion Transformers and Domain-Specific LLMs for Real-World Industrial Sensor Forecasting and Decision Support

2026-04-22T03:07:32Z

Leveraging Temporal Fusion Transformers and Domain-Specific LLMs for Real-World Industrial Sensor Forecasting and Decision Support Ben Yosef, Ori In process industries, large volumes of sensor data are generated continuously, yet much remains underutilized for proactive decision-making. This thesis explores a novel architecture that combines deep learning and large language models (LLMs) to forecast, interpret and prevent process threshold violations in an industrial process facility. A Temporal Fusion Transformer (TFT) model was trained on 3 months of real-world, multivariate sensor data (1-minute resolution across 31 sensors) to predict 12-minute-ahead process parameter exceedances. Forecast outputs were passed to a costume-built domain-specific GPT-4.1 model, configured using prompt engineering, graph interpretation capabilities, and a retrieval-augmented generation (RAG) system incorporating expert literature and process knowledge. The GPT model synthesized probabilistic forecasts into well-structured team-based Five Whys root cause analyses, where virtual domain experts questioned each other to refine the diagnosis, a long term mitigation plan to remove the root causes found, and simulation-driven, per-unit prevention plan, generated by testing alternative process settings with the trained deep learning model and selecting the minimal production disturbance configuration that prevented the predicted violation, all while leveraging domain-specific knowledge to ensure operational feasibility and engineering trustworthiness by explicitly referencing authoritative sources from its RAG library, such as procedures and technical text books, to maintain compliance with stakeholder needs. Evaluation showed that GPU-trained deep learning model significantly outperformed CPU-trained equivalents in mean quantile loss metrics. Subject matter expert evaluation of the LLM’s responses indicates that the LLM’s insight quality improved as more domain knowledge was added leading to greater specificity, unit level differentiation in recommendations. This dual-model system demonstrates a scalable approach to combining forecasting and interpretability in one pipeline, offering preventative, actionable, domain-specific support for engineers, operators, and managers in complex industrial environments.

Evaluating the Influence of Surface Characteristics on Boiling and CHF Predictions with the MITB Model in Subcooled Flows

2026-04-22T03:08:36Z

Evaluating the Influence of Surface Characteristics on Boiling and CHF Predictions with the MITB Model in Subcooled Flows Ding, Alice The MIT Boiling Model (MITB) offers a detailed physics-based alternative to the empirical, correlation-based approaches of subchannel codes which are still the nuclear industry standard while limited in general applicability. In contrast, MITB is designed for integration into multiphase computational fluid dynamic (M-CFD) frameworks, enabling detailed resolution of three-dimensional effects introduced by features such as spacer grids and mixing vanes. When coupled with models for critical heat flux (CHF), MITB enhances predictive capabilities for CHF onset estimation. While its potential has been demonstrated in capturing boiling mechanisms and CHF behavior, an important challenge remains in establishing model sensitivity to surface characteristics. These characteristics play a central role the ebullition cycle, heterogeneously affecting bubble nucleation and the consequent bubble dynamics on the boiling surface. As dominant effects, surface roughness impacts nucleation site density and bubble formation, while wettability affects bubble contact angles and their nucleation and detachment dynamics. In this study, the predictive capabilities of MITB were evaluated based on its ability to capture the effects of surface characteristics, such as roughness and wettability, on boiling behavior. The contribution of surface properties to the model's ability to predict CHF was also examined. The MITB model was first implemented in a point-averaged form to assess the sensitivity of individual closure models to varying flow conditions and surface attributes. Closure relations for bubble departure diameter (BDD), bubble departure frequency (BDF), and nucleation site density (NSD) were selected based on their physical consistency and accuracy in predicting boiling curves, as validated against experimental data from the MIT flow boiling loop at low-pressure conditions. The influence of localized flow conditions, particularly void fraction distributions, was also considered to assess the impact of the extension of the MITB formulation to a M-CFD framework. In the work, two different CHF prediction methods were tested: a mechanistic and physically consistent surface-dependent model based on the work of Demarly and a more traditional empirical local void-fraction-based criterion. Comparative analysis reveals that both models tend to underpredict CHF, particularly for highly wettable rough surfaces. The mechanistically-based model demonstrates lower overall error, but has significant sensitivity to the treatment of the bubble growth time parameter. The void-fraction-based criterion method, though less accurate, offers improved computational robustness.

Symmetries in Neural Network Functions and Parameters

2026-04-22T03:04:54Z

Symmetries in Neural Network Functions and Parameters Lim, Derek Modern neural networks are large, complex objects, which can be difficult to study and work with. In this thesis, I analyze and improve neural networks from the perspective of symmetries, with particular focus on function symmetries and parameter symmetries. Function symmetries are transformations of the input that lead to predictable changes in the output, which can be enforced in neural network architectures to improve performance on data with symmetry structures. Parameter symmetries are transformations of parameters that leave the underlying neural network function unchanged, and they have impacts on various empirical phenomena in neural networks. In Part I of this thesis, I focus on function symmetries, and develop new methods and analysis techniques for equivariant neural networks that have function symmetries baked into their architectures. I apply these techniques primarily on eigenvector-valued data, resulting in the first provably expressive neural network architectures that respect the symmetries of eigenvector data. In Part II, I focus on parameter-symmetries, and analyze their impact in various empirical phenomena of neural networks, as well as their impact in the open-weight ecosystem of models with publicly-shared parameters. In Part III, I consider both function and parameter symmetries to construct metanetworks: models that take in the parameters of other neural networks as input. Since the input to metanetworks are parameters, I develop metanetworks that are invariant or equivariant to the parameter symmetries of the input networks. All in all, my work shows that accounting for function and parameter symmetries is both theoretically and empirically beneficial across diverse types of data, learning tasks, neural network architectures, and other parts of the deep learning pipeline.

Optimizing the Use of Enriched Lithium in Fusion Reactor Blankets: A Neutronics-Based Economic Analysis

2026-04-22T03:07:42Z

Optimizing the Use of Enriched Lithium in Fusion Reactor Blankets: A Neutronics-Based Economic Analysis Webber, Logan D. Deuterium-tritium (D-T) fusion is the principal reaction of interest for near-term fusion energy development. Because tritium, the third isotope of hydrogen, occurs naturally only in trace amounts, it must be produced onsite to sustain the fuel cycle of any fusion reactor utilizing the D-T pathway. This is typically achieved through neutron capture reactions with lithium in the tritium breeding blanket (TBB). In this regard, it is deuterium and lithium, not tritium, that constitute the primary resources required for D-T fusion. Lithium-6 (Li-6) more readily breeds tritium across most neutron energies than lithium-7 (Li-7); however, its natural abundance is only ~7.5%. This has motivated TBB designs that require various levels of Li-6 enrichment. However, the supply chains and industrial capacity needed to produce enriched Li-6 at scale do not currently exist. To address this critical barrier to fusion deployment, this thesis evaluates the neutronics performance, shielding implications, and economic tradeoffs of lithium-based breeder materials for use in TBBs. A particular focus is placed on the role of Li-6 enrichment. A comprehensive modeling workflow was developed to assess five candidate breeder materials: 2LiF-BeF2 (“FLiBe”), the eutectic Li17Pb83 (“PbLi”), pure lithium metal, lithium titanate (Li2TiO3), and lithium orthosilicate (Li4SiO4). A simplified cylindrical blanket model was constructed to enable straightforward comparative analysis across a broad parameter space of enrichment and breeder thickness, with geometry loosely approximating the proposed ARC reactor, designed at the Massachusetts Institute of Technology. Neutronics simulations were conducted using OpenMC to calculate the tritium breeding ratio (TBR) for over 800 blanket configurations. A one-dimensional analytical model was then applied to estimate the minimum shielding thickness required to protect the toroidal field (TF) coils for each blanket configuration, based on neutron leakage fraction tallies and conservative fluence limits for REBCO high-temperature superconductor (HTS) tape. These results were integrated into a materials-based economic analysis to quantify tradeoffs between tritium production, Li-6 requirements, and system costs. Key findings include the identification of material-specific scaling behaviors and diminishing returns in TBR performance with increasing enrichment and thickness. FLiBe demonstrated robust performance at minimal enrichment with compact radial builds. In contrast, PbLi offered higher peak TBRs while requiring less lithium by mass, but at the cost of greater enrichment and shielding. Lithium metal exhibited flexible performance across enrichment levels, albeit with significant performance sensitivity and greater technical uncertainty under practical constraints. Across all materials, economically optimal configurations were found to strike a balance between tritium self-sufficiency and Li-6 minimization, while maintaining compact geometry. These insights provide actionable guidance for fusion reactor blanket design under existing Li-6 supply constraints.

Advanced Federated Learning Algorithms Leveraging Selective Forgetting for Data-Constrained Environments

2026-04-22T03:05:49Z

Advanced Federated Learning Algorithms Leveraging Selective Forgetting for Data-Constrained Environments Alotaibi, Abdulrahman Federated Learning (FL) enables collaborative training of machine learning models without centralizing raw data, offering a practical framework for real-world AI. Yet real-world deployments face challenges in data-constrained environments, where client datasets are both limited and heterogeneous, as well as from broader adversarial risks and complex regulatory requirements. This dissertation addresses these challenges by integrating Knowledge Evolution (KE) and Later-Layer Forgetting (LLF) into the FL paradigm, and by analyzing their combined impact on security and compliance. The proposed FL-KE and FL-LLF frameworks introduce selective forgetting mechanisms that prune less salient representations, reallocate model capacity, and enable iterative refinement over generations. Experimental evaluations on diverse image classification datasets, including Flower-102, CUB-200, MIT-67, and Stanford Dogs, demonstrate accelerated convergence, improved generalization, and robustness under data scarcity compared to baseline FL. Beyond performance, this work examines the security implications of FL-KE and FL-LLF through a comprehensive threat model covering poisoning, backdoor, inference, free-rider, Sybil, and Byzantine attacks. Analysis reveals that selective forgetting can reduce the persistence of malicious updates, mitigating certain attack vectors while coexisting with robust aggregation and secure aggregation protocols. Finally, this dissertation explores the intersection of FL and data privacy regulations through an empirical survey of stakeholders across the Gulf Cooperation Council (GCC) region. The findings reveal a gap between regulatory awareness and operational compliance, as well as opportunities for FL especially in its KE and LLF enhanced forms to align with legal principles of data minimization, purpose limitation, and user rights. By combining methodological advances, defenses against adversarial threats, and attention to regulatory requirements, this work offers a framework for building the next generation of federated learning systems that are effective, secure, and compliant in varied settings. These contributions also support the broader goal of trusted and safe machine learning, where the demand for robust, data-sharing, and regulation-aware systems is central to preventing harmful outcomes, promoting fairness, and protecting the integrity of AI in sensitive fields such as healthcare, finance, and government. The findings presented here carry direct implications for deploying federated AI in high-stakes environments and highlight promising directions for future research at the intersection of machine learning, security, and policy.

Intuitive but Wrong: Uncovering Student Misconceptions About Force and Motion With Bayesian Item-Response Methods

2026-04-22T03:01:52Z

Intuitive but Wrong: Uncovering Student Misconceptions About Force and Motion With Bayesian Item-Response Methods Segado, Martin Alan Students often enter our classrooms with many preexisting beliefs about force and motion learned through a lifetime of hands-on experience. Unfortunately, many of these beliefs are not correct, leading to a wide variety of misconceptions about mechanics concepts. Such misconceptions are often highly resistant to traditional instruction and can seriously impede deep conceptual learning in mechanical engineering and other STEM fields; it is therefore essential to develop ways of understanding, measuring, and correcting these in our students. Previous efforts to address this problem have led to the development of research-based multiple-choice tests such as the Force Concept Inventory (FCI), a pioneering instrument whose incorrect answer choices ("distractors") were mined from student responses and implicitly encode many common misconceptions. While numerous studies have undertaken to explore the structure of the FCI by analyzing student responses, very few of these make full use of the information present in the students' specific choice of distractors, and those that do are limited in the types of associations they are able to find. This thesis demonstrates that, by combining highly-flexible psychometric models, modern Bayesian inference methods, and traditional factor analysis techniques to analyze a large dataset of student responses to the FCI, it is possible to discover a much greater number of interpretable student misconceptions than previously found, and to do so without prior labeling of test content beyond identifying the correct answers. Our approach is based on the Multidimensional Nominal Categories IRT Model (MNCM) which leverages information present in both correct and incorrect student responses to identify a plausible set of latent (unobserved) student traits governing response behavior. To provide robustness to infrequent responses and guard against overfitting, we implement this as a Bayesian model with hierarchical priors; approximate variational inference methods enable this approach to scale to larger datasets, and standard factor rotation techniques facilitate the discovery of small subsets of distractors most likely to encode real psychological constructs. We apply our method to a dataset comprising both pre- and post-instruction FCI submissions from ~17,000 students across eight North American colleges and universities. We take a principled approach to rotation, comparing both subjective interpretability and several indicators of solution quality, including similarity of our results across both schools and instruction as well as a metric of overall simplicity (the hyperplane count). Our results inform the generation of several candidate solutions, which we further examine for robustness via non-parametric bootstrapping and from which we choose a subset of 27 unique, partly-overlapping sets of distractors for additional investigation. By examining both the content of the most heavily-loaded distractors in these sets and the broader context of their associated questions, we find 22 partly-overlapping dimensions corresponding to misconceptions and misunderstandings. Most of these are consistent with prior research on specific misconceptions in introductory mechanics, and two appear to be novel. We further observe that many of our misconceptions correspond to previously-accepted historical theories about force and motion. Finally, we present a simple method for assessing the prevalence of each misconception and apply it to our data both before and after instruction. We identify two major classes of misconceptions---standard and naive---which differ both in which students they affect and in how resistant they are to instruction. Our results support and extend prior findings about misconceptions by showing that (1) it is primarily the standard misconceptions which are most resistant to instruction, while naive misconceptions appear better remediated in our data; and that (2) current instruction appears to be noticeably less effective in dispelling misconceptions in learners with average or below-average pre-test scores, further underscoring the need for improved instructional techniques. Importantly, though, our work also provides educators and researchers alike with a new tool to address this challenge. By providing a catalog of existing misconceptions as well as the means to discover and measure new ones, we hope this research will lead to the development of focused interventions for specific misconceptions, aid in the evaluation of new instructional techniques, and provide teachers with a clearer lens through which to understand and adapt to the students in their classrooms.

RF-Visual Perception with Applications to Mobile Sensing, Robotics, & Augmented Reality

2026-04-22T03:05:53Z

RF-Visual Perception with Applications to Mobile Sensing, Robotics, & Augmented Reality Boroushaki, Tara Mobile and wireless sensing technologies play a critical role in Internet of Things, Augmented Reality (AR), robotics, environmental monitoring, and human health monitoring. Despite their ubiquity, each of today’s mobile sensing technologies is limited in a fundamental way: cameras can capture high-resolution images but are limited to the line of sight and cannot sense behind occlusions; wireless sensing (e.g., using WiFi or Bluetooth) can traverse occlusions but has limited sensing resolution; inertial sensors can track with high accuracy and speed but suffer from spatiotemporal drift. This thesis introduces algorithms, learning models, and systems to fuse different sensing modalities, particularly radio frequency (RF) and cameras. However, RF and vision are inherently two different sensing modalities, which makes their fusion a complex task. To address this challenge, this thesis leverages and develops advanced signal processing techniques and mathematical modeling that combine different sensing modalities. As a result, it unlocks new capabilities for IoT-connected and mobile systems, such as AR headsets and robots, and enables new perception, interaction, and manipulation tasks. The thesis has three core components, the first focuses on RF-Visual perception for cyber-physical systems. We present the design, implementation, and evaluation of RFusion, a robotic system that can search for and retrieve RFID-tagged items in line-of-sight, non-line-of-sight, and fully-occluded settings. We then introduce FuseBot, a robotic system that rather than requiring all target items in a pile to be RFID-tagged, leverages the mere existence of an RFID-tagged item in the pile to benefit retrieval of both tagged and untagged items. The second component focuses on RF-Visual perception for cyber-human systems. We present the design, implementation, and evaluation of X-AR, an AR system with non-line-of-sight perception. X-AR augments AR headsets with RF sensing to enable users to see things that are otherwise invisible to the human eye or to state-of-the-art AR systems. We then explore how to exploit synergies between AR headsets and RFID localization to improve both user experience and localization accuracy. Using fundamental mathematical formulations for RFID localization, we derive confidence metrics and display guidance to the user to improve their experience and enable them to retrieve items faster. The final component of this thesis answers the question of how we can perform non-line-of-sight perception without any RF-tags altogether. To do this tag-less perception, we use millimeter wave (mmWave) signals and off-the-shelf mmWave radars. We introduce a real-world dataset of mmWave images of everyday objects and an open-source simulation tool that can be used to generate synthetic mmWave images for any 3D triangle mesh. We demonstrate the usefulness of this dataset and simulation tool in multiple perception tasks in non-line-of-sight. The combination of these three component introduces now sensing modalities that opens up important capabilities for a variety of applications in manufacturing, warehousing, logistics, and supply chain as well as Human Computer Interaction (HCI) and Human Robot Interaction (HRI).

A New Town Scale Consensus: Propagational Voting in Urban Decision Making

2026-04-22T03:06:06Z

A New Town Scale Consensus: Propagational Voting in Urban Decision Making Sakai, Yasushi Cities represent spaces where diverse populations negotiate collective living, creating potential for both conflict and cooperation. The city or "Polis" triggered the term "Polites" (one lives in cities), which the term we know "Politics" refers to techniques and activities to live together including building consensus. This is not metaphorical, we today have exactly this issue when urban planning processes often face a tension: while communities express broad support for development and housing, they frequently resist specific local projects. Current community engagement mechanisms compound this problem by reducing complex, layered citizen concerns into simplified decisions made by a few officials/commissioners, while at the same time, creating procedural bottlenecks and gridlock. In Boston, even small development projects face years of review time, with community input processes that amplify rather than resolve underlying contradictions between collective need and individual experience. This thesis introduces Propagational Voting (PV), a voting mechanism designed to capture the organic, multi-layered way people naturally reason about civic issues. PV allows participants to split their single unit of voting power across multiple targets at different levels of abstraction from specific policy proposals to broader values while enabling delegation to trusted individuals, groups, or institutions. This method accommodates plurality in engagement styles: direct decision-making, selective delegation, and flexible resolution levels that mirror how people actually think and relate in small-group settings. Basing Suzuki Ken’s original concept, this thesis further develops the concept through both theoretical modeling and empirical testing via workshops and surveys focused on urban planning challenges. The system not only aggregates preferences but reveals how influence flows through trust networks, providing insights into decision-making structures. Chapter 2 to 4 establishes the theoretical and computational foundations of PV, while Chapter 5 to 7 explores its application to real urban planning scenarios through case-based analysis and participatory testing. This foundational thesis do have limitations, yet these gaps point toward creative solutions including AI-augmented delegation with safety guardrails, and dynamic consensus mechanisms applicable to real urban issues that adapt across multiple abstraction levels. This work presents a foundational framework designed for structured exploration at smaller civic scales, with empirical testing demonstrating feasibility while identifying pathways for broader application. Voting should go hand in hand with the deliberation and co-design process. The core contribution bridges deliberation and voting by embracing rather than reducing complexity. Its easier to dismiss contradictory responses as obstructive, PV treats them as natural and valid expressions of layered reasoning. This foundational framework, designed for structured civic experimentation, points toward more peaceful, pluralistic governance treating democracy as a continuous direction of improvement toward systems that honor natural patterns of trust and contextual organic decision making; to help live together in cities.

Physics-based and Machine Learning Hybrid Modeling of Oil Transport and Oil Consumption in Internal Combustion Engines - a Digital Twin

2026-04-22T03:06:04Z

Physics-based and Machine Learning Hybrid Modeling of Oil Transport and Oil Consumption in Internal Combustion Engines - a Digital Twin Zhong, Xinlin A considerable amount of resources is invested by the automotive industry in combating internal combustion (IC) engine emissions resulting from lubrication oil consumption (LOC). Reducing LOC requires a thorough understanding of the oil transport in the piston ring pack, which is a multi-physics process with different length and time scales. Particularly, the multi-time scale nature has never been addressed in the field. This thesis develops a comprehensive digital twin framework to model oil transport in the piston ring pack across engine cycles and to predict LOC. A modular model architecture is employed, and a hybrid approach combining machine learning and physics-based models accurately and efficiently resolves each mechanism. The model incorporates essential physics responsible for the oil flow from the bottom of the ring pack to the top, including piston ring dynamics, gas flow, oil transfer between ring and liner, oil redistribution on piston land and grooves, oil vaporization, and ring rotation. The effects of expansive design and operating parameters are also considered. Three LOC sources, namely, oil vaporization on the liner, reverse gas flow, and the top ring up-scraping, are identified, and they are all found to be highly dependent on the ever-changing relative ring gap locations. As such, without knowing the ring gap locations, only a range of LOC can be predicted. To shed light on the enduring myth of ring rotation, a model is developed for the first time. An investigation into the driving mechanisms reveals piston secondary motion and bore expansion as the dominant factors in determining the rotation behavior. It was found that under a constant friction coefficient between the ring and the groove, stationary points inevitably arise for the top two rings, and a non-uniform distribution is needed to render any ring rotation patterns. By applying a gradient-based calibration procedure to the ring groove friction coefficient distribution, this work identifies the necessary ring groove lubrication condition to match experimentally observed ring rotation patterns. The Digital Twin framework is a valuable tool for studying how the lubricating oil is supplied, distributed, released, and consumed in the piston system, and the effects of design and operating parameters. The framework integrates processes at different length scales and traces the evolution of the oil distribution over engine cycles. For the first time, the unsteadiness of the oil transport in the ring pack under a steady engine operation is modeled with a reasonable computation time. The model is a foundation for future ring pack design optimization tasks where a balance between oil consumption and proper lubrication must be reached. Based on the current framework, future improvements on ring/groove lubrication conditions and the liner finish effects can expand the predictability of the model in both time and length scales.

Seeing Spirulina: Within, Between, and Beyond Institutional Research

2026-04-22T03:02:39Z

Seeing Spirulina: Within, Between, and Beyond Institutional Research Ganapathy, Turga This three-part dissertation is born from diverse—sometimes overlapping, and even contradicting—rituals of contemporary Spirulina engineering and science-making. Drawing from my participation in institutional Spirulina research (Spirudemia) and do-it-yourself (DIY) algoculture, the text opens with stories foregrounding the skilled practices and material cultures of home growers, small-scale farmers, Spirulina academics (Spirudemics), commercial cultivators, philanthropists, and third worlding engineers whose ways of conceptualizing, cultivating, experimenting with, and relating to Spirulina have sculpted my own technoscientific vision. Next, I follow voyaging, botanizing agents of empire and Spirulina’s earliest industrialists to establish the emergence of Spirudemia. Diving into Spirudemia’s beginnings, I situate Spirudemics’ taken-for-granted traditions—and their attendant research trajectories and material outcomes—in a long arc of historical entanglements with colonial bioprospecting missions, land dispossession, and plantation-scale monoculture. Emerging from critique in the final chapter, I channel my labor into realities and futures in which Spirulina cultivation is collaborative, decentralized, and democratized. Combining microscopy and geometry-based computer vision, I showcase a partially-automated image processing protocol that tracks the helicities of Spirulina trichomes grown in low-cost, plug-and-play, lab-scale photobioreactors (PBRs). I demonstrate how time-series shape data collected daily across cultivation periods allows humans to interface with the filamentous cyanobacteria via a “language of shape.” Colony morphology histories reveal Spirulina’s preferred growth mechanisms, alterations in bacterial cell wall architecture, morphological acclimation timescales, and the emergence of multiple morphotypes within a single population. From a more-than-human engineering standpoint, shape data may inform maintenance interventions, and enable assessments of culture health and growth consistencies within typically black-boxed bioreactor ecosystems from the perspectives of their microbial inhabitants.

Low-cost and Low-emissions Strategies for Implementation of Hydrogen as Next Generation Fuel

2026-04-22T03:02:56Z

Low-cost and Low-emissions Strategies for Implementation of Hydrogen as Next Generation Fuel Bhat, Maanasa Net Zero Emissions 2050 (NZE 2050) outlines an ambitious goal of complete reduction of net CO₂ emissions to zero in the next two decades. This is aimed to be achieved through maximum decarbonization of energy production, transportation, residential and industrial sectors. One of the active strategies for decarbonization is switching from fossil-based energy sources to renewable energy sources which is expected to contribute approximately 40 % towards net zero. However, analysis of current technology readiness levels of renewable energy utilization and prediction of future technology development suggest that switching to renewable sources might not be sufficient to reach net zero by 2050. In addition to renewable energy, carbon-free or carbon-neutral fuels are attractive options for replacing fossil-based fuels and accelerating emissions reduction. Hydrogen is the most prominent carbon-free fuel under consideration and hydrogen and its derivatives are expected to have a significant role in the overall CO₂ emissions reduction strategy. Hydrogen is a high energy density fuel and is currently used in industries such as chemicals production, iron and steel and refineries. NZE 2050 roadmap predicts the expansion of hydrogen usage to transportation, electricity production, heating by blending in gas grid and many other sectors. However, the current hydrogen infrastructure is not sufficient for large scale hydrogen utilization and a wide range of improvements in technology development, technology scale-up, economic feasibility analysis and policy development are required. In this regard, this thesis contributes towards low-cost and low-emissions strategies for hydrogen infrastructure development in three different stages of the hydrogen life cycle: production, storage and transportation. In the hydrogen production stage, a low-cost spray synthesis process is investigated for the production of mixed metal oxide materials which are potential catalysts that improve hydrogen production efficiency. Synthesis of mixed metal oxides is highly challenging due to separation of the constituent elements during the product material formation process. In this section, the evaporation of the spray droplet is visualized to improve understanding of the element separation during the product particle formation. It is shown that addition of just 2 wt % urea to the precursor spray solution causes violent bubbling and mixing of elements in the droplet at 150 °C. This strategy can be used to mitigate the element separation and form uniform product particles as desired. In the hydrogen storage stage, the development of highly sensitive hydrogen leak detection sensors to improve operational safety is discussed. Storage and transportation of hydrogen is dangerous due to low ignition energy and high flame speeds. Chemiresistive metal oxide sensors are suitable leak detection devices with a simple design, minimal energy requirement and capability of detecting sub-ppm level of hydrogen. The response of these sensors to low concentrations of hydrogen leaks is known to be dependent on the structure of the sensing metal oxide film. However, the effect of sensing film structural features such as film porosity, constituent particle size and packing density on the sensor response is not yet well understood. In this section, an attempt is made to elucidate this structure-response relationship in sensors by preparing varied sensing film structures following established methods and evaluating sensor response for ppm level hydrogen exposure. The results show that including only structural features that accelerate surface reactions in the sensing mechanism may not be sufficient to get a good sensor response and a more holistic approach considering all stages of the sensing mechanism is required to design high performing sensors. For the hydrogen transportation stage, Techno-economic analysis (TEA) and Life cycle analysis (LCA) methods are used to estimate the costs and emissions of using hydrogen carriers as means of long-distance hydrogen transportation. With increasing hydrogen demand in various sectors in accordance with NZE 2050, development of a global hydrogen supply chain is essential to ensure uniform usage. Pipeline transportation of gaseous hydrogen becomes too complicated and expensive for distances above 1800 km. A strategy to overcome this challenge is converting hydrogen to higher density liquid materials called hydrogen carriers which can be easily transported globally using tanker ships. This section discusses the estimation of eco- nomic feasibility and emissions reduction potential for long-distance hydrogen transportation from Australia to Japan via four hydrogen carriers methanol, methane, ammonia and liquid hydrogen using TEA and LCA methodologies. The analysis shows that when the end-use for the hydrogen carriers is fixed as 20% cofiring in natural gas power plants, ammonia is the cheapest hydrogen carrier with a delivered cost of $40/GJ-LHV and adds $50/MWh to the produced electricity cost. The thesis outlines implementable and economically and environmentally feasible strategies for improvements in efficiency of hydrogen production, safe operation through leak detection and long-distance transportation. The thesis contributes towards accelerating the development of hydrogen infrastructure to achieve the CO₂ emissions reduction goals set by NZE 2050.

The Effect of Breaching on Coastal Pond Water Quality

2026-04-22T03:09:46Z

The Effect of Breaching on Coastal Pond Water Quality Brooks, Faith Coastal pond breaching is used as a management strategy to improve water quality and reduce the frequency of harmful algal blooms (HABs) in many New England coastal ponds. At Sesachacha Pond, on Nantucket Island, MA, breaching currently is conducted twice annually with the goal of flushing eutrophic pond water and maintaining a brackish ecosystem through ocean input. Recent breaches have been too short to allow for significant water exchange, raising questions about the effectiveness of breaching as a water quality management method because the total nitrogen (TN) concentration remains four times in exceedance of the total maximum daily load (TMDL). The high level of eutrophication in the pond has led to anoxic and hypoxic conditions, resulting in an increase in HABs that poses a danger to the ecosystem and those interacting with it. Observations collected during the December 2024 breach using nutrient sampling and spectral data suggest that even brief connections may lower TN concentrations, but greater and sustained improvements to pond water quality likely depend on extending breach duration to enable greater water exchange. Field observations identified the marsh culvert stream and Cains Pond as nutrient sources and potential sites of algal growth. Modest decreases in TN and reflectance at 703 nm indicate that more effective breaches may limit nutrient availability and help mitigate HAB formation. With procedural adjustments and the potential addition of a third breach with greater timing flexibility, it may be possible to reduce nitrogen levels further and restore healthier pond conditions. Although recent breaches have not achieved lasting improvements, this appears to reflect logistical limitations rather than a failure of the breaching process itself. The findings provide a framework for improving breach execution and informing future coastal pond water quality management strategies.

Model Based Digital Engineering: Accelerating Digital Transformation through Integrated Data and Model Management Framework

2026-04-22T03:08:14Z

Model Based Digital Engineering: Accelerating Digital Transformation through Integrated Data and Model Management Framework Pradhan, Jayanta Kumar The increasing complexity of engineered systems and the demand for rapid innovation are straining traditional, siloed engineering practices. Despite the adoption of digital tools, many organizations suffer from fragmented environments, leading to inefficiencies, communication failures, and recurring challenges with project cost, scheduling, and design validation. To address these issues, this thesis introduces a comprehensive framework for Model-Based Digital Engineering (MBDE) designed to unify disparate systems into a cohesive digital ecosystem. Central to this is a vendor-neutral architecture that treats data and models as core enterprise assets. Recognizing that many digital transformations stall due to a lack of data and model governance, the proposed framework establishes the essential architectural, procedural, and governance mechanisms for a scalable MBDE implementation, enabling seamless traceability and consistency. By bridging the gap between strategy and execution, this work offers an actionable methodology for transitioning to an integrated, model-based enterprise. The results underscore that structured data interoperability, cross-disciplinary collaboration, and lifecycle model continuity are critical for achieving measurable gains in engineering efficiency, program agility, and system performance.

Inverse Discrete Design

2026-04-22T03:02:25Z

Inverse Discrete Design Abdel-Rahman, Amira Historically, engineering and manufacturing have been dominated by analog processes—manual, continuous, and heavily reliant on human intuition and trial-and-error. Digital technologies have revolutionized communication and computing, enabling unprecedented scalability, precision, and complexity, yet our methods for designing and fabricating physical structures remain largely analog, fragmented across disciplines, and resistant to integration, significantly limiting innovation across complex, multidisciplinary domains. We need a parallel digital revolution in the physical realm that implements principles of discretization, modularity, hierarchy, and error correction to create new possibilities for physical design and fabrication. By applying these principles to physical structures, we can enable precise placement of functional materials with embedded electrical and mechanical properties. These `Digital Material' structures bring digital programmability to the physical realm, mirroring the transformative impact of digital technologies in other fields. This transformation requires new design methodologies to faithfully model our complex environment and enable us to design a new world where the physical and digital become indistinguishable. My thesis introduces a fully declarative and inverse workflow for designing and building scalable Digital Material systems. These workflows allow users to model and design systems that span scales (micro, meso, macro) and disciplines (electrical, mechanical, aerospace, architectural engineering) without being experts in all—or any—of these fields. The introduced workflow uses domain knowledge as priors and universal design representations across all stages of design, simulation, optimization, fabrication, and control. Leveraging the discrete and hierarchical nature of the Digital Material system, I first introduce a multi-scale multi-physics simulation tool for mechanical metamaterial. I then present target-based multi-scale optimization workflows for the design of the geometry as well as the growth of cellular structures and soft robots. Next, I introduce advanced path planning algorithms—swarm, recursive, and hierarchical—for scalable robotic swarm assembly; as well shape-control optimization for the reconfiguration of these robots. The work extends to introduce a comprehensive design tool to design and build intelligence and physical computing, and culminates in an end-to-end inverse design workflow of electromechanical structures, that transforms text specifications into physical designs. The workflow is used to design a plethora of static and dynamic structures, ranging from bridges and shelters to aerospace structures, robots, and electronics.

A Deep Learning Framework for Acoustic Condition Monitoring in Machining Nickel Superalloy Turbine Blades

2026-04-22T03:10:29Z

A Deep Learning Framework for Acoustic Condition Monitoring in Machining Nickel Superalloy Turbine Blades Placzek, Luke Alexander The machining of gas turbine blade cooling holes presents persistent manufacturing challenges due to the difficulty of working with single-crystal nickel superalloys, ceramic coatings, and intricate geometries. In the machining of the cooling holes on GE Vernova's 7HA.03 stage 1 turbine blades, over 60 carbide tools are typically consumed per blade, and machining interruptions due to tool breakage occur on average every 30 minutes, leading to over 5 hours of additional process time per blade. While modern CNC machines provide machine state logs through systems like Vimana, due to the size of tools, the RoboDrills in use are only able to detect tool breakage after hole completion, making it impossible to determine when or where within a hole the tool fails. This work was done in conjunction with fellow M.Eng. student Alexander Brush [1], who worked on the statistical modeling of current tool breakage and modification of process parameters to reduce tool breakage. This thesis builds a data pipeline for collecting audio at a 96kHz sample rate, visualize it, and combine it with the machine data and breakage statistics to segment the audio into appropriate files which can be used to train Machine Learning (ML) models to detect tool failures. To test this framework, multiple classification models were trained on 32 hours of segmented audio data synchronized with Vimana machine logs. The data pipeline automatically segments continuous audio recordings into labeled training examples based on machine state, with augmentation methods to mitigate the severe class imbalance. Additionally, a Statistical Process Control (SPC) system was implemented to track tool breakage rates in real-time, using Nelson rules to detect significant changes in process performance. This enables engineers to identify when process modifications impact tool life and correlate changes with specific machines, rows of holes, or operational parameters. The combination of acoustic monitoring for precise breakage detection and SPC for trend analysis provides engineers with actionable insights for process improvement. While the current model accuracy requires improvement before production deployment, this work establishes the infrastructure and methodology for enhanced tool condition monitoring in high-value manufacturing.

Key factors controlling the radiation tolerance of REBCO tapes in a nuclear fusion environment

2026-04-22T03:06:37Z

Key factors controlling the radiation tolerance of REBCO tapes in a nuclear fusion environment Devitre, Alexis R. Superconducting magnets based on Rare-Earth Barium Copper Oxide (REBCO) enable energy break-even in compact fusion power plants (FPPs). Given the critical role of the magnet system, the radiation tolerance of REBCO informs the thickness of the neutron shield, which has direct consequences on the ultimate FPP size and cost. The performance of fusion magnets is often gauged by the maximum current that these can carry without resistance, known as the critical current, I_c. However, predicting the radiation response of REBCO remains challenging because defect formation, evolution, and their net effect on I_c depend on the initial microstructure and operating conditions. An empirical approach is needed to identify the factors controlling I_c degradation in a fusion environment and to efficiently reassess the radiation tolerance of new REBCO tape formulations. To address this, we designed and commissioned a cryogenic ion-irradiation platform capable of exposing REBCO tape to controlled, homogeneous light and heavy ion beams to emulate the consequences of fusion neutron bombardment. The system controls temperature (20-300 K) and assesses the superconducting properties of REBCO tapes with transport current measurements (100 nA-100 A, δV ≈ 0.1 μV) before, during and after irradiation — capturing for the first time all aspects of fusion magnet operating conditions, with the recent addition of a 0.33~T permanent magnet embedded in the target holder. The results reveal that I_c degrades 1.6x faster at cryogenic temperatures than at 300 K—the latter is typical of in-core neutron irradiations previously used for lifetime dose assessments. Real-time measurements during irradiation also show a reversible suppression of I_c due to ion beam heating, enabling a direct determination of the beam spot temperature. Careful experimental design and statistical analysis were used to isolate and exclude the effect of ballistic displacements relevant to neutron irradiation. Preliminary results further suggest an acceleration of I_c degradation in the presence of a background magnetic field. The main conclusion of this thesis is that radiation tolerance of REBCO tapes may have been overestimated in prior assessments. Our results provide the first dataset capturing all major operational factors and will inform future "design rules" for optimizing the radiation shielding of superconducting magnets in fusion power plants.

Photonic Control of Incoherent Emission In and Out of Equilibrium

2026-04-22T03:03:15Z

Photonic Control of Incoherent Emission In and Out of Equilibrium Pajovic, Simo Spontaneous emission is the quantum mechanical process by which matter excited to a higher energy level transitions to a lower one by emitting light in the form of a photon. It is partly characterized by its incoherence (i.e., emitted photons have random frequencies and directions) and can occur in systems that are in or out of thermodynamic equilibrium. Thermal emission is the archetypal example of equilibrium spontaneous emission, while an example of nonequilibrium spontaneous emission is scintillation—light emission when an X-ray passes through a material, notable for its wide use in medical imaging and particle physics. In equilibrium systems, the design of incoherent emitters is enabled by Kirchhoff’s law of radiation, traditionally equating the emissive properties of materials to their absorptive properties, which are much easier to compute. Recently, Kirchhoff’s law was generalized to nonequilibrium systems, but additional complications such as broken time reversal symmetry (via magnetic fields and/or time modulation) require further generalizations that may not take the form of an equality between emission and absorption. Using thermal emission and scintillation as canonical and technologically relevant examples of incoherent emission, this thesis explores the control of both equilibrium and nonequilibrium light emission by applying strategies from nanophotonics within the framework of Kirchhoff’s law and its generalized forms. In the first part, I show how magnetic fields and nanophotonic patterning can be used to tailor thermal emission from nano- to macroscale, for applications in energy and medical imaging. First, I use modeling to show that combining magnetocaloric materials (which change temperature in response to magnetic fields) with magneto-optic materials can enable switchable radiative heat transfer. Then, I experimentally demonstrate nonreciprocal (or directionally asymmetric) reflectivity in a magneto-optic material, which is tantamount to the breakdown of the traditional form of Kirchhoff’s law, i.e., emissivity no longer equals absorptivity. Finally, I revisit X-ray tube design through the lens of photonics and show that nanophotonic patterning can enhance radiative cooling in these systems, leading to superior performance and reduced risk of failure in X-ray imaging. In the second part, I show how nanophotonics provides an avenue to control scintillation, emphasizing approaches that can increase the physical size and dimensionality (i.e., from surface to volumetric nanophotonic patterning) of so-called “nanophotonic scintillators.” I demonstrate the imaging of biological samples using scintillators that are nanophotonically patterned over areas as large as 4×4 cm—competitive with commercially available flat-panel detectors—and measure sixfold scintillation emission enhancement over a relatively large area. Then, I experimentally probe volumetric nanophotonic scintillators that are patterned extremely deep into the material, even all the way through (∼0.5 mm, two orders of magnitude greater than the wavelength of emitted light). In particular, I report a new phenomenon in which the patterns can outcouple light emitted farther away in the bulk without being directly excited themselves, which is promising for applications such as remote particle detection. In summary: using nanophotonic strategies grounded in newly derived generalizations of Kirchhoff’s law, my work demonstrates how incoherent emission can be tailored both in and out of equilibrium. This thesis contributes to the development of more efficient, engineered emitters and absorbers for applications including energy conversion, sensing, and medical imaging.

Toward Quantitative Evaluation of Public R&D Investments: A Framework Concerning Japan’s Semiconductor Strategy

2026-04-22T03:09:14Z

Toward Quantitative Evaluation of Public R&D Investments: A Framework Concerning Japan’s Semiconductor Strategy Nakata, Shogo Semiconductors serve as a cornerstone of national industrial infrastructure and are critically important strategic technologies and materials from the perspective of economic security. In recent years, particularly with regard to next-generation semiconductors, governments around the world have provided unprecedented levels of support, intensifying international competition. However, public investment decisions in the technology sector may rely heavily on internal heuristics within government institutions, and resource allocation is not always optimal. As the physical limits of the miniaturization approach, the importance of back-end processes is increasing, and evaluation metrics are becoming more diverse. In the absence of systematic use of indicators such as Figures of Merits(FOMs) and Technology Readiness Levels (TRLs), evaluation criteria vary across projects, leading to inconsistencies in resource allocation priorities and potentially reducing the overall efficiency of public support. This study adopts a systems thinking approach to compare semiconductor policies across countries and focuses on technology roadmapping as a method for enabling quantitative evaluation using FOMs and TRLs. Based on this methodology, the study proposes an evaluation framework for government Research and Development (R&D) projects. Specifically, it analyzes the status of FOM implementation within Japan’s nextgeneration semiconductor policy and examines the feasibility of quantitative decisionmaking through a hypothetical case study focusing on high-bandwidth memory (HBM). The comparative analysis of Japan and the United States reveals that Japan’s top-down, concentrated investment strategy offers strengths in terms of speed and consistency, while the US model excels in long-term infrastructure development, broad stakeholder engagement, and flexible resource allocation. The study also finds that Japan lacks a comprehensive national strategy that clearly outlines investment priorities by technology area. As a result, resource allocation tends to be fragmented and dispersed. Furthermore, at the policy level, technology roadmaps based on FOMs and clear strategic guidance are insufficient. At the project level, many initiatives lack quantitative goals and evaluation metrics, and the use of FOMs remains limited and inconsistent. To address this issue, evaluation, and portfolio management methods that incorporate FOMs and TRLs may provide a basis for improving alignment with policy goals and enabling transparent and rational investment decisions. This study suggests that developing national-level technology roadmaps and establishing a consistent evaluation system based on FOMs represent promising strategies for enhancing the strategic coherence and operational efficiency of Japan’s semiconductor policy.

Return Paths: Tracing the Sound of Black, Asian, and Indigenous Technologies

2026-04-22T03:10:13Z

Return Paths: Tracing the Sound of Black, Asian, and Indigenous Technologies Briët, Kayla Luray In Return Paths: Tracing the Sound of Black, Asian, and Indigenous Technologies, I explore the intertwined histories of sound, craft, and computation across diasporic Black, Asian, and Indigenous communities. Moving across four technological eras— acoustic instruments, radio and spectrum, amplification and electrification, and digital machines—I reframe music technologies not as neutral tools to uphold a mythology of the cutting edge, but as carriers of land, labor, and memory. Through archival storytelling, personal narrative, and craft-based research, I amplify muted stories of invention, foregrounding how improvisation, relationality, and self-sovereignty function as survival technologies against colonial erasure. Chapter One situates improvisation and luthiery as acts of survivance, recalling instruments such as the African banjo and bomba barrel drum as vessels of ancestral memory and resistance. Chapter Two reframes the radio spectrum as kin, tracing Black and Indigenous broadcasting and mutual-aid radio as acts of self-determination, cultural continuity, and collective care. Chapter Three highlights Afro-Asian collaborations in Jamaican sound system culture, linking militarized knowledge and indigenous rhythms to sound and memory. Chapter Four weaves my conception of an Indigenous machine epistemology with global Black, Asian, and Indigenous contributions in computing, from the Haudenosaunee wampum belt and Quechua khipu to Diné women’s craftsmanship in semiconductor assembly. The chapter ends with how the integrated circuit reshaped the digital soundscape and with Don Lewis’ unsung invention of a serial digital protocol that predated MIDI by a decade. Together, these threads illuminate the politics of the archive: who is remembered as an inventor, and under what conditions? Here, my metaphor of the return path—an electrical current’s continuity to its source—serves as a method for ancestral recall. Rather than situating Black, Asian, and Indigenous histories as parallel, I argue that they are interwoven, forming a circuitous loop of innovation, collaboration, and repair. By tracing these return paths, I call for new epistemologies of technology that honor multiplicity beyond geographic and racial fixity, celebrate survival and ancestry, and create futures rooted in kinship, sovereignty, and self-determined invention. With these seeds, we may create for ourselves a beautiful, meaningful, dignified, and storied life. Above all, I write to those with broken and scattered archives, to those who exist in the in-between, mapping belonging across borders. We cannot study ourselves into existence. Instead, we can take the shattered pieces of the past, examine them, and arrange them to create a mosaic — something new and equally true. This path is not for the faint hearted, but this forge reveals the sanctuary within ourselves. This sanctuary is the path to becoming. Becoming is the path to freedom.

Electronic Phases in Crystalline Lanthanide Systems

2026-04-22T03:03:27Z

Electronic Phases in Crystalline Lanthanide Systems Debbas, Maximilien F. Crystalline lanthanide systems are known to realize a multitude of disparate electronic phases arising from lattices of localized f-electrons in the crystal. These phases include magnetic phenomena such as ferromagnetism and antiferromagnetism as well as nonmagnetic Fermi liquid phases such as heavy fermion systems. Lanthanide systems can moreover realize exotic phases such as heavy fermion superconductivity, multipolar order, and hidden order. The latter two may be driven not solely by the magnetic dipole moment of the localized f-electrons, but additionally by higher-order multipolar moments such as the electric quadrupole which are often significant due to the highly anisotropic nature of the f-electron orbital wavefunction. The orbital wavefunction is also highly sensitive to the local crystal field surrounding it via interaction with the crystal electric field (CEF). This crystal field can lower the symmetry of the environment wherein the f-electron lives to strongly modify the behavior of the free-space electronic state by lifting energetic degeneracies and introducing strong anisotropy. This thesis will investigate the physics of three different lanthanide systems: R₃Ni₃₀B₁₀ (R = La, Ce), Ce₂SnS₅, and Gd₂B₅. These materials will provide opportunities to discuss multipolar/anomalous order in Ce₃Ni₃₀B₁₀, anisotropic magnetism in Ce2SnS5, and the mapping of a rich, highly anisotropic phase diagram in Gd₂B₅. Experimental characterization techniques employed in the study of these materials include electrical transport, SQUID magnetometry, torque magnetometry, and heat capacity. This thesis will also leverage group theory and its power to shed light on otherwise abstruse electronic phenomena. Due to the wide range of electronic phenomena accessible through the study of lanthanide systems, these materials provide an exciting platform for the realization of material engineering of potentially tunable quantum systems. This thesis will motivate these systems by emphasizing the richness of the myriad of electronic phenomena attainable through the manipulation of the local environment of the lanthanide f-electrons.

Connecting UV-Mediated Property Changes to Surface Cracking in Low-Density Polyethylene: Toward Understanding the Generation of Microplastic Pollution

2026-04-22T03:02:52Z

Connecting UV-Mediated Property Changes to Surface Cracking in Low-Density Polyethylene: Toward Understanding the Generation of Microplastic Pollution Lehn-Bradshaw, Andrea M. The vast production and disposal of synthetic polymers have led to widespread microplastic (MP) pollution. MPs are nominally defined as polymer-based particles with at least one size dimension in the 1 μm to 1 mm range. MPs pose risks to ecosystems, human health, and they may impact climate processes. Understanding MP formation is important to mitigating these impacts. A primary source of MP generation is the fragmentation of larger plastics through environmental weathering, with UV radiation likely playing a significant role due to its effects on polymers. The overarching goal of this research is to bridge the gap between UV-mediated molecular-scale changes in polymers and surface cracking by applying principles from fracture mechanics. MPs extracted from the environment show signs of UV-induced changes, and observational studies in the lab confirm that UV exposure accelerates fragmentation, producing smaller, more abundant particles. Studies of polymer degradation in the lab and in the field also show that UV, combined with factors such as temperature and humidity, lead to property gradients and sometimes surface cracking. However, there is disagreement about the depth and severity of these gradients, and it is difficult to isolate the effect of UV on surface cracking. Residual stress arising from gradient formation may lead to cracking, but residual stress generated by UV exposure has not been studied. Thus, while UV-induced molecular changes are well-known, the mechanisms by which UV exposure causes surface cracking and fragmentation remains unclear, and predictive models are lacking. We address this gap by: 1) quantifying property gradients, residual stress, and surface cracking in a model polymer, additive-free low-density polyethylene (LDPE), exposed to UV radiation, and 2) applying a film-on-substrate fracture mechanics model to relate surface cracking to mechanical property gradients, residual stress (if present), geometry, and external loading in UV-exposed LDPE. In the first part of the thesis, we quantify UV-mediated property gradients and residual stresses in LDPE subjected to varying durations of UV exposure in a custom-built benchtop chamber. A depth-profiling method, based on a glancing angle cut, enabling high-resolution property analysis as a function of depth, is developed. Chemical, morphological, and mechanical property gradients are measured using x-ray photoelectron spectroscopy (XPS), Raman microscopy, and nanoindentation. Our results show that although a gradient in mechanical properties emerges, it is not sufficient to induce residual stress or surface cracking in the absence of external loading, under the examined conditions. Crystallinity increases with UV exposure, but no depth-varying trend emerges. At the chemical level, O2 content varies in a way that suggests UV in combination with other factors contributes to oxidation, since similar changes in oxidation emerge for irradiated samples and for control samples placed in the chamber that are shielded from UV. In the second part of the thesis, we evaluate the relevance of a film-on-substrate model to predict surface crack density in UV-irradiated LDPE. Film-on-substrate models offer valuable insights into how material heterogeneities, such as residual stress and elastic property gradients, influence fracture behavior. We select a finite fracture mechanics model that predicts surface crack density as a function of mechanical property gradients, geometry, and applied tensile or bending strain. UV-irradiated LDPE samples with known property gradients and geometry are subjected to varying levels of tensile strain using a universal tensile tester and then observed using scanning electron microscopy (SEM). Surface crack density is determined by visual inspection and compared to model predictions. A pattern of parallel surface cracks emerges in specimens exposed to UV, but not in controls. Crack density increases with increasing strain, with the direction of crack propagation orthogonal to the direction of tensile loading. Crack density and orientation are consistent with model predictions. To our knowledge, this is the first experimental study to explicitly connect UV-mediated molecular-scale changes to cracking behavior in a predictive manner by applying fracture mechanics principles. The mechanical property gradient formation observed is consistent with literature, showing surface softening followed by stiffening as a function of depth. We found that the formation of mechanical and chemical property gradients did not coincide with residual stress or surface cracking under the conditions of this study, suggesting that additional factors, such as external bending and tensile loads, in combination with UV exposure, are required for cracking to occur. Surface cracking patterns are consistent with patterns observed for LDPE exposed to UV and other factors. This study demonstrates that film-on-substrate fracture models are promising to improve our understanding of cracking behavior in UV-irradiated LDPE. In the future, this framework could be expanded to investigate cracking under a broader range of weathering conditions (e.g., temperature, humidity, or cyclic exposure), as well as in different polymers exhibiting similar responses to UV, such as high-density polyethylene (HDPE) or polypropylene (PP). Additionally, the influence of polymer additives on gradient formation and subsequent cracking behavior could be explored. This study used an additive-free polymer, and many commercial polymers, which end up as MP pollution, use various additives and stabilizers. This thesis serves as a stepping stone for clarifying the relationship between UV exposure and the generation of MP pollution via UV-mediated fragmentation.

Development of a Physics-Constrained Machine Learning Model for Generalized Quasi-Linear Diffusion Coefficient Prediction in Lower Hybrid Current Drive Scenarios

2026-04-22T03:08:32Z

Development of a Physics-Constrained Machine Learning Model for Generalized Quasi-Linear Diffusion Coefficient Prediction in Lower Hybrid Current Drive Scenarios Pyeon, Gyeonghun In this thesis, machine learning models for predicting the quasi-linear diffusion coefficient (D_QL) are explored, compared, and evaluated. A novel machine learning framework with physical constraints specifically designed to predict D_QL is proposed. The proposed D_QL prediction model successfully reproduces the ground truth D_QL obtained from GENRAYCQL3D simulations. Furthermore, when the predicted DQL replaces the conventionally computed D_QL in GENRAY-CQL3D, the resulting radial current drive profiles exhibit consistent behavior with those from the original simulation, while achieving a substantial reduction in computational time. These results demonstrate that the developed surrogate model not only accurately captures key wave–particle interaction features but also significantly accelerates simulation efficiency while maintaining accuracy. Accurate prediction of the quasi-linear diffusion coefficient is regarded as crucial for describing wave–particle interactions and non-inductive current drive in tokamak plasmas. However, conventional methods for evaluating D_QL rely on computationally intensive wave propagation and Fokker–Planck simulations. To address this challenge, a machine learning-based surrogate model capable of predicting D_QL both rapidly and accurately is developed. Initially, to predict D_QL, dimensionality reduction methods combined with neural networks are explored. Although these approaches enable improved computational efficiency, they struggle to fully preserve fine-scale details essential for accurate current profile prediction. To overcome these limitations, a hybrid architecture combining a U-Net and an autoregressive neural network (ARNN) is introduced. This hybrid architecture effectively captures the physics inherent in D_QL, while incorporating physical constraints via the Potential Power Deposition (PPD) method. Overall, the final U-Net–ARNN framework consistently outperforms other approaches, delivering precise wave–particle interaction modeling with greatly enhanced computational efficiency.

Analysis of Runaway-Electron-Driven Whistler Waves on MST

2026-04-22T03:08:05Z

Analysis of Runaway-Electron-Driven Whistler Waves on MST Xiao, Yiru This thesis presents a detailed linear and quasilinear analysis of runaway-electron-driven whistler waves using experimental data from shot 1220511040 on the Madison Symmetric Torus (MST). A key novel aspect of this work is the investigation of the previously overlooked normal Doppler resonance (n=+1), which offers a new perspective on wave-particle interactions. The analysis concludes that while both anomalous and normal Doppler resonances can be excited by runaway electrons, they correspond to distinct wave propagation characteristics: the anomalous Doppler branch implies forward-propagating waves, whereas the normal Doppler branch implies backward-propagating waves. Theoretically, the normal Doppler branch is subject to damping and requires an additional source of free energy for net growth. This work proposes that the spatial gradient of the runaway electron density can provide this necessary free energy, enabling the instability and potentially explaining key experimental observations.

Complete and continuous methane pyrolysis to produce carbon construction materials and hydrogen

2026-04-22T03:06:14Z

Complete and continuous methane pyrolysis to produce carbon construction materials and hydrogen Bichnevicius, Michael Apart from being considered for prospective energy applications, hydrogen (H₂) today plays a pivotal role in growing food for the global population, serving as a key ingredient for ammonia-based fertilizers. However, unabated steam methane reforming (SMR)—the predominant method of H₂ production—produces CO₂, accounting for 2–3% of global CO₂ emissions. While clean alternative approaches such as water electrolysis have struggled to compete with the cost of SMR, various embodiments of methane pyrolysis (MP) hold promise. The key problems with MP, however, have been (1) realizing complete conversion or high yield; (2) navigating the engineering challenge of removing the solid carbon byproduct, enabling continuous operation without clogging; and (3) creating a valuable carbon product, providing the complementary value allowing the H₂ price to compete with SMR. Here, we report for the first time a prototype system that addresses each of these challenges, paving the way to make a useful carbon product and H₂.

Designing for Human Systems: Integrating Organizational Psychology into Systems-Theoretic Safety Analysis for Extreme Teams

2026-04-22T03:07:30Z

Designing for Human Systems: Integrating Organizational Psychology into Systems-Theoretic Safety Analysis for Extreme Teams Carey, Tyler K. As the complexity of high-risk domains such as space exploration and energy production grows, safety analysis must account for cognitive, organizational, and team-based factors that shape human performance. System-Theoretic Process Analysis is a leading method for identifying unsafe control actions and loss scenarios in sociotechnical systems, yet its representation of complex human teams in extreme environments is limited. This thesis investigates whether STPA with the Engineering for Humans extension can capture important decision-making dynamics of a land-based drilling rig crew. The study draws on seven semi-structured interviews, 127 excerpts coded into 215 tags, and structured surveys over a 14-day work rotation with 32 of 42 responses. Three team-level themes emerged: performance demands, shared mental models, and hierarchical challenges. Applying STPA to bottom hole assembly handling produced four representative unsafe control actions and two detailed loss scenarios that link operator cognition and context to hazardous states. To address identified gaps, the thesis proposes an enhanced control structure that explicitly represents distributed cognition and cross-level information flows. This visualization layer revealed additional loss mechanisms related to delayed feedback, cognitive load, and cross-boundary misalignment, and supported three actionable mitigations in the case context. The findings show that STPA offers value beyond traditional methods, while indicating opportunities to adapt it to meaningfully represent distributed cognition and social dynamics in extreme teams, without altering core STPA steps. This work also contributes actionable insights for the case context examined here, as well as other high-risk extreme domains facing similar challenges. Disclosure: The author takes full responsibility for the content provided in this thesis. While the ideas and writing are the author’s own, an AI-based tool was utilized to develop and refine the material.

Mechanisms of Solar-Thermal and Super Solar-Thermal Evaporation of Water

2026-04-22T03:01:24Z

Mechanisms of Solar-Thermal and Super Solar-Thermal Evaporation of Water Zhang, James H. The phase change from liquid water to vapor is fundamentally important for many technologies, including electronic cooling, refrigeration, critical mineral harvesting, and desalination. Solar desalination uses solar energy to evaporate water from brine resources to produce clean water through a distillation process. This technology is particularly attractive for decentralized communities with abundant sunlight due to its low capital costs compared to other desalination technologies. Advances in interfacial solar evaporators using porous materials have enhanced the efficiencies of solar stills by concentrating the solar energy near the evaporating interface. A critical challenge in the large-scale application of solar desalination technologies is the high latent heat of evaporation of water. The maximum amount of water that can be produced is about 1.45 to 1.49 kg m-2 h-1 using the latent heat, sensible heat, and standard 1 sun intensity incident on the evaporating interface. This value is commonly referred to as the solar-thermal limit of evaporation. The latent heat of evaporation is about 3 orders of magnitude higher than the minimum energy needed to separate water from seawater using a membrane process. These low water production rates make it challenging to compete with other desalination technologies on a large-scale. Surprisingly, numerous works over the last decade have shown that highly microporous materials can exceed the solar-thermal limit of evaporation by 2 to 3 times, which we call super solar-thermal evaporation. These materials seem to violate energy conservation, and there needs to be a physical mechanism to explain these observations. Despite the plethora of experimental works, the field currently lacks critical analysis and understanding of the fundamental mechanisms for why super solar-thermal evaporation occurs. The lack of understanding has stalled the translation of this technology into readily deployable devices that can enhance the water production rates of solar stills immensely. In this thesis, we conducted a critical analysis using coupled transport phenomena to elucidate the mechanisms behind super solar-thermal evaporation. We first reexamined and disproved the prevalent reduced latent heat hypothesis that is commonly used to explain super solar-thermal evaporation rates through a combination of modeling, simulations, and experimental methods. This work also illustrated that water cluster evaporation is likely the only physical mechanism that can satisfy both the vapor transport and energy transport arguments in super solar-thermal evaporation rates. Next, we elucidate the underlying mechanisms for super solar-thermal evaporation rates from 3D interfacial solar evaporators. We show that the enhanced evaporation rates from these materials are driven by continuous dry air flowing near its sidewalls, leading to greater opportunities in outdoor open environments such as brine pond treatment than in closed environments such as solar still devices. Afterwards using our knowledge of coupled heat and mass transport, we designed and built a high-performing reverse solar still prototype for outdoor usage that is an order of magnitude larger in area than almost all other prototypes in the field with a similar configuration. Our understanding of the underlying coupled transport of these devices has given new insights on the optimization pathways of solar stills. Finally, we did further theoretical work on the photomolecular effect. The photomolecular effect is a new theory proposed by our group on how surface interactions between light and the air-water interface can excite water clusters out, leading to super solar-thermal 2D evaporation. We used simulations to understand how cluster dissociation kinetics can couple into the boundary layers above the evaporating surface. These works represent major steps in understanding both solar-thermal and super solar-thermal evaporation mechanisms.

Study of turbulent electron temperature fluctuations and their cross-phase angles with electron density fluctuations at the ASDEX Upgrade tokamak

2026-04-22T03:03:54Z

Study of turbulent electron temperature fluctuations and their cross-phase angles with electron density fluctuations at the ASDEX Upgrade tokamak Yoo, Christian Turbulence is generally the dominant mechanism for energy transport in tokamak fusion reactors and has thus far prevented tokamaks from producing net energy. The measurement of plasma turbulence is important for improving our understanding of turbulent transport, for the validation of physics-based transport models, and ultimately for guiding the design of future fusion reactors. This Thesis presents an investigation of turbulent electron temperature fluctuations, their cross-phase angles with electron density fluctuations, and the impact of turbulent transport on plasma confinement at the ASDEX Upgrade (AUG) tokamak. This work spans three different confinement regimes - the low (L), improved (I), and high (H) confinement modes - as well as the physics of both core and edge turbulence. As part of this Thesis, a new Correlation Reflectometer Radiometer (CRR) diagnostic was designed and installed on AUG. Novel measurements in the edge of an H-mode plasma across a transition from small Edge Localized Modes (ELMs) to ELM-free operation show that the cross-phase angle between turbulent electron density and temperature fluctuations changes from approximately 90 degrees to 120 degrees, indicating changes in the physics driving transport. Of note is that this relatively large change in the cross-phase angle occurred despite only small changes in the edge profiles and gradients, indicating that the CRR diagnostic and the measured turbulence are particularly sensitive to small variations in plasma conditions that yield changes in ELM stability. Experiments and turbulence measurements using the Correlation Electron Cyclotron Emission (CECE) diagnostic were also conducted across the Linear Ohmic Confinement to Saturated Ohmic Confinement (LOC-SOC) transition in an ohmically-heated L-mode plasma at AUG. A multi-regime confinement database was then built to include I-mode and H-mode plasmas. A comparative analysis of these plasmas was performed. The results demonstrate for the first time that these three distinct confinement regimes, distinguished primarily by their different edge characteristics, nevertheless exhibit similar trends in energy confinement and density peaking as the core plasma effective collisionality increases in conditions with strong electron heating. One possible explanation for this result is that similar changes in core turbulence drive are occurring in all three confinement regimes. This study also found that two additional effects, including the strength of the neutral beam injection (NBI) particle source relative to transport effects as well as fast ion activity, are correlated with the observed trends. Therefore, multiple mechanisms could be playing a role in the underlying physics. Novel methods were also developed to improve the spatial and temporal resolution of turbulence diagnostics. In addition, real-time applications of turbulence diagnostics relevant to tokamak operation were explored. Overall, the results of this thesis research contribute to our understanding of turbulence and transport across multiple confinement regimes and detail new methods for measuring turbulent fluctuation amplitudes and cross-phase angles.

Broadband Acoustic Characterization of the Deep Scattering Layer

2026-04-22T03:05:46Z

Broadband Acoustic Characterization of the Deep Scattering Layer Zhuang, Zhaozhong Sound-scattering layers are found throughout the global ocean at depths between 200 and 1000 meters, referred to as the deep scattering layers (DSLs). DSLs are biological in nature, primarily formed by organisms such as zooplankton and mesopelagic fish, many of which undertake diel vertical migration (DVM). Despite their recognized ecological and biogeochemical significance, quantitative understanding of DSLs remains limited due to their vast spatial extent, deep habitat, complex biological composition, and dynamic coupling with ocean physics. This thesis leverages long-term broadband acoustic observations and signal processing methods to characterize the DSL from the individual to the population level, with the goal of improving biomass estimates and elucidating DSL dynamics in relation to oceanographic processes. Over two years, a deep-submerged mooring equipped with a broadband split-beam echosounder (36–44 kHz) was deployed in the slope sea off the New England continental shelf. The system continuously monitored individual organisms in the DSL between ~545 and 575 m depth. Tracking ~820,000 individual organisms, this study reveals a remarkably consistent target strength (TS) distribution across years, dominated by two distinct groups, with mean TS values of –53 dB and –49 dB, associated with migratory and non-migratory species, respectively. Variations in TS distribution at depth were linked to DVM, resident depth, and water masses, highlighting the importance of in situ TS observations for biomass estimation. A focused case study characterized the DSL within a warm-core anticyclonic eddy occupied by tagged sharks. The DSL inside the eddy exhibited high organism abundance and a unique vertical composition, with an upper layer dominated by fluid-like organisms. Energetic, near-inertial, oscillatory population movements with pronounced vertical shear were observed, likely driven by the "inertial drainpipe" effect of anticyclonic eddies that channel wind-generated near-inertial waves to depth. The superposition of wave and eddy velocity fields may result in a bowl-shaped DSL with persistent finescale vertical structures, i.e., thin layers. The distinct biological and physical environment within the eddy likely contributes to its role as a hotspot for marine top predators. To enhance and advance the capability of broadband split-beam echosounders in complex environments like the DSL, the thesis further revises and introduces methods for angle estimation of both single and overlapping echoes. A revised split-aperture correlator improves the angle estimation of single echoes from targets with highly frequency-dependent backscattering responses, while a broadband maximum likelihood estimator enables angle and spectral estimation of overlapping echoes from multiple targets, validated through laboratory experiments.

Monitoring Preferential Flow of Water in Sand Using Thermoacoustics Wave Imaging

2026-04-22T03:12:05Z

Monitoring Preferential Flow of Water in Sand Using Thermoacoustics Wave Imaging Liu, Chang; Mao, Xu; Wang, Chang; Liyanage, Rebecca; Heredia Juesas, Juan; Juanes, Ruben; Martinez‐Lorenzo, Jose Angle Accurate predictions of fluid flow, mass transport, and reaction rates critically impact the efficiency and reliability of subsurface exploration and sustainable use of subsurface resources. Quantitative dynamical sensing and imaging can play a pivotal role in the ability to make such predictions. Geophysical thermoacoustic technology has the potential to provide the aforementioned capabilities, since it builds upon the principle that electromagnetic and mechanical wave fields can be coupled through a thermodynamic process. In this letter, we present laboratory experiments featuring the efficacy of thermoacoustic imaging in the monitoring of preferential flow of water in porous media. Our laboratory experimental equipment can be readily packaged in a form factor that fits in a borehole, and the use of multiple acoustic transducers—which can be combined with volumetric coding techniques—has the potential to provide quasi‐real‐time imaging (0.5 Hz video rate) of regions in close proximity (a few meters) of an open field well.

An Analytic Model for Tropical Cyclone Outer Winds

2026-04-22T03:11:24Z

An Analytic Model for Tropical Cyclone Outer Winds Cronin, Timothy W The variation of Tropical cyclone azimuthal wind speed (V) with distance from storm center (r) is a fundamental aspect of storm structure with important implications for risk and damages. The theoretical model of Emanuel (2004, https://doi.org/10.1017/CBO9780511735035.010), which applies outside the rainy core of the storm, matches radiatively-driven subsidence and Ekman suction rates just above the boundary layer to obtain a nonlinear differential equation for dV/dr. This model is appealing because of its strong physical foundation, but lacks a known analytic solution for V(r). In this paper, I obtain an analytic solution to V(r) for the Emanuel (2004, https://doi.org/10.1017/CBO9780511735035.010) outer wind model. Following previous work, I then use this solution to explore properties of merged wind models that combine the outer model with an inner model, which applies to the rainy core of a storm.

Comment on “Phosphine in the Venusian Atmosphere: A Strict Upper Limit From SOFIA GREAT Observations” by Cordiner et al.

2026-04-22T03:11:49Z

Comment on “Phosphine in the Venusian Atmosphere: A Strict Upper Limit From SOFIA GREAT Observations” by Cordiner et al. Greaves, Jane S; Petkowski, Janusz J; Richards, Anita MS; Sousa‐Silva, Clara; Seager, Sara; Clements, David L Searches for phosphine in Venus' atmosphere have sparked a debate. Cordiner et al. (2022, https://doi.org/10.1029/2022gl101055) analyze spectra from the Stratospheric Observatory For Infrared Astronomy (SOFIA) and infer <0.8 ppb of PH3. We noticed that some spectral artifacts arose from non-essential calibration-load signals. By-passing these signals allows simpler post-processing and a 5.7σ candidate detection, suggesting ∼3 ppb of PH3 above the clouds. Compiling six phosphine results hints at an inverted abundance trend: decreasing above the clouds but rising again in the mesosphere from some unexplained source. However, no such extra source is needed if phosphine is undergoing destruction by sunlight (photolysis), to a similar degree as on Earth. Low phosphine values/limits are found where the viewed part of the super-rotating Venusian atmosphere had passed through sunlight, while high values are from views moving into sunlight. We suggest Venusian phosphine is indeed present, and so merits further work on models of its origins. Article relates to: Cordiner, M. A., Villanueva, G. L., Wiesemeyer, H., Milam, S. N., dePater, I., Moullet, A., et al. (2022). Phosphine in the Venusian atmosphere: A strict upper limit from SOFIA GREAT observations. Geophysical Research Letters, 49, e2022GL101055. https://doi.org/10.1029/2022GL101055

Re‐Assessing the Need for Apatite‐ and Dolomite‐Specific Calibrations of the Carbonate Clumped Isotope Thermometer

2026-04-22T03:13:16Z

Re‐Assessing the Need for Apatite‐ and Dolomite‐Specific Calibrations of the Carbonate Clumped Isotope Thermometer Anderson, NT; Bonifacie, M; Jost, AB; Siebert, J; Bontognali, T; Horita, J; Müller, IA; Bernasconi, SM; Bergmann, KD A few key methodological uncertainties remain for the carbonate clumped isotope community. One is how to compare data among published data sets that are not anchored to the InterCarb Carbon Dioxide Equilibrium Scale (I‐CDES). A second is how temperature calibrations of calcite compare to those of other carbonate minerals in the I‐CDES—particularly dolomite and apatite—which can elucidate several Earth system dynamics. Previous calibrations of the clumped isotope thermometer for dolomite are discrepant from one another and variably (dis)agree with calibrations developed for calcite; apatite calibrations have not yet been compared between laboratories using carbonate‐based standardization. Here we report I‐CDES standardized values for a suite of 11 carbonates that are commonly measured by the clumped isotope community to aid future comparisons of non‐I‐CDES data sets. In addition, 17 dolomite samples (25–1,200°C) and five apatite samples (1–38°C) of known precipitation temperature were measured using carbonate‐based standardization. Excellent agreement between calcites and dolomites heated to similar temperatures (1,100–1,200°C) suggests no mineral‐specific differences in absolute acid fractionation factor. We show that calcite and dolomite regressions largely agree but are sensitive to sample characteristics, regression method, and how equations are statistically compared. We suggest that there is no need for a dolomite‐specific clumped isotope calibration, although our results suggest that further work is necessary to determine the influence of sample characteristics on this relationship. The apatite calibration equation defined in this study is statistically indistinguishable from calcite‐based calibrations; we corroborate previous findings that an apatite‐specific calibration is unnecessary.

Melt Network Reorientation and Crystallographic Preferred Orientation Development in Sheared Partially Molten Rocks

2026-04-22T03:12:40Z

Melt Network Reorientation and Crystallographic Preferred Orientation Development in Sheared Partially Molten Rocks Seltzer, Cassandra; Peč, Matěj; Zimmerman, Mark E; Kohlstedt, David L We investigated the co-evolution of melt, shape, and crystallographic preferred orientations (MPOs, SPOs, and CPOs) in experimentally deformed partially molten rocks, from which we calculated the influence of MPO and CPO on seismic anisotropy. Olivine-basalt aggregates containing 2 to 4 wt% melt were deformed in general shear at a temperature of 1,250°C under a confining pressure of 300 MPa at shear stresses of τ ≤ 175 MPa to shear strains of γ ≤ 2.3. Grain-scale melt pockets developed a MPO parallel to the loading direction by γ < 0.4. At higher strains, the grain-scale MPO remained parallel to the loading direction, while incipient sample-scale melt bands formed at ∼20° to the grain-scale MPO. An initial SPO and CPO were induced during sample preparation, with [100] and [001] axes girdled perpendicular to the long axis of the starting material. At the highest explored strain, a strong SPO was established subperpendicular to the loading direction, and the [100] axes of the CPO clustered nearly parallel to the shear plane. Our results demonstrate that grain-scale and sample-scale alignments of melt pockets are distinct. Furthermore, the melt and the solid microstructures evolve on different timescales: in planetary bodies, changes in the stress field will drive a relatively fast reorientation of the melt network and a relatively slow realignment of the crystallographic axes. Rapid changes to seismic anisotropy in a deforming partially molten aggregate are thus caused by MPO rather than CPO.

Data-Driven Design of Recycling-Friendly Aluminium Alloys

2026-04-22T03:06:32Z

Data-Driven Design of Recycling-Friendly Aluminium Alloys Montanelli, Luca Francesco Improving material efficiency through the increased use of secondary material is an unambiguous goal for the aluminium industry. The main barrier in achieving this is the compositional mismatch between scrap and alloy. Among the many approaches to address this, one promising direction is to design alloys that compositionally match scrap streams, a practice called recycling-friendly alloy design (RFAD). However, previous efforts have largely focused on creating good scrap sinks without considering the system-wide nature of scrap consumption. In a supply chain governed by interdependent material flows, alloys must function not only as sinks but also as future sources of scrap. In order to increase overall scrap consumption, we propose an RFAD framework combining dynamic material flow analysis, economic feedback, and plant level batch-design to include a systemic assessment of end-of-life material generation and consumption. Each of these components is integrated in a Bayesian optimisation framework with comprehensive exploration of the alloy design space to balance the pervasive source-sink tension of elemental limits. Finally, we include a natural language processing module to assist in identifying thermodynamic and property constraints used to filter the design space. Using our framework, we find that the optimal alloys for the North American transport sector achieve a 7% increase in scrap usage, corresponding to 200-400 kt/year of additional scrap consumption. We also demonstrate that alloys designed for short lifetime sectors (e.g., transport) tend to be good sources whereas those designed for longer lifetime sectors (e.g., building & construction) are good sinks due to their limited capacity to influence the composition and therefore the consumption of the post-consumer scrap stream. The capabilities of our model also allow us to simulate RFAD under different future scenarios. In the automotive sector, adoption of electric vehicles is driving a decrease in cast alloy production, a traditional sink. Thankfully, we show that the lower the production of cast alloys by 2040, the higher the scrap usage attainable with RFAD along with better properties. In the can sector, unialloys have been theorised to increase recycling by lowering compositional variability. Our simulations show that RFAD for a can unialloy leads to an additional 3.2% scrap usage increase versus RFAD for a non unialloy. Finally, exploring the Pareto frontier between yield strength and scrap usage, we find that 2xxx alloys offer the best trade-off, suggesting the potential for high-strength alloys sourced from automotive scrap.

Theory, Modeling, and Design Principles of Redox-mediated Electrochemical Systems

2026-04-22T03:03:35Z

Theory, Modeling, and Design Principles of Redox-mediated Electrochemical Systems Matteucci Jr., Nicholas J. Electrochemical systems offer a pathway toward directly harnessing sustainable electricity to manage intermittency in renewable power production, decarbonize transportation, and unlock new routes for chemical and material production. However, to compete with their thermochemical counterparts, emerging electrochemical processes of interest must achieve lower system costs, higher efficiencies, and longer operating lifetimes. Redox-mediated electrochemical systems are an emerging technology concept that could aid in addressing these challenges. These devices utilize a chemical looping approach, where a soluble mediator species is circulated between an electrochemical reactor – where it is activated – and a chemical reactor – where the activated mediator drives an “off-electrode” chemical redox reaction. This decoupling of the desired chemical transformation of interest from the electrochemical reactor simultaneously alters the underlying design landscape, offering new routes toward cost-effective reactor architectures and conversions. Mediated processes have been demonstrated to improve the energy density of redox flow batteries, catalyze impractical or inefficient electrochemical transformations, increase selectivity and efficiency of separations and recycling, and facilitate spatial and temporal flexibility in chemical manufacturing. However, the complex interplay of the reactors and multiple active species obfuscate the underlying behavior of these systems, hindering efforts to understand, improve, and scale. In this dissertation, I develop a continuum model framework capable of capturing and rationalizing the thermodynamics, reaction kinetics, and transport phenomena that govern the performance dynamics of redox-mediated systems for energy storage and conversion. Leveraging (electro)chemical engineering principles and drawing upon mixed potential theory (originally developed to describe metallic corrosion), the framework qualitatively tracks the results of multiple experimental redox-mediated systems. Such findings suggest the framework is capable of providing insight into new avenues to probe questions related to system design and operation. I begin by relating physical and operating parameters to system-level performance trends in redoxmediated flow batteries, including revealing performance regimes and a dimensionless “collapsed relationship” for solid utilization and tradeoffs in system energy and power. Guided by the assumptions and consequences of the underlying theory, I then pursue a suite of modeling and empirical analyses to uncover the implications of different system designs and operational protocol. Specifically, I investigate how “delocalized” charge transfer in conductive composites, cycling protocol, active material degradation, non-ideal thermodynamics, and intraparticle transport are anticipated to alter the underlying dynamics and system performance of redoxmediated flow batteries. Toward the end of this work, I shift to analyzing emerging redox-mediated systems relevant to industrial chemical transformation, exploring case studies in continuously mediated solid transformations and mediated gas evolving reactions. Finally, I depart from the theme of this dissertation to briefly explore how dynamic thixotropy structure parameter expressions can dynamically regularize viscoplasticity in rheological models, which could aid in advancing non-Newtonian fluid mechanics simulations of emerging devices employing flowable suspension electrodes. Ultimately, the goal of this dissertation is to aid in the development of theory, analysis tools, and design principles that guide the study, development, and enhancement of emerging redox-mediated systems for sustainable energy conversion and storage.

Understanding Model Representation of Aerosol Composition: From Secondary Inorganic Aerosols to Phosphorus

2026-04-22T03:04:46Z

Understanding Model Representation of Aerosol Composition: From Secondary Inorganic Aerosols to Phosphorus Norman, Olivia Aerosols in our atmosphere impact climate, human health, air quality, and biogeochemical cycling. Models can be a valuable tool for predicting aerosol abundance depending on how well they describe the processes controlling aerosols (e.g., emissions, thermodynamic partitioning, chemical production and loss, deposition). This thesis aims to improve our understanding of two aerosol components, secondary inorganic aerosols and phosphorus, through model development, evaluation, and exploration. In the second chapter of this thesis, we focus on secondary inorganic aerosols, which dominate fine particulate matter (PM2.5) in many regions of the world. We evaluate how well the trends and magnitude of secondary inorganic aerosols (sulfate, nitrate, and ammonium) are captured in a global chemical transport model using a suite of aircraft campaigns and identify high biases in ammonium nitrate. We also explore how remaining uncertainties in certain processes (emissions, deposition) may contribute to these biases and how others (thermodynamic partitioning, several chemical processes) are less likely to be the cause of current model bias. In the third and fourth chapters, we focus on phosphorus, which is an essential nutrient that can control ecosystem productivity. In the third chapter, we develop a comprehensive description of atmospheric phosphorus in a global atmospheric chemistry model. Leveraging a wide range of observational data, we evaluate our current understanding of the processes that control phosphorus (emissions, chemical aging) and highlight two improvements to our phosphorus description that result in less phosphorus from two sources (dust and combustion). In the fourth chapter of this thesis, we apply the atmospheric phosphorus scheme we developed to investigate how atmospheric phosphorus will evolve under projected anthropogenic land use changes. Looking at two scenarios for the future (SSP1 and SSP3) that have the most significant (and opposing) changes in land use compared to the present day, we find that global phosphorus deposition increases or decreases minimally (<±3%), but with substantial regional changes (>±50%). In aggregate, this thesis advances our understanding of aerosol composition by identifying key gaps in how aerosol composition (e.g., secondary inorganic aerosols and phosphorus) is represented and provides insight into how atmospheric composition responds to changing loss and production processes.

Quantifying ocean carbon and oxygen cycles using Biogeochemical Argo

2026-04-22T03:01:15Z

Quantifying ocean carbon and oxygen cycles using Biogeochemical Argo Park, Ellen Ryunhwa The ocean plays an important role in the global carbon and oxygen cycles. Not only has it taken up one third of total anthropogenic carbon dioxide emissions, but it also produces 50% of the oxygen in the atmosphere due to phytoplankton that live in the surface ocean. This thesis explores different aspects of the ocean carbon and oxygen cycles using Biogeochemical (BGC) Argo, which is a global array of autonomous drifting profiling floats. Chapter 2 of this thesis addresses the question of: “How can we improve BGC-Argo float oxygen measurement and address biases related to sensor response time?” by characterizing the temperature and flow speed dependence of a suite of oxygen optodes that are or may become suitable for profiling applications. This work provides a novel approach for estimating sensor response time based on physical principles from temperature, salinity, and flow speed measurements. In Chapter 3, particulate backscatter data from the global BGC Argo float array is used to better constrain metrics of the biological carbon pump for both small and large sinking particles. Results from this work find that the transfer efficiency, which is the fraction of carbon exported out of the surface ocean that makes it to a specified depth horizon and is an important metric because deeper export results in longer carbon sequestration times, is high at low latitudes and low at high latitudes. In Chapters 4 and 5, BGC-Argo oxygen data is used in combination with other observational oxygen datasets to evaluate how effectively current ocean observing systems can spatially interpolate sparse and irregularly gridded observations onto a uniform grid, using temperature and salinity variability, in the subpolar North Atlantic. These Chapters leverage machine learning and compare results to previously published gridded oxygen products. Ultimately, this thesis work spans various spatial and temporal scales, tackling important questions in ocean observing from the sensor to global scale, with additional benefits of reducing uncertainties in sensor measurements and global fluxes, all of which are necessary for understanding and modeling the current state of the ocean to better model the future one.

Toward new maps of basal melt rate in Antarctic grounding zones through inverse modeling of tidal flexure with ICESat-2

2026-04-22T03:01:29Z

Toward new maps of basal melt rate in Antarctic grounding zones through inverse modeling of tidal flexure with ICESat-2 Hendley Elgart, Faye M. In Antarctica, the grounding line is the triple junction where ice meets ocean meets earth, and grounded ice sheets become floating ice shelves. In the past decade, the surrounding area, termed the grounding zone, has come to be recognized as one of the most dynamic and sensitive indicators of change in the Antarctic. Ice shelves float up and down on ocean tides, but in the grounding zone, the ice shelf is mechanically coupled to the upstream grounded ice and flexes rather than floats. Here, we develop an observationally-constrained inversion based on the tidal flexure of ice shelves and ICESat-2 laser altimetry data to make new estimates of ice thickness, ice thickness gradient, ice rheological properties, and ultimately, basal melt rate, in grounding zones of the two largest Antarctic Ice Shelves. We find that the effective Young’s modulus of ice varies significantly in space, both on and among ice shelves, and that estimates of ice thickness in the grounding zone made by assuming ice shelves are in hydrostatic equilibrium may locally underestimate ice thickness on the order of 10-15 percent. Further, we calculate basal melt rate near the grounding line based on conservation of mass and find that ice thickness gradient is a first-order control on the spatial variation of basal melt rate, and using a flexure-inferred versus hydrostatically-inferred ice thickness requires differing interpretations of the oceanographic context close to the grounding line. As basal melt rate is one of the single greatest sources of uncertainty in modeling the rate and amount of future sea level rise from Antarctica, this may have far-reaching implications for ice sheet modeling.

From Extraction to Retrieval: A Graph-Enhanced Framework for Spatio-Temporal Reasoning in Historical Legal Documents

2026-04-22T03:07:05Z

From Extraction to Retrieval: A Graph-Enhanced Framework for Spatio-Temporal Reasoning in Historical Legal Documents Liu, Yifeng Accurate understanding and querying of historical legal property documents remains a significant challenge for urban planning research. These records typically exist only in analog format—scanned images with inconsistent quality, archaic language, and no structured metadata. This limitation severely hinders systematic analysis of how discriminatory housing practices, particularly racial covenants, shaped city development patterns. While researchers have begun applying Generative AI systems to assist with legal documentation work,1 fundamental challenges persist in retrieval accuracy, model hallucination, and reliable extraction of structured facts from unstructured historical text. Using racial covenant analysis as a test case, this thesis addresses two interconnected challenges. Information Extraction: How can structured spatio-temporal information be accurately extracted from degraded historical documents? Reasoning at Scale: How can retrieval systems maintain accuracy when answering complex queries requiring multi-hop reasoning across thousands of documents with temporal and spatial constraints? To address the first challenge, a document processing pipeline was developed and applied to 569 historical deed records from a test subset of the Massachusetts Covenants Project, spanning 1861–1930 in North Middlesex County. The pipeline integrates optical character recognition, named entity extraction, and geographic information systems to transform scanned deeds into structured spatio-temporal data. The case study demonstrates successful extraction of policy-relevant keywords and accurate geolocation, achieving 64.9% complete accuracy across all documents (or 76.3% when considering only documents containing extractable geographic information), validating the e!ectiveness of this approach for systematically analyzing discriminatory housing patterns. The pipeline has been released as an open-source tool2 and is currently being packaged for deployment by MassHousing, a state a!ordable housing agency, to support ongoing collaborative research. While the pipeline produces structured data, querying this information at scale presents its own challenges. To address this second problem, a Graph RAG (Graph-based RetrievalAugmented Generation) framework is adapted and optimized, with its knowledge graph structure designed to specifically encode the spatio-temporal relationships inherent in historical deeds. Through controlled experiments on 2,000 synthetic documents mirroring real deed characteristics, it is demonstrated that the spatio-temporal-optimized Graph RAG achieves an overall F1 score of 0.598, compared to 0.007 for traditional vector-based RAG—an absolute improvement of 0.591 in F1 score. Notably, vector RAG performance degrades significantly when scaling from 100 to 2,000 documents, while the proposed method maintains stable accuracy. Performance was further benchmarked across a five-level query complexity hierarchy, with Graph RAG achieving particularly strong results on multi-hop queries (F1: 0.923) and temporal reasoning tasks, highlighting the critical role of graph construction and query parsing for complex spatio-temporal tasks. These dual contributions establish a replicable framework for historical document analysis in urban planning research, comprising an open-source processing pipeline achieving 64.9% complete accuracy (76.3% on valid samples) and a retrieval architecture that maintains performance at scale. The methods generalize to legal document review, housing policy analysis, and other domains requiring structured, scalable reasoning over large archival collections.

Essays in Macroeconomics and Labor Economics

2026-04-22T03:02:35Z

Essays in Macroeconomics and Labor Economics Di Tella, Isabel This dissertation examines how institutional and behavioral frictions shape aggregate economic outcomes. Combining quasi-experimental methods from applied microeconomics with theoretical frameworks from macroeconomics, I analyze three dimensions of how labor market policies and norms influence employment dynamics and macroeconomic fluctuations. The first chapter investigates how labor market institutions affect the propagation of nominal shocks in inflationary environments. Conventional wisdom suggests that inflation can "grease" labor market adjustments by facilitating real wage changes, but institutional wage-setting may alter this mechanism. We address this question by studying Brazil's annually indexed minimum wage in a high-inflation context. Using administrative data, we show that workers exposed to the policy experience fewer month-to-month wage increases before indexation events, and that firms anticipate the policy by rigidifying wages of workers who will be newly bound. We evaluate the macroeconomic implications of these features by introducing a cost-push shock to a New Keynesian model with heterogeneous labor and an indexed minimum wage. While staggered indexation amplifies the inflation-as-grease mechanism by introducing nominal rigidities, anticipation dampens it via intertemporal substitution. Overall, amplification dominates since cost-push shocks have a stronger "greasing" effect compared to a setting where the minimum wage indexes every period. Our findings demonstrate that even in high-inflation environments, the institutional structure of wage-setting fundamentally shapes how shocks propagate through the economy. The second chapter broadens the analysis of how institutions shape middle-income country's labor markets, focusing on informality. We analyze the Venezuelan refugee crisis in Colombia to separately identify the effect of informal immigration and work permit policies on labor markets. Using a Synthetic Instrumental Variables design, we find that the informal labor supply shock displaced native workers in both the informal and formal sectors, indicating high substitutability between worker types (elasticity approximately 11). Using a triple difference-in-differences design, we find that work permits reduced competition in the informal sector while increasing it in the formal sector. Using a model, we estimate this created 24,440 new formal jobs and approximately $43 million in annual tax revenue. Our results also suggest that work permits create productivity spillovers through reduced skill mismatch, providing economic rationale for immigrant integration policies. The third chapter investigates whether gender norms lead to inefficient resource reallocation following employment shocks. Leveraging rich longitudinal data from the UK on couples' employment, time use, and self-reported gender norms, we test whether gender norms prevent households from achieving income-maximizing arrangements after negative employment shocks. We find that after a layoff, men are more likely than women to return to work in the long run. This asymmetry is fully explained by women who subscribe to traditional gender norms: progressive women have the same labor market response to layoffs as men. These findings reveal how deeply embedded behavioral norms can generate persistent labor market frictions with macroeconomic consequences. Together, these chapters demonstrate how institutional design and behavioral patterns create frictions that fundamentally alter aggregate outcomes, providing policy-relevant insights for middle-income economies and beyond. JEL Codes: E24, E26, E31, J16, J61, O54

Coordination Chemistry at Boron: Reactivity, Electronic Structure, and Optical Properties Studies Across the Main Group

2026-04-22T03:04:39Z

Coordination Chemistry at Boron: Reactivity, Electronic Structure, and Optical Properties Studies Across the Main Group Frey, Nathan C. Boron, owing to its vacant pz orbital, has historically enabled the synthesis of diverse Lewis acid-base adducts. The research herein focuses on the structure, bonding, and reactivity of a range of Lewis base-stabilized neutral, cationic, radical, and anionic boron-containing systems. The utilization of various Lewis basic ligands, such as N- heterocyclic carbenes (NHCs), cyclic alkyl(amino) carbenes (CAACs), and phosphines, has enabled the synthesis and study of electron-rich boron-centered radicals, anions, and frustrated Lewis pairs (FLPs). These systems have been shown to participate in discrete bond activation processes, including single electron transfer and nucleophilic attack. In parallel, carbene and carbone ligands have facilitated the isolation of luminescent borafluorenium ions with tunable optoelectronic and stimuli-responsive properties. As a result, boron-centered molecules can be leveraged as key tools for various applications, including hydrogen storage, bond activation reactions, novel heterocycle formation, and stimuli-responsive materials. In conjunction with experimental results, density functional theory has been used to propose reaction mechanisms, identify bonding patterns, and understand key optoelectronic properties within these systems. These results collectively advance the understanding of coordination chemistry involving redox-flexible boron molecules while highlighting their reactivity and potential as functional molecular materials. Chapter 2 focuses on the synthesis of dimethylamineborane-bound magnesium(II) centers supported by NHC ligands. These species were shown to dehydrocouple dimethylaminebo-rane, yielding stoichiometric amounts of dihydrogen. By using various techniques, including NMR spectroscopy and DFT, the migratory behavior of dimethylamine borane within the magnesium complexes is investigated. Chapter 3 describes the activation of elemental sulfur (S 8 ) and diphenyl dichalcogenides using the CAAC-stabilized borafluorene radical and anion. From these non-selective reactions, sulfur chains of varying length (S 8 , S 7 , S 4 , and S 2 ) were each identified and analyzed using experimental and theoretical methods. The corresponding borafluorene-phenylchalcogenide species were analyzed using spectroscopic techniques and displayed characteristics consistent with an internal heavy atom effect. Chapter 4 highlights the ring expansion reactivity of a borinine-containing FLP towards various chalcogen-containing substrates. Subsequent reduction of these species yielded di- boracyclonone molecules bridged by a µ 2 chalcogenide atom. Halide abstraction from the 3 parent FLP yielded an analogous species with a formal chloronium ion bridging the two boron atoms. DFT was used to provide insight into reaction mechanisms and structure and bonding within each species. Chapter 5 depicts the synthesis and photophysical characterization of borafluorenium ions stabilized by hexaphenylcarbodiphosphorane. By installing 3,3’-dimethoxy substituents on the borafluorene core and changing the counteranion from bromide to tetrakis(3,5- bis(trifluoromethyl)phenyl)borate, pronounced changes in stability and optical properties were observed. Substantial increases in fluorescence quantum yields were invoked from solution state to solid state, indicating aggregation-induced emission behavior.

Advances in Bootstrap Embedding Towards Large Molecular Systems

2026-04-22T03:02:47Z

Advances in Bootstrap Embedding Towards Large Molecular Systems Weisburn, Leah P. In the pursuit of a quantitative understanding of molecular energetics, theoretical chemistry methods must carefully balance accuracy with computational cost. This challenge is particularly acute in quantum chemical approaches, which aim to describe electronic structure and interactions with high fidelity but whose exact solutions scale exponentially with system size. Quantum embedding techniques address this challenge by exploiting the locality of electron correlation to reduce computational expense. Among these, bootstrap embedding (BE), developed in the Van Voorhis group, offers a compelling compromise between the steep scaling of correlated wavefunction methods and their ability to accurately describe chemical systems. BE achieves this by partitioning molecules or solids into fragments, treating each fragment at a high level of theory, and recombining the fragment results to recover correlated-level descriptions of extended systems. In this thesis, we present several developments that extend this linear-scaling, wavefunction-in-wavefunction embedding framework to larger molecular systems and dense basis sets, often beyond the reach of competing approaches. We demonstrate that localizing orbitals using intrinsic atomic orbitals (IAOs) in large basis sets yields highly accurate results that systematically converge toward full-system energies as fragment sizes increase. This localization also accelerates convergence and enables more efficient integral transformations. Using fragments that extend two coordination shells beyond a central atom, we consistently recover more than 99.7% of the total correlation energy across all tested systems and basis sets. Building on this localization strategy, we introduce an alternative fragment construction scheme designed specifically for large basis sets. This approach employs cluster natural orbitals (CNOs) to capture interactions between each fragment and its environment, augmenting the fragment spaces in a manner inspired by local correlation methods. The inclusion of CNOs significantly improves the accuracy of BE calculations, and when combined with extrapolation techniques, enables the recovery of more than 99% of the total correlation energy in double-zeta basis sets, often outperforming the BE(3) result where calculable. We further introduce a mixed-basis-set framework that enables BE calculations at the CCSD level for systems containing hundreds of atoms in triple-zeta basis sets. We combine a multiscale basis-set description with the ensemble of overlapping fragments, leveraging the locality of chemically-important interactions, to recover over 99% of the target BE energy for systems well beyond traditional size limits. When coupled with QM/MM techniques, this approach is demonstrated on extended biological systems containing approximately 40,000 atoms. In addition, we explore the application of BE in a different computational regime by employing selected configuration interaction (SCI) as the fragment solver. We show that incorporating SCI within the BE framework reliably extends its applicability to systems containing tens of hydrogen atoms and suggests a viable path toward treating carbon-based molecules. This represents a substantial expansion beyond the conventional capabilities of SCI and points toward the possibility of achieving near–full configuration interaction accuracy with a linearly scaling approach. Finally, we describe the implementation of these methods and others in the open-source software package QuEmb, which enables the broad use and further development of BE. Through this platform, we aim to facilitate the application of BE to new classes of systems and encourage collaborative efforts to continue advancing the method.

Power, Land, and the Limits of Reshoring: Infrastructure Sequencing and the New Geography of U.S. Industrial Development

2026-04-22T03:07:02Z

Power, Land, and the Limits of Reshoring: Infrastructure Sequencing and the New Geography of U.S. Industrial Development Deng, Larry The United States is undergoing a renewed push toward domestic industrial production driven by geopolitical fragmentation, supply-chain disruptions, automation, and federal industrial policy. Despite unprecedented levels of announced reshoring investment, realized manufacturing output growth has slowed, revealing a growing gap between intent and execution. This thesis argues that this divergence is not cyclical, but structural. Specifically, it contends that U.S. reshoring is constrained less by capital availability or corporate willingness than by regional capacity to deliver developmentready land and electrical power at scale, in the correct sequence, and within commercially viable timelines. Drawing on reshoring data, trade-flow analysis, industrial real estate research, and utility system planning documents, the thesis demonstrates how land-use constraints, grid congestion, interconnection delays, and fragmented institutional coordination have emerged as binding limits on industrial feasibility. The analysis develops a feasibility framework that links land readiness, logistics infrastructure, construction economics, and energy generation, transmission, and utility sequencing to reshoring outcomes. This framework is applied through a case study of Apex Industrial Park in Southern Nevada, which illustrates how anticipatory infrastructure investment, large-scale industrial zoning, and coordinated utility planning can convert reshoring demand into operational capacity. By integrating national data with a place-based case study, this research provides a structured approach for evaluating where industrial reshoring is likely to succeed and where structural constraints will continue to limit execution, with implications for developers, investors, and policymakers.

Dynamics and impacts of toxic Alexandrium catenella blooms in the Pacific Arctic

2026-04-22T03:02:59Z

Dynamics and impacts of toxic Alexandrium catenella blooms in the Pacific Arctic Fachon, Evangeline Recent warming in the Pacific Arctic region is facilitating the emergence of harmful algal blooms (HABs) in polar waters, posing a significant threat to northern communities and ecosystems. Of particular concern is the dinoflagellate Alexandrium catenella, a cyst-forming species that produces a potent suite of paralytic shellfish toxins (PSTs). In this thesis, Pacific Arctic bloom dynamics are explored in the context of the meroplanktonic life cycle of Alexandrium, revealing the lasting impacts of bloom events upon this region. Building upon previous work that characterized a massive accumulation of resting Alexandrium cysts on the Chukchi shelf, the implications of cyst bed disturbance and resuspension for bloom initiation were investigated. Although there is currently a narrow seasonal window within which physiological and environmental conditions align to promote bloom initiation, this interval may become wider under continued Arctic borealization. In the summer of 2022, a large, concentrated, and highly toxic bloom of Alexandrium was tracked in real-time as it progressed northward through the Bering Strait region. The high resolution at which this bloom was observed provided the opportunity to initiate community event response, as well investigate the hydrodynamic drivers contributing to the extraordinary density, scale, and toxicity of this event. Little is currently known about the uptake and depuration of PSTs in Arctic taxa, introducing significant challenge into modeling trophic toxin transfer. Following the 2022 bloom, toxicity in a bivalve population of Macoma calcarea on the southern Chukchi shelf was measured over three years, showing that this species serves as a reservoir for PSTs in the food web by retaining significant toxin levels for over a year after a bloom event. Beyond the persistence of PSTs in the ecosystem, advected blooms have the potential to create lasting impacts by sustaining and shaping the Chukchi shelf Alexandrium cyst bed. A population study of Alexandrium isolates using microsatellite markers revealed high diversity and little spatial differentiation across the study region. However, a significant shift in population genetic structure was detected over time, likely resulting from new cyst inputs by advected blooms from the Bering Sea. These results highlight the dynamic nature of the Chukchi cyst bed, which serves as a terminal deposition site that is formed and maintained by sub-Arctic sources. This thesis adds significantly to our understanding of the relationship between bloom dynamics and algal toxin risks in warming Pacific Arctic waters, providing insights that can be used to shape future risk mitigation and response efforts.

The Tide is High: Tidally-Associated Genetic Markers of an Invasive Coastal Crab in the Northwest Atlantic

2026-04-22T03:06:58Z

The Tide is High: Tidally-Associated Genetic Markers of an Invasive Coastal Crab in the Northwest Atlantic Bakari, Kela The ecology of invasive marine species holds many mysteries, ranging from the precise mechanics of adaption in foreign environments to the heritability of behaviors in the native range, and the globally-invasive crab Carcinus maenas (L.) provides a good model for looking into the connection between environment, genetics and behavior in invasive populations. To investigate this relationship, I sampled Carcinus maenas adults and juveniles from an array of sites along the Northwest Atlantic coast and examined how local tidal conditions affected genetic variation and allele frequency patterns in four candidate markers hypothesized to be associated with tidal amplitude. Expecting the patterns I revealed to closely mirror those previously discovered in the native range, I found that one of the four tidal markers I used displayed the same allele frequency trend in both the native and invasive ranges. This suggests that tidal amplitude is a likely driver of selection in this species in both native and foreign environments, but sampling more sites in the future is needed for a more conclusive assertion of this idea. Nevertheless, the results of this study offer a promising lead on the relationship between tidal amplitude and genetics in Carcinus maenas, bringing us closer to solving the mystery of environment-behavioral coupling in marine species.

Tunnels in the ocean: formation and propagation of interannual water mass anomalies in global subtropical cells

2026-04-22T03:05:11Z

Tunnels in the ocean: formation and propagation of interannual water mass anomalies in global subtropical cells Hersh, Cora Alden Subtropical cells connect subducting subtropical waters to upwelling sites along the equator. Water mass properties (e.g. salinity, temperature) are set at the surface and subducted along ventilated pathways that flow toward upwelling sites in the tropics. This tight link between the subtropics and the tropics, on a scale of 5-15 years, is well-established in a time-averaged sense by modeling and observations. Recently, evidence has emerged of spice and potential vorticity anomaly persistence along mean flow pathways on isopycnals. In Chapter 2, we provide the first global view of subtropical water mass anomaly propagation, using both an observational dataset and the Estimating the Circulation and Climate of the Ocean (ECCO) state estimate Version 4 Release 4. We find long-lived interannual water mass anomalies that translate along mean advective pathways in all ventilated subtropical gyres. These anomalies, in spice (densitycompensated temperature and salinity) and potential vorticity (ratio of Coriolis parameter to density layer thickness), are detectable over multiple years and several thousand kilometers. Anomalies sometimes reach western boundary and equatorial current systems before being entirely eroded, and thus could form ocean “tunnels” to impact remote climate variability. ECCO successfully captures these phenomena, motivating further study of subtropical thermocline variability using modified-forcing experiments in the ECCO-configured Massachusetts Institute of Technology General Circulation Model (MITgcm) in subsequent chapters. In Chapter 3, we remove the interannual frequency of the input surface forcing over different ocean regions. This allows us to describe the pathways and quantify the impact strength of subtropically-forced water mass anomalies on equatorial variability. We find that interannual forcing over each of the five global subtropical basins creates a detectable impact on equatorial water properties, although the strength varies greatly between basins. We also investigate impacts in the reverse direction, from variability forced over the equator toward the subtropics, and demonstrate potential ocean-based mechanisms through which such variability can propagate. Finally, in Chapter 4, we perform additional ECCO experiments in which we remove the interannual band of wind or buoyancy forcing in order to understand surface forcing drivers of the observed spice variability. We see that while the interannual wind forcing is responsible for fast-moving, high-frequency wave perturbation features in the spice record along subducting pathways, it also creates large, low-frequency spice variability which is anticorrelated with the buoyancy-forced variability. These experiments emphasize the role of the outcrop migration mechanism of spice formation, in which sea surface temperature anomalies cause an isopycnal outcrop to move across the background surface salinity field, particularly in the South Pacific.

Implications of seasonality and asymmetry for ENSO’s predictability and future changes

2026-04-22T03:01:42Z

Implications of seasonality and asymmetry for ENSO’s predictability and future changes Carr, Theo The El Niño - Southern Oscillation (ENSO), a coupled ocean-atmosphere phenomenon in the tropical Pacific, is the dominant year-to-year driver of variations in Earth’s climate. Its impacts on temperature and precipitation are felt globally, and particularly in the tropics, where a large fraction of the world’s population lives. While a century of research since the discovery of the Southern Oscillation has advanced understanding of ENSO, there is little consensus about how it will change in the rest of the 21st century, owing to disagreement between different state-of-the-art climate models. In an effort to understand this disagreement, and improve understanding of ENSO’s future changes, this thesis investigates aspects of two ENSO features – seasonality and asymmetry – whose projected changes haven’t been well-studied, despite their prominence in observations. First, we evaluate an alternative framework – based on the Koopman operator – for representing asymmetry and seasonality in a highly idealized, conceptual model of ENSO. While conceptual ENSO models are widely used to diagnose ENSO stability in observations and global climate models, they rely on nonlinear parameterizations which may misrepresent the processes giving rise to ENSO asymmetry. We show that the Koopman-based approach, based on finding a nonlinear transformation which makes ENSO look symmetric, can reproduce the most salient aspects of ENSO’s evolution asymmetry, including the faster decay of El Niños during boreal spring and the higher frequency of multi-year La Niñas. Next, we study projected changes in ENSO’s seasonality and asymmetry in a global climate model over the course of the 21st century. We find that changes in asymmetry contribute significantly to the late-21st century decrease in ENSO amplitude projected by the model, with a weak increase in La Niña intensity opposing a much larger decrease in El Niño intensity. We show that this change is consistent with the projected weakening of the equatorial Pacific’s zonal temperature gradient, a change which limits the potential intensity of El Niños, but not La Niñas. Finally, we switch our focus to precipitation variability in the U.S. Midwest, and in particular, the ocean-to-land atmospheric moisture transport which fuels this variability. Oceanic moisture sources (vs. those from land) have become more important in recent decades, and we quantify the relative contributions from Pacific and Atlantic moisture sources and how they vary seasonally and over time. We find that an intensification of the Great Plains Low-Level Jet is correlated with an increase in the fraction of Midwest rainfall that originates from the ocean (vs. from land surfaces). While this effect is observed on interannual timescales and likely modulated by ENSO, it is most pronounced on synoptic timescales, when upper-level disturbances amplify the low-level jet and moisture transport to the Midwest; thus here we investigate it independent of ENSO. Overall, this thesis highlights the potential importance of nonlinearity and seasonality in ENSO’s future changes and suggests one alternative framework for their representation in conceptual models.

Prediction of dissolution and nucleation in silicates using machine learning force fields

2026-04-22T03:01:49Z

Prediction of dissolution and nucleation in silicates using machine learning force fields Roy, Swagata Understanding how silicates react in water is crucial in a range of fields, from geology and cement chemistry to the synthesis of zeolite catalysts and precipitated silica. Although reactive simulations offer powerful insights at the molecular level, balancing accuracy and scale in these condensed-phase calculations remains a significant challenge. With the advent of machine-learned potential for materials, large-scale calculations seem achievable with high accuracy. In this thesis, machine learning and data-driven analyses are extended to capture complexities inherent to silica gelation, which were then applied to advance the mechanistic understanding of silica polymerization under different environmental conditions using molecular dynamics and kinetic Monte Carlo simulations. We developed a robust reactive machine-learned potential to accurately capture silicate-water reactivity. To ensure the robustness of the model, we introduce a new and general active learning strategy based on the attribution of the model uncertainty that automatically isolates uncertain regions of bulk simulations to be calculated as small-sized clusters. The potential reproduces the static and dynamic properties of liquid water and solid crystalline silicates, despite having been trained exclusively on cluster data. We applied the potential to obtain energy barriers for the formation of silica oligomers of any shape and size. Using this, we used a kinetic Monte Carlo study to study the effect of pH and temperature on the dynamics evolution of amorphous silica gel by identifying small building units leading to precipitated silica or crystalline zeolite depending on different conditions. Realizing limitations in the assumptions involved in the Kinetic Monte Carlo study and to incorporate long-range interactions in the study, we trained a coarse grained potential to replicate the silica polymerization dynamics. The potential was trained successfully by distilling a foundation model and was able to accurately capture the time evolution of silicate clusters but had its own limitations in terms of size and time. In general, this thesis provides a detailed description of the preliminary stages of silica oligomerization that lead to an amorphous gel acting as precursors for precipitated silica or zeolites.

Probing the Logic of DnaJB6 Oligomerization

2026-04-22T03:08:48Z

Probing the Logic of DnaJB6 Oligomerization Petty, Aaron Protein misfolding and aggregation, a characteristic molecular phenotype of neurodegenerative disease, is mediated by a class of proteins called molecular chaperones. One such protein, DnaJB6, potently suppresses the aggregation of polypeptides such as amyloid-β and tau and has been observed to self-oligomerize, but the connection between these properties is unknown. Previous work to understand DnaJB6 has not elucidated this connection, in part because the dynamic nature of these oligomers makes them difficult to analyze by conventional techniques. I hypothesize that DnaJB6 oligomers serve as inactive reservoirs from which dimers, the active subunit, dissociate upon binding to misfolding client and engage Hsp70s to facilitate processing. To test this hypothesis, I have generated point variants of DnaJB6 to perturb oligomerization and tested their biophysical properties and biochemical activity. My analysis demonstrates that aromatic residues in the disordered region are critical for oligomerization and affirms my hypothesis that smaller assemblies of the chaperone are responsible for its anti-aggregation activity. This work enhances our foundational biophysical understanding of DnaJB6, which serves as a basis for the development of DnaJB6-inspired therapeutics to combat neurodegenerative disease.

Planning EV Charger Placements with Heterogeneous Charging Technologies

2026-04-22T03:06:48Z

Planning EV Charger Placements with Heterogeneous Charging Technologies Allen, Julia R. When developing public urban charging infrastructure for electric vehicles (EVs), key questions are how many chargers to deploy, where to locate them, and what charger technology to utilize. This paper introduces a facility location model with multiple facility types that jointly optimizes the placement and type of EV chargers to minimize total infrastructure costs while meeting spatially distributed demand. We then study the benefit of a hybrid mix of chargers relative to single-technology solutions, demonstrating that the gains from hybrid solutions depend critically on demand pooling structures. In particular, hybrid solutions yield the highest benefit when there is heterogeneity in the amount of demand served at each location. We complement our theoretical results through a data-driven case study based on the City of Detroit, developing an end-to-end pipeline to solve the problem for real cities. First a computer vision model finds feasible curbside charging locations by analyzing images from Google Street View, and then an optimization model determines the optimal placement and technology of chargers in Detroit. This pipeline is demonstrably more effective than either machine learning or optimization alone. This work provides both analytical insight and a scalable methodology to support cities in designing cost-effective EV charging networks.

Structuring Heterogeneous Real Estate Market Evidence Using LLMs: A Provenance-Aware Analytical Framework

2026-04-22T03:07:09Z

Structuring Heterogeneous Real Estate Market Evidence Using LLMs: A Provenance-Aware Analytical Framework Hahmann, Luca; Xie, Richard Real estate market analysis at early analytical stages relies on evidence drawn from heterogeneous public and semi-public sources, including brokerage research, administrative datasets, planning documents, and narrative commentary. These inputs differ systematically in scope, definition, temporal framing, and institutional construction. Market indicators are frequently consumed through static reports and summary tables that obscure provenance, comparability constraints, and evidentiary gaps. As a result, analytical conclusions often depend on implicit assumptions about how market information is constructed and aligned before formal underwriting or quantitative modeling begins. This thesis develops an evidence-centric framework for structuring and inspecting real estate market information prior to inference, using large language models (LLMs) to translate unstructured report artifacts into structured evidence objects. The framework treats market indicators, narrative claims, and source dependencies as constructed analytical objects. Each observation is represented together with explicit metadata describing geographic scope, temporal reference, definitional disclosure, and upstream data dependencies. This representation supports disciplined comparison across sources and makes uncertainty, non-equivalence, and missing context explicit. The methodological contribution consists of a KPI taxonomy, a context-aware data model, and a layered processing architecture. Observations are preserved with their original construction context and aligned only when comparability conditions are satisfied. Uncertainty is encoded through coverage, recency, and dispersion indicators. Visualization functions as an interface for evidentiary inspection, enabling users to navigate indicators, examine parallel representations, and trace reported values to their sources. The framework is demonstrated through an application to U.S. multifamily market reports. A single-source case study illustrates how brokerage reports combine headline KPIs, narrative claims, submarket tables, time-series elements, and transaction summaries within a single artifact. A multi-source case study examines contemporaneous reports for the same market and shows how differences in segmentation logic, measurement conventions, and temporal aggregation shape apparent agreement and disagreement. In both cases, structural non-equivalence remains visible as an analytical feature. The results show that explicit representation of evidentiary structure supports clearer interpretation of market claims independent of predictive modeling. The thesis positions LLM-enabled structuring as foundational infrastructure for real estate market analysis and as a prerequisite for downstream quantitative and causal research. Future work may extend the framework to additional data sources, longitudinal analysis of reporting behavior, and institutional deployment across investment workflows.

Limits of Literature-Conditioned Large Language Models for Predicting Behavioral Experiments

2026-04-22T03:07:22Z

Limits of Literature-Conditioned Large Language Models for Predicting Behavioral Experiments Na, Robin Large language models (LLMs) have recently shown potential in various capabilities contributing to scientific progress. Recent work shows that they can predict experimental outcomes as accurately as human forecasters. Other works show that retrieval-augmented generation (RAG)– conditioning LLMs on relevant documents or databases–can improve the quality of model outputs across various research synthesis tasks. Here, we combine these two streams of work and ask: does conditioning LLMs on published research articles improve their predictions of outcomes in new behavioral experiments? We test this using 20 new experiments on peer punishment in cooperation dilemmas, where the prediction task is to determine how much punishment mechanisms increase or decrease group welfare across different settings. Consistent with prior findings, the baseline offthe-shelf GPT-4.1 model performance matches or exceeds every human (laypeople and experts) forecaster we tested. We then condition the model on 1,398 published papers studying punishment, testing both individual papers and collections constructed by grouping papers in different ways (e.g., theory-focused versus empirical studies, recent versus older publications, high-impact versus lower-impact journals). To our surprise, conditioning on individual papers rarely reduces prediction error, and in many cases it makes predictions worse. Conditioning on collections substantially increases the model’s confidence without increasing its accuracy. Simply providing research articles to language models does not seem to improve predictions of outcomes in new experiments, suggesting that more effective systems may require different approaches to representing and processing scientific evidence.

Modeling Complex Evolution of Slow to Fast Earthquakes on Simple and Heterogeneous Faults

2026-04-22T03:06:19Z

Modeling Complex Evolution of Slow to Fast Earthquakes on Simple and Heterogeneous Faults Sun, Yudong The goal of my PhD research is to understand the behavior of earthquakes and slow slip events on fault systems ranging from simple planar geometries to complex fault networks with structural and material heterogeneity. To achieve this, I developed both analytical and numerical models to investigate the processes of nucleation, propagation, and termination. I investigate the occurrence of back-propagating earthquake fronts—secondary rupture fronts that travel in the reverse direction of the main rupture. Through dynamic rupture simulations using rate-and-state friction, I demonstrate that such phenomena can emerge even on frictionally homogeneous faults, provided that the rupture is unilateral and driven by stress gradients. An analytical model shows that velocity-dependent friction destabilizes steady crack-like propagation, leading to transitions between pulse and crack modes and generating back-propagating fronts. These findings suggest that back propagation may be more common than previously recognized, and does not require structural heterogeneity. I examine how fault roughness affects the dynamics of slow slip events (SSEs). Using quasi-dynamic simulations of rough faults with rate-and-state friction, I show that variations in normal stress, serving as a proxy for geometric roughness, induce irregular rupture patterns. SSEs exhibit temporal clustering, diverse rupture speeds, and back-propagating fronts. These arise from stress oscillations caused by roughness-modulated frictional properties, offering a new mechanism for the complex SSE evolution seen in geophysical observations. I perform quasi-dynamic and fully-dynamic earthquake simulations using rate-and-state friction to reproduce laboratory experiments on 760 mm-long faults with three velocity-weakening patches separated by velocity-strengthening barriers composed of PMMA and Teflon. Simulations show that multi-patch ruptures become more frequent under higher normal stress and narrower barriers, consistent with laboratory observations. Fully-dynamic simulations, which incorporate elastic wave propagation, allow ruptures to more easily overcome barriers and better reproduce the observed rupture size and speed compared to quasi-dynamic simulations. I develop novel 2.5D earthquake cycle simulations using boundary element methods to study the evolution of foreshocks and aftershocks in fault systems. This approach offers the computational efficiency of 2D modeling while incorporating more realistic 3D stress transfer effects. Collectively, these works provide new insights into the fundamental physical mechanisms driving both fast and slow fault slip with implications for seismic hazard assessment and earthquake forecasting.

Chemical complexity in high-entropy materials

2026-04-22T03:05:57Z

Chemical complexity in high-entropy materials Sheriff, Killian Materials properties depend strongly on chemical composition, i.e., the relative amounts of each chemical element. In metallic alloys, small changes in composition can alter mechanical strength, thermal conductivity, corrosion resistance, and phase stability. This sensitivity arises from changes in the local chemical environments that atoms experience—features that vary significantly across chemically ordered and disordered phases. In ordered phases, specific atomic motifs repeat periodically, whereas in chemically disordered phases, local chemical environments vary widely with composition and temperature. Both types of phases are essential to high-performance structural alloys, high-entropy materials, and chemically complex catalytic surfaces. Yet, the configurational complexity of disordered phases poses a particular challenge for predictive modeling. This thesis addresses the fundamental question: "What does it mean to be chemically complex?" We will see that the understanding of chemical complexity is key to enabling predictive modeling of high-entropy materials across composition, particularly for the modeling of metallic alloys, where disordered solid solutions are the prevalent phases across their phase diagrams. Traditional approaches to materials design rely on simplified models that fail to capture the intricate interplay between chemical disorder, local atomic environments, and emergent properties. This thesis bridges this gap by combining geometric deep learning with large-scale molecular dynamics simulations to characterize, predict, and control chemical complexity at the atomic scale, ultimately opening up the rational design of next-generation disordered materials with tailored properties.

Benthic meiofaunal and micro-eukaryotic inhabitation of extreme environments: Submerged caves and hydrocarbon seeps

2026-04-22T03:01:32Z

Benthic meiofaunal and micro-eukaryotic inhabitation of extreme environments: Submerged caves and hydrocarbon seeps Rohret, Shari M. Benthic meiofauna and microbial eukaryotes comprise diverse taxa that influence biogeochemical cycling and food-web dynamics in aquatic ecosystems. Many inhabit “extreme” environments with steep gradients in parameters such as oxygen, salinity, and organic matter availability, serving as natural laboratories for studying community structure and resilience. This thesis integrates molecular, geochemical, and microscopy-based approaches to investigate the biodiversity, community structure, and cytology of benthic eukaryotes in two chemically stratified systems: submerged caves (Chapters 2 and 3) and hydrocarbon seeps (Chapter 4). Chapter 2 explores the community composition and ecological structuring of benthic meiofauna and microbial eukaryotes across environmental gradients in submerged cave systems of the Yucatan Peninsula and Cozumel Island, Mexico using metabarcoding analysis. Despite a large percentage of unidentified sequences, diverse protistan, metazoan, and fungal taxa were detected, spanning a range of feeding strategies, with community composition differing both between caves and within a single system. Chapter 3 focuses on benthic foraminiferal communities in submerged cave systems using traditional morphological identification and metabarcoding analysis. Living and total (i.e., dead plus living) assemblages of foraminifera in picked samples were compared using a fluorogenic probe to identify living specimens, and a non-vital stain to identify recently living individuals. Living foraminiferal assemblages were recovered in the two cave systems studied, one with anoxic to hypoxic conditions, and the other an oxygenated system. Metabarcoding of cave sediments using foraminifera-specific primers yielded coarse taxonomic resolution, highlighting the need for an improved molecular reference database. Chapter 4 examines the cellular ultrastructure of foraminifera inhabiting hydrocarbon seeps using Transmission Electron Microscopy (TEM). Two extant foraminiferal groups (monothalamids and miliolids) representing early-evolving lineages showed ultrastructural variability between species, with differing feeding strategies observed. Potential adaptations to seep conditions include lipid proliferation, extensive peroxisome-endoplasmic reticulum complexes, and abundant “empty” vacuoles. These studies offer new insights into benthic eukaryotic life in geochemically dynamic systems, highlighting community-level patterns (Chapters 2-3) and organism-level characteristics (Chapter 4). As anthropogenic stressors increasingly impact marine systems, potentially increasing the prevalence of hypoxic, acidic, and eutrophic conditions, understanding how meiofaunal and microbial communities respond will be critical for predicting ecosystem resilience and future biogeochemical change.

Mathematical Formulation and Numerical Methods for the Optimization of Novel Biotherapeutics Manufacturing

2026-04-22T03:06:00Z

Mathematical Formulation and Numerical Methods for the Optimization of Novel Biotherapeutics Manufacturing Inguva, Pavan Krishna The emergence of multiple classes of novel biotherapeutics, such as nucleic acids, and cell and gene therapies, are enabling the efficacious treatment of many diseases. A key part of unlocking the full potential of these novel therapeutics and delivering these life-saving medications to patients is the development of efficient biomanufacturing processes that are able to ensure product quality at scale. To that end, digitalization is currently seen as one of the key enabling technologies for driving advances in the industry. A vital component of digitalization process is a computational model of the system, which can then be leveraged for various objectives such as accelerating process development and deploying advanced process control strategies. In the case of novel biotherapeutics, not only are the manufacturing processes less well understood, these processes and the therapeutic itself can be more complex compared to those for conventional biologics or small molecule drugs. Consequently, the development of mechanistic models for these systems is much harder as there is less established process insight and experimental data available. To address this challenges, this thesis applies concepts and methods from process systems engineering to formulate, develop, and deploy mechanistic models for the production of two classes of biotherapeutics: lipid nanoparticles (LNPs) for nucleic acid delivery, and viral particles. In parallel, ancillary work to develop numerical algorithms to enable the efficient and accurate solution of these models is pursued. This thesis can be broken down into three parts: Part 1 explores the conceptualization and formulation of mechanistic models for LNP and viral particle production. Recognizing the paucity of modeling approaches available in the literature for these systems, the use of first principle concepts such as mass and energy balances and insights from adjacent fields in formulating the models are discussed. Part 2 outlines the development of numerical algorithms for the solution of two classes of partial differential equation systems that are essential for modeling these systems, namely population balance models (PBMs) and phase-field models (PFMs). The proposed method for PBMs uses a combination of variable transformations, specially constructed meshes, operator splitting, and solving the equation at the limit of numerical stability to achieve efficient (in some cases, as low as memory reallocation) and accurate (in some cases to machine precision) solution of many classes of PBMs. For the PFM, a spline approximation is used to regularize the logarithmic nonlinearity found in physically-accurate forms of the Cahn–Hilliard equation which enables its solution at model parameter values otherwise inaccessible. Part 3 showcases the development and deployment of these models with the development of a multi-scale model for LNP production, and the application of advanced process control for a continuous viral bioreactor. The proposed multi-scale model for LNP production adopts incorporates a computational fluid dynamics (CFD) model at the mixer-scale and thermodynamic modeling at the molecular-scale, to yield salient information which is cascaded into a PFM model at the particle-scale. For the control of the continuous viral bioreactor, the use of nonlinear economic model predictive control is demonstrated to be able to achieve a variety of process objectives such as maximizing yield and purity and is also shown to be robust under plant-model mismatch.

Electrochemical Mechanisms in Thermochemical Catalysis

2026-04-22T03:01:20Z

Electrochemical Mechanisms in Thermochemical Catalysis Lodaya, Kunal Electrochemistry and thermochemical catalysis have historically been viewed as disparate fields with distinct mechanistic paradigms. This divide, along with an inadequate experimental and methodological electrochemical toolkit, has impeded understanding of electrochemical mechanisms in thermochemical catalysis. This dissertation applies both legacy and newly-developed electrochemical approaches to assess the role of coupled half-reactions in three different thermochemical catalytic reactions. We first examine the hydrogenation of nitrate on bimetallic PdCu/C catalysts (Chapter 2) and determine that the reaction proceeds via the coupling of electrochemical hydrogen oxidation on Pd and nitrate reduction on Cu. This understanding is employed to optimize catalyst formulation, determine new bimetallic catalyst pairings with nitrate hydrogenation activity, and improve activity in the state-of-the-art catalyst. In Chapter 3, this mechanistic framework is extended to the Pd-catalyzed vinyl acetate synthesis. We use a combination of liquid-phase and gas-phase electrochemical measurements, with the latter being a new tool developed in our laboratory, to investigate the reaction. This study reveals the bifunctionality of homogeneous Pd(II) species and heterogeneous Pd/C in facilitating the overall oxidation, revising the legacy mechanism for this industrial catalytic reaction. Building on the mechanistic analysis and electrochemical probes developed in Chapters 2 and 3, we investigate the synthesis of ammonia (Chapter 4). Our study challenges the historical mechanism for the reaction, revealing the essential role of interfacial charge transfer in activating and reducing dinitrogen with driving force derived from the coupling of electrochemical half-reactions. Taken together, these studies unravel the role of galvanic coupling in redox thermochemical catalysis, and serve as a call to action for the application of electrochemical methods in mechanistic investigations of catalysis.

Oxidation Chemistry and Secondary Organic Aerosol Formation in Indoor and Outdoor Atmospheres

2026-04-22T03:04:50Z

Oxidation Chemistry and Secondary Organic Aerosol Formation in Indoor and Outdoor Atmospheres Tahsini, Nadia Particulate matter exerts a significant influence on climate and air quality, including harmful human health impacts. A substantial fraction of particulates are organic aerosols, comprised largely of secondary organic aerosol (SOA) formed from photochemical reactions of volatile organic compounds (VOCs). SOA formation occurs across scales, ranging from the outdoor atmosphere to indoor environments, and improved understanding of this chemistry is required to more accurately quantify impacts on climate and human health. This thesis investigates oxidation chemistry and SOA formation in indoor and outdoor atmospheres, including a focus on peroxy radical (RO₂) chemistry. In the first part of this work, we examine the oxidation of isoprene, an atmospherically abundant biogenic VOC. Most global models currently use SOA yields for isoprene that assume NOₓ linearity, limiting the accuracy under intermediate conditions. Using environmental chamber experiments, we characterize the product distributions and SOA formation from isoprene oxidation under the full atmospheric range of RO₂ regimes. We provide yields for major gas-phase species and aerosol as a function of key RO₂ parameters, highlighting the non-linear NOₓ dependence of SOA contrary to traditional assumptions, which can be used to improve the treatment of isoprene SOA in climate models. The second part of this work investigates how chemistry and volatility interplay for the temperature dependence of SOA formation, focusing on the ozonolysis of α-pinene, another prominent biogenic VOC emitted into the atmosphere. While SOA formation has been shown to be temperature dependent based on volatility effects, the temperature effect of chemistry - especially RO₂ isomerization - is less explored. With environmental chamber experiments and box modeling, we examine how chemistry versus volatility effects impact the overall temperature dependence of SOA, using speciated gas-phase measurements and aerosol-phase analysis. This work provides novel insight on how chemistry and volatility contribute to SOA formation across temperatures, including a focus on RO₂ isomerization effects and corresponding changes in α-pinene product distributions. Finally, we turn to indoor atmospheres, examining the impact of commercial air cleaners on indoor photochemistry and air quality under 222 nm germicidal lamps (GUV₂₂₂). GUV₂₂₂ is effective at killing airborne pathogens, but it generates ozone in the indoor environment, thus initiating unwanted chemistry including SOA formation. Through environmental chamber experiments and modeling, we quantify the reduction of ozone and secondary pollutants by running commercial air cleaners concurrently with GUV₂₂₂. These results highlight the potential benefits of air cleaners to improve indoor air quality across GUV₂₂₂ and non-GUV₂₂₂ settings. Overall, this thesis connects several relevant areas of atmospheric photochemistry, helping improve our understanding of RO₂ chemistry and SOA formation from oxidation of biogenic VOCs, as well as characterizing indoor air quality implications of GUV₂₂₂ and commercial air cleaning technologies.

Metabolic drivers of community dynamics in marine microorganisms

2026-04-22T03:02:44Z

Metabolic drivers of community dynamics in marine microorganisms Jahns, Max A. Marine microorganisms adopt metabolic strategies, the biochemical means by which they acquire energy and nutrients, in part, because of the interactions between biological entities in their community. The eco-evolutionary strategies adopted by marine microbes both shape and are shaped by the environments in which they can live and remain competitive, the flow of carbon and nutrients through the environment, and the structure of the marine ecosystem. While many theories exist to explain the metabolic underpinnings of plankton ecology, there remains significant gaps in our understanding of how organisms fill metabolic niches and the mechanisms underlying these dynamics. This work focuses on three biotic relationships (parasitism, predation, and competition) and how these community dynamics both shape and are shaped by organismal metabolism using planktonic laboratory systems. To test the effects of parasitism and predation, we compared the cosmopolitan marine cyanobacteria Prochlorococcus MED4 lipidome under ideal conditions to its lipidome modulated due to top-down pressures from P-SSP7, a T7-like phage, and Paraphysomonas bandaiensis, a phagotrophic nanoflagellete predator. From cultures containing only Prochlorococcus MED4, we assemble the first complete lipidome of the abundant marine cyanobacteria Prochlorococcus MED4. Using this core lipidome as a baseline, we identify several characteristic ways phage infection alters the fatty acid, phospholipid, and other lipid metabolic pathways in Prochlorococcus. We also describe an unknown mechanism by which the energy storage metabolism of Paraphysomonas bandaiensis is affected by grazing on P-SSP7 infected prey. Next, to test how competitive dynamics shape metabolic niche partitioning, we assemble a novel model system using multiple strains of the marine mixotroph Ochromonas, with differing metabolic requirements. These mixotrophs were grown in competition with Paraphysomonas bandaiensis in batch and chemostatic culture. Generally, mixotrophic metabolism is assumed to confer a competitive advantage through the acquisition of energy and nutrients from multiple pools and the ability to relieve competitive pressures by switching trophic investments away from limiting pathways. Despite this, both obligately phagotrophic strains of Ochromonas tested instead ‘doubled-down’ on phagotrophy in response to competition with Paraphysomonas bandaiensis. Whereas the facultatively phagotrophic strain used in this study, Ochromonas 1391, followed typical mixotrophic competitive behavior of increased investment in phototrophy. These strains also experienced significant growth and energy efficiency tradeoffs because of this investment. We find evidence that this tradeoff may be due to prey nutrition needs or prey quotas in these organisms. Additionally, we show how metabolic landscapes, i.e. multidimensional conceptualizations of metabolism, can inform metabolic tradeoffs and realized metabolic niches in response to community dynamics. Our results underscore the powerful effects of community interactions and metabolism, and vice versa. The dynamics captured within this dissertation research, inform microbial metabolic niches and how energy and nutrients flow through mixed planktonic communities with complex biotic interactions.

Early life-history ecology of Arctic benthic invertebrates

2026-04-22T03:02:02Z

Early life-history ecology of Arctic benthic invertebrates Schrage, Kharis In this dissertation I explore the abiotic and biotic factors impacting early life-history bottlenecks in the Arctic Ocean and Woods Hole, MA. Many marine invertebrates have a biphasic life-history, with larvae that disperse in the water column then settle to the seafloor. Extreme population bottlenecks occur at larval and juvenile stages, which fundamentally alter adult communities. Yet, marine early life-history ecology is severely understudied, leaving us ill-prepared to predict impacts of a changing environment. The Arctic is warming rapidly, making it a natural laboratory for studying change. In addition, the polar oceans are a severe, cold habitat with extreme seasonality in light and food availability. Thus, directly studying the organisms in this region rather than extrapolating based on patterns at lower latitudes is imperative. My primary objective was to elucidate the early life-history bottlenecks shaping seafloor assemblages in the rapidly warming Arctic. Chapter 2 investigates the natural history of larvae during the poorly understood polar night. This multi-month period of complete darkness with no primary production seems ill suited to support larval development, however, my survey of plankton communities in a fjord in Svalbard in January 2023 revealed fifty larval taxa. My samples included many recently spawned embryos, suggesting active reproduction by multiple species, feeding, and active metamorphosis and settlement. Chapter 3 investigates the drivers of dispersal and connectivity of larvae in the Fram Strait. I documented the distribution of marine invertebrate larvae in the summer, demonstrating a significant difference in larval community composition between Atlantic and Arctic water masses, as well as a relationship to phytoplankton community composition. Larvae of many coastal species were observed offshore, suggesting high connectivity and/or high wastage. I also modeled the dispersal of these taxa to understand their origins, finding non-native taxa prevalent in the region suggesting potential for invasion. Finally, I explored how post-settlement processes shape community structure. I utilized a recently developed autonomous camera tool, CATAIN (CAmera To Analyze INvertebrates), which allows observation of settlement and post-settlement mortality in remote environments for the first time. In Chapter 4, I developed and deployed CATAIN over a one-year period in Woods Hole, MA. I captured sub-daily settlement data and found a novel relationship between time of day and settlement frequency for multiple taxa. I also found that post-settlement mortality patterns varied among commonly occurring species, challenging entrenched ideas about this critical stage. In Chapter 5, I deployed CATAIN in Svalbard twice for a total of 20 months. There, I discovered high rates of mortality for the barnacle Semibalanus balanoides, and their ecology differed from the same species in temperate regions. I tied both settlement and mortality to background biotic conditions and punctuated abiotic events in situ. Overall, this work broadens knowledge of Arctic meroplankton, and the unique season of the polar night, and highlights the important processes occurring during early life stages that have lasting implications for seafloor communities.

Pooling Heuristics and Credit Risk Distribution in Conduit CMBS: Evidence from a Controlled Re-Pooling Experiment

2026-04-22T03:09:21Z

Pooling Heuristics and Credit Risk Distribution in Conduit CMBS: Evidence from a Controlled Re-Pooling Experiment Galler, Leo Conduit commercial mortgage-backed securities (CMBS) pool loans from multiple borrowers and properties and allocate cash flows and losses across tranched bonds. While prior work emphasizes how collateral characteristics and deal structure affect tranche performance, less attention has been paid to how the pooling rule itself shapes credit risk distribution across pools. This thesis studies that question in a controlled experiment that holds the loan universe fixed and varies only the pooling heuristic. Using SEC ABS-EE Exhibit 102 loan-level disclosures, the thesis builds a dataset of 389 commercial mortgage loans from ten U.S. conduit CMBS transactions issued between 2022 and 2025. The loans are reallocated into four synthetic pools under four pooling rules: Random, DSCR stratification, LTV stratification, and a Defensive rule that disperses jointly weak loans (low DSCR and high LTV). Each pool is evaluated using a stylized refinance-at-maturity loss mapping and a common three-tranche waterfall (10/20/70). Results are reported for a base case and seven stress scenarios. Three findings emerge. First, single-signal stratification reduces cross-pool dispersion relative to random assignment, with DSCR stratification achieving the lowest dispersion in every scenario. In the combined rate-plus-NOI stress, cross-pool standard deviation is 0.98 percentage points under DSCR stratification versus 2.81 under random assignment. Second, dispersion minimization and worst-pool containment are distinct objectives: the Defensive rule can improve worst-pool outcomes in jointstress states even when overall dispersion remains higher. Third, distance-to-impairment margins for mezzanine and senior tranches vary materially across heuristics, indicating that pooling design can affect tranche-level tail risk even when total collateral loss is fixed within a scenario. The results imply a practical tradeoff for issuers between dispersion control and tail containment under joint stress. For investors and credit analysts, they motivate supplementing weighted-average metrics with worst-pool diagnostics. While the framework is intentionally stylized and results are comparative rather than predictive, the findings suggest that pooling warrants explicit attention in deal structuring and credit assessment.

Hydrogen Carriers: A Path Forward

2026-04-22T03:03:46Z

Hydrogen Carriers: A Path Forward Biswas, Sayandeep Hydrogen carriers offer a promising pathway to overcome the storage, transport, and infrastructure challenges that limit the large-scale deployment of molecular hydrogen. Yet, their broader adoption is hindered by conversion inefficiencies, system-integration barriers, and economic constraints. This thesis advances the practical utilization of hydrogen carriers by developing improved production pathways, demonstrating efficient hydrogen-release technologies, and evaluating their performance across multiple carrier systems. First, ammonium formate is investigated as a safe, non-flammable, and energy-dense solid hydrogen carrier. An electrochemical dehydrogenation system is demonstrated at 105 °C, where ammonium formate forms an ionic liquid. The system achieves near-quantitative Faradaic efficiencies at both electrodes and exploits coupled thermal–electrochemical driving forces, enabling modular hydrogen release with the potential for net-zero carbon emissions. Second, a comprehensive costing and emissions analysis is conducted for blue, green, and integrated blue–green ammonia production pathways. The integrated pathway uses oxygen from electrolysis to feed the autothermal reformer, eliminating the need for an air-separation unit and significantly reducing energy use and capital cost, making it a strong candidate for transitional decarbonization. Third, the thesis evaluates onboard dehydrogenation of Liquid Organic Hydrogen Carriers (LOHCs) as an alternative to centralized hydrogen release for heavy-duty trucking. This perspective identifies key reactor, catalyst, and engine-integration requirements for enabling an LOHC-fueled powertrain and shows that LOHCs charged with blue hydrogen can approach cost parity with diesel. Crucially, the final component of the thesis directly addresses these requirements. A pilot-scale benzyltoluene dehydrogenation reactor is designed, built, and experimentally validated to demonstrate the core enabling technology for the proposed powertrain. The reactor uses high-temperature exhaust gas directly to supply the endothermic heat demand and generates high-pressure hydrogen compatible with engine-based systems. The results align strongly with model predictions and confirm, at pilot scale, the technical feasibility of the onboard LOHC utilization pathway articulated in the third work. Together, these contributions advance both the scientific foundations and engineering readiness of hydrogen carriers, providing new strategies to improve conversion efficiency, reduce system costs, and enable scalable hydrogen distribution and utilization in future low-carbon energy systems.

Accelerating Autonomous Molecular Discovery through Automated Quantum Chemistry and Artificial Intelligence

2026-04-22T03:04:07Z

Accelerating Autonomous Molecular Discovery through Automated Quantum Chemistry and Artificial Intelligence Wu, Haoyang (Oscar) The grand challenges in medicine, energy, and materials science are fundamentally molecular discovery problems. However, the vastness of chemical space renders traditional experimental exploration inefficient and insufficient. Autonomous Molecular Discovery promises to accelerate this process by integrating artificial intelligence (AI), computation, and automation in chemistry, but it faces a critical trilemma: balancing accuracy, speed, and scalability. This thesis documents a systematic effort to alleviate this tension by developing and integrating novel computational frameworks that synergize the first-principles rigor of quantum mechanics (QM) with the predictive efficiency of machine learning (ML) and the scalable automation enabled by AI. This thesis began by focusing on developing the first ab initio kinetic models for the liquid-phase oxidative degradation of Active Pharmaceutical Ingredients. This demonstrates the feasibility and predictive power of automated mechanistic modeling in complex chemical environments, and highlights the acute need for more accurate thermochemical and kinetic data to handle real-world complexity. To address this, we developed a framework for computing systematic thermochemical corrections, and conducted an extensive benchmark of 284 model chemistries, establishing protocols to efficiently achieve chemical accuracy (~1 kcal/mol) from QM simulations. Recognizing the limitations of speed and data scarcity, we engineered physics-informed ML architectures, notably the QM-GNN, which fuses Graph Neural Networks (GNN) with QM descriptors. This approach significantly improves predictive performance and data efficiency, particularly for reaction regioselectivity in low-data regimes. Finally, to deploy these advances at scale, we designed QuantumPioneer, an automated, high-throughput platform for generating large-scale, high-fidelity QM thermo-kinetic datasets. This platform has produced an extensive database for oxidation reactions, enabling the development of novel ML models for predicting molecular stability and solvation energies. Collectively, this thesis provides a cohesive framework for accelerating molecular discovery, demonstrating that the strategic integration of first-principles simulation and data-driven intelligence can overcome key bottlenecks hindering autonomous chemical design and discovery.

Tomographic Investigation of Bonding and Microstructure in Supersonic Microparticle Impacts

2026-04-22T03:02:30Z

Tomographic Investigation of Bonding and Microstructure in Supersonic Microparticle Impacts Panova, Veera The Laser Induced Particle Impact Test (LIPIT) enables particle-wise controlled launch of micron-scale metallic powders at velocities relevant to cold spray deposition. Many cold spray studies are empirical and discuss the effects of process parameters on porosity, density, and only aggregate material properties. As such, LIPIT is a powerful platform for probing the fundamental mechanisms governing high-velocity deformation and solid-state bonding. This method has been extensively used to study the subsurface features associated with bonding: the oxide layer thickness, jetting, and microstructural evolution; however, the true morphology of these features has remained largely inferred due to the limitations of conventional two-dimensional characterization. This thesis proposes a synergistic use of the LIPIT and FIB-SEM tomography characterization to study Cu-on-Cu impacts in 3D and provide detailed quantitative insights into fundamental aspects of a coating: its bonding and microstructure. First, the true geometry of bonded interfaces in single impacts is revealed for the first time, and a mechanistic analytical model for the onset of the bonding regime is developed, enabling a prediction of optimal bonding conditions. The 3D methodology is then applied to the study of microstructure to measure and model the extent of metadynamic recrystallization which occurs immediately following the high-velocity impact due to the presence of residual heat. The second half of the thesis discusses two-particle LIPIT stacks where individual impacts are aimed directly on top of one another. Such stacks enable careful study of the coating buildup step, characterized by particle-particle bonding and the evolution of the particle-substrate interface. Typically, isolating the impact conditions of any one particle during a coating application is impossible, and other “few-particle” experiments still lack the detail to decouple the variables. We first revisit our developed model for the onset of bonding and modify it for the new scenario to show that “peening”, the deformation of the underlying particles by the incoming high-velocity impacts typically associated with bonding enhancement in cold spray, is a two-step process. Finally, the effects of surface roughness and hardness on particle-particle adhesion are decoupled. These comprehensive 3D studies of several phenomena help inform the solid-state manufacturing process.

Interpretable Approaches for Optimizing the Pulse Diagnostics and Formation for Lithium-ion Batteries

2026-04-22T03:03:39Z

Interpretable Approaches for Optimizing the Pulse Diagnostics and Formation for Lithium-ion Batteries Rhyu, Jinwook Lithium-ion Batteries (LiBs) are widely used in electronic devices and energy storage systems owing to their high energy density, long lifespan, and low cost. To further improve their performance and safety, we focus on optimizing two necessary yet time-consuming LiB applications: pulse diagnostics that enable degradation mechanism-level diagnosis, and the formation step that can greatly impact the battery performance. Interpretability plays a key role in the optimization scheme. Interpretability allows us to break down result-oriented, time-consuming objective functions into tractable problems, enhances the extrapolation capability of the optimization solver, and determines the depth of physical insights we gain from the optimization scheme. Thus, we aim for interpretable approaches for optimizing the pulse diagnostics and formation step. First, we optimize voltage-pulse diagnostics using the fitness model, a mechanistic model that describes the behavior of degraded LiBs under voltage pulses, as a source of interpretability. The optimal set of diagnostic protocols is found from the Pareto front plot with two objective functions, each representing the practical identifiability of the degradation parameters and the total diagnostic time. We discuss on why film resistance is challenging to identify, and how to improve the practical identifiability using redundant voltage pulses. Next, we demonstrate how the formation protocols can be optimized using the systematic feature engineering framework as the source of interpretability. Using two features designed from our framework, we achieve reliable evaluation of new formation protocols during the formation step, with a < 10% cycle life prediction error. Combined with the domain knowledge, our feature engineering work guides the development of the mechanistic distributed-resistance model, providing physical meaning to the data-driven features. Finally, we present a reliable classification method of full-cell Acoustic Emission (AE) signals based on their acoustic sources, which is the first step for utilizing AEs for optimizing the formation step. AEs capture microscopic mechanical phenomena, such as gas generation or particle fracture. Using the simplest convolutional neural network structure for interpretability, we observe a possible transferability of the classifier from half-cell to full-cell AEs, and across similar cell chemistries.

Noncovalent PROTAC Antibody Drug Conjugates

2026-04-22T03:02:49Z

Noncovalent PROTAC Antibody Drug Conjugates Krum, David Proteolysis Targeting Chimeras (PROTACs) are a new class of heterobifunctional drugs that induce proximity between an E3 ligase and a given protein of interest (POI), causing the POI to be ubiquitinated and targeted for proteasomal degradation. While the event-driven, irreversible pharmacodynamics of PROTACs offer advantages over traditional occupancy-driven small molecule inhibitors, their large molecular weight and existence outside of Lipinski chemical space give rise to poor pharmacokinetic behavior. To ameliorate these issues, PROTACs can be conjugated to anti-tumor antibodies to improve circulation time in vivo and promote accumulation of the drug in the tumor. The conjugation chemistry and purification of antibody drug conjugates is time and labor intensive, and we therefore propose a facile, noncovalent, “plug-and-play” conjugation strategy whereby an anti-PROTAC antibody fragment is included in a bispecific antibody. In this work, we develop a pharmacokinetic model describing the kinetics of reversible, noncovalent antibody drug conjugates. We use yeast surface display to develop high-affinity antibodies that bind to the most common small molecules employed in Proteolysis Targeting Chimeras (PROTACs) guided by observations from our modelling. We test the binding and delivery kinetics of these non-covalent antibody drug conjugates in vitro and demonstrate that that the antibodies induce internalization of the drug, leading to target protein degradation and cytotoxicity in murine and human cell lines. Finally, we investigate the in vivo pharmacokinetics and anti-tumor efficacy of various PROTAC drugs, demonstrating an improvement relative to drugs without conjugation to an antibody.

Interpolation Methods in Random Optimization and Deep Learning

2026-04-22T03:03:07Z

Interpolation Methods in Random Optimization and Deep Learning El Cheairi, Houssam This thesis studies three problems in high-dimensional probability and statistics: (1) the densest subgraph problem in dense random graphs, where the goal is to estimate and recover the subgraph with the highest edge density; (2) the maximum cut problem on sparse random graphs, where the goal is to find a partition of the graph’s vertices that maximizes the number of edges between the two sets; and (3) the theoretical feasibility of compression for multilayer perceptrons via pruning and quantization. In Chapter 2, we derive new asymptotics for the density of densest k-subgraphs in random graphs in the sublinear regime k = n^α , α ∈ (0, 1). Our derivation is specified for both the dense Erdös-Rényi model and Gaussian-weighted random graphs. We also show using the interpolation method that the densest subgraph density is distributionally invariant for sub-Gaussian distributions, thus establishing universality. We leverage the asymptotics in the dense Erdös-Rényi setting to study the algorithmic landscape of the Hidden Clique model, and show that it exhibits a form of the Overlap Gap Property (OGP), which constitutes an algorithmic barrier for a family of Markov chain Monte Carlo (MCMC) algorithms. In Chapter 3, we study the performance of a class of Low-Degree Polynomial (LDP) algorithms for the maximum cut problem (Max-Cut) on random Erdös-Rényi graphs. We show that tree-structured LDPs based on the Approximate Message Passing (AMP) framework are near-optimal for finding ground states of the Sherrington-Kirkpatrick (SK) model. Then, using an interpolation argument, we show optimality of the same algorithms on sparse random graphs. This effectively proves algorithmic universality for this class of LDPs. In Chapter 4, we provide a theoretical justification for the post-training compression of wide multilayer perceptrons (MLPs). By analyzing a randomized greedy algorithm akin to Optimal Brain Damage (OBD) via an interpolation method, we unify the treatment of unstructured compression (pruning and quantization) and structured pruning. Our results rigorously establish the existence of sparse and quantized subnetworks that maintain competitive performance. In particular, we show that pruning at linear sparsities is achievable for sufficiently wide MLPs. The derived bounds, which are free of data assumptions, formally showcase a tradeoff between an MLP’s width and its compressibility

Predictive Methods and Datasets for Thermodynamic and Kinetic Modeling of Ionic Solutes

2026-04-22T03:06:44Z

Predictive Methods and Datasets for Thermodynamic and Kinetic Modeling of Ionic Solutes Zheng, Jonathan The ability to quantitatively describe ionization phenomena is essential to designing medicines, developing novel materials, and modeling the time-evolution of many relevant liquid-phase chemical systems. In liquid phase chemistry, acid-base phenomena result in the formation of solvated ions. Biochemical reactions, synthesis steps, and pharmacological mechanisms-of-action also often involve charged reactants and products. A key property is the “elusive” solvation free energy of the ion, which itself is not directly measurable but for many relevant ions is tied to other observable properties such as the acid dissociation constant (pKₐ). However, modeling approaches tend to perform poorly for predicting solvation free energies of ionic solutes. Existing solvation models often have average errors exceeding 3 kcal mol-1. Literature data are also scarce, precluding the parameterization of physics-based solvation models, development of data-driven methods, and benchmarking of such techniques for ionic solutes. The work in this thesis seeks to address these issues of data scarcity and modeling. The work can be divided into three components: • Acid-base phenomena. First, the data curation aspects of pKa are discussed. Existing inconsistencies in data usage and terminology have confused the literature, and are clarified herein. Curated datasets for aqueous and non-aqueous pKa values, based on IUPAC collections of data, are also presented here. However, even with these datasets, many solvent systems still do not have much data (several, for instance, have less than 100 datapoints). To address this data scarcity issue, a method for generating high-quality pKa predictions in non-aqueous solvents is presented and benchmarked, and then utilized to create “synthetic” data for roughly 3,000 acids in 29 solvents (a total of nearly 80,000 data points). • Thermodynamics of ionic solutes. As aforementioned, pKₐ, along with other measurable thermodynamic properties, can be linked back to the ions’ solvation energies. This section describes how this thermodynamic relationship is used to generate a new database of hydration free energies for ions, which can then be used to develop simple corrections to existing solvation models to reduce error by roughly 60%. This method is expanded to a large scale, used to generate nearly 6,000 values across 8 solvents. For comparison, prior to these efforts, the largest such database included only 300 datapoints. Finally, a machine learning model was trained to predict solvation free energies (as well as the substituent properties), the first machine learning model to our knowledge that can predict anionic solvation energies and gas-phase acidities. • Kinetics. The methods of the previous chapters are expanded to reactions involving zwitterions, radicals, and singly-charged anions (S_N 2 reactions). The H-abstraction reaction is examined through a dataset of approximately 100 million solvation free energies previously computed in our group. The dataset consists mostly of uncharged closed-shell and open-shell solutes, but contains some zwitterions as well. The error of the calculation method is examined, with a focus on the barrier heights of the reactions. The conformational effects of the zwitterions are examined, showing the surprisingly high sensitivity of the solvation energies to the optimized geometries of such solutes. Next, S_N 2 reactions are examined. A dataset of S_N 2 rate data was digitized and provided, and used to benchmark a quantum-chemical approach to prediction relative rate coefficients. The method shows good predictive quality, demonstrating the usefulness of solvation models for relative properties despite their very high errors for absolute energies. In sum, these efforts combine cheminformatics, quantum chemistry, data science, and machine learning to enable quicker and more accurate prediction of properties related to acid-base and ionization phenomena.

Identifying and Managing Risks in Mezzanine Financing for Real Estate: Towards Optimized Risk-Adjusted Investment Decisions

2026-04-22T03:10:25Z

Identifying and Managing Risks in Mezzanine Financing for Real Estate: Towards Optimized Risk-Adjusted Investment Decisions Park, Hazel Sanghi The evolution of capital structure in U.S. commercial real estate has been shaped by historical and institutional events, including financial crises, regulatory reforms, and shifts in capital market participation. Within this context, the mezzanine tranche has been the most adaptive element of the capital stack, continually reshaping itself to meet shifting market conditions, bridge funding shortfalls, and broaden the range of capital providers. At the same time, this evolution has introduced new type of risks and increased the need for intercreditor alignment and more structured agreement terms. This thesis identifies the inherent risks associated with mezzanine lender’s position in the capital stack and its underlying collateral structure. It further examines the contractual and structural mechanisms that have been developed to protect investor rights and mitigate these risks. Drawing on loan documents, case studies, and professional interviews, the thesis illustrates how these mechanisms operate, particularly in distressed scenarios, and discusses their practical implications. Specifically, it examines (1) the historical and institutional drivers and outcomes of market evolution including various capital stack instruments; (2) the contractual features of mezzanine loans through text-based analysis of loan agreements; (3) lessons from defaults or stress scenarios in selected case studies; and (4) the contractual mechanisms designed to protect investors and mitigate risk, followed by the development of a risk-protection matrix to support the systematic assessment of risk–return trade-offs. By integrating historical analysis, case studies, and contract-level evaluation, this research aims to provide a framework for identifying underlying risks in investment opportunities and supporting sound investment decision-making through the assessment of reasonable risk-adjusted returns.

From Feasibility to Inventory: A Predictive Framework for Multifamily Supply Analysis

2026-04-22T03:09:55Z

From Feasibility to Inventory: A Predictive Framework for Multifamily Supply Analysis Muro Moleiro, Luis Fernando This thesis introduces a predictive framework for multifamily supply analysis by reversing the conventional sequence, moving from feasibility to inventory rather than inventory to feasibility. Traditional supply metrics rely heavily on delivered units, active pipeline counts, and historical absorption, all of which describe supply after developers have already made their decision to build. This backward-looking approach obscures the underlying conditions that actually determine whether new inventory will be built. This thesis proposes a model that begins instead with the economics of development feasibility, incorporating construction costs, achievable rents, capitalization rates, and the spread from YOC to Cap Rates that developers typically require for new projects to proceed. By estimating the rent levels necessary for a hypothetical development to achieve this spread— and comparing those requirements to current and projected market rents—the framework identifies where new supply is financially viable, where it is marginal, and where it is economically impossible. By anchoring supply forecasting in the feasibility thresholds that guide developer behavior, this approach transforms supply-side analysis into a forward-looking tool capable of predicting future inventory expansion with greater accuracy and insight than pipeline data alone.

Long Term Asset Management Planning for Housing Security and Climate Stability: A Case Study of Decarbonization Roadmapping for Allston Brighton Community Development Corporation’s Affordable Multifamily Rental Housing Portfolio

2026-04-22T03:08:25Z

Long Term Asset Management Planning for Housing Security and Climate Stability: A Case Study of Decarbonization Roadmapping for Allston Brighton Community Development Corporation’s Affordable Multifamily Rental Housing Portfolio Blacklow, Arielle a Boston-based multifamily affordable housing provider pursuing net-zero emissions by 2040, this project develops a mechanism and framework for portfolio decarbonization roadmapping of existing residential housing assets. The research links decarbonization planning to equitable long-term asset management by aligning efforts of emission reductions with housing affordability, capital needs, financial capacity, climate adaptation, accessibility compliance, and resident engagement. Grounded in real-world constraints faced by mission-driven affordable housing organizations, the study draws on practitioner interviews, scenario planning, multi-criteria decision-making, and ABCDC’s experience with recent retrofit projects to develop actionable planning tools. The research produces adaptable property-level decarbonization planning dashboards that inform a dynamic portfolio-wide energy projection and tracking dashboard, providing user-friendly, datadriven mechanisms to support organizational decision-making. Through iterative scenario analysis, the thesis explores a combination of Zero over Time (ZOT) and Simultaneous Energy Upgrade (SEU) strategies for ABCDC’s portfolio in order to balance energy, regulatory, cost, and logistical factors. The research also introduces a prioritization mechanism for portfolios facing resource constraints and develops resident engagement frameworks to promote equitable and participatory decarbonization planning processes. The thesis contributes a practical bridge between technical decarbonization modeling and long-term asset management planning while identifying key implementation challenges, including funding constraints, data limitations, and knowledge transmission barriers. Although reliant on a single case study, the framework developed through this thesis offers transferable insights for affordable housing providers seeking to disrupt the reinforcing cycle between housing insecurity and climate instability.

Kinetic Manipulation of Self-Assembling Systems for Scalable Nanomaterials Synthesis

2026-04-22T03:02:32Z

Kinetic Manipulation of Self-Assembling Systems for Scalable Nanomaterials Synthesis Ye, Matthew Dan Nanoparticle assembly is an approach to materials development in which macroscopic materials are constructed through the autonomous self-organization of nanoparticle-based building blocks. Since individual nanoparticles can be synthesized with tailored size, shape, composition, and chemical functionality, this synthetic strategy offers extremely fine control over material structure at the nanometer length scale. However, because an enormous quantity of these building blocks is typically required to produce macroscopic materials, the scalability of this approach has remained a significant obstacle to large scale production. In this dissertation, I demonstrate that polymer-grafted nanoparticles (PGNPs) can be used to not only address these scalability challenges, but also generate novel microscale and nanoscale architectures that are otherwise difficult to realize through bulk processing. First, I show that in concentrated colloidal dispersions of PGNPs, adjusting the solvent quality enables sufficiently precise control over polymer-polymer interactions to yield ordered nanoparticle arrays on much shorter timescales than previously reported assembly techniques. This facile and rapid assembly methodology is highly desirable because it uses generic polymer ligands that are amenable to large scale production, can be performed without specialized equipment, and is easily adapted to particles of arbitrary composition. Second, I show that by using polymer ligands end-modified with supramolecular binding moieties, nanoparticles can be assembled into previously unobserved nonequilibrium crystal habits. Interestingly, the prevalence of these kinetically-driven morphologies follow exactly the opposite trend of what would be expected in conventional crystal growth, revealing new mechanistic pathways in the morphogenesis of crystals derived from colloidal constituents. Finally, I show that ordered PGNP arrays can be thermally processed to induce interparticle sintering, resulting in interconnected porous networks. This allows nanoparticle assembly to serve as a versatile route to mesoporous metals, ceramics, and even heterostructures with multiple components, thus providing a powerful platform for generating catalytic materials with tunable surface area and activity. Collectively, these findings reveal that kinetic control underpins the practical scalability and structural diversity of PGNP-based materials and establishes nanoparticle assembly as a kinetically tunable platform for engineering complex nanoscale architectures.

Seasonal predictability and interannual variability in coastal planktonic communities

2026-04-22T03:03:19Z

Seasonal predictability and interannual variability in coastal planktonic communities Santos, Miraflor Padilla Understanding the predictability and variability of coastal planktonic communities is essential for forecasting ecosystem responses to environmental change and managing marine resources. This thesis examines seasonal predictability, temperature-phenology relationships, and interannual bloom variability in coastal plankton using two decades of high-resolution observations from the Martha's Vineyard Coastal Observatory (MVCO). Through automated flow cytometry, we analyzed community dynamics across multiple taxa representing diverse functional groups and seasonal strategies. The first component investigates seasonal predictability patterns across the planktonic community using wavelet analysis, novel cyclicity indices, and lag-adjusted seasonal models. Wavelet analysis reveals that annual periodicity dominates temporal variability, with $71\%$ of taxa exhibiting consistent annual cycles throughout the observation period. However, substantial variation exists in predictability among taxa, with some species serving as reliable seasonal anchors while others display more erratic dynamics. Critically, we demonstrate that shifts in bloom amplitude contribute more to interannual variability than changes in seasonal timing, and that individual taxa often exhibit higher predictability than their aggregated functional groups, challenging common modeling approaches that rely on broad taxonomic categories. The second component examines the mechanistic basis for seasonal predictability by quantifying relationships between temperature timing and bloom phenology. Using year-day crossing models, we assess how temperature threshold transitions predict bloom onset and decline across four representative taxa: \textit{Synechococcus} (spring), \textit{Pseudo-nitzschia }(summer), \textit{Akashiwo} (fall), and \textit{Ditylum brightwellii} (winter). Results show that bloom onset timing is more tightly coupled to temperature transitions than decline timing, with positive correlations indicating that earlier warming leads to earlier bloom initiation. This onset-decline asymmetry suggests temperature functions as a reliable seasonal cue for bloom initiation, while termination depends on more complex, multi-factorial processes including nutrient depletion, grazing pressure, and biological interactions. The third component characterizes interannual variability in bloom dynamics through episode-based analyses of frequency, integrated intensity, peak magnitude, and timing. Community-scale patterns reveal distinct ecological strategies for packaging biomass through time, with some taxa favoring infrequent but intense episodes while others maintain consistent moderate-level activity. Temporal trends in bloom characteristics differ markedly among seasonal taxa, with spring bloomers showing declining magnitudes, summer diatoms exhibiting increasing peak concentrations, and winter taxa displaying variable intensity patterns. Event-based temperature-timing analyses demonstrate that only a subset of strongly seasonal taxa exhibit significant phenological coupling, with most community members showing weak or inconsistent temperature relationships when examined at the episode level. These findings have important implications for understanding and predicting coastal ecosystem dynamics. The dominance of amplitude over timing variability suggests that forecasting efforts should prioritize predicting bloom magnitude rather than precise timing. The stronger temperature control on onset versus decline indicates that warming-driven phenological advances are likely, but termination timing will remain more stochastic. The superior predictability of individual taxa compared to functional groups argues for maintaining taxonomic resolution in monitoring and modeling efforts. Together, these results provide a comprehensive framework for understanding how coastal plankton communities respond to environmental variability and change, informing both fundamental ecological theory and practical forecasting applications for marine ecosystem management.

AI-driven Exploration of Topological Quantum Materials

2026-04-22T03:07:14Z

AI-driven Exploration of Topological Quantum Materials Cheng, Mouyang Topological quantum materials have emerged as promising platforms for next-generation technologies in energy, computing, and information processing. However, their exploration faces two major challenges: reliably detecting exotic topological states from experimental signals and identifying materials that balance quantum functionality with economic and environmental sustainability. In this thesis, I develop artificial intelligence (AI)-driven approaches to address both fronts. First, I introduce a machine learning framework that distinguishes Majorana zero modes (MZMs) from spurious zero bias peaks in scanning tunneling spectroscopy, integrating quantum transport simulations, topological data analysis, and deep learning to achieve robust classification even in noisy experimental regimes. Second, I establish a data-driven methodology to screen over 16,000 topological materials by combining thermodynamic stability, supply-chain resilience, toxicity, and environmental footprint with the quantum weight, an AI-predicted metric quantifying electronic quantumness. The analysis uncovers a striking correlation between enhanced quantum behavior and increased sustainability costs, and identifies a small set of viable candidates that optimize both. Together, these efforts demonstrate how AI can bridge the gap between theoretical prediction, experimental verification, and sustainable design of topological quantum materials, charting a pathway toward their scalable deployment in future quantum and energy technologies.

Enhancing the Mechanical Recycling of Plastics

2026-04-22T03:02:21Z

Enhancing the Mechanical Recycling of Plastics Hocken, Alexis The plastic waste problem has become a serious environmental issue as over 400 million tons of plastic waste are generated annually. Unfortunately, plastic production is only continuing to rise. Most plastic waste is landfilled, mismanaged, or accidentally released into the environment, while only a small portion is recycled (about 9% in the US). This underscores the need to develop more sophisticated technology to maximize plastics recovery and minimize unnecessary use of virgin petroleum feedstocks. Unrecovered plastics represent a missed opportunity for economic and environmental benefit. This thesis explores potential technological schemes that could be deployed in material recovery facilities (MRFs) to enhance post-consumer plastic recovery. The first two parts of this work probe a cyclone separation process to improve retention of small format plastics within MRFs. By leveraging the density difference between glass and plastic, the plastics can be sorted out with higher than 90% efficiency. Additionally, environmental and technoeconomic feasibility studies demonstrated that the introduction of the cyclone separation process would be a beneficial addition to MRF operations. Further experiments explored the limits of this technology by investigating the impact of feed shape, size, infill, and aspect ratio on separation performance. The experiments were further explained by computational fluid dynamics (CFD) simulations and modeling. The last two parts of this thesis study how machine learning could be employed within plastics sorting. An expansion of optical sorting technology to support polyester bioplastics was investigated using reflectance spectroscopy and machine learning techniques. Random forest and k-nearest neighbor models were trained on a spectral dataset and demonstrated classification accuracies of 92% and higher. Additional probing into sample attributes, feature importance, and tolerance to external noise was performed. Finally, an alternate sorting scheme in recycling based on sorting by brand rather than plastic type was demonstrated by leveraging AI. It was shown that this new sorting technology could be used to implement an economically-viable extended producer responsibility (EPR) scheme that would better align the incentives of all participants in the plastics system in order to improve recycling rates.

Supercoiling as a regulator: the role of biophysical feedback in transcription and chromatin dynamics

2026-04-22T03:02:12Z

Supercoiling as a regulator: the role of biophysical feedback in transcription and chromatin dynamics Johnstone, Christopher P. Transcription induces a wave of DNA supercoiling, altering the binding affinity of RNA polymerases and reshaping the biochemical landscape of gene regulation. Instead of transcription being a simply biochemical progress acting over a static energy landscape, this supercoiling diffuses outward, dynamically reshaping the regulation of proximal genes and forming a complex feedback loop. While supercoiling is well studied as a biophysics problem, a theoretical framework is needed to integrate biophysical regulation with biochemical transcriptional regulation. To investigate the role of supercoiling-mediated feedback within multi-gene systems, we model transcriptional regulation—and especially transcriptional initiation—under the influence of supercoiling-mediated polymerase dynamics, allowing us to identify patterns of expression that result from physical inter-gene coupling. Gene syntax—–the relative ordering and orientation of genes—–defines the expression profiles, variance, burst dynamics, and inter-gene correlation of two-gene systems. In turn, this can enhance or weaken biochemical regulation. Together, the modeling results suggest that supercoiling couples behavior between neighboring genes, providing a regulatory mechanism that tunes transcriptional variance in engineered gene networks and explains the behavior of co-localized native circuits. With these predictions in hand, we use integrated reporter circuits in human cells and demonstrate the reciprocal effects of transcription and DNA supercoiling: supercoiling-mediated biophysical feedback. This feedback effect regulates expression of adjacent genes in a syntaxspecific manner, as predicted. Using Region Capture Micro-C and GapRUN, two recently published genomics techniques, we measure induction-dependent formation of supercoiled plectonemes and syntax-specific chromatin structures in human induced pluripotent stem cells. This first demonstration of supercoiling measurements in synthetic, integrated circuits in mammalian cells gives us unprecedented detail into the regulation of two-gene circuits. Applying syntax as a design parameter, we built compact gene circuits, tuning the mean, variance, and stoichiometries of expression across diverse delivery methods and cell types and improve both the titer of antibody production circuits and the efficacy of all-in-one inducible lentiviral circuits. Integrating supercoiling-mediated feedback into models of gene regulation will expand our understanding of native systems and enhance the design of synthetic gene circuits.

Genetic Modifications and Other Tools for the Enhancement and Optimization of Recombinant Protein Manufacturing in Komagataella phaffii

2026-04-22T03:03:58Z

Genetic Modifications and Other Tools for the Enhancement and Optimization of Recombinant Protein Manufacturing in Komagataella phaffii Ford, Hayley The global demand for high-quality recombinant proteins is rapidly growing. Meeting this demand while sustaining supply chains and improving worldwide accessibility requires scalable, cost-efficient production platforms. Komagataella phaffii (K. phaffii) is a promising host for addressing these challenges and has been used to produce a wide range of recombinant proteins. However, for complex molecules such as monoclonal antibodies (mAbs), space–time yields in K. phaffii remain below those of traditional hosts. This thesis presents several studies that narrowed this productivity gap by enhancing the volumetric productivity of K. phaffii through host-cell engineering and novel reactor operating strategies. First, we explored how disrupting non-essential genes affected the production of recombinant proteins in K. phaffii. Using CRISPR-Cas9, we characterized the essentiality of every gene in the K. phaffii genome. We used these results in conjunction with biological hypotheses to identify targets for genetic engineering. We disrupted 29 genes associated with secretion pathways and observed significant improvements in mAb production (up to 9x) for 19 of those strains. The best performing strains were evaluated across multiple scales, including fed-batch fermentation. We also assessed the effectiveness of combining the most beneficial genetic engineering changes. We next investigated whether transcriptomic data could be used in conjunction with essentiality to identify promising targets for genetic engineering. We found no correlation between gene expression level and the effectiveness of gene disruptions; however, our screen did uncover five genetic edits that significantly improved mAb production in K. phaffii. Beyond genetic engineering of the host organism, genomic integration significantly impacts volumetric productivity – specifically, the number of copies of a heterologous transgene encoding a recombinant protein significantly influences yield and genetic stability. Current methods for determining the copy number of recombinant transgenes have limited quantitative resolution for large constructs or constructs with high copy numbers. We developed a pipeline that uses long-read sequencing data to determine transgene copy number for large, complex, and repetitive integrants. This pipeline can be used to characterize existing strains, evaluate the impact of copy number on productivity, support optimization of process conditions, inform strain engineering decisions, and minimize confounding variables in comparative studies. We also explored novel reactor operating conditions for engineered strains of K. phaffii. We developed a stepped perfusion fermentation method that allowed for testing of up to 11 different operating conditions within a single reactor run. We used this method to identify promising process parameters for the commercial production of NIST mAb in perfusion fermentation. We also developed a novel feeding strategy for fed-batch fermentation of trastuzumab-producing strains. Closing the productivity gap between K. phaffii and traditional manufacturing hosts could unlock considerable economic and operational advantages for agile and flexible manufacturing. This thesis introduces specific genetic engineering changes and operational fermentation modes that significantly improved the productivity of several proteins of interest in K. phaffii. The strategies and tools developed and presented here provide a foundation for continued engineering of this organism to achieve target productivities and can be broadly applied to enhance the production of recombinant proteins across diverse microbial systems.

Interpretable Learning and Control of Physical Dynamical Systems

2026-04-22T03:02:17Z

Interpretable Learning and Control of Physical Dynamical Systems Cohen, Alexander Ethan The laws of physics are encoded as differential equations that describe how systems evolve in time and space. These equations are traditionally derived by analyzing fundamental physical interactions. However, in many settings the observed phenomenon is too complex or high-dimensional to yield tractable differential equation models, resulting in partially or completely unknown governing equations. Advances in machine learning have yielded powerful predictive and generative models for complex and high-dimensional systems, but applications of these techniques to scientific problems often lack interpretability and are prone to violating physical constraints, especially in data-limited settings. Hybrid methods that integrate physical structure with data-driven analyses have emerged as a promising approach for accurate, interpretable modeling of complex physical systems. This thesis develops frameworks for learning interpretable dynamical models by combining first-principles structure with modern data-driven and machine learning methods. The resulting models aim to be accurate, computationally efficient, interpretable, and useful for prediction, control, and scientific understanding. The first part of this thesis develops tools for modeling biophysical dynamics, with a focus on animal motion, behavior, and neural activity. These systems are challenging to model due to their high dimensionality, nonlinearities, and limited availability of comprehensive datasets. To address these challenges, this thesis introduces spectral mode representations as a low-dimensional and interpretable representation space for biological dynamics. While the form of the models for these biological systems is unknown, general physical symmetries and biological constraints are known. Accordingly, methods for incorporating physical constraints into dynamical models in mode space are developed and applied to animals whose dynamics can be effectively described by centerline motion. For linear dynamics, the constrained models are Hermitian, and this structure is utilized to compare dynamics across species and behavioral states. The framework is then extended to nonlinear, stochastic dynamics and neural control to build generative models of neural activity, motion, and behavior. Learning is facilitated by decomposing the vector field via a Helmholtz decomposition into gradient and divergence-free components, which are optimized separately using score matching, diffusion models, and generalized Hamiltonian dynamics. Coupling the dynamical system to neural activity enables prediction of motion from neural signals and the design of neural control strategies for steering motion. Applied to C. elegans, this model reproduces posture statistics across behavioral states, captures stereotyped dynamics, and links neural activity to interpretable modes of motion. The second part of this thesis applies similar hybrid techniques to complex materials described by nonlinear partial differential equations (PDEs). These systems are challenging to model because key parameters and functions are often unknown and the equations are computationally intensive to simulate and optimize. To address these challenges, an open-source, GPU-accelerated framework is developed for optimization and control of phase-separating and pattern-forming PDEs, enabling efficient gradient-based optimization of arbitrary PDE parameters in complex domains. This tool is then leveraged to solve representative, experimentally relevant learning and control problems in energy and quantum materials, illustrating how physically structured optimization can be used to learn models for and control pattern formation. Building on this framework, this thesis develops methods for learning continuum models from molecular dynamics simulations of phase separation. Starting from particle-based simulations of a phase-separating Lennard–Jones fluid, coarse-grained continuum fields are constructed via diffusion-like smoothing operators and effective dynamical equations are learned in this continuum space. The coarse-graining procedure is linked to diffusion models from machine learning by demonstrating that the learned dynamics correspond to score functions of the coarse-grained distributions. Finally, this thesis demonstrates the broader applicability of these ideas through several collaborative projects, including models for controlling indoor airborne disease transmission and for learning mosquito behavioral responses to multi-sensory stimuli. Across these domains, the thesis advances a common perspective: integrating physical structure with data-driven models yields dynamical systems that are both interpretable and useful for prediction and control problems in many areas of science and engineering.

From Real Estate to Hybrid (Real Estate + Tech) How Public Markets Reprice Data Center REITs

2026-04-22T03:07:35Z

From Real Estate to Hybrid (Real Estate + Tech) How Public Markets Reprice Data Center REITs Oh, Andrew This thesis investigates the evolving pricing identity of U.S. listed data center REITs from 2015 to 2025. Data centers occupy an ambiguous position in public markets: they are structured as real estate investment trusts yet increasingly perceived as technology infrastructure. This study asks whether public market pricing reflects this ambiguity and, if so, how the pricing relationship has changed over time. Using weekly return data for a two-constituent data center REIT basket (Equinix and Digital Realty), an equal-weight REIT benchmark excluding data centers, and two technology equity ETFs (XLK and SMH), the analysis employs three complementary methods: 52-week rolling correlations, regime-level two-factor identity regressions with Newey-West standard errors, and rolling two-factor betas. The sample is partitioned into four regimes aligned with macroeconomic conditions: pre-COVID baseline (2015–2019), COVID shock and aftermath (2020–2021), rate-hike stress (2022–2023), and post-hike period (2024–2025). The findings reveal a pronounced identity shift. In the baseline regime, data center REITs exhibited strong REIT alignment and near-zero technology exposure, consistent with traditional real estate pricing. During 2020–2021, REIT alignment collapsed while technology alignment emerged under the broad tech proxy, though not under semiconductors. Post2022, a hybrid identity crystallized: both REIT and technology coefficients became positive and statistically significant across proxy specifications. Rolling estimates confirm that this hybrid structure is persistent rather than episodic. The results contribute to understanding how public markets classify assets with hybrid characteristics. The shift from REIT-only to hybrid pricing identity suggests that investors increasingly view data center REITs through a blended real estate and technology lens, with implications for benchmarking, portfolio construction, and the organizational boundary between operating platforms and property ownership. The pricing identity of listed data center REITs has evolved from predominantly real estate to a durable hybrid of real estate and technology.

When Negotiation Hurts and When It Doesn’t: Relational Consequences of Negotiation in Rental Markets

2026-04-22T03:07:40Z

When Negotiation Hurts and When It Doesn’t: Relational Consequences of Negotiation in Rental Markets Wang, Laura Changlan Negotiation is widely encouraged as a means of improving economic outcomes, yet concerns persist that negotiating may undermine interpersonal relationships. Integrating research on negotiation, relationships, and social norms, I argue that the relational consequences of negotiation depend on whether negotiating aligns with contextual norms. Using 19-month longitudinal field data from the U.S. rental real estate market (N = 2,678 tenants), I examined how the occurrence of a negotiation predicts tenants’ subjective value during lease signing and downstream behavioral outcomes. Tenants who negotiated during the lease-signing process reported lower subjective value than those who did not, but only when negotiation was perceived as violating prevailing norms. Specifically, the perception that the landlord had communicated lease terms were negotiable—particularly the perception that this was communicated publicly— and perceived market conditions favorable to tenants attenuated or eliminated these negative associations. Lower subjective value, in turn, predicted shorter planned length of stay, which subsequently predicted a shorter actual tenancy one year later. These findings show that negotiation is not inherently harmful to relationships; rather, its interpersonal consequences hinge on normative context. By identifying social norms as a key boundary condition, this research advances understanding of when negotiation undermines relationships and how early interactions shape long-term outcomes.

Sharpness-Aware Minimization (SAM) Improves Classification Accuracy of Bacterial Raman Spectral Data Enabling Portable Diagnostics

2026-04-22T03:08:58Z

Sharpness-Aware Minimization (SAM) Improves Classification Accuracy of Bacterial Raman Spectral Data Enabling Portable Diagnostics Zareno, Kaitlin Antimicrobial resistance is expected to claim 10 million lives per year by 2050, and resource-limited regions are most affected. In these regions, access to quality hospital-level clinical care and microbiological testing is limited. Thus, it is important to develop a solution for bacterial infection diagnosis that is rapid, reliable, and compact. Two techniques that can help contribute to more reliable diagnostic tools are Raman spectroscopy and deep learning (DL). Raman spectroscopy is a novel pathogen diagnostic approach that promises rapid and portable antibiotic resistance, and can provide results significantly faster then culturing methods. Despite its advantages, current algorithms for Raman spectral analysis 1) are unable to generalize well on limited datasets across diverse patient populations; and 2) require increased complexity due to the low-quality nature of Raman spectral data. In this work, we address the intrinsic challenges of working with Raman spectral data by utilizing Sharpness-Aware Minimization-based (SAM) optimizers for Raman spectral data analysis. We specifically observe this in clinical and non-clinical bacterial isolate classification tasks. We demonstrate that SAM-based optimizers achieve accuracy improvements on bacterial classification tasks. These results display the capability of SAM to advance the application of AI-powered Raman spectroscopy tools, enabling progress towards the translation of rapid on-device pathogen diagnostics for patients in need.

Environmental Drivers of Coastal Evolution

2026-04-22T03:04:59Z

Environmental Drivers of Coastal Evolution Sanborn, Lily H. Coastal evolution is shaped by the processes that influence sediment transport at coastlines. The present morphology, sedimentology, and distribution of modern coastal landforms reflect the influence of Holocene environmental processes that have altered the availability and movement of sediment at the coast. In this thesis, I leverage sediment transport modeling, sedimentary field data, and geospatial analysis to constrain the influence of specific environmental drivers of coastal change since the last deglaciation. I present case studies from near-field (North Atlantic) and far-field (tropical Pacific) coastal environments. Chapter 2 models the effects of glacial ice retreat on surface hydrology and quantifies resulting effects on fluvial sediment transport, showing that landscape changes that accompany deglaciation shape the evolution of rivers and the delivery of sediment to the coast. Chapter 3 explores interactions between paleohurricanes and evolving barrier coastlines, examining how coastal development influences the deposition and preservation of sedimentary paleohurricane records that can be used to understand long-term storm recurrence. Chapter 4 reconstructs the influence of Holocene environmental processes on the development of Pacific atoll islands, demonstrating that both sea-level change and temporal variations in storm-driven sediment transport mediated the timing and pace of island formation. Chapter 5 examines processes affecting atoll island development at the regional scale, quantifying patterns in the distribution of islands on atoll reefs relative to local wave climates. Together, these chapters connect coastal sedimentary records and modern morphologies to environmental processes that have shaped coastal development. Through quantitative examination of coastal sedimentary environments, we gain insight into the influence of environmental processes on coastal development across timescales longer than permitted by historic observations. By constraining the magnitude and nature of environmental influences on coastal sediment transport, this thesis contributes to a broader understanding of how environmental change has shaped the evolution of coastlines.

Lithium Garnet Development and Advances for Use in Next-Generation Solid-State Batteries

2026-04-22T03:05:03Z

Lithium Garnet Development and Advances for Use in Next-Generation Solid-State Batteries Hinricher, Jesse J. Batteries power our modern society, but their development is outpaced by technology that requires increasing power output and time between charges. Lithium metal is the most energy dense anode choice available and is the most attractive anode material due to its low density, high specific capacity, and low electrochemical potential. Lithium anodes form dendrites which grow from the anode and, if they contact the cathode, lead to short circuit which could result in thermal runaway and ignition of the organic liquid electrolyte that is the most used electrolyte. Instead, solid-state electrolytes (SSEs) have the potential to omit liquid electrolytes, making them a safer alternative with Li₇La₃Zr₂O₁₂ (LLZO), one of the most commonly used SSEs. Fabrication is typically in the form of ceramic pellets or tapes that require high-temperature processing exceeding 1000 ºC. In contrast, the fabrication method used herein relies on a direct liquid precursor to solid film spray method, Sequential Decomposition Synthesis (SDS), that requires a maximum processing temperature of only 500-750 ºC and yields a low thickness of several microns comparable to standard polymer separators in the battery field. Collectively, this contributes to increasing energy density to more than 30% for next generation batteries, by utilizing a solid-state electrolyte with a low-potential lithium metal anode. In this thesis, two major areas were focused on: in the first part, SDS-synthesized LLZO has been integrated into batteries for the first time and its cycling stability determined in operating conditions. A distinct advantage of SDS is its low synthesis cost combined with a naturally resulting film thickness in a range difficult to achieve by other methods (1-20 µm), making it easier to incorporate these films into existing battery architectures. It is demonstrated that SDS LLZO electrolyte layers can successfully be deposited on glass fiber support structures and integrated to hybrid semi liquid/solid demonstration cell architecture, suitable for next generation battery prototypes. Further, an amorphous LLZO phase was used to mitigate substrate temperature requirements and grain boundary lithium dendrite propagation. Successful cell cycling of 150 cycles at >80% capacity retention has been demonstrated. The successful demonstration of such low-processing temperature films is significant in the field of energy storage where the majority of projected costs from solid-state electrolyte fabrication is born by processing, rather than raw materials cost. The second thesis part examines the ability of direct-liquid precursor-to-solid-state electrolyte synthesis via SDS for high throughput and Bayesian optimization-guided synthesis of a high number LLZO chemistries and ceramic nanostructures. In general, solid-state electrolyte development takes many years of labor and resource-intensive brute-force methods driven by humans to optimize conductivity, phase, or processing parameters (e.g. temperature). Instead, a data-driven approach using Bayesian optimization can better traverse a large sample space while requiring fewer samples than a simple grid search which could take months or years to reach the same optimization. We demonstrate that this approach can optimize a surrogate objective function to maximize conductivity by applying it to the well-studied LLZO material. Raman spectroscopy is non-destructive, quick, and information rich and fast which aids in rapid advances. In the first for the field, an SDS system was constructed to mix precursor solutions on demand to span an experimentally plausible composition range. In total, fewer than 100 samples were required to achieve a phase-pure and conductive film of LLZO. The computer-guided campaign optimized the cubic-LLZO phase and elucidated limits to its composition and doping that could be applied to guide future studies. This breakthrough platform enables rapid development of solid-state electrolytes for next-generation batteries. This approach can be adapted to novel solid-state materials that haven’t been synthesized before, enabling faster development of next-generation electrolytes and their batteries.

Bridging the Logics of Technocracy and Communitarianism: Translational Micropractices in Housing and Urban Development

2026-04-22T03:02:27Z

Bridging the Logics of Technocracy and Communitarianism: Translational Micropractices in Housing and Urban Development Gade, Anisha Our current, entrenched housing crisis requires attending to frictions and conflicting agendas between resident groups and developers of many stripes. Each of these sets of actors maintains distinctive aims, knowledge-creation methods, measures of success and relations to power - all of which amount to a bundle of communicative and meaning-making practices that I term a ‘logic.’ The two logics entail, on the one hand, the technocratic logic indicative of developers, landlords, and sometimes, planners who are oriented to apolitical neutrality, deploy empirical data analysis, establish meaning through quantitative metrics and financial valuations, and are proximate to power. On the other hand, communitarian logics deployed by certain resident groups and their advocates prioritize the redress of harms, trust lived experience, establish meaning through longstanding cultural practices, and are typically distant from power. This dissertation seeks to understand how proponents of these two incommensurable logics struggle to become legible to and negotiate with one another. Facilitating the institutional contexts where such tensions are translated and negotiated are intermediaries of many kinds, including, at times, planners. I explore the process of translation and negotiation between the two logics (a phenomenon that I call ‘bridging’) through three, in-depth case studies. Each of my cases exemplifies an experimental effort at realigning technocratic practices, to varying degrees, with the priorities of residents, even as these priorities are not monolithic. The first case is the creation of a novel cultural preservation district and anti-displacement zoning overlay. The second involves two parallel projects: a participatory action research study linking housing stability to social determinants of health and a real estate equity fund whose ROI metrics are tied to the public health study. And thirdly, public housing administrators deploy digital tenant screening services—proptech—to streamline housing allocation and therefore, improve housing access for poor and disabled populations. With the three cases, I assess the efficacy of realignment efforts by examining the varying degrees of bridging between the two logics. I find that the highest degree of bridging is achieved through the deployment of translational “micropractices” (Healey 2012a). In the cases where this is prevalent, the experimental solution had the greatest potential to make good on its promises. Following the lessons from my cases, to overcome the deep intractability of the current housing crisis, I argue that planners, advocates and scholars must engage with the institutional–and not just economic or market-related–contexts where the political economic dynamics of the relationship between the housing industry and community development proponents play out.

Development of Small Angle Neutron Scattering Tools for Probing the Phase-Behavior and Self-Assembly of Sequence-Defined Polymers

2026-04-22T03:01:27Z

Development of Small Angle Neutron Scattering Tools for Probing the Phase-Behavior and Self-Assembly of Sequence-Defined Polymers Dai, Kexin (Charlotte) Sequence control in polymers offer a powerful platform for programming self-assembly and phase behavior with monomer-level precision, bridging the structural control of biological macromolecules and the functional tunability of synthetic polymers. However, understanding how primary sequence dictates structure and material properties remains a fundamental challenge due to the vast design space and the lack of high-throughput characterization tools and predictive models. This thesis addresses these challenges by combining recombinant polymer synthesis, coarse-grained simulations, and information-guided scattering techniques to interrogate how molecular-level sequence features drive mesoscale structure, phase behavior, and self-assembly in dilute and concentrated aqueous environments. To enable efficient structural screening, a high-throughput small-angle neutron scattering (SANS) workflow was developed to rapidly evaluate large libraries of sequence-defined polymers under diverse solvent and temperature conditions. By analyzing three classes of model polymer systems and simulating reduced-count datasets, it is shown that accurate parameter estimates (within 5–10% of full-count values) can be obtained using only 1–50% of the original counts, depending on the sample and parameter. Both AIC and BIC can successfully identify the correct model from a candidate set, even with limited data. This approach provides a practical framework to optimize SANS beamtime and supports efficient structural characterization of material libraries. On the experimental end, elastin like polypeptides (ELPs) were used as a model system to understand sequence-property relationships. Thermoresponsive behavior of ABA triblock ELPs was systematically investigated. Two variants differing only in midblock hydrophobicity revealed distinct phase behaviors: one formed temperature-triggered micellar aggregates with syneresis, while the other remained viscoelastic and unstructured across conditions. SANS, rheology, and depolarized light scattering collectively demonstrated how sequence-encoded hydrophobicity and block architecture govern micelle formation, water partitioning, and gel properties near the LCST. The sequence-property landscape was mapped using a series of ELPs with varied guest residues. Turbidimetry in water/ethanol/salt mixtures uncovered how hydrophobicity and charge combine define cononsolvency boundaries and critical transition temperatures, highlighting the role of solvation competition in shaping UCST- and LCST-type transitions. Finally, a highly coarse-grained dumbbell model was developed for globular protein– polymer bioconjugates. Molecular dynamics simulations demonstrated that treating the protein as a hard sphere tethered to a soft polymer tail as a soft sphere captures the essential physics of self-assembly in those systems. Simulated phase diagrams aligned with experimental trends and provided a predictive model for designing protein-polymer bioconjugates and understanding the thermodynamic driving force of self-assembly. By integrating high-throughput structural characterization, insightful coarse-grained models, and large amount of experimental data, this thesis provides quantitative design rules for encoding self-assembly and transitions across molecular, mesoscale, and macroscopic regimes— paving the way for next-generation programmable polymer materials.

Always just over the horizon: Reprocessing and the perpetually promised future

2026-04-22T03:07:00Z

Always just over the horizon: Reprocessing and the perpetually promised future Ridzuan Chun, Noor Maslinda binti After the collapse of nuclear commercial reprocessing ventures in the 1970s in the U.S., reprocessing became a policy taboo; yet in the following decades, it repeatedly resurfaced and receded—returning as promise, as policy, as horizon. This paper asks why reprocessing endures in expert and governmental imaginaries despite sustained consensus regarding unfavorable economics, environmental liabilities, and proliferation risks. It argues that reprocessing persists not as an effective backend solution but as a speculative remainder: an infrastructure and imaginary whose institutional life was never conclusively closed and whose promissory futures remain suspended, available for reactivation whenever waste, scarcity, or sovereignty demands technical redemption. Through paired case studies of West Valley, New York—the nation’s only operating commercial reprocessing plant (1966–1972), subsequently converted by the 1980 West Valley Demonstration Project into a federally managed cleanup—and Barnwell, South Carolina—a fully constructed facility stranded by the post-1974 nonproliferation turn, evolving nuclear safeguards, and waste-solidification requirements—the essay traces a migration of purpose from mid-century abundance (fuel extension, breeder futures, a “closed” cycle) to later rationales of waste mitigation, energy security, and, most recently, climate policy. Methodologically, this paper reads reprocessing as speculative infrastructure, drawing on STS scholarship on co-production, sociotechnical imaginaries, anticipatory governance, and the “politics of impossibility” that renders long-lived harm administratively manageable while deferring resolution. The historiographical intervention is twofold: first, to reposition reprocessing from a marginal cautionary tale to a central device through which the U.S. nuclear enterprise sustains legitimacy—a placeholder that promises eventual closure of waste and fuel constraints while enabling continued front-end expansion; second, to reconceptualize energy history as governance by deferral and remainder rather than a linear arc of innovation and obsolescence, wherein breakdown yields repurposing and policy displacement rather than decommissioning. Seen in this light, the Ford–Carter moratoria, Reagan-era revivalism, the Bush-era Global Nuclear Energy Partnership, and twenty-first-century “advanced recycling” campaigns appear as cyclical reanimations that attach new vocabularies to an unresolved core, keeping the future—deliberately—just over the horizon.

Building the Infrastructure for Innovation: A Market Entry Strategy for Cambridge Innovation Center in India

2026-04-22T03:09:23Z

Building the Infrastructure for Innovation: A Market Entry Strategy for Cambridge Innovation Center in India Kalra, Shagun This thesis examines whether the Cambridge Innovation Center’s ecosystem-based model can be adapted to India’s rapidly evolving innovation landscape. CIC’s global approach integrates space, community, and advisory functions to strengthen coordination among research institutions, corporates, and government actors. While this model emerged in highly coherent environments such as Kendall Square and later expanded successfully to cities like Rotterdam and Tokyo, its applicability to India requires careful evaluation given the country’s institutional fragmentation and regulatory complexity. The analysis draws on market research, expert conversations, policy review, and comparative international precedent. It assesses India’s feasibility across five dimensions: market demand, financial return potential, policy environment, institutional partnership strength, and social and environmental alignment. The resulting feasibility index, though interpretive, indicates that none of these dimensions pose structural barriers to CIC’s entry. Instead, India’s deepening capital markets, state-level innovation programs, and rising corporate interest in open innovation position the country as a promising but operationally demanding opportunity. Based on these findings, the thesis proposes a phased entry strategy anchored in a joint-venture or management agreement structure with a credible Indian partner. A focused pilot campus would test partner reliability and market fit, followed by broader institutional integration and, eventually, a distributed multi-city network. While risks related to regulation, currency exposure, and market volatility require deliberate governance and design, they are manageable within a structured partnership model. Taken together, the analysis suggests that India offers a viable and strategically meaningful expansion path for CIC.

Revealing the Chemistry, Structure, and Self-Assembly of the Lead Sulfide Nanocrystal Ligand Shell through Small-Angle Neutron Scattering

2026-04-22T03:03:31Z

Revealing the Chemistry, Structure, and Self-Assembly of the Lead Sulfide Nanocrystal Ligand Shell through Small-Angle Neutron Scattering Price, Eliza K. Colloidal, semiconducting nanocrystals (NCs), or quantum dots, are composed of an inorganic core coated in an organic ligand shell and are of interest for next-generation sensing, photovoltaic, and computing devices. Successful integration of NCs into devices requires rigorous synthetic control and materials characterization. In pursuit of this goal, the impact of the inorganic core of NCs on devices has been thoroughly studied, where the size, composition, and monodispersity of the core determine the bandgap of NCs through quantum confinement effects. The organic ligand shell is also critical to the processibility, self-assembly, and functional properties of NCs, but the structure-property relationships of ligands are not well understood due to the insensitivity of common structural characterization methods to the ligand shell. This thesis advances understanding of the structure and impact of the organic ligand shell coating the surface of semiconducting nanocrystals by studying the chemistry, structure, and self-assembly behavior of the ligand shell for the model system of lead sulfide (PbS) NCs. We apply a combination of experimental techniques and thermodynamic modeling, and in particular, leverage small-angle neutron scattering (SANS) to directly characterize the structure of the ligand shell, resolving details of NC surface termination, ligand shell thickness, and ligand shell solvation that cannot be seen with other methods. This thesis highlights best practices for ligand shell characterization and new insights for the synthesis of NCs and design of NC SLs in our studies of PbS NCs. First, we show that the surface termination and ligand passivation of PbS NCs are sensitive to the post-synthetic purification method. Using a combination of SANS, 1H-NMR, and optical characterization, we distinguish the core size, optical emission, and ligand exchange properties of PbS[RNH3+Cl-] and PbS@PbClx NCs. Then, we apply a suite of scattering techniques, including SANS and small-angle X-ray scattering (SAXS), to quantify how the colloidal structure of the ligand shell coating PbS NCs impacts interparticle interactions and self-assembly. We observe unexpected trends in the colloidal structure of the NC ligand shell, arising from curvature and solvent-dependent interactions, and find that the colloidal ligand shell structure correlates with the structure of self-assembled NC superlattices. Finally, we show that ligand entropy impacts the coherent orientation of NCs within close-packed NC superlattices. Since SANS methods are nascent within the field of colloidal nanocrystals, this thesis documents the theory of scattering methods and presents an optimal experimental design framework for SANS studies. Using statistical information theory, we quantify how the uncertainty of the structural parameters describing the NC ligand shell varies with SANS experimental design. Ultimately, the experimental design framework provides a rational framework through which to navigate tradeoffs of cost, measurement time, and parameter certainty in SANS experiments. This thesis advances understanding of the structure-property relationships of the organic ligand shell coating semiconducting NCs, pioneering SANS as a uniquely sensitive method to understand organic ligands. These studies lay the groundwork for future efforts of ligand shell design with unprecedented structural understanding through SANS.

Cracks of Opportunity: Unveiling the Impact of Misconduct Disclosure on Workers’ Careers

2026-04-22T03:09:33Z

Cracks of Opportunity: Unveiling the Impact of Misconduct Disclosure on Workers’ Careers Garg, Moksh Organizational misconduct and its disclosure can have lasting effects on the careers of key organizational members, yet little is known about its impact on lower-ranked employees. Prior research focuses primarily on elites, offering only a partial understanding of how disclosure shapes outcomes across the broader workforce. To extend this line of inquiry, we develop a duallens theoretical framework that conceptualizes misconduct disclosure as both discrediting and reformative. While disclosure can stigmatize employees through association with wrongdoing, it can also disrupt exploitative organizational arrangements, potentially creating opportunities for upward mobility among lower-ranked workers. Leveraging detailed firm-level microdata and Brazil’s major state-led anti-corruption crackdown as an exogenous shock, we find evidence consistent with our framework: upper white-collar workers experience significant wage declines and are more likely to exit the formal sector, whereas blue-collar employees who remain in or reenter formal employment experience large and persistent wage gains. These effects hold across gender and racial groups and withstand multiple robustness checks, suggesting a wage-leveling mechanism triggered by the disruption of exploitative pay structures. Together, our findings reveal how misconduct disclosure, while punitive for some, can unintentionally advantage others by reshaping organizational hierarchies and reducing pay inequality.

What Do Large Factor Models Learn? Self-Induced Regularization, Cost of Overfitting, and Self-Adaptivity

2026-04-22T03:09:10Z

What Do Large Factor Models Learn? Self-Induced Regularization, Cost of Overfitting, and Self-Adaptivity Xiong, Xin This paper studies the out-of-sample performance of large, overparameterized linear factor models for stochastic discount factor (SDF) estimation. Motivated by recent advances in finance and machine learning, we analyze the all-inclusive ridge estimator that incorporates all candidate factors without ex-ante screening or dimension reduction. Our new non-asymptotic pricing error bounds reveal that including many low-variance principal components implicitly increases the effective penalty on high-variance components, shrinking the estimated SDF and biasing performance. We further show that exact interpolation in the low-variance space leads to bounded out-of-sample deterioration, as the fitted coefficients behave effectively like zero and incur little additional error relative to underspecification. In addition, ridge regression self-adapts by emphasizing top principal components without knowing which factors are important, effectively mimicking a data-driven cutoff in principal component regression. These findings extend benign overfitting theory to the inherently misspecified setting of SDF estimation, where classical linear regression assumptions fail. Empirically, we validate these insights using U.S. equity data and large factor sets constructed via Random Fourier Features (RFF). We find that noise factors degrade performance through bias rather than variance inflation, and that adding weak but priced factors can hurt model performance. Lastly, we show that mildly negative ridge penalties can enhance model performance, consistent with our theoretical prediction that they partially offset self-induced regularization.

Development of a Biohybrid Tendon Interface for Muscle-Powered Robots

2026-04-22T03:09:53Z

Development of a Biohybrid Tendon Interface for Muscle-Powered Robots Castro, Nicolas S. Unlike metal and plastic, biological materials can communicate with their surroundings, adapt to stimuli, and self-repair damage. Incorporating these materials into engineered systems could foster smarter, more adaptable machines. We have shown that engineered skeletal muscle stretched around an elastomer ‘skeleton’ can generate force and drive locomotion. However, the interface between the biotic and abiotic components of this robotic system are friction-based, leading to inefficient force transmission. In the body, muscle is covalently tethered to bone via tendons, which efficiently transmit force. Thus, we have developed a bioinspired synthetic tendon to act as a biohybrid interface, enabling the design of more modular and adaptive biohybrid machines. We developed a tough adhesive hydrogel tendon, composed of a poly(acrylic acid) hydrogel functionalized for tissue adhesion with N-Hydroxysuccinimide ester groups, in collaboration with the Zhao Lab at MIT. Muscle tissues were manufactured from C2C12 mouse myoblasts seeded into a fibrin and Matrigel matrix. Peel tests of these differentiated and undifferentiated muscle tissues bound to the synthetic tendon revealed that the biotic-abiotic interface could withstand forces >500mN before breaking. This is significantly greater than those generated from the contraction of engineered muscle (~300uN), demonstrating a robust binding. A cell viability assay and pH exposure test confirmed that the synthetic tendon had no significant impact on muscle health, indicating general biocompatibility. Furthermore, we have been able to bind strips of mature engineered skeletal muscle tissue between two strips of synthetic tendon, akin to myotendinous junctions in vivo. We have leveraged this tendon-muscle-tendon (TMT) construct as a modular actuator that can be mechanically coupled to robotic skeletons to generate force and produce motion. We are evaluating the effects of varying synthetic tendon stiffness and preload tension on the force production capability of these units, enabling the optimized design and deployment of TMT actuators in untethered machines. In short, we have developed a hydrogel tendon system that serves as a robust biocompatible musculoskeletal interface.

Online Education Opens up Learning Opportunities Agnostic of Age and Geography

2026-04-27T12:32:52Z

Online Education Opens up Learning Opportunities Agnostic of Age and Geography Datta, Shoumen An array of biased clues (suggestions; planning tools) for autodidacts to explore and pursue paths known and parts unknown.

A Global Assessment of the Controls on the Fractionation of Arc Magmas

2026-04-18T03:08:21Z

A Global Assessment of the Controls on the Fractionation of Arc Magmas Klein, Benjamin Z; Jagoutz, Oliver; Schmidt, Max W; Kueter, Nico During the differentiation of arc magmas, fractionating liquids follow a series of cotectics, where the co-crystallization of multiple minerals control the melt compositional trajectories, commonly referred to as liquid lines of descent (LLD). These cotectics are sensitive to intensive properties, including fractionation pressure and melt H2O concentration, and changes in these variables produce systematic differences in the LLDs of arc lavas. Based on a compilation of experimental studies, we develop two major element proxies that exploit differences in LLDs to constrain the fractionation conditions of arc magmas. Near-primary fractionating magmas evolve along the olivine-clinopyroxene cotectic, which is pressure-sensitive. We use this sensitivity to develop a proxy for early fractionation pressure based on the normative mineral compositions of melts with 8 ± 1 wt.% MgO. Fractionation in more evolved magmas is controlled by the clinopyroxene-plagioclase cotectic, which is strongly sensitive to magmatic H2O contents. We use this relationship to develop an H2O proxy that is calibrated to the normative mineral components of melts with 2–4 wt.% MgO. These two proxies provide new tools for estimating the variations in pressure and temperature between magmatic systems. We applied these proxies to compiled major element data and phenocryst assemblages from modern volcanic arcs and show that in island arcs early fractionation is relatively shallow and magmas are dominantly H2O-poor, while continental arcs are characterized by more hydrous and deeper early fractionation. These differences likely reflect variations in the relative contributions of decompression and flux melting in combination with distinct upper plate controls on arc melt generation.

A Systems Analysis of Sustainability Impacts of Agricultural Policies in India

2026-04-18T03:05:15Z

A Systems Analysis of Sustainability Impacts of Agricultural Policies in India Maji, P; Selin, NE We apply a systems framework for analyzing the overall sustainability impacts of interventions to a case of the rice‐wheat cropping system of Punjab (India), where agricultural practices lead to air pollution‐related health impacts, over‐exploitation of groundwater, over‐use of fertilizers and reduced local crop diversity. We use this case to quantify how varying degrees of change in interventions result in sustainability impacts using an inclusive wealth‐based approach. We show that either improving the existing cropping system or inducing fundamental changes in the cropping system, can lead to substantial and wide‐ranging sustainability benefits. We also show that interventions that improve human health show the largest quantitative benefit due to the assumed high marginal value of human life. Accurate localized estimates of marginal values of stocks are needed for estimating overall sustainability impacts.

Oriented Bedrock Samples Drilled by the Perseverance Rover on Mars

2026-04-18T03:06:13Z

Oriented Bedrock Samples Drilled by the Perseverance Rover on Mars Weiss, Benjamin P; Mansbach, Elias N; Carsten, Joseph L; Kaplan, Kyle W; Maki, Justin N; Wiens, Roger C; Bosak, Tanja; Collins, Curtis L; Fentress, Jennifer; Feinberg, Joshua M; Goreva, Yulia; Kennedy Wu, Megan; Estlin, Tara A; Klein, Douglas E; Kronyak, Rachel E; Moeller, Robert C; Peper, Nicholas; Reyes‐Newell, Adriana; Sephton, Mark A; Shuster, David L; Simon, Justin I; Williford, Kenneth H; Stack, Kathryn W; Farley, Kenneth A A key objective of the Perseverance rover mission is to acquire samples of Martian rocks for future return to Earth. Eventual laboratory analyses of these samples would address key questions about the evolution of the Martian climate, interior, and habitability. Many such investigations would benefit greatly from samples of Martian bedrock that are oriented in absolute Martian geographic coordinates. However, the Mars 2020 mission was designed without a requirement for orienting the samples. Here we describe a methodology that we developed for orienting rover drill cores in the Martian geographic frame and its application to Perseverance's first 20 rock samples. To orient the cores, three angles were measured: the azimuth and hade of the core pointing vector (i.e., vector oriented along the core axis) and the core roll (i.e., the solid body angle of rotation around the pointing vector). We estimated the core pointing vector from the attitude of the rover's Coring Drill during drilling. To orient the core roll, we used oriented images of asymmetric markings on the bedrock surface acquired with the rover's Wide Angle Topographic Sensor for Operations and eNgineering (WATSON) camera. For most samples, these markings were in the form of natural features on the outcrop, while for four samples they were artificial ablation pits produced by the rover's SuperCam laser. These cores are the first geographically-oriented (<2.7° 3σ total uncertainty) bedrock samples from another planetary body. This will enable a diversity of paleomagnetic, sedimentological, igneous, tectonic, and astrobiological studies on the returned samples.

A Post‐Launch Summary of the Science of NASA's Psyche Mission

2026-04-18T03:06:55Z

A Post‐Launch Summary of the Science of NASA's Psyche Mission Dibb, Steven D; Asphaug, Erik; Bell, James F; Binzel, Richard P; Bottke, William F; Cambioni, Saverio; Christoph, John M; Elkins‐Tanton, Linda T; Jaumann, Ralf; Lawrence, David J; Oran, Rona; O’Rourke, Joseph G; Polansky, Carol; Weiss, Benjamin P; Wieczorek, Mark; Williams, David A Astronomical observations indicate that asteroid (16) Psyche is a large, high-density (likely >3,400 kg·m−3), metal-rich (30–55 vol. %) asteroid. Psyche may be remnant core material or it could be a primordial, undifferentiated metal-rich object. We discuss the science objectives of the upcoming Psyche mission, which will employ three instruments (the Magnetometer, Multispectral Imager, and Gamma-Ray and Neutron Spectrometer) and will use Doppler tracking of the spacecraft to explore the asteroid. This mission will shed light on the nature and origins of metal-rich objects in the solar system and beyond, including the cores of the terrestrial planets.

Remotely Sensed Soil Moisture Can Capture Dynamics Relevant to Plant Water Uptake

2026-04-18T03:08:17Z

Remotely Sensed Soil Moisture Can Capture Dynamics Relevant to Plant Water Uptake Feldman, Andrew F; Short Gianotti, Daniel J; Dong, Jianzhi; Akbar, Ruzbeh; Crow, Wade T; McColl, Kaighin A; Konings, Alexandra G; Nippert, Jesse B; Tumber‐Dávila, Shersingh Joseph; Holbrook, Noel M; Rockwell, Fulton E; Scott, Russell L; Reichle, Rolf H; Chatterjee, Abhishek; Joiner, Joanna; Poulter, Benjamin; Entekhabi, Dara A frequently expressed viewpoint across the Earth science community is that global soil moisture estimates from satellite L‐band (1.4 GHz) measurements represent moisture only in a shallow surface layer (0–5 cm) and consequently are of limited value for studying global terrestrial ecosystems because plants use water from deeper rootzones. Using this argumentation, many observation‐based land surface studies avoid satellite‐observed soil moisture. Here, based on peer‐reviewed literature across several fields, we argue that such a viewpoint is overly limiting for two reasons. First, microwave soil emission depth considerations and statistical considerations of vertically correlated soil moisture information together indicate that L‐band measurements carry information about soil moisture extending below the commonly referenced 5 cm in many conditions. However, spatial variations of effective depths of representation remain uncertain. Second, in reviewing isotopic tracer field studies of plant water uptake, we find a prevalence of vegetation that primarily draws moisture from these upper soil layers. This is especially true for grasslands and croplands covering more than a third of global vegetated surfaces. Even some deeper‐rooted species (i.e., shrubs and trees) preferentially or seasonally draw water from the upper soil layers. Therefore, L‐band satellite soil moisture estimates are more relevant to global vegetation water uptake than commonly appreciated (i.e., relevant beyond only shallow soil processes like soil evaporation). Our commentary encourages the application of satellite soil moisture across a broader range of terrestrial hydrosphere and biosphere studies while urging more rigorous estimates of its effective depth of representation.

Electrospun Liquid-Infused Membranes for Emulsified Oil/Water Separation

2026-04-18T03:05:10Z

Electrospun Liquid-Infused Membranes for Emulsified Oil/Water Separation Song, Chen; Rutledge, Gregory C From an environmental perspective, microfiltration membranes are attractive for the separation of emulsified oils from contaminated water. However, fouling of the membrane is a major drawback of the technology. "Liquid-infused membranes" (LIMs) have the potential to eliminate membrane fouling. Here, we demonstrate the practical application of LIMs for the separation of oil from a stable oil-in-water emulsion and characterize their resistance to fouling. The base membrane is an electrospun nonwoven fibrous layer of the fluorinated copolymer poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-co-HFP). The surface energy of the PVDF-co-HFP fibers was lowered by the covalent attachment of a fluorinated silane (PFOCTS), and then, the membrane was infused with a perfluoropolyether. The membrane was then challenged with model emulsions of dodecane in water in a cross-flow configuration. This PFOCTS-modified LIM showed better infused liquid stability, permeation selectivity, higher permeate flux than the unmodified LIM, and better anti-fouling properties than the bare membrane without infused liquid. We also examine the mechanism for transport of the dispersed oil phase through the liquid-infused membrane. We find a linear relationship between the dodecane flux and dodecane concentration in the feed and a higher dodecane flux through the PFOCTS-modified membrane than the unmodified one, which suggests that the capture of dodecane droplets from the feed plays an important role in determining the overall rate of permeation. Other factors such as lower viscosity of the infused liquid, larger pore size, and higher operating pressure also improved the permeate flux through the LIMs. Overall, this work provides some guidelines on the design of composite membranes comprising infused liquids and the choice of operating conditions for the filtration process.

Experiments and Modeling of Flow-Enhanced Nucleation in LLDPE

2026-04-18T03:07:10Z

Experiments and Modeling of Flow-Enhanced Nucleation in LLDPE Nicholson, David A; Andreev, Marat; Kearns, Kenneth L; Chyasnavichyus, Marius; Monaenkova, Daria; Moore, Jonathan; den Doelder, Jaap; Rutledge, Gregory C A computational and experimental framework for quantifying flow-enhanced nucleation (FEN) in polymers is presented and demonstrated for an industrial-grade linear low-density polyethylene (LLDPE). Experimentally, kinetic measurements of isothermal crystallization were performed by using fast-scanning calorimetry (FSC) for melts that were presheared at various strain rates. The effect of shear on the average conformation tensor of the melt was modeled with the discrete slip-link model (DSM). The conformation tensor was then related to the acceleration in nucleation kinetics by using an expression previously validated with nonequilibrium molecular dynamics (NEMD). The expression is based on the nematic order tensor of Kuhn segments, which can be obtained from the conformation tensor of entanglement strands. The single adjustable parameter of the model was determined by fitting to the experimental FSC data. This expression accurately describes FEN for the LLDPE, representing a significant advancement toward the development of a fully integrated processing model for crystallizable polymers.

Statistics of Gaussian polymer chains in harmonic applied fields

2026-04-18T03:06:29Z

Statistics of Gaussian polymer chains in harmonic applied fields Mikhail, John P; Rutledge, Gregory C The model of an ideal polymer chain in a harmonic applied field has broad applicability in situations involving polymer confinement and deformation due to applied stress. In this work we (1) formulate a general analytical model for a continuous Gaussian chain under a harmonic applied potential and (2) evaluate the statistical mechanics of this model given the potential, obtaining partition functions and moment generating functions (MGFs) that describe the chain configurations. Closed-form expressions for the squared radius of gyration, potential energy, partition function, and MGF for the center of mass are obtained for a general and multidimensional harmonic field. The expressions are compared with results of Monte Carlo simulations of a discrete Gaussian chain as well as results for related systems obtained from the literature. The theory derived here is used to test the applicability of the current model assumptions to relations from the literature describing polymer confinement and deformation in experiment, theory, and simulations.

Development of Electrospun Polyacrylonitrile Aerosol Filter Media for Respiratory Personal Protective Equipment

2026-04-18T03:04:58Z

Development of Electrospun Polyacrylonitrile Aerosol Filter Media for Respiratory Personal Protective Equipment Ewell, Nathan; Lee, Jeonyoon; Mulherin, Kristen; Rutledge, Gregory C The COVID-19 pandemic highlighted the importance of respiratory personal protective equipment (PPE) as a means of reducing the spread of disease via aerosolized droplets. For years, N95-type filtering facepiece respirators based on meltblown polypropylene nonwovens have been the technology of choice for healthcare professionals and personal use. However, their reliance on electrostatic charges to achieve an acceptable trade-off between filtration efficiency and pressure drop has led to concerns about shelf life, reusability, quality control, and versatility of materials. In this study, we show that media in which an electrospun polyacrylonitrile (PAN) nonwoven serves as the active layer, comprising fibers of much smaller diameter than typical meltblown fibers, can achieve high levels of filtration efficiency combined with low pressure drop without the assistance of electrostatic charging. Moreover, the aerosol filtration data is well-described by the slip flow-modified Kuwabara model for pressure drop and a single fiber efficiency model that takes into account particle collection via diffusion, interception, and impaction. These models may be used to guide the further design of nanofiber filters. Combined with a spunbond substrate, the proposed filtration media resolves practical concerns regarding mechanical robustness and residual solvent, and it has been fabricated into filtering facepiece respirators that meet N95 filtration standards when tested by standard methods.

Heterogeneous Nucleation of High-Density Polyethylene Crystals on Graphene within Microdomains

2026-04-18T03:07:56Z

Heterogeneous Nucleation of High-Density Polyethylene Crystals on Graphene within Microdomains Volchko, Nathan W; Rutledge, Gregory C In polymer processes, nucleating agents are often used to control the kinetics of crystallization, but their application remains largely a matter of trial and error. Thermodynamically, the efficiency of a nucleating agent can be quantified by the difference in substrate/crystal, crystal/melt, and substrate/melt interfacial energies, Δσ. In this work, the efficiency of graphene nanoplatelets (GNP) as nucleating agents for high-density polyethylene (HDPE) is investigated. HDPE nucleates and crystallizes rapidly, so Δσ can be especially difficult to measure experimentally. To overcome this difficulty, blends of HDPE+GNP are confined to microdomains so that crystallization becomes nucleation limited. Two methods of microdomain formation are employed. In the first, HDPE+GNP is melt-blended with an immiscible matrix of polystyrene (PS), and crystallization is characterized using differential scanning calorimetry (DSC); in the second, dispersions of HDPE+GNP in toluene are sprayed onto PS substrates, and crystallization is characterized with polarized optical microscopy (POM). Heterogeneous nucleation rates at several crystallization temperatures and for several GNP loadings were measured by these two methods and found to give excellent agreement across GNP loadings. The value of Δσ for HDPE+GNP is calculated to be 0.83 ± 0.18 erg/cm2. This value is only 2.8 times larger than that reported for HDPE nucleated heterogeneously on a HDPE fiber, a nearly ideal nucleating agent for HDPE, and much smaller than many of the best nucleating agents reported for other polymers. We conclude that GNP is an efficient nucleating agent for HDPE.

Three-Dimensional Imaging of Emulsion Separation through Liquid-Infused Membranes Using Confocal Laser Scanning Microscopy

2026-04-18T03:08:05Z

Three-Dimensional Imaging of Emulsion Separation through Liquid-Infused Membranes Using Confocal Laser Scanning Microscopy Song, Chen; Rutledge, Gregory C The removal of emulsified oils from water has always been a challenge due to the kinetic stability resulting from the small droplet size and the presence of stabilizing agents. Membrane technology can treat such mixtures, but fouling of the membrane leads to dramatic reductions in the process capacity. Liquid-infused membranes (LIMs) can potentially resolve the issue of fouling. However, their low permeate flux compared with conventional hydrophilic membranes remains a limitation. To gain insight into the mechanism of transport, we use 3D images acquired by confocal laser scanning microscopy (CLSM) to reconstruct the sequence of events occurring during startup and operation of the LIM for removal of dispersed oil from oil-in-water emulsions. We find evidence for coalescence of oil droplets on the surface of and formation of oil channels within the LIM. Using image analysis, we find that the rate at which oil channels are formed within the membrane and the number of channels ultimately govern the permeate flux of oil through the LIMs. Oil concentration in the feed affects the rate of coalescence of oil on the surface of the LIM, which, in turn, affects the channel formation dynamics. The channel formation dynamics also depend on the viscosity of the infused liquid and the operating pressure. A higher affinity to the pore wall for infused liquid than permeating liquid is essential to antifouling behavior. Overall, this work offers insight into the selective permeation of a dispersed liquid phase through a LIM.

Shape-Stable Composites of Electrospun Nonwoven Mats and Shear-Thickening Fluids

2026-04-18T03:07:19Z

Shape-Stable Composites of Electrospun Nonwoven Mats and Shear-Thickening Fluids Hao, Junli; Ding, Jie; Rutledge, Gregory C To improve the flexibility of the fabric stacks used in protective clothing, shear-thickening fluids (STFs) have previously been incorporated into woven microfiber fabrics to enhance their impact resistance. However, the microfiber-STF composites can exhibit loss of the STF from the composite over time due to the large interstitial spaces between fibers, resulting in limited long-term shape stability. In this study, nonwoven mats of electrospun ultrafine fibers (UFFs) were used in place of woven microfiber fabrics to improve the STF retention within the fiber-STF composites by taking advantage of high specific surface area, small pore size, and large capillary force. The UFF-STF composite, comprising an electrospun polyamide (PA 6,6) UFF mat and a fumed silica (FS) STF, exhibited excellent shape stability with high breakthrough pressure and improved STF retention compared to composites based on conventional microfiber fabrics. The mechanical response of the composite is shown to depend on the rate of deformation. At strain rates lower than the shear-thickening threshold of the STF, the introduction of STF resulted in no stiffening or strengthening of fiber mats, allowing the composite to remain flexible. At high deformation rates above the onset of shear thickening, the incorporation of STF improved both the elasticity and the viscosity of the material. In addition, the shape stability and the mechanical properties of the composite were influenced by the STF viscosity and the UFF morphology. STF with high particle loading and UFF with small fiber diameter resulted in a more pronounced enhancement to membrane performance.

MIT Faculty Newsletter Vol. XXXVIII No. 3, November/December 2025

2026-04-18T03:01:12Z

MIT Faculty Newsletter Vol. XXXVIII No. 3, November/December 2025 MIT Faculty Newsletter

MIT Faculty Newsletter Vol. XXXVIII No. 2, October 2025: Special Edition

2026-04-18T03:01:04Z

MIT Faculty Newsletter Vol. XXXVIII No. 2, October 2025: Special Edition MIT Faculty Newsletter

MIT Faculty Newsletter Vol. XXXVIII No. 1, September/October 2025

2026-04-18T03:01:11Z

MIT Faculty Newsletter Vol. XXXVIII No. 1, September/October 2025 MIT Faculty Newsletter

MIT Faculty Newsletter Vol. XXXVII No. 5, May/June 2025

2026-04-18T03:01:14Z

MIT Faculty Newsletter Vol. XXXVII No. 5, May/June 2025 MIT Faculty Newsletter

Propagation in the Drought Cascade: Observational Analysis Over the Continental US

2026-04-17T03:06:28Z

Propagation in the Drought Cascade: Observational Analysis Over the Continental US Entekhabi, Dara Persistent deficits in meteorological, hydrological and ecological variables define different types of drought that are often linked together in a cascade. The drought cascade is an emergent phenomenon in the climatic system. A quantitative evaluation of the drought cascade based on observations is both a rigorous test of how different components of numerical Earth System models interact with one‐another and a useful tool for practitioners concerned with drought impacts on water resource and ecosystem services. In this study the drought cascade is characterized over the continental US using remote sensing data and in situ observations. Remote sensing fields of coincident vegetation photosynthesis and above‐ground biomass anomalies are introduced into the cascade to assess the role of the terrestrial biosphere within the cascade. The propagation of the diverse drought types, in terms of amplitude‐dampening and phase‐delays, are quantified. It is shown that woody and herbaceous vegetation have contrasting responses to prolonged soil moisture deficit that is traceable to access and storage of water as well as the dual effects of water‐ and light‐limitation. The observations also show that prolonged precipitation deficit in itself is not adequate to trigger the intense soil moisture and vegetation stress responses and that excessive atmospheric evaporative demand needs to be coincident with the precipitation anomalies.

Land Surfaces at the Tipping‐Point for Water and Energy Balance Coupling

2026-04-17T03:06:45Z

Land Surfaces at the Tipping‐Point for Water and Energy Balance Coupling Dong, Jianzhi; Akbar, Ruzbeh; Feldman, Andrew F; Gianotti, Daniel Short; Entekhabi, Dara The surface water and energy balances can be coupled or uncoupled depending on whether the evaporation regime is water‐limited or energy‐limited. As the landscape loses soil moisture during drydowns, a transition between the regimes may occur, which signifies a nonlinear change in water‐energy‐carbon coupling. Regions that switch often between these two regimes, that is, are dominated by neither regime, are particularly vulnerable to climate variability and change. To robustly identify these tipping points, we identify drydown events based on global soil moisture data sets from remote sensing. The event identification does not rely on precipitation information and is robust with respect to measurement noise. Then, the soil moisture thresholds delineating the evaporation regime transitions are determined by Sequential Monte Carlo Sampling and a two‐stage parametrization strategy. Based on the estimated soil moisture thresholds across the globe, we estimate observation‐based water availability indices which quantify the nonlinear controls of soil moisture on evaporation. This framework is tested and applied globally using Soil Moisture Active Passive soil moisture retrievals. Combined with a new tippling‐point metric that describes the frequency of evaporation regime transitions, we identify regions that switch often between different evaporation regimes at the global scale. Given unit shifts in soil moisture, these regions will experience the most change in how their surface water and energy are coupled.

Experimental Investigations of Fracture Deformation, Flow, and Transport Using a Pressure‐Controlled Hele‐Shaw Cell and Digital Fabrication

2026-04-17T03:07:12Z

Experimental Investigations of Fracture Deformation, Flow, and Transport Using a Pressure‐Controlled Hele‐Shaw Cell and Digital Fabrication Villamor‐Lora, Rafael; Germaine, John T; Einstein, Herbert H In this paper we present a novel pressure‐controlled Hele‐Shaw cell to investigate different physical processes in rough fractures using 3D‐printed rock analogs. Our system can measure high‐resolution fracture aperture and tracer concentration maps under relevant field stress conditions. Using a series of hydraulic and visual measurements, combined with numerical simulations, we investigate the evolving fracture geometry characteristics, pressure‐dependent hydraulic transmissivity, flow channeling, and the nature of mass transport as a function of normal stress. Our experimental results show that as the fracture closes and deforms under increasing normal loading: (a) the contact areas grow in number and size; (b) the flow paths become more focused and tortuous; and (c) the transport dynamics of conservative tracers evolve toward a higher dispersive regime. Moreover, under the applied experimental conditions, we observed excellent agreement between the simulated‐ and the experimentally measured‐hydraulic behavior.

Modeling the Production of Heinrich Layers With a Sediment‐Enabled Iceberg Model

2026-04-17T03:05:46Z

Modeling the Production of Heinrich Layers With a Sediment‐Enabled Iceberg Model Fendrock, Michaela; Condron, Alan; McGee, David In the North Atlantic, relatively coarse grained sediments can be found periodically throughout sediment cores spanning the Last Glacial Period. These sediments were rafted by icebergs released from the Laurentide Ice Sheet (LIS) in so‐called Heinrich Events. These “Heinrich Layers” coincide with records of global climate change, suggesting that the impact of these events was propagated beyond the North Atlantic. In order to best understand the climate context and significance of Heinrich Events, it is important to constrain the mechanism for their release from the LIS and the nature of the ice sheet itself. One approach for investigating the source of Heinrich Events is to understand the sediment load of icebergs involved, information that would inform interpretations of how those icebergs were produced. By simulating Heinrich Events in a high resolution global climate model (20–40 times the resolution of previous studies), this work investigates the processes involved in the deposition of Heinrich Layers in the North Atlantic. In these simulations, the same volume of sediment is distributed differently through the same volume of icebergs, producing profoundly different sediment records. Due to the high resolution of the model, these simulated sedimentary layers can be inspected in great detail, revealing nuances of the deposit. Only when sediment is distributed throughout the entire iceberg does the model produce a sediment pattern in agreement with observations, yet icebergs with this sediment distribution are not observed in the modern‐day.

Evaluating the Drivers of Quaternary Dust Fluxes to the Western North Pacific: East Asian Dustiness and Northern Hemisphere Gustiness

2026-05-08T03:13:05Z

Evaluating the Drivers of Quaternary Dust Fluxes to the Western North Pacific: East Asian Dustiness and Northern Hemisphere Gustiness Abell, Jordan T; Winckler, Gisela; Pullen, Alex; Kinsley, Christopher W; Kapp, Paul A; Middleton, Jennifer L; Pavia, Frank J; McGee, David; Ford, Heather L; Raymo, Maureen E Quantifying variability in, and identifying the mechanisms behind, East Asian dust production and transport across the last several million years is essential for constraining future dust emissions and deposition. Our current understanding of East Asian dust dynamics through the Quaternary is primarily limited to low‐resolution records from the North Pacific Ocean, those from the Chinese Loess Plateau (CLP), and paleoenvironmental reconstructions from arid basins. All are susceptible to sediment winnowing and focusing as well as input of poorly constrained or unidentified non‐dust detrital material. To avoid these limitations, we examine high‐resolution, constant flux proxy‐derived dust fluxes from the North Pacific and find evidence for higher glacial dust fluxes in the late Pliocene‐early Pleistocene compared to the late Pleistocene‐Holocene. Our results suggest decreasing dust transported to the mid‐latitude North Pacific Ocean from eastern Asia across the Quaternary. This observation is ostensibly at odds with previous dust records from marine sediments and the CLP, and with the perception of higher East Asian dust production and transport during the late Pleistocene associated with the amplification of glaciations. We provide three possible scenarios to describe the ∼2,700‐ky evolution of eastern Asia glacial dust dynamics, and discuss them in the context of sediment production, availability, and atmospheric circulation. Our data and proposed driving mechanisms not only raise questions about the framework typically used to interpret dust archives from East Asia and the North Pacific Ocean, but also provide a roadmap for hypothesis testing and future work necessary to produce better‐constrained records of paleo‐dust fluxes.

Capturing Equatorial Pacific Variability With Multivariate Sr‐U Coral Thermometry

2026-05-08T03:14:18Z

Capturing Equatorial Pacific Variability With Multivariate Sr‐U Coral Thermometry Mollica, NR; Cohen, AL; Horton, F; Oppo, DW; Solow, AS; McGee, D Sr-U, a coral-based paleothermometer, corrects for the effects of Rayleigh Fractionation on Sr/Ca by regressing multiple, paired U/Ca and Sr/Ca values. Prior applications of Sr-U captured mean annual sea surface temperatures (SSTs), inter-annual variability, and long-term trends. However, because many Sr/Ca-U/Ca pairs are needed for a single Sr-U value as originally formulated, the temporal resolution of the proxy is typically limited to 1 year. Here, we address this limitation by applying laser ablation inductively coupled plasma mass spectrometry (LA-ICPMS) to three Porites colonies from Jarvis and Nikumaroro Islands in the central equatorial Pacific (CEP), generating ∼25 Sr/Ca-U/Ca pairs per month of skeletal growth. Both Sr/Ca and U/Ca vary significantly over small (sub-mm) length scales and support the calculation of Sr-U values using the original regression method. Over the represented temperature range of 24–31°C, the Sr/Ca-U/Ca-SST relationships are nonlinear, a finding consistent with predictions of the Rayleigh model. To reflect this non-linearity, we developed a calibration using multivariate nonlinear regression. The multivariate, three-coral calibration was applied to 20 years of monthly resolved Sr/Ca and U/Ca of a coral interval not included in the calibration, yielding RMSE = 0.73°C and r2 = 0.85 (p < 0.05; df = 256). The multivariate calibration performed significantly better than Sr/Ca alone (r2 = 0.28). Applying the new calibration to a subfossil Porites from Kiritimati Atoll, CEP (2200 Before Present) yields equivalent phase and amplitude of interannual variability, but water temperatures ∼1.6°C cooler than they are in this region today.

The Flux‐Differencing Discontinuous Galerkin Method Applied to an Idealized Fully Compressible Nonhydrostatic Dry Atmosphere

2026-04-17T03:07:39Z

The Flux‐Differencing Discontinuous Galerkin Method Applied to an Idealized Fully Compressible Nonhydrostatic Dry Atmosphere Souza, AN; He, J; Bischoff, T; Waruszewski, M; Novak, L; Barra, V; Gibson, T; Sridhar, A; Kandala, S; Byrne, S; Wilcox, LC; Kozdon, J; Giraldo, FX; Knoth, O; Marshall, J; Ferrari, R; Schneider, T Dynamical cores used to study the circulation of the atmosphere employ various numerical methods ranging from finite-volume, spectral element, global spectral, and hybrid methods. In this work, we explore the use of Flux-Differencing Discontinuous Galerkin (FDDG) methods to simulate a fully compressible dry atmosphere at various resolutions. We show that the method offers a judicious compromise between high-order accuracy and stability for large-eddy simulations and simulations of the atmospheric general circulation. In particular, filters, divergence damping, diffusion, hyperdiffusion, or sponge-layers are not required to ensure stability; only the numerical dissipation naturally afforded by FDDG is necessary. We apply the method to the simulation of dry convection in an atmospheric boundary layer and in a global atmospheric dynamical core in the standard benchmark of Held and Suarez (1994, https://doi.org/10.1175/1520-0477(1994)075〈1825:apftio〉2.0.co;2).

An optimal transport approach to estimating causal effects via nonlinear difference-in-differences

2026-05-08T03:14:09Z

An optimal transport approach to estimating causal effects via nonlinear difference-in-differences Torous, William; Gunsilius, Florian; Rigollet, Philippe We propose a nonlinear difference-in-differences (DiD) method to estimate multivariate counterfactual distributions in classical treatment and control study designs with observational data. Our approach sheds a new light on existing approaches like the changes-in-changes estimator and the classical semiparametric DiD estimator, and it also generalizes them to settings with multivariate heterogeneity in the outcomes. The main benefit of this extension is that it allows for arbitrary dependence between the coordinates of vector potential outcomes and includes higher-dimensional unobservables, something that existing methods cannot provide in general. We demonstrate its utility on both synthetic and real data. In particular, we revisit the classical Card & Krueger dataset, which reports fast food restaurant employment before and after a minimum wage increase. A reanalysis with our methodology suggests that these restaurants substitute full-time labor with part-time labor on aggregate in response to a minimum wage increase. This treatment effect requires estimation of the multivariate counterfactual distribution, an object beyond the scope of classical causal estimators previously applied to this data.

A Method for Estimating Global Subgrid‐Scale Orographic Gravity‐Wave Temperature Perturbations in Chemistry‐Climate Models

2026-04-17T03:07:32Z

A Method for Estimating Global Subgrid‐Scale Orographic Gravity‐Wave Temperature Perturbations in Chemistry‐Climate Models Weimer, M; Wilka, C; Kinnison, DE; Garcia, RR; Bacmeister, JT; Alexander, MJ; Dörnbrack, A; Solomon, S Many chemical processes depend non-linearly on temperature. Gravity-wave-induced temperature perturbations have been shown to affect atmospheric chemistry, but accounting for this process in chemistry-climate models has been a challenge because many gravity waves have scales smaller than the typical model resolution. Here, we present a method to account for subgrid-scale orographic gravity-wave-induced temperature perturbations on the global scale for the Whole Atmosphere Community Climate Model. Temperature perturbation amplitudes urn:x-wiley:19422466:media:jame21928:jame21928-math-0001 consistent with the model's subgrid-scale gravity wave parameterization are derived and then used as a sinusoidal temperature perturbation in the model's chemistry solver. Because of limitations in the parameterization, we explore scaling of urn:x-wiley:19422466:media:jame21928:jame21928-math-0002 between 0.6 and 1 based on comparisons to altitude-dependent urn:x-wiley:19422466:media:jame21928:jame21928-math-0003 distributions of satellite and reanalysis data, where we discuss uncertainties. We probe the impact on the chemistry from the grid-point to global scales, and show that the parameterization is able to represent mountain wave events as reported by previous literature. The gravity waves for example, lead to increased surface area densities of stratospheric aerosols. This increases chlorine activation, with impacts on the associated chemical composition. We obtain large local changes in some chemical species (e.g., active chlorine, NOx, N2O5) which are likely to be important for comparisons to airborne or satellite observations, but the changes to ozone loss are more modest. This approach enables the chemistry-climate modeling community to account for subgrid-scale gravity wave temperature perturbations interactively, consistent with the internal parameterizations and are expected to yield more realistic interactions and better representation of the chemistry.

New biological solutions to the many problems of our time

2026-05-08T03:14:12Z

New biological solutions to the many problems of our time Lauffenburger, Douglas A; Koeppl, Heinz Douglas A. Lauffenburger is Ford Professor of Engineering at the Massachusetts Institute of Technology (MIT). He is faculty member in the Department of Biological Engineering, which he co-founded in 1998. In an interview with Heinz Koeppl, Director of the Centre for Synthetic Biology at Technical University Darmstadt, he explains how Biological Engineering differs from traditional bioengineering, what the education program in this discipline at MIT is based on and why we need biological engineering to solve many of our current challenges from climate change to world food supplies.

Automated Machine Learning to Evaluate the Information Content of Tropospheric Trace Gas Columns for Fine Particle Estimates Over India: A Modeling Testbed

2026-04-17T03:07:52Z

Automated Machine Learning to Evaluate the Information Content of Tropospheric Trace Gas Columns for Fine Particle Estimates Over India: A Modeling Testbed Zheng, Zhonghua; Fiore, Arlene M; Westervelt, Daniel M; Milly, George P; Goldsmith, Jeff; Karambelas, Alexandra; Curci, Gabriele; Randles, Cynthia A; Paiva, Antonio R; Wang, Chi; Wu, Qingyun; Dey, Sagnik India is largely devoid of high-quality and reliable on-the-ground measurements of fine particulate matter (PM2.5). Ground-level PM2.5 concentrations are estimated from publicly available satellite Aerosol Optical Depth (AOD) products combined with other information. Prior research has largely overlooked the possibility of gaining additional accuracy and insights into the sources of PM using satellite retrievals of tropospheric trace gas columns. We evaluate the information content of tropospheric trace gas columns for PM2.5 estimates over India within a modeling testbed using an Automated Machine Learning (AutoML) approach, which selects from a menu of different machine learning tools based on the data set. We then quantify the relative information content of tropospheric trace gas columns, AOD, meteorological fields, and emissions for estimating PM2.5 over four Indian sub-regions on daily and monthly time scales. Our findings suggest that, regardless of the specific machine learning model assumptions, incorporating trace gas modeled columns improves PM2.5 estimates. We use the ranking scores produced from the AutoML algorithm and Spearman’s rank correlation to infer or link the possible relative importance of primary versus secondary sources of PM2.5 as a first step toward estimating particle composition. Our comparison of AutoML-derived models to selected baseline machine learning models demonstrates that AutoML is at least as good as user-chosen models. The idealized pseudo-observations (chemical-transport model simulations) used in this work lay the groundwork for applying satellite retrievals of tropospheric trace gases to estimate fine particle concentrations in India and serve to illustrate the promise of AutoML applications in atmospheric and environmental research.

Processes Controlling Methane Emissions From a Tropical Peatland Drainage Canal

2026-04-17T03:06:46Z

Processes Controlling Methane Emissions From a Tropical Peatland Drainage Canal Somers, Lauren D; Hoyt, Alison; Cobb, Alexander R; Isnin, Suhailah; Suhip, Muhammad Asri Akmal bin Haji; Sukri, Rahayu S; Gandois, Laure; Harvey, Charles Most peat domes in Southeast Asia are crisscrossed by networks of drainage canals. These canals are a potentially important source of methane to the atmosphere because the groundwater that discharges into them carries high concentrations of dissolved methane that is produced within peat. In this study, we present an isotope‐enabled numerical model that simulates transport, degassing, and oxidation of methane and dissolved inorganic carbon (DIC) along a drainage canal. We then estimate methane fluxes through a 5‐km canal that crosses a disturbed, forested, but undeveloped, peat dome in Brunei Darussalam by applying this model to field data: concentrations and stable carbon isotopic ratios of both methane and dissolved inorganic carbon from both peat porewater and canal water. We estimate that approximately 70% of the methane entering the canal is oxidized within the canal, 26% is degassed to the atmosphere, and 4% is transported toward the ocean, under low to moderate flow conditions. The flux of methane to the atmosphere is lowest at the maximum elevation of the canal, where flow is stagnant and methane concentrations are highest. Downstream, as flow velocity increases, methane emissions plateau even as methane concentrations decrease. The resulting methane emissions from the canal are large compared to emissions from the peat surface and vegetation on a per‐area basis. However, since the canal covers only a small portion of the catchment area, the canal may be a substantial but not dominant source of methane from the peatland.

Future trends in stratosphere-to-troposphere transport in CCMI models

2026-05-08T03:13:03Z

Future trends in stratosphere-to-troposphere transport in CCMI models Abalos, Marta; Orbe, Clara; Kinnison, Douglas E; Plummer, David; Oman, Luke D; Jöckel, Patrick; Morgenstern, Olaf; Garcia, Rolando R; Zeng, Guang; Stone, Kane A; Dameris, Martin One of the key questions in the air quality and climate sciences is how tropospheric ozone concentrations will change in the future. This will depend on two factors: changes in stratosphere-to-troposphere transport (STT) and changes in tropospheric chemistry. Here we aim to identify robust changes in STT using simulations from the Chemistry Climate Model Initiative (CCMI) under a common climate change scenario (RCP6.0). We use two idealized stratospheric tracers to isolate changes in transport: stratospheric ozone (O3S), which is exactly like ozone but has no chemical sources in the troposphere, and st80, a passive tracer with fixed volume mixing ratio in the stratosphere. We find a robust increase in the tropospheric columns of these two tracers across the models. In particular, stratospheric ozone in the troposphere is projected to increase 10 %–16 % by the end of the 21st century in the RCP6.0 scenario. Future STT is enhanced in the subtropics due to the strengthening of the shallow branch of the Brewer–Dobson circulation (BDC) in the lower stratosphere and of the upper part of the Hadley cell in the upper troposphere. The acceleration of the deep branch of the BDC in the Northern Hemisphere (NH) and changes in eddy transport contribute to increased STT at high latitudes. These STT trends are caused by greenhouse gas (GHG) increases, while phasing out of ozone-depleting substances (ODS) does not lead to robust transport changes. Nevertheless, the decline of ODS increases the reservoir of ozone in the lower stratosphere, which results in enhanced STT of O3S at middle and high latitudes. A higher emission scenario (RCP8.5) produces stronger STT trends, with increases in tropospheric column O3S more than 3 times larger than those in the RCP6.0 scenario by the end of the 21st century.

Vegetation‐Driven Seasonal Sediment Dynamics in a Freshwater Marsh of the Mississippi River Delta

2026-04-17T03:08:00Z

Vegetation‐Driven Seasonal Sediment Dynamics in a Freshwater Marsh of the Mississippi River Delta Beltrán‐Burgos, M; Esposito, CR; Nepf, HM; Baustian, MM; Di Leonardo, DR Deltaic wetlands are critically important coastal environments, upon which hundreds of millions of people depend. Managing and preserving them in the face of sea level rise will be a challenging task over the next century that will require land managers to devise restoration strategies that maximize the delivery and storage of mineral sediments, and to apportion limited sediment resources to priority locations. We collected a unique field data set characterizing sediment delivery to, and retention in, a deltaic wetland throughout a period of rapidly changing emergent and submerged vegetation conditions in the spring of 2019. Our results demonstrate that wetland deposition is extremely sensitive to the timing of the flood pulse with respect to vegetation conditions, and that vegetation alone can adjust sediment delivery and retention by one or more orders of magnitude over a period of weeks. In planning for wetland management operations, it will be critical for managers to assess these rapidly changing conditions as they influence project success.

The Erosional Signature of Drainage Divide Motion Along the Blue Ridge Escarpment

2026-05-08T03:13:12Z

The Erosional Signature of Drainage Divide Motion Along the Blue Ridge Escarpment Stokes, Maya F; Larsen, Isaac J; Goldberg, Samuel L; McCoy, Scott W; Prince, Philip P; Perron, J Taylor The planform rearrangement of river basins is recognized as an important process for landscape evolution. The boundaries of river basins can shift either through gradual drainage divide migration or discrete river captures, but the methods for identifying these processes often rely on topographic evidence that remains otherwise untested. Moreover, efforts to understand the relative importance of either process are hampered by a lack of age constraints on river captures. We use 10Be-derived erosion rates to test whether, and how, divide motion is occurring at three locations along the Blue Ridge Escarpment in the Appalachian Mountains. In the Pee Dee River basin, we find that the escarpment is migrating inland up to 45 m/Myr, consistent with topographic evidence for gradual divide migration. In the Dan River basin, erosion rates support the topographic evidence for river capture, and we use a forward model of river incision to estimate that the capture likely occurred in the past 12.5 Myr. In the South Fork Roanoke River basin, where the presence of a knickzone has been interpreted as evidence that a river capture initiated a pulse of faster erosion, we instead measure nearly uniform tributary erosion rates above and within the mainstem knickzone. Simulations show that river incision into a more erodible layer of rock, with or without a river capture, could produce the observed topography and erosion rates in the South Fork Roanoke River. Our results show how multiple lines of evidence can illuminate the rates and mechanisms of river basin reorganization.

The significant impact of aerosol vertical structure on lower atmosphere stability and its critical role in aerosol–planetary boundary layer (PBL) interactions

2026-05-08T03:14:11Z

The significant impact of aerosol vertical structure on lower atmosphere stability and its critical role in aerosol–planetary boundary layer (PBL) interactions Su, Tianning; Li, Zhanqing; Li, Chengcai; Li, Jing; Han, Wenchao; Shen, Chuanyang; Tan, Wangshu; Wei, Jing; Guo, Jianping The aerosol–planetary boundary layer (PBL) interaction was proposed as an important mechanism to stabilize the atmosphere and exacerbate surface air pollution. Despite the tremendous progress made in understanding this process, its magnitude and significance still have large uncertainties and vary largely with aerosol distribution and meteorological conditions. In this study, we focus on the role of aerosol vertical distribution in thermodynamic stability and PBL development by jointly using micropulse lidar, sun photometer, and radiosonde measurements taken in Beijing. Despite the complexity of aerosol vertical distributions, cloud-free aerosol structures can be largely classified into three types: well-mixed, decreasing with height, and inverse structures. The aerosol–PBL relationship and diurnal cycles of the PBL height and PM2.5 associated with these different aerosol vertical structures show distinct characteristics. The vertical distribution of aerosol radiative forcing differs drastically among the three types, with strong heating in the lower, middle, and upper PBL, respectively. Such a discrepancy in the heating rate affects the atmospheric buoyancy and stability differently in the three distinct aerosol structures. Absorbing aerosols have a weaker effect of stabilizing the lower atmosphere under the decreasing structure than under the inverse structure. As a result, the aerosol–PBL interaction can be strengthened by the inverse aerosol structure and can be potentially neutralized by the decreasing structure. Moreover, aerosols can both enhance and suppress PBL stability, leading to both positive and negative feedback loops. This study attempts to improve our understanding of the aerosol–PBL interaction, showing the importance of the observational constraint of aerosol vertical distribution for simulating this interaction and consequent feedbacks.

The impacts of biomass burning activities on convective systems over the Maritime Continent

2026-04-17T03:07:15Z

The impacts of biomass burning activities on convective systems over the Maritime Continent Lee, Hsiang-He; Wang, Chien Convective precipitation associated with Sumatra squall lines and diurnal rainfall over Borneo is an important weather feature of the Maritime Continent in Southeast Asia. Over the past few decades, biomass burning activities have been widespread during summertime over this region, producing massive fire aerosols. These additional aerosols, when brought into the atmosphere, besides influencing the local radiation budget through directly scattering and absorbing sunlight, can also act as cloud condensation nuclei or ice nuclei to alter convective clouds and precipitation over the Maritime Continent via so-called aerosol indirect effects. Based on 4-month simulations with or without biomass burning aerosols, conducted using the Weather Research and Forecasting model coupled with a chemistry module (WRF-Chem), we have investigated the aerosol–cloud interactions associated with biomass burning aerosols over the Maritime Continent. Results from selected cases of convective events have specifically shown the significant impact of fire aerosols on weak convections by their increasing of the quantities of hydrometeors and rainfall in both the Sumatra and Borneo regions. Statistical analysis over the fire season also suggests that fire aerosols have impacts on the nocturnal convections associated with the local anticyclonic circulation in western Borneo and weaken nocturnal rainfall intensity by about 9 %. Such an effect is likely to have come from the near-surface heating due to absorbing aerosols emitted from fires, which could weaken land breezes and thus the convergence of anticyclonic circulation.

AI to Identify Strain-Sensitive Regions of the Optic Nerve Head Linked to Functional Loss in Glaucoma

2026-04-17T03:07:08Z

AI to Identify Strain-Sensitive Regions of the Optic Nerve Head Linked to Functional Loss in Glaucoma Chuangsuwanich, Thanadet; Nongpiur, Monisha E; Braeu, Fabian A; Prasad, Shimna Clara; Tun, Tin A; Thiéry, Alexandre; Perera, Shamira; Ho, Ching Lin; Buist, Martin; Barbastathis, George; Aung, Tin; Girard, Michaël JA Purpose: The purposes of this study were to assess whether optic nerve head (ONH) biomechanics, quantified by tissue strain, improves classification of progressive visual field (VF) loss patterns in glaucoma beyond morphology, and to use saliency maps to identify ONH regions associated with the predictions. Methods: We recruited 249 patients with glaucoma (mean age 69 ± 5 years, 54% female patients). One eye per subject was imaged under (1) primary gaze and (2) primary gaze with IOP elevated to approximately 35 millimeters of mercury (mm Hg) via ophthalmo-dynamometry. Twelve subjects were excluded due to poor scan quality/limited lamina cribrosa (LC) visibility. Experts classified subjects into four categories based on the presence of specific visual field defects (VFDs): (1) superior nasal step (N = 26), (2) superior partial arcuate (N = 62), (3) full superior hemifield defect (N = 25), and (4) other/non-specific defects (N = 124). Automatic segmentation and digital volume correlation computed neural tissue and LC strains. Biomechanical and structural features were input to a PointNet model. Three classification tasks were performed to detect: (1) superior nasal step, (2) superior partial arcuate, and (3) full superior hemifield defect. Data were split 80/20 (train/test). Area under the curve (AUC) assessed performance. Saliency maps (an explainable artificial intelligence [XAI] technique) highlighted ONH regions most critical to classification. Results: Models achieved AUCs of 0.77 to 0.88 across VFD classifications. The structure-only model reached an AUC of 0.83 ± 0.02 for superior arcuate defects, which significantly improved to 0.87 ± 0.02 (P < 0.05) with the addition of strain information, demonstrating that ONH biomechanics enhance prediction beyond morphology. Strain-sensitive regions were localized to the inferior and inferotemporal rim, expanding with increasing severity of VF loss. Conclusions: ONH strain enhances classification of glaucomatous VF loss patterns. The neuroretinal rim, rather than the LC, was most critical, suggesting rim strain may play a dominant role in axonal injury and functional loss.

Intact Perceptual Interactions of Interocular Temporal Phase and Contrast Disparities in Amblyopia

2026-05-08T03:14:27Z

Intact Perceptual Interactions of Interocular Temporal Phase and Contrast Disparities in Amblyopia Kosovicheva, Anna; Ahmed, Zain; Chaudhry, Nikhil; Lim, Brooke; Bex, Peter J.; Wiecek, Emily K.; Gaier, Eric D. Purpose: Interocular temporal and contrast differences have been applied separately to improve binocular integration and treat amblyopia. However, the extent to which binocular integration in amblyopia can be improved by combining interocular differences in timing and contrast has yet to be tested. We evaluated how these parameters interact in individuals with amblyopia and in normally sighted controls. Methods: We developed an interocular flicker integration task in which a pair of dichoptic gratings flickered sinusoidally in counterphase (2 hertz [Hz], 90-degree spatial and temporal offset), producing the appearance of motion. We determined the interocular delay required for optimal integration by adding phase delays to the 90-degree phase offset (left or right eye leading, −80 degrees to +80 degrees). Delays were tested across different interocular contrast conditions: 50%-50%, 70%-30%, or 85%-15%. Amblyopic (n = 12) and control (n = 12) participants reported the perceived motion direction. Results: Both groups showed broad temporal tuning of flicker integration. Accuracy in the contrast-balanced condition was highest with no added phase delay, and Gaussian fits to the data showed peak performance at a negligible delay (−0.3 degrees). Across both groups, an 85%-15% contrast disparity reduced overall accuracy and shifted peak accuracy to a delay of 28.6 degrees in the higher contrast eye. Conclusions: Our data demonstrate that interocular temporal and contrast disparities interact similarly in normally sighted individuals and those with amblyopia. In this task, temporal delays and contrast imbalance manipulations interact predictably, such that the effects of imbalanced contrast are counteracted by a leading temporal offset for the eye with the lower contrast stimulus.

Evaluating Blood Flow Speed in Retinal Microaneurysms Secondary to Diabetic Retinopathy Using Variable Interscan Time Analysis OCTA

2026-04-17T03:06:13Z

Evaluating Blood Flow Speed in Retinal Microaneurysms Secondary to Diabetic Retinopathy Using Variable Interscan Time Analysis OCTA Takahashi, Hiroyuki; Hwang, Yunchan; Won, Jungeun; Jamil, Muhammad Usman; Yaghy, Antonio; Liang, Michelle C; Baumal, Caroline R; Witkin, Andre J; Ohno-Matsui, Kyoko; Duker, Jay S; Fujimoto, James G; Waheed, Nadia K Purpose: To quantify the blood flow speed within retinal microaneurysms (MAs) and investigate the relationship between blood flow speed and clinical characteristics in eyes with diabetic retinopathy (DR). Methods: Variable interscan time analysis (VISTA) quantifies blood flow speed in the vasculature by measuring how fast optical coherence tomography (OCT) angiography (OCTA) saturates for different interscan times. Macular OCTA imaging was performed in eyes with DR using a high-speed swept-source OCT prototype instrument operating at a 600-kHz A-scan rate. The presence of MAs was determined using OCT B-scans, and three-dimensional MA masks were generated. VISTA flow speed (VFS) was determined within MAs and the retinal capillary plexus (RCP). Intraluminal reflectivity, axial location within the RCP, and the presence of intraretinal fluid (IRF) around the MAs were evaluated. Results: A total of 123 MAs were detected from 24 eyes of 20 patients with DR. Mean VFS was 1.26 ms-1 (95% confidence interval, 1.16-1.35). MAs with medium and high intraluminal reflectivity had slower VFS than those with low intraluminal reflectivity (P < 0.01) and often had slower VFS than the RCP (P < 0.01). Sixty-six MAs were located near IRF and had slower VFS than the other 57 MAs without surrounding IRF (1.16 ms-1 vs. 1.37 ms-1; P = 0.03). Conclusions: VISTA OCTA can assess blood flow speed of MAs in relation to other structural features in DR. Decreased blood flow speed in MAs is correlated with the presence of IRF around MAs. Translational Relevance: We offer a new method that quantifies the blood flow speed of MAs to study the development of diabetic macular edema.

A Simple Model for Interpreting Temperature Variability and Its Higher-Order Changes

2026-04-17T03:06:42Z

A Simple Model for Interpreting Temperature Variability and Its Higher-Order Changes Tamarin-Brodsky, Talia; Hodges, Kevin; Hoskins, Brian J; Shepherd, Theodore G Atmospheric temperature distributions are often identified with their variance, while the higher-order moments receive less attention. This can be especially misleading for extremes, which are associated with the tails of the probability density functions (PDFs), and thus depend strongly on the higher-order moments. For example, skewness is related to the asymmetry between positive and negative anomalies, while kurtosis is indicative of the “extremity” of the tails. Here we show that for near-surface atmospheric temperature, an approximate linear relationship exists between kurtosis and skewness squared. We present a simple model describing this relationship, where the total PDF is written as the sum of three Gaussians, representing small deviations from the climatological mean together with the larger-amplitude cold and warm temperature anomalies associated with synoptic systems. This model recovers the PDF structure in different regions of the world, as well as its projected response to climate change, giving a simple physical interpretation of the higher-order temperature variability changes. The kurtosis changes are found to be largely predicted by the skewness changes. Building a deeper understanding of what controls the higher-order moments of the temperature variability is crucial for understanding extreme temperature events and how they respond to climate change.

Diabatic Rossby Vortex World: Finite-Amplitude Effects in Moist Cyclogenesis

2026-05-08T03:12:30Z

Diabatic Rossby Vortex World: Finite-Amplitude Effects in Moist Cyclogenesis Kohl, Matthieu; O’Gorman, Paul A Diabatic Rossby vortices (DRVs) are a special class of heavily precipitating extratropical cyclone in which latent heating effects play a key role. As such, their dynamics defies the classic mechanism of midlatitude storm formation and poses challenges to modeling and theoretical understanding. Here, we build on recent theoretical advances on the growth of DRV modes in small-amplitude moist instability calculations by exploring the structure of finite-amplitude DRV storms in a hierarchy of models of moist macroturbulence. Simulations of moist quasigeostrophic turbulence show a transition to a DRV-dominated flow (DRV world) when the latent heating is strong. The potential vorticity (PV) structure of the DRVs is similar to the PV structure from small-amplitude DRV modal theory. Simulations of the moist primitive equations also transition to DRV world when both the latent heating is strong and the Rossby number is sufficiently low. At high Rossby numbers, however, the PV structure of storms with strong latent heating is bottom intensified compared to DRV modal theory due to higher-order effects beyond quasigeostrophy, and the macroturbulent flow has both DRV-like storms and frontal structures. A 1D model of the vertical structure of PV is solved for different Rossby numbers and stratification profiles to reconcile the PV structures of DRVs in the simulations, small-amplitude modal theory, and observations.

RES.11-003 Climate Justice Instructional Toolkit, Fall 2023

2026-04-16T17:25:17Z

RES.11-003 Climate Justice Instructional Toolkit, Fall 2023 Rabe, Christopher; Fernandez, John; Meyers, Sarah The primary goal of these resources and programming, created as part of a larger initiative to expand climate justice education at MIT, is to provide support to faculty members and instructors across disciplines in integrating climate justice content and related instructional approaches into their courses. Funded by the Alumni Class Funds Grant, the Toolkit houses a wide range of climate-justice-adaptable teaching modules, a starter guide for teaching climate justice, resources for students, and climate justice data sets that can serve as supportive tools to enhance teaching content and approaches.

Petrological Traverse of the Olivine Cumulate Séítah Formation at Jezero Crater, Mars: A Perspective From SuperCam Onboard Perseverance

2026-05-07T03:13:43Z

Petrological Traverse of the Olivine Cumulate Séítah Formation at Jezero Crater, Mars: A Perspective From SuperCam Onboard Perseverance Séítah is the stratigraphically lowest formation visited by Perseverance in the Jezero crater floor. We present the data obtained by SuperCam: texture by imagery, chemistry by Laser‐Induced Breakdown Spectroscopy, and mineralogy by Supercam Visible and Infrared reflectance and Raman spectroscopy. The Séítah formation consists of igneous, weakly altered rocks dominated by millimeter‐sized grains of olivine with the presence of low‐Ca and high‐Ca pyroxenes, and other primary minerals (e.g., plagioclase, Cr‐Fe‐Ti oxides, phosphates). Along a ∼140 m long section in Séítah, SuperCam analyses showed evidence of geochemical and mineralogical variations, from the contact with the overlying Máaz formation, going deeper in the formation. Bulk rock and olivine Mg#, grain size, olivine content increase gradually further from the contact. Along the section, olivine Mg# is not in equilibrium with the bulk rock Mg#, indicating local olivine accumulation. These observations are consistent with Séítah being the deep ultramafic member of a cumulate series derived from the fractional crystallization and slow cooling of the parent magma at depth. Possible magmatic processes and exhumation mechanisms of Séítah are discussed. Séítah rocks show some affinity with some rocks at Gusev crater, and with some Martian meteorites suggesting that such rocks are not rare on the surface of Mars. Séítah is part of the Nili Fossae regional olivine‐carbonate unit observed from orbit. Future exploration of Perseverance on the rim and outside of the crater will help determine if the observations from the crater floor can be extrapolated to the whole unit or if this unit is composed of distinct sub‐units with various origins.

The Complex Exhumation History of Jezero Crater Floor Unit and Its Implication for Mars Sample Return

2026-04-16T03:06:59Z

The Complex Exhumation History of Jezero Crater Floor Unit and Its Implication for Mars Sample Return Quantin‐Nataf, C; Alwmark, S; Calef, FJ; Lasue, J; Kinch, K; Stack, KM; Sun, V; Williams, NR; Dehouck, E; Mandon, L; Mangold, N; Beyssac, O; Clave, E; Walter, SHG; Simon, JI; Annex, AM; Horgan, B; Rice, James W; Shuster, D; Cohen, B; Kah, L; Sholes, Steven; Weiss, BP During the first year of NASA's Mars 2020 mission, Perseverance rover has investigated the dark crater floor unit of Jezero crater and four samples of this unit have been collected. The focus of this paper is to assess the potential of these samples to calibrate the crater‐based Martian chronology. We first review the previous estimation of crater‐based model age of this unit. Then, we investigate the impact crater density distribution across the floor unit. It reveals that the crater density is heterogeneous from areas which have been exposed to the bombardment during the last 3 Ga to areas very recently exposed to bombardment. It suggests a complex history of exposure to impact cratering. We also display evidence of several remnants of deposits on the top of the dark floor unit across Jezero below which the dark floor unit may have been buried. We propose the following scenario of burying/exhumation: the dark floor unit would have been initially buried below a unit that was a few tens of meters thick. This unit then gradually eroded away due to Aeolian processes from the northeast to the west, resulting in uneven exposure to impact bombardment over 3 Ga. A cratering model reproducing this scenario confirms the feasibility of this hypothesis. Due to the complexity of its exposure history, the Jezero dark crater floor unit will require additional detailed analysis to understand how the Mars 2020 mission samples of the crater floor can be used to inform the Martian cratering chronology.

Samples Collected From the Floor of Jezero Crater With the Mars 2020 Perseverance Rover

2026-04-16T03:04:51Z

Samples Collected From the Floor of Jezero Crater With the Mars 2020 Perseverance Rover The first samples collected by the Mars 2020 mission represent units exposed on the Jezero Crater floor, from the potentially oldest Séítah formation outcrops to the potentially youngest rocks of the heavily cratered Máaz formation. Surface investigations reveal landscape‐to‐microscopic textural, mineralogical, and geochemical evidence for igneous lithologies, some possibly emplaced as lava flows. The samples contain major rock‐forming minerals such as pyroxene, olivine, and feldspar, accessory minerals including oxides and phosphates, and evidence for various degrees of aqueous activity in the form of water‐soluble salt, carbonate, sulfate, iron oxide, and iron silicate minerals. Following sample return, the compositions and ages of these variably altered igneous rocks are expected to reveal the geophysical and geochemical nature of the planet's interior at the time of emplacement, characterize martian magmatism, and place timing constraints on geologic processes, both in Jezero Crater and more widely on Mars. Petrographic observations and geochemical analyses, coupled with geochronology of secondary minerals, can also reveal the timing of aqueous activity as well as constrain the chemical and physical conditions of the environments in which these minerals precipitated, and the nature and composition of organic compounds preserved in association with these phases. Returned samples from these units will help constrain the crater chronology of Mars and the global evolution of the planet's interior, for understanding the processes that formed Jezero Crater floor units, and for constraining the style and duration of aqueous activity in Jezero Crater, past habitability, and cycling of organic elements in Jezero Crater.

Hand Magnets and the Destruction of Ancient Meteorite Magnetism

2026-04-16T03:05:07Z

Hand Magnets and the Destruction of Ancient Meteorite Magnetism Vervelidou, Foteini; Weiss, Benjamin P; Lagroix, France Meteorites provide invaluable records of planetary formation and evolution. Studies of their paleomagnetism have constrained accretion in the protoplanetary disk, the thermal evolution and differentiation of planetesimals, and the history of planetary dynamos. Yet, the potential of these magnetic records in advancing the field of planetary science is severely hindered by a widely used technique: application of hand magnets to assist in meteorite classification. Touching a meteorite with a magnet results in near‐instantaneous destruction of its magnetic record. Here, we showcase the destructive effects of exposing meteorites to magnets through numerical modeling, a controlled remagnetization experiment on a terrestrial basalt, and a paleomagnetic study of the oldest known Martian meteorite, the Northwest Africa (NWA) 7034 pairing group. NWA 7034 is a polymict regolith breccia containing zircon crystals with crystallization ages older than 4.4 billion years. As such it contains materials that are sufficiently old to have formed during the time Mars is most likely to have had a core dynamo. Unfortunately, we found that all nine paired stones of NWA 7034 that we investigated were remagnetized by hand magnets, as has been observed for many other hot desert meteorites. We recommend that magnets not be applied to meteorites during collection and curation. Instead, a low‐field susceptibility meter is a far more sensitive and completely nondestructive tool for meteorite classification.

Carbonate Detection With SuperCam in Igneous Rocks on the Floor of Jezero Crater, Mars

2026-04-16T03:07:24Z

Carbonate Detection With SuperCam in Igneous Rocks on the Floor of Jezero Crater, Mars Perseverance explored two geological units on the floor of Jezero Crater over the first 420 Martian days of the Mars2020 mission. These units, the Máaz and Séítah formations, are interpreted to be igneous in origin, with traces of alteration. We report the detection of carbonate phases along the rover traverse based on laser-induced breakdown spectroscopy (LIBS), infrared reflectance spectroscopy (IRS), and time-resolved Raman (TRR) spectroscopy by the SuperCam instrument. Carbonates are identified through direct detection of vibrational modes of CO3 functional groups (IRS and TRR), major oxides content, and ratios of C and O signal intensities (LIBS). In Séítah, the carbonates are consistent with magnesite-siderite solid solutions (Mg# of 0.42–0.70) with low calcium contents (<5 wt.% CaO). They are detected together with olivine in IRS and TRR spectra. LIBS and IRS also indicate a spatial association of the carbonates with clays. Carbonates in Máaz are detected in fewer points, as: (a) siderite (Mg# as low as 0.03); (b) carbonate-containing coatings, enriched in Mg (Mg# ∼0.82) and spatially associated with different salts. Overall, using conservative criteria, carbonate detections are rare in LIBS (∼30/2,000 points), IRS (∼15/2,000 points), and TRR (1/150 points) data. This is best explained by (a) a low carbonate content overall, (b) small carbonate grains mixed with other phases, (c) intrinsic complexity of in situ measurements. This is consistent with orbital observations of Jezero crater, and similar to compositions of carbonates previously reported in Martian meteorites. This suggests a limited carbonation of Jezero rocks by locally equilibrated fluids.

Resonance Ionization Spectroscopy Experiment at Facility for Rare Isotope Beams

2026-04-16T03:06:18Z

Resonance Ionization Spectroscopy Experiment at Facility for Rare Isotope Beams Brinson, AJ; Rickey, BJ; Allmond, JM; Dockery, A; Chiu, A Fernandez; Ruiz, RF Garcia; Gray, TJ; Karthein, J; King, TT; Minamisono, K; Ortiz-Cortes, A; Pineda, SV; Rasco, BC; Reponen, M; Udrescu, SM; Vernon, AR; Wilkins, SG This manuscript reports on the commissioning of the Resonance Ionization Spectroscopy Experiment (RISE) at the BEam COoler and LAser spectroscopy (BECOLA) facility at Facility for Rare Isotope Beams (FRIB). The instrument implements the collinear resonance ionization spectroscopy technique for sensitive measurements of isotope shifts and hyperfine structure of short-lived isotopes produced at FRIB. The existing BECOLA beamline was extended to integrate an electrostatic ion-beam bender and an ion detector at ultrahigh vacuum. An injection-seeded titanium-sapphire laser and a multiharmonic pulsed neodymium-doped yttrium aluminum garnet laser were installed to perform resonant excitation and selective ionization. Commissioning tests were performed to demonstrate the capabilities of the instrument by measuring the hyperfine structure of stable27 Al produced in an offline ion source. The RISE instrument is ready and operational for future studies of short-lived isotopes at FRIB.

Exceptional topology on nonorientable manifolds

2026-04-16T03:04:53Z

Exceptional topology on nonorientable manifolds König, J Lukas K; Yang, Kang; Fonseca, André Grossi; Vaidya, Sachin; Soljačić, Marin; Bergholtz, Emil J We classify gapped phases and characteristic nodal points of non-Hermitian band structures on two-dimensional nonorientable parameter spaces. Such spaces arise in a wide range of physical systems in the presence of nonsymmorphic parameter space symmetries. For gapped phases, we find that nonorientable spaces provide a natural setting for exploring fundamental structural problems in braid group theory, such as torsion and conjugacy. Gapless systems, which host exceptional points (EPs), explicitly violate fermion doubling, even in two-band models. We demonstrate that EPs traversing the nonorientable parameter space exhibit non-Abelian charge inversion. These braided phases and their transitions leave distinct signatures in the form of bulk Fermi arc degeneracies, offering a concrete route toward experimental realization and verification.

Ionization potential of radium monofluoride

2026-04-16T03:06:07Z

Ionization potential of radium monofluoride The ionization potential (IP) of radium monofluoride (RaF) was measured to be 4.969(2)[10] eV, revealing a relativistic enhancement in the series of alkaline earth monofluorides. The results are in agreement with a relativistic coupled-cluster prediction of 4.981(7) eV, incorporating up to quantum electrodynamics corrections. Using the same computational methodology, an improved calculation for the dissociation energy (𝐷0) of 5.54(5) eV is presented. This confirms that RaF joins the group of diatomic molecules for which 𝐷0>IP, paving the way for precision control and interrogation of its Rydberg states.

Trade-off between path entanglement and quantum sensitivity

2026-04-16T03:06:54Z

Trade-off between path entanglement and quantum sensitivity Lou, Benjamin; Loughlin, Hudson A; Mavalvala, Nergis Entanglement often increases quantum measurement schemes’ sensitivity. However, we find that in precision measurements with zero-mean Gaussian states, such as squeezed states, entanglement between different paths degrades measurement sensitivity. We prove an inverse relationship between entanglement entropy and sensitivity for measurements of single-mode phase shifts in multimode systems and for phase shifts on both modes in two-mode systems. In the two-mode case, which models devices such as interferometers, we find that entanglement strongly degrades differential phase sensitivity. Finally, we show that minimizing entanglement between paths maximizes the phase sensitivity of 𝑁-mode systems with zero-mean Gaussian state inputs.

Classification of fragile topology enabled by matrix homotopy

2026-04-16T03:07:41Z

Classification of fragile topology enabled by matrix homotopy Lee, Ki Young; Wong, Stephan; Vaidya, Sachin; Loring, Terry A.; Cerjan, Alexander Flat bands in twisted materials have attracted considerable attention due to the emergence of correlated phases that can be associated with the non-Wannier-representable nature of its single-particle states. Specifically, these bands can exhibit a class of topology that can be nullified by the addition of trivial bands, termed fragile topology, which has required an expansion of prior classification schemes. However, existing approaches for predicting fragile topology rely on momentum-space methods, e.g., Wilson loops, presenting a fundamental challenge for using fragile topology as a predictor of correlated phases in aperiodic systems, such as incommensurate twist angles in moiré materials. Here, we develop a Z2 energy-resolved topological marker for classifying fragile phases using a system’s position-space description, enabling the direct classification of finite, disordered, and aperiodic materials. By translating the physical symmetries protecting the system’s fragile topological phase into matrix symmetries of the system’s Hamiltonian and position operators, we use matrix homotopy to construct our topological marker while simultaneously yielding a quantitative measure of topological robustness. We demonstrate our framework’s effectiveness in both a low-energy tight-binding model and a continuum photonic crystal model of C2T -symmetric systems, and find that fragile topology can both persist under strong disorder and even exhibit disorder-induced reentrant phase transitions. Our photonic crystal results also demonstrate the robustness of fragile topology, and the applicability of our approach, to heterostructures lacking a bulk spectral gap. Overall, our framework serves as an efficient tool for elucidating fragile topology, offering guidance for the prediction and discovery of correlated phases in both crystalline and aperiodic materials.

Universality of the perturbative definition of the Flory-Huggins parameter

2026-04-16T03:06:24Z

Universality of the perturbative definition of the Flory-Huggins parameter Petrov, Artem; Hernández-Mendoza, Guillermo A; Alexander-Katz, Alfredo The effective Flory-Huggins parameter χe is one of the most important characteristics of any multicomponent polymer system. This parameter characterizes the free energy of interaction between monomers of different types and controls the phase behavior of polymer mixtures. In this work, we developed a perturbation theory and derived how χe depends on the details of an arbitrary coarse-grained polymer model. After defining χe in this way, we found that the models of symmetric polymer blends and diblock copolymer melts behaved universally: Their phase transition points, free energies, and mesoscopic invariant structure factors depended solely on the chain architecture, the invariant chain length N¯, and the interaction parameter χeN. To parametrize our perturbative expression for χe, only the effective coordination number distribution in a reference homogeneous system needs to be measured. This distribution, in turn, can be directly mapped onto coarse-grained model parameters set prior to simulation, which clarifies how model construction influences χe. Our definition of χe enables straightforward quantitative comparison of models with each other and with experiments, which will facilitate the computational design of multicomponent polymer materials.

Asymmetry of the Distribution of Vertical Velocities of the Extratropical Atmosphere in Theory, Models, and Reanalysis

2026-04-16T03:07:02Z

Asymmetry of the Distribution of Vertical Velocities of the Extratropical Atmosphere in Theory, Models, and Reanalysis Kohl, Matthieu; O’Gorman, Paul A The vertical velocity distribution in the atmosphere is asymmetric with stronger upward than downward motion. This asymmetry is important for the distribution of precipitation and its extremes and for an effective static stability that has been used to represent the effects of latent heating on extratropical eddies. Idealized GCM simulations show that the asymmetry increases as the climate warms, but current moist dynamical theories based around small-amplitude modes greatly overestimate the increase in asymmetry with warming found in the simulations. Here, we first analyze the changes in asymmetry with warming using numerical inversions of a moist quasigeostrophic omega equation applied to output from the idealized GCM. The inversions show that increases in the asymmetry with warming in the GCM simulations are primarily related to decreases in moist static stability on the left-hand side of the moist omega equation, whereas the dynamical forcing on the right-hand side of the omega equation is unskewed and contributes little to the asymmetry of the vertical velocity distribution. By contrast, increases in asymmetry with warming for small-amplitude modes are related to changes in both moist static stability and dynamical forcing leading to enhanced asymmetry in warm climates. We distill these insights into a toy model of the moist omega equation that is solved for a given moist static stability and wavenumber of the dynamical forcing. In comparison to modal theory, the toy model better reproduces the slow increase of the asymmetry with climate warming in the idealized GCM simulations and over the seasonal cycle from winter to summer in reanalysis.

Large-Scale Circulations and Dry Tropical Cyclones in Direct Numerical Simulations of Rotating Rayleigh–Bénard Convection

2026-04-16T03:06:14Z

Large-Scale Circulations and Dry Tropical Cyclones in Direct Numerical Simulations of Rotating Rayleigh–Bénard Convection Velez-Pardo, Martin; Cronin, Timothy W The organization of convection into relatively long-lived patterns of large spatial scales, like tropical cyclones, is a common feature of Earth’s atmosphere. However, many key aspects of convective aggregation and its relationship with tropical cyclone formation remain elusive. In this work, we simulate highly idealized setups of dry convection, inspired by the Rayleigh–Bénard system, to probe the effects of different thermal boundary conditions on the scale of organization of rotating convection, and on the formation of tropical cyclone–like structures. We find that in domains with sufficiently high aspect ratios, moderately turbulent (Raf 109), moderately rotating (Roc 1) convection organizes more persistently and at larger scales when thermal boundary conditions constrain heat fluxes rather than temperatures. Furthermore, for some thermal boundary conditions with asymmetric heat fluxes, convection organizes into persistent vortices with the essential properties of mature tropical cyclones: a warm core, high axisymmetry, a strong azimuthal circulation, and substantially larger size than individual buoyant plumes. We argue that flux asymmetry results in a persistent and localized input of buoyancy, which allows spatially aggregated convection to sustain a warm core in a developing large-scale vortex. Crucially, the most intense and axisymmetric cyclone forms for setups where the bottom heat flux is enhanced by the nearby flow and the top boundary is insulating, as long as the convective Rossby number is higher than about 1. Our results demonstrate the great potential for dialogue between classical turbulence research and the study of convective aggregation and tropical cyclones.

The Quasi-Linear Relation between Planetary Outgoing Longwave Radiation and Surface Temperature: A Climate Footprint of Radiative and Nonradiative Processes

2026-04-16T03:05:59Z

The Quasi-Linear Relation between Planetary Outgoing Longwave Radiation and Surface Temperature: A Climate Footprint of Radiative and Nonradiative Processes Cai, Ming; Sun, Jie; Ding, Feng; Kang, Wanying; Hu, Xiaoming The slope of the quasi-linear relation between planetary outgoing longwave radiation (OLR) and surface temperature (TS ) is an important parameter measuring the sensitivity of Earth’s climate system. The primary objective of this study is to seek a general explanation for the quasi-linear OLR–TS relation that remains valid regardless of the strength of the atmospheric window’s narrowing effect on planetary thermal emission at higher temperatures. The physical understanding of the quasi-linear OLR–TS relation and its slope is gained from observation analysis, climate simulations with radiative–convective equilibrium and general circulation models, and a series of online feedback suppression experiments. The observed quasi-linear OLR–TS relation manifests a climate footprint of radiative (such as the greenhouse effect) and nonradiative processes (poleward energy transport). The former acts to increase the meridional gradient of surface temperature and the latter decreases the meridional gradient of atmospheric temperatures, causing the flattening of the meridional profile of the OLR. Radiative processes alone can lead to a quasi-linear OLR–TS relation that is more steeply sloped. The atmospheric poleward energy transport alone can also lead to a quasi-linear OLR–TS relation by rerouting part of the OLR to be emitted from a warmer place to a colder place. The combined effects of radiative and nonradiative processes make the quasi-linear OLR–TS relation less sloped with a higher degree of linearity. In response to anthropogenic radiative forcing, the slope of the quasi-linear OLR–TS relation is further reduced via stronger water vapor feedback and enhanced poleward energy transport.

Earth System Reanalysis in Support of Climate Model Improvements

2026-04-16T03:04:41Z

Earth System Reanalysis in Support of Climate Model Improvements Stammer, Detlef; Amrhein, Daniel E; Alonso Balmaseda, Magdalena; Bertino, Laurent; Bonavita, Massimo; Buontempo, Carlo; Caltabiano, Nico; Counillon, Francois; Fenty, Ian; Ferrari, Raffaele; Fujii, Yosuke; Gaikwad, Shreyas Sunil; Gentine, Pierre; Gettelman, Andrew; Gopalakrishnan, Ganesh; Heimbach, Patrick; Hersbach, Hans; Hill, Chris; Kobayashi, Shinya; Köhl, Armin; Kushner, Paul J; Mazloff, Matthew; Nakamura, Hisashi; Penny, Stephen G; Slivinski, Laura; Tegtmeier, Susann; Zanna, Laure What: More than 70 experts from research and operational centers came together in a hybrid meeting held at the Massachusetts Institute of Technology (MIT) to discuss the state of climate-model-related data assimilation and to inspire future work required to make coupled data assimilation and Earth system reanalysis a reality. When: 12–14 June 2023 Where: Massachusetts Institute of Technology, Cambridge, Massachusetts

A conserved disruption of nucleocytoplasmic compartmentalization in meiosis is controlled by a kinase-phosphatase pair in Saccharomyces cerevisiae

2026-04-16T03:04:44Z

A conserved disruption of nucleocytoplasmic compartmentalization in meiosis is controlled by a kinase-phosphatase pair in Saccharomyces cerevisiae Walsh, Madison E; Chetlapalli, Keerthana; Styler, Benjamin S; Upadhyayula, Srigokul; King, Grant A; Ünal, Elçin In eukaryotic organisms, the nucleus is remodeled to accommodate the space required for chromosome segregation. Remodeling strategies range from closed division, where the nuclear envelope remains intact, to open division, where the nuclear envelope is temporarily disassembled. While the budding yeast Saccharomyces cerevisiae (S. cerevisiae) undergoes closed mitosis, its meiotic nuclear division strategy is less understood. Here, we investigate nucleocytoplasmic compartmentalization during budding yeast meiosis and discover that meiosis II represents a semi-closed division marked by bidirectional mixing between the nucleus and cytoplasm. This includes nuclear entry of the Ran GTPase activating protein (RanGAP), typically cytoplasmic, although RanGAP relocalization appears to be a consequence, rather than a cause of permeability changes. This intercompartmental mixing occurs without nuclear envelope breakdown or dispersal of nucleoporins and is independent of known nuclear pore complex remodeling events. This phenomenon, termed virtual nuclear envelope breakdown (vNEBD), represents a unique mechanism distinct from other semi-closed divisions. We demonstrate that vNEBD is integrated into the meiotic program and regulated by the conserved meiotic kinase Ime2, and the meiosis-specific protein phosphatase 1 regulatory subunit, Gip1. Remarkably, the vNEBD event is conserved between S. cerevisiae and the distantly related Schizosaccharomyces pombe (S. pombe), indicating a fundamental role in meiosis.

Cultivating PhD Aspirations during College

2026-04-16T03:06:56Z

Cultivating PhD Aspirations during College Jones, Daniela S; Gillette, Devyn D; Cooper, Paige E; Salinas, Raquel Y; Hill, Jennifer L; Black, Sherilynn J; Lew, Daniel J; Canelas, Dorian A Science, technology, engineering, and mathematics (STEM) career barriers persist for individuals from marginalized communities due to financial and educational inequality, unconscious bias, and other disadvantaging factors. To evaluate differences in plans and interests between historically underrepresented (UR) and well-represented (WR) groups, we surveyed more than 3000 undergraduates enrolled in chemistry courses. Survey responses showed all groups arrived on campus with similar interests in learning more about science research. Over the 4 years of college, WR students maintained their interest levels, but UR students did not, creating a widening gap between the groups. Without intervention, UR students participated in lab research at lower rates than their WR peers. A case study pilot program, Biosciences Collaborative for Research Engagement (BioCoRE), encouraged STEM research exploration by undergraduates from marginalized communities. BioCoRE provided mentoring and programming that increased community cohesion and cultivated students’ intrinsic scientific mindsets. Our data showed that there was no statistical significant difference between BioCoRE WR and UR students when surveyed about plans for a medical profession, graduate school, and laboratory scientific research. In addition, BioCoRE participants reported higher levels of confidence in conducting research than non-BioCoRE Scholars. We now have the highest annual number of UR students moving into PhD programs in our institution’s history.

Remodeling of cytoskeleton, chromatin, and gene expression during mechanical rejuvenation of aged human dermal fibroblasts

2026-04-16T03:06:39Z

Remodeling of cytoskeleton, chromatin, and gene expression during mechanical rejuvenation of aged human dermal fibroblasts Sornapudi, Trinadha Rao; Yuan, Luezhen; Braunger, Jana M; Uhler, Caroline; Shivashankar, GV Aging is associated with a progressive decline in cellular function. To reset the aged cellular phenotype, various reprogramming approaches, including mechanical routes, have been explored. However, the epigenetic mechanisms underlying cellular rejuvenation are poorly understood. Here, we studied the cytoskeletal, genome-wide chromatin and transcriptional changes in young, aged, and mechanically rejuvenated fibroblasts using immunofluorescence, RNA sequencing, and Hi-C experiments. The mechanically rejuvenated aged fibroblasts, that had partially reset their transcription to a younger cell state, showed a local reorganization of the interchromosomal contacts and lamina-associated domains. Interestingly, the observed chromatin reorganization correlated with the transcriptional changes. Immunofluorescence experiments in the rejuvenated state confirmed increased actomyosin contractility like younger fibroblasts. In addition, the rejuvenated contractile properties were maintained over multiple cell passages. Overall, our results give an overview of how changes in the cytoskeleton, chromatin, and gene activity are connected to aging and rejuvenation.

Rickettsia parkeri forms extensive, stable contacts with the rough endoplasmic reticulum

2026-04-16T03:05:03Z

Rickettsia parkeri forms extensive, stable contacts with the rough endoplasmic reticulum Acevedo-Sánchez, Yamilex; Woida, Patrick J; Anderson, Caroline; Kraemer, Stephan; Lamason, Rebecca L Upon invasion into the host cell, a subset of bacterial pathogens resides exclusively in the cytosol. While previous research revealed how they reshape the plasma membrane during invasion, subvert the immune response, and hijack cytoskeletal dynamics to promote their motility, it was unclear if these pathogens also interacted with the organelles in this crowded intracellular space. Here, we examined if the obligate intracellular pathogen Rickettsia parkeri interacts with the endoplasmic reticulum (ER), a large and dynamic organelle spread throughout the cell. Using live-cell microscopy and transmission and focused-ion-beam scanning electron microscopy, we show that R. parkeri forms extensive contacts with the rough ER that are ∼55 nm apart and cover more than half the bacterial surface. Depletion of the ER-specific tethers VAPA and VAPB reduced rickettsia–ER contacts, and VAPA and VAPB were localized around intracellular rickettsiae. Overall, our findings illuminate an interkingdom ER contact uniquely mediated by rickettsiae that mimics some characteristics of traditional host membrane contact sites.

Combining genomics and epidemiology to track mumps virus transmission in the United States

2026-04-16T03:05:37Z

Combining genomics and epidemiology to track mumps virus transmission in the United States Unusually large outbreaks of mumps across the United States in 2016 and 2017 raised questions about the extent of mumps circulation and the relationship between these and prior outbreaks. We paired epidemiological data from public health investigations with analysis of mumps virus whole genome sequences from 201 infected individuals, focusing on Massachusetts university communities. Our analysis suggests continuous, undetected circulation of mumps locally and nationally, including multiple independent introductions into Massachusetts and into individual communities. Despite the presence of these multiple mumps virus lineages, the genomic data show that one lineage has dominated in the US since at least 2006. Widespread transmission was surprising given high vaccination rates, but we found no genetic evidence that variants arising during this outbreak contributed to vaccine escape. Viral genomic data allowed us to reconstruct mumps transmission links not evident from epidemiological data or standard single-gene surveillance efforts and also revealed connections between apparently unrelated mumps outbreaks.

An algorithmic framework for synthetic cost-aware decision making in molecular design

2026-04-15T03:04:50Z

An algorithmic framework for synthetic cost-aware decision making in molecular design Fromer, Jenna C; Coley, Connor W Small molecules exhibiting desirable property profiles are often discovered through an iterative process of designing, synthesizing and testing sets of molecules. The selection of molecules to synthesize from all possible candidates is a complex decision-making process that typically relies on expert chemist intuition. Here we propose a quantitative decision-making framework, SPARROW, that prioritizes molecules for evaluation by balancing expected information gain and synthetic cost. SPARROW integrates molecular design, property prediction and retrosynthetic planning to balance the utility of testing a molecule with the cost of batch synthesis. We demonstrate, through three case studies, that the developed algorithm captures the non-additive costs inherent to batch synthesis, leverages common reaction steps and intermediates, and scales to hundreds of molecules.

RDCanon: A Python Package for Canonicalizing the Order of Tokens in SMARTS Queries

2026-04-15T03:06:04Z

RDCanon: A Python Package for Canonicalizing the Order of Tokens in SMARTS Queries Mahjour, Babak A; Coley, Connor W SMARTS is a widely used language in cheminformatics for defining substructural queries for database lookups, reaction templates for chemical transformations, and other applications. As an extension to SMILES, many SMARTS patterns can represent the same query. Despite this, no canonicalization algorithm invariant of the line notation sequence or atomic numbering is publicly available. Here, we introduce RDCanon, an open-source Python package that can be used to standardize SMARTS queries. RDCanon is designed to ensure that the sequence of atomic queries remains consistent for all graphs representing the same substructure query and to ensure a canonical sequence of primitives within each individual atom query; furthermore, the algorithm can be applied to canonicalize the order of reactants, agents, and products and their atom map numbers in reaction SMARTS templates. As part of its canonicalization algorithm, RDCanon provides a mechanism in which the canonicalized SMARTS is optimized for speed against specific molecular databases. Several case studies are provided to showcase improved efficiency in substructure matching and retrosynthetic analysis.

Automation of air-free synthesis

2026-04-15T03:05:52Z

Automation of air-free synthesis Mahjour, Babak A; Coley, Connor W Cutting-edge chemistry is often performed in non-atmospheric conditions. Continued development of the Chemputer platform now enables the utilization of sensitive compounds in automated synthetic protocols.

PyLabRobot: An open-source, hardware-agnostic interface for liquid-handling robots and accessories

2026-04-15T03:05:28Z

PyLabRobot: An open-source, hardware-agnostic interface for liquid-handling robots and accessories Wierenga, Rick P; Golas, Stefan M; Ho, Wilson; Coley, Connor W; Esvelt, Kevin M Liquid-handling robots are often limited by proprietary interfaces that are only compatible with a single type of robot and operating system, restricting method sharing and slowing development. Here, we present PyLabRobot, an open-source, cross-platform Python interface capable of programming diverse liquid-handling robots, including Hamilton STARs and Vantages, Tecan EVOs, and Opentron OT-2s. PyLabRobot provides an interface for a universal set of commands and deck layout representations while enabling the control of diverse accessory devices. The interface can work with any liquid-handling robot capable of aspirating and dispensing precise volumes of liquid within a Cartesian coordinate system. In addition to the already integrated robots, we include guidance on integrating new liquid-handling systems and accessories. We validated the framework through unit tests and application demonstrations, including a browser-based simulator, a position calibration tool, and a path-teaching tool for complex movements. PyLabRobot provides a flexible, open, and collaborative programming environment for laboratory automation.

Predictive Chemistry Augmented with Text Retrieval

2026-04-15T03:06:54Z

Predictive Chemistry Augmented with Text Retrieval Qian, Yujie; Li, Zhening; Tu, Zhengkai; Coley, Connor; Barzilay, Regina This paper focuses on using natural language descriptions to enhance predictive models in the chemistry field. Conventionally, chemoinformatics models are trained with extensive structured data manually extracted from the literature. In this paper, we introduce TextReact, a novel method that directly augments predictive chemistry with texts retrieved from the literature. TextReact retrieves text descriptions relevant for a given chemical reaction, and then aligns them with the molecular representation of the reaction. This alignment is enhanced via an auxiliary masked LM objective incorporated in the predictor training. We empirically validate the framework on two chemistry tasks: reaction condition recommendation and one-step retrosynthesis. By leveraging text retrieval, TextReact significantly outperforms state-of-the-art chemoinformatics models trained solely on molecular data.

Scientific discovery in the age of artificial intelligence

2026-04-15T03:07:29Z

Scientific discovery in the age of artificial intelligence Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. Both developers and users of AI tools need a better understanding of when such approaches need improvement, and challenges posed by poor data quality and stewardship remain. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation.

MIST-CF: Chemical Formula Inference from Tandem Mass Spectra

2026-04-15T03:06:44Z

MIST-CF: Chemical Formula Inference from Tandem Mass Spectra Goldman, Samuel; Xin, Jiayi; Provenzano, Joules; Coley, Connor W Chemical formula annotation for tandem mass spectrometry (MS/MS) data is the first step toward structurally elucidating unknown metabolites. While great strides have been made toward solving this problem, the current state-of-the-art method depends on time-intensive, proprietary, and expert-parametrized fragmentation tree construction and scoring. In this work, we extend our previous spectrum Transformer methodology into an energy-based modeling framework, MIST-CF: Metabolite Inference with Spectrum Transformers for Chemical Formula prediction, for learning to rank chemical formula and adduct assignments given an unannotated MS/MS spectrum. Importantly, MIST-CF learns in a data-dependent fashion using a Formula Transformer neural network architecture and circumvents the need for fragmentation tree construction. We train and evaluate our model on a large open-access database, showing an absolute improvement of 10% top 1 accuracy over other neural network architectures. We further validate our approach on the CASMI2022 challenge data set, achieving nearly equivalent performance to the winning entry within the positive mode category without any manual curation or postprocessing of our results. These results demonstrate an exciting strategy to more powerfully leverage MS2 fragment peaks for predicting MS1 precursor chemical formulas with data-driven learning.

Annotating metabolite mass spectra with domain-inspired chemical formula transformers

2026-04-15T03:07:40Z

Annotating metabolite mass spectra with domain-inspired chemical formula transformers Goldman, Samuel; Wohlwend, Jeremy; Stražar, Martin; Haroush, Guy; Xavier, Ramnik J; Coley, Connor W Metabolomics studies have identified small molecules that mediate cell signaling, competition and disease pathology, in part due to large-scale community efforts to measure tandem mass spectra for thousands of metabolite standards. Nevertheless, the majority of spectra observed in clinical samples cannot be unambiguously matched to known structures. Deep learning approaches to small-molecule structure elucidation have surprisingly failed to rival classical statistical methods, which we hypothesize is due to the lack of in-domain knowledge incorporated into current neural network architectures. Here we introduce a neural network-driven workflow for untargeted metabolomics, Metabolite Inference with Spectrum Transformers (MIST), to annotate tandem mass spectra peaks with chemical structures. Unlike existing approaches, MIST incorporates domain insights into its architecture by encoding peaks with their chemical formula representations, implicitly featurizing pairwise neutral losses and training the network to additionally predict substructure fragments. MIST performs favorably compared with both standard neural architectures and the state-of-the-art kernel method on the task of fingerprint prediction for over 70% of metabolite standards and retrieves 66% of metabolites with equal or improved accuracy, with 29% strictly better. We further demonstrate the utility of MIST by suggesting potential dipeptide and alkaloid structures for differentially abundant spectra found in an inflammatory bowel disease patient cohort.

QMugs 1.1: Quantum mechanical properties of organic compounds commonly encountered in reactivity datasets

2026-04-15T03:05:10Z

QMugs 1.1: Quantum mechanical properties of organic compounds commonly encountered in reactivity datasets Neeser, Rebecca M; Isert, Clemens; Stuyver, Thijs; Schneider, Gisbert; Coley, Connor W Here, the Quantum Mechanical Properties of Drug-like Molecules (QMugs) dataset is expanded to facilitate its use as training data for surrogate machine learning models to predict quantum mechanical properties for tasks related to chemical reactivity. Small molecules from reaction databases as well as charged and boron-containing compounds from ChEMBL were added. Each of these compounds was passed through a pipeline of MMFF94s/UFF conformer generation, followed by GFN2-xTB optimization and finally a density functional theory single-point calculation at the ωB97X-D/def2-SVP level of theory. In total, 71,632 new molecules were evaluated in this manner. Steric (SASA) and dispersion (P int) descriptors were computed at the semiempirical GFN2-xTB level of theory for the lowest energy conformer of all species in the enlarged QMugs dataset. The expanded dataset aims to facilitate the construction of surrogate models of much broader scope than the original QMugs dataset which was limited to biologically active compounds.

The Americas’ Transportation: a Cross-country Comparison

2026-04-15T03:25:50Z

The Americas’ Transportation: a Cross-country Comparison Ruiz Peraza, Vivian Andrea; Dayrell Mendonça, Guilherme; Villena Bayona, José Andrés The road freight mode is the backbone of the national transportation systems in the Americas. Each country has different characteristics that have a direct impact on how transportation is procured and managed in the region. This paper conducted a cross-country comparison analysis of the most significant road freight transport systems of Latin America (Brazil, Argentina, Colombia and Mexico) and North America (United States) based on the new proposed concept of Truck Transport Performance Index (TTPI). The objective is to provide practitioners and researchers an extensive overview of the main transportation practices regarding procurement, truck drivers’ labour market, innovation, cost management, environmental topics and public policies. Semi-structured interviews with main transportation stakeholders such as carriers' companies, multinationals and transportation agencies conducted and final results presented based on qualitative coding methodology.

Behavioral flexibility is associated with changes in structure and function distributed across a frontal cortical network in macaques

2026-04-15T03:06:58Z

Behavioral flexibility is associated with changes in structure and function distributed across a frontal cortical network in macaques Sallet, Jérôme; Noonan, MaryAnn P; Thomas, Adam; O’Reilly, Jill X; Anderson, Jesper; Papageorgiou, Georgios K; Neubert, Franz X; Ahmed, Bashir; Smith, Jackson; Bell, Andrew H; Buckley, Mark J; Roumazeilles, Léa; Cuell, Steven; Walton, Mark E; Krug, Kristine; Mars, Rogier B; Rushworth, Matthew FS One of the most influential accounts of central orbitofrontal cortex—that it mediates behavioral flexibility—has been challenged by the finding that discrimination reversal in macaques, the classic test of behavioral flexibility, is unaffected when lesions are made by excitotoxin injection rather than aspiration. This suggests that the critical brain circuit mediating behavioral flexibility in reversal tasks lies beyond the central orbitofrontal cortex. To determine its identity, a group of nine macaques were taught discrimination reversal learning tasks, and its impact on gray matter was measured. Magnetic resonance imaging scans were taken before and after learning and compared with scans from two control groups, each comprising 10 animals. One control group learned discrimination tasks that were similar but lacked any reversal component, and the other control group engaged in no learning. Gray matter changes were prominent in posterior orbitofrontal cortex/anterior insula but were also found in three other frontal cortical regions: lateral orbitofrontal cortex (orbital part of area 12 [12o]), cingulate cortex, and lateral prefrontal cortex. In a second analysis, neural activity in posterior orbitofrontal cortex/anterior insula was measured at rest, and its pattern of coupling with the other frontal cortical regions was assessed. Activity coupling increased significantly in the reversal learning group in comparison with controls. In a final set of experiments, we used similar structural imaging procedures and analyses to demonstrate that aspiration lesion of central orbitofrontal cortex, of the type known to affect discrimination learning, affected structure and activity in the same frontal cortical circuit. The results identify a distributed frontal cortical circuit associated with behavioral flexibility.

The effect of decay and lexical uncertainty on processing long-distance dependencies in reading

2026-04-15T03:05:33Z

The effect of decay and lexical uncertainty on processing long-distance dependencies in reading Stone, Kate; von der Malsburg, Titus; Vasishth, Shravan To make sense of a sentence, a reader must keep track of dependent relationships between words, such as between a verb and its particle (e.g. turn the music down). In languages such as German, verb-particle dependencies often span long distances, with the particle only appearing at the end of the clause. This means that it may be necessary to process a large amount of intervening sentence material before the full verb of the sentence is known. To facilitate processing, previous studies have shown that readers can preactivate the lexical information of neighbouring upcoming words, but less is known about whether such preactivation can be sustained over longer distances. We asked the question, do readers preactivate lexical information about long-distance verb particles? In one self-paced reading and one eye tracking experiment, we delayed the appearance of an obligatory verb particle that varied only in the predictability of its lexical identity. We additionally manipulated the length of the delay in order to test two contrasting accounts of dependency processing: that increased distance between dependent elements may sharpen expectation of the distant word and facilitate its processing (an antilocality effect), or that it may slow processing via temporal activation decay (a locality effect). We isolated decay by delaying the particle with a neutral noun modifier containing no information about the identity of the upcoming particle, and no known sources of interference or working memory load. Under the assumption that readers would preactivate the lexical representations of plausible verb particles, we hypothesised that a smaller number of plausible particles would lead to stronger preactivation of each particle, and thus higher predictability of the target. This in turn should have made predictable target particles more resistant to the effects of decay than less predictable target particles. The eye tracking experiment provided evidence that higher predictability did facilitate reading times, but found evidence against any effect of decay or its interaction with predictability. The self-paced reading study provided evidence against any effect of predictability or temporal decay, or their interaction. In sum, we provide evidence from eye movements that readers preactivate long-distance lexical content and that adding neutral sentence information does not induce detectable decay of this activation. The findings are consistent with accounts suggesting that delaying dependency resolution may only affect processing if the intervening information either confirms expectations or adds to working memory load, and that temporal activation decay alone may not be a major predictor of processing time.

FrameAxis: characterizing microframe bias and intensity with word embedding

2026-04-15T03:05:14Z

FrameAxis: characterizing microframe bias and intensity with word embedding Kwak, Haewoon; An, Jisun; Jing, Elise; Ahn, Yong-Yeol Framing is a process of emphasizing a certain aspect of an issue over the others, nudging readers or listeners towards different positions on the issue even without making a biased argument. Here, we propose FrameAxis, a method for characterizing documents by identifying the most relevant semantic axes (“microframes”) that are overrepresented in the text using word embedding. Our unsupervised approach can be readily applied to large datasets because it does not require manual annotations. It can also provide nuanced insights by considering a rich set of semantic axes. FrameAxis is designed to quantitatively tease out two important dimensions of how microframes are used in the text. Microframe bias captures how biased the text is on a certain microframe, and microframe intensity shows how prominently a certain microframe is used. Together, they offer a detailed characterization of the text. We demonstrate that microframes with the highest bias and intensity align well with sentiment, topic, and partisan spectrum by applying FrameAxis to multiple datasets from restaurant reviews to political news. The existing domain knowledge can be incorporated into FrameAxis by using custom microframes and by using FrameAxis as an iterative exploratory analysis instrument. Additionally, we propose methods for explaining the results of FrameAxis at the level of individual words and documents. Our method may accelerate scalable and sophisticated computational analyses of framing across disciplines.

Design of a Two-echelon Last-mile Delivery Model

2026-04-15T03:25:56Z

Design of a Two-echelon Last-mile Delivery Model Piña-Pardo, Juan Carlos; Moreno, Matheo; Bastos Barros, Miguel; Lapria Faria, Alexandre Vinicius Due to high city congestion and growing demand for last-mile delivery services, several companies have been implemented two-echelon distribution strategies over the past few years. Notably, the installation of urban transshipment points has gained increasing attention, used by logistics operators to transfer goods from large freight trucks to smaller, more agile vehicles for last-mile delivery. Nevertheless, the main challenge is how to deciding the number and location of these facilities in the presence of demand uncertainty. In this paper, we develop a two-stage stochastic program to design two-echelon last-mile delivery networks under demand uncertainty. This approach decomposes the problem into strategic decisions (facility location) and operational decisions (daily distribution of goods). We solve the model through the sample average approximation technique to address large-scale instances and estimate the optimal routing costs using continuous approximation methods. Using a real-world case study with more than 1,300 customers from New York City, our results provide several managerial insights regarding the mix of transportation modes, facility location, and the impact of allowing the outsourcing of customer demand. We validate the results using a simulation-based approach that employs a deterministic Mixed-Integer Linear Program.

Computer-aided structure redesign of high speed electromechanical systems for improved control

2026-04-14T03:02:10Z

Computer-aided structure redesign of high speed electromechanical systems for improved control Rai, Sudhendu. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1993; Includes bibliographical references (leaves 101).

Demonstration of heat pipe self-deployment

2026-04-14T03:04:34Z

Demonstration of heat pipe self-deployment Woloshun, Keith Albert. Thesis: M.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1986; Bibliography: leaves 109-112.

PETWORLD, an animal behavior system using rules

2026-04-14T03:05:12Z

PETWORLD, an animal behavior system using rules Coderre, William H. Thesis: B.S., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 1986; Bibliography: leaves 34-35.

An active constrained layer damper for vibrational control of a cantilever beam

2026-04-14T03:05:10Z

An active constrained layer damper for vibrational control of a cantilever beam Wright, Christopher Allen. Thesis: B.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1985; Bibliography: leaf 25.

Spectrometric temperature measurements of thermal radiators

2026-04-14T03:02:48Z

Spectrometric temperature measurements of thermal radiators Hunter, Gordon Bruce. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Materials Science and Engineering, 1984; Vita.; Includes bibliographical references.

High angular momentum Rydberg atoms : the production and study

2026-04-14T03:02:20Z

High angular momentum Rydberg atoms : the production and study Hulet, Randall Gardner. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 1984; Includes bibliographical references.

A general fluid dynamical theory of unstable spiral modes in disk-shaped galaxies.

2026-04-14T03:02:34Z

A general fluid dynamical theory of unstable spiral modes in disk-shaped galaxies. Pannatoni, Ronald Francis. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 1979; Vita.; Includes bibliographical references.

Counseling nightline : developing a model for decision making for a student organization

2026-04-14T03:05:07Z

Counseling nightline : developing a model for decision making for a student organization Warshaw, Meredith G. Thesis: B.S., Massachusetts Institute of Technology, Department of Urban Studies and Planning, 1979

Cost-effectiveness analysis of urban transit alternatives.

2026-04-14T03:04:37Z

Cost-effectiveness analysis of urban transit alternatives. Rouleau, Eloi Marie. Thesis: M.S., Massachusetts Institute of Technology, Department of Civil Engineering, 1977; Bibliography: leaves 84-87.

Cooling of glazed solar collectors

2026-04-14T03:05:06Z

Cooling of glazed solar collectors Suchon, Mark Theodore. Thesis: B.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1976; Includes bibliographical references.

Primitives of BP (BP) modulo an invariant prime ideal.

2026-04-14T03:02:36Z

Primitives of BP (BP) modulo an invariant prime ideal. Moreira, Manuel Ricardo Falcão. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 1975; Vita.; Includes bibliographical references.

Model based specification for developing safety-critical system-software

2026-04-14T03:04:28Z

Model based specification for developing safety-critical system-software Karasi, Anand K. (Anand Kumar), 1975- Thesis: S.M., Massachusetts Institute of Technology, Technology and Policy Program, 2000; Includes bibliographical references (leaves 62-63).

Introduction of scientific production methods in a hard rubber products plant

2026-04-14T03:04:57Z

Introduction of scientific production methods in a hard rubber products plant Cooperstein, Benjamin.; Schmalz, George. Thesis: B.S., Massachusetts Institute of Technology, Department of Business and Engineering Administration, 1936; Includes bibliographical references (leaf 53).

Thermal contact resistance

2026-04-14T03:02:26Z

Thermal contact resistance Henry, John J. (John Jewett), 1938- Thesis: Sc. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1964; Vita.; Includes bibliographical references (leaves 54-61).

Theory of time delay control and application to magnetic bearings

2026-04-14T03:02:43Z

Theory of time delay control and application to magnetic bearings Reddy, Suresh B. (Suresh Baddam), 1965- Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1992; Includes bibliographical references (leaves 318-325).

Drive-by Environmental Sensing Strategy to Reach Optimal and Continuous Spatio-Temporal Coverage Using Local Transit Network

2026-04-14T03:08:21Z

Drive-by Environmental Sensing Strategy to Reach Optimal and Continuous Spatio-Temporal Coverage Using Local Transit Network Ariss, Mayar; Wang, An; Sabouri, Sadegh; Duarte, Fabio; Ratti, Carlo Monitoring environmental features, such as air pollution, carbon dioxide emissions, noise, and heat, gives cities key data-driven insights to advise sustainable policies and city design. However, given the high variability of the environmental data, achieving good spatio-temporal resolution and coverage remains a major challenge. Even in well-monitored cities, such as Amsterdam, environmental sensors are usually placed in very few fixed locations, implying limited spatial coverage and an inability to adapt to changes in the urban environment. As cities evolve, they experience shifts in pollution sources, and fixed sensors might not adequately capture these changes without a costly and time-consuming reconfiguration process. To monitor the environmental qualities of Amsterdam’s roads, we present a “drive-by” sensing solution for a structured network of vehicles, meaning that sensors are designed to be deployed on buses and tramways, the trajectories and schedules of which are known. We propose a deployment strategy that combines the available fleets to reach optimal spatio-temporal coverage for different environmental features. For example, by optimizing the deployment of sensors on public transit vehicles, our proposal significantly enhances the monitoring of pollution-sensitive areas in Amsterdam. Depending on the desired spatio-temporal granularity and noting that one vehicle only hosts one sensor, the required number of sensors to be deployed on the structured network varies between 43 and 142, with the latter achieving the finest possible resolution.

Seismic assessment of unreinforced masonry façades from images using macroelement-based modeling

2026-04-14T03:08:23Z

Seismic assessment of unreinforced masonry façades from images using macroelement-based modeling Ariss, Mayar; Pantoja-Rosero, Bryan German; Duarte, Fabio; Klimenka, Mikita; Ratti, Carlo Despite the variability of urban infrastructure, unreinforced masonry buildings remain globally prevalent. Constructed from brick, hollow concrete blocks, stone, or other masonry materials, these structures account for a significant proportion of fatalities during seismic events—particularly in regions with limited access to early warning systems. Due to the complex behavior of masonry, accurately assessing structural vulnerabilities is highly dependent on the chosen modeling strategy. Yet, scalable, cost-effective approaches based on simple RGB imagery can still offer valuable insights. In this context, building on a previously developed digitalization methodology, this study proposes an automated, image-based framework for the rapid, non-invasive seismic evaluation of façades, addressing important research gaps in disaster resilience. The framework links image data with structural simulation by extracting visual and geometric features and translating them into consistent macroelement models using computer vision techniques, enabling nonlinear analyses under in-plane cyclic loading. The adopted numerical strategy has been extensively validated in prior work, with predictions closely aligning with experimental results. While the outcomes are predictive rather than diagnostic, future integration with publicly accessible urban imagery may enable the development of real-time, cross-border seismic risk maps.

A Mars 2020 Perseverance SuperCam Perspective on the Igneous Nature of the Máaz Formation at Jezero Crater and Link With Séítah, Mars

2026-04-14T03:08:27Z

A Mars 2020 Perseverance SuperCam Perspective on the Igneous Nature of the Máaz Formation at Jezero Crater and Link With Séítah, Mars The Máaz formation consists of the first lithologies in Jezero crater analyzed by the Mars 2020 Perseverance rover. This formation, investigated from Sols (Martian days) 1 to 201 and from Sols 343 to 382, overlies the Séítah formation (previously described as an olivine-rich cumulate) and was initially suggested to represent an igneous crater floor unit based on orbital analyses. Using SuperCam data, we conducted a detailed textural, chemical, and mineralogical analyses of the Máaz formation and the Content member of the Séítah formation. We conclude that the Máaz formation and the Content member are igneous and consist of different lava flows and/or possibly pyroclastic flows with complex textures, including vesicular and non-vesicular rocks with different grain sizes. The Máaz formation rocks exhibit some of the lowest Mg# (=molar 100 × MgO/MgO + FeO) of all Martian igneous rocks analyzed so far (including meteorites and surface rocks) and show similar basaltic to basaltic-andesitic compositions. Their mineralogy is dominated by Fe-rich augite to possibly ferrosilite and plagioclase, and minor phases such as Fe-Ti oxides and Si-rich phases. They show a broad diversity of both compositions and textures when compared to Martian meteorites and other surface rocks. The different Máaz and Content lava or pyroclastic flows all originate from the same parental magma and/or the same magmatic system, but are not petrogenetically linked to the Séítah formation. The study of returned Máaz samples in Earth-based laboratories will help constrain the formation of these rocks, calibrate Martian crater counting, and overall, improve our understanding of magmatism on Mars.

Compositions and Interior Structures of the Large Moons of Uranus and Implications for Future Spacecraft Observations

2026-04-11T07:29:15Z

Compositions and Interior Structures of the Large Moons of Uranus and Implications for Future Spacecraft Observations Castillo‐Rogez, Julie; Weiss, Benjamin; Beddingfield, Chloe; Biersteker, John; Cartwright, Richard; Goode, Allison; Melwani Daswani, Mohit; Neveu, Marc The five large moons of Uranus are important targets for future spacecraft missions. To motivate and inform the exploration of these moons, we model their internal evolution, present-day physical structures, and geochemical and geophysical signatures that may be measured by spacecraft. We predict that if the moons preserved liquid until present, it is likely in the form of residual oceans less than 30 km thick in Ariel, Umbriel, and less than 50 km in Titania, and Oberon. The preservation of liquid strongly depends on material properties and, potentially, on dynamical circumstances that are presently unknown. Miranda is unlikely to host liquid at present unless it experienced tidal heating a few tens of million years ago. We find that since the thin residual layers may be hypersaline, their induced magnetic fields could be detectable by future spacecraft-based magnetometers. However, if the ocean is maintained primarily by ammonia, and thus well below the water freezing point, then its electrical conductivity may be too small to be detectable by spacecraft. Lastly, our calculated tidal Love number (k2) and dissipation factor (Q) are consistent with the Q/k2 values previously inferred from dynamical evolution models. In particular, we find that the low Q/k2 estimated for Titania supports the hypothesis that Titania currently holds an ocean.

Intermodel Spread in Walker Circulation Responses Linked to Spread in Moist Stability and Radiation Responses

2026-04-11T07:29:18Z

Intermodel Spread in Walker Circulation Responses Linked to Spread in Moist Stability and Radiation Responses Duffy, Margaret L; O’Gorman, Paul A The response of the Pacific Walker circulation (WC) to long‐term warming remains uncertain. Here, we diagnose contributions to the WC response in comprehensive and idealized general circulation model (GCM) simulations. We find that the spread in WC response is substantial across both the Coupled Model Intercomparison Project (CMIP6) and the Atmospheric Model Intercomparison Project (AMIP) models, implicating differences in atmospheric models in the spread in projected WC strength. Using a moist static energy (MSE) budget, we evaluate the contributions to changes in the WC strength related to changes in gross moist stability (GMS), horizontal MSE advection, radiation, and surface fluxes. We find that the multimodel mean WC weakening is mostly related to changes in GMS and radiation. Furthermore, the spread in WC response is related to the spread in GMS and radiation responses. The GMS response is potentially sensitive to parameterized convective entrainment which can affect lapse rates and the depth of convection. We thus investigate the role of entrainment in setting the GMS response by varying the entrainment rate in an idealized GCM. The idealized GCM is run with a simplified Betts‐Miller convection scheme, modified to represent entrainment. The weakening of the WC with warming in the idealized GCM is dampened when higher entrainment rates are used. However, the spread in GMS responses due to differing entrainment rates is much smaller than the spread in GMS responses across CMIP6 models. Therefore, further work is needed to understand the large spread in GMS responses across CMIP6 and AMIP models.

Spatial Variability of Movement, Structure, and Formation of Warm Core Rings in the Northwest Atlantic Slope Sea

2026-04-11T07:29:16Z

Spatial Variability of Movement, Structure, and Formation of Warm Core Rings in the Northwest Atlantic Slope Sea Silver, Adrienne; Gangopadhyay, Avijit; Gawarkiewicz, Glen; Andres, Magdalena; Flierl, Glenn; Clark, Jenifer Gulf Stream Warm Core Rings (WCRs) have important influences on the New England Shelf and marine ecosystems. A 10‐year (2011–2020) WCR dataset that tracks weekly WCR locations and surface areas is used here to identify the rings' path and characterize their movement between 55 and 75°W. The WCR dataset reveals a very narrow band between 66 and 71°W along which rings travel almost due west along ∼39°N across isobaths – the “Ring Corridor.” Then, west of the corridor, the mean path turns southwestward, paralleling the shelfbreak. The average ring translation speed along the mean path is 5.9 cm s−1. Long‐lived rings (lifespan >150 days) tend to occupy the region west of the New England Seamount Chain (NESC) whereas short‐lived rings (lifespan <150 days) tend to be more broadly distributed. WCR vertical structures, analyzed using available Argo float profiles indicate that rings that are formed to the west of the NESC have shallower thermoclines than those formed to the east. This tendency may be due to different WCR formation processes that are observed to occur along different sections of the Gulf Stream. WCRs formed to the east of the NESC tend to form from a pinch‐off mechanism incorporating cores of Sargasso Sea water and a perimeter of Gulf Stream water. WCRs that form to the west of the NESC, form from a process called an aneurysm. WCRs formed through aneurysms comprise water mostly from the northern half of the Gulf Stream and are smaller than the classic pinch‐off rings.

Paleomagnetic Constraint on the Age of the Shyok Suture Zone

2026-04-11T07:29:26Z

Paleomagnetic Constraint on the Age of the Shyok Suture Zone Martin, Craig R; Jagoutz, Oliver; Upadhyay, Rajeev; van Tongeren, Jill A; Mueller, Paul A; Weiss, Benjamin P The India‐Eurasia collision is a key case study for understanding the influence of plate tectonic processes on Earth's crust, atmosphere, hydrosphere, and biosphere. However, the timing of the final India‐Eurasia continental collision is debated due to significant uncertainty in the age of the collision between the Kohistan‐Ladakh arc (KLA) and Eurasia along the Shyok suture zone. Here we present paleomagnetic results that constrain the Karakoram terrane in northwest India to a paleolatitude of 19.9 ± 8.9°N between 93 and 75 million years ago (Ma). Our results show that the Karakoram terrane was situated on the southern margin of Eurasia in the Late‐Cretaceous. Our results indicate that the KLA and Eurasian continent had a not converged until <61.6 Ma, placing a Paleocene older limit on the age of final closure of the Shyok suture zone. This suggests that the India‐Eurasia collision in northwestern India likely occurred after the closure of the oceanic basin between the KLA and Eurasia. The Paleocene collision event affecting India that has been widely interpreted to represent final India‐Eurasia collision instead records the arc‐continent collision between the KLA and the northern edge of India prior to final India‐Eurasia collision. Final India‐Eurasia collision in northwest India most likely occurred after the closure of the oceanic basin between the KLA and Eurasia.

High‐Pressure Mechanical Properties of Talc: Implications for Fault Strength and Slip Processes

2026-04-11T07:29:13Z

High‐Pressure Mechanical Properties of Talc: Implications for Fault Strength and Slip Processes Boneh, Y; Pec, M; Hirth, G The hydrous mineral talc is stable over a relatively large P‐T field and can form due to fluid migration and metamorphic reactions in mafic and ultramafic rocks and in faults along plate boundary interfaces. Talc is known to be one of the weakest minerals, making it potentially important for the deformation dynamics and seismic characteristics of faults. However, little is known about talc's mechanical properties at high temperatures under confining pressures greater than 0.5 GPa. We present results of deformation experiments on natural talc cylinders exploring talc rheology under 0.5–1.5 GPa and 400–700°C, P‐T conditions simulating conditions at deep faults and subducted slab interface. At these pressures, the strength of talc is highly temperature‐dependent where the thermal weakening is associated with an increased tendency for localization. The strength of talc and friction coefficient inferred from Mohr circle analysis is between 0.13 at 400°C to ∼0.01 at 700°C. Strength comparison with other phyllosilicates highlights talc as the weakest mineral, a factor of ∼3–4 weaker than antigorite and a factor of ∼2 weaker than chlorite. The observed friction coefficients for talc are consistent with those inferred for subducted slabs and the San Andreas fault. We conclude that the presence of talc may explain the low strength of faults and of subducted slab interface at depths where transient slow slip events occur.

Microseismic Constraints on the Mechanical State of the North Anatolian Fault Zone 13 Years After the 1999 M7.4 Izmit Earthquake

2026-04-11T07:29:10Z

Microseismic Constraints on the Mechanical State of the North Anatolian Fault Zone 13 Years After the 1999 M7.4 Izmit Earthquake Beaucé, Eric; van der Hilst, Robert D; Campillo, Michel The 17 August 1999 Mw7.4 Izmit earthquake ruptured the western section of the North Anatolian Fault Zone and strongly altered the fault zone properties and stress field. Consequences of the co- and post-seismic stress changes were seen in the spatio-temporal evolution of the seismicity and in the surface slip rates. Thirteen years after the Izmit earthquake, in 2012, the seismic network Dense Array for North Anatolia (DANA) was deployed for 1.5 years. We built a new catalog of microseismicity (M < 2) by applying our automated detection and location method to the DANA data set. Our method combines a systematic backprojection of the seismic wavefield and template matching. We analyzed the statistical properties of the catalog by computing the Gutenberg-Richter b-value and by quantifying the amount of temporal clustering in groups of nearby earthquakes. We found that the microseismicity mainly occurs off the main fault and that the most active regions are the Lake Sapanca step-over and near the Akyazi fault. Based on previous studies, we interpreted the b-values and temporal clustering (a) as indicating that the Akyazi seismicity is occurring in high background stresses and is driven by the Izmit earthquake residual stresses, and (b) as suggesting evidence that an intricate combination of seismic and aseismic slip was taking place on heterogeneous faults at the eastern Lake Sapanca, near the brittle-ductile transition. Combined with geodetic evidence for enhanced north-south extension around Lake Sapanca following the Izmit earthquake, the seismicity supports the possibility of slow slip at depth in the step-over.

Higher‐Resolution Tropopause Folding Accounts for More Stratospheric Ozone Intrusions

2026-04-11T07:29:04Z

Higher‐Resolution Tropopause Folding Accounts for More Stratospheric Ozone Intrusions Bartusek, Samuel; Wu, Yutian; Ting, Mingfang; Zheng, Cheng; Fiore, Arlene; Sprenger, Michael; Flemming, Johannes Ozone in the troposphere is a pollutant and greenhouse gas, and it is crucial to better understand its transport from the ozone-rich stratosphere. Tropopause folding, wherein stratospheric air intrudes downward into the troposphere, enables stratosphere-to-troposphere ozone transport (STT). However, systematic analysis of the relationship between folding and tropospheric ozone, using data that can both capture folding's spatial scales and accurately represent tropospheric chemistry, is limited. Here, we compare folding in high-resolution reanalysis ERA5 (0.25° horizontal, <21 hPa vertical) and low-resolution chemical reanalysis CAMSRA (0.75°, <40 hPa), against CAMSRA ozone, over 1 year. Folding becomes dramatically more frequent at high resolution, with vertical resolution overwhelmingly responsible. Deeper, more filamentary folding is almost entirely unrepresented at low resolution. Higher-resolution folding is better-correlated with tropospheric ozone (especially along midlatitude storm tracks, where deep folding is most common); STT is therefore likely more attributable to tropopause folding than coarsely-resolved folding can capture.

Phosphine in the Venusian Atmosphere: A Strict Upper Limit From SOFIA GREAT Observations

2026-04-11T07:29:19Z

Phosphine in the Venusian Atmosphere: A Strict Upper Limit From SOFIA GREAT Observations Cordiner, MA; Villanueva, GL; Wiesemeyer, H; Milam, SN; de Pater, I; Moullet, A; Aladro, R; Nixon, CA; Thelen, AE; Charnley, SB; Stutzki, J; Kofman, V; Faggi, S; Liuzzi, G; Cosentino, R; McGuire, BA The presence of phosphine (PH3) in the atmosphere of Venus was reported by Greaves et al. (2021, https://doi.org/10.1038/s41550-020-1174-4), based on observations of the J = 1–0 transition at 267 GHz using ground-based, millimeter-wave spectroscopy. This unexpected discovery presents a challenge for our understanding of Venus's atmosphere, and has led to a reappraisal of the possible sources and sinks of atmospheric phosphorous-bearing gases. Here we present results from a search for PH3 on Venus using the German REceiver for Astronomy at Terahertz Frequencies instrument aboard the Stratospheric Observatory for Infrared Astronomy aircraft, over three flights conducted in November 2021. Multiple PH3 transitions were targeted at frequencies centered on 533 and 1,067 GHz, but no evidence for atmospheric PH3 was detected. Through radiative transfer modeling, we derived a disk-averaged upper limit on the PH3 abundance of 0.8 ppb in the altitude range 75–110 km, which is more stringent than previous ground-based studies.

Crystal Shape Control on the Repacking and Jamming of Crystal‐Rich Mushes

2026-04-11T07:29:11Z

Crystal Shape Control on the Repacking and Jamming of Crystal‐Rich Mushes Hoyos, Susana; Florez, Darien; Pec, Matej; Huber, Christian The rheology of crustal mushes is a crucial parameter controlling melt segregation and magma flow. However, the relations between mush dynamics and crystal size and shape distribution remain poorly understood because of the complexity of melt‐crystal and crystal‐crystal interactions. We performed analog experiments to characterize the mechanisms that control pore space reduction associated with repacking. Three suspensions of monodisperse particles with different geometries and aspect ratios (1:1, 2:1, 4:1) in a viscous fluid were tested. Our results show that particle aspect ratios strongly control the melt extraction processes. We identify two competing mechanisms that enable melt extraction at grain scale. The first mechanism leads to continuous deformation and melt extraction and is associated with “diffuse” frictional dissipation between neighboring particles. The second is stochastic, localized, and nearly instantaneous and is associated with the development and destruction of force chains percolating through the granular assembly.

Abundances of Uranium and Thorium Elements in Earth Estimated by Geoneutrino Spectroscopy

2026-05-02T03:12:56Z

Abundances of Uranium and Thorium Elements in Earth Estimated by Geoneutrino Spectroscopy The decay of the primordial isotopes 238U, 235U, 232Th, and 40K has contributed to the terrestrial heat budget throughout the Earth's history. Hence, the individual abundance of those isotopes are key parameters in reconstructing contemporary Earth models. The geoneutrinos produced by the radioactive decays of uranium and thorium have been observed with the Kamioka Liquid-Scintillator Antineutrino Detector (KamLAND). Those measurements have been improved with more than 18-year observation time, and improvement in detector background levels mainly with an 8-year nearly reactor-free period, which now permit spectroscopy with geoneutrinos. Our results yield the first constraint on both uranium and thorium heat contributions. The KamLAND result is consistent with geochemical estimations based on elemental abundances of chondritic meteorites and mantle peridotites. The High-Q model is disfavored at 99.76% C.L. and a fully radiogenic model is excluded at 5.2σ assuming a homogeneous heat producing element distribution in the mantle.

Unexpected Repartitioning of Stratospheric Inorganic Chlorine After the 2020 Australian Wildfires

2026-04-10T03:07:37Z

Unexpected Repartitioning of Stratospheric Inorganic Chlorine After the 2020 Australian Wildfires Strahan, Susan E; Smale, Dan; Solomon, Susan; Taha, Ghassan; Damon, Megan R; Steenrod, Stephen D; Jones, Nicholas; Liley, Ben; Querel, Richard; Robinson, John The inorganic chlorine (Cly) and odd nitrogen (NOy) chemical families influence stratospheric O3. In January 2020 Australian wildfires injected record-breaking amounts of smoke into the southern stratosphere. Within 1–2 months ground-based and satellite observations showed Cly and NOy were repartitioned. By May, lower stratospheric HCl columns declined by ∼30% and ClONO2 columns increased by 40%–50%. The Cly perturbations began and ended near the equinoxes, increased poleward, and peaked at the winter solstice. NO2 decreased from February to April, consistent with sulfate aerosol reactions, but returned to typical values by June - months before the Cly recovery. Transport tracers show that dynamics not chemistry explains most of the observed O3 decrease after April, with no significant transport earlier. Simulations assuming wildfire smoke behaves identically to sulfate aerosols couldn't reproduce observed Cly changes, suggesting they have different composition and chemistry. This undermines our ability to predict ozone in a changing climate.

Organosulfur Aerosols Likely Carried Sulfur MIF Signatures in the Early Earth’s Atmosphere

2026-04-10T03:07:29Z

Organosulfur Aerosols Likely Carried Sulfur MIF Signatures in the Early Earth’s Atmosphere Oduro, Harry; Ono, Shuhei; Alrasheed, Faisal; Eldridge, Daniel L Signatures of mass-independent fractionation (MIF) of sulfur in Archean sulfide and sulfate minerals are widely thought to record an anoxic early Earth’s atmosphere. While experiments of ultraviolet irradiation of SO2 produce significant sulfur mass-independent fractionation (S-MIF) in reaction products (elemental sulfur and residual sulfur dioxide), they have not been able to reproduce the isotope patterns, in particular Δ36S/Δ33S ratios, observed in the geologic rock record. Studies that focused on organic sulfur gases and hazes in Archean did not report organosulfur aerosol photoproducts as major contributors to Archean S-MIF chemistry. Here we show, for the first time, that photochemical reactions of SO2 in the presence of gaseous hydrocarbons (CH4, C2H2, and C2H4) produce haze-like organosulfur aerosols bearing S-MIF with variable Δ36S/Δ33S ratios. The isotope trends for the organosulfur photoproducts produced in our experiments suggest that in addition to elemental sulfur, organosulfur compounds—in particular methanesulfonic acid—are a key component of S-MIF signals from the atmosphere to the ocean and sediments with possible links to Archean atmosphere warmed by a methane greenhouse.

High‐Resolution Magnetic‐Geochemical Mapping of the Serpentinized and Carbonated Atlin Ophiolite, British Columbia: Toward Establishing Magnetometry as a Monitoring Tool for In Situ Mineral Carbonation

2026-04-10T03:07:39Z

High‐Resolution Magnetic‐Geochemical Mapping of the Serpentinized and Carbonated Atlin Ophiolite, British Columbia: Toward Establishing Magnetometry as a Monitoring Tool for In Situ Mineral Carbonation Tominaga, Masako; Beinlich, Andreas; Lima, Eduardo A; Pruett, Paiden; Vento, Noah R; Weiss, Benjamin P We address in situ serpentinization and mineral carbonation processes in oceanic lithosphere using integrated field magnetic measurements, rock magnetic analyses, superconducting quantum interference device (SQUID) microscopy, microtextural observations, and energy dispersive spectroscopy phase mapping. A representative suite of ultramafic rock samples were collected, within the Atlin ophiolite, along a 100‐m long transect across a continuous outcrop of mantle harzburgite with several alteration fronts: serpentinite, soapstone (magnesite + talc), and listvenite (magnesite + quartz). Strong correlations between changes in magnetic signal strengths and amount of alteration are shown with distinctive contrasts between serpentinite, transitional soapstone, and listvenite that are linked to the formation and breakdown of magnetite. While previous observations of the Linnajavri ultramafic complex indicated that the breakdown of magnetite occurred during listvenite formation from the precursor soapstone (Tominaga et al., 2017, https://doi.org/10.1038/s41467-017-01610-4), results from our study suggest that magnetite destabilization already occurred during the replacement of serpentinite by soapstone (i.e., at lower fluid CO2 concentrations). This difference is attributed to fracture‐controlled flow of sulfur‐bearing alteration fluid at Atlin, causing reductive magnetite dissolution in thin soapstone zones separating serpentinite from sulfide‐mineralized listvenite. We argue that magnetite growth or breakdown in soapstone provides insight into the mode of fluid flow and the composition, which control the scale and extent of carbonation. This conclusion enables us to use magnetometry as a viable tool for monitoring the reaction progress from serpentinite to carbonate‐bearing assemblages in space and time with a caution that the three‐dimensionality of magnetic sources impacts the scalability of measurements.

Estimating the Net Magnetic Moment of Geological Samples From Planar Field Maps Using Multipoles

2026-04-10T03:07:27Z

Estimating the Net Magnetic Moment of Geological Samples From Planar Field Maps Using Multipoles Lima, Eduardo A; Weiss, Benjamin P; Borlina, Caue S; Baratchart, Laurent; Hardin, Douglas P Recent advances in magnetic microscopy have enabled studies of geological samples whose weak and spatially nonuniform magnetizations were previously inaccessible to standard magnetometry techniques. A quantity of central importance is the net magnetic moment, which reflects the mean direction and the intensity of the magnetization states of numerous ferromagnetic crystals within a certain volume. The planar arrangement of typical magnetic microscopy measurements, which originates from measuring the field immediately above the polished surface of a sample to maximize sensitivity and spatial resolution, makes estimating net moments considerably more challenging than with spherically distributed data. In particular, spatially extended and nonuniform magnetization distributions often cannot be adequately approximated by a single magnetic dipole. To address this limitation, we developed a multipole fitting technique that can accurately estimate net moment using spherical harmonic multipole expansions computed from planar data. Given that the optimal location for the origin of such expansions is unknown beforehand and generally unconstrained, regularization of this inverse problem is critical for obtaining accurate moment estimates from noisy experimental magnetic data. We characterized the performance of the technique using synthetic sources under different conditions (noiseless data, data corrupted with simulated white noise, and data corrupted with measured instrument noise). We then validated and demonstrated the technique using superconducting quantum interference device microscopy measurements of impact melt spherules from Lonar crater, India and dusty olivine chondrules from the CO chondrite meteorite Dominion Range 08006.

A >200 ka U-Th Based Chronology From Lacustrine Evaporites, Searles Lake, CA

2026-04-10T03:07:15Z

A >200 ka U-Th Based Chronology From Lacustrine Evaporites, Searles Lake, CA Stroup, Justin S; Olson, Kristian J; Lowenstein, Tim K; Jost, Adam B; Mosher, Hayley M; Peaple, Mark D; Feakins, Sarah J; Chen, Christine Y; Lund, Steven P; McGee, David Well‐dated lacustrine records are essential to establish the timing and drivers of regional hydroclimate change. Searles Basin, California, records the depositional history of a fluctuating saline‐alkaline lake in the terminal basin of the Owens River system draining the eastern Sierra Nevada. Here, we establish a U‐Th chronology for the ∼76‐m‐long SLAPP‐SLRS17 core collected in 2017 based on dating of evaporite minerals. Ninety‐eight dated samples comprising nine different minerals were evaluated based on stratigraphic, mineralogic, textural, chemical, and reproducibility criteria. After the application of these criteria, a total of 37 dated samples remained as constraints for the age model. A lack of dateable minerals between 145 and 110 ka left the age model unconstrained over the penultimate glacial termination (Termination II). We thus established a tie point between plant wax δD values in the core and a nearby speleothem δ18O record at the beginning of the Last Interglacial. We construct a Bayesian age model allowing stratigraphy to inform sedimentation rate inflections. We find that the >210 ka SLAPP‐SRLS17 record contains five major units that correspond with prior work. The new dating is broadly consistent with previous efforts but provides more precise age estimates and enables a detailed evaluation of evaporite depositional history. We also offer a substantial revision of the age of the Bottom Mud‐Mixed Layer contact, shifting it from ∼130 ka to 178 ± 3 ka. The new U‐Th chronology documents the timing of mud and salt layers and lays the foundation for climate reconstructions.

The spinorial energy for asymptotically Euclidean Ricci flow

2026-05-02T03:11:54Z

The spinorial energy for asymptotically Euclidean Ricci flow Baldauf, Julius; Ozuch, Tristan This article introduces a functional generalizing Perelman’s weighted Hilbert-Einstein action and the Dirichlet energy for spinors. It is well defined on a wide class of noncompact manifolds; on asymptotically Euclidean manifolds, the functional is shown to admit a unique critical point, which is necessarily of min-max type, and the Ricci flow is its gradient flow. The proof is based on variational formulas for weighted spinorial functionals, valid on all spin manifolds with boundary.

Emergent symmetries and phases in quantum spin chains coupled to a Kuramoto model

2026-04-10T03:07:17Z

Emergent symmetries and phases in quantum spin chains coupled to a Kuramoto model Bastidas, V. M. Floquet theory is a widely used framework to describe the dynamics of periodically driven quantum systems. The usual scenario to describe such systems is to consider the effect of an external control with a definite period in time that can act either locally or globally on the system of interest. However, apart from periodicity, such drives typically lack classical correlations or additional structure. In this work, we consider drives with intrinsic dynamics that undergo self-organization, leading to periodic steady states with emergent symmetries. To substantiate our results, we consider two examples of one-dimensional quantum spin chains coupled to a classical Kuramoto model. First, we investigate a Kuramoto model with all-to-all coupling driving a one-dimensional quantum Ising chain into a time-periodic steady state with an emergent translational symmetry. Next, we consider a Kuramoto model in a zigzag lattice driving an XX spin chain. The dynamics of traveling waves in the Kuramoto model trimerizes the lattice, effectively inducing topological behavior that can be exploited to perform topological pumping. Our results can be experimentally implemented in digital and analog near-term quantum devices.

Relativistic electrodynamics with a universal length scale

2026-04-10T03:07:38Z

Relativistic electrodynamics with a universal length scale Pedergnana, Tiemo; Kogelbauer, Florian We derive the analogues of the Dirac and Pauli equations from a spatially fourth-order Klein-Gordon equation with a universal length scale. Starting from a singularly perturbed variant of Maxwell's equations, we deduce a 32-dimensional variant of the Dirac equation for spin-1/2 particles through an algebraic factorization procedure. We illustrate an experimental test of the theory from the split lines of the electron beam in a Stern-Gerlach experiment. This hyperfine splitting leads to four distinct eigenvalues of the spin operator, which can be grouped into two pairs centered around the classic values of ±ℏ/2. The modified electrodynamic framework features an oriented, micropolar spacetime.

The Imminent Data Desert: The Future of Stratospheric Monitoring in a Rapidly Changing World

2026-04-10T03:07:34Z

The Imminent Data Desert: The Future of Stratospheric Monitoring in a Rapidly Changing World Salawitch, Ross J; Smith, Jessica B; Selkirk, Henry; Wargan, Krzysztof; Chipperfield, Martyn P; Hossaini, Ryan; Levelt, Pieternel F; Livesey, Nathaniel J; McBride, Laura A; Millán, Luis F; Moyer, Elisabeth; Santee, Michelle L; Schoeberl, Mark R; Solomon, Susan; Stone, Kane; Worden, Helen M The Atmospheric Chemistry Experiment–Fourier Transform Spectrometer (ACE-FTS) on SCISAT-1 and Microwave Limb Sounder (MLS) on NASA’s Aura satellite have contributed significantly to understanding the impacts of human activities on the stratospheric ozone layer. The two-decade-long data record from these instruments has allowed quantification of ozone depletion caused by human-released ozone-depleting substances, the effects of extreme natural events like major volcanic eruptions including Hunga in 2022, and events amplified by human-caused climate change such as wildfires that inject material into the stratosphere, as happened over Australia in early 2020. The Aura platform is nearing the end of its operational lifetime, and SCISAT-1 is over 20 years old. Their decommissioning will cause a substantial gap in the measurement of critical atmospheric components, including water vapor, inorganic chlorine species, and tracers of stratospheric transport. This upcoming “data desert” poses significant challenges for monitoring the recovery of the ozone layer and assessing the effects on stratospheric composition of future extreme events, threats posed by increases in space debris from satellite burn-up, and the possible injection of stratospheric aerosol to mitigate global warming. The lack of confirmed future missions that can provide daily near-global profile measurements of stratospheric composition highlights the need for observational strategies to bridge this impending gap. This paper discusses the essential role of ACE-FTS and MLS in advancing our understanding of the stratosphere, the impact of data loss after the cessation of one or both instruments, and the urgency of developing strategies for mitigating the impact of these observational losses at a time marked by dramatic changes in the stratosphere due to human and natural factors. Significance Statement We highlight the critical role that data from the ACE-FTS and Microwave Limb Sounder (MLS) satellite instruments have played in advancing our understanding of stratospheric composition and the impacts of human activities on the ozone layer. As these instruments near the end of their operational lifetimes, the imminent loss of data, particularly of stratospheric water vapor, chlorine species, and tracers of transport, portends profound and irrevocable gaps in atmospheric observations. This loss of observational capability will occur at a time of rapid climate change and hinder our understanding of the stratosphere’s response to, and its coupled role in, continued climate forcing. This paper emphasizes the urgency of addressing this data desert, highlighting the need for sustained, coordinated, global measurement capabilities for these crucial constituents.

Identifying Climate Patterns Using Clustering Autoencoder Techniques

2026-04-10T03:07:22Z

Identifying Climate Patterns Using Clustering Autoencoder Techniques Kurihana, Takuya; Mastilovic, Ilijana; Wang, Lijing; Meray, Aurelien; Praveen, Satyarth; Xu, Zexuan; Memarzadeh, Milad; Lavin, Alexander; Wainwright, Haruko The complexity of growing spatiotemporal resolution of climate simulations produces a variety of climate patterns under different projection scenarios. This paper proposes a new data-driven climate classification workflow via an unsupervised deep learning technique that can dimensionally reduce the vast volume of spatiotemporal numerical climate projection data into a compact representation. We aim to identify distinct zones that capture multiple climate variables as well as their future changes under different climate change scenarios. Our approach leverages convolutional autoencoders combined with k-means clustering (standard autoencoder) and online clustering based on the Sinkhorn–Knopp algorithm (clustering autoencoder) across the conterminous United States (CONUS) to capture unique climate patterns in a data-driven fashion from the Geophysical Fluid Dynamics Laboratory Earth System Model with GOLD component (GFDL-ESM2G). The developed approach compresses 70 years of GFDL-ESM2G simulation at 0.125° spatial resolution across the CONUS under multiple warming scenarios to a lower-dimensional space by a factor of 660 000 and then tested on 150 years of GFDL-ESM2G simulation data. The results show that five climate clusters capture physically reasonable and spatially stable climatological patterns matched to known climate classes defined by human experts. Results also show that using a clustering autoencoder can reduce the computational time for clustering by up to 9.2 times when compared to using a standard autoencoder. Our five unique climate patterns resulting from the deep learning–based clustering of the lower-dimensional space thereby enable us to provide insights on hydrometeorology and its spatial heterogeneity across the conterminous United States immediately without downloading large climate datasets. Significance Statement This paper presents a data-driven climate classification approach using unsupervised deep learning to dimensionally reduce climate model outputs and to identify distinct climate regions for their future changes. Our approach compresses climate information for 70 years of Geophysical Fluid Dynamics Laboratory Earth System Model data across the conterminous United States (CONUS) at 0.125° spatial resolution. The results reveal that five climate clusters capture reasonable and stable climatological patterns matched to known climate patterns. The embedded clustering process in deep learning provides ×9.2 times faster execution than the k-means clustering technique. These results give us insight about climate spatial patterns and heterogeneity of hydrological patterns across the conterminous United States without downloading large climate datasets.

Machine Learning Approach for Spatiotemporal Multivariate Optimization of Environmental Monitoring Sensor Locations

2026-05-02T03:11:41Z

Machine Learning Approach for Spatiotemporal Multivariate Optimization of Environmental Monitoring Sensor Locations Siddiquee, Masudur R; Meray, Aurelien O; Xu, Zexuan; Gonzalez-Raymat, Hansell; Danielson, Thomas; Upadhyay, Himanshu; Lagos, Leonel E; Eddy-Dilek, Carol; Wainwright, Haruko M Long-term environmental monitoring is critical for managing the soil and groundwater at contaminated sites. Recent improvements in state-of-the-art sensor technology, communication networks, and artificial intelligence have created opportunities to modernize this monitoring activity for automated, fast, robust, and predictive monitoring. In such modernization, it is required that sensor locations be optimized to capture the spatiotemporal dynamics of all monitoring variables as well as to make it cost-effective. The legacy monitoring datasets of the target area are important to perform this optimization. In this study, we have developed a machine-learning approach to optimize sensor locations for soil and groundwater monitoring based on ensemble supervised learning and majority voting. For spatial optimization, Gaussian process regression (GPR) is used for spatial interpolation, while the majority voting is applied to accommodate the multivariate temporal dimension. Results show that the algorithms significantly outperform the random selection of the sensor locations for predictive spatiotemporal interpolation. While the method has been applied to a four-dimensional dataset (with two-dimensional space, time, and multiple contaminants), we anticipate that it can be generalizable to higher-dimensional datasets for environmental monitoring sensor location optimization.

Subseasonal Prediction of Central European Summer Heatwaves with Linear and Random Forest Machine Learning Models

2026-04-10T03:07:30Z

Subseasonal Prediction of Central European Summer Heatwaves with Linear and Random Forest Machine Learning Models Weirich-Benet, Elizabeth; Pyrina, Maria; Jiménez-Esteve, Bernat; Fraenkel, Ernest; Cohen, Judah; Domeisen, Daniela IV Heatwaves are extreme near-surface temperature events that can have substantial impacts on ecosystems and society. Early warning systems help to reduce these impacts by helping communities prepare for hazardous climate-related events. However, state-of-the-art prediction systems can often not make accurate forecasts of heatwaves more than two weeks in advance, which are required for advance warnings. We therefore investigate the potential of statistical and machine learning methods to understand and predict central European summer heatwaves on time scales of several weeks. As a first step, we identify the most important regional atmospheric and surface predictors based on previous studies and supported by a correlation analysis: 2-m air temperature, 500-hPa geopotential, precipitation, and soil moisture in central Europe, as well as Mediterranean and North Atlantic sea surface temperatures, and the North Atlantic jet stream. Based on these predictors, we apply machine learning methods to forecast two targets: summer temperature anomalies and the probability of heatwaves for 1–6 weeks lead time at weekly resolution. For each of these two target variables, we use both a linear and a random forest model. The performance of these statistical models decays with lead time, as expected, but outperforms persistence and climatology at all lead times. For lead times longer than two weeks, our machine learning models compete with the ensemble mean of the European Centre for Medium-Range Weather Forecast’s hindcast system. We thus show that machine learning can help improve subseasonal forecasts of summer temperature anomalies and heatwaves. Significance Statement Heatwaves (prolonged extremely warm temperatures) cause thousands of fatalities worldwide each year. These damaging events are becoming even more severe with climate change. This study aims to improve advance predictions of summer heatwaves in central Europe by using statistical and machine learning methods. Machine learning models are shown to compete with conventional physics-based models for forecasting heatwaves more than two weeks in advance. These early warnings can be used to activate effective and timely response plans targeting vulnerable communities and regions, thereby reducing the damage caused by heatwaves.

Waveform-based estimation of Q and scattering properties for zero-offset vertical seismic profile data

2026-04-10T03:07:24Z

Waveform-based estimation of Q and scattering properties for zero-offset vertical seismic profile data Nakata, Rie; Lumley, David; Hampson, Gary; Nihei, Kurt; Nakata, Nori Estimating Q using downgoing waves in zero-offset vertical seismic profiles (VSPs) can be challenging when scattered waves from near-borehole heterogeneities interfere with direct arrivals. In any Q estimation method that assumes a downgoing plane wave, constructive and destructive wave-mode interference can cause errors in the estimate. For example, in the spectral-ratio method, such interference modulates the amplitude spectra introducing significant variations and even nonphysical negative Q (amplification) estimates. We have investigated this phenomenon using synthetic and field data sets from offshore Australia and developed a two-step waveform-based method to characterize scattering anomalies and improve Q estimates. Waveform information is key to deal with closely spaced bandlimited seismic events. First, we solve an inverse problem to locate and characterize scatterers by minimizing the traveltime and waveform misfits. Then, using the estimated parameters, we model the scatterers’ contribution to the VSP data and remove it from the observed waveforms. The resulting spectra resemble those that would have been acquired in the absence of the scatterers and are much more suitable for the spectral-ratio method. By assuming a 1D medium and a simple scatterer shape (i.e., circular), we parameterize a scattering heterogeneity using five parameters (depth, distance, size, velocity, and density) and seek a solution using a grid search to handle the nonuniqueness of the VSP inversion. Instead, adaptive subtraction is required to fine-tune the modeled interference to better fit the observation. We successfully use this method to characterize and mitigate the strongest wave interference in the field data. The final Q estimates contain milder variations and much less nonphysical negative Q. Our results demonstrate that the proposed method, readily extendible to multiple scatterer cases, can locate discrete scatterers, remove the effects of their interference, and thus significantly improve the Q estimates from VSP data.

Reservoir geophysics’ contributions to the energy transition and climate change challenge — Introduction

2026-04-10T03:07:14Z

Reservoir geophysics’ contributions to the energy transition and climate change challenge — Introduction He, Zhiliang; Mukerji, Tapan; Grana, Dario; Fu, Liyun; Cao, Danping; Cai, Xiaohui; Qu, Luping; Liu, Mingliang; Farquharson, Colin G Achieving net-zero greenhouse gas emissions by 2050 represents one of the key scientific and societal challenges of our time. The energy industry plays a central role in this transition, which requires a rapid shift from conventional fossil fuel-based systems toward renewable energy resources and the large-scale deployment of subsurface solutions such as geological CO₂ sequestration, underground hydrogen storage, and natural hydrogen exploration. These emerging energy systems critically depend on accurate static subsurface characterization and robust dynamic monitoring of complex reservoir environments. In this context, reservoir geophysics provides essential tools for imaging, quantifying, and monitoring subsurface processes across spatial and temporal scales.

Revisiting numerical validation of Gassmann’s equations: Open-pore boundary condition

2026-04-10T03:07:31Z

Revisiting numerical validation of Gassmann’s equations: Open-pore boundary condition Alkhimenkov, Yury Gassmann’s equations are widely used to predict the effective moduli of fluid-saturated rocks from dry properties and basic petrophysical parameters, yet pore-scale validations that explicitly demonstrate numerical convergence remain limited. Furthermore, recent publications have questioned the logical consistency of the derivation of Gassmann’s equations, arguing instead for the consistency of the Brown and Korringa (1975) formulation with a “mean” compressibility. A pore-scale validation of Gassmann’s relations was performed under an open-pore boundary geometry, where the pore network intersects the external surface and the imposed macroscopic displacement acts simultaneously on solid and fluid at the boundary. Three-dimensional finite-element simulations were used that couple linear elasticity of the solid skeleton with the quasistatic, compressible Navier–Stokes equations that govern fluid flow. Direct relaxation tests on monomineral, isotropic (cubic) models with generic pore topology (narrow throats linked to a wider pore body) were conducted across a sequence of mesh refinements. In the low-frequency limit, the numerically evaluated undrained bulk modulus converged to the Gassmann prediction to within numerical precision. Comparisons with the Brown and Korringa (1975) formulation indicated that the “mean” compressibility converges to the grain compressibility, causing the formulation to collapse to the classical Gassmann result as resolution increases. Together with earlier closed-pore analysis, the results confirmed that for such models, Gassmann’s equations remain valid for open- and closed-pore boundary conditions, provided that uniform pore pressure is achieved in the quasistatic limit.

Full rock anisotropy characterization using laser ultrasonics

2026-04-10T03:07:25Z

Full rock anisotropy characterization using laser ultrasonics Mandal, Partha Pratim; Simpson, Jonathan; Sarout, Joel; Kovalyshen, Yevhen; Adam, Ludmila; Wijk, Kasper van Reliable seismic imaging and the estimate of the distribution of subsurface stress depend on the accurate assessment of elastic anisotropy in shaly rock formation. Anisotropy was typically evaluated in the lab with contacting transducers. However, these measurements frequently reported significant uncertainty due to variations in mechanical coupling, limitations on the number of ray paths analyzed, and the relative sizes of transducers. We assessed the effectiveness of the contactless laser ultrasonic pulse transmission technique, which uses a source and probing laser and a cylindrical rock sample placed on a rotating stage, to reduce uncertainties in the estimation of Thomsen’s anisotropy parameters (TAPs). The ambiguity of propagation distance was eliminated from the smaller imprint of the source and receiver lasers on the core sample, suggesting that group velocity was effectively estimated, and the observed wave attenuation was solely indicative of the rock. Three cylindrical samples of multilayer manufactured material, namely, phenolic grade CE (Canvas Electrical), which were rather homogeneous and oriented differently (0, 45°, and 90° with respect to the bedding), were examined for P-wave velocity over approximately 630 separate ray pathways. The most precise estimation of TAPs in a vertical transverse isotropic medium without knowledge of the symmetry axis was achieved by using the laser ultrasonic method on a phenolic grade CE sample that is extracted horizontally to the bedding. Multiple dip angles, dense sampling, and multipath inversion of these datasets reduced Thomsen’s δ parameter uncertainty by 20%. Application of the same technique on an anisotropic and heterogeneous shale sample suggested that (i) the mineralogy-controlled density heterogeneity observed from the 3D X-ray computed tomography images could be detected and identified from high-density laser ultrasonic data using reservoir monitoring techniques imported from field-scale geophysics and (ii) TAPs in the homogeneous and anisotropic sub-volume of the shale sample could be reliably estimated once heterogeneity was accounted for.

MEDiCINe: Motion Correction for Neural Electrophysiology Recordings

2026-04-10T03:07:18Z

MEDiCINe: Motion Correction for Neural Electrophysiology Recordings Watters, Nicholas; Buccino, Alessio; Jazayeri, Mehrdad Electrophysiology recordings from the brain using laminar multielectrode arrays allow researchers to measure the activity of many neurons simultaneously. However, laminar microelectrode arrays move relative to their surrounding neural tissue for a variety of reasons, such as pulsation, changes in intracranial pressure, and decompression of neural tissue after insertion. Inferring and correcting for this motion stabilizes the recording and is critical to identify and track single neurons across time. Such motion correction is a preprocessing step of standard spike-sorting methods. However, estimating motion robustly and accurately in electrophysiology recordings is challenging due to the stochasticity of the neural data. To tackle this problem, we introduce MEDiCINe (Motion Estimation by Distributional Contrastive Inference for Neurophysiology), a novel motion estimation method. We show that MEDiCINe outperforms existing motion estimation methods on an extensive suite of simulated neurophysiology recordings and leads to more accurate spike sorting. We also show that MEDiCINe accurately estimates the motion in primate and rodent electrophysiology recordings with a variety of motion and stability statistics. We open-source MEDiCINe, usage instructions, examples integrating MEDiCINe with common tools for spike sorting, and data and code for reproducing our results. This open software will enable other researchers to use MEDiCINe to improve spike sorting results and get the most out of their electrophysiology datasets.

pAI/MSc: ML Theory Research with Humans on the Loop

2026-04-09T03:00:18Z

pAI/MSc: ML Theory Research with Humans on the Loop Abdelmoneum, Mahmoud; Beneventano, Pierfrancesco; Poggio, Tomaso We present pAI/MSc3, an open-source, customizable, modular multi-agent system for academic research workflows. Our goal is not autonomous scientific ideation, nor fully automated research. It is narrower and more practical: to reduce by orders of magnitude the human steering required to turn a specified hypothesis into a literature-grounded, mathematically established, experimentally supported, submission-oriented manuscript draft. pAI/MSc is built with a current emphasis on machine learning theory and adjacent quantitative fields.

Size Ranges of Magnetic Domain States in Tetrataenite

2026-05-08T03:12:24Z

Size Ranges of Magnetic Domain States in Tetrataenite Mansbach, Elias N; Shah, Jay; Williams, Wyn; Maurel, Clara; Bryson, James FJ; Weiss, Benjamin P Paleomagnetic studies of meteorites provide unique constraints on the evolution of magnetic fields in the early solar system. These studies rely on the identification of magnetic minerals that can retain stable magnetizations over ≳4.5 billion years (Ga). The ferromagnetic mineral tetrataenite (γ''-Fe0.5Ni0.5) is found in iron, stony-iron and chondrite meteorite groups. Nanoscale intergrowths of tetrataenite have been shown to carry records of paleomagnetic fields, although the effect of magnetostatic interactions on their magnetic remanence acquisition remains to be fully understood. Tetrataenite can also occur as isolated, non-interacting, nanoscale grains in many meteorite groups, although the paleomagnetic potential of these grains is particularly poorly understood. Here, we aim to improve our understanding of tetrataenite magnetization to refine our knowledge of existing paleomagnetic analyses and broaden the spectrum of meteorite groups that can be used for future paleomagnetic studies. We present the results of analytical calculations and micromagnetic modeling of isolated tetrataenite grains with various geometries. We find that tetrataenite forms a stable single domain state at grain lengths between 6 and ∼160 nm dependent on its elongation. It also possesses a magnetization resistant to viscous remagnetization over the lifetime of the solar system at 293 K. At larger grain sizes, tetrataenite's lowest energy state is a lamellar two-domain state, stable at Ga-scale timescales. Unlike many other magnetic minerals, tetrataenite does not form a single-vortex domain state due to its large uniaxial anisotropy. Our results show that single domain and two-domain tetrataenite grains carry an extremely stable magnetization and therefore are promising for paleomagnetic studies.

Coincident Biogenic Nitrite and pH Maxima Arise in the Upper Anoxic Layer in the Eastern Tropical North Pacific

2026-05-08T03:13:41Z

Coincident Biogenic Nitrite and pH Maxima Arise in the Upper Anoxic Layer in the Eastern Tropical North Pacific Cinay, Timur; Dumit, Diana; Woosley, Ryan J; Boles, Elisabeth L; Kwiecinski, Jarek V; Mullen, Susan; Tamasi, Tyler J; Wolf, Martin J; Kelly, Colette L; Travis, Nicole M; Casciotti, Karen L; Babbin, Andrew R The Eastern Tropical North Pacific (ETNP), like the other marine oxygen deficient zones (ODZs), is characterized by an anoxic water column, nitrite accumulation at the anoxic core, and fixed nitrogen loss via nitrite reduction to N2O and N2 gases. Here, we constrain the relative contribution of biogeochemical processes to observable features such as the secondary nitrite maximum (SNM) and local pH maximum by simultaneous measurement of inorganic nitrogen and carbon species. High-resolution sampling within the top 1 km of the water column reveals consistent chemical features previously unobserved in the region, including a tertiary nitrite maximum. Dissolved inorganic carbon measurements show that pH increases with depth at the top of the ODZ, peaking at the potential density of the SNM at σθ = 26.15 ± 0.06 (1 s.d.). We developed a novel method to determine the relative contributions of anaerobic ammonium oxidation (anammox), denitrification, nitrite oxidation, dissimilatory nitrate reduction to nitrite, and calcium carbonate dissolution to the nitrite cycling in the anoxic ODZ core. The calculated relative contributions of each reaction are slightly sensitive to the assumed C:N:P ratio and the carbon oxidation state of the organic matter sinking through the ODZ. Furthermore, we identify the source of the pH increase at the top of ODZ as the net consumption of protons via nitrite reduction to N2 by the denitrification process. The increase in pH due to denitrification impacts the buffering effect of calcite and aragonite dissolving in the ETNP.

Subdaily Slow Fault Slip Dynamics Captured by Low‐Frequency Earthquakes

2026-05-08T03:13:14Z

Subdaily Slow Fault Slip Dynamics Captured by Low‐Frequency Earthquakes Mouchon, Caroline; Frank, William B; Radiguet, Mathilde; Poli, Piero; Cotte, Nathalie Geodetic positioning is the geophysical record of reference for slow slip events, but typical daily solutions limit studies of the evolution of slow slip to its long‐term dynamics. Accompanying seismic low‐frequency earthquakes located precisely in time and space provide an opportunity to image slow slip dynamics at subdaily time scales. Here we show that a high‐resolution time history of low‐frequency earthquake fault slip alone can reproduce the geodetic record of slow slip that we observe to be dominated by subdaily fault slip dynamics. However, a simple linear model cannot accommodate the complex dynamics present throughout the slow slip cycle, and an analysis of different phases of the slow slip cycle shows that the ratio of geodetic to seismic fault slip varies as a function of time. This suggests that the low‐frequency earthquake source region saturates as slow slip grows in moment and area. We propose that rheological heterogeneities at the plate boundary associated with low‐frequency earthquakes do not play a significant role in the slow slip rupture process, thus implying that their activity is incidental to the driving aseismic slip.

Fast Response of Amazon Rivers to Quaternary Climate Cycles

2026-04-24T03:15:25Z

Fast Response of Amazon Rivers to Quaternary Climate Cycles Goldberg, Samuel L; Schmidt, Morgan J; Perron, J Taylor Large alluvial rivers transport water and sediment across continents and shape lowland landscapes. Repeated glacial cycles have dominated Earth's recent climate, but it is unclear whether these rivers are sensitive to such rapid changes. The Amazon River system, the largest and highest‐discharge in the world, features extensive young terraces that demonstrate geologically rapid change temporally correlated with changes in runoff from Quaternary climate cycles. To test the plausibility of a causal relationship, we use a simple model to estimate from empirical measurements how quickly a river profile responds to changes in discharge or sediment supply. Applying this model to data from 30 gauging stations along alluvial rivers throughout the Brazilian Amazon, we find that many rivers of the Amazon basin can respond faster than glacially induced changes in runoff or sediment flux. The Amazon basin is unusually responsive compared to other large river systems due to its high discharge and sediment flux, narrow floodplains, and low slopes. As a result, we predict that the Amazon basin has been highly dynamic during Quaternary glacial cycles, with cyclical aggradation and incision of lowland rivers driving repeated habitat and environmental change throughout the region. This dynamic landscape may have contributed to the exceptional biodiversity of the region and patterns of ancient human settlement.

Stratosphere‐Troposphere Exchanges of Air Mass and Ozone Concentration in the Last Glacial Maximum

2026-04-09T03:08:13Z

Stratosphere‐Troposphere Exchanges of Air Mass and Ozone Concentration in the Last Glacial Maximum Wang, Mingcheng; Fu, Qiang; Solomon, Susan; Alexander, Becky; White, Rachel H Stratosphere‐troposphere exchange (STE) of ozone represents a significant source term in the tropospheric ozone budget and can impact surface ozone concentrations, tropospheric oxidation capacity, and methane lifetime. Using the Whole Atmosphere Community Climate Model 6, changes in the air mass and ozone STEs in the Last Glacial Maximum (LGM) as compared with preindustrial (PI) climate are investigated. We use dynamic isentropic surfaces that are determined by fitting to the tropical tropopauses as the upper boundary of the lowermost stratosphere in a mass budget approach, a method particularly suitable for estimating air mass and ozone STEs across different climates. Relative to the PI, the magnitude of ozone STE in the LGM is decreased by 14%–19%, 18%–24%, 18%–23%, 16%–21%, and 15%–21% over the Northern hemisphere extratropics, Southern hemisphere extratropics, the tropics, the extratropics, and the globe, respectively. The extratropical and global decreases are mainly caused by decreased ozone in the extratropical lower stratosphere associated with a weakening of Brewer‐Dobson circulation, while changes in air mass fluxes play a minor role because the effects of weakening Brewer‐Dobson circulation and increased isentropic density partly cancel each other. Analysis of the modeled tropospheric ozone budget indicates that the ozone STE in the LGM is 28% of the tropospheric ozone production rate, as compared to about 9% in the modern climate (year 2000) and 19% in the PI.

Philipp Strack, 2024 Clark Medalist

2026-05-08T03:13:57Z

Philipp Strack, 2024 Clark Medalist Fudenberg, Drew The 2024 John Bates Clark Medal of the American Economic Association was awarded to Philipp Strack, Professor of Economics at Yale University, for his pathbreaking contributions to the study of individual decision making, which have introduced new techniques, improved our understanding of important economic phenomena, and helped spark a new wave of research on the economics of information while building bridges between modern economic theory and a wide range of adjacent disciplines. This article summarizes some of Philipp’s papers, and explains how they build on and improve previous work.

Anisotropic Exciton Transport in a Lamellar CsPbBr₃ Nanocrystal Superlattice

2026-04-09T03:08:07Z

Anisotropic Exciton Transport in a Lamellar CsPbBr₃ Nanocrystal Superlattice Sheehan, Thomas J.; Sekh, Taras V.; Bodnarchuk, Maryna I.; Kovalenko, Maksym V.; Tisdale, William A. Colloidal self-assembly is one strategy for engineering anisotropic properties into otherwise isotropic materials. In this work, we demonstrate anisotropic exciton transport in an A2B-type superlattice containing columns of 5.3 nm CsPbBr3 nanocubes assembled into a hexagonal lattice around 6.5 nm LaF3 nanodisks. Using transient photoluminescence microscopy, we determined that diffusivity along the fast axis of the superlattice is more than twice as large as the slow axis at T = 5 K, but that anisotropy is greatly suppressed at room temperature. Calculations of the diffusivity anisotropy ratio based on Förster theory overestimate the measured values, highlighting the limitations of this theory in completely describing exciton transport. Overall, our results demonstrate how self-assembly of colloidal nanocrystals can be used to engineer directional energy transport, and raise more questions about the microscopic nature of dipole coupling in CsPbBr3 NC superlattices.

Greedy Algorithm Almost Dominates in Smoothed Contextual Bandits

2026-04-09T03:08:02Z

Greedy Algorithm Almost Dominates in Smoothed Contextual Bandits Raghavan, Manish; Slivkins, Aleksandrs; Vaughan, Jennifer Wortman; Wu, Zhiwei Steven Online learning algorithms, widely used to power search and content optimization onthe web, must balance exploration and exploitation, potentially sacrificing the experience of currentusers in order to gain information that will lead to better decisions in the future. While necessary inthe worst case, explicit exploration has a number of disadvantages compared to the greedy algorithmthat always ``exploits"" by choosing an action that currently looks optimal. We determine under whatconditions inherent diversity in the data makes explicit exploration unnecessary. We build on a recentline of work on the smoothed analysis of the greedy algorithm in the linear contextual bandits model.We improve on prior results to show that the greedy algorithm almost matches the best possibleBayesian regret rate of any other algorithm on the same problem instance whenever the diversityconditions hold. The key technical finding is that data collected by the greedy algorithm sufficesto simulate a run of any other algorithm. Further, we prove that under a particular smoothnessassumption, the Bayesian regret of the greedy algorithm is at most \~O(T 1/3) in the worst case, whereT is the time horizon.

Spectral Methods from Tensor Networks

2026-04-24T03:15:55Z

Spectral Methods from Tensor Networks Moitra, Ankur; Wein, Alexander S A tensor network is a diagram that specifies a way to “multiply” a collection of tensors together to produce another tensor (or matrix). Many existing algorithms for tensor problems (such as tensor decomposition and tensor PCA), although they are not presented this way, can be viewed as spectral methods on matrices built from simple tensor networks. In this work we leverage the full power of this abstraction to design new algorithms for certain continuous tensor decomposition problems. An important and challenging family of tensor problems comes from orbit recovery, a class of inference problems involving group actions (inspired by applications such as cryo-electron microscopy). Orbit recovery problems over finite groups can often be solved via standard tensor methods. However, for infinite groups, no general algorithms are known. We give a new spectral algorithm based on tensor networks for one such problem: continuous multi-reference alignment over the infinite group SO(2). Our algorithm extends to the more general heterogeneous case.

Coupling Techniques for Nonlinear Ensemble Filtering

2026-05-08T03:13:40Z

Coupling Techniques for Nonlinear Ensemble Filtering Spantini, Alessio; Baptista, Ricardo; Marzouk, Youssef We consider filtering in high-dimensional non-Gaussian state-space models with intractable transition kernels, nonlinear and possibly chaotic dynamics, and sparse observations in space and time. We propose a novel filtering methodology that harnesses transportation of measures, convex optimization, and ideas from probabilistic graphical models to yield robust ensemble approximations of the filtering distribution in high dimensions. Our approach can be understood as the natural generalization of the ensemble Kalman filter (EnKF) to nonlinear updates, using stochastic or deterministic couplings. The use of nonlinear updates can reduce the intrinsic bias of the EnKF at a marginal increase in computational cost. We avoid any form of importance sampling and introduce non-Gaussian localization approaches for dimension scalability. Our framework achieves state-of-the-art tracking performance on challenging configurations of the Lorenz-96 model in the chaotic regime.

Lifting for Simplicity: Concise Descriptions of Convex Sets

2026-05-08T03:14:21Z

Lifting for Simplicity: Concise Descriptions of Convex Sets Fawzi, Hamza; Gouveia, Joao; Parrilo, Pablo A; Saunderson, James; Thomas, Rekha R This paper presents a selected tour through the theory and applications of lifts of convex sets. A lift of a convex set is a higher-dimensional convex set that projects onto the original set. Many convex sets have lifts that are dramatically simpler to describe than the original set. Finding such simple lifts has significant algorithmic implications, particularly for optimization problems. We consider both the classical case of polyhedral lifts, described by linear inequalities, as well as that of spectrahedral lifts, defined by linear matrix inequalities, with a focus on recent developments related to spectrahedral lifts. Given a convex set, ideally we would like to either find a (low-complexity) polyhedral or spectrahedral lift or find an obstruction proving that no such lift is possible. To this end, we explain the connection between the existence of lifts of a convex set and certain structured factorizations of its associated slack operator. Based on this characterization, we describe a uniform approach, via sums of squares, to the construction of spectrahedral lifts of convex sets and illustrate the method on several families of examples. Finally, we discuss two flavors of obstruction to the existence of lifts: one related to facial structure, and the other related to algebraic properties of the set in question. Rather than being exhaustive, our aim is to illustrate the richness of the area. We touch on a range of different topics related to the existence of lifts and present many examples of lifts from different areas of mathematics and its applications.

Cortical Face-Selective Responses Emerge Early in Human Infancy

2026-05-08T03:13:49Z

Cortical Face-Selective Responses Emerge Early in Human Infancy Kosakowski, Heather L; Cohen, Michael A; Herrera, Lyneé; Nichoson, Isabel; Kanwisher, Nancy; Saxe, Rebecca In human adults, multiple cortical regions respond robustly to faces, including the occipital face area (OFA) and fusiform face area (FFA), implicated in face perception, and the superior temporal sulcus (STS) and medial prefrontal cortex (MPFC), implicated in higher-level social functions. When in development, does face selectivity arise in each of these regions? Here, we combined two awake infant functional magnetic resonance imaging (fMRI) datasets to create a sample size twice the size of previous reports (n = 65 infants; 2.6–9.6 months). Infants watched movies of faces, bodies, objects, and scenes, while fMRI data were collected. Despite variable amounts of data from each infant, individual subject whole-brain activation maps revealed responses to faces compared to nonface visual categories in the approximate location of OFA, FFA, STS, and MPFC. To determine the strength and nature of face selectivity in these regions, we used cross-validated functional region of interest analyses. Across this larger sample size, face responses in OFA, FFA, STS, and MPFC were significantly greater than responses to bodies, objects, and scenes. Even the youngest infants (2–5 months) showed significantly face-selective responses in FFA, STS, and MPFC, but not OFA. These results demonstrate that face selectivity is present in multiple cortical regions within months of birth, providing powerful constraints on theories of cortical development.

Synchronous Measurements of Extracellular Action Potentials and Neurochemical Activity with Carbon Fiber Electrodes in Nonhuman Primates

2026-05-08T03:14:02Z

Synchronous Measurements of Extracellular Action Potentials and Neurochemical Activity with Carbon Fiber Electrodes in Nonhuman Primates Amjad, Usamma; Choi, Jiwon; Gibson, Daniel J; Murray, Raymond; Graybiel, Ann M; Schwerdt, Helen N Measuring the dynamic relationship between neuromodulators, such as dopamine, and neuronal action potentials is imperative to understand how these fundamental modes of neural signaling interact to mediate behavior. We developed methods to measure concurrently dopamine and extracellular action potentials (i.e., spikes) in monkeys. Standard fast-scan cyclic voltammetric (FSCV) electrochemical (EChem) and electrophysiological (EPhys) recording systems are combined and used to collect spike and dopamine signals, respectively, from an array of carbon fiber (CF) sensors implanted in the monkey striatum. FSCV requires the application of small voltages at the implanted sensors to measure redox currents generated from target molecules, such as dopamine. These applied voltages create artifacts at neighboring EPhys measurement sensors which may lead to misclassification of these signals as physiological spikes. Therefore, simple automated temporal interpolation algorithms were designed to remove these artifacts and enable accurate spike extraction. We validated these methods using simulated artifacts and demonstrated an average spike recovery rate of 84.5%. We identified and discriminated cell type-specific units in the monkey striatum that were shown to correlate to specific behavioral task parameters related to reward size and eye movement direction. Synchronously recorded spike and dopamine signals displayed contrasting relations to the task variables, suggesting a complex relationship between these two modes of neural signaling. Future application of our methods will help advance our understanding of the interactions between neuromodulator signaling and neuronal activity, to elucidate more detailed mechanisms of neural circuitry and plasticity mediating behaviors in health and in disease.

Functional genetics reveals modulators of antimicrotubule drug sensitivity

2026-04-24T03:15:56Z

Functional genetics reveals modulators of antimicrotubule drug sensitivity Su, Kuan-Chung; Radul, Elena; Maier, Nolan K; Tsang, Mary-Jane; Goul, Claire; Moodie, Brittania; Marescal, Océane; Keys, Heather R; Cheeseman, Iain M Microtubules play essential roles in diverse cellular processes and are important pharmacological targets for treating human disease. Here, we sought to identify cellular factors that modulate the sensitivity of cells to antimicrotubule drugs. We conducted a genome-wide CRISPR/Cas9-based functional genetics screen in human cells treated with the microtubule-destabilizing drug nocodazole or the microtubule-stabilizing drug paclitaxel. We further conducted a focused secondary screen to test drug sensitivity for ∼1,400 gene targets across two distinct human cell lines and to additionally test sensitivity to the KIF11 inhibitor, STLC. These screens defined gene targets whose loss enhances or suppresses sensitivity to antimicrotubule drugs. In addition to gene targets whose loss sensitized cells to multiple compounds, we observed cases of differential sensitivity to specific compounds and differing requirements between cell lines. Our downstream molecular analysis further revealed additional roles for established microtubule-associated proteins and identified new players in microtubule function.

Chemical transformation of the multibudding yeast, Aureobasidium pullulans

2026-04-24T03:14:36Z

Chemical transformation of the multibudding yeast, Aureobasidium pullulans Wirshing, Alison CE; Petrucco, Claudia A; Lew, Daniel J Aureobasidium pullulans is a ubiquitous polymorphic black yeast with industrial and agricultural applications. It has recently gained attention amongst cell biologists for its unconventional mode of proliferation in which multinucleate yeast cells make multiple buds within a single cell cycle. Here, we combine a chemical transformation method with genome-targeted homologous recombination to yield ∼60 transformants/μg of DNA in just 3 days. This protocol is simple, inexpensive, and requires no specialized equipment. We also describe vectors with codon-optimized green and red fluorescent proteins for A. pullulans and use these tools to explore novel cell biology. Quantitative imaging of a strain expressing cytosolic and nuclear markers showed that although the nuclear number varies considerably among cells of similar volume, total nuclear volume scales with cell volume over an impressive 70-fold size range. The protocols and tools described here expand the toolkit for A. pullulans biologists and will help researchers address the many other puzzles posed by this polyextremotolerant and morphologically plastic organism.

Tunable DNMT1 degradation reveals DNMT1/DNMT3B synergy in DNA methylation and genome organization

2026-05-08T03:12:33Z

Tunable DNMT1 degradation reveals DNMT1/DNMT3B synergy in DNA methylation and genome organization Scelfo, Andrea; Barra, Viviana; Abdennur, Nezar; Spracklin, George; Busato, Florence; Salinas-Luypaert, Catalina; Bonaiti, Elena; Velasco, Guillaume; Bonhomme, Frédéric; Chipont, Anna; Tijhuis, Andréa E; Spierings, Diana CJ; Guérin, Coralie; Arimondo, Paola; Francastel, Claire; Foijer, Floris; Tost, Jӧrg; Mirny, Leonid; Fachinetti, Daniele DNA methylation (DNAme) is a key epigenetic mark that regulates critical biological processes maintaining overall genome stability. Given its pleiotropic function, studies of DNAme dynamics are crucial, but currently available tools to interfere with DNAme have limitations and major cytotoxic side effects. Here, we present cell models that allow inducible and reversible DNAme modulation through DNMT1 depletion. By dynamically assessing whole genome and locus-specific effects of induced passive demethylation through cell divisions, we reveal a cooperative activity between DNMT1 and DNMT3B, but not of DNMT3A, to maintain and control DNAme. We show that gradual loss of DNAme is accompanied by progressive and reversible changes in heterochromatin, compartmentalization, and peripheral localization. DNA methylation loss coincides with a gradual reduction of cell fitness due to G1 arrest, with minor levels of mitotic failure. Altogether, this system allows DNMTs and DNA methylation studies with fine temporal resolution, which may help to reveal the etiologic link between DNAme dysfunction and human disease.

Can Development Programs Counter Insurgencies? Evidence from a Field Experiment in Afghanistan

2026-04-08T03:07:35Z

Can Development Programs Counter Insurgencies? Evidence from a Field Experiment in Afghanistan Beath, Andrew; Christia, Fotini; Enikolopov, Ruben We exploit a randomized controlled trial conducted between 2007 and 2011 to identify the effect of Afghanistan's largest local governance and development program on the strength of the insurgency. We find that the program reduced violence, improved economic outcomes, and increased government support in interior regions of the country, but increased violence in villages close to the Pakistani border, where foreign insurgents were more numerous. The results suggest that development programs can be effective in suppressing locally driven insurgencies, but may be counterproductive where insurgents are not reliant on the local population for support. (JEL C93, D74, F35, O15, O17, O18)

Wage Stagnation and the Decline of Standardized Pay Rates, 1974–1991

2026-04-08T03:07:27Z

Wage Stagnation and the Decline of Standardized Pay Rates, 1974–1991 Massenkoff, Maxim; Wilmers, Nathan Using new establishment-by-occupation microdata, we show that the use of discretionary wage setting significantly expanded in the 1970s and 1980s. Increasingly, wages for blue-collar workers were not standardized by job title or seniority but instead subject to managerial discretion. When establishments abandoned standardized pay rates, wages fell, particularly for the lowest-paid workers in a job and for those in establishments that previously paid above market rates. This shift away from standardized pay rates, in context of a broader decline in worker bargaining power, accelerated the decline in real wages experienced by blue-collar workers in the 1980s. (JEL J31, J33, J52, M52, O33)

Multiscale affinity maturation simulations to elicit broadly neutralizing antibodies against HIV

2026-04-08T03:07:47Z

Multiscale affinity maturation simulations to elicit broadly neutralizing antibodies against HIV Conti, Simone; Ovchinnikov, Victor; Faris, Jonathan G; Chakraborty, Arup K; Karplus, Martin; Sprenger, Kayla G The design of vaccines against highly mutable pathogens, such as HIV and influenza, requires a detailed understanding of how the adaptive immune system responds to encountering multiple variant antigens (Ags). Here, we describe a multiscale model of B cell receptor (BCR) affinity maturation that employs actual BCR nucleotide sequences and treats BCR/Ag interactions in atomistic detail. We apply the model to simulate the maturation of a broadly neutralizing Ab (bnAb) against HIV. Starting from a germline precursor sequence of the VRC01 anti-HIV Ab, we simulate BCR evolution in response to different vaccination protocols and different Ags, which were previously designed by us. The simulation results provide qualitative guidelines for future vaccine design and reveal unique insights into bnAb evolution against the CD4 binding site of HIV. Our model makes possible direct comparisons of simulated BCR populations with results of deep sequencing data, which will be explored in future applications.

How persistent infection overcomes peripheral tolerance mechanisms to cause T cell–mediated autoimmune disease

2026-04-08T03:07:39Z

How persistent infection overcomes peripheral tolerance mechanisms to cause T cell–mediated autoimmune disease Yin, Rose; Melton, Samuel; Huseby, Eric S; Kardar, Mehran; Chakraborty, Arup K T cells help orchestrate immune responses to pathogens, and their aberrant regulation can trigger autoimmunity. Recent studies highlight that a threshold number of T cells (a quorum) must be activated in a tissue to mount a functional immune response. These collective effects allow the T cell repertoire to respond to pathogens while suppressing autoimmunity due to circulating autoreactive T cells. Our computational studies show that increasing numbers of pathogenic peptides targeted by T cells during persistent or severe viral infections increase the probability of activating T cells that are weakly reactive to self-antigens (molecular mimicry). These T cells are easily re-activated by the self-antigens and contribute to exceeding the quorum threshold required to mount autoimmune responses. Rare peptides that activate many T cells are sampled more readily during severe/persistent infections than in acute infections, which amplifies these effects. Experiments in mice to test predictions from these mechanistic insights are suggested.

Enantioselective Copper-Catalyzed Synthesis of Hydroxylamines via Hydrofunctionalization of Alkenes using Nitroalkanes

2026-04-08T03:07:24Z

Enantioselective Copper-Catalyzed Synthesis of Hydroxylamines via Hydrofunctionalization of Alkenes using Nitroalkanes Law, James A; Mai, Binh Khanh; Hung, Hsuan-Min; Hendon, Tobianna; Dong, Yuyang; Zhang, Yu; Liu, Peng; Buchwald, Stephen L Herein, we report that nitroalkanes are competent electrophiles for the enantioselective copper hydride (CuH)-catalyzed alkene hydrofunctionalization of vinyl(hetero)arenes to generate hydroxylamines in good yields and with high levels of enantioselectivity. Control experiments and density functional theory (DFT) calculations suggest that the nitro group constitutes the active electrophile. The direct addition of the enantioenriched alkyl copper intermediate to the nitro group outcompetes competitive reduction or deprotonation of the nitroalkane. DFT calculations indicate that the addition of the stereoenriched alkyl copper intermediate to nitroalkane electrophiles occurs through a six-membered cyclic transition state featuring dearomatization of the vinyl arene. Overall, this process constitutes a one-step route to access enantioenriched N-alkylhydroxylamine from vinylarenes and nitroalkanes.

Monte Carlo toolkit for designing and validating step-range-filter spectrometer designs

2026-04-08T03:07:45Z

Monte Carlo toolkit for designing and validating step-range-filter spectrometer designs Johnson, TM; Lahmann, B; Russell, L; Vanderloo, NL; Cufari, MJ; Reichelt, BL; Chang, CW; Birkel, A; Kabadi, NV; Sutcliffe, GD; Adrian, PJ; Pearcy, JA; Kunimune, JH; Dannhoff, SG; Evans, TE; Johnson, M Gatu; Séguin, FH; Petrasso, RD; Li, CK; Frenje, JA Here, we present a Monte Carlo toolkit for validating step range filter (SRF) spectrometer designs. Geant4 is used to transport charged particles through the SRF filters to generate synthetic SRF data that include realistic CR-39 effects. Synthetic SRF spectra generated by this method inherently account for instrument response and allow for the quantification of SRF performance before shots. The usefulness of this toolkit is demonstrated through its application to a number of problems. A new broadband SRF for the ∼10 MeV wide 3He3He proton spectrum is validated, and an analysis method for analyzing 3He3He-p SRF data that accounts for instrument response is put forth. In addition, an SRF design for the compact recoil-proton spectrometer (CRS) on the Z-machine is validated. Finally, a new calibration technique for the DD-p SRF is proposed and validated.

Accelerating plasma and radiation surface science using transient grating spectroscopy

2026-04-08T03:07:36Z

Accelerating plasma and radiation surface science using transient grating spectroscopy Wylie, APC; Woller, KB; Rae, M; Lanzrath, AT; Dacus, BR; Ferry, SE; Short, MP A facility for the investigation of in situ radiation-materials and plasma-materials interaction is demonstrated with tungsten, using transient grating spectroscopy as a probe of thermal diffusivity and surface acoustic wave speed. Helium plasma exposure at 645 °C to 1.18 × 1018 cm−2 helium, until the growth of tungsten fuzz, showed an increase in surface acoustic wave speed at the near-surface from 2542 ± 1 m s−1 up to 2565 ± 1 m s−1, followed by a greater drop to 2499 ± 7 m s−1. No observable change in thermal diffusivity was present for plasma exposure alone. A separate 10.26 MeV self-ion-irradiation of tungsten to a dose of 7.92 dpa showed a reduction in both thermal diffusivity from 61.4 ± 1.4 mm2 s−1 to 36.0 ± 0.7 mm2 s−1, following trends seen in existing studies, and surface acoustic wave speed from 2647.8 ± 0.6 m s−1 to 2640.0 ± 0.4 m s−1. Facilities like these are poised to rapidly close critical knowledge gaps regarding the coupled effects of plasma and radiation damage for materials in fusion systems.

Enhanced sampling of robust molecular datasets with uncertainty-based collective variables

2026-04-08T03:07:30Z

Enhanced sampling of robust molecular datasets with uncertainty-based collective variables Tan, Aik Rui; Dietschreit, Johannes CB; Gómez-Bombarelli, Rafael Generating a dataset that is representative of the accessible configuration space of a molecular system is crucial for the robustness of machine-learned interatomic potentials. However, the complexity of molecular systems, characterized by intricate potential energy surfaces, with numerous local minima and energy barriers, presents a significant challenge. Traditional methods of data generation, such as random sampling or exhaustive exploration, are either intractable or may not capture rare, but highly informative configurations. In this study, we propose a method that leverages uncertainty as the collective variable (CV) to guide the acquisition of chemically relevant data points, focusing on regions of configuration space where ML model predictions are most uncertain. This approach employs a Gaussian Mixture Model-based uncertainty metric from a single model as the CV for biased molecular dynamics simulations. The effectiveness of our approach in overcoming energy barriers and exploring unseen energy minima, thereby enhancing the dataset in an active learning framework, is demonstrated on alanine dipeptide and bulk silica.

Thermal enhancement of defect motion for optimizing periodic poling of x-cut thin-film lithium niobate

2026-04-08T03:07:25Z

Thermal enhancement of defect motion for optimizing periodic poling of x-cut thin-film lithium niobate Doshi, Sagar P; West, Gavin N; Gray, Dodd; Ram, Rajeev J Patterning of stable, spatially tailored ferroelectric domains in thin-film lithium niobate enables efficient nonlinear optical interactions through quasi-phase matching. The engineering of domain structure is limited by the uncontrolled distribution of defects, which disrupt domain wall motion. Here, we fabricate quasi-phase matching gratings in thin-film lithium niobate with sub 20 nm of period variation. We demonstrate that annealing processed samples at 350 or 500 °C for 48 h, prior to E-field poling, can dramatically reduce the duty cycle variation. We show that maintaining an elevated temperature of 200 °C during poling enhances defect mobility, which leads to more rectangular inverted domains. Moreover, poling at elevated temperatures also increases inversion depth without sacrificing the periodic domain pattern's accuracy or precision. Elevating the temperature prior to and during poling resulted in near-ideal square wave patterning of ferroelectric domains (50% mean duty cycle, sub 10% domain width variation, and 100% depth inversion). This enables effective quasi-phase matching for second harmonic generation in 5.6 mm-long waveguides fabricated from MgO-doped x-cut thin-film lithium niobate.

Ultra-fast single-crystal CVD diamonds in the particle time-of-flight (PTOF) detector for low yield burn-history measurements on the NIF (invited)

2026-04-08T03:07:46Z

Ultra-fast single-crystal CVD diamonds in the particle time-of-flight (PTOF) detector for low yield burn-history measurements on the NIF (invited) The Particle Time of Flight (PTOF) diagnostic is a chemical vapor deposition diamond-based detector and is the only diagnostic for measuring nuclear bang times of low yield (<1013) shots on the National Ignition Facility. Recently, a comprehensive study of detector impulse responses revealed certain detectors with very fast and consistent impulse responses with a rise time of <50 ps, enabling low yield burn history measurements. At the current standoff of 50 cm, this measurement is possible with fast 14 MeV neutrons from deuterium–tritium (DT) fusion plasmas. PTOF-inferred DT burn width numbers compare well with widths inferred from the gamma reaction history diagnostic on midyield (1013–1015) shots, where both systems are capable of making this measurement. These new capabilities could be extended to 2.5 MeV deuterium–deuterium neutrons from D plasmas and to even lower yield by reducing the detector standoff distance to 10 cm; a design for this is also presented.

Machine learning driven measurement of high-aspect-ratio nanostructures using Mueller matrix spectroscopic ellipsometry

2026-04-08T03:07:41Z

Machine learning driven measurement of high-aspect-ratio nanostructures using Mueller matrix spectroscopic ellipsometry Mudide, Shiva; Keller, Nick; Andrew Antonelli, G; Cruz, Geraldina; Hart, Julia; Bruccoleri, Alexander R; Heilmann, Ralf K; Schattenburg, Mark L Accurate fabrication of high-aspect ratio (HAR) structures in applications from semiconductor devices to x-ray observatories is essential for their optimal performance because their performance directly depends on their structure. High-efficiency critical-angle transmission (CAT) gratings enable high-resolution x-ray spectroscopy in astrophysics, but their performance is only ideal when certain performance-critical parameters, like the bar tilts introduced during deep reactive-ion etching, are tuned to precise values. Traditional measurement methods like small-angle x-ray scattering (SAXS) are accurate, but limit the development of robust control algorithms to nudge performance-critical parameters toward favorable values because they are slow and often destructive. We present a fast, accurate, nondestructive measurement method using Mueller matrix spectroscopic ellipsometry and machine learning. Given a HAR structure, we train on rigorous coupled-wave analysis simulation data to predict Mueller matrix spectra from input performance-critical parameter values. We then invert this forward problem by freezing our network weights, measuring experimental Mueller matrix spectra, and vanilla gradient descending on performance-critical parameters to values that correspond to the input Mueller matrix spectra. Introducing machine learning to invert the forward problem reduces computation time, and experimental results demonstrate close agreement between our method’s determined tilt and SAXS measurements. Our accurate, fast measurement method paves the way for the development of robust control algorithms that adjust fabrication parameters in response to measurement, ensuring optimal performance in not only CAT gratings but also HAR structures embedded in applications from semiconductor to microelectromechanical systems fabrication.

Self-aligned fabrication of vertical, fin-based structures

2026-04-08T03:07:38Z

Self-aligned fabrication of vertical, fin-based structures Perozek, Joshua; Palacios, Tomás Modern power devices rely on complex, three-dimensional, vertical designs to increase their power density, ease their thermal management, and improve their reliability. However, fabrication techniques have historically relied on 2D processes for patterning lateral features. This work presents a new technology that uses multiple steps of angled depositions to fabricate self-aligned vertical, fin-based devices that avoid fundamental lithography resolution and alignment limitations. The fabrication flows of two devices, the self-aligned vertical finFET and the high-κ dielectric fin diode, are presented to demonstrate how angled depositions can readily achieve transistors with submicrometer, vertical gates in a source-first process and also create high-aspect ratio GaN fins with a record 70:1 aspect ratio.

Evaluating deuterated-xylene for use as a fusion neutron spectrometer

2026-04-08T03:07:49Z

Evaluating deuterated-xylene for use as a fusion neutron spectrometer Ball, JL; Panontin, E; Mackie, S; Tinguely, RA; Raj, P The spectrum of neutrons emitted by thermonuclear plasmas encodes information about the fuel ion distribution function. Measuring these fast neutron spectra with sufficient resolution allows for the measurement of plasma properties such as the ion temperature and strength and energy of fast ion populations. Liquid organic scintillators are a commonly used fast neutron detection technology because of their high detection efficiency and ability to discriminate between neutrons and gammas. However, performing detailed spectroscopy with these detectors is difficult because of the isotropic nature of neutron scattering on protons, the dominant mechanism of interaction. Deuterium-based scintillators have shown promise as a superior spectrometer technology because of the anisotropic nature of neutron scattering on deuterium, which significantly improves the condition number of the detector response matrix [Lawrence et al., Nucl. Instrum. Methods Phys. Res., Sect. A 729, 924 (2013)]. Deuterated-xylene, now available commercially, has advantages in light output and safety over benzene-based deuterated scintillators [Becchetti et al., Nucl. Instrum. Methods Phys. Res., Sect. A 820, 112 (2016)]. We present experimental spectrum unfoldings made by 2 in. right cylindrical protiated-xylene and deuterated-xylene detectors with response matrices generated with Geant4 and additional data from the literature. We compare their performance by measuring the neutron spectrum produced by an AmBe source and deuterium–tritium (DT) neutron generators. We find that the deuterated scintillator outperforms the protiated one for AmBe and DT spectra, suggesting deuterated-xylene should be considered for future fusion neutron spectrometry applications.

Immersed boundary method for dynamic simulation of polarizable colloids of arbitrary shape in explicit ion electrolytes

2026-04-08T03:07:34Z

Immersed boundary method for dynamic simulation of polarizable colloids of arbitrary shape in explicit ion electrolytes Krucker-Velasquez, Emily; Swan, James W; Sherman, Zachary We develop a computational method for modeling electrostatic interactions of arbitrarily shaped, polarizable objects on colloidal length scales, including colloids/nanoparticles, polymers, and surfactants, dispersed in explicit ion electrolytes and nonionic solvents. Our method computes the nonuniform polarization charge distribution induced in a colloidal particle by both externally applied electric fields and local electric fields arising from other charged objects in the dispersion. This leads to expressions for electrostatic energies, forces, and torques that enable efficient molecular dynamics and Brownian dynamics simulations of colloidal dispersions in electrolytes, which can be harnessed to accurately predict structural and transport properties. We describe an implementation in which colloidal particles are modeled as rigid composites of small spherical beads that tessellate the surface of the particle. The electrostatics calculations are accelerated using a spectrally accurate particle-mesh-Ewald technique implemented on a graphics processing unit and regularized such that the electrostatic calculations are well-defined even for overlapping bodies. We illustrate the effectiveness of this approach with a comprehensive set of calculations: the induced dipole moments and forces for individual, paired, and lattice configurations of spherical colloids in an electric field; the induced dipole moment and torque for anisotropic particles subjected to an electric field; the equilibrium ion distribution in the double layer surrounding charged colloids; the dynamics of charged colloids; and the behavior of ions in the double layer of a polarizable colloid under the influence of an electric field.

Errors in the field reconstruction using CR-39 proton radiographs with high fluence variation

2026-04-08T03:07:28Z

Errors in the field reconstruction using CR-39 proton radiographs with high fluence variation Foo, BC; Buschmann, BI; Cufari, M; Dannhoff, SG; DeVault, A; Evans, TE; Johnson, TM; Kunimune, JH; Lawrence, Y; Pearcy, JA; Reichelt, BL; Russell, L; Vanderloo, N; Vargas, J; Wink, CW; Johnson, M Gatu; Séguin, FH; Petrasso, RD; Frenje, JA CR-39 proton radiography is an experimental charged-particle backlighter platform fielded and used at OMEGA and the NIF to image electric and magnetic fields in a subject plasma. Processing a piece of CR-39 involves etching it in hot NaOH, and the etch time can greatly impact the background-to-signal ratio (BSR) in low-fluence (≲4 × 104 cm−2) regions and detection efficiency in high-fluence regions (≳7 × 105 cm−2). For CR-39 data with high fluence variation, these effects mean that any single etch time will result in ≳15% error in the measured signal in either the high- or low-fluence regions. This study aims to quantify the impact of the etch time on the BSR and efficiency losses and how these affect the field reconstruction. Experiments at the MIT Linear Electrostatic Ion Accelerator provided empirical values of the BSR and efficiency losses as a function of the fluence and etch time for fluences ranging from 3 × 103 to 7 × 105 cm−2. Synthetic radiographs were generated with known fields and modulated based on empirical values of BSR and efficiency losses. The fields were reconstructed using a Monge–Ampère code with the modulated radiographs as input. The results indicate that combining short and long etches allows for more accurate analysis of radiographs with high fluence variation, with the mean squared error of the reconstructed fields decreasing by factors of 1.2–7 compared to the reconstructions using only one etch time.

Atomic structure of self-buffered BaZr(S, Se)3 epitaxial thin film interfaces

2026-04-08T03:07:16Z

Atomic structure of self-buffered BaZr(S, Se)3 epitaxial thin film interfaces Xu, Michael; Ye, Kevin; Sadeghi, Ida; Jaramillo, R; LeBeau, James M Understanding and controlling the growth of chalcogenide perovskite thin films through interface design is important for tailoring film properties. Here, the film and interface structure of BaZr(S, Se)3 thin films grown on LaAlO3 by molecular beam epitaxy and postgrowth anion exchange is resolved using aberration-corrected scanning transmission electron microscopy. Epitaxial films are achieved from selfassembly of an interface “buffer” layer, which accommodates the large film/substrate lattice mismatch of nearly 40% for the alloy film studied here. The self-assembled buffer layer, occurring for both the as-grown sulfide and post-selenization alloy films, is shown to have rock-salt-like atomic stacking akin to a Ruddlesden–Popper phase. These results provide insights into oxide-chalcogenide heteroepitaxial film growth, illustrating a process that yields relaxed, crystalline, epitaxial chalcogenide perovskite films that support ongoing studies of optoelectronic and device properties.

Synthetic measurements of runaway electron synchrotron emission in the SPARC tokamak

2026-04-08T03:07:43Z

Synthetic measurements of runaway electron synchrotron emission in the SPARC tokamak Tinguely, RA; Rosenthal, AM; Silva Sa, M; Jean, M; Abramovic, I With plasma currents up to 8.7 MA, the SPARC tokamak runs the risk of forming multi-MA beams of relativistic “runaway” electrons (REs), which could damage plasma facing components if unmitigated. The infrared (IR) and visible imaging and visible spectroscopy systems in SPARC are designed with measurements of synchrotron emission from REs in mind. Synchrotron radiation is emitted by REs along their direction of motion, opposite the plasma current. Matched clockwise and counterclockwise wide views are proposed to detect synchrotron and background radiation, allowing observation of RE synchrotron emission in both plasma current configurations. Due to SPARC’s high toroidal magnetic field strength, 12.2 T on axis, the synchrotron light spectrum is expected to peak in the visible-IR wavelength range. The synthetic diagnostic tool, Synchrotron Orbit-Following Toolkit, is used to model synchrotron images and spectra for three scenarios, with appropriate magnetic equilibria for each: REs generated during plasma current ramp-up, steady-state flat-top (although unlikely, but serving as a reference), and disruptions. Required time resolutions, achievable spatial coverage, and appropriate spectral ranges for various RE energies are assessed.

Quantifying the effects of neutron fluence on proton signal retention in CR-39

2026-04-08T03:07:32Z

Quantifying the effects of neutron fluence on proton signal retention in CR-39 Russell, L; Johnson, TM; Lawrence, Y; Reichelt, B; Vanderloo, N; Cufari, M; Buschmann, BI; Dannhoff, S; DeVault, A; Doeg, E; Evans, T; Foo, BC; Frankel, R; Kunimune, JH; Pearcy, JA; Vargas, J; Gatu Johnson, M; Frenje, J This paper reports on investigations on the impact of higher neutron fluences on the detection efficiency of protons with CR-39, a charged particle track detector. CR-39 is widely used as a diagnostic for inertial fusion applications and is an integral component of numerous particle diagnostics at the OMEGA laser facility and National Ignition Facility. As experiments continue to produce higher and higher yields, existing diagnostics are impacted by higher particle fluences than they were originally designed for. This paper presents data from experiments measuring proton signal on pieces of CR-39 with different levels of neutron fluence with two different etch times. The experiments show a decrease in signal recovery with increased neutron fluence, which is exacerbated at longer etch times. At 3 h etch time, data suggest a 17% ± 7% signal loss at 1.3 × 105 neutron-induced tracks per cm2 and a 67% ± 21% loss at 6 h etch time. Careful signal isolation techniques can recover most of the proton tracks even with moderate neutron fluence.

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.

Data Driven Discovery of Modular Biological Systems

2026-04-07T03:03:26Z

Data Driven Discovery of Modular Biological Systems Altae-Tran, Han This dissertation presents a big data-driven approach for biological and biomedical discovery. The topics covered include the evolution and diversity of CRISPR systems, the identification and analysis of hypervariable protein systems, the identification of ancestral systems, and the development of RNA-guided systems for genome editing and therapeutic applications. Additionally, one of the chapters focuses on population-scale longitudinal mapping of COVID-19 symptoms, behavior, and testing, providing valuable insights for public health officials during the early stages of the pandemic. Through the development of novel methodologies and the utilization of big data-driven methods, this dissertation contributes to the expanding landscape of biomedical research. Chapter II delves into the origins of Cas9 and Cas12, examining the evolutionarily conserved non-coding RNA associated with IscB and the diverse RNA-guided nucleases encoded by IS200/IS605 elements. Through phylogenetic analysis and experimental characterization, we gain insight into the evolutionary history and diversity of IscB systems, and their potential biological functions. In Chapter III, we explore the diversity and function of Obligate Mobile Element Guided Activity (OMEGA) systems, focusing on TnpB and its relationship with Cas12 systems. We examine the taxonomy, genomic features, and evolution of these systems, as well as their mobility and potential exaptation. Chapter IV is dedicated to optimizing OMEGA RNA-guided systems for therapeutic applications. We screen natural IscB variants for efficient genome editing and engineer OrufIscB for enhanced activity, demonstrating its potential as a versatile genome interrogation tool. In Chapter V, we employ deep terascale clustering to discover functionally diverse CRISPR systems. Using a fast locality-sensitive hashing algorithm, we identify rare CRISPR systems, such as DinG-HNH, Type I Cascade components with HNH domains, and the Type VII CRISPR system, which is a precise RNA-guided RNA endonuclease complex containing a β-CASP nuclease. In Chapter VII, we investigate compact RNA editors, focusing on the discovery and characterization of Cas13bt. We repurpose Cas13bt for base editing and deliver these base editors to human cells using adeno-associated viruses (AAV), demonstrating their potential for therapeutic applications. Chapter VIII focuses on the identification and analysis of hypervariable protein systems with repeat signatures, seeking to find generalizations of concepts from other repeat systems such as CRISPR and TALENs. A computational pipeline is established to identify hypervariable repeat signatures in proteins, resulting in candidate systems that were characterized in additional detail. Multiple new mechanisms of modularity (two functions that are decoupled via an interchangeable domain or structure, such as repeats) were identified, pointing to a greater landscape of hypervariable protein systems than previously thought. These findings have implications for the understanding of protein architectures and may also provide valuable insights for the design of novel protein-based tools and therapeutics. Finally, Chapter IX presents one of the early large-scale studies conducted during the COVID pandemic, focusing on population-scale longitudinal mapping of COVID-19 symptoms, behavior, and testing. The study was conducted relatively early during the pandemic and collected data from a large user base of the How We Feel application. The data-driven approach employed various data analysis techniques, such as logistic regression, UMAP, and prediction models, to identify factors associated with testing propensity, symptoms associated with COVID, and behavior of patients after contracting COVID. The findings from this study could have provided valuable insights in the early stages of the pandemic, informing policymakers and public health officials such as the state of Connecticut to make data-driven decisions. Overall, this dissertation presents methods for and results from applying big data-driven methods to discovery from large biomedical databases. It specifically focuses on the exploration of diverse CRISPR systems, ancestors of CRISPR systems, hypervariable protein systems, and protein engineering for therapeutics and genome editing applications. From examining the evolutionary origins of Cas9 and Cas12 to investigating the diversity of OMEGA systems and optimizing them for therapeutic use, this work deepens our understanding of these complex biological systems. The discovery of rare CRISPR systems and compact RNA editors further broadens the landscape of genetic tools with potential therapeutic applications. Additionally, the identification and analysis of hypervariable protein systems reveal new mechanisms of modularity, with implications for protein architectures and the development of novel protein-based tools. Finally, the large-scale study of COVID-19 symptoms, behavior, and testing during the early stages of the pandemic demonstrates the power of data-driven approaches in informing public health decisions. Collectively, this research contributes significantly to our understanding of complex biological systems and highlights the potential for their application in advancing human health and biotechnology.

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.

Carbon Nanotube Based Biosensors Using Corona Phase Molecular Recognition (CoPhMoRe): Development and Applications

2026-04-07T03:03:30Z

Carbon Nanotube Based Biosensors Using Corona Phase Molecular Recognition (CoPhMoRe): Development and Applications Jin, Xiaojia Molecular recognition sites that specifically bind a target molecule are essential for clinical research, disease diagnosis, and therapeutic development. To this end, a promising technique developed by the Strano laboratory at MIT is Corona Phase Molecular Recognition (CoPhMoRe), which uses amphiphilic polymers or macromolecules adsorbed onto a nanoparticle surface to generate a synthetic recognition site. The underlying nanoparticle, which can also function as the sensor transducer, pins the polymer to a specific 3D confirmation using non-covalent interactions, resulting in a binding pocket analogous to the antigen binding domain of a natural antibody. While CoPhMoRe has proven considerable versatile in recognizing small organic molecules such as vitamins, neurotransmitters, pharmaceutical drugs and steroid hormones, the recognition of large molecules such has viral proteins has been less explored. Macromolecular analytes introduce a much wider set of potential interactions, requiring refined analysis and new insights into mechanisms. This thesis focuses on (i) constructing CoPhMoRe sites for protein analytes, (ii) exploring new methods and mechanistic understanding to inform CoPhMoRe recognitions and also (iii) translating CoPhMoRe phases to interfaces that can be incorporated into biosensors for specific applications. Towards Aim (i), this thesis has developed CoPhMoRe sites for protein based disease biomarkers, including interleukin-6, nucleocapsid and spike proteins of SARS-CoV-2, enabling rapid and label-free near-IR fluorescence detection of target analytes with dose dependent responses in complex environments. Towards Aim (ii), this thesis investigates new methods and analyses for CoPhMoRe characterization, such as the expansion of the Molecular Probe Absorption (MPA) technique. This technique measures the accessible surface area of a CoPhMoRe based sensor by using a fluorescent molecule as a probe that quenches upon interacting with the corona phase. Further advances involve instrumentation and mathematical models to analyze the chiroptical properteis of corona phases, facilitating the CoPhMoRe handedness determination at the single molecule level with circularly polarized excitation sources. Towards Aim (iii), this thesis explores form factor advancements to broaden the utility of CoPhMoRe sensors. It includes profiling cellular immune heterogeneity by integrating the optical nanosensor arrays into microfluidics to interrogate chemical species efflux from individual cells in real-time using Nanosensor Chemical Cytometry (NCC). Furthermore, nanosensors are encapsulated into stable hydrogels for integration into acoustic tags to track hormone levels in marine animals. Together, the successful development of CoPhMoRe nanosensors opens new pathways for synthetic molecular recognition that enables the detection of biological macromolecules, and holds great promise for life science applications.

Design, Synthesis and Applications of Versatile Porous Poly(arylene ether)s

2026-04-07T03:03:15Z

Design, Synthesis and Applications of Versatile Porous Poly(arylene ether)s Wu, Yifan This thesis describes the synthesis of various porous poly(arylene ether)s for the applications in heterogeneous catalysis and gas separation. In Chapter I, we offer an overview of the structure and properties of porous poly(arylene ether)s, and introduce the key challenges in heterogeneous catalysis and gas separation. In Chapter II, we present the development of a solution-processable microporous organic polymer catalyst that displays high catalytic performance and size-selectivity in the heterogeneous SuzukiMiyaura coupling reaction. The catalyst can be used to create catalytic impellers that simplifies its use and recovery, thereby conforming to green chemistry principles. In Chapter III, we demonstrate a tunable synthetic platform for the advent of eight representative microporous poly(arylene ether)s with tertiary-amine functional groups. We compare the competition enhancements in sorption affinity for H₂S and CO₂ to those of primary-amine functional membranes and provide explanation for the reason why the slight enhancements do not fully translate to enhanced separation performance. In Chapter IV, we explore the potential of free volume manipulation in enhancing acid gas separation for microporous polymer membranes. By incorporating labile functional groups onto a microporous poly(arylene ether) and employing thermal treatment with oxygen, we improve the combined acid gas selectivity and increase membrane resistance to plasticization. In Chapter V, we synthesize a series of ionic poly(arylene ethers), and evaluate their potential applications in propylene/propane separation, proton exchange and heterogenous catalysis. We study the separation performance of carboxylate-functionalized polymer, and the ion exchange capacity and efficiency as solid acid catalyst for sulfonated polymers to demonstrate their promise.

Explaining Allied Military Postures: Extended Deterrence, the U.S. Nuclear Umbrella, and the Search for Security

2026-04-07T03:03:29Z

Explaining Allied Military Postures: Extended Deterrence, the U.S. Nuclear Umbrella, and the Search for Security Kwon, Jung Jae How do non-nuclear allies of the United States bolster deterrence even as they rely on the United States and its nuclear umbrella for security? How do they exercise agency in operationalizing the deterrence extended to them by the United States against their nuclear-armed adversaries? Although the existing literature offers valuable insights into the measures the United States employs to signal commitment and enhance the credibility of its security guarantees, it has paid far less attention to the role of allies themselves. This dissertation addresses that gap by introducing allied integration theory, a new framework for understanding allies’ incentives and choices. The theory explains and predicts variation in allies’ peacetime military postures. I first develop a typology of four ideal types, each reflecting allies’ different choices regarding capabilities, doctrine, tolerance for escalation risk, and integration with U.S. forces, including U.S. nuclear forces. I then argue that allies’ decisions are shaped primarily by their threat environment. Specifically, I highlight two key factors: the threat of strikes (conventional, WMD, or nuclear) and the threat of invasion from their adversaries. I then test the theory through two pairs of comparative case studies. The first pair examines Japan and South Korea in the post-Cold War period; the second compares West Germany and Norway, two frontline states of NATO, during the later Cold War (1970s-1980s). This research design allows for examining both within-case and cross-case variation while holding constant many potential confounders within each pair. Drawing on extensive fieldwork, elite interviews, foreign-language sources, U.S. archival materials, and secondary sources, I analyze how each ally made key decisions on their postures and evaluate the performance of allied integration theory. The findings contribute to the broader debates on alliance politics in the nuclear era and the interplay between conventional and nuclear domains. The research also offers practical insights for addressing growing security challenges faced by U.S.-led alliances amid intensifying geopolitical competition with multiple nuclear-armed adversaries.

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.

Discovery and characterization of diverse microbial RNA-guided systems

2026-04-07T03:03:19Z

Discovery and characterization of diverse microbial RNA-guided systems Kannan, Soumya Precise modification of nucleic acids is a powerful technique to enable understanding of the relationship between variation in genetic information and biological phenotypes and to treat genetic diseases. Over the past decade, the microbial adaptive immune system CRISPR-Cas has revolutionized genome editing, largely due to ease of target reprogramming. Cas nucleases can be retargeted by changing a short sequence in the associated non-coding RNA, which acts to guide the enzyme or complex to a complementary nucleic acid target. Exploration of the natural diversity of CRISPR-Cas systems has uncovered numerous variants with widely differing protein and domain architectures that exhibit distinct substrate specificities and activities, tying RNA-guided nucleic acid recognition to diverse enzymatic functions. This diversity has fueled the development of CRISPR-Cas systems for additional applications beyond genome editing, such as transcriptome editing, rapid diagnostics and molecular recording tools, and has aided in optimization of existing technologies via identification of systems that naturally exhibit desirable properties. In this thesis, we explore the evolution and diversity of RNA-guided microbial systems and characterize and engineer them for use in human cells. First, we investigate the origins of Cas9 and Cas12, the most widely used Cas proteins for genome editing. We find that they evolved from compact transposon-encoded nucleases, which we termed OMEGA, that already employed an RNA-guiding mechanism. Next, we develop OMEGA systems as genome editing tools that, due to their small size, are more compatible with therapeutic delivery vectors. We further explore microbial genomes and metagenomes to discover Cas13b-t, a similarly compact RNA-targeting system that we develop as a deliverable RNA editing platform. Finally, we extended the theme of mining sequence databases to comprehensively catalog proteins and domains associated with CRISPR-Cas systems. Through this analysis, we identify and characterize several novel CRISPR-Cas types and subtypes with potential for development as biotechnologies.

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.

Understanding Ion Conduction in Polymer-Ceramic Composite Electrolytes for Lithium Ion Batteries

2026-04-07T03:03:13Z

Understanding Ion Conduction in Polymer-Ceramic Composite Electrolytes for Lithium Ion Batteries Sand, Sara Catherine In the transition to safer, more energy-dense solid state batteries, polymer-ceramic composite electrolytes may offer a potential route to achieve simultaneously high lithium ion conductivity and enhanced mechanical stability. Despite numerous studies on the polymer-ceramic composite electrolytes, disagreements persist on whether the polymer or the ceramic are the primary conduction pathway and how each phase is impacted by the proximity of the other. This lack of understanding limits the design of effective composite solid electrolytes. Therefore, the goal of this thesis is to establish the role that the polymer plays in the ionic conduction and what changes in this phase to allow for enhanced conduction. I present a collection of well-controlled experiments using model systems and minimizing the parameter space, as well as utilizing advanced characterization techniques. In doing so, we probe the underlying mechanisms of how lithium ion conductivity is affected in polymer-ceramic composites. In particular, the use of positron annihilation spectroscopy has been instrumental in revealing the primary mechanisms that alter lithium ion conductivity in the polymer matrix. First, we present a comprehensive and in-depth review of the literature in Chapter 2. This chapter statistically analyzes the trends in ionic conductivities achieved in the field and present the various arguments for ion conduction pathways and interfacial effects made throughout the last three decades. As a result of this field-wide analysis, we hypothesize that the major component whose ionic conductivity is affected in the composite is the polymer. Thus the following experiments and results focus on how the polymer is altered structurally and/or chemically. Second, in Chapter 3 we present a study utilizing thin polymer films deposited on substrates of varying acidity, as a model system. Comparison of the polymer film conductivities, chemistry and structures allow us to deduce that the modification of polymer structure near a ceramic interface is a major factor, while the ceramic-polymer interface chemistry has a negligible effect on the conductivity. In particular, the crystallinity and free volume of the polymer change appreciably to result in a higher ionic conductivity in thin films as compared to bulk materials. In Chapter 4, we assess well-controlled composite electrolytes with inert, non-lithium conducting ceramic fillers, and active, lithium conducting ceramic fillers in the polymer, in particular by consistently controlling the particle size and filler volume fraction. We find that the increase in the free volume of the polymer phase plays a crucial role in the conductivity of both types of composites. The addition of active fillers only minorly improves conductivity over inert fillers, solely due to the conduction path introduced by the active ceramic particles. In Chapter 5, we assess composites containing inert nanometer-site particles to those with inert micrometer-size particles. We find that the ionic conductivity improves with nanoparticles but not with the micrometer-size particles, and again we are able to correlate this improvement with the free volume present in the polymer matrix. Lastly, in Chapter 6, we will summarize several experiments that, though less pertinent to the main aims of this thesis, are valuable to the field in understanding the techniques for characterizing these materials. Through this thesis, we provide a strong argument for the importance of the polymer’s structure in the composite electrolytes, and particularly an increase in the free volume present in the polymer phase. Such analysis of free volume has been limited in the field thus far. Thus, this thesis fills an important knowledge gap that has been limiting the understanding and control of how ceramic fillers increase lithium ion conductivity in polymer-ceramic composite electrolytes.

Magnetization Dynamics in Multi-sublattice Magnetic Materials

2026-04-07T03:03:10Z

Magnetization Dynamics in Multi-sublattice Magnetic Materials Lee, Byung Hun Magnon spintronics explores the solid-state devices that utilize electric control of spin currents carried by magnons, the quanta of spin waves. The magnon, the dynamic eigen-excitation of magnetically ordered materials, offers promising opportunities for next-generation information processing and communication systems. It can be engineered through various parameters, such as the orientation and magnitude of applied magnetic fields, choice of magnetic material, and sample geometry, enabling versatile engineering of wave-based computing technologies. Beyond the conventional ferromagnetic magnons, the final goal of this thesis is to obtain a better understanding of antiferromagnetic dynamics, which gains huge attention due to its unique characteristics including ultrafast magnetization dynamics, low damping, and negligible dipole fields. To achieve this, the study focuses on the magnetization dynamics within magnetic materials ranging from ferromagnet, ferrimagnet, and antiferromagnet, using Brillouin Light Scattering (BLS) spectroscopy. Starting from the understanding of ferromagnetic magnetization dynamics, the interface-driven effects on magnon physics are studied in ferromagnetic insulator/metal bilayers. The study reveals previously unidentified interface-driven changes in magnetization dynamics that can be exploited to locally control and modulate magnonic properties in thin-film heterostructures. As the ferromagnetic film transitions towards antiferromagnetic behavior, notable changes in BLS spectra are observed, including significant linewidth broadening. These experimental results highlight the crucial role of antiferromagnetic exchange interactions in multi-sublattice magnetic materials' magnon dynamics. Motivated by the observed reduction in magneto-optical (MO) signal for ferrimagnetic thin films, we conducted a comprehensive study on canted antiferromagnet, focusing on thin hematite (𝛼 — Fe₂O₃) film. Through both experimental and theoretical analyses of the MO properties of hematite films, we demonstrate that the canted net magnetic moment induced by the Dzyaloshinskii-Moriya interaction (DMI) generates a significant linear MO effect, while the quadratic MO signal arises from the Néel vector. Attributing to the observation, we further demonstrate that the secondary MO effect from dynamical net magnetic moment induces the significant BLS signal, in both theoretical and experimental manners. Furthermore, we demonstrate the strong correlation between magnetic domain structure and local magnon spectra, which is identified for the first time in thin 𝛼 — Fe₂O₃ film. Our study extends to the direct observation of a non-thermalized magnon state with spin current injection, indicative of a characteristic excitation mechanism with linearly polarized modes in an easy-plane antiferromagnet. The magnon thermalization and relaxation processes are further elucidated between the two linearly polarized modes. The result shows the significant contribution of high k magnons to long-distance spin transport in hematite using non-local electric measurements. Finally, we characterize the magnetic anisotropy within hematite films, distinguishing each contribution to its origin. These findings offer insights into magnetization dynamics and the manipulation of AFM spin textures via optical methods.

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.

Controlling Protein and Cell Adhesion Through Interfacial Engineering for More Efficient Biomanufacturing

2026-04-07T03:02:50Z

Controlling Protein and Cell Adhesion Through Interfacial Engineering for More Efficient Biomanufacturing McCue, Caroline Interfaces between biological and synthetic materials present both challenges and opportunities within biomanufacturing. Protein and cell adhesion to surfaces can be controlled to improve steps in a typical biologic manufacturing process from cell culture to protein separation and purification. Within downstream processes, in protein purification, chromatography columns are used to separate target proteins from the output of a bioreactor. This work explores how crystallization could be used as an alternative purification process, by utilizing functionalized nanoparticles to nucleate protein crystals faster and at lower concentrations. We demonstrate significant improvements in nucleation induction time and nucleation rates using bioconjugate-functionalized nanoparticles. Within upstream processes, in adherent cell culture, cells are typically detached from surfaces using trypsin, an enzyme that can damage sensitive cells, and result in genetic mutations. This work studies how passive surface textures and active coatings can impact cell growth, morphology and adhesion. We show that micropost surfaces can significantly reduce cell-surface adhesion, and how electrically active surfaces can be used to detach cells on demand without the use of trypsin. These interfacial platforms present opportunities to reduce the costs of traditional biomanufacturing processes, reduce damage to cells, and enable new high throughput platforms.

Structural Investigation of Allosteric Regulation in Class III Ribonucleotide Reductases

2026-04-07T03:02:56Z

Structural Investigation of Allosteric Regulation in Class III Ribonucleotide Reductases Andree, Gisele A. Ribonucleotide reductases (RNRs) are essential enzymes that use radical-based chemistry to catalyze the reduction of ribonucleotides to deoxyribonucleotides. Each RNR class uses a different cofactor to generate a catalytically essential thiyl radical in the active site. Anaerobic (class III) RNRs employ an oxygen-sensitive glycyl radical cofactor installed within the adjacent glycyl radical domain. To maintain intracellular nucleotide pool balance, RNRs are allosterically regulated. For the prototypical class Ia RNR from Escherichia coli, this regulation involves the Nterminus of the catalytic subunit in a region known as the ‘cone domain’. ATP or dATP binding to the cone domain results in an association between it and the radical-generating subunit that either allows or prevents, respectively, radical transfer. Most class III RNRs have a cone domain and are allosterically regulated, but the mechanism of how this regulation proceeds is not well understood. Allosteric activity regulation for such a class III RNR, the class III RNR from Streptococcus thermophilus (StNrdD) is the focus of this thesis. We have developed a universal liquid chromatography tandem mass spectrometry based activity assay, which was adapted for use in class III RNR, and used the assay to show that ATP is an allosteric activity enhancer and dATP is an allosteric activity inhibitor of StNrdD. We used a combination of cryogenic electron microscopy (cryo-EM) and X-ray crystallography to show that ATP and dATP bind to the StNrdD cone domain and that the cone domains adopt exceptionally different conformations in the presence of either allosteric effector. Mutagenesis assays and hydrogendeuterium exchange mass spectrometry data show that the flexible region between the cone domain and the core is important for catalysis. Changes between ATP- and dATP-binding in the cone domains underlie the observed conformational and allosteric changes. This work answers some long-standing questions surrounding class III allosteric regulation and lays the groundwork to continue improving our molecular-level understanding of this complex enzyme system.

Real-time Anticipation and Entrainment in Human-Robot Interaction

2026-04-07T03:02:38Z

Real-time Anticipation and Entrainment in Human-Robot Interaction Fourie, Christopher Kurt In this work, reactive control methodologies, alongside real-time methodologies for dense human motion prediction, are utilized to facilitate real-time anticipation and entrainment in human robot interaction. The technical contributions of this thesis include: extensions to dynamical systems-based modulation approaches that enable real-time circumnavigation of non-convex obstacles (NOMAD), a trajectory clustering approach based on a relaxation of dynamic time warping (TRACER), a real-time human modelling and prediction approach (HABITS), and the integration of these technologies into an anticipation and entrainment controller that enables real-time adaptive synchronization between a human and a robot. NOMAD introduces several on-manifold strategies that enable real-time navigation in the presence of non-convex obstacles, alongside a methodology for the eff icient representation of dense environments that can represent up to 240k points while maintaining a 1ms loop. TRACER is a probabilistic trajectory clustering algorithm that uses the expectation-maximization algorithm and a relaxation of dynamic time warping (Soft-DTW), with demonstrable improvement over non-probabilistic techniques such as kMedoids or DBSCAN. HABITS is an event-driven probabilistic filtering and incremental profiling framework that provides robust segmentation, prediction, and alignment estimation in real-time (25Hz) for emergent interactions in structured settings. The combination of these technologies is then demonstrated to enable both effective real-time anticipation (de-conflicting a workspace), as well as to support entrainment (long-term human-robot synchronization) in human-robot interaction.

Approaches to quantitatively analyze protein complex assembly and regulation

2026-04-07T03:03:08Z

Approaches to quantitatively analyze protein complex assembly and regulation Kinman, Laurel F. Biological macromolecular complexes occupy high-dimensional conformational landscapes corresponding to their assembly and regulation. For many protein complexes, these conformational landscapes encode diverse functional states of the complex, and indeed, there is a growing appreciation of the critical significance of protein dynamics in driving protein function. As such, there is a need for approaches to experimentally and quantitatively resolve these conformational landscapes to better understand: 1) the diverse structural states these complexes sample; 2) how these states and their dynamics are linked to biological function; and 3) how these landscapes are modulated by binding partners or environmental signals, or over the course of complex assembly. State-of-the-art structural approaches including heterogeneous cryogenic electron microscopy (cryo-EM) offer one potentially powerful mechanism for resolving these landscapes, and we present here approaches to resolve large numbers (100s-1,000s) of volumes from a single dataset. Moreover, I explore methods to analyze the resulting large volume ensembles using supervised and unsupervised approaches, and demonstrate the power of these approaches by applying them to identify a proofreading role for the universally-conserved methyltransferase KsgA in biogenesis of the bacterial small ribosomal subunit. However, many of the most highly dynamic protein complexes – involved in critical cellular processes like environmental sensing and signal transduction – remain inaccessible to structural techniques like heterogeneous cryo-EM. I suggest that combining structural approaches with high-throughput techniques, including proteomic and library-based approaches, has substantial power to shed light on the function and regulation of such complexes. As an example, I couple a high-throughput reporter assay with deep mutational scanning, finding highly distributed phosphorylation regulated assembly of the Atg1 complex in Saccharomyces cerevisiae in response to diverse environmental cues. Taken together, my work generates a toolbox of complementary approaches for quantitatively characterizing the assembly and regulation of protein complexes, and I anticipate substantial utility in applying these approaches to broadly characterize the functional and regulatory landscapes of protein complexes.

Full Body, Continuous, Wearable Ultrasound

2026-04-07T03:02:54Z

Full Body, Continuous, Wearable Ultrasound Wang, Chonghe In the rapidly evolving field of healthcare technology, the development of wearable ultrasound systems emerges as a groundbreaking innovation with the potential to transform patient monitoring and disease detection methodologies. This thesis, titled "Full Body, Continuous, Wearable Ultrasound," by Chonghe Wang, explores the design, implementation, and impact of two pioneering wearable ultrasound technologies: the Bioadhesive Ultrasound (BAUS) and the Adjustable Wearable Ultrasound (AWUS) systems. Aimed at enabling continuous, non-invasive monitoring of internal organ health, these systems represent a significant leap forward in medical imaging and healthcare management. At the core of the BAUS system is an innovative bioadhesive hydrogel-elastomer hybrid couplant, which facilitates the attachment of ultrasound probes directly to the skin, ensuring stable, long-term imaging. Conversely, the AWUS system employs phase-transition hydrogel couplants for dynamic adjustment of probe orientation and position, allowing for optimized imaging across multiple organs. Together, these systems offer unparalleled capabilities for full-body monitoring, with potential applications ranging from early disease detection to enhanced patient care and a deeper understanding of human physiology. Through rigorous experimental validation, this research assesses the imaging quality, physiological monitoring accuracy, durability, and comfort of the BAUS and AWUS systems. Employing advanced image processing and machine learning techniques, the study analyzes data collected from continuous imaging sessions, highlighting the systems' ability to capture dynamic physiological events and detect pathological changes. This thesis underscores the transformative potential of wearable ultrasound technology in healthcare and medical research. By shifting the paradigm from episodic to continuous monitoring, it opens new avenues for proactive health management, promising to improve patient outcomes, reduce healthcare costs, and advance our understanding of the human body.

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.

Geometry, packing, and synchronization in three-dimensional (3D) multicellular development and diseases

2026-04-07T03:03:05Z

Geometry, packing, and synchronization in three-dimensional (3D) multicellular development and diseases Tang, Wenhui Three-dimensional (3D) multicellular systems, along with their developmental and pathological processes, present unique features compare to 2D flat systems. Both the spatial organization and temporal cell dynamics can be influenced by fundamental difference in cell-microenvironment interactions, cell mechanics, as well as cell physical properties. However, the effort to understanding the fundamental physics and mechanics is limited due to the constraint in 3D techniques. Moreover, the evolving multicellular properties during biological processes are unclear. In this thesis, we systematically study the geometry, packing, and synchronization in three-dimensional multicellular development and diseases. First, we give a perspective on the emerging evidence of 3D tissue geometry in guiding morphogenesis and abnormal growth during diseases. We review the effort in fabricating various tissue-mimicking structures to study collective cell behaviors, and emphasize how tissue geometry influences physical, mechanical, and biological properties. At the end, we propose future directions, challenges, and potential applications in geometry-induced stem cell therapeutics. Second, we report a surprising result that cells can feel the substrate curvature that they live on. We find that epithelial cells tend to form hexagonal packs to minimize the free energy; cells in these hexagonal packs are more solid-like compared to the cells out of packs. As a result, when the substrate curvature increases, the size of these hexagonal cell packs decreases to release bending energy. Therefore, on more curved surface, we observe a more fluid-like cell monolayer with more active cell dynamics and less collectiveness. Such behavior is observed not only on fabricated geometries, but also on a spontaneously growing human lung alveolosphere system in 3D derived from human induced pluripotent stem cells. Third, as building blocks of life, cells actively coordinate their positions to form various structures and perform functions. A central question is how do cells in three-dimensional organisms accurately arrange their positions and morphology on curved structures during development? We observe an emergence of topological order, specifically a topological gas-to-liquid transition, during the growth of human lung alveolospheres, regulated by the increasing nucleus-to-cell size ratio. Our finding reveals the critical role of cell nuclear size in regulating cell packing during tissue development, and suggests the importance of topological phase changes in establishing tissue stability. Last, from a temporal perspective, we report an experimental observation of large-scale synchronized oscillations generated by sustained contraction and expansion, revealing un- expected phase dynamics in epithelia. We then apply this temporal analysis to studying proliferating epithelia undergoing jamming transition and cancer invasion across various degrees of malignancy. Remarkably, this method provides an original perspective to assess the stage of tissue development, estimate cancer malignancy level, as well as distinguish healthy tissues from the diseased ones.

Multidimensional MOF Mixed Matrix Membranes for Efficient Gas Separation

2026-04-07T03:02:53Z

Multidimensional MOF Mixed Matrix Membranes for Efficient Gas Separation Lee, Hyunhee Membrane separations are crucial in the chemical industry, with polymeric materials traditionally used due to their cost and mechanical benefits. However, they face challenges in permeability–selectivity trade-off, and in stability. Metal–organic frameworks (MOFs) offer potential solutions with their customizable properties but are difficult to manufacture. Mixed-matrix membranes (MMMs), which incorporate MOFs into polymers, mitigate some issues, yet high MOF loading can lead to aggregation and voids. This thesis investigates the promising potential of MMMs for efficient and improved gas separation, leveraging unique morphologies and understanding the dynamics of MOF–polymer interactions. First, the novel branch-shaped ZIF-8 (BZ) was developed and incorporated into polymer matrix, which successfully established a percolated network at loadings as low as 20 wt%, showing permeability boost. Also, it showed suppressed polymer chain dynamics and a smaller diffusion cut-off than traditional ZIF-8, which resulted in an enhanced membrane stability and superior performance in H₂-based separations. BZ was studied further by investigating temperature-dependent properties of MMMs. BZ and control ZIF-8 (CZ) MMMs exhibited unique gas transport behaviour in relation to temperature shifts, with BZ MMM demonstrating more significant temperature dependence for H₂-based separations. As temperature decreases, the H₂/CH₄ permselectivity of BZ MMMs drastically increases, with minor changes in H₂ permeability. Conversely, at higher temperatures, separation performance aligns with that of CZ MMM, showing continuous yet broad control over the gas performance. To understand the origin of this selectivity difference, facet-specific gas transport in polymer nanocomposites was studied with the hypothesis of BZ consist of facet 100, which characterize less thermally stable polymorph, cubes. A key finding is the interaction between 100 facet and polyimides, which enhances hydrogen-based and ethylene/ethane separation, particularly at subambient temperatures, which is consistent with the trend observed for BZ MMMs. In conclusion, this thesis addresses the enhancement of MMMs through innovative morphological approaches, where percolated network enhances permeability and 100 facet termination may restrict the MOF–polymer interphase confinement, leading to high selectivity for small gas pairs, which is very difficult to achieve at the same time. The temperature effects and facet-termination effects on gas transport in MMMs can also offer substantial contributions to the development and optimization of mixed matrix membranes for efficient gas separations.

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.

Chemically Circumventing the Oxidative Instability of Boronic Acids for Biological Applications

2026-04-07T03:03:03Z

Chemically Circumventing the Oxidative Instability of Boronic Acids for Biological Applications FitzGerald, Forrest Grant Boronic acids are more than just chemical reagents used in synthetic organic chemistry. They are life-saving chemo- and radiotherapeutics, optical imaging agents, chemical sensors of biological stress, hydrogels, nanomaterials, and so much more. While the literature is rich with applications of boronic acids, there is much room for improvement. From the environment we live in, to the biochemical transformations that drive cellular respiration, oxidation is a common thread. Boronic acids are highly susceptible to oxidation by reactive oxygen species that are continuously generated in these environments, and though boronic acids have found wide use, their utility is limited by their short lifetimes due to this form a degradation. We sought to find chemical solutions for this instability. Benzoxaborolone, a boralactone, was previously developed in the Raines lab as an oxidatively stable arylboronic acid. In this current work, we demonstrate that this oxidative stability is enhanced by electron-withdrawing functional groups and reduced by electron-donating functional groups appended to the aryl ring. Applying principles of physical organic chemistry, we reasoned that this electronic impact is predictable depending on the identity of the functional group, with valuable insights for use in medicinal chemistry and chemical biology. Next, we demonstrated that a benzoxaborolone–fluorophore conjugate is an equally effective glycan imaging reagent compared to the commonly used phenylboronic acid-bearing reagent, yet is more stable and thus versatile. We designed this reagent to be a general-use minimalistic glycan-binding reagent with high diffusivity and modularity. We then used this reagent to explore the diverse organ-specific glycome in mice, leveraging recently reported techniques in expansion microscopy to acquire fluorescence images in nanoscale resolution. Finally, we took a new approach to circumventing the chemical instability of boronic acids in biological settings. Bortezomib is a highly potent alkylboronic acid chemotherapeutic that has been a mainstay in the clinic for the treatment of multiple myeloma for nearly twenty-five years. Unfortunately, it is highly susceptible to oxidation and thus prone to inactivation during synthesis, storage, and administration. Substituting in an aromatic, oxidatively stable benzoxaborolone is not an effective strategy for bortezomib, as it would completely change the chemical structure and abrogate activity. Instead, we leveraged a popular boronic acid protecting group, Nmethyliminodiacetic acid (MIDA), used primarily in organic chemistry, to create a bench-stable, oxidation-resistant bortezomib that retains its cytotoxic potency toward cancer cells. Furthermore, we uncovered for the first time that the MIDA protecting group may be susceptible to esterase-mediated hydrolysis, paving a path towards potential prodrug applications in the future.

Report to the President for year ended June 30, 2025, Dean, Sloan School

2026-04-07T03:53:28Z

Report to the President for year ended June 30, 2025, Dean, Sloan School Locke, Richard This report contains the following sections: Introduction, Faculty and Research, Academic Programs, Executive Education, Office of External Relations, MIT Sloan Management Review, MIT Sloan Global Programs, and Conclusion.

Report to the President for year ended June 30, 2025, Division of Student Life

2026-04-07T03:53:20Z

Report to the President for year ended June 30, 2025, Division of Student Life Nelson, Suzy This report contains the following sections: DSL Mission, Goals, Accomplishments, and Program Updates.

Transcriptome-wide mapping reveals a diverse dihydrouridine landscape including mRNA

2026-04-25T03:13:34Z

Transcriptome-wide mapping reveals a diverse dihydrouridine landscape including mRNA Draycott, Austin S; Schaening-Burgos, Cassandra; Rojas-Duran, Maria F; Wilson, Loren; Schärfen, Leonard; Neugebauer, Karla M; Nachtergaele, Sigrid; Gilbert, Wendy V Dihydrouridine is a modified nucleotide universally present in tRNAs, but the complete dihydrouridine landscape is unknown in any organism. We introduce dihydrouridine sequencing (D-seq) for transcriptome-wide mapping of D with single-nucleotide resolution and use it to uncover novel classes of dihydrouridine-containing RNA in yeast which include mRNA and small nucleolar RNA (snoRNA). The novel D sites are concentrated in conserved stem-loop regions consistent with a role for D in folding many functional RNA structures. We demonstrate dihydrouridine synthase (DUS)-dependent changes in splicing of a D-containing pre-mRNA in cells and show that D-modified mRNAs can be efficiently translated by eukaryotic ribosomes in vitro. This work establishes D as a new functional component of the mRNA epitranscriptome and paves the way for identifying the RNA targets of multiple DUS enzymes that are dysregulated in human disease.

SARS-CoV-2 antibodies protect against reinfection for at least 6 months in a multicentre seroepidemiological workplace cohort

2026-04-24T03:16:22Z

SARS-CoV-2 antibodies protect against reinfection for at least 6 months in a multicentre seroepidemiological workplace cohort Finch, Emilie; Lowe, Rachel; Fischinger, Stephanie; de St Aubin, Michael; Siddiqui, Sameed M; Dayal, Diana; Loesche, Michael A; Rhee, Justin; Beger, Samuel; Hu, Yiyuan; Gluck, Matthew J; Mormann, Benjamin; Hasdianda, Mohammad A; Musk, Elon R; Alter, Galit; Menon, Anil S; Nilles, Eric J; Kucharski, Adam J Identifying the potential for SARS-CoV-2 reinfection is crucial for understanding possible long-term epidemic dynamics. We analysed longitudinal PCR and serological testing data from a prospective cohort of 4,411 United States employees in 4 states between April 2020 and February 2021. We conducted a multivariable logistic regression investigating the association between baseline serological status and subsequent PCR test result in order to calculate an odds ratio for reinfection. We estimated an odds ratio for reinfection ranging from 0.14 (95% CI: 0.019 to 0.63) to 0.28 (95% CI: 0.05 to 1.1), implying that the presence of SARS-CoV-2 antibodies at baseline is associated with around 72% to 86% reduced odds of a subsequent PCR positive test based on our point estimates. This suggests that primary infection with SARS-CoV-2 provides protection against reinfection in the majority of individuals, at least over a 6-month time period. We also highlight 2 major sources of bias and uncertainty to be considered when estimating the relative risk of reinfection, confounders and the choice of baseline time point, and show how to account for both in reinfection analysis.

Persona2vec: a flexible multi-role representations learning framework for graphs

2026-04-24T03:16:18Z

Persona2vec: a flexible multi-role representations learning framework for graphs Yoon, Jisung; Yang, Kai-Cheng; Jung, Woo-Sung; Ahn, Yong-Yeol Graph embedding techniques, which learn low-dimensional representations of a graph, are achieving state-of-the-art performance in many graph mining tasks. Most existing embedding algorithms assign a single vector to each node, implicitly assuming that a single representation is enough to capture all characteristics of the node. However, across many domains, it is common to observe pervasively overlapping community structure, where most nodes belong to multiple communities, playing different roles depending on the contexts. Here, we propose persona2vec, a graph embedding framework that efficiently learns multiple representations of nodes based on their structural contexts. Using link prediction-based evaluation, we show that our framework is significantly faster than the existing state-of-the-art model while achieving better performance.

Chemically induced reprogramming to reverse cellular aging

2026-04-25T03:13:28Z

Chemically induced reprogramming to reverse cellular aging Yang, Jae-Hyun; Petty, Christopher A; Dixon-McDougall, Thomas; Lopez, Maria Vina; Tyshkovskiy, Alexander; Maybury-Lewis, Sun; Tian, Xiao; Ibrahim, Nabilah; Chen, Zhili; Griffin, Patrick T; Arnold, Matthew; Li, Jien; Martinez, Oswaldo A; Behn, Alexander; Rogers-Hammond, Ryan; Angeli, Suzanne; Gladyshev, Vadim N; Sinclair, David A A hallmark of eukaryotic aging is a loss of epigenetic information, a process that can be reversed. We have previously shown that the ectopic induction of the Yamanaka factors OCT4, SOX2, and KLF4 (OSK) in mammals can restore youthful DNA methylation patterns, transcript profiles, and tissue function, without erasing cellular identity, a process that requires active DNA demethylation. To screen for molecules that reverse cellular aging and rejuvenate human cells without altering the genome, we developed high-throughput cell-based assays that distinguish young from old and senescent cells, including transcription-based aging clocks and a real-time nucleocytoplasmic compartmentalization (NCC) assay. We identify six chemical cocktails, which, in less than a week and without compromising cellular identity, restore a youthful genome-wide transcript profile and reverse transcriptomic age. Thus, rejuvenation by age reversal can be achieved, not only by genetic, but also chemical means.

High-Level Expression and Biochemical Properties of A Thermo-Alkaline Pectate Lyase From Bacillus sp. RN1 in Pichia pastoris With Potential in Ramie Degumming

2026-04-24T03:16:26Z

High-Level Expression and Biochemical Properties of A Thermo-Alkaline Pectate Lyase From Bacillus sp. RN1 in Pichia pastoris With Potential in Ramie Degumming Zheng, Xueyun; Zhang, Yimin; Liu, Xiaoxiao; Li, Cheng; Lin, Ying; Liang, Shuli Pectate lyases play an essential role in textiles, animal feed, and oil extraction industries. Pichia pastoris can be an ideal platform for pectate lyases production, and BspPel (a thermo-alkaline pectate lyase from Bacillus sp. RN1) was overexpressed by combined strategies, reaching 1859 U/mL in a 50 L fermentator. It displayed the highest activity at 80°C, and maintained more than 60% of the activity between 30 and 70°C for 1 h. It showed an optimal pH of 10.0, and exhibited remarkable stability over a wider pH range (3.0-11.0), retaining more than 80.0% of enzyme activity for 4 h. The Km and kcat of BspPel on PGA (polygalacturonic acid) was 2.19 g L–1 and 116.1 s–1, respectively. The activity was significantly enhanced by Ca2+, Mn2+, and Cu2+, and a slight increase was observed with the addition of Ba2+ and Mg2+. Scanning electron microscope was used to show the degumming efficiency of BspPel on ramie fibers. The loss weight was 9.2% when treated with crude enzyme supernatant and 20.8% when treated with the enzyme-chemical method, which was higher than the 14.2% weight loss in the positive control treated with 0.5% (w/v) NaOH alone. In conclusion, BspPel could be a good candidate for the ramie degumming industry.

Growth Factor Engineering Strategies for Regenerative Medicine Applications

2026-04-25T03:15:44Z

Growth Factor Engineering Strategies for Regenerative Medicine Applications Ren, Xiaochen; Zhao, Moyuan; Lash, Blake; Martino, Mikaël M; Julier, Ziad Growth factors are critical molecules for tissue repair and regeneration. Therefore, recombinant growth factors have raised a lot of hope for regenerative medicine applications. While using growth factors to promote tissue healing has widely shown promising results in pre-clinical settings, their success in the clinic is not a forgone conclusion. Indeed, translation of growth factors is often limited by their short half-life, rapid diffusion from the delivery site, and low cost-effectiveness. Trying to circumvent those limitations by the use of supraphysiological doses has led to serious side-effects in many cases and therefore innovative technologies are required to improve growth factor-based regenerative strategies. In this review, we present protein engineering approaches seeking to improve growth factor delivery and efficacy while reducing doses and side effects. We focus on engineering strategies seeking to improve affinity of growth factors for biomaterials or the endogenous extracellular matrix. Then, we discuss some examples of increasing growth factor stability and bioactivity, and propose new lines of research that the field of growth factor engineering for regenerative medicine may adopt in the future.

Quantification of muscle fiber malformations using edge detection to investigate chronic muscle pressure ulcers

2026-04-25T03:14:09Z

Quantification of muscle fiber malformations using edge detection to investigate chronic muscle pressure ulcers Ong, Charlene ZL; Nasir, N Jannah M; Welsch, Roy E; Tucker-Kellogg, Lisa; Rajapakse, Jagath C Background: Microscopy of regenerated tissue shows different morphologies between the healing of acute wounds and chronic wounds. This difference can be seen manually by biologists, but computational methods are needed to automate the characterization of morphology and regenerative quality in regenerated muscle tissue. Results: From the detected edge segments, we computed several imaging biomarkers of interest, such as median tortuosity, number of edge segments normalized by area, median edge segment distance and interquartile range of orientation angles of edge segments of the microscope images of successful and unsuccessful muscle regeneration. We observed that muscle fibers in saline-treated pressure ulcers had a larger interquartile range of orientation angles of the edge segments (p = 0.05) and shorter edge segment distances (p = 0.003) compared to those of acute cardiotoxin injuries. Conclusion: Our edge detection method was able to identify statistically significant differences in some of the imaging biomarkers between saline-treated pressure ulcers and cardiotoxin injuries, suggesting that chronic pressure ulcers have increased muscle fiber malformations compared to cardiotoxin injuries.

Fast and Fourier: extreme mass ratio inspiral waveforms in the frequency domain

2026-04-25T03:14:48Z

Fast and Fourier: extreme mass ratio inspiral waveforms in the frequency domain Speri, Lorenzo; Katz, Michael L; Chua, Alvin JK; Hughes, Scott A; Warburton, Niels; Thompson, Jonathan E; Chapman-Bird, Christian EA; Gair, Jonathan R Extreme Mass Ratio Inspirals (EMRIs) are one of the key sources for future spacebased gravitational wave interferometers. Measurements of EMRI gravitational waves are expected to determine the characteristics of their sources with subpercent precision. However, their waveform generation is challenging due to the long duration of the signal and the high harmonic content. Here, we present the first ready-to-use Schwarzschild eccentric EMRI waveform implementation in the frequency domain for use with either graphics processing units (GPUs) or central processing units (CPUs). We present the overall waveform implementation and test the accuracy and performance of the frequency domain waveforms against the time domain implementation. On GPUs, the frequency domain waveform takes in median 0.044 s to generate and is twice as fast to compute as its time domain counterpart when considering massive black hole masses ≥ 2 × 106 M⊙ and initial eccentricities e0 > 0.2. On CPUs, the median waveform evaluation time is 5 s, and it is five times faster in the frequency domain than in the time domain. Using a sparser frequency array can further speed up the waveform generation, reaching up to 0.3 s. This enables us to perform, for the first time, EMRI parameter inference with fully relativistic waveforms on CPUs. Future EMRI models, which encompass wider source characteristics (particularly black hole spin and generic orbit geometries), will require significantly more harmonics. Frequency domain models will be essential analysis tools for these astrophysically realistic and important signals.

Eigenvalue lower bounds and splitting for modified Ricci flow

2026-04-25T03:15:36Z

Eigenvalue lower bounds and splitting for modified Ricci flow Colding, Tobias Holck; Minicozzi II, William P We prove sharp lower bounds for eigenvalues of the drift Laplacian for a modified Ricci flow. The modified Ricci flow is a system of coupled equations for a metric and weighted volume that plays an important role in Ricci flow. We will also show that there is a splitting theorem in the case of equality.

Propagation of symmetries for Ricci shrinkers

2026-04-25T03:13:54Z

Propagation of symmetries for Ricci shrinkers Colding, Tobias Holck; Minicozzi II, William P We will show that if a gradient shrinking Ricci soliton has an approximate symmetry on one scale, this symmetry propagates to larger scales. This is an example of the shrinker principle which roughly states that information radiates outwards for shrinking solitons.

The effects of morphology, mobility size, and secondary organic aerosol (SOA) material coating on the ice nucleation activity of black carbon in the cirrus regime

2026-05-08T03:12:11Z

The effects of morphology, mobility size, and secondary organic aerosol (SOA) material coating on the ice nucleation activity of black carbon in the cirrus regime Zhang, Cuiqi; Zhang, Yue; Wolf, Martin J; Nichman, Leonid; Shen, Chuanyang; Onasch, Timothy B; Chen, Longfei; Cziczo, Daniel J There is evidence that black carbon (BC) particles may affect cirrus formation and, hence, global climate by acting as potential ice nucleating particles (INPs) in the troposphere. Nevertheless, the ice nucleation (IN) ability of bare BC and BC coated with secondary organic aerosol (SOA) material remains uncertain. We have systematically examined the IN ability of 100–400 nm size-selected BC particles with different morphologies and different SOA coatings representative of anthropogenic (toluene and n-dodecane) and biogenic (β-caryophyllene) sources in the cirrus regime (−46 to −38 ∘C). Several BC proxies were selected to represent different particle morphologies and oxidation levels. Atmospheric aging was further replicated with the exposure of SOA-coated BC to OH. The results demonstrate that the 400 nm hydrophobic BC types nucleate ice only at or near the homogeneous freezing threshold. Ice formation at cirrus temperatures below homogeneous freezing thresholds, as opposed to purely homogeneous freezing, was observed to occur for some BC types between 100 and 200 nm within the investigated temperature range. More fractal BC particles did not consistently act as superior INPs over more spherical ones. SOA coating generated by oxidizing β-caryophyllene with O3 did not seem to affect BC IN ability, probably due to an SOA-phase state transition. However, SOA coatings generated from OH oxidation of various organic species did exhibit higher IN-onset supersaturation ratio with respect to ice (SSi), compared with bare BC particles, with the toluene-SOA coating showing an increase in SSi of 0.1–0.15 while still below the homogeneous freezing threshold. Slightly oxidized toluene SOA coating seemed to have a stronger deactivation effect on BC IN ability than highly oxidized toluene SOA, which might be caused by oligomer formation and the phase state transition of toluene SOA under different oxidation levels. n-dodecane and β-caryophyllene-derived SOA-coated BC only froze in the homogeneous regime. We attribute the inhibition of IN ability to the filling of the pores on the BC surface by the SOA material coating. OH exposure levels of n-dodecane and β-caryophyllene SOA coating experiments, from an equivalent atmospheric exposure time from 10 to 90 d, did not render significant differences in the IN potential. Our study of selected BC types and sizes suggests that increases in diameter, compactness, and/or surface oxidation of BC particles lead to more efficient IN via the pore condensation freezing (PCF) pathway, and that coatings of common SOA materials can inhibit the formation of ice.

A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1

2026-05-08T03:13:16Z

A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1 Nicely, Julie M; Duncan, Bryan N; Hanisco, Thomas F; Wolfe, Glenn M; Salawitch, Ross J; Deushi, Makoto; Haslerud, Amund S; Jöckel, Patrick; Josse, Béatrice; Kinnison, Douglas E; Klekociuk, Andrew; Manyin, Michael E; Marécal, Virginie; Morgenstern, Olaf; Murray, Lee T; Myhre, Gunnar; Oman, Luke D; Pitari, Giovanni; Pozzer, Andrea; Quaglia, Ilaria; Revell, Laura E; Rozanov, Eugene; Stenke, Andrea; Stone, Kane; Strahan, Susan; Tilmes, Simone; Tost, Holger; Westervelt, Daniel M; Zeng, Guang The hydroxyl radical (OH) plays critical roles within the troposphere, such as determining the lifetime of methane (CH4), yet is challenging to model due to its fast cycling and dependence on a multitude of sources and sinks. As a result, the reasons for variations in OH and the resulting methane lifetime (τCH4 ), both between models and in time, are difficult to diagnose. We apply a neural network (NN) approach to address this issue within a group of models that participated in the Chemistry-Climate Model Initiative (CCMI). Analysis of the historical specified dynamics simulations performed for CCMI indicates that the primary drivers of τCH4 differences among 10 models are the flux of UV light to the troposphere (indicated by the photolysis frequency JO 1D), the mixing ratio of tropospheric ozone (O3), the abundance of nitrogen oxides (NOx ≡ NO + NO2), and details of the various chemical mechanisms that drive OH. Water vapour, carbon monoxide (CO), the ratio of NO : NOx , and formaldehyde (HCHO) explain moderate differences in τCH4 , while isoprene, methane, the photolysis frequency of NO2 by visible light (JNO2), overhead ozone column, and temperature account for little to no model variation in τCH4 . We also apply the NNs to analysis of temporal trends in OH from 1980 to 2015. All models that participated in the specified dynamics historical simulation for CCMI demonstrate a decline in τCH4 during the analysed timeframe. The significant contributors to this trend, in order of importance, are tropospheric O3, JO 1D, NOx , and H2O, with CO also causing substantial interannual variability in OH burden. Finally, the identified trends in τCH4 are compared to calculated trends in the tropospheric mean OH concentration from previous work, based on analysis of observations. The comparison reveals a robust result for the effect of rising water vapour on OH and τCH4 , imparting an increasing and decreasing trend of about 0.5 % decade−1 , respectively. The responses due to NOx , ozone column, and temperature are also in reasonably good agreement between the two studies.

High-Resolution OCT Reveals Age-Associated Variation in the Region Posterior to the External Limiting Membrane

2026-04-24T03:14:41Z

High-Resolution OCT Reveals Age-Associated Variation in the Region Posterior to the External Limiting Membrane Jamil, Muhammad Usman; Won, Jungeun; Ploner, Stefan B; Marmalidou, Anna; Takahashi, Hiroyuki; Kaiser, Stephanie; Hwang, Yunchan; Abu-Qamar, Omar; Yaghy, Antonio; Witkin, Andre J; Zhao, Peter Y; Desai, Shilpa; Duker, Jay S; Maier, Andreas; Fujimoto, James G; Waheed, Nadia K Purpose: To evaluate visibility of a sub-band posterior to the external limiting membrane (ELM) and assess its age-associated variation. Methods: In a retrospective cross-sectional study, normal eyes were imaged using a high-resolution spectral-domain optical coherence tomography (SD-OCT) prototype (2.7-µm axial resolution). Volume fusion of six sequential scans (each 500 × 500 A-scans over 6 mm × 6 mm) was performed in the motion correction and volume reconstruction in OCT (MoReOCT) framework to enhance feature visibility in OCT. The subjects were divided into three groups: young (21-40 years old), middle (41-60 years old), and older (>60 years old). Three expert graders assessed the visibility of the sub-band on B-scans, and its A-scan intensity relative to ELM intensity (peak intensity ratio) was measured. Results: Forty-four eyes of 44 subjects were imaged. The sub-band, tentatively attributed to the photoreceptor myoid, can be visualized under high-resolution OCT. The B-scan gradings showed that sub-band visibility increased with age (visible in 16.7%, 47.2%, and 66.7% of the young, middle, and older age groups, respectively). The gradings were statistically different among age groups at 1 mm and 2 mm nasal and 1 mm and 2 mm temporal (P < 0.04) from the foveal center. Similarly, the mean peak intensity ratios of the sub-band to the ELM were 71.6%, 77.5%, and 85.2% in the young, middle, and older age groups, respectively, and were positively correlated with age at 1 mm temporal (P = 0.012) and 2 mm temporal (P < 0.001). Conclusions: High-resolution OCT, combined with advanced volume fusion, enables visualization of the photoreceptor myoid and investigation of its age-associated variations. Translational Relevance: Investigating the sub-band can advance our understanding of photoreceptors and their association with aging and disease pathogenesis.

Topographic Measurement of the Subretinal Pigment Epithelium Space in Normal Aging and Age-Related Macular Degeneration Using High-Resolution OCT

2026-04-24T03:14:31Z

Topographic Measurement of the Subretinal Pigment Epithelium Space in Normal Aging and Age-Related Macular Degeneration Using High-Resolution OCT Won, Jungeun; Takahashi, Hiroyuki; Ploner, Stefan B; Karbole, Wenke; Abu-Qamar, Omar; Yaghy, Antonio; Marmalidou, Anna; Kaiser, Stephanie; Hwang, Yunchan; Lin, Junhong; Witkin, Andre; Desai, Shilpa; Baumal, Caroline R; Maier, Andreas; Curcio, Christine A; Waheed, Nadia K; Fujimoto, James G Purpose: A micrometer scale hyporeflective band within the retinal pigment epithelium basal lamina - Bruch's membrane complex (RPE-BL-BrM) was topographically measured in aging and age-related macular degeneration (AMD). Methods: In a prospective cross-sectional study, 90 normal eyes from 76 subjects (range = 23-90 years) and 53 dry AMD eyes from 47 subjects (range = 62-91 years) were enrolled. Isotropic volume raster scans over 6 mm × 6 mm (500 × 500 A-scans) were acquired using a high-resolution (2.7 µm axial resolution) spectral-domain optical coherence tomography (SD-OCT) prototype instrument. Six consecutive optical coherence tomography (OCT) volumes were computationally motion-corrected and fused to improve feature visibility. A boundary regression neural network was developed to measure hyporeflective band thickness. Topographic dependence was evaluated over a 6-mm-diameter Early Treatment Diabetic Retinopathy Study (ETDRS) grid. Results: The hyporeflective band thickness map (median of 4.3 µm and 7.8 µm in normal and AMD eyes, respectively) is thicker below and radially symmetric around the fovea. In normal eyes, age-associated differences occur within 0.7 to 2.3 mm from the foveal center (P < 0.05). In AMD eyes, the hyporeflective band is hypothesized to be basal laminar deposits (BLamDs) and is thicker within the 3-mm ETDRS circle (P < 0.0002) compared with normal eyes. The inner ring is the most sensitive location to detect age versus AMD-associated changes within the RPE-BL-BrM. AMD eyes with subretinal drusenoid deposits (SDDs) have a significantly thicker hyporeflective band (P < 0.001) than those without SDDs. Conclusions: The hyporeflective band is a quantifiable biomarker which differentiates AMD from aging. Longitudinal studies are warranted. The hyporeflective band may be a useful biomarker for risk stratification and disease progression.

Assessment of Choriocapillaris Flow Prior to Nascent Geographic Atrophy Development Using Optical Coherence Tomography Angiography

2026-04-24T03:14:52Z

Assessment of Choriocapillaris Flow Prior to Nascent Geographic Atrophy Development Using Optical Coherence Tomography Angiography Greig, Eugenia Custo; Moult, Eric M; Despotovic, Ivana N; Hodgson, Lauren AB; Pramil, Varsha; Fujimoto, James G; Waheed, Nadia K; Guymer, Robyn H; Wu, Zhichao Purpose: To assess the relationship between choriocapillaris (CC) loss and the development of nascent geographic atrophy (nGA) using optical coherence tomography angiography (OCTA) imaging. Methods: In total, 105 from 62 participants with bilateral large drusen, without late age-related macular degeneration (AMD) or nGA at baseline, were included in this prospective, longitudinal, observational study. Participants underwent swept-source OCTA imaging at 6-month intervals. CC flow deficit percentage (FD%) and drusen volume measurements were determined for the visit prior to nGA development or the second-to-last visit if nGA did not develop. Global and local analyses, the latter based on analyses within superpixels (120 × 120-µm regions), were performed to examine the association between CC FD% and future nGA development. Results: A total of 15 (14%) eyes from 12 (19%) participants developed nGA. There was no significant difference in global CC FD% at the visit prior to nGA development between eyes that developed nGA and those that did not (P = 0.399). In contrast, CC FD% was significantly higher in superpixels that subsequently developed nGA compared to those that did not (P < 0.001), and a model utilizing CC FD% was significantly better at predicting foci of future nGA development at the superpixel level than a model using drusen volume alone (P ≤ 0.040). Conclusions: This study showed that significant impairments in CC blood flow could be detected locally prior to the development of nGA. These findings add to our understanding of the pathophysiologic changes that occur with atrophy development in AMD.

Stochastic Network Utility Maximization in Strategic Queueing Systems: A Game-Theoretic Approach

2026-04-24T03:14:54Z

Stochastic Network Utility Maximization in Strategic Queueing Systems: A Game-Theoretic Approach Nguyen, Quang Minh; Berry, Randall; Modiano, Eytan Stochastic Network Utility Maximization (NUM) has been a dominant framework for many queueing network resource allocation and control problems. Its original model seeks to optimize social welfare, which usually takes the form of the sum of local utilities of participating entities. However, such a centralized utility maximization approach is unsuitable for many modern multi-agent systems, in which each agent may selfishly optimize its local utility without regard to the overall utility. In this paper, we formulate the stochastic NUM problem in strategic queueing systems as a repeated game with queue stability constraints. In particular, the agents repeatedly make decisions to satisfy both their local constraints and global constraints, shared among them, while maintaining queue stability. The goal is to design a policy that constitutes a generalized Nash equilibrium (GNE) for the game. We first derive the fluid model characterization of the strategic queueing NUM problem via a static one-shot game formulation. This characterization motivates a primal-dual algorithm that constitutes an approximate GNE by ensuring last-iterate convergence to a solution of the regularized static one-shot game. However, similar to primal-dual methods developed for the classical NUM problem, this approach does not leverage real-time queue lengths in decision making, leading to suboptimal queueing delay in practice, and has no explicit performance guarantees. To this end, we propose the Strategic Drift-plus-Penalty (SDP) algorithm and show that it constitutes an $\varepsilon$-GNE and has a uniformly bounded expected queue length of order $O\big(\frac{1}{\varepsilon^3} \big)$ for any $\varepsilon > 0$. Under an additional mild assumption that holds for a wide class of problems, we show that our algorithms achieve long-term average social welfare arbitrarily close to that of a welfare-maximizing GNE policy. Simulations validate our theory and demonstrate the favorable performance of our algorithms.

Pop-Up Encounters with Spot: Shaping Public Perceptions of Robots through Hands-On Experience

2026-05-08T03:14:01Z

Pop-Up Encounters with Spot: Shaping Public Perceptions of Robots through Hands-On Experience Park, Hae Won; Van de Zande, Georgia D.; Zhang, Xiajie; Wendell, Dawn; Hodgins, Jessica Public attitudes toward robots are often shaped by indirect exposure (e.g., media, staged demos), leaving open how direct, hands-on experience influences acceptance. In this study, we investigate how interacting with Boston Dynamics’ Spot, an agile, state-of-the-art quadruped robot, in a public pop-up booth affects perceptions of comfort and suitability across everyday and high-stakes environments. In a walk-up, 10-week pop-up booth, participants (N=753) completed pre–post surveys before and after driving Spot within curated Drive Scenes (Factory, Home, Hospital, Outdoor/Disaster). Measures captured comfort encountering robots and perceived suitability across Rated Contexts (RCs), affective reactions, and open-ended reflections. Hands-on control significantly increased comfort across all RCs, with the largest gains in Outdoor/Disaster, and increased perceived suitability—most in Home/Office/Hospital where baselines were lower. Improvements generalized beyond the experienced Drive Scene to other contexts. Age, gender, and prior familiarity moderated baseline levels and some changes, but hands-on exposure raised scores for all groups and attenuated several gaps. Thematic analysis showed memorable moments tied to locomotion, terrain adaptation, and expressive tilt; imagined roles consistently emphasized domestic assistance (e.g., cleaning, mobility), with entertainment/play and companionship emerging post-interaction. Together, these results demonstrate that brief, agency-granting encounters with a high-capability quadruped can broaden where people see robots as appropriate and diversify envisioned roles, offering a scalable model for public-facing HRI that fosters comfort, enthusiasm, and acceptance. HRI ’26, Edinburgh, Scotland, UK

Who’s the Boss? Children Negotiate Robot Control across Role and Context

2026-04-24T03:14:28Z

Who’s the Boss? Children Negotiate Robot Control across Role and Context Pu, Isabella; Rogers, Kantwon; Dinh, Linh Dieu; Alghowinem, Sharifa; Breazeal, Cynthia Children regularly negotiate questions of authority and control in home and school life, but little is known about how they believe robots should fit into these dynamics. We conducted a 75-minute design session with 17 children (ages 6-9) to examine when robots should take, share, or defer control, and how expectations shift when robots are framed as teachers, classmates, or mentees. Children resisted robot control, particularly in adult-regulated domains and areas tied to personal skill or self-expression. They were more open to robot control in domains where they felt less competent, or where robots, perceived as less legitimate authorities than humans, could substitute for adult control. Role framing further shaped expectations: teacher robots were granted autonomy, classmate robots were expected to act as peers, and mentee robots were expected to defer. These findings show that children apply context- and role-sensitive rules when negotiating control with robots. We conclude with design considerations for robots in children's everyday lives that respect children's agency, calibrate autonomy by domain, and align behavior with children's context-sensitive expectations. HRI ’26, Edinburgh, Scotland, UK

WiReSens Toolkit: An Open-source Platform towards Accessible Wireless Tactile Sensing

2026-04-24T03:15:11Z

WiReSens Toolkit: An Open-source Platform towards Accessible Wireless Tactile Sensing Murphy, Devin; Zhu, Junyi; Gadre, Akshay; Torralba, Antonio; Liang, Paul Pu; Matusik, Wojciech; Luo, Yiyue Past research has widely explored the design and fabrication of resistive matrix-based tactile sensors for creating touch-sensitive devices. However, real-world deployment of resistive tactile sensing systems remains difficult for individuals with limited prior experience in embedded sensing due to challenges of portability, adaptivity, and efficiency. We introduce the WiReSens Toolkit, an accessible, open-source platform to bridge this gap. Central to our approach is adaptive hardware for interfacing with resistive sensors and a web-based GUI that streamlines access to advanced features for building scalable tactile sensing systems, including multi-device programming and wireless visualization across three communication protocols, autocalibration for adaptive sensitivity, and intermittent data transmission for low-power use. We validated the toolkit’s usability through a user study with 11 novice participants, who, on average, configured a tactile sensor with over 95% accuracy in under five minutes, calibrated sensors 10× faster than baseline methods, and showed improved sense-making of tactile data. TEI ’26, Chicago, IL, USA

PixBric: Precision Morphological Control of Pre-Stretched Fabrics Through Tessellated Primitive Geometries

2026-05-08T03:13:10Z

PixBric: Precision Morphological Control of Pre-Stretched Fabrics Through Tessellated Primitive Geometries Youn, Hye Jun; Choe, Jun Kyu; Ahn, SooYeon; Tania, Marcello; Sara, Serena Xin Wei; Ishii, Hiroshi 3D printing onto pre-stretched fabrics has emerged as a promising technique for fabricating self-shaping textiles. However, resulting morphing behaviors are often dictated by heuristics or arbitrarily selected parameters. We present PixBric, a pixel-based design framework that enables precise morphological control through tessellated primitive geometries printed onto biaxially stretched fabrics. Upon release, these units buckle into programmed 3D forms including undulations, curling, and bistable snapping. PixBric integrates parametric modeling, mechanical simulation, and empirical evaluation to map geometric parameters to deformation outcomes. We demonstrate applications spanning morphable typography, wearable rings, and reconfigurable surfaces. PixBric bridges digital simulation (tide) with the mechanical constraints of elastic substrates (tied), transforming complex material behaviors into accessible tools for learning, experimentation, and creative fabrication. TEI ’26, Chicago, IL, USA

Synthetic Network Data Generation for Analyst Training

2026-04-02T03:08:55Z

Synthetic Network Data Generation for Analyst Training Smith, Liam; Wright, Matthew Rapidly evolving cyber threats demand continuous, high-fidelity training for defense analysts. However, generating realistic network traffic datasets creates a significant barrier to entry, often requiring extensive virtualization infrastructure, specialized hardware, and knowledge in cyber range administration. This paper introduces a streamlined architecture, called Generative Packet Captures (GenCap), built upon the foundational capabilities of the FOSR benign traffic generator and the ID2T attack injector. By abstracting these complex tools behind an automated orchestration layer, it enables users to generate scenario-specific PCAP files on demand. This approach democratizes access to training data, allowing analysts to create rigorous network defense scenarios without the need for complex provisioning or systems engineering knowledge.

ChromoLCD: LCD-based Compact Reprogrammer for On-the-fly High-Resolution Images on Photochromic Surfaces

2026-04-24T03:15:30Z

ChromoLCD: LCD-based Compact Reprogrammer for On-the-fly High-Resolution Images on Photochromic Surfaces Zhu, Yunyi; Li, Qingyuan; Yan, Katherine; Guan, Emily; Luchianov, Alexandru; Hen, Eden; Mueller, Stefanie Color-changing materials, such as photochromic pigments, allow objects to have reprogrammable multicolor surface images. Existing systems that reprogram these images are based on projectors and LEDs, each with advantages and limitations in device portability and image resolution. In this paper, we present ChromoLCD, a surface reprogrammer that uses a liquid crystal display (LCD) to achieve a compact handheld device without sacrificing image resolution. ChromoLCD consists of an LCD panel with a custom backlight containing R,G,B and UV LEDs, forming high-resolution light patterns with the required wavelengths. The compact form factor of ChromoLCD enables on-the-fly reprogramming of everyday surfaces. Our technical evaluation shows that ChromoLCD achieves a resolution of 25 ppi, which is 8 times better than the prior work. We demonstrate ChromoLCD with three applications, including the stamping of reprogrammable AR markers on a kitchen counter, on-the-fly designs on personal accessories, and reference pictures on a whiteboard. TEI ’26, Chicago, IL, USA

Natural Convection Heat Transfer from an Inclined Cylinder

2026-04-02T03:07:39Z

Natural Convection Heat Transfer from an Inclined Cylinder Jaffer, Aubrey Based on Jaffer’s (2023) heat engine analysis of natural convection, this investigation mathematically derives a novel, comprehensive formula predicting the natural convective heat transfer from an inclined cylinder given its length, diameter, angle, and Rayleigh number and the fluid’s Prandtl number and thermal conductivity. The present formula was tested with 93 inclined cylinder measurements having length-to-diameter ratios between 1.48 and 104 in nine data-sets from three peer-reviewed studies, yielding (data-set) root-mean-squared relative error values between 1.9% and 4.7%.

Stability and Reactivity of Cyclopentane Nucleoside Analogs in 98% w/w Sulfuric Acid

2026-05-08T03:14:16Z

Stability and Reactivity of Cyclopentane Nucleoside Analogs in 98% w/w Sulfuric Acid Seager, Sara; Seager, Maxwell D.; Visser, Ton; Marinus, Nittert; Poizat, Mael; van Wiltenburg, Jim; Poelert, Martin; Petkowski, Janusz J. We synthesized seven carbocyclic nucleoside analogs featuring a cyclopentane ring in place of the (deoxy)ribose sugar, which serves as a linker in DNA/RNA nucleosides. We assessed the stability of cyclopentane nucleosides in 98% w/w sulfuric acid at room temperature via 1H and 13C NMR spectroscopy. We observe that adenine (A1, A4), guanine (G1) and thymine (T1) cyclopentane nucleoside analogs remain stable for at least two weeks at room temperature, with only minor (~4%) degradation in A1. In contrast, the cytosine analog (C1) rapidly degrades to release a soluble cytosine. Methyl-substituted adenine analogs mimicking polymer backbone attachments at positions prone to tertiary carbocation formation (A2, A3) prove unstable and release soluble adenine. Only the 3,3-dimethylcyclopentyl adenine analog (A4) exhibits sufficient stability. Our findings reveal that cyclopentane serves as a viable stable linker in concentrated sulfuric acid for select nucleic acid bases, provided that the backbone connections avoid tertiary carbons susceptible to carbocation-mediated cleavage. We thus identify one potential key structural feature for engineering examples of genetic-like polymers that could potentially persist in Venus’s concentrated sulfuric acid cloud environment.

Operation of a Modular 3D-Pixelated Liquid Argon Time-Projection Chamber in a Neutrino Beam

2026-05-08T03:12:43Z

Operation of a Modular 3D-Pixelated Liquid Argon Time-Projection Chamber in a Neutrino Beam Abbaslu, S.; Abud, A. Abed; Acciarri, R.; Accorsi, L. P.; Acero, M. A.; Adames, M. R.; Adamov, G.; Adamowski, M.; Adriano, C.; Akbar, F.; Alemanno, F.; Alex, N. S.; Allison, K.; Alrashed, M.; Alton, A.; Alvarez, R.; Alves, T.; Aman, A.; Amar, H.; Amedo, P. The 2x2 Demonstrator, a prototype for the Deep Underground Neutrino Experiment (DUNE) liquid argon (LAr) Near Detector, was exposed to the Neutrinos from the Main Injector (NuMI) neutrino beam at Fermi National Accelerator Laboratory (Fermilab). This detector is a prototype of a new modular design for a liquid argon time-projection chamber (LArTPC), comprising a two-by-two array of four modules, each further segmented into two optically isolated LArTPCs. The 2x2 Demonstrator features a number of pioneering technologies, including a low-profile resistive field shell to establish drift fields, native 3D ionization pixelated imaging, and a high-coverage dielectric light readout system. The 2.4-tonne active mass detector is flanked upstream and downstream by supplemental solid-scintillator tracking planes, repurposed from the MINERvA experiment, which track ionizing particles exiting the argon volume. The antineutrino beam data collected by the detector over a 4.5 day period in 2024 include over 30,000 neutrino interactions in the LAr active volume—the first neutrino interactions reported by a DUNE detector prototype. During its physics-quality run, the 2x2 Demonstrator operated at a nominal drift field of 500 V/cm and maintained good LAr purity, with a stable electron lifetime of approximately 1.25 ms. This paper describes the detector and supporting systems, summarizes the installation and commissioning, and presents the initial validation of collected NuMI beam and off-beam self-triggers. In addition, it highlights observed interactions in the detector volume, including candidate muon antineutrino events.

15.S21 Nuts and Bolts of Business Plans, January IAP 2014

2026-03-31T19:01:36Z

15.S21 Nuts and Bolts of Business Plans, January IAP 2014 Hadzima, Joseph The nuts and bolts of preparing a New Venture Plan and launching the venture will be explored in this twenty-fifth annual course offering. The course is open to members of the MIT Community and to others interested in entrepreneurship. It is particularly recommended for persons who are interested in starting or are involved in a new business or venture. Because some of the speakers will be judges of the MIT $100K Entrepreneurship Competition, persons who are planning to enter the Competition should find the course particularly useful. In the past approximately 50% of the class has been from the Engineering / Science / Architecture Schools and 50% from the Sloan School of Management. The course is offered during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month.

Technology-Policy Handbook for Trans-Atlantic Nuclear Maritime Corridors: Ports, Infrastructure, and Safety

2026-04-07T03:06:12Z

Technology-Policy Handbook for Trans-Atlantic Nuclear Maritime Corridors: Ports, Infrastructure, and Safety Valiaveedu, Anthony; Edmonds, Nat On September 18, 2025, the United States and the United Kingdom published a Memorandum of Understanding (MoU) on scientific and technological advancement. This new partnership focuses on understanding and deploying disruptive technologies in Artificial Intelligence, quantum, and civil nuclear energy. Less highlighted was a single sentence within the MoU outlining efforts to "explore opportunities" for establishing a "maritime shipping corridor" between the US and UK. So far, research on civilian nuclear ships has generally prioritized ship design and operation analysis. This paper will instead analyze port, regulatory, and infrastructural issues within this space and provide a path forward for technology policy solutions supporting systems safety.

Accelerated Full Waveform Inversion by Deep Compressed Learning

2026-04-24T03:14:50Z

Accelerated Full Waveform Inversion by Deep Compressed Learning Gelboim, Maayan; Adler, Amir; Araya-Polo, Mauricio We propose and test a method to reduce the dimensionality of Full Waveform Inversion (FWI) inputs as a computational cost mitigation approach. Given modern seismic acquisition systems, the data (as an input for FWI) required for an industrial-strength case is in the teraflop level of storage; therefore, solving complex subsurface cases or exploring multiple scenarios with FWI becomes prohibitive. The proposed method utilizes a deep neural network with a binarized sensing layer that learns by compressed learning seismic acquisition layouts from a large corpus of subsurface models. Thus, given a large seismic data set to invert, the trained network selects a smaller subset of the data, then by using representation learning, an autoencoder computes latent representations of the shot gathers, followed by K-means clustering of the latent representations to further select the most relevant shot gathers for FWI. This approach can effectively be seen as a hierarchical selection. The proposed approach consistently outperforms random data sampling, even when utilizing only 10% of the data for 2D FWI, and these results pave the way to accelerating FWI in large scale 3D inversion.

A Time-Symmetric and Retrocausal Resolution of the EPR Paradox

2026-04-23T03:15:39Z

A Time-Symmetric and Retrocausal Resolution of the EPR Paradox Heaney, Michael B. The Copenhagen Interpretation of quantum mechanics explains the Einstein, Podolsky, and Rosen (EPR) experiments with “spooky action at a distance” and nonlocal wavefunction collapse. A time-symmetric and retrocausal interpretation of quantum mechanics explains the same experiments without spooky action at a distance or nonlocal wavefunction collapse. An experiment that can distinguish between the Copenhagen and Time-Symmetric Interpretations is described.

Ionic Liquid Biospheres

2026-04-01T03:08:00Z

Ionic Liquid Biospheres Seager, Sara; Bains, William; Iakubivskyi, Iaroslav; Agrawal, Rachana; Jenkins, John; Shinde, Pranav; Petkowski, Janusz J. Liquid is a fundamental requirement for life as we understand it, but whether that liquid has to be water is not known. We propose the hypothesis that ionic liquids (ILs) and deep eutectic solvents (DES) constitute a class of non-aqueous planetary liquids capable of persisting on a wide range of bodies where stable liquid water cannot exist. This hypothesis is motivated by key physical properties of ILs and DES. Many exhibit vapor pressures orders of magnitude lower than that of water and remain liquid across exceptionally wide temperature ranges, from cryogenic to well above terrestrial temperatures. These properties permit stable liquids to exist where liquid water would rapidly evaporate or freeze and outside of bulk phases as persistent microscale reservoirs—such as thin films and pore-filling droplets. In other words, ILs and DES can persist in environments without requiring oceans, thick atmospheres, or narrowly regulated climate conditions. We further hypothesize that ILs and DES could act as solvents for non-Earth-like life, based on their polar nature and the demonstrated stability and functionality of proteins and other biomolecules in ionic liquids. More speculatively, our hypothesis extends to the idea that ILs and DES could enable prebiotic chemistry by providing long-lived, protective liquid environments for complex organic molecules on bodies such as comets and asteroids, where liquid water is absent. Additionally, based on the occurrence of DES-like mixtures as protective intracellular liquids in desiccation-tolerant plants, we propose that ILs and DES might be solvents that life elsewhere purposefully evolves. We review protein and other biomolecule studies in ILs and DES and outline planetary environments in which ILs and DES might occur by discussing available anions and cations. We present strategies to advance the IL/DES solvent hypothesis using laboratory studies, computational chemistry, planetary missions, analysis of existing spectroscopic datasets, and modeling of liquid microniches and chemical survival on small bodies.

Nomenclatural Recommendations for Genera Assigned to the Arcobacteraceae from the Subcommittee on the Taxonomy of Campylobacter and Related Bacteria

2026-05-08T03:12:50Z

Nomenclatural Recommendations for Genera Assigned to the Arcobacteraceae from the Subcommittee on the Taxonomy of Campylobacter and Related Bacteria On, Stephen L. W.; Figueras, Maria J.; Fox, James G.; Houf, Kurt; Mégraud, Francis; Miller, William G.; Stolz, John; Takai, Ken; Vandamme, Peter The taxonomy of the genus Arcobacter has been subject to substantive turmoil in recent years following a proposal to subdivide the genus into six genera. This proposal has been challenged by a number of multidisciplinary studies employing phenotypic, genomic, and phylogenetic analyses. Following several discussions among members of the International Committee on Systematics of Prokaryotes (ICSP) subcommittee on the taxonomy of Campylobacter and related bacteria, this group now unanimously recommends the use of the genus term Arcobacter to refer to these species.

Local Geographic Atrophy Growth Rates Not Influenced by Close Proximity to Non-Exudative Type 1 Macular Neovascularization

2026-04-23T03:15:27Z

Local Geographic Atrophy Growth Rates Not Influenced by Close Proximity to Non-Exudative Type 1 Macular Neovascularization Trivizki, Omer; Moult, Eric M; Wang, Liang; Iyer, Prashanth; Shi, Yingying; Gregori, Giovanni; Feuer, William; Fujimoto, James G; Rosenfeld, Philip J Purpose: The local growth rates of geographic atrophy (GA) adjacent to non-exudative type 1 macular neovascularization (MNV) were investigated to determine if MNV influenced GA growth. Methods: Eyes with GA and non-exudative type 1 MNV were followed for at least 1 year. Both GA and the MNV were imaged and measured using swept-source optical coherence tomography angiography (SS-OCTA) scans. Pearson correlations were computed between local growth rates of GA, which were estimated using a biophysical GA growth model, and local distances-to-MNV. Corresponding P values for the null hypothesis of no Pearson correlation were computed using a Monte Carlo approach that adjusts for spatial autocorrelations. Results: Nine eyes were included in this study. There were positive correlations (Pearson's r > 0) between distance-to-MNV and local GA growth in eight (89%) of the eyes; however, in all but one eye (11%), correlations were relatively weak and statistically nonsignificant after Bonferroni correction (corrected P > 0.05). Conclusions: SS-OCTA imaging combined with GA growth modeling and spatial statistical analysis enabled quantitative assessment of correlations between local GA growth rates and local distances-to-MNV. Our results are not consistent with non-exudative type 1 MNV having a strong inhibitory effect on local GA growth rates.

Optical trapping of SrOH molecules for dark matter and T-violation searches

2026-04-01T03:08:08Z

Optical trapping of SrOH molecules for dark matter and T-violation searches Sawaoka, Hiromitsu; Nasir, Abdullah; Lunstad, Annika; Li, Mingda; Mango, Jack; Lasner, Zack D; Doyle, John M We report an optical dipole trap of strontium monohydroxide (SrOH) with 1400(300) trapped molecules. Through optical pumping, we access vibrational states that are proposed for improved probes of the electron’s electric dipole moment (eEDM) and ultralight dark matter (UDM). For each of these states, the lifetime of trapped molecules is measured and found to be consistent with spontaneous radiative decay and blackbody excitation limits, making this platform viable for these eEDM and UDM searches.

Drosophila Fog/Cta and T48 pathways have overlapping and distinct contributions to mesoderm invagination

2026-05-08T03:12:58Z

Drosophila Fog/Cta and T48 pathways have overlapping and distinct contributions to mesoderm invagination Horo, Uzuki; Clarke, D Nathaniel; Martin, Adam C The regulation of the cytoskeleton by multiple signaling pathways, sometimes in parallel, is a common principle of morphogenesis. A classic example of regulation by parallel pathways is Drosophila gastrulation, where the inputs from the Folded gastrulation (Fog)/Concertina (Cta) and the T48 pathways induce apical constriction and mesoderm invagination. Whether there are distinct roles for these separate pathways in regulating the complex spatial and temporal patterns of cytoskeletal activity that accompany early embryo development is still poorly understood. We investigated the roles of the Fog/Cta and T48 pathways and found that, by themselves, the Cta and T48 pathways both promote timely mesoderm invagination and apical myosin II accumulation, with Cta being required for timely cell shape change ahead of mitotic cell division. We also identified distinct functions of T48 and Cta in regulating cellularization and the uniformity of the apical myosin II network, respectively. Our results demonstrate that both redundant and distinct functions for the Fog/Cta and T48 pathways exist.

Tools for live-cell imaging of cytoskeletal and nuclear behavior in the unconventional yeast, Aureobasidium pullulans

2026-05-08T03:12:55Z

Tools for live-cell imaging of cytoskeletal and nuclear behavior in the unconventional yeast, Aureobasidium pullulans Petrucco, Claudia A; Crocker, Alex W; D’Alessandro, Alec; Medina, Edgar M; Gorman, Olivia; McNeill, Jessica; Gladfelter, Amy S; Lew, Daniel J Aureobasidium pullulans is a ubiquitous fungus with a wide variety of morphologies and growth modes including “typical” single-budding yeast, and interestingly, larger multinucleate yeast than can make multiple buds in a single cell cycle. The study of A. pullulans promises to uncover novel cell biology, but currently tools are lacking to achieve this goal. Here, we describe initial components of a cell biology toolkit for A. pullulans, which is used to express and image fluorescent probes for nuclei as well as components of the cytoskeleton. These tools allowed live-cell imaging of the multinucleate and multibudding cycles, revealing highly synchronous mitoses in multinucleate yeast that occur in a semiopen manner with an intact but permeable nuclear envelope. These findings open the door to using this ubiquitous polyextremotolerant fungus as a model for evolutionary cell biology.

Tracking the Spatio‐Temporal Evolution of Foreshocks Preceding the Mw 6.1 2009 L’Aquila Earthquake

2026-03-28T03:07:07Z

Tracking the Spatio‐Temporal Evolution of Foreshocks Preceding the Mw 6.1 2009 L’Aquila Earthquake Cabrera, Leoncio; Poli, Piero; Frank, William B How faulting processes lead to a large earthquake is a fundamental question in seismology. To better constrain this pre‐seismic stage, we create a dense seismic catalog via template matching to analyze the precursory phase of the Mw 6.1 L’Aquila earthquake that occurred in central Italy in 2009. We estimate several physical parameters in time, such as the coefficient of variation, the seismic moment release, the effective stress drop, and analyze spatio‐temporal patterns to study the evolution of the sequence and the earthquake interactions. We observe that the precursory phase experiences multiple accelerations of the seismicity rate that we divide into two main sequences with different signatures and features: the first part exhibits weak earthquake interactions, quasi‐continuous moment release, slow spatial migration patterns, and a lower effective stress drop, pointing to aseismic processes. The second sequence exhibits strong temporal clustering, fast seismicity expansion, and a larger effective stress drop typical of a stress transfer process. We interpret the differences in seismicity behaviors between the two sequences as distinct physical mechanisms that are controlled by different physical properties of the fault system. We conclude that the L’Aquila earthquake is preceded by a complex preparation, made up of different physical processes over different time scales on faults with different physical properties.

A High‐Resolution Atlas of the Eastern Tropical Pacific Oxygen Deficient Zones

2026-03-28T03:07:13Z

A High‐Resolution Atlas of the Eastern Tropical Pacific Oxygen Deficient Zones Kwiecinski, Jarek V; Babbin, Andrew R Oxygen deficient zones (ODZs) are important biogeochemical provinces of the global oceans wherein standing dissolved oxygen concentrations decrease to nanomolar levels. Despite their confinement, these regions are disproportionally important to the ocean's role in modulating Earth's climate through the interactions between the marine nitrogen cycle and that of carbon. Moreover, the spatial domain of low oxygen regions of the ocean is predicted to change as a consequence of ocean warming, increased stratification, and changes in circulation and productivity. However, the expanse of the modern ODZs is poorly resolved due to a dearth of direct sampling compounded with errors that arise in the processing and gridding of the sparse measurements that do exist. Here, we take a novel approach to map the horizontal and vertical extent of the two major ODZs of the eastern tropical Pacific via analysis of meter-scale resolution electrode sensors from both ship casts and Argo profiles, rather than from discretized bottle measurements. The resulting three-dimensional data product is based on a compendium of nearly 15 million measurements taken across three decades and provides the precise locations of low oxygen water, elucidating the ODZs' three-dimensional structures. It can be utilized by researchers to validate models, plan cruise occupations, and as a comparison for future change. Calculations made with this high-resolution atlas also provide the volumes, layers of maximal areal extent, and other descriptive statistics for both Pacific ODZs. Finally, the atlas reveals fine-scale features of oxygenated water mass intrusions and regional differences across these anoxic zones.

Orbital‐ and Millennial‐Scale Variability in Northwest African Dust Emissions Over the Past 67,000 years

2026-03-28T03:07:12Z

Orbital‐ and Millennial‐Scale Variability in Northwest African Dust Emissions Over the Past 67,000 years Kinsley, Christopher W; Bradtmiller, Louisa I; McGee, David; Galgay, Michael; Stuut, Jan‐Berend; Tjallingii, Rik; Winckler, Gisela; deMenocal, Peter B Reconstructions of aeolian dust flux to West African margin sediments can be used to explore changing atmospheric circulation and hydroclimate over North Africa on millennial to orbital timescales. Here, we extend West African margin dust flux records back to 37 ka in a transect of sites from 19° to 27°N, and back to 67 ka at Ocean Drilling Program (ODP) Hole 658C, in order to explore the interplay of orbital and high-latitude forcings on North African climate and make quantitative estimates of dust flux during the core of the Last Glacial Maximum (LGM). The ODP 658C record shows a Green Sahara interval from 60 to 50 ka during a time of high Northern Hemisphere summer insolation, with dust fluxes similar to levels during the early Holocene African Humid Period, and an abrupt peak in flux during Heinrich event 5a (H5a). Dust fluxes increase from 50 to 35 ka while the high-latitude Northern Hemisphere cools, with peaks in dust flux associated with North Atlantic cool events. From 35 ka through the LGM dust deposition decreases in all cores, and little response is observed to low-latitude insolation changes. Dust fluxes at sites from 21° to 27°N were near late Holocene levels during the LGM time slice, suggesting a more muted LGM response than observed from mid-latitude dust sources. Records along the northwest African margin suggest important differences in wind responses during different stadials, with maximum dust flux anomalies centered south of 20°N during H1 and north of 20°N during the Younger Dryas.

Methyl Chloroform Continues to Constrain the Hydroxyl (OH) Variability in the Troposphere

2026-03-28T03:07:10Z

Methyl Chloroform Continues to Constrain the Hydroxyl (OH) Variability in the Troposphere Patra, PK; Krol, MC; Prinn, RG; Takigawa, M; Mühle, J; Montzka, SA; Lal, S; Yamashita, Y; Naus, S; Chandra, N; Weiss, RF; Krummel, PB; Fraser, PJ; O'Doherty, S; Elkins, JW Trends and variability in tropospheric hydroxyl (OH) radicals influence budgets of many greenhouse gases, air pollutant species, and ozone depleting substances. Estimations of tropospheric OH trends and variability based on budget analysis of methyl chloroform (CH3CCl3) and process-based chemistry transport models often produce conflicting results. Here we use a previously tested transport model to simulate atmospheric CH3CCl3 for the period 1985–2018. Based on mismatches between model output and observations, we derive consistent anomalies in the inverse lifetime of CH3CCl3 (KG) using measurements from two independent observational networks (National Oceanic and Atmospheric Administration and Advanced Global Atmospheric Gases Experiment). Our method allows a separation between “physical” (transport, temperature) and “chemical” (i.e., abundance) influences on OH + CH3CCl3 reaction rate in the atmosphere. Small increases in KG due to “physical” influences are mostly driven by increases in the temperature-dependent reaction between OH and CH3CCl3 and resulted in a smoothly varying increase of 0.80% decade−1. Chemical effects on KG, linked to global changes in OH sources and sinks, show larger year-to-year variations (∼2%–3%), and have a negative correlation with the El Niño Southern Oscillation. A significant positive trend in KG can be derived after 2001, but it persists only through 2015 and only if we assume that CH3CCl3 emissions decayed more slowly over time than our best estimate suggests. If global CH3CCl3 emissions dropped below 3 Gg year−1 after 2015, recent CH3CCl3 measurements indicate that the 2015–2018 loss rate of CH3CCl3 due to reaction with OH is comparable to its value 2 decades ago.

InterCarb: A Community Effort to Improve Interlaboratory Standardization of the Carbonate Clumped Isotope Thermometer Using Carbonate Standards

2026-03-28T03:07:16Z

InterCarb: A Community Effort to Improve Interlaboratory Standardization of the Carbonate Clumped Isotope Thermometer Using Carbonate Standards Increased use and improved methodology of carbonate clumped isotope thermometry has greatly enhanced our ability to interrogate a suite of Earth-system processes. However, interlaboratory discrepancies in quantifying carbonate clumped isotope (Δ47) measurements persist, and their specific sources remain unclear. To address interlaboratory differences, we first provide consensus values from the clumped isotope community for four carbonate standards relative to heated and equilibrated gases with 1,819 individual analyses from 10 laboratories. Then we analyzed the four carbonate standards along with three additional standards, spanning a broad range of δ47 and Δ47 values, for a total of 5,329 analyses on 25 individual mass spectrometers from 22 different laboratories. Treating three of the materials as known standards and the other four as unknowns, we find that the use of carbonate reference materials is a robust method for standardization that yields interlaboratory discrepancies entirely consistent with intralaboratory analytical uncertainties. Carbonate reference materials, along with measurement and data processing practices described herein, provide the carbonate clumped isotope community with a robust approach to achieve interlaboratory agreement as we continue to use and improve this powerful geochemical tool. We propose that carbonate clumped isotope data normalized to the carbonate reference materials described in this publication should be reported as Δ47 (I-CDES) values for Intercarb-Carbon Dioxide Equilibrium Scale.

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.

Age Set versus Kin: Culture and Financial Ties in east Africa

2026-03-28T03:07:15Z

Age Set versus Kin: Culture and Financial Ties in east Africa Moscona, Jacob; Seck, Awa Ambra We study how social organization shapes patterns of economic interaction and the effects of national policy, focusing on the distinction between age-based and kin-based groups in sub-Saharan Africa. Motivated by ethnographic accounts suggesting that this distinction affects redistribution, we analyze a cash transfer program in Kenya and find that in age-based societies there are consumption spillovers within the age cohort, but not the extended family, while in kin-based societies we find the opposite. Next, we document that social structure shapes the impact of policy by showing that Uganda’s pension program had positive effects on child nutrition only in kin-based societies. (JEL H23, I12, I38, J13, O15, Z13).

Social Media and Mental Health

2026-03-27T07:23:38Z

Social Media and Mental Health Braghieri, Luca; Levy, Ro’ee; Makarin, Alexey We provide quasi-experimental estimates of the impact of social media on mental health by leveraging a unique natural experiment: the staggered introduction of Facebook across US colleges. Our analysis couples data on student mental health around the years of Facebook’s expansion with a generalized difference-in-differences empirical strategy. We find that the rollout of Facebook at a college had a negative impact on student mental health. It also increased the likelihood with which students reported experiencing impairments to academic performance due to poor mental health. Additional evidence on mechanisms suggests the results are due to Facebook fostering unfavorable social comparisons. (JEL D91, I12, I23, L82)

Is Journalistic Truth Dead? Measuring How Informed Voters Are about Political News

2026-03-27T07:23:39Z

Is Journalistic Truth Dead? Measuring How Informed Voters Are about Political News Angelucci, Charles; Prat, Andrea To investigate general patterns in news information in the United States, we combine a protocol for identifying major political news stories, 11 monthly surveys with 15,000 participants, and a model of news discernment. When confronted with a true and a fake news story, 47 percent of subjects confidently choose the true story, 3 percent confidently choose the fake story, and the remaining half are uncertain. Socioeconomic differences are associated with large variations in the probability of selecting the true news story. Partisan congruence between an individual and a news story matters, but its impact is up to an order of magnitude smaller. (JEL D72, D83, L82).

How Merchant Towns Shaped Parliaments: From the Norman Conquest of England to the Great Reform Act

2026-03-27T07:23:34Z

How Merchant Towns Shaped Parliaments: From the Norman Conquest of England to the Great Reform Act Angelucci, Charles; Meraglia, Simone; Voigtländer, Nico We study the emergence of urban self-governance in the late medieval period. We focus on England after the Norman Conquest of 1066, building a novel comprehensive dataset of 554 medieval towns. During the Commercial Revolution (twelfth to thirteenth centuries), many merchant towns obtained Farm Grants: the right of self-governed tax collection and law enforcement. Self-governance, in turn, was a stepping stone for parliamentary representation: Farm Grant towns were much more likely to be summoned directly to the medieval English Parliament than otherwise similar towns. We also show that self-governed towns strengthened the role of Parliament and shaped national institutions over the subsequent centuries. (JEL D02, D72, D73, K11, K34, N43, N93).

Shock response of periodic interpenetrating phase composites

2026-03-27T07:23:37Z

Shock response of periodic interpenetrating phase composites Taylor, Spencer V; Gonzales, Manny; Cordero, Zachary C In this work, we examine the macroscale and fine-scale shock responses of interpenetrating phase composites comprising a body-centered cubic steel lattice embedded in an aluminum matrix. Through plate impact simulations, we find that the complex mesoscale geometry reduces shock velocity relative to monolithic constituents, slowing and spreading the shock front via reflection and redirection. The periodicity of the mesoscale composite geometry is also reflected by quasi-steady-wave behavior. On the fine-scale, we can predict several aspects of the oscillatory pressure and longitudinal velocity responses by tracking internal wave reflections. We also observe that the post-shock maximum temperature increases with structural openness and temperature hotspots form at interfaces parallel to the shock direction. The findings in this work provide novel structure–property linkages in the dynamic response of architectured interpenetrating phase composites.

ℏ2 expansion of the transmission probability through a barrier

2026-03-27T07:23:42Z

ℏ2 expansion of the transmission probability through a barrier Pollak, Eli; Cao, Jianshu Ninety years ago, Wigner derived the leading order expansion term in ℏ2 for the tunneling rate through a symmetric barrier. His derivation included two contributions: one came from the parabolic barrier, but a second term involved the fourth-order derivative of the potential at the barrier top. He left us with a challenge, which is answered in this paper, to derive the same but for an asymmetric barrier. A crucial element of the derivation is obtaining the ℏ2 expansion term for the projection operator, which appears in the flux-side expression for the rate. It is also reassuring that an analytical calculation of semiclassical transition state theory (TST) reproduces the anharmonic corrections to the leading order of ℏ2. The efficacy of the resulting expression is demonstrated for an Eckart barrier, leading to the conclusion that especially when considering heavy atom tunneling, one should use the expansion derived in this paper, rather than the parabolic barrier approximation. The rate expression derived here reveals how the classical TST limit is approached as a function of ℏ and, thus, provides critical insights to understand the validity of popular approximate theories, such as the classical Wigner, centroid molecular dynamics, and ring polymer molecular dynamics methods.

Nonlinear exceptional-point lasing with ab initio Maxwell– Bloch theory

2026-03-27T07:23:22Z

Nonlinear exceptional-point lasing with ab initio Maxwell– Bloch theory Benzaouia, Mohammed; Stone, AD; Johnson, Steven G We present a general analysis for finding and characterizing nonlinear exceptional point (EP) lasers above threshold using steady-state ab initio Maxwell–Bloch equations. For a system of coupled slabs, we show that a nonlinear EP is obtained for a given ratio between the external pumps in each resonator and that it is associated with a kink in the output power and lasing frequency, confirming coupled-mode theory predictions. Through numerical linear stability analysis, we confirm that the EP laser can be stable for a large enough inversion relaxation rate. We further show that the EP laser can be characterized by scattering a weak signal off the lasing cavity so that the scattering frequency spectrum exhibits a quartic divergence. Our approach can be applied to arbitrary scatterers with multi-level gain media.

The impact of semiconductor surface states on vacuum field emission

2026-03-27T07:23:40Z

The impact of semiconductor surface states on vacuum field emission Kim, Taeyoung; Joishi, Chandan; Shih, Pao-Chuan; Palacios, Tomás; Rajan, Siddharth This work presents a theoretical analysis of the impact of surface states on vacuum field emission currents in semiconductors. In wide and ultra-wide bandgap semiconductors such as GaN and AlGaN, low electron affinity has been proposed as a benefit for field emission into vacuum. However, in these materials, the surface Fermi level at the surface is pinned well below the conduction band, and the surface depletion barriers due to the surface Fermi level pinning can be comparable to or higher than the electron affinity. Therefore, analysis of field emission requires consideration of not only the vacuum potential barrier set by electron affinity, but also the depletion region near the semiconductor surface. In this paper, we develop analytical models to predict field emission currents with careful consideration of the impact of surface states on the energy band alignment. The results are used to provide guidelines for design of field emitters that could benefit from the low electron affinity of semiconductors such as Al(Ga)N.

A robust computational approach to Lees–Dorodnitsyn laminar hypersonic boundary layers with temperature-dependent properties

2026-03-27T07:23:35Z

A robust computational approach to Lees–Dorodnitsyn laminar hypersonic boundary layers with temperature-dependent properties Onyeador, CN; Hodge, A; Harris, W The Lees–Dorodnitsyn (L–D) boundary layer equations for two-dimensional, non-reactive, laminar, hypersonic, boundary layer flows, and an assumption of an isentropic external flow are examined. They are applied to various geometries for which the Thin Shear Layer assumptions are valid. This study expands on previous work to develop a novel and robust methodology for computing high-temperature hypersonic flows using a uniform and compact computational stencil implemented through a computational tool, the Bulk-property Boundary Layer (BuBL) solver. In particular, we explore the impact of treating high-temperature effects present in hypersonic flows, namely, treating air as a thermally perfect gas with temperature-variable properties. The ability to solve these flows computationally using second-order finite difference methods is evaluated as are various models for viscosity, Prandtl number, and specific heat. The methodology for solving the external flow properties in the transformed L–D computational domain is also discussed. It is shown that the L–D equations evaluated using the “box” computational stencil are an effective means for evaluating laminar hypersonic boundary layer flows. Solutions for displacement and momentum thicknesses, skin friction, and Stanton number variations are obtained as a function of Prandtl number, specific heat model, and Mach number. Verification and validation measures are performed for the code. Excellent agreement is found in comparisons between BuBL and other computational fluid dynamics and experimental results, thus demonstrating the utility of the proposed methodology.

Plasmas for in situ resource utilization on Mars: Fuels, life support, and agriculture

2026-03-27T07:23:31Z

Plasmas for in situ resource utilization on Mars: Fuels, life support, and agriculture Guerra, V; Silva, T; Pinhão, N; Guaitella, O; Guerra-Garcia, C; Peeters, FJJ; Tsampas, MN; van de Sanden, MCM This work discusses the potential of combining non-thermal plasmas and conducting membranes for in situ resource utilization (ISRU) on Mars. By converting different molecules directly from the Martian atmosphere, plasmas can create the necessary feed-stock and base chemicals for processing fuels, breathing oxygen, building materials, and fertilizers. Different plasma sources operate according to different principles and are associated with distinct dominant physicochemical mechanisms. This diversity allows exploring different energy transfer pathways leading to CO2 dissociation, including direct electron-impact processes, plasma chemistry mediated by vibrationally and electronically excited states, and thermally driven dissociation. The coupling of plasmas with membranes is still a technology under development, but a synergistic effect between plasma decomposition and oxygen permeation across conducting membranes is anticipated. The emerging technology is versatile, scalable, and has the potential to deliver high rates of production of molecules per kilogram of instrumentation sent to space. Therefore, it will likely play a very relevant role in future ISRU strategies.

Bridging the gap between H- and J-aggregates: Classification and supramolecular tunability for excitonic band structures in two-dimensional molecular aggregates

2026-03-27T07:23:33Z

Bridging the gap between H- and J-aggregates: Classification and supramolecular tunability for excitonic band structures in two-dimensional molecular aggregates Deshmukh, Arundhati P; Geue, Niklas; Bradbury, Nadine C; Atallah, Timothy L; Chuang, Chern; Pengshung, Monica; Cao, Jianshu; Sletten, Ellen M; Neuhauser, Daniel; Caram, Justin R Molecular aggregates with long-range excitonic couplings have drastically different photophysical properties compared to their monomer counterparts. From Kasha's model for one-dimensional systems, positive or negative excitonic couplings lead to blue or red-shifted optical spectra with respect to the monomers, labeled H-and J-aggregates, respectively. The overall excitonic couplings in higher dimensional systems are much more complicated and cannot be simply classified from their spectral shifts alone. Here, we provide a unified classification for extended 2D aggregates using temperature dependent peak shifts, thermal broadening, and quantum yields. We discuss the examples of six 2D aggregates with J-like absorption spectra but quite drastic changes in quantum yields and superradiance. We find the origin of the differences is, in fact, a different excitonic band structure where the bright state is lower energy than the monomer but still away from the band edge. We call this an “I-aggregate.” Our results provide a description of the complex excitonic behaviors that cannot be explained solely on Kasha's model. Furthermore, such properties can be tuned with the packing geometries within the aggregates providing supramolecular pathways for controlling them. This will allow for precise optimizations of aggregate properties in their applications across the areas of optoelectronics, photonics, excitonic energy transfer, and shortwave infrared technologies.

Design and construction of a compact, high-repetition-rate ultrafast electron diffraction instrument

2026-03-27T07:23:29Z

Design and construction of a compact, high-repetition-rate ultrafast electron diffraction instrument Freelon, Byron; Rohwer, Timm; Zong, Alfred; Kogar, Anshul; Zhou, Hengyun; Wong, Liang Jie; Ergeçen, Emre; Gedik, Nuh We present the design and performance of a compact ultrafast electron diffraction instrument. The diffractometer provides a means of examining time-resolved ultrafast dynamical properties of solids. The system’s utilization is discussed in terms of instrument parameters and diffraction data from selected condensed matter samples. The difractometer’s performance is highlighted in terms of detection sensitivity, instrumental temporal resolution, and the electron beam transverse coherence length. Following specific details of the construction, we present a practical discussion of parameters such as repetition rate and provide advice on general construction approaches for laboratory-based, keV ultrafast electron diffractometers. In addition, design guidance for constructing a compact electron gun source that is well-suited for studying diffraction from hard condensed matter is given. A unique data acquisition scheme, utilizing high laser repetition rates, is presented.

"POOR ECONOMICS" Creating Markets: BOU needs POOR SOFTWARE (for BOU BAZAR transactions)

2026-05-03T19:06:40Z

"POOR ECONOMICS" Creating Markets: BOU needs POOR SOFTWARE (for BOU BAZAR transactions) Datta, Shoumen Hiding in plain sight is the immense demand for "poor software" from billions of potential impecunious users in nations where "poor economics" is not just the weather of the day but the climate of the century. Unbeknownst to many in the affluent world, billions of users (BOU) who are perhaps capable of paying only pennies (PAPPU - pay a penny per use) are in need of "poor" software for daily (hourly) transactions related to FEWSH (food, energy, water, sanitation, healthcare) marketplaces (BAZAR). Even the ecclesiastical apparat will be made inconsequential for any intervention if the potential for escalation between the "haves" (1%) and the "have-nots" (99%) may reach eschatological dimensions. There may be a potential bridge over the chasm which separates billions from the billionaires. BOU could be that bridge. Billions of users (BOU) are creating demand for mobile software systems to transact their daily needs. BOU is driven by demographics (~6.5 billion poor people) in Asia and Africa. BOU is characteristic of less affluent nations and it is not usually a part of our discussion, yet this unmet demand is as real as their (and our) daily need for FEWSH (food, energy, water, sanitation, health and wellness). Software innovation in material management systems (SIMMS) may be able to capture transactions in the FEWSH verticals and monetize the system through PAPPU. This article explores ideas with respect to SIMMS helping BOU based on principles of economics and the practice of ethical profitability (however oxymoronic it may sound, ethical profitability may be that metaphorical fulcrum between need and greed).

EXPANSE: A time-of-flight EXPanded Angle Neutron Spin Echo spectrometer at the Second Target Station of the Spallation Neutron Source

2026-03-26T03:07:32Z

EXPANSE: A time-of-flight EXPanded Angle Neutron Spin Echo spectrometer at the Second Target Station of the Spallation Neutron Source Do, Changwoo; Ashkar, Rana; Boone, Cristina; Chen, Wei-Ren; Ehlers, Georg; Falus, Peter; Faraone, Antonio; Gardner, Jason S; Graves, Van; Huegle, Thomas; Katsumata, Reika; Kent, Darian; Lin, Jiao YY; McHargue, Bill; Olsen, Bradley; Wang, Yangyang; Wilson, Danielle; Z, Y EXPANSE, an EXPanded Angle Neutron Spin Echo instrument, has been proposed and selected as one of the first suite of instruments to be built at the Second Target Station of the Spallation Neutron Source at the Oak Ridge National Laboratory. This instrument is designed to address scientific problems that involve high-energy resolution (neV–μeV) of dynamic processes in a wide range of materials. The wide-angle detector banks of EXPANSE provide coverage of nearly two orders of magnitude in scattering wavenumbers, and the wide wavelength band affords approximately four orders of magnitude in Fourier times. This instrument will offer unique capabilities that are not available in the currently existing neutron scattering instruments in the United States. Specifically, EXPANSE will enable direct measurements of slow dynamics in the time domain over wide Q-ranges simultaneously and will also enable time-resolved spectroscopic studies. The instrument is expected to contribute to a diverse range of science areas, including soft matter, polymers, biological materials, liquids and glasses, energy materials, unconventional magnets, and quantum materials.

Thermal diffusivity in ion-irradiated single-crystal iron, chromium, vanadium, and tungsten measured using transient grating spectroscopy

2026-04-24T03:15:31Z

Thermal diffusivity in ion-irradiated single-crystal iron, chromium, vanadium, and tungsten measured using transient grating spectroscopy Wylie, APC; Woller, KB; Al Dajani, SAA; Dacus, BR; Pickering, EJ; Preuss, M; Short, MP The speed-up of radiation science development with the advent of ion-irradiation experiments has, until recently, been omitted in the post-irradiation examination technique. This paper reports the results of transient grating spectroscopy—a rapid, non-destructive, in situ photothermal surface technique—of ion-irradiated single-crystals of iron, chromium, vanadium, and tungsten at room temperature. Thermal diffusivity was used to track damage development throughout irradiation, with 5 MeV self-ion irradiated iron, chromium, and vanadium showing little to no change up to damages of the order of 1 dpa. 5 MeV Si3+-ion irradiated tungsten exhibits a reduction of thermal diffusivity from 0.78(7) to 0.29(2) cm2 s−1 with logarithmically increasing dose over a similar damage range. A comparison to literature of transient grating spectroscopy thermal diffusivity values past and present shows good agreement; radiation-induced change can be clearly distinguished from differences between mono- and poly-crystalline tungsten.

Thickness and temperature dependence of the atomic-scale structure of SrRuO3 thin films

2026-05-08T03:12:28Z

Thickness and temperature dependence of the atomic-scale structure of SrRuO3 thin films Zhang, Xuanyi; Penn, Aubrey N; Wysocki, Lena; Zhang, Zhan; van Loosdrecht, Paul HM; Kornblum, Lior; LeBeau, James M; Lindfors-Vrejoiu, Ionela; Kumah, Divine P The temperature-dependent layer-resolved structure of 3 to 44 unit cell thick SrRuO3 (SRO) films grown on Nb-doped SrTiO3 substrates is investigated using a combination of high-resolution synchrotron x-ray diffraction and high-resolution electron microscopy to understand the role that structural distortions play in suppressing ferromagnetism in ultra-thin SRO films. The oxygen octahedral tilts and rotations and Sr displacements characteristic of the bulk orthorhombic phase are found to be strongly dependent on temperature, the film thickness, and the distance away from the film–substrate interface. For thicknesses, t, above the critical thickness for ferromagnetism (t > 3 uc), the orthorhombic distortions decrease with increasing temperature above TC. Below TC, the structure of the films remains constant due to the magneto-structural coupling observed in bulk SRO. The orthorhombic distortions are found to be suppressed in the 2–3 interfacial layers due to structural coupling with the SrTiO3 substrate and correlate with the critical thickness for ferromagnetism in uncapped SRO films.

Hyperbolic phonon polaritons with positive and negative phase velocities in suspended α -MoO3

2026-04-24T03:16:16Z

Hyperbolic phonon polaritons with positive and negative phase velocities in suspended α -MoO3 Shen, Jialiang; Zheng, Zhiren; Dinh, Thao; Wang, Chuanyu; Chen, Mingyuan; Chen, Pengyu; Ma, Qiong; Jarillo-Herrero, Pablo; Kang, Lixing; Dai, Siyuan Sample suspension is a valuable method to improve the mechanical, thermal, electronic, and optical properties of low-dimensional materials. In terms of confined light-matter waves—the polaritons, sample suspension can elongate the wavelength of polaritons with a positive phase velocity. Previous work demonstrates a wavelength elongation of ∼10% for hyperbolic phonon polaritons (HPPs) in uniaxial crystals of hexagonal boron nitride (hBN). In this work, we report the alteration of HPPs in biaxial α-phase molybdenum trioxide (α-MoO3) by sample suspension. Our combined infrared nano-imaging experiments and electromagnetic theory reveal a wavelength elongation > 60% and a propagation length increase > 140%, due to the simultaneous wavelength elongation and dissipation elimination in the suspended specimen. We have also examined HPPs in α-MoO3 with a negative phase velocity. The sample suspension shortens the HPP wavelength and simultaneously reduces the dissipation due to the unique permittivity tensor. The HPPs with improved figures of merits in the suspended specimen may be developed for nano-polaritonic circuits, biochemical sensing, emission engineering, and energy transfer.

Hole opening from growing interfacial voids: A possible mechanism of solid state dewetting

2026-05-08T03:12:49Z

Hole opening from growing interfacial voids: A possible mechanism of solid state dewetting Curiotto, Stefano; Chame, Anna; Müller, Pierre; Thompson, Carl V; Pierre-Louis, Olivier Vacancies at interfaces between a film and a substrate can affect material properties and could play a role in solid state dewetting. Using kinetic Monte Carlo simulations, we show that interfacial mono-vacancies diffuse and coalesce to form vacancy clusters and voids. The film/substrate excess energy ES, which is related to the apparent contact angle, controls the mechanisms of coalescence. Depending on ES, voids emerging at the film surface form a hole that can be filled by the film or can lead to dewetting of the film from the substrate.

2D materials-enabled optical modulators: From visible to terahertz spectral range

2026-04-24T03:15:13Z

2D materials-enabled optical modulators: From visible to terahertz spectral range Gan, Xuetao; Englund, Dirk; Van Thourhout, Dries; Zhao, Jianlin Two-dimensional (2D) materials with layered structures have a variety of exceptional electronic and optical attributes for potentially developing basic functions of light wave technology from light-emitting to -modulating and -sensing. Here, we present state-of-the-art 2D materials-enabled optical intensity modulators according to their operation spectral ranges, which are mainly determined by the optical bandgaps of the 2D materials. Leveraging rich electronic structures from different 2D materials and the governed unique light–matter interactions, the working mechanisms and device architectures for the enabled modulators at specific wavelength ranges are discussed. For instance, the tunable excitonic effect in monolayer transition metal dichalcogenides allows the modulation of visible light. Electro-absorptive and electro-refractive graphene modulators could be operated in the telecom-band relying on their linear dispersion of the massless Dirac fermions. The bendable electronic band edge of the narrow bandgap in few-layer black phosphorus promises the modulation of mid-infrared light via the quantum-confined Franz–Keldysh or Burstein–Moss shift effect. Electrically and magnetically tunable optical conductivity in graphene also supports the realizations of terahertz modulators. While these modulators were demonstrated as proof of concept devices, part of them have great potential for future realistic applications, as discussed with their wavelength coverage, modulation depth, insertion loss, dynamic response speed, etc. Specifically, benefiting from the well-developed technologies of photonic chips and optical fibers in telecom and datacom, the 2D materials-based modulators integrated on these photonic structures are expected to find applications in fiber and chip optical communications. The free-space mid-infrared and terahertz modulators based on 2D materials can expect application in chemical bond spectroscopy, free-space communications, and environment/health sensing.

Current-induced switching of a ferromagnetic Weyl semimetal Co2MnGa

2026-03-26T03:07:18Z

Current-induced switching of a ferromagnetic Weyl semimetal Co2MnGa Han, Jiahao; McGoldrick, Brooke C; Chou, Chung-Tao; Safi, Taqiyyah S; Hou, Justin T; Liu, Luqiao The introduction of magnetic moments to topological materials provides rich opportunities for studying the interplay among magnetism, electron correlation, and topological orders, which can give rise to exotic magnetoelectric effects and allow one to manipulate the topological band structure via spintronic approaches. Here, we report current-induced switching in a thin film of ferromagnetic Weyl semimetal Co2MnGa with perpendicular magnetic anisotropy, via the spin–orbit torque from a neighboring heavy metal Pt. The reversal of the large anomalous Hall signal indicates an effective electrical control of the Berry curvatures associated with the Weyl nodes in the topological band structure. The efficiency of the spin–orbit torque switching is calibrated to be comparable to that in conventional ferromagnets. Given the compatibility of Co2MnGa films with various spintronic devices and techniques, our work represents an essential step toward memory and computing devices built by topological ferromagnetic materials.

Super-resolved second harmonic generation imaging by coherent image scanning microscopy

2026-05-08T03:14:06Z

Super-resolved second harmonic generation imaging by coherent image scanning microscopy Raanan, Dekel; Song, Man Suk; Tisdale, William A; Oron, Dan We extend image scanning microscopy to second harmonic generation (SHG) by extracting the complex field amplitude of the second-harmonic beam. While the theory behind coherent image scanning microscopy (ISM) is known, an experimental demonstration was not yet established. The main reason is that the naive intensity-reassignment procedure cannot be used for coherent scattering as the point spread function is now defined for the field amplitude rather than for the intensity. We use an inline interferometer to demonstrate super-resolved phase-sensitive SHG microscopy by applying the ISM reassignment machinery on the resolved field. This scheme can be easily extended to third harmonic generation and stimulated Raman microscopy schemes.

Sub-50 cm/s surface recombination velocity in InGaAsP/InP ridges

2026-03-26T03:07:22Z

Sub-50 cm/s surface recombination velocity in InGaAsP/InP ridges Andrade, Nicolas M; Hooten, Sean; Kim, Yunjo; Kim, Jeehwan; Yablonovitch, Eli; Wu, Ming C The III–V InP/InGaAsP/InGaAs material family is important for photonic devices due to its optical emission and absorption in the 1.55 and 1.3 lm telecommunication bands for optical interconnects. However, InGaAsP/InGaAs generally suffer from relatively high surface recombination velocity—compared to Si [Das et al., in 2020 47th IEEE Photovoltaic Specialists Conference (PVSC) (IEEE, Calgary, AB, 2020), pp. 1167–1170] and InP [Joyce et al., Nano Lett. 12, 5325–5330 (2012)], which reduces the efficiency and can increase the noise in nanophotonic devices. Here, we demonstrate an efficient method to passivate the surface using a combination of sulfur-saturated ammonium sulfide and atomic layer deposition. After annealing, the surface passivation led to a surface recombination velocity as low as 45 cm/s, corresponding to a >180 increase in the photoluminesence of a nanoscale light-emitting device with 200 nm width.

Emerging GaN technologies for power, RF, digital, and quantum computing applications: Recent advances and prospects

2026-05-08T03:13:27Z

Emerging GaN technologies for power, RF, digital, and quantum computing applications: Recent advances and prospects Hoo Teo, Koon; Zhang, Yuhao; Chowdhury, Nadim; Rakheja, Shaloo; Ma, Rui; Xie, Qingyun; Yagyu, Eiji; Yamanaka, Koji; Li, Kexin; Palacios, Tomás GaN technology is not only gaining traction in power and RF electronics but is also rapidly expanding into other application areas including digital and quantum computing electronics. This paper provides a glimpse of future GaN device technologies and advanced modeling approaches that can push the boundaries of these applications in terms of performance and reliability. While GaN power devices have recently been commercialized in the 15–900 V classes, new GaN devices are greatly desirable to explore both higher-voltage and ultra-low-voltage power applications. Moving into the RF domain, ultra-high frequency GaN devices are being used to implement digitized power amplifier circuits, and further advances using the hardware–software co-design approach can be expected. On the horizon is the GaN CMOS technology, a key missing piece to realize the full-GaN platform with integrated digital, power, and RF electronics technologies. Although currently a challenge, high-performance p-type GaN technology will be crucial to realize high-performance GaN CMOS circuits. Due to its excellent transport characteristics and ability to generate free carriers via polarization doping, GaN is expected to be an important technology for ultra-low temperature and quantum computing electronics. Finally, given the increasing cost of hardware prototyping of new devices and circuits, the use of high-fidelity device models and data-driven modeling approaches for technology-circuit co-design are projected to be the trends of the future. In this regard, physically inspired, mathematically robust, less computationally taxing, and predictive modeling approaches are indispensable. With all these and future efforts, we envision GaN to become the next Si for electronics.

Underscreening and hidden ion structures in large scale simulations of concentrated electrolytes

2026-05-08T03:12:09Z

Underscreening and hidden ion structures in large scale simulations of concentrated electrolytes Krucker-Velasquez, Emily; Swan, James W The electrostatic screening length predicted by Debye–Hückel theory decreases with increasing ionic strength, but recent experiments have found that the screening length can instead increase in concentrated electrolytes. This phenomenon, referred to as underscreening, is believed to result from ion–ion correlations and short-range forces such as excluded volume interactions among ions. We use Brownian Dynamics to simulate a version of the Restrictive Primitive Model for electrolytes over a wide range of ion concentrations, ionic strengths, and ion excluded volume radii for binary electrolytes. We measure the decay of the charge–charge correlation among ions in the bulk and compare it against scaling trends found experimentally and determined in certain weak coupling theories of ion–ion correlation. Moreover, we find that additional large scale ion structures emerge at high concentrations. In this regime, the frequency of oscillations computed from the charge–charge correlation function is not dominated by electrostatic interactions but rather by excluded volume interactions and with oscillation periods on the order of the ion diameter. We also find the nearest neighbor correlation of ions sharing the same charge transitions from negative at small concentrations to positive at high concentrations, representing the formation of small, like-charge ion clusters. We conclude that the increase in local charge density due to the formation of these clusters and the topological constraints of macroscopic charged surfaces can help explain the degree of underscreening observed experimentally.

Quantum Chemistry Common Driver and Databases (QCDB) and Quantum Chemistry Engine (QCEngine): Automation and interoperability among computational chemistry programs

2026-04-24T03:15:19Z

Quantum Chemistry Common Driver and Databases (QCDB) and Quantum Chemistry Engine (QCEngine): Automation and interoperability among computational chemistry programs Community efforts in the computational molecular sciences (CMS) are evolving toward modular, open, and interoperable interfaces that work with existing community codes to provide more functionality and composability than could be achieved with a single program. The Quantum Chemistry Common Driver and Databases (QCDB) project provides such capability through an application programming interface (API) that facilitates interoperability across multiple quantum chemistry software packages. In tandem with the Molecular Sciences Software Institute and their Quantum Chemistry Archive ecosystem, the unique functionalities of several CMS programs are integrated, including CFOUR, GAMESS, NWChem, OpenMM, Psi4, Qcore, TeraChem, and Turbomole, to provide common computational functions, i.e., energy, gradient, and Hessian computations as well as molecular properties such as atomic charges and vibrational frequency analysis. Both standard users and power users benefit from adopting these APIs as they lower the language barrier of input styles and enable a standard layout of variables and data. These designs allow end-to-end interoperable programming of complex computations and provide best practices options by default.

A deep learning augmented genetic algorithm approach to polycrystalline 2D material fracture discovery and design

2026-04-24T03:14:45Z

A deep learning augmented genetic algorithm approach to polycrystalline 2D material fracture discovery and design Lew, Andrew J; Buehler, Markus J The gestalt of computational methods including physics-based molecular dynamics simulations, data-driven machine learning (ML) models, and biologically-inspired genetic algorithms affords a powerful toolbox for tackling materials mechanism discovery and design problems. Here, we leverage these methods to investigate the complex multidimensional problem of polycrystalline 2D material fracture. We focus first on graphene and in doing so, demonstrate a practical workflow for exploring the structural dependencies of fracture energy. Despite training our ML model on exclusively single crystal fracture in increments of 10° orientations, we can identify a crack branching mechanism responsible for high bicrystal toughness centered at initial crystal orientation angles of 19° and 41°. These high peaks span only a few degrees in range and are completely overlooked by a search with stride 10°. Furthermore, we can discover qualitative physical phenomena such as collective fracture branch termination and extract quantitative trends relating angular dispersion and mis-orientation angles of crystal grains to fracture energy. None of these complex polycrystalline behaviors were presented in the training data, and the predictive power of the model ultimately allows us to expeditiously generate polycrystalline graphene structures with bespoke fracture paths, a task with great implications in industrial design applications and mechanism discovery. Furthermore, the approach is not limited to graphene specifically, as we demonstrate by retraining the model for another more complex 2D material—MoS2—and achieve polycrystalline fracture predictions of comparable accuracy.

Estimating the task content of work: workforce design for AI-driven human-robot collaboration in intralogistics

2026-05-08T03:14:13Z

Estimating the task content of work: workforce design for AI-driven human-robot collaboration in intralogistics Bouquet, Pierre; Bagnoli, Nicolò Piergiovanni; Sheffi, Yossi This paper addresses the challenge of strategic workforce planning for AI-driven human-robot collaboration (AI-HRC) in intralogistics. We ask two questions: how can task-level full-time equivalent (FTE) estimates be constructed from existing labour statistics, and how can these estimates, combined with AI exposure metrics, inform strategic AI-HRC design and workforce planning? Drawing on U.S. Bureau of Labor Statistics employment data, O∗NET occupational profiles, and task-level AI exposure scores, we develop a stochastic task-time framework that decomposes occupations into tasks and models task frequencies as probability vectors on the simplex. A covariance-completion procedure reconstructs task covariance matrices consistent with survey standard errors, enabling the translation of occupational data into task-level and detailed work activity (DWA)-level FTE estimates with uncertainty bounds. Applying the framework to the U.S. intralogistics workforce, we find that approximately 370,000 FTEs (about 17% of workers) are concentrated in the top 15% most AIexposed DWAs. These results provide task-specific insight into AI-driven automation and support scenario-based workforce planning by linking alternative AI-HRC adoption paths to task-level FTE impacts, uncertainty bands, and upskilling priorities, thereby offering an analytical foundation for resilient, human-centered AI-HRC systems.

The stress in a dispersion of mutually polarizable spheres

2026-04-24T03:14:47Z

The stress in a dispersion of mutually polarizable spheres Reed, KM; Swan, JW Dispersions of dielectric and paramagnetic nanoparticles polarize in response to an external electric or magnetic field and can form chains or other ordered structures depending on the strength of the applied field. The mechanical properties of these materials are of interest for a variety of applications; however, computational studies in this area have so far been limited. In this work, we derive expressions for two important properties for dispersions of polarizable spherical particles with dipoles induced by a uniform external field—the isothermal stress tensor and the pressure. Numerical calculations of these quantities, evaluated using a spectrally accurate Ewald summation method, are validated using thermodynamic integration. We also compare the stress obtained using the mutual dipole model, which accounts for the mutual polarization of particles, to the stress expected from calculations using a fixed dipole model, which neglects mutual polarization. We find that as the conductivity of the particles increases relative to the surrounding medium, the fixed dipole model does not accurately describe the dipolar contribution to the stress. The thermodynamic pressure, calculated from the trace of the stress tensor, is compared to the virial expression for the pressure, which is simpler to calculate but inexact. We find that the virial pressure and the thermodynamic pressure differ, especially in suspensions with a high volume fraction of particles.

Wind farm yaw control set-point optimization under model parameter uncertainty

2026-04-24T03:14:29Z

Wind farm yaw control set-point optimization under model parameter uncertainty Howland, Michael F Wake steering, the intentional yaw misalignment of certain turbines in an array, has demonstrated potential as a wind farm control approach to increase collective power. Existing algorithms optimize the yaw misalignment angle set-points using steady-state wake models and either deterministic frameworks or optimizers that account for wind direction and yaw misalignment variability and uncertainty. Wake models rely on parameterizations of physical phenomena in the mean flow field, such as the wake spreading rate. The wake model parameters are uncertain and vary in time at a wind farm depending on the atmospheric conditions, including turbulence intensity, stability, shear, veer, and other atmospheric features. In this study, we develop a yaw set-point optimization approach that includes model parameter uncertainty in addition to wind condition variability and uncertainty. To enable computationally efficient online set-point optimization under model parameter uncertainty, a simplified, approximate parameter distribution estimation method is used. The optimization is tested in open-loop control numerical experiments using utility-scale wind farm operational data for which the set-point optimization framework with parametric uncertainty has a statistically significant impact on the wind farm power production for certain wind turbine layouts at low turbulence intensity, but the results are not significant for all layouts considered nor at higher turbulence intensity. The set-point optimizer is also tested for closed-loop wake steering control of a model wind farm in large eddy simulations of a convective atmospheric boundary layer (ABL). The yaw set-point optimization with model parameter uncertainty reduced the sensitivity of the closed-loop wake steering control to increases in the yaw controller update frequency. Increases in wind farm power production were not statistically significant due to the high ambient power variability in the turbulent, convective ABL.

Native oxide reconstructions on AlN and GaN (0001) surfaces

2026-05-08T03:12:45Z

Native oxide reconstructions on AlN and GaN (0001) surfaces Mirrielees, Kelsey J; Dycus, J Houston; Baker, Jonathon N; Reddy, Pramod; Collazo, Ramón; Sitar, Zlatko; LeBeau, James M; Irving, Douglas L Properties of AlN/GaN surfaces are important for realizing the tunability of devices, as the presence of surface states contributes to Fermi level pinning. This pinning can influence the performance of high electron mobility transistors and is also important for passivation of the surface when developing high-power electronic devices. It is widely understood that both AlN and GaN surfaces oxidize. Since there are many possible reconstructions for each surface, it is a challenge to identify the relevant surface reconstructions in advance of a detailed simulation. Because of this, different approaches are often employed to down select initial structures to reduce the computational load. These approaches usually rely on either electron counting rules or oxide stoichiometry, as both of these models tend to lead to structures that are energetically favorable. Here we explore models from these approaches but also explore a reconstruction of the (0001) surface directly observed using scanning transmission electron microscopy with predictive density functional theory simulations. Two compositions of the observed surface reconstruction—one which obeys oxide stoichiometry and one which is cation deficient and obeys electron counting—are compared to reconstructions from the previous work. Furthermore, surface states are directly calculated using hybrid exchange-correlation functionals that correct for the underestimation of the bandgaps in AlN and GaN and improve the predicted positions of surface states within the gap. It is found that cation deficiency in the observed reconstruction yields surface states consistent with the experiment. Based on all of these results, we provide insight into the observed properties of oxidized AlGaN surfaces.

Chemical kinetic mechanisms and scaling of two-dimensional polymers via irreversible solution-phase reactions

2026-04-23T03:15:48Z

Chemical kinetic mechanisms and scaling of two-dimensional polymers via irreversible solution-phase reactions Zhang, Ge; Zeng, Yuwen; Gordiichuk, Pavlo; Strano, Michael S Two-dimensional (2D) polymers are extended networks of multi-functional repeating units that are covalently linked together but confined to a single plane. The past decade has witnessed a surge in interest and effort toward producing and utilizing 2D polymers. However, facile synthesis schemes suitable for mass production are yet to be realized. In addition, unifying theories to describe the 2D polymerization process, such as those for linear polymers, have not yet been established. Herein, we perform a chemical kinetic simulation to study the recent synthesis of 2D polymers in homogeneous solution with irreversible chemistry. We show that reaction sites for polymerization in 2D always scale unfavorably compared to 3D, growing as molecular weight to the 1/2 power vs 2/3 power for 3D. However, certain mechanisms can effectively suppress out-of-plane defect formation and subsequent 3D growth. We consider two such mechanisms, which we call bond-planarity and templated autocatalysis. In the first, although single bonds can easily rotate out-of-plane to render polymerization in 3D, some double-bond linkages prefer a planar configuration. In the second mechanism, stacked 2D plates may act as van der Waals templates for each other to enhance growth, which leads to an autocatalysis. When linkage reactions possess a 1000:1 selectivity (γ) for staying in plane vs rotating, solution-synthesized 2D polymers can have comparable size and yield with those synthesized from confined polymerization on a surface. Autocatalysis could achieve similar effects when self-templating accelerates 2D growth by a factor β of 106. A combined strategy relaxes the requirement of both mechanisms by over one order of magnitude. We map the dependence of molecular weight and yield for the 2D polymer on the reaction parameters, allowing experimental results to be used to estimate β and γ. Our calculations show for the first time from theory the feasibility of producing two-dimensional polymers from irreversible polymerization in solution.

Ionic thermoelectric materials for near ambient temperature energy harvesting

2026-04-14T03:39:20Z

Ionic thermoelectric materials for near ambient temperature energy harvesting Liu, Weishu; Qian, Xin; Han, Cheng-Gong; Li, Qikai; Chen, Gang Ionic thermoelectric (i-TE) materials, using ions as the energy carrier, can generate a voltage under a temperature difference, bearing similarities to the Seebeck effect of electrons and holes in solid-state materials. Recent experiments have demonstrated large thermopower of quasi-solid-state i-TE materials, which are attractive for harvesting ambient heat as large enough voltage can be generated under a small temperature difference to match the voltage input needs of sensors for internet-of-things applications. In this perspective article, we discuss similarities and differences of i-TE materials from electronic-based thermoelectric materials and also different i-TE thermoelectric effects including the thermodiffusion (Soret) effect and the thermogalvanic effect, in which the latter includes redox reaction entropy and the Soret effect. Strategies to improve performances of materials and devices are elaborated, together with needs for future research in understanding microscopic origins of different effects.

Ultrafast low-temperature metal–insulator interface phonon dynamics and heat transport in a Pt/Gd3Fe5O12 heterostructure

2026-04-14T03:39:15Z

Ultrafast low-temperature metal–insulator interface phonon dynamics and heat transport in a Pt/Gd3Fe5O12 heterostructure Sri Gyan, Deepankar; Li, Ni; Chen, Zhantao; Geprägs, Stephan; Dietlein, Maxim; Gross, Rudolf; Sato, Takahiro; Sun, Yanwen; Hoffmann, Matthias C; Zhu, Diling; Haskel, Daniel; Strempfer, Jörg; Li, Mingda; Mannix, Danny; Evans, Paul G Interfacial thermal and acoustic phenomena have an important role in quantum science and technology, including in spintronic and spincaloritronic materials and devices. Simultaneous measurements of the low-temperature thermal and acoustic properties of a metal/insulator heterostructure reveal distinct dynamics in the characteristic phonon frequency ranges of acoustic and thermal transport. The measurements probed a heterostructure consisting of a thin film of Pt on the ferrimagnetic insulator gadolinium iron garnet (Gd3Fe5O12, GdIG) grown epitaxially on a gadolinium gallium garnet substrate. Ultrafast structural dynamics within the Pt layer were tracked using time-resolved ultrafast x-ray diffraction and analyzed to probe interfacial acoustic and thermal properties. The rapid heating of the Pt layer by a 400 nm wavelength femtosecond-duration optical pulse produced transient structural changes that provided the stimulus for these measurements. Rapid heating produced a broadband acoustic pulse that was partially reflected by the Pt/GdIG interface. Temporal frequencies up to 740 GHz, corresponding to angular frequencies of several THz, were detected in a wavelet analysis of the acoustic oscillations of the strain in the Pt layer. The structural results were analyzed to determine (i) the acoustic damping coefficient and phonon mean free path in Pt at frequencies of hundreds of GHz and (ii) the Grüneisen anharmonicity parameter. The thermal conductance of the Pt/GdIG interface was tracked using the slower, tens-of-picosecond-scale, dynamics of the initial cooling of the heated Pt layer. Analysis using a model based on the Boltzmann transport equation shows that the phonon transmission is lower at the phonon frequencies relevant to thermal transport than for subterahertz regime acoustics.

Two-dimensional electron gases as non-Newtonian fluids

2026-04-14T03:39:18Z

Two-dimensional electron gases as non-Newtonian fluids Kryhin, Serhii; Levitov, Leonid Two-dimensional electron systems offer an appealing platform to explore long-lived excitations arising due to collinear carrier scattering enabled by phase-space constraints at the Fermi surface. Recently it was found that these effects can boost excitation lifetimes over the fundamental bound set by Landau’s Fermi-liquid theory by a factor as large as (TF/T)α with α≈2. Long-lived degrees of freedom possess the capability to amplify the response to weak perturbations, producing lasting collective memory effects. This leads to non-Newtonian hydrodynamics in 2D electron fluids driven by multiple viscous modes with scale-dependent viscosity. We describe these modes as Fermi surface modulations of odd parity evolving in space and time, and discuss their implications for experimental studies of electron hydrodynamics.

Humpback whale (Megaptera novaeangliae) breathing sound characteristics from simultaneous above and underwater measurements

2026-04-14T03:39:18Z

Humpback whale (Megaptera novaeangliae) breathing sound characteristics from simultaneous above and underwater measurements Radermacher, Max K; Schinault, Matthew E; Seri, Sai Geetha; Mohebbi-Kalkhoran, Hamed; Makris, Nicholas C; Ratilal, Purnima Humpback whale breathing-related sounds were recorded on elements of a coherent hydrophone array subaperture deployed vertically at the Great South Channel on the US Northeastern continental shelf in Fall 2021, where half of the hydrophones were in-air and the rest submerged underwater. In-air hydrophones recorded breathing sounds with approximately 2.5 s duration, but smaller bandwidths compared to underwater hydrophones where signal energies extended beyond 50 kHz, and a mean underwater source level of 161 ± 4 dB re 1 μPa at 1 m, based on measurements at 22.9 m. The underwater recorded humpback whale breathing sound spectra displayed a broadband dip centered at 15.7 kHz, with approximately 400 Hz half-power bandwidth, likely caused by attenuation from propagation through pulsating air bubbles. The air bubble radius for natural frequency of oscillations at 15.7 kHz is estimated to be 0.205–0.21 mm. These bubbles are capable of removing energy from the forward propagated humpback breathing sounds via resonance absorption most pronounced at and near bubble natural oscillation frequency. Humpback whale distances from the vertically deployed hydrophones are estimated and tracked by matching the curved nonlinear travel-time wavefront of its breathing sounds, since the whale was in the near-field of the subarray.

Estimation of the spatial variability of the New England Mud Patch geoacoustic properties using a distributed array of hydrophones and deep learning

2026-04-14T03:39:17Z

Estimation of the spatial variability of the New England Mud Patch geoacoustic properties using a distributed array of hydrophones and deep learning Vardi, Ariel; Dahl, Peter H; Dall'Osto, David; Knobles, David; Wilson, Preston; Leonard, John; Bonnel, Julien This article presents a spatial environmental inversion scheme using broadband impulse signals with deep learning (DL) to model a single spatially-varying sediment layer over a fixed basement. The method is applied to data from the Seabed Characterization Experiment 2022 (SBCEX22) in the New England Mud-Patch (NEMP). Signal Underwater Sound (SUS) explosive charges generated impulsive signals recorded by a distributed array of bottom-moored hydrophones. The inversion scheme is first validated on a range-dependent synthetic test set simulating SBCEX22 conditions, then applied to experimental data to predict the lateral spatial structure of sediment sound speed and its ratio with the interfacial water sound speed. Traditional geoacoustic inversion requires significant computational resources. Here, a neural network enables rapid single-signal inversion, allowing the processing of 1836 signals along 722 tracks. The method is applied to both synthetic and experimental data. Results from experimental data suggest an increase in both absolute compressional sound speed and sound speed ratio from southwest to northeast in the NEMP, consistent with published coring surveys and geoacoustic inversion results. This approach demonstrates the potential of DL for efficient spatial geoacoustic inversion in shallow water environments.

The JIBO Kids Corpus: A speech dataset of child-robot interactions in a classroom environment

2026-04-14T03:39:14Z

The JIBO Kids Corpus: A speech dataset of child-robot interactions in a classroom environment Shankar, Natarajan Balaji; Afshan, Amber; Johnson, Alexander; Mahapatra, Aurosweta; Martin, Alejandra; Ni, Haolun; Park, Hae Won; Perez, Marlen Quintero; Yeung, Gary; Bailey, Alison; Breazeal, Cynthia; Alwan, Abeer This paper describes an original dataset of children's speech, collected through the use of JIBO, a social robot. The dataset encompasses recordings from 110 children, aged 4–7 years old, who participated in a letter and digit identification task and extended oral discourse tasks requiring explanation skills, totaling 21 h of session data. Spanning a 2-year collection period, this dataset contains a longitudinal component with a subset of participants returning for repeat recordings. The dataset, with session recordings and transcriptions, is publicly available, providing researchers with a valuable resource to advance investigations into child language development.

Range-dynamical low-rank split-step Fourier method for the parabolic wave equation

2026-04-14T03:39:16Z

Range-dynamical low-rank split-step Fourier method for the parabolic wave equation Charous, Aaron; Lermusiaux, Pierre FJ Numerical solutions to the parabolic wave equation are plagued by the curse of dimensionality coupled with the Nyquist criterion. As a remedy, a new range-dynamical low-rank split-step Fourier method is developed. The integration scheme scales sub-linearly with the number of classical degrees of freedom in the transverse directions. It is orders of magnitude faster than the classic full-rank split-step Fourier algorithm and saves copious amounts of storage space. This enables numerical solutions of the parabolic wave equation at higher frequencies and on larger domains, and simulations may be performed on laptops rather than high-performance computing clusters. Using a rank-adaptive scheme to optimize the low-rank equations further ensures the approximate solution is highly accurate and efficient. The methodology and algorithms are demonstrated on realistic high-resolution data-assimilative ocean fields in Massachusetts Bay for two three-dimensional acoustic configurations with different source locations and frequencies. The acoustic pressure, transmission loss, and phase solutions are analyzed in the two geometries with seamounts and canyons across and along Stellwagen Bank. The convergence with the rank of the subspace and the properties of the rank-adaptive scheme are demonstrated, and all results are successfully compared with those of the full-rank method when feasible.

Replayed reef sounds induce settlement of Favia fragum coral larvae in aquaria and field environments

2026-04-14T03:39:13Z

Replayed reef sounds induce settlement of Favia fragum coral larvae in aquaria and field environments Aoki, Nadège; Weiss, Benjamin; Jézéquel, Youenn; Apprill, Amy; Mooney, T Aran Acoustic cues of healthy reefs are known to support critical settlement behaviors for one reef-building coral, but acoustic responses have not been demonstrated in additional species. Settlement of Favia fragum larvae in response to replayed coral reef soundscapes were observed by exposing larvae in aquaria and reef settings to playback sound treatments for 24–72 h. Settlement increased under 24 h sound treatments in both experiments. The results add to growing knowledge that acoustically mediated settlement may be widespread among stony corals with species-specific attributes, suggesting sound could be one tool employed to rehabilitate and build resilience within imperiled reef communities.

A deep learning method for reflective boundary estimation

2026-04-14T03:39:19Z

A deep learning method for reflective boundary estimation Arikan, Toros; Weiss, Amir; Vishnu, Hari; Deane, Grant B; Singer, Andrew C; Wornell, Gregory W Environment estimation is a challenging task in reverberant settings such as the underwater and indoor acoustic domains. The locations of reflective boundaries, for example, can be estimated using acoustic echoes and leveraged for subsequent, more accurate localization and mapping. Current boundary estimation methods are constrained to high signal-to-noise ratios or are customized to specific environments. Existing methods also often require a correct assignment of echoes to boundaries, which is difficult if spurious echoes are detected. To evade these limitations, a convolutional neural network (NN) method is developed for robust two-dimensional boundary estimation, given known emitter and receiver locations. A Hough transform-inspired algorithm is leveraged to transform echo times of arrival into images, which are amenable to multi-resolution regression by NNs. The same architecture is trained on transform images of different resolutions to obtain diverse NNs, deployed sequentially for increasingly refined boundary estimation. A correct echo labeling solution is not required, and the method is robust to reverberation. The proposed method is tested in simulation and for real data from a water tank, where it outperforms state-of-the-art alternatives. These results are encouraging for the future development of data-driven three-dimensional environment estimation with high practical value in underwater acoustic detection and tracking.

Dynamically orthogonal narrow-angle parabolic equations for stochastic underwater sound propagation. Part I: Theory and schemes

2026-03-21T03:07:16Z

Dynamically orthogonal narrow-angle parabolic equations for stochastic underwater sound propagation. Part I: Theory and schemes Ali, Wael H; Lermusiaux, Pierre FJ Robust informative acoustic predictions require precise knowledge of ocean physics, bathymetry, seabed, and acoustic parameters. However, in realistic applications, this information is uncertain due to sparse and heterogeneous measurements and complex ocean physics. Efficient techniques are thus needed to quantify these uncertainties and predict the stochastic acoustic wave fields. In this work, we derive and implement new stochastic differential equations that predict the acoustic pressure fields and their probability distributions. We start from the stochastic acoustic parabolic equation (PE) and employ the instantaneously-optimal Dynamically Orthogonal (DO) equations theory. We derive stochastic DO-PEs that dynamically reduce and march the dominant multi-dimensional uncertainties respecting the nonlinear governing equations and non-Gaussian statistics. We develop the dynamical reduced-order DO-PEs theory for the Narrow-Angle parabolic equation and implement numerical schemes for discretizing and integrating the stochastic acoustic fields.

Dynamically orthogonal narrow-angle parabolic equations for stochastic underwater sound propagation. Part II: Applications

2026-03-21T03:07:14Z

Dynamically orthogonal narrow-angle parabolic equations for stochastic underwater sound propagation. Part II: Applications The stochastic dynamically orthogonal (DO) narrow-angle parabolic equations (NAPEs) are exemplified and their properties and capabilities are described using three new two-dimensional stochastic range-independent and range-dependent test cases with uncertain sound speed field, bathymetry, and source location. We validate results against ground-truth deterministic analytical solutions and direct Monte Carlo (MC) predictions of acoustic pressure and transmission loss fields. We verify the stochastic convergence and computational advantages of the DO-NAPEs and discuss the differences with normal mode approaches. Results show that a single DO-NAPE simulation can accurately predict stochastic range-dependent acoustic fields and their non-Gaussian probability distributions, with computational savings of several orders of magnitude when compared to direct MC methods. With their coupling properties and their adaptation in range to the dominant uncertainties, the DO-NAPEs are shown to predict accurate statistics, from mean and variance to multiple modes and full probability distributions, and to provide excellent reconstructed realizations, from amplitudes and phases to other specific properties of complex realization fields.

Office AI Automation using Existing DAF-Approved Software

2026-03-21T03:08:21Z

Office AI Automation using Existing DAF-Approved Software Cui, Wei; Kennedy, Laura The Department of the Air Force (DAF) continues to face mounting administrative workloads that hinder mission focus and operational efficiency. Executive officers and staff officers spend substantial time generating reports, managing emails, routing documents, and organizing taskers across multiple systems. This paper presents the Smart Executive Assistant, an office AI initiative to automate repetitive administrative tasks using existing DAF-approved technologies without a new Authority- To-Operate (ATO). By integrating DAF 365 applications, Power Automate, and approved large language models (LLMs) within secure IL5 and IL6 environments, this solution seeks to reduce time spent on low-value administrative processes by 90% while maintaining compliance and data security.

AI for Scalable Defensive Cyber Log Analysis

2026-03-21T03:08:17Z

AI for Scalable Defensive Cyber Log Analysis Schofield, Catherine; Jananthan, Hayden; Kepner, Jeremy Centralized cyber logging platforms ingest large volumes of heterogeneous telemetry, yet high dimensionality and query-driven workflows often limit scalable analytic insight on these systems. This work presents an automated pipeline for ingesting, characterizing, and analyzing large-scale hostbased logs using sparse representations and distribution-aware statistics. A systematic dimensional analysis reduces hundreds of raw log fields to a small set of informative dimensions suitable for aggregation across extended time windows. Temporal analysis of the reduced representation reveals coordinated deviations in activity volume and distributional behavior that are not apparent in individual log streams. The results demonstrate that dimensional reduction enables scalable, interpretable analysis of enterprise cyber telemetry. Furthermore, these results were obtained using host-based sensors designed for event-oriented point-defense and demonstrate the feasibility of integrating such sensors to enable long-range, long-duration area defense.

Cross-Aircraft Flight Phase Classification Using ADS-B Data and Transfer Learning

2026-03-21T03:08:22Z

Cross-Aircraft Flight Phase Classification Using ADS-B Data and Transfer Learning Kiefer, Jacob; Alemany, Sheila Flight phase identification (FPI) approaches that apply traditional machine learning techniques are expensive to scale, difficult to generalize across platforms, and frequently unavailable in permissive or distributed training environments. We propose a scalable, data-driven pipeline for automatic FPI using open-source Automatic Dependent Surveillance-Broadcast (ADS-B) data, with an emphasis on cross-aircraft generalization through transfer learning. Leveraging ADS-B telemetry from USAF Initial Flight Training aircraft, a neural network classifier is trained on Diamond DA-20 flight data and evaluated on Texan T-6 aircraft under zero-shot and fine-tuned transfer learning conditions. We describe a robust ADS-B preprocessing pipeline integrating digital elevation model (DEM) data, a data labeling strategy using unsupervised learning, and a transfer learning approach enabling adaptation across aircraft types with limited labeled data. Our results demonstrate that transfer learning significantly improves classification accuracy for flight phases with limited data, highlighting the potential of ADS-B-based models to support scalable, behavior-aware airspace intelligence across heterogeneous fleets and permissive environments. This research advances FPI capabilities for USAF training analysis and broader operational priorities in autonomy, situational awareness, and data-driven decision support.

Neural Networks for Stress Intensity Factor Vertex Prediction

2026-03-21T03:08:24Z

Neural Networks for Stress Intensity Factor Vertex Prediction Hokaj, Ian; Ghanem, Janelle Structural fatigue in aging metallic aircraft is a primary driver of sustainment costs for the U.S. Air Force, significantly impacting fleet readiness. Fatigue life prediction tools like AFGROW depend on interpolating between computationally expensive stress intensity factors (K-solutions) to approximate unknown values. However, interpolation errors in the current approach introduce uncertainty and force overly conservative maintenance schedules. This paper investigates the use of a machine learning surrogate to replace AFGROW’s dimensionreduction interpolation for the finite-width c orner-cracked hole geometry. We developed a robust data processing pipeline for a large FEA dataset and trained a neural network model. Our results reveal a critical insight: the surrogate model offers substantial performance gains over AFGROW’s interpolation in low-data regimes, emphasizing both the potential of the model and its sensitivity to dataset size. For the original, sparse dataset—which is characteristic of computationally expensive problems—the neural network significantly o utperformed the baseline interpolation, reducing the mean absolute percentage error (MAPE) by over 40% (from 2.77% to 1.60%) and achieving an R² value exceeding 0.99. However, experiments on synthetically generated dense datasets showed that the traditional interpolation method becomes more accurate as the data grid becomes less sparse. This study concludes that while neural network surrogates offer a powerful, high-fidelity solution for computationally intensive engineering problems, their adoption should be guided by a careful analysis of data density after dataset has been cleaned of outliers. It also highlights the necessity of employing rigorous, application-relevant validation strategies that move beyond simplistic random splits to accurately assess model performance.

Evaluating Adaptive AI for Contracting Officer Readiness: Design and Pedagogical Proposal for the Warrant Board RAG Chatbot

2026-03-21T03:08:23Z

Evaluating Adaptive AI for Contracting Officer Readiness: Design and Pedagogical Proposal for the Warrant Board RAG Chatbot Mullen, Julia; Grosvenor, Sarah The United States Air Force (USAF) requires a sustained and expanding pool of warranted Contracting Officers (COs) to meet growing operational and fiscal demands across its global enterprise. The authority to obligate funds and bind the government contractually—granted through the issuance of a warrant—requires successful completion of a multi-stage evaluation process culminating in a scenario-based oral board. This final interview assesses a candidate’s ability to interpret and apply acquisition policy under complex and ambiguous conditions. This paper proposes the design of an adaptive artificial intelligence (AI) training system—the Warrant Board Retrieval- Augmented Generation (RAG) Chatbot—to serve as a simulated board-preparation environment. This chatbot inverts the common ’user question, AI answer’ model, and instead has the chatbot ask the learner a series of critical thinking scenariobased questions. The prototype design adopts a model-agnostic LLM gateway capable of operation through either commercial APIs (e.g., OpenAI) or secure, government-hosted environments such as GenAI.mil, ensuring accessibility within unclassified Air Force networks. This research contributes to the emerging field of AI-assisted professional education by developing a transparent, auditable, and pedagogically grounded framework for formative learning in acquisition training.

Weird A.I.

2026-03-21T03:07:04Z

Weird A.I. Lindquist, Benjamin This essay sketches out what I call “weird AI”: probabilistic, associative systems that behave as if they feel. It shows how midcentury architects of artificial neural networks deliberately courted the uncanny as they engineered space for intuition, emotion, and nonrational thought, even as standard histories cast computing as an Enlightenment project of calculation and control. To make sense of artificial intelligence’s past and present, historians must move beyond an information-centric framework and reckon with the affective undercurrents that have shaped the field from its start.

Vibrotactile stimulation at gamma frequency mitigates pathology related to neurodegeneration and improves motor function

2026-03-20T06:10:15Z

Vibrotactile stimulation at gamma frequency mitigates pathology related to neurodegeneration and improves motor function Suk, Ho-Jun; Buie, Nicole; Xu, Guojie; Banerjee, Arit; Boyden, Edward S; Tsai, Li-Huei The risk for neurodegenerative diseases increases with aging, with various pathological conditions and functional deficits accompanying these diseases. We have previously demonstrated that non-invasive visual stimulation using 40 Hz light flicker ameliorated pathology and modified cognitive function in mouse models of neurodegeneration, but whether 40 Hz stimulation using another sensory modality can impact neurodegeneration and motor function has not been studied. Here, we show that whole-body vibrotactile stimulation at 40 Hz leads to increased neural activity in the primary somatosensory cortex (SSp) and primary motor cortex (MOp). In two different mouse models of neurodegeneration, Tau P301S and CK-p25 mice, daily exposure to 40 Hz vibrotactile stimulation across multiple weeks also led to decreased brain pathology in SSp and MOp. Furthermore, both Tau P301S and CK-p25 mice showed improved motor performance after multiple weeks of daily 40 Hz vibrotactile stimulation. Vibrotactile stimulation at 40 Hz may thus be considered as a promising therapeutic strategy for neurodegenerative diseases with motor deficits.

Traffic management protocols for advanced air mobility

2026-03-20T06:10:09Z

Traffic management protocols for advanced air mobility Chin, Christopher; Qin, Victor; Gopalakrishnan, Karthik; Balakrishnan, Hamsa The demand for advanced air mobility (AAM) operations is expected to be at a much larger scale than conventional aviation. Additionally, AAM flight operators are likely to compete in providing a range of on-demand services in congested airspaces, with varying operational costs. These characteristics motivate the need for the development of new traffic management algorithms for advanced air mobility. In this paper, we explore the use of traffic management protocols (“rules-of-the-road” for airspace access) to enable efficient and fair operations. First, we show that it is possible to avoid gridlock and improve efficiency by leveraging the concepts of cycle detection and backpressure. We then develop a cost-aware traffic management protocol based on the second-price auction. Using simulations of representative advanced air mobility scenarios, we demonstrate that our traffic management protocols can help balance efficiency and fairness, in both the operational and the economic contexts.

If blockchain is the solution, robot security is the problem

2026-03-20T06:10:04Z

If blockchain is the solution, robot security is the problem Ferrer, Eduardo Castelló Robotics systems of all types are revolutionizing a wide variety of industries—transportation, manufacturing, and even healthcare—and yet, many essential ingredients for robotics systems in the real world are not technologically ready for deployment. Currently, robots lack the protocols and standards required to be safe and secure outside factories. In an attempt to close this gap, recent research has demonstrated the security benefits of combining robotics systems with blockchain-based and related technologies (e.g., smart contracts, zero-knowledge proofs, Merkle trees). In this perspective article, I argue that blockchain-based robotics is starting to provide innovative solutions (e.g., secure data sharing, consensus mechanisms, and new interaction methods) to urgent problems of robot security. I list the most important takeaways so far from this emerging field of research that I helped establish together with a growing community. I close the article by discussing the implications of the security challenges that the robotics research community is facing, and possible ways for us to move forward.

Quantification of heat vulnerability using system dynamics

2026-03-20T06:10:23Z

Quantification of heat vulnerability using system dynamics Bayomi, Norhan; Fernandez, John E One of the major climate threats is extreme heat events, as they pose significant risks to public health that are well documented in the epidemiologic literature. The effects of extreme heat events have been evident over the past years by several extreme heat events worldwide. With the growing concerns of future heat exposure, numerous studies in the literature have developed heat vulnerability indices based on determinants that have heat-related impacts. However, there has been limited guidance on heat vulnerability assessment that accounts for the impacts of the characteristics of the built environment and changes in population dynamics over time. This paper focuses on developing the methodology for heat vulnerability assessment in urban areas using System Dynamics (SD) based on integrating three levels of the physical urban environment: the urban level, the building level, and the human adaptive capacity to heat exposure. We examine the viability of using SD modeling as an approach to examine the key drivers in heat vulnerability assessment in urban areas. Thus, the paper assesses the dynamic relationship between heat vulnerability components, namely, Susceptibility, Exposure, Coping Capacity, and Adaptive Capacity, and their effect on increased or decreased vulnerability under extreme heat events. The paper concludes with an applied case study in Cairo, Egypt, to test the use of the SD approach in assessing heat vulnerability in urban settings. Results from the proposed SD model confirm the underlying hypothesis that vulnerability from heat exposure is dynamically linked to the coping and adaptive capacity of the surrounding built environment with the urban population’s socioeconomic characteristics. The main contribution of this approach is that it allows for parallel examination of the effect of the human system that simulation models cannot include and the performance of the built environment system that epidemic heat vulnerability studies cannot capture.

Predicting one-year left ventricular mass index regression following transcatheter aortic valve replacement in patients with severe aortic stenosis: A new era is coming

2026-03-20T06:10:20Z

Predicting one-year left ventricular mass index regression following transcatheter aortic valve replacement in patients with severe aortic stenosis: A new era is coming Asheghan, Mohammad Mostafa; Javadikasgari, Hoda; Attary, Taraneh; Rouhollahi, Amir; Straughan, Ross; Willi, James Noel; Awal, Rabina; Sabe, Ashraf; de la Cruz, Kim I; Nezami, Farhad R Aortic stenosis (AS) is the most common valvular heart disease in the western world, particularly worrisome with an ever-aging population wherein postoperative outcome for aortic valve replacement is strongly related to the timing of surgery in the natural course of disease. Yet, guidelines for therapy planning overlook insightful, quantified measures from medical imaging to educate clinical decisions. Herein, we leverage statistical shape analysis (SSA) techniques combined with customized machine learning methods to extract latent information from segmented left ventricle (LV) shapes. This enabled us to predict left ventricular mass index (LVMI) regression a year after transcatheter aortic valve replacement (TAVR). LVMI regression is an expected phenomena in patients undergone aortic valve replacement reported to be tightly correlated with survival one and five year after the intervention. In brief, LV geometries were extracted from medical images of a cohort of AS patients using deep learning tools, and then analyzed to create a set of statistical shape models (SSMs). Then, the supervised shape features were extracted to feed a support vector regression (SVR) model to predict the LVMI regression. The average accuracy of the predictions was validated against clinical measurements calculating root mean square error and R2 score which yielded the satisfactory values of 0.28 and 0.67, respectively, on test data. Our work reveals the promising capability of advanced mathematical and bioinformatics approaches such as SSA and machine learning to improve medical output prediction and treatment planning.

Aesthetic chills cause an emotional drift in valence and arousal

2026-03-20T06:10:12Z

Aesthetic chills cause an emotional drift in valence and arousal Jain, Abhinandan; Schoeller, Felix; Horowitz, Adam; Hu, Xiaoxiao; Yan, Grace; Salomon, Roy; Maes, Pattie Aesthetic chills are an embodied peak emotional experience induced by stimuli such as music, films, and speeches and characterized by dopaminergic release. The emotional consequences of chills in terms of valence and arousal are still debated and the existing empirical data is conflicting. In this study, we tested the effects of ChillsDB, an open-source repository of chills-inducing stimuli, on the emotional ratings of 600+ participants. We found that participants experiencing chills reported significantly more positive valence and greater arousal during the experience, compared to participants who did not experience chills. This suggests that the embodied experience of chills may influence one’s perception and affective evaluation of the context, in favor of theoretical models emphasizing the role of interoceptive signals such as chills in the process of perception and decision-making. We also found an interesting pattern in the valence ratings of participants, which tended to harmonize toward a similar mean after the experiment, though initially disparately distributed. We discuss the significance of these results for the diagnosis and treatment of dopaminergic disorders such as Parkinson’s, schizophrenia, and depression.

On projection and the shadow of [wh]

2026-04-24T03:15:48Z

On projection and the shadow of [wh] Newman, Elise Much work has shown that wh-movement is subject to several kinds of locality restrictions cross-linguistically. I propose that these restrictions arise when [wh] projects past the maximal projection that it came from. When this happens, it intervenes for whmovement, trapping the wh-element in its base position, unless it can escape through other means. The need to escape the domain of [wh] is proposed to capture the distribution of successive-cyclic movement and interactions with Voice in different languages. Different locality conditions in different languages are captured by different distributions of the same features and probes.

Discussion of “Experimental Design and Modeling for Forward-Inverse Maps” by R. Barton & M. Morris, appearing in Technometrics

2026-04-24T03:16:13Z

Discussion of “Experimental Design and Modeling for Forward-Inverse Maps” by R. Barton & M. Morris, appearing in Technometrics Marzouk, Youssef M. Inverse design—essentially the problem of finding system parameter values that achieve a given performance metric—is an enormously important problem across a wide range of engineering fields. Typical methods for inverse design employ a forward model for example, a complex computer simulation mapping design parameters to performance metrics, and embed it in an optimization loop. If the forward model is a black box, for which direct evaluation of gradients is intractable, then one must resort to derivative-free or so-called “zeroth order” optimization approaches (e.g., Močkus Citation1975; Jones, Schonlau, and Welch Citation1998; Conn, Scheinberg, and Vicente Citation2009; Larson, Menickelly, and Wild Citation2019). Most of these approaches iteratively construct a metamodel for the forward map during optimization. Barton and Morris (henceforth “the authors” or “BM”) propose instead to build an inverse metamodel, that is, a computationally inexpensive approximation of the performance metric-to-parameter map. The promise of such an inverse metamodel is that it makes inverse design much faster and more direct, bypassing the need for explicit optimization.

Where Are All the CFOs?

2026-04-17T03:13:34Z

Where Are All the CFOs? Wright, Randall S. In my 38 years with MIT’s Office of Corporate Relations, where I worked with and advised executives, I saw chief executive officers (CEOs), company presidents, managing directors, general managers, chief information officers (CIOs), chief technology officers (CTOs), vice presidents, chief scientists, chief counsels, directors of innovation hubs, managers of strategic alliances, technology entrepreneurs, managers of supply chains, presidents of European Trade Commissions, scientific attachés, senior NATO officers, ministers of economics, speakers of parliaments, chancellors, governors, Members of Congress . . .

An Alternative to Probability Tables for Modeling Unresolved Resonance Behavior

2026-03-19T03:07:12Z

An Alternative to Probability Tables for Modeling Unresolved Resonance Behavior Ridley, Gavin; Forget, Benoit We first review the requirements of Monte Carlo neutron transport codes to accurately model the phenomena associated with the unresolved resonance regime and present the first rigorous justification for the modeling assumption employed by probability tables for unresolved resonances. We present a new method named AURA (analytic unresolved resonance algorithm) for modeling unresolved resonance cross-sections with the normal inverse Gaussian distribution. The new method accurately models temperature dependence in the unresolved resonance region (URR), requires fewer data than previous methods, and outperforms the conventional URR treatment on GPUs.

Direct Sampling of the Egelstaff-Schofield Scattering Law and the Phonon Sampling Method for Liquids

2026-04-17T03:14:27Z

Direct Sampling of the Egelstaff-Schofield Scattering Law and the Phonon Sampling Method for Liquids Ridley, Gavin; Forget, Benoit The phonon sampling method (PSM) enables accurate and efficient simulation of thermal neutron scattering from solids; it obviates a substantial degree of discretization, enabling continuous representations of scattering law behavior across a variety of conditions. Despite these strengths, the PSM relies on the phonon expansion, which only applies to solid materials. To remove this limitation, we demonstrate an efficient sampling algorithm for the nonsymmetric translational 𝒮⁡(𝛼,𝛽) distribution of Egelstaff and Schofield, and show how this can be used to extend the PSM for the simulation of thermal neutron scattering in materials exhibiting mixed translational and vibrational behavior, such as water or molten salts. We demonstrate the correctness of the method with example results for scattering from room temperature water at a few incident neutron energies.

Preliminary Verification of Fixed-Source Sensitivity Analysis in OpenMC

2026-04-24T03:15:40Z

Preliminary Verification of Fixed-Source Sensitivity Analysis in OpenMC Ebiwonjumi, Bamidele; Forget, Benoit; Peterson, Ethan Sensitivity analysis capabilities have yet to find extensive use in fusion reactor design applications where they can help understand the impact of nuclear data uncertainties on the tritium breeding ratio (TBR), shutdown dose rates, and nuclear heating. Significant uncertainty exists in nuclear data for fusion applications, and the goal of this work is to explore whether adjoint- and perturbation theory–based eigenvalue and generalized response sensitivity methods recently developed within the OpenMC Monte Carlo code can be extended to fixed-source Monte Carlo radiation transport simulations. This paper presents a derivation for the extended sensitivity analysis method. The adjoint-based sensitivity coefficients are compared with Monte Carlo SERPENT sensitivity coefficients for a TBR calculation in a simplified ARC-class tokamak. Further verification with the Monte Carlo MCSEN and deterministic ASUSD sensitivity coefficients for the tritium production rate in the Frascati neutron generator helium-cooled pebble bed test blanket module mock-up experiment was performed. The OpenMC sensitivity coefficients were found to agree with the other code systems. The use of the sensitivity coefficients with nuclear data covariance within the sandwich rule for cross-section uncertainty propagation also showed good agreement with the reference total Monte Carlo nuclear data uncertainty.

Supply chain mapping through retrieval-augmented generation: applications to the electronics industry

2026-04-17T03:14:01Z

Supply chain mapping through retrieval-augmented generation: applications to the electronics industry Jackson, Ilya; Saénz, Maria Jesus; Ivanov, Dmitry; Ma, Benedict Jun This paper presents a novel methodology for automated multi-tier supply chain mapping, leveraging Retrieval-Augmented Generation (RAG) and network science techniques. We developed an RAG-based approach that extracts supplier-customer relationships from unstructured public data sources, including SEC 10-K filings and earnings calls. The extracted entities are structured into a directed supply chain graph and analysed using network science metrics such as centrality, modularity, and path length. The case study focuses on three of the largest contract manufacturers in the electronics industry: Hon Hai Precision Industry (Foxconn), Flex Ltd., and Jabil Inc. Our findings demonstrate that Generative AI (GAI), specifically LLMs enhanced with RAG, can construct scalable and comprehensive supply chain graphs. The proof of concept is successful, as evidenced by the construction of a directed supply chain graph encompassing 4,644 nodes and 8,341 edges, covering three of the largest contract manufacturers in the electronics industry.

Quantifying Flash Flood Inundation and Assessing Damage Using Satellite Earth Observations: The Case of 2022 Flash Flood in Bangladesh

2026-04-17T03:13:38Z

Quantifying Flash Flood Inundation and Assessing Damage Using Satellite Earth Observations: The Case of 2022 Flash Flood in Bangladesh Sariful, Md; Mitra, Juthi Rani; Rahman, Munshi Khaledur Floods pose significant threats to Bangladesh, frequently causing the loss of lives, properties, infrastructure, and livelihoods. This study examines the spatial and temporal dynamics of the 2022 Sylhet flood by integrating Sentinel-1 Synthetic Aperture Radar (SAR) imagery with socioeconomic datasets. Otsu thresholding method was used to estimate water extent before, during, and after the 2022 flood event in the Sylhet region. Results suggest that over 1,600 km² of land in Sylhet division was inundated, with Habiganj district experiencing the highest surge. More than half a million people were directly impacted, alongside significant damage to cropland and urban areas. Furthermore, the study highlighted substantial impacts on urban built-up areas and cropland across districts. Cumulative cropland damage increased from 3376.30 km² in June to 4701.30 km² in July, indicating severe consequences for agricultural productivity. Concurrently, the affected built-up areas were found to be 40.9 km², emphasizing the impact on human settlements. By providing a detailed assessment of flood extent and damage, this study provides valuable insights for policymakers, planners, and disaster management authorities. The findings support the development of targeted strategies for flood risk reduction, agricultural resilience, and the mitigation of future disaster impacts in vulnerable regions of Bangladesh.

GeoXCP: uncertainty quantification of spatial explanations in explainable AI

2026-04-17T03:14:10Z

GeoXCP: uncertainty quantification of spatial explanations in explainable AI Lou, Xiayin; Luo, Peng; Li, Ziqi; Gao, Song; Meng, Liqiu Understanding and explaining complex geographic phenomena—ranging from climate change to socioeconomic disparities—is a central focus in both geography and the broader scientific community. Various methods have been developed to elucidate relationships between variables, from coefficient estimates in linear regression models to the increasingly dominant use of feature attribution scores in Explainable AI (XAI) techniques. However, explanations generated by XAI methods often carry uncertainty, stemming from the model itself and the data used to train the model. Despite the critical importance of accounting for such uncertainty, this issue remains largely overlooked in the geospatial domain. In this study, we developed an uncertainty quantification framework for XAI explanations based on conformal prediction, termed Geospatial eXplanation Conformal Prediction (GeoXCP). By incorporating spatial dependence into the modeling process, GeoXCP produced spatially adaptive explanations with calibrated uncertainty estimates. We validated the effectiveness of GeoXCP through extensive simulation experiments and real-world datasets. The results demonstrated that GeoXCP provided reliable explanations while effectively quantifying uncertainty across diverse geospatial scenarios. Our approach represented a significant advancement in explainable geospatial machine learning, enabling decision-makers to better assess the trustworthiness of model-driven insights. The proposed framework was implemented in a python package, named GeoXCP.

Rapidly innovating firms: patent lifecycle and support for trade and IP enforcement

2026-04-24T03:16:15Z

Rapidly innovating firms: patent lifecycle and support for trade and IP enforcement Cha, Sujin; Lee, Jieun; Osgood, Iain; Park, Sojun The rate of technological innovation and the speed with which old technologies are discarded are fundamental features of industries in the modern economy. How do these factors shape the politics of trade and trade agreements? We describe two potential channels. In one, rapid innovators support trade agreements and trade liberalization because of their privileged competitive position at the cutting edge of innovation. In the other, rapid innovators support trade agreements and intellectual property (IP) enforcement due to their particular need for speed in patent approval and enforcement. By matching data on patent lifecycles to data on United States (US) tariffs and corporate support for trade agreements and IP enforcement, we test these two theoretical accounts. We find evidence consistent with both. We conclude that rapidly innovating firms are strong supporters of globalization, though their political successes have also contributed to contention over the contemporary international order, both at home and abroad.

Grounding infrastructure: community ownership of an international cooperation project in Kibera, Nairobi

2026-03-19T03:07:23Z

Grounding infrastructure: community ownership of an international cooperation project in Kibera, Nairobi Williams, Sarah; Carolini, Gabriella International infrastructure projects are deeply ensconced in debates about power, intentions, and imaginaries of progress that also stir debate about what it means to decolonize international relations and projects that nominally build improvements in low-income environments. Traditional prioritization of technical expertise, the criticality of contextual knowledge, and the dynamics of uneven partnerships involved are all central elements of these pronounced tensions in the practice of international infrastructure development. This paper instead describes an international infrastructure project that is centred on community stewardship and co-design. The infrastructure project Living Data Hubs (LDH), highlighted here, is a small-scale information and communication technology (ICT) and data management project in the informal settlement of Kibera in Nairobi, Kenya. In its development, equal attention was afforded to technical, conceptual, and procedural knowledge production, ensuring that both universal and contextual elements of the ICT infrastructure and data management were explicitly discussed and valued. We argue that international partnerships like LDH that give space to technical, conceptual, and procedural capacity building alike can produce community stewarded infrastructure that is sustainable and puts forward a pathway for diminishing uneven power relations and enhancing community well-being.

Writing revolutions:1 From Haiti to MIT to Palestine through the lenses of linguistics and history for decolonization and liberation

2026-05-08T03:12:22Z

Writing revolutions:1 From Haiti to MIT to Palestine through the lenses of linguistics and history for decolonization and liberation DeGraff, Michel In this reflexive essay, I explore, through historical and linguistic lenses, the similarities between the freedom struggles of Haitians and Palestinians. One of my goals is to uncover certain pervasive mechanisms of oppression and resistance. As I do so, I reflect on my personal and professional journey as a Haitian scholar–activist at MIT, highlighting the political repression I have faced after proposing an elective “Special Topics” seminar on language and linguistics for decolonization and liberation in Haiti, Palestine and Israel. Employing distinct yet complementary analytical approaches, I dissect the complex interplay between language and history in the Haitian and Palestinian struggles. By integrating historical perspectives with linguistic analysis, I illustrate how these elements collectively contribute to forging paths toward decolonization and liberation, at both the individual and collective levels.

Advancing Pedestrian Models: A Comparative Review and Vision for the Future

2026-04-24T03:14:09Z

Advancing Pedestrian Models: A Comparative Review and Vision for the Future Zafri, Niaz Mahmud; Sevtsuk, Andres Problem, research strategy, and findings Pedestrian mobility is essential for creating sustainable, healthy, and equitable cities, yet pedestrian modeling remains underdeveloped compared with vehicle-centric approaches. To advance the state of the art, we critically review four available pedestrian modeling frameworks—urban network analysis (UNA), multi-agent transport simulation (MATSim), model of pedestrian demand (MoPeD), and spatial design network analysis (sDNA)—through the lens of the traditional four-step transportation modeling framework. We assess their methodological foundations, capabilities, practical applications, and limitations and outline future directions for improving modeling practice. UNA and sDNA offer high-resolution, trip-based network analyses; MATSim supports agent- and activity-based multimodal simulations; and MoPeD estimates grid-level pedestrian demand. Despite these strengths, several key gaps remain: Most models focus predominantly on utilitarian walking, neglect leisure and social activities, typically assume homogeneous pedestrian behavior by overlooking sociodemographic variations, face shortcomings with mode choice estimation, and are rarely applied in informal urban contexts. Furthermore, limited availability of pedestrian count data continues to constrain effective model calibration and validation. Takeaway for practice We propose that researchers and planners adopt a human-centered, inclusive, and policy-aligned modeling agenda, emphasizing simple yet intuitive metrics that capture the full spectrum of walking benefits, supporting early-stage planning even in data-scarce contexts, fostering stronger collaboration with practitioners, and promoting a modular, adaptable modeling ecosystem. Ultimately, reorienting pedestrian models as flexible decision support tools—rather than narrowly focused forecasting instruments—can meaningfully support the development of more walkable cities.

Reasons for Non-Agents

2026-03-18T03:07:07Z

Reasons for Non-Agents Watkins, Eliot According to a standard picture, normative reasons do not extend beyond the boundaries of agency. If something isn’t an agent, then there can’t be normative reasons for it to do one thing rather than another. This paper argues that the standard picture is false. There are reasons for smoke detectors to alarm when exposed to smoke, and for Venus Flytraps to close around their prey when stimulated. I argue that the collapse of the standard picture has important implications for philosophical debates about reasons, and especially serious consequences for theories that analyse normative reasons in terms of the standards of good practical reasoning.

Fully 3D-Printed Electric Motor Manufactured via Multi-Modal, Multi-Material Extrusion

2026-04-24T03:14:56Z

Fully 3D-Printed Electric Motor Manufactured via Multi-Modal, Multi-Material Extrusion Cañada, Jorge; Bigelow, Zoey; Velásquez-García, Luis Fernando Material extrusion additive manufacturing can process a wide variety of functional materials including electrically conductive, magnetic, and mechanically compliant polymer composites. While filaments developed for 3D printing often exhibit limited functionality, highly loaded functional composites originally formulated for specialised manufacturing processes can be processed via material extrusion. In this work, a commercial multi-material extrusion 3D printer was modified to process conductive inks, soft and hard magnetic composite pellets, and rigid and compliant polymeric filaments. Using this system, solenoids, hard magnets, and springs were fabricated. These components were combined through straightforward assembly to demonstrate the first fully 3D-printed electric motor — a linear actuator composed of five distinct functional materials: dielectric, electrically conductive, soft magnetic, hard magnetic, and flexible. The solenoids produced up to 2.03 mT magnetic fields, the magnets generated up to 71 mT magnetic fields, and the linear actuator attained a maximum displacement of 318 μm at its resonant frequency (41.6 Hz). This study demonstrates the capability of multi-modal, multi-material extrusion 3D printing to fabricate all critical components of electrical machines, with magnetisation of the hard magnets being the only post-printing step. This milestone advances multi-material, multi-functional 3D printing towards implementing in-situ, customised, low-waste, and low-cost functional hardware.

Partial Identification of Individual-Level Parameters Using Aggregate Data in a Nonparametric Model

2026-05-08T03:13:38Z

Partial Identification of Individual-Level Parameters Using Aggregate Data in a Nonparametric Model Moon, Sarah I develop a methodology to partially identify linear combinations of conditional mean outcomes when the researcher only has access to aggregate data. Unlike the existing literature, I only allow for marginal, not joint, distributions of covariates in my model of aggregate data. Bounds are obtained by solving an optimization program and can easily accommodate additional polyhedral shape restrictions. I provide a procedure to construct confidence intervals on the identified set and demonstrate the performance of my method in a simulation study. In an empirical illustration of the method using Rhode Island standardized exam data, I find that conditional pass rates vary across student subgroups and across counties.

Shaping suburbia: the one-and-a-half-century evolution of setback regulations in American neighbourhood development

2026-05-08T03:13:23Z

Shaping suburbia: the one-and-a-half-century evolution of setback regulations in American neighbourhood development Zhu, Chenhao; Ben-Joseph, Eran A comprehensive understanding of setback regulations is vital to American neighbourhood development and ongoing zoning reform efforts; however, the topic remains underexplored. By examining zoning codes, historical documents, norms, and development plans, this study traces the evolution of setback regulations from the 19th century to the present. The results map out the historical trajectory of setback regulations, highlight their evolving roles in shaping neighbourhood development, and synthesize the regulatory and dimensional trends. Building on the findings, insights are offered into the multifaceted roles of setbacks, the implications of identified historical periods, and recommendations for innovating future setback regulations.

The European Union’s CBAM: averting emissions leakage or promoting the diffusion of carbon pricing?

2026-04-24T03:15:54Z

The European Union’s CBAM: averting emissions leakage or promoting the diffusion of carbon pricing? Mehling, Michael A.; Dolphin, Geoffroy; Ritz, Robert A. Adopted in 2023, the Carbon Border Adjustment Mechanism (CBAM) is a significant component of the European Union’s ambitious decarbonization strategy under the European Green Deal. This article questions the output effectiveness of the CBAM in achieving its stated objective, prevention of carbon leakage, while demonstrating its impact effectiveness as an instrument for advancing the global diffusion of carbon pricing. Empirical evidence for carbon leakage remains sparse, and implementation challenges might limit the capacity of the CBAM to counteract leakage even where it occurs. Nonetheless, the CBAM has already demonstrated a powerful spillover effect by incentivizing the acceleration of carbon pricing roadmaps across EU trade partners, suggesting that trade-related climate measures can effectively encourage global climate action. As the EU navigates the complexities of operationalizing the CBAM, it must balance several tradeoffs to maintain this important spillover effect. If successful, the CBAM could catalyze a virtuous cycle of carbon pricing adoption, reinforcing its potentially pivotal role in the EU’s toolbox to manage the environment-trade nexus.

Shadow players of the eviction crisis: identifying and characterizing professional evicting attorneys in Massachusetts

2026-04-24T03:14:33Z

Shadow players of the eviction crisis: identifying and characterizing professional evicting attorneys in Massachusetts Aizman, Asya; Huntley, Eric Robsky This paper examines an under-studied class of actors in the housing system: attorneys representing landlords. We use a cluster-detection algorithm to identify salient clusters of attorneys based on their scale of operations. We then characterize these groups—what we call professional, active, less active, and least active evicting and tenant attorneys—using metrics related to the geographic scope of their practice, their prevalence in eviction court, case outcomes, and client base. We find that there are large differences between the practices of professional and less active landlord attorneys: professional attorneys’ cases are resolved more quickly, more regularly result in executions, affect more varied geographies, and have a greater proportion of institutional clients. Placed in a growing body of literature on eviction and advocacy for tenants’ Right to Counsel, we argue that professional evicting attorneys—most of whom are named or founding principal—are not neutral actors, but are contributing to the worsening eviction crisis in class solidarity with landlords.

Adaptive Digital Twin Modeling with Control: Integration of Extended Kalman Filter-Based Recursive Sparse Nonlinear Identification with Model Predictive Control

2026-05-08T03:13:32Z

Adaptive Digital Twin Modeling with Control: Integration of Extended Kalman Filter-Based Recursive Sparse Nonlinear Identification with Model Predictive Control Wang, Jingyi; Cao, Liang; Cao, Yankai; Gopaluni, Bhushan The adoption of digital twins has revolutionized industrial process simulation, monitoring, and control effectiveness. However, practical implementations of digital twins are hindered by substantial challenges, including extended development time, diminishing model accuracy, and restricted interactive capabilities. Addressing these critical issues, this paper proposes a comprehensive digital twin development framework that integrates digital twin identification, real-time model updating, and advanced process control. The proposed approach first identifies the offline digital twin model through the sparse identification of a nonlinear dynamics algorithm, reducing the digital twin development time while maintaining model fidelity. Then, the identified model is updated by the extended Kalman filter to mitigate the problem of diminishing accuracy. Finally, incorporating the latest updated model into the model predictive control facilitates the control inputs optimization and enhances the interactive capacity of digital twins. Through one industrial case study and two simulation examples, the advantages of the proposed algorithm are demonstrated.

FastComposer: Tuning-Free Multi-subject Image Generation with Localized Attention

2026-05-08T03:12:54Z

FastComposer: Tuning-Free Multi-subject Image Generation with Localized Attention Xiao, Guangxuan; Yin, Tianwei; Freeman, William T.; Durand, Frédo; Han, Song 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. 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 $$\times $$ × –2500 $$\times $$ × 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. Code, model, and dataset are available here ( https://github.com/mit-han-lab/fastcomposer ).

Molecular Mechanisms of Liver Metastasis: Connecting Biology, Biomarkers, and Outcomes

2026-04-24T03:14:25Z

Molecular Mechanisms of Liver Metastasis: Connecting Biology, Biomarkers, and Outcomes Wagner, Doris; Balzer, Felix; Margonis, Georgios Antonios In our Special Issue titled “Molecular Mechanisms of Liver Metastases,” we aimed to attract articles that connect metastasis mechanisms and biomarkers with clinical disease characteristics and patient outcomes. Starting with the review paper by the Ohio State group, the authors provide valuable insight into the complex, multi-step molecular mechanisms underpinning liver metastasis [1]. Moving from pathogenesis to the prognostic and predictive role of clinically actionable proxies of tumor biology, the University of California, San Francisco (UCSF) group reviews current evidence on the prognostic and predictive relevance of key alterations—including RAS, BRAF, mismatch repair (MMR) genes, TP53, and SMAD4—in surgically treated colorectal liver metastases (CRLMs) [2]. For RAS and BRAF in particular—given the breadth of existing evidence—the authors emphasize the largest and/or the most recently published studies to provide the most robust and contemporary overview for readers.

Stability and Reactivity of Alternative Nucleobases in Concentrated Sulfuric Acid

2026-05-08T03:13:58Z

Stability and Reactivity of Alternative Nucleobases in Concentrated Sulfuric Acid Huang, Jingcheng; Seager, Sara; Seager, Maxwell D.; Petkowski, Janusz J. Recent findings demonstrate that concentrated sulfuric acid supports rich organic chemistry, including the stability of the canonical DNA bases adenine, thymine, guanine and cytosine. Yet, due to full protonation in concentrated sulfuric acid, these bases may not pair as effectively as they do in water. We are therefore motivated to study nucleic acid bases that pair via hydrophobic and van der Waals interactions instead of canonical hydrogen bonding. Here, we investigate the stability of 14 selected, commercially available alternative nucleobases in concentrated sulfuric acid to evaluate their potential for forming DNA-like polymers in this solvent. The reactivity of compounds 1–14 have not been previously investigated in concentrated sulfuric acid. We incubate the selected compounds in 98% and 81% w/w sulfuric acid and monitor their stability using 1H and 13C NMR spectroscopy over 3 weeks at room temperature. In 98% w/w sulfuric acid, six bases—benzo[c][1,2,5]thiadiazole (1), 2,2′-bipyridine (2), 1,1′-biphenyl (3), 1-methoxy-3-methylbenzene (MMO2) (7) and 1-chloro-3-methoxybenzene (ClMO) (13), and 2,4-difluorotoluene (14)—remain soluble and stable with no detectable degradation. A few compounds show non-destructive reactivity, like sulfonation (compound 3) or H/D exchange (compounds 7, 13, 14). The other compounds react rapidly or are insoluble in 98% w/w sulfuric acid. In 81% w/w sulfuric acid, only compounds 1 and 2 remain stable and soluble, while other selected compounds are insoluble or unstable. Our findings identify a subset of alternative bases stable in concentrated sulfuric acid, advancing efforts towards the design of an example genetic-like polymer in this unusual solvent. Our work further highlights sulfuric acid’s potential for supporting complex organic chemistry, with implications for astrobiology, planetary science of Venus and synthetic biology.

Examining the Uptake and Effectiveness of the Climate Justice Instructional Toolkit

2026-04-24T03:14:59Z

Examining the Uptake and Effectiveness of the Climate Justice Instructional Toolkit Rabe, Chris; Schlegel, Madeline; Mitchell, Pearl Climate justice is an accelerating global movement in which education plays a vital role in building awareness, understanding and catalyzing action. Yet, many institutions of higher education face persistent barriers to integrating climate justice across disciplines, including limited institutional focus, lack of faculty expertise, and, more recently, heightened political constraints. In response, climate justice educators have proposed a range of strategies—such as conducting faculty workshops, hosting events, offering incentives, increasing funding, and developing curricular tools—though few studies have examined how curricular resources themselves can advance climate justice education. The Climate Justice Instructional Toolkit (CJIT) was developed to address this gap by providing accessible, adaptable materials to help instructors, even those with limited prior experience, incorporate climate and environmental justice into diverse disciplines. Using a novel survey tool across multiple educational contexts, this study used a mixed methods approach to analyze 76 survey responses to evaluate the CJIT’s effectiveness and user experiences among postsecondary instructors, K–12 teachers, and students. The findings showed broad positive feedback regarding the toolkit’s adaptability and utility while underscoring the need for stronger inclusion of Indigenous perspectives, greater socioeconomic accountability, and more attention to solutions and resilience. Respondents also emphasized that inclusive design, community engagement, and institutional support are critical for advancing climate justice integration and empowering educators in this work.

GraphHash: Graph Clustering Enables Parameter Efficiency in Recommender Systems

2026-04-23T03:15:18Z

GraphHash: Graph Clustering Enables Parameter Efficiency in Recommender Systems Wu, Xinyi; Loveland, Donald; Chen, Runjin; Liu, Yozen; Chen, Xin; Neves, Leonardo; Jadbabaie, Ali; Ju, Mingxuan; Shah, Neil; Zhao, Tong Deep recommender systems rely heavily on large embedding tables to handle high-cardinality categorical features such as user/item identifiers, and face significant memory constraints at scale. To tackle this challenge, hashing techniques are often employed to map multiple entities to the same embedding and thus reduce the size of the embedding tables. Concurrently, graph-based collaborative signals have emerged as powerful tools in recommender systems, yet their potential for optimizing embedding table reduction remains unexplored. This paper introduces GraphHash, the first graph-based approach that leverages modularity-based bipartite graph clustering on user-item interaction graphs to reduce embedding table sizes. We demonstrate that the modularity objective has a theoretical connection to message-passing, which provides a foundation for our method. By employing fast clustering algorithms, GraphHash serves as a computationally efficient proxy for message-passing during preprocessing and a plug-and-play graph-based alternative to traditional ID hashing. Extensive experiments show that GraphHash substantially outperforms diverse hashing baselines on both retrieval and click-through-rate prediction tasks. In particular, GraphHash achieves on average a 101.52% improvement in recall when reducing the embedding table size by more than 75%, highlighting the value of graph-based collaborative information for model reduction. Our code is available at https://github.com/snap-research/GraphHash. WWW ’25, April 28-May 2, 2025, Sydney, NSW, Australia

Mindful young brains and minds: a systematic review of the neural correlates of mindfulness-based interventions in youth

2026-05-08T03:12:07Z

Mindful young brains and minds: a systematic review of the neural correlates of mindfulness-based interventions in youth Jande, Jovan; Treves, Isaac N.; Ely, Samantha L.; Gowatch, Leah C.; Carpenter, Carmen; Shampine, MacKenna; Webb, Christian A.; Sacchet, Matthew D.; Gabrielli, John D. E.; Marusak, Hilary A. This systematic narrative review examines neuroimaging studies that investigated the neural correlates of mindfulness-based interventions in youth (ages 0–18). We extracted 13 studies with a total of 467 participants aged 5–18 years from the MEDLINE database on February 21st, 2024. These studies included both typically developing youth and those at risk of developing or recovering from neuropsychiatric disorders. Most studies (76.9%) utilized a pre-post intervention design, with resting-state functional magnetic resonance imaging (fMRI) being the most common imaging modality (46.1%), followed by task-based fMRI (38.4%), diffusion-weighted imaging (15.4%), and structural MRI (7.7%). Despite substantial heterogeneity across study designs and findings, several consistent patterns emerged. Resting-state fMRI studies generally reported increased functional connectivity within and between networks, notably involving the salience network, frontoparietal network, and default mode network. Studies using diffusion-weighted imaging indicated enhancements in white matter microstructural properties, supporting overall connectivity improvements. Several task-based fMRI studies identified decreased activation of the default mode network and heightened reactivity of the salience network during or after mindfulness practice, with real-time neurofeedback further amplifying these effects. While preliminary, the reviewed studies suggest that mindfulness interventions may alter both functional and structural connectivity and activity in youth, potentially bolstering self-regulation and cognitive control. Nonetheless, the variability in methodologies and small sample sizes restricts the generalizability of these results. Future research should prioritize larger and more diverse samples, and standardized mindfulness-based interventions to deepen our understanding of the neural mechanisms underlying mindfulness-based interventions in youth and to optimize their efficacy.

Theory Instead of Experiment (TIE): A Creator Valuation System at Tencent

2026-05-08T03:13:19Z

Theory Instead of Experiment (TIE): A Creator Valuation System at Tencent Huang, Lei; Zhang, Juanjuan Experiments are informative but should be used judiciously as a costly resource. Well-constructed theory may serve as a substitute. We develop a ''Theory Instead of Experiment'' (TIE) framework and, in collaboration with Tencent, apply the framework to assess how much value (e.g., user clicks) each creator contributes to its WeChat Official Accounts Platform. This TIE application models content demand and supply upon the counterfactual departure of a creator. The demand model predicts user clicks based on estimated user preferences, while the supply model captures the platform's content distribution response. Together, they predict how each creator influences user engagement through the platform's content distribution strategy. We test the predictions of the TIE system with 168 experiments, each examining a different mix of creators and involving more than 9 million unique users. The TIE system and the experiments demonstrate a 97% correlation on the key performance metric (change in user clicks). Based on its low costs, high accuracy, granular output, and minimal latency, Tencent has deployed the TIE system as the default approach to creator valuation, assessing tens of millions of creators each day while avoiding a 2.5% user click loss associated with a typical experiment. KDD ’25, Toronto, ON, Canada

The Seasonal Cycle of Atmospheric Heating and Temperature

2026-03-17T03:10:21Z

The Seasonal Cycle of Atmospheric Heating and Temperature Donohoe, Aaron; Battisti, David S The seasonal cycle of the heating of the atmosphere is divided into a component due to direct solar absorption in the atmosphere and a component due to the flux of energy from the surface to the atmosphere via latent, sensible, and radiative heat fluxes. Both observations and coupled climate models are analyzed. The vast majority of the seasonal heating of the northern extratropics (78% in the observations and 67% in the model average) is due to atmospheric shortwave absorption. In the southern extratropics, the seasonal heating of the atmosphere is entirely due to atmospheric shortwave absorption in both the observations and the models, and the surface heat flux opposes the seasonal heating of the atmosphere. The seasonal cycle of atmospheric temperature is surface amplified in the northern extratropics and nearly barotropic in the Southern Hemisphere; in both cases, the vertical profile of temperature reflects the source of the seasonal heating. In the northern extratropics, the seasonal cycle of atmospheric heating over land differs markedly from that over the ocean. Over the land, the surface energy fluxes complement the driving absorbed shortwave flux; over the ocean, they oppose the absorbed shortwave flux. This gives rise to large seasonal differences in the temperature of the atmosphere over land and ocean. Downgradient temperature advection by the mean westerly winds damps the seasonal cycle of heating of the atmosphere over the land and amplifies it over the ocean. The seasonal cycle in the zonal energy transport is 4.1 PW. Finally, the authors examine the change in the seasonal cycle of atmospheric heating in 11 models from phase 3 of the Coupled Model Intercomparison Project (CMIP3) due to a doubling of atmospheric carbon dioxide from preindustrial concentrations. The seasonal heating of the troposphere is everywhere enhanced by increased shortwave absorption by water vapor; it is reduced where sea ice has been replaced by ocean, which increases the effective heat storage reservoir of the climate system and thereby reduces the seasonal magnitude of energy fluxes between the surface and the atmosphere. As a result, the seasonal amplitude of temperature increases in the upper troposphere (where atmospheric shortwave absorption increases) and decreases at the surface (where the ice melts).

Mechanistic modeling explains the production dynamics of recombinant adeno-associated virus with the baculovirus expression vector system

2026-04-23T03:14:38Z

Mechanistic modeling explains the production dynamics of recombinant adeno-associated virus with the baculovirus expression vector system Destro, Francesco; Joseph, John; Srinivasan, Prasanna; Kanter, Joshua M; Neufeld, Caleb; Wolfrum, Jacqueline M; Barone, Paul W; Springs, Stacy L; Sinskey, Anthony J; Cecchini, Sylvain; Kotin, Robert M; Braatz, Richard D Current manufacturing processes for recombinant adeno-associated viruses (rAAVs) have less-than-desired yields and produce significant amounts of empty capsids. The increasing demand and the high cost of goods for rAAV-based gene therapies motivate development of more efficient manufacturing processes. Recently, the US Food and Drug Administration (FDA) approved the first rAAV-based gene therapy product manufactured in the baculovirus expression vector system (BEVS), a technology that demonstrated production of high titers of full capsids. This work presents a first mechanistic model describing the key extracellular and intracellular phenomena occurring during baculovirus infection and rAAV maturation in the BEVS. The model predictions are successfully validated for in-house and literature experimental measurements of the vector genome and of structural and non-structural proteins collected during rAAV manufacturing in the BEVS with the TwoBac and ThreeBac constructs. A model-based analysis of the process is carried out to identify the bottlenecks that limit full capsid formation. Vector genome amplification is found to be the limiting step for rAAV production in Sf9 cells using either the TwoBac or ThreeBac system. In turn, vector genome amplification is hindered by limiting Rep78 levels. Transgene and non-essential baculovirus protein expression in the insect cell during rAAV manufacturing also negatively influences the rAAV production yields.

PixBric: Precision Morphological Control of Pre-Stretched Fabrics Through Tessellated Primitive Geometries

2026-03-17T03:09:57Z

PixBric: Precision Morphological Control of Pre-Stretched Fabrics Through Tessellated Primitive Geometries Youn, Hye Jun; Sara, Serena; Ishii, Hiroshi 3D printing patterns onto pre-stretched fabrics has emerged as a promising method for the rapid fabrication of self-shaping textiles. However, the influence of design parameters on morphing behavior remains insufficiently explored, often resulting in heuristic-driven decisions. This study introduces PixBric, a pixel-based textile methodology composed of primitive geometries designed to induce controlled morphing behaviors—such as undulation and bending—and mechanical properties including multistability. By parametrically adjusting geometry, thickness, and inter-pixel spacing, PixBric enables precise morphing outcomes. The framework includes a morphing simulation tool and a design chart linking geometric variables to deformation results. We also propose a streamlined fabrication protocol using biaxial pre-stretching with magnetic framing. These contributions establish a systematic design approach for the functional and interactive deployment of self-shaping textile structures. UIST Adjunct ’25, Busan, Republic of Korea

Cellular anatomy of the mouse primary motor cortex

2026-05-08T03:13:48Z

Cellular anatomy of the mouse primary motor cortex An essential step toward understanding brain function is to establish a structural framework with cellular resolution on which multi-scale datasets spanning molecules, cells, circuits and systems can be integrated and interpreted1. Here, as part of the collaborative Brain Initiative Cell Census Network (BICCN), we derive a comprehensive cell type-based anatomical description of one exemplar brain structure, the mouse primary motor cortex, upper limb area (MOp-ul). Using genetic and viral labelling, barcoded anatomy resolved by sequencing, single-neuron reconstruction, whole-brain imaging and cloud-based neuroinformatics tools, we delineated the MOp-ul in 3D and refined its sublaminar organization. We defined around two dozen projection neuron types in the MOp-ul and derived an input–output wiring diagram, which will facilitate future analyses of motor control circuitry across molecular, cellular and system levels. This work provides a roadmap towards a comprehensive cellular-resolution description of mammalian brain architecture.

The true, the good and the justified: Essays on epistemic normativity and value

2026-03-17T03:04:09Z

The true, the good and the justified: Essays on epistemic normativity and value Gélineau, Félix-Antoine This dissertation about the role that value—in particular, the value of truth—plays in the explanation of epistemic norms, the norms that should govern our belief-formation and belief-revision processes. It is a pervasive practice for us to assess one another’s beliefs, decrying some and commending others: we find something amiss in a belief formed by wishful thinking, and deem proper a belief arrived at via meticulous consideration of one’s evidence. It is natural to think that there is a close connection between what epistemic norms sanction, truth, and the fact that truth matters. In what sense is truth valuable? and what does that entail for how we should conceive of epistemic norms? These questions drive my dissertation. In chapter 1, “The true, the good and the justified”, I argue that the teleological conception of epistemic justification, the view that for a belief to be justified is for it to be formed in a way that is conducive to truth, is safe from the main objections it faces. These import assumptions about value that belong to ethical consequentialism; the epistemic teleologist need not and should not accept them. “Epistemic value” is best understood as a ‘placeholder’, not as a term denoting value in any substantive sense. The upshot is that endorsing a teleological explanation of epistemic norms does not commit one to the idea that true beliefs ought to be promoted in the way the good ought to be promoted according to ethical consequentialists. In chapter 2, “Why be antisocial (about epistemic normativity)”, I examine whether epistemic normativity is grounded in the usefulness of truth for communities and argue that it is not. If what we ought to believe were to be explained in terms of the usefulness of truth for communities, we should expect our epistemic norms to be wildly different from what they are: they could condone trade-offs between true and false beliefs across a community when doing so would be useful for it; they would often fail to prescribe believing over other doxastic attitudes when the epistemic aims of the community would be equally well-served by either; finally, there is no way to answer in a satisfying manner the question of what isolated individuals should believe. In chapter 3, “What’s truth got to do with it?”, I develop an account of epistemic normativity that does justice to the idea that truth matters but avoids the shortcomings of other value-based accounts. I argue that, given any plausible account of the value of truth, the value of truth can explain some, but not all epistemic norms. The account of epistemic normativity that I present is pluralistic: I distinguish between substantive norms, which are explained by the value of truth, and basal norms, which stem from the good functioning of mechanisms of belief-formation and belief-revision, independently of the value of truth. This account is shown to be superior to other accounts discussed in the course of the dissertation.

Ions in Electrically Conductive and Insulating Metal-Organic-Frameworks: Transport and Energy Storage

2026-03-17T03:04:08Z

Ions in Electrically Conductive and Insulating Metal-Organic-Frameworks: Transport and Energy Storage Su, Alice Yue Ion transport in metal-organic frameworks (MOFs) is attracting increasing attention since ions can be easily incorporated into porous MOF structures as guest species, promising a variety of possible applications. While electronically insulating but ionically conductive MOFs show great potential as solid electrolytes, the precise structure and tunability of MOFs also enables rational combination of electronic and ionic conductivity to create intrinsic mixed conductors. The combination of both conduction pathways is highly relevant for energy storage applications where ions interact with and insert into electronically conductive electrode active materials. This thesis first explores ion transport in an anionic MOF electrolyte, conducting mono- and divalent cations. Transport studies give insight into how MOF structure and ion-related variables impact ionic conductivity and activation energy. Studies on this material paint a picture that furthers our understanding of fundamental ion transport mechanism in MOF electrolytes. Incorporating electronic transport as an additional layer of complexity, new mixed proton-electron conductive two-dimensional MOFs based on aromatic azaborine ligands are synthesized. Their dual conductive nature is confirmed by separating the ionic and electronic contributions to the overall transport. Lastly, a family of triazatruxene-based two-dimensional electronically conductive MOFs are explored as pseudocapacitors. Here, the diffusion of ions inside the pore network as well as their interaction with MOF active sites depending on the interlayer spacing are investigated for their impact on capacitance.

Plurals of politeness and the morphosyntax of number

2026-03-17T03:04:13Z

Plurals of politeness and the morphosyntax of number Sinha, Yash Many languages use plural pronouns to address (and refer to) single individuals with politeness or honorification. In some languages, these plurals of politeness (PoPs) show mixed agreement, triggering plural agreement on some agreement targets and singular on others. In this dissertation, I use PoPs to investigate the internal structure of DPs, focusing on how number features are represented within them. Starting first with pronominal DPs, I adopt the view that these contain two phrases: (i) a noun phrase (NP) headed by a silent noun (Postal 1966 and subsequent work), and (ii) an index phrase (idxP), realized by the pronoun, which occupies the specifier of the DP and introduces a referential index (Jenks 2022; see also Choi 2014, Giusti 2015). My novel claim is that both the NP and the idxP bear their own number features. In most cases, this is not detectable because the number features of the NP and idxP match. I argue, however, that this is not the case for PoPs, which allows us to see that these two number features are in fact distinct. Specifically, I show that the agreement patterns of PoPs are best accounted for by treating them as consisting of a plural idxP with a singular NP (i.e., a plural pronoun with a silent singular noun). This analysis not only derives the mixed agreement of PoPs seen in some languages, but also explains certain cross-linguistic restrictions on the distribution of singular agreement with them. I also extend this analysis to account for nominal PoPs, a type of nominal DP found in a subset of languages with pronominal PoPs. These nominal PoPs have the same semantics and the same agreement patterns as their pronominal counterparts, but crucially, the morphology on the noun in nominal PoPs is singular and not plural. I argue that these similarities and differences can be explained by positing that idxPs are present in nominal DPs as well, but are not realized overtly. This analysis of nominal PoPs is shown to also be able to account for the DP-internal concord patterns seen with them.

Oddness under Discussion

2026-03-17T03:02:36Z

Oddness under Discussion Hénot-Mortier, Adèle At a broad level, this dissertation's main claim is that many cases of pragmatic oddness do not stem from assertions alone, but rather from their interaction with the questions they implicitly evoke. Felicitous assertions, must evoke felicitous questions. To operationalize this claim, a model of compositionally derived implicit question is devised, along with conditions of their well-formedness, drawing from familiar concepts in pragmatics, such as Redundancy and Relevance. This model assigns a central role to the degree of specificity, or granularity, conveyed by assertions. At a more narrow level, this dissertation argues that disjunctions and conditionals fundamentally differ in terms of the questions they evoke, and that this difference has direct consequences on the oddness/felicity profiles of sentences involving these operators. Disjunctions are shown to be prone to Redundancy issues, while conditionals are shown to be prone to Relevance issues. In other words, disjunctions and conditionals typically display distinct flavors of oddness. This is supported by three main classes of sentences. First, sentences that can be seen as equivalent, but which combine conditionals and disjunctions in distinct ways, display varying felicity profiles. Second, "pure" disjunctions and conditionals that can be seen as isomorphic, if not equivalent, display varying felicity profiles. Third, some differences between these disjunctions and conditionals remain when additional pragmatic phenomena, in particular scalar implicatures, are at play, and such differences shift in a way predicted by our approach. This dissertation therefore justifies the appeal to a more elaborate model of (implicit) questions, which, when fed to the pragmatic module, is characterized by a better empirical accuracy on challenging data, than previous model solely based on assertive content.

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.

Illumination-Robust Terrain Relative Navigation for Planetary Descent

2026-03-17T03:04:17Z

Illumination-Robust Terrain Relative Navigation for Planetary Descent Mitchell, Adriana Macieira Missions to explore planetary bodies (e.g., Moon, Mars, Titan, Europa, Enceladus) through surface exploration have been planned for the next decades. These missions depend on autonomous optical navigation capabilities for safe entry, descent, and landing near key scientific areas of interest, potentially near hazardous terrain. Terrain Relative Navigation (TRN) enables autonomous precision landing by matching descent images to an a priori orbital map. However, performance degrades significantly when large differences in solar illumination exist between the map and descent imagery, particularly under high azimuth angle changes, due to terrain-induced shading inversions that break assumptions of photometric consistency in both frequency and intensity-based correlation methods. This thesis presents a set of methods to robustify TRN performance under large directional illumination changes, addressing three core challenges: understanding the failure of existing TRN methods, improving feature matching robustness to varying Sun angles, and generating reliable navigation maps from incomplete orbital imagery. First, the physical cause of correlation failure is characterized through a frequency-domain analysis of shading effects. Building on the azimuth impact matrix, which models the directional dependence of shading-induced phase reversals, this work applies it as a frequencydomain correction to frequency correlation to improve correlation peak accuracy across large solar azimuth differences. Second, a novel frequency-domain photometric correction method, Solar Orientation Layering via Frequency Image eXtraction (SOLFIX), is introduced to produce corrected map products aligned with expected descent conditions by layering spatial frequency content aligned with known solar angles from multi-resolution, multi-illumination orbital imagery. Third, a predictive illumination-aware map is developed to identify terrain regions likely to yield reliable correlations. This map integrates solar azimuth geometry, terrain aspect from low-resolution digital elevation models, and spatial frequency information to pre-filter unreliable areas of the map prior to localization. The proposed methods are validated on Mars orbital datasets, simulated terrain renderings, and NASA JPL’s field test imagery, each with Sun angle variations. Despite wide Sun angle differences, the proposed methods recover accurate localization where baseline TRN methods fail due to the bias from shading inversions caused by large solar azimuth differences. These contributions enable reliable optical navigation in scenarios where acquiring new orbital maps is infeasible and support future planetary missions operating under severe illumination constraints.

Verification Planning for Efficient Uncertainty Reduction of Space Science Systems

2026-03-17T03:04:12Z

Verification Planning for Efficient Uncertainty Reduction of Space Science Systems Stenzel, June Verification for a complex engineering system is essential for mitigating risks associated with quantitative uncertainty and ensuring confidence in the system’s performance. Verification planning is the problem of deciding how to allocate resources in this process, and verification for some systems may allocate more time and money to certain verification activities than is necessary to achieve a desired level of certainty. The need for efficient and effective verification is especially great for space systems and space science instruments, which are often subject to active cost and schedule constraints that make verification planning a challenge. This research proposes the Uncertainty Quantification Verification Planning Methodology (UQVM) for designing optimal-under-uncertainty verification plans in a systematic, quantitative, model-based way. Uncertainty quantification methods are used to model instrument performance, determine sources and magnitudes of parametric uncertainty, and perform sensitivity analysis. Stochastic models of potential verification activities are also developed with subject matter expertise, and are subjected to uncertainty quantification. A novel approach to optimal Bayesian experimental design (OBED) is developed to determine sets of verification activities that minimize effort and maximize certainty of system performance. A comprehensive systems engineering approach brings together these techniques of uncertainty quantification and experimental design methods, so that systems engineers can optimize design of AI&T and V&V campaigns with respect to programmatic cost and confidence of system performance, and can refine those plans as new verification data is obtained. UQVM is demonstrated for space science system case studies. A study of verification planning for CCD performance shows that optimally uncertainty-reducing plans within a cost cap can be identified that reduce the variance of predicted performance by 67%, and that iterative data-informed verification planning can reduce the variance of predicted performance by 96%. A retrospective analysis of the sensitivity verification for the Large Lenslet Array Magellan Spectrograph (LLAMAS) shows that optimized plans can reduce testbed time by 94% without a loss in uncertainty reduction, and that plans can be identified that perform better than historical plans with a confidence of greater than 90%. An early-phase analysis of precision control verification for the James Webb Space Telescope (JWST) shows that tests can be ranked in order of benefit-at-cost.

Molecular Mechanisms Defining and Driving Receptivity in Conversion of Fibroblasts to Motor Neurons

2026-03-17T03:04:11Z

Molecular Mechanisms Defining and Driving Receptivity in Conversion of Fibroblasts to Motor Neurons Beitz, Adam M. Layers of regulation stabilize cellular identity and prevent aberrant cell-fate transitions. Cell fate conversion processes, such as the conversion of fibroblasts into motor neurons, attempt to induce a cell of one type to become a cell of another type by activating genes and gene networks associated with the desired final cell type. Cell fate conversion processes have the potential to revolutionize drug discovery and drive the development of novel cell-based therapies, but their translational potential is limited by poor conversion rates. Forced overexpression of lineage-specifying transcription factors is rarely sufficient to induce a complete change in cellular identity. We find that overexpression of known oncogenes can enhance a cell’s receptivity to conversion by increasing proliferation rates and increase conversion yields 100x, even when converting to a non-proliferative state. In this thesis, we use the model system of mouse embryonic fibroblast to motor neuron conversion to determine how oncogenic mutants of HRAS and the tumor suppressor protein p53 induce a receptive cell state and enhance conversion. We find that cells that proliferate at high rates early in conversion attain the motor neuron identity at higher rates than cells that do not proliferate as much. We isolate cells that attain high rates of proliferation and define the subcellular properties of these conversion-receptive cells. Receptive cells display more compact chromatin as proliferation destabilizes chromatin structures generally, including those that reinforce the starting cell type identity. An increase in trimethylation of H3K27, a histone mark known to induce chromatin compaction and reduce transcriptional output, supports this this compaction and is associated with a global decrease in transcription rates. Finally, we make a counter-intuitive finding that the interaction between mutant and native p53 enhances conversion beyond its role in promoting proliferation that is dependent on the presence of native p53. The p53 mutant induces accumulation of native p53 in a subpopulation of cells. We developed a tool to track p53 levels in mouse embryonic fibroblasts in order to track p53 accumulation during conversion. By developing tools to isolate cells with different conversion-receptivity during oncogene-enhanced conversion, we can characterize the subcellular features that promote conversion. We expect future cell fate conversions may be engineered to systematically guide cells through a receptive state without oncogenes.

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

Structural and biochemical investigations of collagen-I trimerization

2026-03-17T03:02:52Z

Structural and biochemical investigations of collagen-I trimerization Srinivasa, Sorin Asha The fibrillar collagens display a unique assembly process, with assembly initiating at the C-terminal propeptide (C-Pro) domain, a small globular domain that encodes vast amounts of information for chain selection, stoichiometry, and molecular recognition. The C-Pro domain is responsible for ensuring that strands that assemble together are of the same type of collagen, and, in the case of certain collagens, that the stoichiometry between strands is correct. In addition to being involved in assembly, the C-Pro domain has also been shown to play a significant role in collagen proteostasis. Quality control factors in the cell are able to specifically recognize misfolded C-Pro domains in the context of disease-causing mutations, demonstrating a vital role for C-Pro folding in this process. In chapter 2, we report progress towards the answer to a long-elusive question: why do certain collagens favor heterotrimeric assemblies even when homotrimeric assemblies are possible? We show progress towards a high-resolution structure of the collagen-I C-Pro 2:1 heterotrimer, and investigate the role of Ca2+ coordination in dictating assembly behavior and chain association. In chapter 3, we characterize the assembly and proteostasis defects associated with a set of C-Pro mutations that have been observed in patients with osteogenesis imperfecta. Our data reveal that, while some variants are effectively recognized by the cell’s quality control mechanisms and retained intracellularly, others are not and secrete at similar levels to the wild-type. We also demonstrate that even severely assembly-deficient C-Pro domains can escape quality control, likely resulting in severe or lethal disease. Collectively, the work presented here significantly advances our understanding of how collagen-I assembly occurs in healthy biological systems, and how it can be disrupted in disease, setting the stage for a variety of future investigations into this challenging system.

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.

Structured Bayesian Inference for Spatio-Temporal Systems with Applications in Remote Sensing

2026-03-17T03:02:50Z

Structured Bayesian Inference for Spatio-Temporal Systems with Applications in Remote Sensing Leung, Kelvin Man Yiu Satellite-based remote sensing observing systems are a key source of information for understanding Earth system dynamics. Bayesian inference provides a principled framework for retrieving physical parameters from satellite observations while quantifying uncertainty. However, the high dimensionality and spatio-temporal complexity of remote sensing problems pose major computational challenges for traditional inference methods. This thesis develops scalable algorithms for Bayesian inference for remote sensing systems by leveraging low-rank structure and sparse conditional dependence structure. The resulting methods enable accurate and efficient posterior characterization at scales relevant for modern satellite missions. The first theme of this thesis is identifying low-rank structure in problems where the scientific goal is to estimate a small number of quantities of interest (QoIs) that are a function of the unknown parameters. Using a gradient-based dimension reduction framework, we construct informative subspaces of the observation space that are tailored to specific QoIs. This framework is integrated with transport maps to enable simulation-based inference directly for the QoIs, without the need to recover the full posterior of the high-dimensional parameters. We demonstrate this approach on imaging spectroscopy data from NASA’s upcoming Surface Biology and Geology (SBG) mission and show that it achieves inference accuracy comparable to Markov chain Monte Carlo (MCMC) while requiring orders of magnitude less computational time. In addition, we examine the role of preconditioning in dimension reduction and demonstrate that the optimal choice of preconditioner depends on the nonlinearity of the forward model. Next, we explore how conditional independence structure can be used to improve the scalability of inference algorithms relevant to remote sensing systems. We first consider a single-pixel setting and exploit within-state conditional independence to build sparse transport maps for hyperspectral retrievals. These sparse maps reduce computation over standard nonGaussian inference methods while preserving accuracy. Extending beyond individual pixels, we develop an information filter that leverages spatio-temporal conditional independencies in satellite observing systems. By incorporating sparse inverse covariance structure into the filtering equations, we achieve significant improvements in both scalability and inference accuracy on data relevant to NASA’s OCO-2, EMIT, and SBG missions. Building on this structure, this thesis also explores extensions of large-scale spatio-temporal inference to the continuous non-Gaussian setting using measure transport. Drawing inspiration from belief propagation algorithms for Gaussian graphical models, we construct decomposed transport maps tailored to spatio-temporal graphical structures. These methods enable scalable inference while capturing non-Gaussian features of the posterior. We demonstrate their application to spatio-temporal systems, providing a viable framework for high-fidelity uncertainty quantification.

Towards Intelligent Psycho-Social Support to Augment Behavioral Health Management in Isolated Environments

2026-03-17T03:02:19Z

Towards Intelligent Psycho-Social Support to Augment Behavioral Health Management in Isolated Environments Nguyen, Golda Long-duration spaceflight requires astronauts to live in isolated, confined, and extreme environments while physically and socially separated from support systems for up to years at a time. This thesis explores how intelligent, autonomous tools may augment behavioral health management when real-time, Earth-based support is inaccessible in deep space. Such intelligent psycho-social support must be able to: 1) characterize individual risks, 2) assess health state accurately, and 3) deliver appropriate interventions or countermeasures. To augment these system functions, techniques from statistical modeling, natural language processing, and conversational AI are investigated across three case studies of isolation: wide-scale isolation during the COVID-19 pandemic, isolation in a space analog habitat, and social isolation in the general public. To explore risk characterization, statistical models were constructed on trait-based, behavioral, and social environment factors in relation to mood and anxiety state in isolation. Models of civilian risk under isolation were developed to inform automated risk characterization for future private astronauts. To explore augmenting psychological assessment, a feasibility analysis of natural language processing (NLP) for automated affect classification was conducted. Transformer-based NLP techniques were tested against lexicon-based and other machine learning (ML)-based techniques on affect classification of personal journal text from analog astronauts. Transformer-based models demonstrated improved detection of negative affect classes, but overall, lexicon-based models were still comparable to ML-based models. Finally, to explore augmenting countermeasures, a study of engagement and disclosure in AI-augmented reflection was conducted. In this study, participants shared more content volume and spent more time in a hybrid reflection condition (reflecting alone first before chatting with a prompted chatbot) than in separated conditions (journaling alone or reflecting only with the bot). Higher loneliness was associated with lower comfort and engagement, but behavioral benefits were similar across lonely and non-lonely users, suggesting further tailoring is needed to support isolated individuals. Through these case studies, this work presents a point of departure towards a vision of intelligent psychosocial support systems that are not meant to replace human connection, but to augment behavioral healthcare for individuals in isolated settings on Earth and in space.

Discovery and characterization of plateau potentials in cortical neurons of awake mice

2026-03-17T03:04:06Z

Discovery and characterization of plateau potentials in cortical neurons of awake mice Mojica Soto-Albors, Raúl E. Plateau potentials are large calcium-dependent regenerative depolarizations that support burst firing and facilitate behavioral time scale synaptic plasticity (BTSP) in the hippocampus. Despite substantial progress in our understanding of these events in CA1, it remains unclear whether they occur in the neocortex and, if so, how do they manifest. To address this, we performed in vivo whole cell patch clamp recordings from layer (L) 2/3, L4, and L5 pyramidal neurons (PNs) in mouse primary visual cortex (V1) to produce the first systematic characterization of cortical plateau potentials. We established functional correlates of plateau potentials and evaluated their role in plasticity induction. First, we described the high prevalence of prolonged somatic depolarizations accompanied by high-frequency spikes (~105 Hz) in 43% of L5 PNs. Cortical plateau potentials closely resembled those previously described in the hippocampus, averaging ~27 mV in amplitude and ~60 ms in duration, with pronounced intraburst spike amplitude attenuation. Recordings obtained from L2/3 and L4 neurons revealed that cells in these layers do not generate plateaus, indicating a unique generation site in L5. Within L5, neurons exhibiting plateaus had lower input resistance than those that did not, suggesting plateaus may be specific to thick-tufted extratelencephalic (ET) PNs. We further described how the incidence of plateaus in L5 PNs was surprisingly not increased by visual stimulation. Intriguingly, their prevalence more than tripled during periods of behavioral arousal. Furthermore, plateau initiation was more likely during the rising phase of the extracellular theta rhythm (5-10 Hz) in V1, suggesting that cortical plateaus are modulated by internal state and network rhythms rather than visual stimuli alone. Finally, we investigated the role of cortical plateaus in plasticity by pairing a non-preferred stimulus with artificially evoked events. In contrast to the BTSP observed in CA1, spiking output remained consistent before and after pairing in V1. However, subthreshold responses indicated some synaptic depotentiation for the preferred stimulus, indicating that plateaus might be sufficient to alter synaptic weights, though in a different way than that demonstrated in hippocampus. Collectively, this work sheds light on an underexplored cortical output mechanism unique to L5 pyramidal neurons, positioning the plateau potential as a cell-type-specific phenomenon that may reshape sensory representations in the neocortex, with implications for cortical computation and biologically inspired learning rules in neural networks.

Comprehensive laboratory studies of organic oxidation across a range of photochemical ages and peroxy radical conditions

2026-03-17T03:04:07Z

Comprehensive laboratory studies of organic oxidation across a range of photochemical ages and peroxy radical conditions Franco Deloya, Lesly Joanne Reactive organic carbon (ROC), defined as all volatile organic compounds (VOCs) and particulate organic carbon except methane, is the largest source of reactive emissions in the atmosphere and therefore plays a central role in atmospheric chemistry. After emission, ROC undergoes rapid chemical transformations driven by sunlight and atmospheric oxidants, forming peroxy radicals (RO₂) whose fate shapes the resulting oxidation products. This sequence of reactions, known as the ROC lifecycle, continues until ROC is removed via wet or dry deposition or fully oxidized to carbon dioxide (CO₂). Throughout its evolution, ROC contributes to the formation of ozone, particulate matter and CO₂, making its study necessary for understanding air quality and climate. While many aspects of this cycle have been explored through modeling, laboratory experiments, and field campaigns, our understanding of the ROC lifecycle remains incomplete due to the large number of compounds and its chemical complexity as it evolves in the atmosphere. This thesis investigates key aspects of the ROC lifecycle, focusing on its multigenerational chemical evolution over extended atmospheric aging and on how RO₂ chemistry shapes gas-phase species distributions. The first part investigates the multigenerational oxidation of ROC through a series of laboratory experiments designed to simulate atmospheric aging over extended timescales. These experiments make comprehensive measurements of both gas- and particle-phase carbon to gain a holistic understanding of ROC evolution in the atmosphere. While models have been used to simulate the full evolution of ROC, few experiments have constrained this process. These experiments provide the first direct constraints on the lifetime of ROC against heterogeneous oxidation, the formation of small and long-lived oxygenated VOCs, and the evolution of carbon properties (e.g. carbon number, carbon oxidation state) over multiweek atmospheric equivalent aging. We then compare these experimental results to long-term aging simulations using several chemical mechanisms that are implemented in models. Most chemical mechanisms achieve near-total carbon closure, however, they differ in species composition, ROC mineralization rates, and OH reactivity, especially at later stages of oxidation. These discrepancies highlight the gaps that remain in our understanding of the long-term chemical evolution of ROC. Finally, we conduct experiments that simulate a variety of RO₂ environments representative of atmospheric conditions. Past chamber experiments have primarily focused on RO₂ chemistry under extreme conditions that involve short bimolecular lifetimes and “low” or “high” NOₓ conditions. Our findings suggest that traditional metrics used to assess species distributions are insufficient to explain the full range of changes with shifts in the RO₂ environment. A more comprehensive accounting of all relevant RO₂ fates is necessary to explain gas-phase species composition, which in turn influences HOₓ and NOₓ cycling, ozone production and ultimately our understanding of secondary organic aerosol formation. Together, these results provide new constraints on the atmospheric evolution of ROC across a wide range of oxidative and photochemical conditions, improving our understanding of ROC and its role in air quality and climate.

Structural characterization and antibiotic development for the Neisseria gonorrhoeae class Ia ribonucleotide reductase

2026-03-17T03:03:59Z

Structural characterization and antibiotic development for the Neisseria gonorrhoeae class Ia ribonucleotide reductase Dorfeuille, Andrew Leonard Jacques Ribonucleotide reductases (RNRs) are essential enzymes that catalyze the reduction of ribonucleotides to deoxyribonucleotides, a critical step in DNA biosynthesis and repair. Class Ia RNRs, found in eukaryotes and many aerobic bacteria including Escherichia coli (E. coli) and Neisseria gonorrhoeae (N. gonorrhoeae), are regulated by complex allosteric mechanisms that control enzymatic activity and substrate specificity. These enzymes function as α₂β₂ complexes, with the α₂ subunit housing regulatory sites and the β₂ subunit providing a catalytic radical. Proper regulation of RNR is vital for maintaining balanced dNTP pools and genomic integrity, making RNRs attractive targets for therapeutic intervention in both infectious disease and cancer. This thesis examines the structural basis of specificity regulation in N. gonorrhoeae class Ia RNR using cryogenic electron microscopy (cryo-EM). Near atomic-resolution structures of the enzyme bound to four canonical substrate/specificity-effector pairs (CDP/dATP, UDP/dATP, GDP/TTP, and ADP/dGTP) reveal that effectors induce conformational changes in loop 2 of the α₂ subunit, which alter hydrogen bonding contacts in the active site, leading to preferential substrate binding. This mechanism is also conserved in the E. coli class Ia RNR, a close homolog. Cryo-EM maps also show weak, non-specific binding of a second nucleotide in the cone domain, likely reflecting artifacts of high nucleotide concentrations used in the cryo-EM experiments rather than physiological relevance. Building on these findings, Chapter III investigates the potential interaction of the cyclic dinucleotide, c-diAMP, with the cone domains of E. coli and N. gonorrhoeae RNRs. We find that c-diAMP binds both enzymes with low micromolar affinity but does not alter the activity of either RNR to a large extent over the no-effector control. Although similar, the behavior of E. coli and N. gonorrhoeae RNRs are not identical, highlighting the potential of targeting the cone domain for species-specific RNR inhibition. This approach could enable the development of novel antibiotics, particularly needed for combatting antibiotic-resistant N. gonorrhoeae.

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.

Generalizable Reinforcement Learning for Network Control

2026-03-17T03:04:02Z

Generalizable Reinforcement Learning for Network Control Wigmore, Jerrod This thesis confronts the critical generalization gap of Deep Reinforcement Learning (DRL) that hinders its effective application to queueing network control, where policies often fail to perform robustly in unseen topologies and traffic conditions upon deployment. We develop and analyze a suite of novel techniques that systematically embed structural domain knowledge and safety considerations to create more robust, efficient, and generalist learning agents. To improve generalization for a large class of queueing network control problems, we first introduce the Switch-Type Network (STN), a policy architecture that embeds the "switch-type" property common in classical control. This architectural prior improves sample efficiency and enables superior zero-shot generalization across varying network parameters. To address generalization across multi-hop networks, we then propose the Multi-Axis Graph Neural Network (MA-GNN), which augments the traditional inter-node message passing operations of a GNN with a novel intranode aggregation mechanism to capture complex, permutation-invariant dependencies between different traffic classes. This allows the MAGNN to learn and output high-level control coefficients that are effective for unseen network topologies. Recognizing the limitations of offline training, we shift to online adaptation and introduce an intervention-assisted DRL framework that guarantees stability in environments with unbounded state-spaces. By partitioning the state space and ceding control to a provably stable policy in high-congestion regions, this framework prevents catastrophic learning failures; its stability is proven via Lyapunov analysis, and foundational policy gradient theorems are extended to support the interventional setting. As a complementary case study in structured exploration, we also develop a Bayesian Hierarchical Bandit model and a Hierarchical Thompson Sampling (HTS) algorithm for the multi-band radio channel selection problem, which leverages environmental correlations to guide exploration and significantly reduce regret. Collectively, these contributions provide a comprehensive framework for creating DRL agents that are more robust and practical, demonstrating that embedding knowledge of policy structure, network topology, safety, and environmental correlations is a crucial step towards deploying autonomous agents in complex, real-world systems.

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.

Local Competition, Number and Definitness

2026-03-17T03:04:00Z

Local Competition, Number and Definitness Doron, Omri In this dissertation, I explore the consequences of local competition inside the DP. I argue that various phenomena, including multiplicity inferences, homogeneity and definiteness, are best explained as locally-triggered scalar implicatures (SIs), when coupled with a view of SIs as presupposed (Bassi et al., 2021). I begin with the puzzle of the multiplicity inferences that arise from the use of plural indefinites, and show that deriving them as presupposed SIs naturally explains their felicity conditions and projection from embedded environments. I then argue that this competition-based system can account for the typology of number marking, and in fact providing us with a parsimonious theory of the crosslinguistic variation. A key result of this argument is that any language which allows for number marking on nouns has both the singular and the plural feature in its inventory. Finally, I suggest that local competition can also derive the inferences stemming from definite descriptions, including uniqueness, maximality and homogeneity.

Engineering DNA-based electrochemical diagnostics for translational applications

2026-03-17T03:02:09Z

Engineering DNA-based electrochemical diagnostics for translational applications Zhou, Xingcheng Low-resource settings are disproportionally burdened by infectious diseases due to limited access to early and accurate disease detection. Current gold standard methods, while effective, have high turnaround times and require costly infrastructure that is often impractical in these environments. Electrochemical biosensors are a promising alternative to due to their sensitivity, selectivity, low-cost, portability, and rapid response time. Among various biorecognition elements, DNA is particularly advantageous due to its versatility, comparable stability, and low production cost. However, there is a significant gap between laboratory proof-of-concept biosensors and commercially viable biosensors. The main challenges associated with commercializing DNA-based sensors include ineffective surface chemistries, limited target range, poor long-term stability, and high-cost, non-scalable manufacturing processes. In my thesis, I resolve these specific challenges by engineering DNA-based electrochemical biosensors systems to support their translation into commercially-viable products. First, I address ineffective surface chemistries for modifying screen-printed carbon electrodes, which are widely used for bioelectrochemical systems due to their low production cost and scalable manufacturing. However, effective modification with biomolecules remains a challenge as the main methods are either non-specific, require harsh reagents, or form weak monolayers. In this project, we develop a new facile, bio-orthogonal, and biocompatible surface chemistry for modifying screen-printed carbon electrodes. This approach enables the modification of electrode surfaces with DNA, whole cells, and proteins while maintaining bioactivity, supporting applications in both biosensing and clean energy. Next, I expand the range of targets for nucleic acid electrochemical detection. Electrochemical hybridization assays are sensitive and specific but are limited to very short nucleic acids. To resolves this, we develop a restriction enzyme-assisted electrochemical hybridization assay for improved nucleic acid detection. By incorporating target-specific restriction enzymes, I detect long nucleic acids, with performance dependent on the location of the cut site relative to the electrode surface. Thus, I establish guidelines for assay design to serve as a generalizable platform for robust electrochemical detection of long nucleic acids. Subsequently, I solve the challenge of long-term storage of sensors. Commercialization of DNA-based electrochemical biosensors is challenged by the stability and shelf-life of the DNA monolayer. There is no technology that allows storage of these sensors long term at room temperature at dry conditions. Here, we report a novel method to preserve DNA-based biosensor through a protective coating of polyvinyl alcohol. We show that the coating significantly improves the shelf life at both room temperature and elevated temperatures. We further demonstrate that the DNA is viable for downstream sensing. Our finding allows facilitates the commercialization of DNA-based biosensors as viable products. Finally, I design and fabricate a multiplexed electrochemical diagnostic device for respiratory viruses. While electrochemical biosensors are a major research area of diagnostics that can be utilized in low resource settings, effectively integrating assays into a seamless, inexpensive fluidic device is difficult. In this project, we first develop an assay to detect three types of respiratory viruses with sensitivity comparable to PCR. We then integrate the workflow into a shelf-stable diagnostic utilizing low-cost materials and a scalable manufacturing process. Our device offers a practical solution for device integration and future disease control for vulnerable populations. Overall, the methods developed in this work have the potential to support the transition of DNA-based electrochemical biosensor from academic research to commercially-viable products, paving the way for more FDA-approved diagnostics for early disease detection and advancing health equity in vulnerable populations.

Essays in Labor and Public Economics

2026-03-17T03:02:02Z

Essays in Labor and Public Economics Martin Richmond, Jane Alexandra This dissertation examines how institutional policies and labor market frictions affect human capital formation, employment outcomes, and economic mobility across generations. The three essays explore distinct but interconnected aspects of how families and workers navigate constraints in education, childcare, and credentialing systems. The first essay is joint with Jonathan Rothbaum and investigates intergenerational spillovers from parental disability insurance receipt, using linked Census/IRS/SSA administrative data covering over 400,000 families. Using US administrative data, we link dependent children of SSDI recipients to their tax filings at age 25. We document a key descriptive fact, that the income of the child at age 25 is increasing in the age at which the parent receives their first SSDI transfer. We show that the probability that a child themselves receives an SSDI transfer as an adult is decreasing on the same margin; however, the pattern persists in the sample of children who do not receive such transfers. We build a model to show that these facts are consistent with a "lost-years" mechanism by which children whose parents become resource constrained earlier in life lose more years of costly intergenerational human capital investment. The second essay examines firms' decisions to remove bachelor's degree requirements from job postings and the subsequent hiring outcomes. By tracking within-role requirements over time, I identify instances where employers have explicitly removed bachelor's degree prerequisites from job advertisements. Linking these changes to aggregated resume data, I analyze whether individuals subsequently hired have different educational credentials, alternative qualifications, or experiential backgrounds. After the removal of degree requirements, the share of individuals hired with a degree falls by 1-3 p.p. Concurrently, the share of people hired that report a non-degree credential is roughly unchanged, and the average years of labor market experience possessed by a candidate increases by 0.5 years. I detail a simple model explaining why firms may be motivated to remove degree requirements and substitute them with other screening mechanisms. To address potential endogeneity concerns, I employ an instrumental variable approach, exploiting staggered state-level policy shifts in the removal of degree requirements from public sector employment, which act as an information treatment to firms. I also show that these firm-level and state-level policy changes are broadly uncorrelated with local labor market conditions. The third essay is joint with Maya Bidanda and analyzes how childcare access affects parents' choice between wage employment and self-employment. In this paper, we exploit variation in subsidized Pre-Kindergarten (Pre-K) to understand how access to childcare impacts mothers’ propensity for self-employment and formal work. We find that mothers of children who are too young for school are more likely to be self-employed and less likely to be in formal work than mothers of older children. We do not find similar trends for fathers. In addition, access to low-cost childcare increases the likelihood for formal work and decreases the likelihood of self-employment. This is evidence that mothers are pushed into self-employment due to frictions or barriers in formal work when childcare is not available. In the last part of the paper we discuss the impact of these patterns on mothers' lifetime income.

Machine Learning for Chemical Reactivity Prediction: Paradigms, Challenges, and Applications

2026-03-17T03:02:30Z

Machine Learning for Chemical Reactivity Prediction: Paradigms, Challenges, and Applications Raghavan, Priyanka The discovery of new therapeutic agents in the pharmaceutical industry is a complex, iterative process, often encapsulated by the Design-Make-Test-Analyze (DMTA) cycle, in which chemists ideate, synthesize, and assay compound targets of interest. A significant bottleneck in this cycle is the "Make" phase, where the synthesis of novel compounds can be time-consuming, resource-intensive, and fraught with unpredictable outcomes. Accurate prediction of chemical reactivity, particularly reaction yields and selectivities, is therefore paramount to accelerating drug discovery by enabling more efficient synthesis planning, reducing material waste, and guiding the design of more synthetically accessible molecules. As such, this dissertation explores the application of machine learning (ML) to address critical challenges in chemical reactivity prediction, with a particular focus on low-data regimes and the integration of predictive models into practical drug discovery workflows. This thesis begins by addressing the pervasive challenge of predicting reaction yields from sparse, literature-derived data. It details the assembly of a large dataset of substrate scopes and evaluates single-task and multi-task ML approaches, highlighting the limitations imposed by data scarcity and noise in real-world chemical literature. Recognizing these challenges, this thesis then provides recommendations for designing experimental datasets that are more conducive to robust machine learning, specifically offering considerations for curating data with the downstream modeling goal in mind. Building on these insights, this thesis then turns toward specific applications of machine learning in medicinal chemistry, first presenting a direct, impactful implementation of ML to enhance synthetic accessibility in drug design by predicting Suzuki cross-coupling yields from a large, historical pharmaceutical library dataset. ML models are shown to often outperform expert intuition and be successfully integrated into existing workflows for library design and rescue, significantly increasing synthesis efficiency. Finally, this thesis expands from chemical reactions to enzymatic reactions, detailing a computational and ML-based workflow for transaminase enzyme selection, to streamline the enantioselective synthesis of valuable chiral amine building blocks used in medicinal chemistry. Collectively, this thesis contributes to the growing field of machine learning in chemistry by addressing fundamental challenges in reactivity prediction, particularly in low-data and real-world industrial settings. It provides novel modeling paradigms for existing data and insights into the limitations of current approaches, offers a conceptual framework for improved data generation, and demonstrates the tangible benefits of integrating ML models into the DMTA pipeline. Throughout, the critical interplay between data quality, molecular representation, and model architecture and evaluation is emphasized, paving the way for more reliable and impactful predictive tools that can accelerate the pace of chemical discovery.

Property prediction with machine learning and ab initio methods for iridium photoactive complexes and metal-organic frameworks

2026-03-17T03:02:22Z

Property prediction with machine learning and ab initio methods for iridium photoactive complexes and metal-organic frameworks Terrones, Gianmarco Guin Data-driven prediction of a chemical’s properties prior to synthesis or use can accelerate chemical discovery by increasing the probability of candidate suitability for the given application. In this thesis, data-driven models and complementary first-principles calculations have been used to study three types of chemistries: iridium photoactive complexes, metal-organic frameworks, and reactions for azetidine synthesis. Iridium photoactive complexes are commonly used in OLED lighting, photocatalysis, and bioimaging due to their unique phosphorescent properties and triplet excited state population. Metal-organic frameworks are studied for heterogeneous catalysis and gas separations and storage due to their tunable metal environments and porous structures. Azetidine-containing molecules have potential for use as pharmaceuticals due to their high stability and good pharmacokinetics. However, despite their advantageous properties, challenges remain in designing these chemistries and informing this design with computation. The excited state properties of iridium complexes are challenging and costly to predict through first-principles methods. Similarly, stability issues often affect metal-organic frameworks, yet these cannot be efficiently modeled by physics-based routines. Lastly, synthetic approaches toward azetidine synthesis are limited, and computational study of novel synthetic approaches to identify desirable reactant characteristics would benefit the future scope of azetidine products. The models developed in this thesis have proven to be complementary tools to first-principles approaches, and have major benefits in their speed of application and ability to train directly on experimental data for properties that challenge methods like DFT. The models are applied to screen chemical space for promising candidates through consideration of hypothetical, not-yet-synthesized iridium complexes and metal-organic frameworks generated through component combination of existing structures. The machine learning models are also used to derive structure-property relationships through feature importance analysis, for example identifying qualities of iridium photoactive complexes that impart longer or shorter excited state lifetime. In addition to model generation, work in this thesis has covered code development of a software package for molecule structure generation, modification, and fingerprinting, and also development of intuitive web interfaces for easy use of data-driven models. It is expected that the tools developed in this thesis will both allow for a greater understanding of iridium complexes, metal-organic frameworks, and azetidine synthesis, and enable low-cost exploration of chemical space for novel material selection.

Spacecraft Autonomy through Computer Vision and Onboard Planning

2026-03-17T03:03:57Z

Spacecraft Autonomy through Computer Vision and Onboard Planning Kacker, Shreeyam Earth observation (EO) from satellite platforms has experienced widespread growth since the commercialization and widespread availability of data, and has had large impacts on applications such as agriculture, disaster monitoring, and defense and intelligence. Observing unpredictable phenomena is still challenging for EO missions due to long lead times from scheduling, uplinking, and executing the image capture onboard the spacecraft. This delay between planning and execution means that conditions can change in between them, causing a task to become unobservable and missed in the meantime, for example due to cloud cover obscuring a target. Dynamic tasking (DT) is a mission concept that aims to mitigate this unpredictability by moving autonomy onboard the spacecraft and quickly reacting to conditions as observed, using several potential perception sources. In this work, we consider DT as applied to a tasked Earth-observing satellite, whose goal is to image Earth’s landmass at predefined targets. The considered goal in this work for DT and onboard autonomy is avoidance of cloud cover, which can cause up to 66% of imaging tasks to be occluded, but factoring real-world constraints on operationalization and onboard edge computing. Instead of using end-to-end learned methods, we build upon existing work on spacecraft scheduling, incorporating a mixed-integer linear program (MILP) scheduler as the primary scheduling algorithm. Rather than directly incorporating DT into a global problem, we instead develop a set of heuristics which can estimate the utility of lookahead actions. We construct these heuristics from two directions: one from a simplified and constrained version of the scheduling problem with order statistics, and a second using a convolutional neural network with large amounts of synthetically generated data. We also consider DT using information from meteorological satellites in geostationary Earth orbit (GEO), parameterizing information delay rather than performing detailed analysis of data pipelines. In cases where all tasks are equally valued, all DT cases tested, including both meteorological and vision cases, outperform the conventional scheduler across all trials, ranging between 40% and 100% increase in total schedule utility based on cloud-free captures, depending on the DT method used. In cases where tasks have Pareto-distributed utility, the gap between the omniscient and conventional schedule shrinks drastically, to within 4% of total utility, and only a single DT method outperforms the conventional schedule consistently, as the environment becomes significantly more challenging due to asymmetric upside and downside risk. We also present methods by which to fractionate global state such that data can be efficiently stored and updated across a satellite constellation, allowing these heuristics to continue working across the constellation with minimal modification.

Large-Scale Multi-Robot Spatial Perception

2026-03-17T03:02:14Z

Large-Scale Multi-Robot Spatial Perception Chang, Yun This thesis addresses the challenge of scalable and robust multi-robot spatial perception, with the goal of supporting autonomous task execution in large-scale environments. The work focuses on two core issues: scaling to large, complex environments, and incorporating highlevel scene understanding to enable autonomy for complex tasks. Current multi-robot systems typically focus on geometric reconstruction for navigation, but often fall short in providing the scene understanding needed for complex decision-making and task execution in real-world environments. Conversely, many recent demonstrations of autonomous task execution are limited to small, controlled environments, with few methods addressing scalability to larger scenes. This work bridges this gap by integrating multi-robot simultaneous localization and mapping (SLAM) with spatial perception in order to support downstream autonomy for complex tasks. We begin by introducing methods to enhance the robustness and efficiency of loop closure detection in centralized multi-robot SLAM, focusing on prioritizing loop closures and mitigating the impact of incorrect loop closures in large-scale environments. We then present the first fully distributed metric-semantic SLAM system for multi-robot teams, which supports real-time semantic mapping and enables large-scale deployments with up to 8 robots and 8 kilometers of traversal. To improve reasoning across robot teams, we extend this work to 3D scene graphs, proposing a framework for collaboratively building and maintaining a shared multi-robot scene graph online. Additionally, we introduce algorithms for task-oriented compression of 3D scene graphs to support communication across robots under bandwidth constraints. Finally, we explore open-set scene understanding made possible by advances in visual-language models and highlight the need for task-driven mapping. Building on this, we propose a novel framework for grounding high-level language commands into scene graphs, enabling robots to decompose high-level tasks into executable subtasks while focusing on task-relevant components of the environment. The contributions of this thesis are validated through experimental evaluations in extreme environments and real-world deployments, where multi-robot teams operate in large-scale settings. These experiments tackle a broad range of tasks, from navigation and object search to executing high-level language commands (e.g., “clean the room”). Our contributions advance multi-robot large-scale spatial perception and have the potential to impact real-world applications such as exploration, service robotics, and search and rescue, where autonomous multi-robot teams are essential for performing complex tasks in large environments.

Locality: a case study from Äiwoo

2026-03-17T03:03:42Z

Locality: a case study from Äiwoo Roversi, Giovanni This thesis explores issues in the syntax of Äiwoo, an understudied Oceanic language from the Solomon Islands. This language showcases an intricate system of clausal alternations, where several parameters vary independently from each other: verbal morphology, word order, and possibilities of Ā-extraction (i.e., which argument(s) may or may not be extracted). As the title indicates, this thesis is set up as a case study, in a sort of “learn by doing” fashion. I attempt to build a descriptively and explanatory adequate formal model of the clausal alternation system in Äiwoo, within a Minimalist framework. By doing so, I examine what a theory of grammar must look like for the model to work as intended. Building such a model of Äiwoo teaches us something about a number of central issues in syntactic theory such as the locality of movement, the A /Ā-distinction, and the syntax of Austronesian languages specifically. I show that conjoining van Urk’s (2015) theory of the A /Ā-distinction and to the independent idea of featurally relativized probes (Béjar 2003), we predict the existence of “non-local A-movement”, that is, movement with the binding-theoretical properties of A-movement that nonetheless does not obey strict DPlocality, and crucially without the need of invoking the notion of “mixed A /Ā-movement”. I show that two instances of this predicted kind of movement are attested in the Äiwoo clause: movement to both spec,TP and spec,CP can target either the subject or – nonlocally – the object, depending on their features. I also propose that features assigned by a probe to a goal (“goal-flagging”; Deal to appear, Clem & Deal 2024) can be further manipulated by the syntax, being searched for by a higher probe. Further, Äiwoo shows an interesting instantiation of the Austronesian “pivot-only” Āextraction restriction, in that it comes with a series of exceptions. I argue that in Äiwoo, this restriction is caused by an Ā-intervention effect, contra analyses of this phenomenon in other Austronesian languages based on phasehood (Rackowski & Richards 2005, Erlewine & C. Lim 2023, Hsieh 2025, a.o.) or DP-intervention (Aldridge 2004, 2008, a.o.). Notably, as soon as the highest DP in a clause does not carry Ā-features, the restriction vanishes, thus allowing for the “exceptional” extraction of lower arguments.

Rapid In-Space Assembly and Manufacturing of Large Reticulated Truss Structures

2026-03-17T03:02:17Z

Rapid In-Space Assembly and Manufacturing of Large Reticulated Truss Structures Bhundiya, Harsh G. Modern deployable space structures have enabled spectacular missions like the James Webb Space Telescope, but they are constrained by the rocket fairing and a tradeoff between deployed size and structural precision that limits their use for future communications and astronomy applications. In-space assembly and manufacturing (ISAM), i.e., the construction of structures in the space environment, offers an approach to overcome these issues and enable novel missions both on orbit and on planetary surfaces. Structures constructed in space can be optimized for loads in space, achieve higher packaging ratios, and increase mission flexibility. Given these benefits and decreasing launch costs from modern reusable rockets, there is a resurgence of interest in ISAM from academic, commercial, and governmental entities. However, current ISAM concepts are hindered by inefficient construction processes with high size, weight, and power requirements and a lack of systems-level design of spacecraft and construction processes. This thesis aims to address these challenges to enable energy-efficient, rapid ISAM of large space structures. The first contribution is an analysis of the fabrication time of large truss structures, considering the constraints of spacecraft power, attitude control authority, and avoidance of flexural vibrations. The analysis shows that angular momentum storage of the spacecraft and flexibility of the structure are dominant constraints on fabrication time of gridshell geometries with diameters over 60 m, while the available power and control torque limit fabrication time for diameters under 60 m. This trade study provides quantitative estimates of the total fabrication time, e.g., five spacecraft constructing a 200 m diameter gridshell in five days, and highlights design tradeoffs to enable rapid ISAM, including using multiple spacecraft and varying the feedstock material based on the structure size. Motivated by the long fabrication timescales, the second contribution is an understanding of spacecraft attitude dynamics with changes in mass properties and environmental disturbances during construction. In particular, variable-mass rigid body dynamics are used to understand the feasibility of gravity gradient capture, a passive approach that exploits the gravity gradient disturbance during ISAM. The concept is illustrated with two case studies on the construction of truss structures, a 2D triangle unit cell and a 3D curved gridshell, by spacecraft in circular orbits. Based on the time reversibility of the equations of motion, initial conditions are computed that result in gravity gradient capture by solving the equations backward in time, considering the changes in mass properties from the prescribed construction sequence. The analysis highlights both the feasibility of passive gravity gradient capture and the sensitivity of initial conditions to small perturbations. It is found that deploying a gravity gradient boom before the start of the construction sequence can decrease this initial condition sensitivity by an order of magnitude, and more generally, designing an ISAM process to maintain the minimum principal inertia axis in the direction of the orbit radius vector can facilitate the robust passive gravity gradient capture of large structures. Finally, to aid the design of attitude control systems for ISAM spacecraft, ground experiments are presented to understand the attitude dynamics during Bend-Forming, a candidate deformation process for fabricating truss structures. The experimental results reveal the effect of metal springback during construction, which causes flexible vibrations and coupled motion of both the truss and spacecraft. Additionally, experiments with closed-loop, thruster-based position and attitude control highlight the possibility of control-structure interactions during construction, due to the decreasing natural frequency of the truss structure. Together, these contributions provide a framework for the efficient design of future ISAM spacecraft and construction processes to enable the next generation of space structures.

Space Optical Interferometry for High-Resolution Coherent Imaging of Astronomical Objects

2026-03-17T03:03:45Z

Space Optical Interferometry for High-Resolution Coherent Imaging of Astronomical Objects Black, Mason R. The hunt for Earth-like exoplanets is one of the great scientific endeavors of the 21st century. To date, the characterization of known exoplanets with instruments like JWST has been spatially unresolved—we can study atmospheric constituents via spectroscopy, but we cannot see continents, synoptic weather, or the geographic distribution of potential spectral biosignatures. The diffraction limit dictates that an optical aperture with sufficient angular resolution to resolve planetary scale features from the vantage point of our solar system would be prohibitively immense—hundreds to thousands of kilometers in diameter at visible wavelengths. Optical interferometry offers perhaps the only path to the required angular resolution by interfering light from multiple telescopes, or sub-apertures, providing Fourier components at a resolution corresponding to the sub-aperture separation. In recent decades, ground-based interferometry has made major strides in sensitivity by calibrating for atmosphere-induced piston errors to enable long coherent integration times. In addition to high-resolution astrometry, these sensitivity gains have allowed for milliarcsecond-scale imaging of bright astronomical objects. Still, optical interferometry from the Earth’s surface faces many fundamental performance limitations, and a space-based system would allow for the study of much dimmer targets at higher diffraction-limited resolutions, taking us a step closer to one day mapping exo-Earths. With recent advances in satellite miniaturization and lower cost-to-orbit, multiple groups have proposed new designs for a first demonstration mission, but no astronomical interferometer has yet flown in space. This work investigates the expected performance of first- and second-generation space optical interferometer concepts for astronomical imaging, focusing on maturing the technology that will be needed to push sensitivity and resolution beyond what is currently possible from the ground. The first mission envisioned is a formation flying pathfinder comprising three CubeSats, aiming to demonstrate the first measurements of interference fringes from starlight collected by separated space telescopes. This feat will require sub-wavelength matching of the optical path lengths traveled by the starlight to maintain the mutual coherence, which is achieved using rapid measurements of the interference fringe itself as a source of path length feedback. The performance of this fringe tracking is modeled via a time-domain control simulation accounting for the micro-vibration disturbance environment that would be expected on a CubeSat platform using reaction wheels for attitude control, which could induce up to 5 µm in optical path length noise and several arcseconds in optical alignment errors if left uncorrected. Also considered are the optical losses and noise sources associated with photon arrival statistics and detection as well as the beam pointing jitter. Simulation results indicate that such a mission would be able to stabilize interference fringes on at least the fifty brightest stars to better than 45 nm even under pessimistic disturbance assumptions, which would be sufficient to demonstrate the feasibility of space interferometry for a subsequent larger mission. The second mission concept analyzed aims to push the limits of faint-object interferometry from space by implementing a dual-feed beam combiner for fringe stabilization and phase referencing using bright off-axis guide stars. A three-telescope interferometer is assessed in its ability to map the surfaces of recently discovered dwarf planets in the outer solar system from a sun-synchronous Earth orbit. The population of stars usable as an interferometric phase reference is found to be within the off-axis field of view permitted by a 1-meter differential optical delay line, aiding image reconstruction via use of the Fourier phase referenced to a fixed point in the sky. Bayesian statistical imaging algorithms are employed to demonstrate recovery of an image from simulated noisy measurements of an 18th magnitude rotating object 37 millarcseconds in diameter, but results indicate that to do this with the integration times permitted by the chosen orbital formation would require approximately Hubble-sized telescope apertures. Potential alternative operational strategies to enable longer integration times with more modest apertures are discussed. Informed by the simulation results, recommendations are presented for near-term technology development in support of space interferometry.

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.

Synthetic Mucins for Microbial Modulation

2026-03-17T03:03:55Z

Synthetic Mucins for Microbial Modulation Barnes, Carolyn E. Mucus is a ubiquitous hydrogel that coats all epithelial cell surfaces. Once thought to be an inert hydrogel, the mucosal barrier is now recognized as a critical component of the innate immune system. It serves not only as a physical and chemical barrier against foreign objects, pathogens, and environmental stressors, but also as a selective interface that shapes an organism’s microbiome. Many of these functions are mediated by the primary structural component of mucus: the mucin protein. Mucins are large, densely glycosylated proteins, with carbohydrates contributing up to 80% of their molecular weight. In addition to mediating microbial interactions, mucins contribute to the biophysical properties of mucus that enable lubrication, adhesion, and protection of tissues. Beyond their physical responsibilities, mucins modulate virulence of microbes, promote cultivation of commensal bacteria through adhesion points and nutrient presentation, and mediate critical immune modulations of the host. However, molecular-level insights remain lacking in understanding mucin structure as well as function. The large size and heterogeneity of mucins and their glycosylation have made it challenging to parse the individual contributions of mucin structural features to biological function. Synthetic mucin mimics, developed using polymer chemistry, offering a promising strategy to probe these structure–function relationships by enabling precise control over molecular weight, glycan identity and density, polymer architecture, and morphology. To address these challenges, we developed novel synthetic mucins to elucidate mucin structure–function relationships. Emphasizing the importance of mucin’s extended morphology, we designed new synthetic strategies to isolate and investigate the impact of mucin structural motifs, such as anionic glycan identity and bottlebrush architectures, on synthetic mucin morphology (Chapter 2). We next demonstrated that synthetic mucins can provide insight into the glycan-binding preferences of probiotic bacteria, highlighting the roles of mucins in shaping microbial organization within the microbiome and emphasizing the potential of synthetic mucins as prebiotics (Chapter 3). Finally, recognizing that mucins key modulators of microbial virulence and infection, we explored the potential of synthetic mucins as antibacterial scaffolds capable of delivering therapeutic cargoes. This work emphasized the importance of understanding how polymer structure influences biological activity and targeting capabilities (Chapter 4). Ultimately, we anticipate that the synthetic mucin platform developed in this work will help address fundamental questions in mucin biology and advance the development of mucin-inspired therapeutic materials.

Techno-Economic Assessment of Grid-Scale Energy Storage Technologies Under Evolving Market and Decarbonization Scenarios: Liquid Air and Lithium-ion Battery Systems

2026-03-17T03:03:40Z

Techno-Economic Assessment of Grid-Scale Energy Storage Technologies Under Evolving Market and Decarbonization Scenarios: Liquid Air and Lithium-ion Battery Systems Cetegen, Shaylin Ashley As global energy systems transition toward low-carbon futures, long-duration energy storage technologies are expected to play a critical role in ensuring grid reliability and flexibility. Liquid air energy storage has emerged as a promising long-duration energy storage solution due to its scalability, ability to be sited flexibly, and environmental sustainability. This thesis investigates the technical modeling and economic feasibility of liquid air energy storage systems under both current and projected future electricity market conditions using a combination of process modeling, mathematical optimization, and techno-economic analysis. The study begins with an exploratory investigation into the modeling of process components in liquid air energy storage systems, emphasizing multistream heat exchangers and the emergence of bifurcation phenomena in thermodynamic models. This is followed by an economic optimization of standalone LAES systems across various electricity markets in the United States and Europe. A mixed-integer linear programming framework is developed to simultaneously optimize system design and hourly operation with the objective of maximizing net present value, providing new insights to inform LAES deployment strategies. Building on these foundations, a forward-looking economic analysis is conducted for 18 electricity regions in the United States under eight distinct decarbonization scenarios. This analysis is based on future electricity price projections from the Cambium 2023 dataset developed by the National Renewable Energy Laboratory. The results identify Texas and Florida as especially favorable markets for liquid air energy storage under a range of decarbonization pathways. Sensitivity analyses reveal that economic incentives such as capital expenditure subsidies have a greater impact on profitability than technical improvements such as increased round-trip efficiency. Finally, the thesis presents a comparative economic assessment of liquid air energy storage systems and lithium-ion battery systems across key metrics, including levelized cost of storage, system lifetime, siting constraints, and cost-effectiveness over multi-hour to multi-day durations. The findings show that liquid air energy storage systems can offer lower cost and greater flexibility than lithium-ion batteries for long-duration applications. This work establishes a generalizable framework for assessing the economic feasibility of emerging energy storage technologies and offers practical insights for decision-makers evaluating the role of liquid air energy storage in future electricity systems.

Active Epistemology

2026-03-17T03:03:38Z

Active Epistemology Fiat, Yonathan Orthodox epistemology deals with what we might call "passive agents:" agents that merely respond to what's given to them. But we care most about are active agents: agents that can sing, dance, seek evidence, make conjectures, etc. In this dissertation, I explore three ways in which this observation is relevant to the traditional questions of epistemology. I argue that it can shine a new light on the nature of knowledge, help us make sense of standard scientific practices, and help solve some famous puzzles in epistemology. Chapter 1 asks how the fact that we know something is related to the question of whether we should seek more evidence. This problem has been discussed in the philosophical literature under the name of "the dogmatism puzzle." In this chapter, I use the multi-armed bandit model to argue for a new solution to the dogmatism puzzle: knowledge often requires proper maintenance. Chapter 2 presents a novel account of significance testing, one of the most important practices in science. It shows that we can make sense of this practice if we accept the claim that predictions are better than accommodation. I then use this account to answer some of the many objections to significance testing. Finally, chapter 3 argues that what we know often depends on our choices, and not merely on what is given to us. We can gain knowledge by choosing something, whereas if we fail to choose, or attempt to choose too many things, we fail to gain knowledge. I then use this idea to offer a new solution to the lottery paradox, to help understand inductive knowledge, and, once again, to make sense of significance testing and related practices.

Mechanisms of genetic risk in Alzheimer’s disease

2026-03-17T03:03:37Z

Mechanisms of genetic risk in Alzheimer’s disease von Maydell, Djuna Sporadic Alzheimer's disease (AD) accounts for the majority of dementia cases worldwide, yet effective treatments remain limited. Genetic variants associated with AD provide insight into disease etiology and highlight potential therapeutic targets. The ε4 allele of the APOE gene is the strongest genetic risk factor for AD, and rare variants in the ABCA7 gene are among the next most significant. Both genes encode lipid transporters, suggesting an important role for lipid metabolism in AD etiology. However, the exact cellular mechanisms through which these variants increase AD risk remain incompletely understood. After a brief introduction in Chapter 1, Chapter 2 demonstrates that damaging ABCA7 variants disrupt neuronal phosphatidylcholine metabolism and mitochondrial function. These defects were reversed by supplementation with the phosphatidylcholine precursor cytidine diphosphate-choline (CDP-choline). Chapter 3 shows that APOE4-expressing oligodendrocytes exhibit altered cholesterol transport and impaired myelination. Pharmacological modulation of cholesterol transport in the brain reversed these defects, improving cognitive function in mouse models. These findings suggest that lipid-related mechanisms represent a class of targetable drivers of AD risk, but it remains unclear whether lipid-targeted treatments would be broadly applicable across AD or restricted to specific disease subtypes. Chapter 4 introduces a practical framework for identifying disease subtypes in high-dimensional biological data based on principles from machine learning and data attribution, and applies it to explore transcriptional subtypes among AD brains. Together, these studies reveal potential mechanisms of genetic risk in AD, highlight lipid disruptions as upstream mediators, and propose a practical framework for uncovering AD subtypes.

The Realm of Grace

2026-03-17T03:03:24Z

The Realm of Grace Mathew, Abraham This dissertation examines three familiar interpersonal phenomena—repentance after wronging another, forgiveness in the wake of being wronged, and reciprocation after being helped—and what they teach us about moral obligation in general. When a wrongdoer sincerely repents, we tend to see that as a reason to forgive them. But why? Chapter 1 offers an answer that I call the ‘Redemptive View’. Repentance, I contend, involves changing how one relates to a past misdeed, acknowledging its wrongness and committing to moral betterment. Consequently, the wrong comes to play a new role in the wrongdoer’s life, becoming a source of moral learning and an impetus towards moral growth. As a result, the wrong is imbued with a new, positive significance that it lacked so far, generating a reason to forgive. But are we ever obliged to forgive our wrongdoers? Many think not. On the orthodox view, all obligations to others correlate with demandable or enforceable rights, and since no one has the standing to demand or enforce forgiveness, no one can be owed it. Chapter 2 disputes this orthodoxy, arguing that forgiveness is sometimes obligatory, even if no wrongdoer has a right to it. In particular, if you’ve previously accepted a gracious offer of forgiveness and are now in a position to extend an equally or less gracious offer to one of your wrongdoers, then you must forgive. Otherwise, you would be imposing on others a harsher standard than you accepted for yourself. It turns out that disparate instances of forgiving and being forgiven within a life are connected in surprising ways: accepting forgiveness in the past can bear on whether present forgiveness is discretionary or obligatory. Chapter 3 turns to cases of standing in debt to someone who helps us—debts we can repay by reciprocating. Some debts are transactional: they can be claimed or waived, and once repaid, the parties return to the status ex ante. Others resist this structure: they cannot be claimed or waived, and reciprocation only generates a persisting, often alternating cycle of mutual aid. What explains this distinctive normative profile? I argue that such non-transactional debts arise when an act of 2 care tacitly proposes a more intimate relationship. When the beneficiary responds in kind, the relationship is reshaped, and new constitutive norms take hold. These norms ensure persistence, but also a normatively healthy pattern of alternation: after all, each act in the cycle is an undemandable gift extended in a time of need, and each response an act of due gratitude. In all, this dissertation challenges the orthodoxy that if we owe something to others, they must have a right to it. We learn that the moral landscape isn’t exhausted by the realm of rights. There is also a ‘realm of grace’—a realm in which we are required to do much for others that they cannot demand of us.

Presuppositionless proxies of politeness: An (eventually) Optimality-Theoretic account

2026-03-17T03:02:31Z

Presuppositionless proxies of politeness: An (eventually) Optimality-Theoretic account Wang, Ruoan Recent research has seen growing interest in the nature of social meaning, “the constellation of qualities and properties that linguistic forms convey about the social identity of language users” (Beltrama 2020, see also Eckert 2008). This dissertation concerns those linguistic forms termed polite pronouns. They convey one particular aspect of social identity: the social distance between language users, where a large social distance mandates the expression of politeness. Most existing work on polite pronouns has modeled polite meaning via dedicated grammatical mechanisms, such as a dedicated feature ([hon], Ackema & Neeleman 2016) or a dedicated dimension of meaning (McCready 2019). This dissertation shows that such dedicated mechanisms are superfluous, as existing grammatical mechanisms can be appropriated to describe and explain both the form and meaning of polite pronouns. Building on Wang (2023), I use a purpose-built typological sample of polite pronouns in >220 genetically and geographically diverse languages to establish the shapes of polite pronouns, showing that polite pronouns are obtained by asymmetrical recruitment of existing ϕ-featural values. Furthermore, the shapes of polite pronouns converge precisely with the shapes of semantic defaults, those ϕ-featural values underspecified in meaning, which must emerge when the context provides little or no information about number or person. To explain this convergence, I use the notion of negative politeness: respecting an interlocutor’s right to be unimpeded (Brown & Levinson 1987). I argue that semantic defaults and polite pronouns are morphologically identical because they are pragmatically identical: underspecification makes them well-suited to be avoidance mechanisms in the service of negative politeness. This captures the intuition that avoidance behaviors are a core component of expressing politeness. Specifically, polite pronouns enable speakers to avoid making presumptions about aspects of the interlocutor. Hence, presuppositionlessness, proxies, and politeness are what this dissertation is all about. I implement this intuition in Optimality-Theoretic terms, where polite grammars are defined by an outranking of Markedness (which mandates speakers to avoid presupposition-rich forms) over Faithfulness (which mandates speakers to be as informative as possible). The resulting system is restrictive but powerful, delivering a factorial typology which exactly mirrors the recruitment asymmetries. With no stipulations specific to polite pronouns, the account is furthermore able to make concrete predictions about politeness phenomena beyond the ϕ-featural domain. Happily, the resulting account is as lean as can be. Its main innovations are methodological and theoretical. Methodologically, the investigation is led by a large cross-linguistic sample, and the additional consideration of use conditions alongside morphological exponence. Theoretically, recruitment affords us with a fresh look at how the universal inventory of features is organized. This dissertation shows that recruitment is not only viable as a meta-grammatical operation, but even desirable for reasons of economy or computational efficiency.

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.

Aft Fuselage Boundary-Layer Ingesting Propulsion Systems for Turbo-Electric Aircraft

2026-03-17T03:03:48Z

Aft Fuselage Boundary-Layer Ingesting Propulsion Systems for Turbo-Electric Aircraft Chen, Zhibo This thesis focuses on a rigorous assessment of a tube-and-wing turbo-electric boundarylayer ingesting (BLI) aircraft, including (i) definitions of the aerodynamic attributes for the best fuel burn benefit, (ii) design guidelines to achieve these attributes, and (iii) a conceptual design of tail-BLI aircraft. The assessment of the BLI benefit relative to a baseline conventional aircraft is based on a TASOPT-CFD analysis framework, using a CFD body-force model for fan representation and a power balance for aircraft performance analysis. The aircraft mission is to carry a 17500 kg payload over 5500 km range at a cruise Mach number of 0.8. The baseline aircraft has two next-generation geared turbofan engines with fan pressure ratio (FPR) of 1.35. The tail-BLI aircraft has six integrated propulsors with electric fans of 1.40 FPR in addition to two underwing turbofans; it is estimated to achieve an 8.5% fuel burn benefit compared to the baseline. To achieve the benefit, the tail-BLI aircraft has a non-axisymmetric aft fuselage that creates axial vorticity in the tail-mounted propulsor inflow, providing co-swirl to reduce rotor incidence variations to improve the fan efficiency and stall margin. The best propulsor configuration, with six 24-inch fans, is established balancing the boundary-layer kinetic energy defect ingestion and propulsion system weight. The integrated propulsor aerodynamic design includes an upstream inlet extension, an inlet leading edge design tailored for each propulsor, a supercritical nacelle, an annular nozzle, an elliptic nozzle plug, and a non-axisymmetric tail cone, to minimize shock loss and achieve attached flow at cruise and takeoff. The BLI fan pressure ratio was selected based on a trade between engine propulsive efficiency and propulsion system weight. Non-axisymmetric stator inlet angles are also used to reduce the stator incidence variations. Fan forcing analysis suggests that the BLI rotor unsteady aerodynamic loading has negligible impact on blade life. Fan stability analysis shows that the BLI inlet distortion reduces the rotor stall margin by 4.5% and 2.7% relative to the rotor in uniform flow, at cruise and takeoff, respectively. The tail-BLI propulsors are thus estimated to have appropriate operability with BLI inlet distortion. In conclusion, the distributed aft fuselage boundary-layer ingesting propulsion system is suggested to offer a relevant pathway for tube-and-wing configurations with a potential major advancement in fuel burn reduction.

Adaptive Optimization Algorithms for Step-Size Selection and Online Problem Parameter Estimation

2026-03-17T03:02:34Z

Adaptive Optimization Algorithms for Step-Size Selection and Online Problem Parameter Estimation Cavalcanti Vilela, João Vítor Optimization algorithms have long been fundamental tools across science and engineering, and now are also at the center of the rise of machine learning and artificial intelligence. However, extracting good practical performance from many of these algorithms depends on careful manual calibration and tuning. In fact, the algorithms with best theoretical guarantees do not always perform best in practice. In this light, reducing the effort and skill required to set up optimization algorithms can save immeasurable amounts of time and resources. This thesis makes two contributions to this end, proposing optimization algorithms that require less supervision by adaptively selecting step-sizes and estimating problem parameters online. First, we revisit the foundational subroutine called backtracking line search (BLS). Typically, a base algorithm calls BLS to search for a parameter (e.g., step-size) such that the iterates of that algorithm satisfy a given condition (e.g., Armijo, descent lemma) that leads to desirable behavior (e.g., reducing the value of the objective function). To find a feasible parameter, BLS successively adjusts a parameter candidate by a constant factor until the given condition is satisfied. We propose to instead adjust the parameter candidate by an adaptive factor that takes into account the degree to which the given condition is violated. This adaptive BLS (ABLS) subroutine adds no computational burden relative to BLS, but can lead to significantly better practical results. Experiments on over fifteen real-world datasets demonstrate that ABLS can be more robust than BLS to problem set ups and require significantly fewer condition evaluations to return higher-quality parameters. At the same time, we prove that ABLS enjoys essentially the same theoretical guarantees of BLS. The second contribution of this thesis is a parameter-free algorithm for smooth and strongly convex objective problems called NAG-free. To our knowledge, NAG-free is the first adaptive algorithm capable of directly estimating the strong convexity parameter without priors or resorting to restart schemes. We prove that NAG-free converges globally at least as fast gradient descent, and achieves accelerated convergence locally if the Hessian is locally smooth and other mild additional assumptions hold. Prominent classes of machine learning problems with locally smooth Hessian include the regularized logistic loss, ridge regression, exponential family negative log-likelihoods with bounded natural parameters, and Moreau envelope smoothing. We present real-world experiments in which NAG-free performs comparably well with restart schemes, demonstrating that it can adapt to better local curvature conditions represented by the smoothness and strong convexity parameters.

Expanding Structural Complexity in Condensed Phosphates: P(V) Reagents for Controlled Phosphoanhydride Bond Construction

2026-03-17T03:03:23Z

Expanding Structural Complexity in Condensed Phosphates: P(V) Reagents for Controlled Phosphoanhydride Bond Construction Qian, Kevin At the outset, Chapter 1 begins by examining the central importance of phosphorus in both nature and industry, situating the chemistry of polyphosphates within a broader historical and conceptual context. A brief overview of the history of polyphosphate research in the life sciences is given, as well as a discussion on the persistent ambiguities in condensed phosphate nomenclature. To frame the work in the following chapters, the idea of the "hydrocarbon analogy" is introduced: a conceptual strategy that draws parallels between the structure and reactivity of organic molecules and that of inorganic phosphate constructs, thus offering a new way of thinking about molecular complexity in this underexplored chemical space. Building from this foundation, Chapter 2 details the discovery of a diphosphorylation reagent, identified to be a mixture of neutral zwitterionic adducts of P4O10 and pyridine. This reagent emerged serendipitously from our efforts to activate trimetaphosphate and has proved to be a powerful tool for synthesizing functionalized cyclic metaphosphates. Chapter 3 is the extrapolation of our strategy to activate otherwise inert metaphosphates by forming ring-strained bicyclic ultraphosphates. We discuss the reactivity of [P5O14]3–, the oligophosphate analog of the bicyclic hydrocarbon housane. Attempts to push this strategy further toward the synthesis of a hexaphosphorylation reagent were ultimately unsuccessful but provided valuable insight into the limitations of this ring-strain activation paradigm. The methods developed in earlier chapters set the stage for our collaboration with the Fielder group, described in Chapter 4. We designed new reagents to chemoselectively conjugate polyphosphates to densely functionalized peptides and proteins. These synthetic strategies pave the way for the study of recently discovered, but poorly characterized post-translational modifications featuring novel phosphorylation modes. Finally, Chapter 5 presents a series of unpublished studies that expand on the themes of the previous chapter. Together, these investigations contribute to a growing body of knowledge aimed at broadening the chemical space of condensed inorganic phosphates.

Unifying electric field-mediated heterogeneous catalysis

2026-03-17T03:03:32Z

Unifying electric field-mediated heterogeneous catalysis Dinakar, Bhavish Electric fields in the catalytic active sites of enzymes, originating from charged amino acid residues in the protein scaffold, have been invoked as a key factor governing the extraordinary activities and selectivities of enzymes for catalyzing chemical transformations. Models and experimental measurements of these electric fields have been utilized to quantitatively rationalize reaction kinetics, understand active-site solvent structures, and design better enzymatic catalysts. Despite the success of using electric fields to study enzymatic systems, this electric field approach has not yet been widely used in studying liquid phase heterogeneous catalysis, largely due to the difficulty in quantifying electric fields at active sites as well as challenges in designing heterogeneous catalysts with precise control of electric fields. In this thesis, we extend this electric field methodology into the realm of liquid-phase heterogeneous catalysis, both to accelerate heterogeneously catalyzed reactions and to understand solvent structures in catalyst pores. First, we design an electrochemical basket reactor system to apply an electric field to the surface of Brønsted-acidic carbon nanotube catalysts via an applied potential, and we examine the effect of electric field on acid-catalyzed alcohol dehydration rates of 1-methylcyclopentanol to 1-methylcyclopentene in an electrolyte-containing acetonitrile solution. We observe that the reaction rate is remarkably sensitive to electric field, with reaction rates increasing ~100,000 fold over 0.5 V of applied potential, and we propose a model that explains the rate-potential scaling and dependence on ionic strength. In further support of our model, we experimentally observe a theorized “isokinetic potential” where the reaction rate is independent of ionic strength. Through reactive base titrations, we quantify the density of active sites and demonstrate that the rate promotion is due to increasing the site activity, rather than increasing the number of active sites. Next, we demonstrate that this electric field effect does not require a special electrochemistry setup to manifest and can also be observed when a catalyst particle is simply touching another electrically conductive material. Through a carefully designed suite of experimental controls, we show that the intrinsic activity of acidic carbon nanotubes changes by an order of magnitude when they are in electrical contact with inert, thermally reduced carbon nanotubes, via the same mechanism that we previously observed when rates changed with an applied potential. We then shift gears to examine solvent structures in confined zeolite pores using vibrational Stark spectroscopy, an infrared spectroscopic technique developed for measuring electric fields at enzyme active sites. Using Ti-Beta zeolite as a test case, we develop a method that quantifies the electric field experienced by a probe molecule at the Ti active sites in solvent-filled pores. We find that the electric field varies with solvent identity and also with zeolite hydrophobicity, indicating that the secondary sphere interactions of the solvent with the zeolite framework result in distinct solvent structures in the hydrophobic and hydrophilic zeolites. Furthermore, we observe that this method identifies distinct electric fields between hydrophobic and hydrophilic zeolites even in the absence of solvent, offering this technique as a method for distinguishing types of active site environments. Finally, we apply this approach to examine hydrogen-bonding interactions inside Ti-Beta zeolite pores when filled with secondary alcohol solvents, motivated by previous work which observed that intraporous solvent structures varied significantly with changes in zeolite hydrophobicity. Using an infrared liquid flow cell that we designed to achieve record signal-to-noise and in-pore selectivity, we find that hydrogen-bonding interactions between a probe molecule and solvent are different between the ensemble-average in-pore environment and at the zeolite active site, revealing that there may be a discrepancy between typically observed intraporous bulk solvent structures invoked to explain kinetic phenomena and the kinetically relevant active-site solvent structures. Collectively, this thesis demonstrates that techniques and models of electric field-induced catalysis previously applied to understand biological systems can also be utilized to improve catalyst activity and understand the structure of solvent under confinement in liquid-phase heterogeneous catalysis. We hope that future work will expand on our methods, applying them to further investigate the effects of applied electric field on reactivity and utilizing vibrational probes to aid in understanding local structure at catalyst active sites.

Development of Modular Strategies for Enhancing Biomanufacturing in Komagataella phaffii

2026-03-17T03:03:18Z

Development of Modular Strategies for Enhancing Biomanufacturing in Komagataella phaffii Shi, Shuting The landscape of protein therapeutics is advancing towards more complex modalities that offer greater medical potential but present significant challenges to existing biomanufacturing frameworks. Alongside these technical challenges, socio-economic factors drive an urgent need for accelerated, cost-effective biomanufacturing to increase rapid response capabilities, ensure equitable access to advanced treatments, and alleviate the economic strain on global healthcare systems. This creates a significant and unresolved gap: the cost-effective production of increasingly complex protein therapeutics under an accelerated timeline. Komagataella phaffii holds great potential to fill this gap, as it is a eukaryotic microorganism well-established for the rapid and cost-effective expression of recombinant proteins. However, realizing this potential requires advanced engineering strategies to extend K. phaffii’s strengths to these complex modalities. This thesis presents the development of modular strategies to enhance the biomanufacturing of such complex therapeutics in K. phaffii. The first part of this thesis focuses on providing alternative manufacturing strategies for VLP vaccines, a promising next-generation vaccine platform whose adoption has been limited by manufacturing challenges. To streamline VLP vaccine development and manufacturing, a modular production framework was developed. This framework’s core strategy involves the secretory production of VLP scaffold subunits in K. phaffii followed by their in vitro assembly, which then allows for flexible "plug-and-display" antigen attachment to the pre-formed scaffold. This approach can be adopted for versatile antigen adaptation and can be stockpiled for rapid response. More importantly, this framework circumvents the manufacturing challenges associated with traditional intracellular VLP production, such as complex downstream purification that leads to increased production cost and decreased yield and quality. This transition not only makes each modular step more suited for current available operational units, especially downstream processing, significantly increasing scalability and reducing cost, but also holds promise for integration into future continuous manufacturing frameworks. A specific implementation and key outcome of this approach was the development of the SpyCatcher::I53-50 modular VLP scaffold, which was then validated by demonstrating the high-fidelity display of a SpyTag-fused HIV Env trimer antigen with preservation of critical neutralizing epitopes, showcasing its potential as a promising modular VLP vaccine platform. Enabling the secretory production of the scaffold’s multimeric subunits required overcoming significant manufacturability challenges. For instance, proteolytic degradation of the SpyCatcher-153-50A fusion was resolved via protein engineering, while secretion of the aggregation-prone 153-50B subunit was achieved through a multi-pronged approach combining process optimization, host engineering, and a novel pseudo-chaperone strategy. These rational engineering efforts were not only crucial for producing these key building blocks but also serve as a practical roadmap for tackling other hard-to-produce targets. The challenges encountered in efficiently optimizing VLP subunit production, even with extensive rational engineering, underscored a broader imperative for more powerful and systematic optimization tools. This realization motivated the second part of this thesis: the development of a modular high-throughput screening (HTS) platform to accelerate the engineering of K. phaffii for enhanced production of complex biologics, using monoclonal antibodies (mAbs) as an industrially significant proof-of-concept. The HTS platform is built on a dual-mode yeast surface display (YSD) system. By establishing a quantitative correlation between surface display and secretion for a full-length mAb, this work unlocks the potential of YSD for reliably screening secretion phenotypes, enabling, for the first time, screening under normal cultivation conditions at an unprecedented scale. Its successful application in a genome-wide CRISPR/Cas9 knockout screen identified numerous novel host gene knockout targets. Individual validation of 20 top-ranked candidates confirmed that 15 led to statistically significant increases in mAb specific productivity, with the most effective target yielding an 8-fold improvement over the baseline strain. This represents a substantial technological advancement for K. phaffii engineering, offering a powerful engine to shift from empirical, low-throughput optimization towards systematic, data-driven cell line development. Designed as a modular platform, it holds promise for screening other perturbation libraries and for optimizing a wide range of protein targets beyond mAbs.

Development of a targeted lipid nanoparticle platform for in vivo RNA delivery to hematopoietic stem and progenitor cells

2026-03-17T03:03:15Z

Development of a targeted lipid nanoparticle platform for in vivo RNA delivery to hematopoietic stem and progenitor cells Shi, Dennis Hematopoietic stem cells (HSCs) are rare cells residing in the bone marrow that are responsible for the generation and maintenance of the body’s immune system through a process known as hematopoiesis. Because of their self-renewal capability and crucial role in producing immune cells, HSCs have garnered a lot of therapeutic interest for the treatment of genetic blood disorders. However, current HSC gene therapies are autologous ex vivo transplantations which consists of three major steps: 1) mobilization and harvest of the patient’s own stem cells, 2) ex vivo editing of those cells, and 3) reinfusion of the edited cells back into the patient. While the results of these ex vivo therapies are quite promising, there are many limitations associated with the current process. First, the manufacturing process is logistically complex which results in high costs and lengthy times. Second, prior to re-infusion of the edited cells, patients must undergo a conditioning regimen (done with a chemotherapeutic) to deplete the existing cells from the bone marrow and make space for the edited cells to graft. This conditioning has many deleterious side effects including organ damage, increased risk of infection, and infertility. One strategy to bypass the existing limitations of ex vivo HSC therapy is to directly edit the HSCs in vivo. Here, we describe the development of a targeted non-viral lipid nanoparticle (LNP) delivery system that can deliver RNA to hematopoietic stem and progenitor cells (HSPCs) in vivo following a single intravenous injection. We targeted CD117, a receptor that is expressed on HSCs, and conjugated an antibody against CD117 to our LNPs for receptormediated delivery of RNA. We demonstrated that modulation of certain LNP parameters such as circulation time and ligand density increase delivery to the bone marrow. Using this targeted platform, we demonstrated LNP uptake and delivery of both siRNA and Cre mRNA into HSPCs. In addition, in the Ai14 mouse model, we showed that HSCs transfected with our targeted LNPs maintain their stemness and functionality to produce mature immune cells. We also evaluated the overall biodistribution of our anti-CD117 LNP and investigated the downstream effects of delivery to organs other than the bone marrow. Finally, we explored in vivo gene editing using a variety of approaches. We optimized our LNP formulation to increase protein expression in bone marrow HSCs and used our optimized formulation for in vivo base editing.

A 3D human liver tissue model of the hepatobiliary junction

2026-03-17T03:03:16Z

A 3D human liver tissue model of the hepatobiliary junction Westerfield, Ashley D. Cholestasis, or disruption in bile flow, is a poorly-understood feature of many liver diseases and is a well-established indication for liver transplant. Despite this clinical significance, many tissue engineering strategies for modeling or treating liver disease fail to recapitulate physiological bile flow. Recent advances in the field of tissue engineering and organoid technology have enabled the culture of human hepatocytes and bile duct cells in vitro, these models lack a key function of the liver which is bile transport. In this thesis, I first describe developments in bioengineering technology that have allowed for the culture and manipulation of bile duct cell organoids. I then present a 3D multicellular spheroid model that captures the structure and function of the human hepatobiliary junction—the interface between liver and bile duct cells that is often disrupted in liver disease. By co-aggregating primary human hepatocytes and bile duct cells, I engineer a liver spheroid model that recapitulates physiological bile flow through a functional connection between the two cell types. These spheroids maintain cell polarity and transport bile from hepatocyte canaliculi to bile duct structures. This function is quantified by leveraging a high-throughput imaging assay with AI-assisted analysis to track junction formation and bile flow over time. I also use this system to model ischemic injury of the bile duct, a common complication of liver transplant, by tuning the oxygen parameters of the spheroid culture. In this injury model, I observe and describe two processes that potentially contribute to injury: a reversible loss of canalicular function during hypoxia, followed by selective bile duct cell death after reoxygenation. This human-based, scalable platform provides a new tool to study bile duct biology, understand mechanisms of biliary injury after liver transplant, and support drug discovery efforts for cholestatic liver diseases.

Modeling and Control of a Continuous Lyophilizer

2026-03-17T03:03:13Z

Modeling and Control of a Continuous Lyophilizer Kadambi, Rohan Patrick Lyophilization, or vacuum freeze-drying, is a separations process in pharmaceutical manufacturing used to stabilize aqueous drug products by removing nearly all the water at cryogenic temperatures. This process is often applied to final-dose formulations of injectable drugs already dispensed in vials. Despite its benefits, freeze-drying is a slow process, with typical cycle times exceeding two days. To meet demand, lyophilization is applied to large batches of thousands to tens of thousands of vials at time. These large batches are plagued by non-uniformity, non-optimal operating conditions, and slow technology transfer across production scales. Continuous manufacturing offers a solution to many of these problems, with its demonstrated history in improving efficiency, homogeneity, and control through its inherent parallelization of operations. However, the need to interact directly with vials, as well as modify heat transfer to them, has delayed the development of a continuous lyophilization process. This work presents the design, control, and implementation of a modular continuous lyophilizer. The continuous lyophilizer is composed of a series of custom aluminum chambers around a magnetic levitation system, which facilitate the heat transfer and motion required. The modular nature ensures that critical geometry is conserved when production rate is scaled between laboratory-, pilot-, and industrial-scales. This system demonstrated continuous operation for four days on various solutions in standard 10R vials. In this work, novel continuous freezing and vacuum systems were designed to support the operation. Additionally, an innovative mass sensor was designed to monitor each vial traveling through the system independently. When combined with per-vial temperature data available from thermal imaging, this system enables a more comprehensive understanding of the process dynamics. This continuous process opens opportunities for expanding the use of lyophilization by simplifying technology transfer during scale-up, improving product uniformity, and increasing process productivity through optimal control.

Atomistic Insights into Disordered Proteins and Condensates via Molecular Simulations

2026-03-17T03:03:10Z

Atomistic Insights into Disordered Proteins and Condensates via Molecular Simulations Wang, Cong Biomolecular condensates formed by intrinsically disordered proteins (IDPs) play essential roles in cellular organization and function, attracting broad scientific interest. In addition to experimental approaches, molecular dynamics simulations—particularly atomistic simulations—serve as powerful tools for gaining mechanistic insights into IDP conformations and inter- and intramolecular interactions within condensates. In this work, we developed a multiscale simulation strategy that balances efficiency and accuracy by combining coarse-grained and atomistic modeling. We further applied dimensionality reduction techniques to extract meaningful features from high-dimensional simulation data. Using this integrated framework, we investigated three scientific problems: (1) the sequence-dependent conformational ensembles of IDPs, (2) nonspecific yet selective condensate–drug interactions, and (3) the mechanism of formation of monocomponent, multiphasic condensates formed by tetrapeptide sequences.

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.

An Analysis on Aircraft Overflight Noise Distribution on Airport Adjacent Communities

2026-03-17T03:03:06Z

An Analysis on Aircraft Overflight Noise Distribution on Airport Adjacent Communities Wang, Z. Juju Aircraft overflight noise and can be a source of noise pollution and can also be the limiting factor in airport operations [1]. This research studies the distribution of overflight noise near 22 airports across 12 metropolitan areas in the United States. It uses a fast noise model to generate noise data from surveillance flight track data and correlates noise data with population data, income, and noise complaints when data is available. Findings reveal that population noise exposure is significantly influenced by urban density and proximity of the airport to city centers. Airports located farther from major cities, particularly newer and larger facilities like IAD, DFW, and DEN, tend to decrease noise exposure due to their expansive land areas and remote siting. Noise exposure is also impacted by operational procedures. Arrival procedures typically follow straight in paths aligned with runway centerlines, concentrating noise along specific corridors. We find a few exceptions to this trend in complex airspaces which have turns in their arrival procedures, often enabled by Area Navigation (RNAV) technology. This allows flights to avoid adjacent airspaces and also overfly targeted lower population areas. The implementation of Area Navigation and Performance Based Navigation technology has enabled greater use of arrival paths involving turns, which were previously limited to visual conditions. In contrast, departure paths and contours from departures are more varied due to routing flexibility. These are often shaped by noise abatement strategies and airspace constraints. Runway orientation relative to urban centers also plays a critical role in determining noise exposure, with runways directed away from cities generally resulting in fewer person-noise events. In a socioeconomic analysis, we find lower-income communities are more frequently located near high-noise zones, though exceptions exist in areas with desirable geographic features near the airport such as waterfront property.

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.

Scaling Bayesian inference for generative models via probabilistic programming

2026-03-17T03:03:02Z

Scaling Bayesian inference for generative models via probabilistic programming Loula Guimarães de Campos, João This thesis addresses these challenges for the field of data science, developing probabilistic programming methods that enable rational AI agents in that domain. The work is organized into two parts: Part I introduces GenLM and Adaptive Weighted Rejection Sampling for translating natural language instructions into structured programs with both syntactic and semantic constraints, outperforming existing approaches across a number of domains. Part II develops Bayesian generative models for tabular data that can answer a wide range of questions, yield stable inferences across subpopulations of different sizes, and scale to hundreds of millions of rows on GPUs; as well as early work on Large Population Models that unify heterogeneous datasets. Together, these contributions provide first steps towards a unified framework for creating AI agents that can rationally formalize and answer questions about data.

Folding and Quality Control Mechanisms of Procollagen

2026-03-17T03:02:10Z

Folding and Quality Control Mechanisms of Procollagen Kim, Seo Yeon Procollagens undergo a complex folding process with the assistance of various ER proteostasis network components and enzymes that introduce post-translational modifications. In Chapter 2, we investigate the biological role of one of the post-translational modifications, the highly conserved N-glycosylation on procollagen C-propeptides by generating and characterizing multiple mouse models that lack the N-glycan on procollagen-I. We show that the lack of N-glycan can affect procollagen folding inside the cells, which translates to reduced bone mass in mice lacking the N-glycan. These data provide new insights into the roles of the evolutionarily conserved, yet underappreciated collagen’s N-glycan. Mutations in procollagen genes and other proteins important for procollagen folding often cause misfolding defects in procollagen that can ultimately lead to disease. In Chapter 3, we explore the various molecular mechanisms that the ER proteostasis network can use to recognize diverse classes of misfolding procollagen variants. Specifically, we apply cell engineering and quantitative mass spectrometry to elucidate the interactomes of diverse osteogenesis imperfecta-causing procollagen α1(I) variants with distinct folding defects, and show that ER proteins differentially engage with these misfolding procollagen variants. These findings provide a foundation for illuminating quality control pathways that the different types of misfolding procollagen variants are subject to during their biogenesis. Taken together, the work in this thesis meaningfully advances our knowledge in the mechanisms of procollagen folding and quality control.

Tribochemistry of Frictional Ignition in High-Pressure Oxygen

2026-03-17T03:03:01Z

Tribochemistry of Frictional Ignition in High-Pressure Oxygen Garcia Jimenez, Andres Frictional heating of metals at sliding contacts in high-pressure oxygen environments can result in catastrophic metal fires. This phenomenon, known as frictional ignition, presents an ongoing challenge in the development of oxygen-compatible components for next-generation reusable rocket engines. Early NASA investigations on frictional ignition indicated that ignition-resistant materials developed a robust oxide tribolayer. Breakdown of this tribolayer was hypothesized to drive the onset of frictional ignition. While this early work ranked the relative ignition resistance of several engineering alloys, it lacked a physical explanation for the mechanisms of tribolayer breakdown and frictional ignition, limiting its utility in predicting ignition conditions and designing ignition-resistant components. This thesis addresses this knowledge gap through a combination of experiments and modeling, which reveal the fundamental mechanisms governing frictional ignition of metals. These insights are then developed into a quantitative framework to predict ignition conditions and identify safe operating limits for sliding systems in high-pressure oxygen environments. The present work considers frictional ignition in the context of thermal ignition theory. Using high-speed dry sliding experiments and finite element modeling, we establish two conditions that must be satisfied for frictional ignition. The first condition is tribolayer breakdown, which we confirm through in situ measurements of the friction coefficient and complementary thermochemical modeling. Three distinct tribolayer breakdown mechanisms are identified – oxide melting, substrate metal melting, and solid-state mechanical failure. The dominant breakdown mechanism is found to depend on alloy chemistry and operating conditions. The second condition for frictional ignition is satisfaction of the thermal ignition criterion for thermal runaway, i.e., when the oxidative heating rate exceeds the rate of heat loss. Numerical modeling shows that the tribolayer acts as a diffusion barrier that slows oxidative heating. Thermal runaway is only possible in the absence of this tribolayer once the temperature at the sliding surface exceeds a critical threshold. The critical temperature provides a conservative estimate for evaluating ignition risk, since below this temperature, ignition cannot occur. This framework is used to explain the ignition behaviors of all materials with available frictional ignition data, highlighting the exceptional ignition resistance of the Nibase alloy MA754. Experiments show that this behavior derives from the unique mechanical properties, microstructure, and growth kinetics of the MA754 tribolayer. The above framework is extended to assess the ignition behaviors of dry dissimilar metal sliding systems. Frictional ignition experiments revealed that tribolayers may form through oxidation of the parent metal or via material transfer between counter-surfaces, potentially resulting in tribolayers with disparate chemistry from the base alloy. The dynamics of material transfer depend on the contact geometry, operating conditions, and material-pair combination. We find that favorable material transfer may result in the formation of thick, lubricating, and protective oxide tribolayers on both surfaces that suppress ignition. We develop expressions to establish safe operating bounds for dissimilar contacts with different geometries – symmetric contacts and a pin-on-disk geometry. These expressions highlight the effects of material-pair combination and contact geometry on ignition conditions. This thesis concludes by providing materials selection and component design strategies for ignition-resistant tribosystems. The first strategy is to select or design materials with high thermal conductivity, low enthalpy of oxidation, and favorable oxidational wear behaviors, i.e., rapid formation of a refractory, delamination-resistant tribolayer that lubricates the rubbing surface and protects against oxidation. The second strategy is to enhance cooling by optimizing component design (e.g., contact geometry) or modifying operating conditions (e.g., working fluid). Collectively, this thesis provides a roadmap for designing ignition-resistant sliding components capable of withstanding extreme oxygen pressures, with direct application to the development of safer oxygen-compatible hardware for next-generation reusable rocket engines.

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.

Personalizing Robot Assistance under Uncertainty about the Human

2026-03-17T03:02:59Z

Personalizing Robot Assistance under Uncertainty about the Human Li, Shen Robots have the potential to enhance human well-being by assisting with daily activities, particularly for older adults and people with disabilities. One example is robot-assisted dressing, where a robot helps a person put on clothing. However, no two individuals are alike. Each person has unique preferences, behaviors, and needs, making personalization essential for effective assistance. A central challenge is that robots often operate under uncertainty about the human they are helping. This uncertainty may involve the person’s preferences, hidden physical states, or reactions to assistance. If not properly addressed, such uncertainty can lead to ineffective, undesired, or even unsafe outcomes. This thesis asks: How should a robot behave when it is uncertain about the human? To answer this, I present a unified framework for uncertainty-aware personalization in humanrobot interaction, spanning three core components of robot intelligence: preference learning, state estimation, and motion planning. I propose methods that (1) reduce uncertainty using implicit cognitive signals, (2) represent and respect uncertainty through set-based state estimation, and (3) act under uncertainty using relaxed safety constraints. First, I introduce an approach that uses response time, a subtle yet informative cognitive signal, as implicit feedback for preference learning. While traditional methods rely solely on binary choices, I developed the first algorithm that integrates both choices and response times to infer not just what a person prefers, but how strongly they feel about those preferences. Theoretical analysis reveals that response times significantly reduce uncertainty about user preferences, especially when users have strong preferences. In simulation studies, this method decreased misidentification of the most preferred option by up to 55 %, enabling faster and more accurate personalization without extra user input. Second, I address the problem of estimating hidden human states during physical interaction. For example, in dressing, parts of the body, such as the elbow, may be occluded. I introduce the first set-based estimator that represents and respects uncertainty from human behavior and sensing models trained on limited data. Instead of outputting a point estimate, the method constructs a geometric set, such as a 3D box, guaranteed with high probability to contain the true human state. In dressing experiments, the estimator achieved 92 % inclusion using significantly smaller boxes than prior methods, balancing reliability and precision, supporting safe and responsive physical assistance. Third, I consider how a robot should plan motion when it is uncertain about future human behavior. Traditional safety constraints typically prohibit any contact, which can cause the robot to freeze when uncertainty is high. I propose a more flexible definition of safety that allows either collision avoidance or low-impact contact. Integrated into a learning-based control framework, this approach enables efficient motion while maintaining safety. In dressing tasks, it reduced task time by 78 % without compromising safety. Together, these contributions show how robots can reduce, represent and respect, and act under uncertainty to personalize their assistance. This thesis lays a foundation for robots that not only respond to commands, but also understand and adapt to the nuanced, evolving, and uncertain nature of human behavior.

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.

Interfacial Engineering of Perovskite Solar Cells

2026-03-17T03:02:48Z

Interfacial Engineering of Perovskite Solar Cells Lu, Yongli The efficiency of the lead halide perovskite solar cells has improved from 3% up to above 27% in a decade. However, the long term stability of the device still need to be improved in order to compete with traditional photovoltaic technologies, such as Silicon and GaAs. Hybrid organic and inorganic interfaces in the devices are the origin of many degradation pathways. Understanding the nature of these interfaces and chemical and physical mechanism behind their evolution under electrical, light and thermal bias is the subject of this thesis. In the following chapters, I focus on developing a electron transport layer (ETL) based on chemical bath deposition (CBD) for the synthesis of a tin dioxide (SnO₂). The conventional CBD recipe uses thioglycolic acid (TGA) to facilitate attachments of SnOx particles onto the substrate. However, nonvolatile TGA is reported to harm the operational stability of PSCs. A volatile oxalic acid (OA) is introduced as an alternative to TGA. OA, a dicarboxylic acid, functions as a chemical linker for the nucleation and attachment of particles to the substrate in the chemical bath. Moreover, OA can be readily removed through thermal annealing followed by a mild H₂O₂ treatment, as shown by FTIR measurements. Synergistically, the mild H₂O₂ treatment selectively oxidizes the surface of the SnOₓ layer, minimizing nonradiative interface carrier recombination. EELS (electron-energy-loss spectroscopy) confirms that the SnOₓ surface is dominated by Sn⁴⁺, while the bulk is a mixture of Sn²⁺ and Sn⁴⁺. This rational design of a CBD SnOₓ layer leads to devices with T₈₅ = 1500h, a significant improvement over the TGA-based device with T₈₀ = 250h. The champion device reached a power conversion effciency of 24.6%. This work offers a rationale for optimizing the complex parameter space of CBD SnOₓ to achieve efficient and stable PSCs. In addition to developing a electron transport layer (ETL) based on chemical bath deposition (CBD) for the synthesis of a tin dioxide (SnO₂), a perovskite ink additive, bis(2- oxo-3-oxazolidinyl) phosphinic chloride (BOP-Cl), was developed with the following benefits: (1) The phosphoryl and two oxo groups form six-membered intermolecular hydrogen-bonded rings with the formamidinium cation (FA), mitigating ion migrations. (2) The hydrogen bonding reduces the electrophilicity of the ammonium protons by donating electron density, therefore reducing its reactivity with the surface oxygen on the metal oxide. Furthermore, the molecule can react to form a protecting group on the nucleophilic oxygen at the tin oxide transport layer surface through the elimination of chlorine. As a result, we achieve perovskite solar cells with an efficiency of 25.0% and improved MPP stability T₉₃ = 1200h at 40–45 °C compared to a control device (T₈₆ = 550h). In addition, we show a negative correlation between the strength of hydrogen bonding of different phosphine oxide derivatives to the organic cations and the degree of metastable behavior (e.g., initial burn-in) of the device.

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.

Colloidal Semiconductor Nanocrystals: Tools For and Insights From First Principles Investigations

2026-03-17T03:02:57Z

Colloidal Semiconductor Nanocrystals: Tools For and Insights From First Principles Investigations Alexander, Ezra A. Colloidal semiconductor nanocrystals, also known as quantum dots (QDs), are at once systems of great promise for diverse applications, systems with complex quantum physics that remain poorly understood at a fundamental level, and systems that are difficult to describe with conventional first principles computational chemistry methods due to their large size. Whereas QDs made from toxic materials like Cd and Pb are able to emit and absorb light efficiently and precisely, non-toxic alternative III-V QDs suffer from difficult to control defects that interfere with their radiative processes. Motivated primarily by the problem of understanding defects in III-V quantum dots, in this thesis we develop and apply new frameworks for the computational study of quantum dots. These frameworks include orbital localization techniques for de-convoluting ground-state band structures, a ∆SCF procedure for modeling the x-ray photoelectron spectra (XPS), and a low-cost machine learning framework for predicting Hamiltonians that can be used to extend molecular dynamics simulations. Through these frameworks, we reveal a more comprehensive picture of the defects that interfere with the optical performance of III-V quantum dots. We find that both three-coordinate indium and phosphorus can cause trap states in InP, explore how geometry and charge modulate trap depth, discover and explain a new source of four-coordinate trap states in InP and GaP, and assign shifts in P 2p XPS to specific III-V surface defects.

Pair Crossing Number, Cutwidth, and Good Drawings on Arbitrary Point Sets

2026-04-24T03:15:43Z

Pair Crossing Number, Cutwidth, and Good Drawings on Arbitrary Point Sets Pi, Oriol S. Determining whether there exists a graph such that its crossing number and pair crossing number are distinct is an important open problem in geometric graph theory. We show that cr ( G ) = O ( pcr ( G ) 3 / 2 ) for every graph G, improving the previous best bound by a logarithmic factor. Answering a question of Pach and Tóth, we prove that the bisection width (and, in fact, the cutwidth as well) of a graph G with degree sequence d 1 , d 2 , ⋯ , d n satisfies bw ( G ) = O ( pcr ( G ) + ∑ k = 1 n d k 2 ) . Then we show that there is a constant C ≥ 1 such that the following holds: For any graph G of order n and any set S of at least n C points in general position on the plane, G admits a straight-line drawing which maps the vertices to points of S and has no more than O log n · pcr ( G ) + ∑ k = 1 n d k 2 crossings. Our proofs rely on a slightly modified version of a separator theorem for string graphs by Lee, which might be of independent interest.

Emergence of a proton exchange-based isomerization and lactonization mechanism in the plant coumarin synthase COSY

2026-04-24T03:15:07Z

Emergence of a proton exchange-based isomerization and lactonization mechanism in the plant coumarin synthase COSY Kim, Colin Y; Mitchell, Andrew J; Kastner, David W; Albright, Claire E; Gutierrez, Michael A; Glinkerman, Christopher M; Kulik, Heather J; Weng, Jing-Ke Plants contain rapidly evolving specialized enzymes that support the biosynthesis of functionally diverse natural products. In coumarin biosynthesis, a BAHD acyltransferase-family enzyme COSY was recently discovered to accelerate coumarin formation as the only known BAHD enzyme to catalyze an intramolecular acyl transfer reaction. Here we investigate the structural and mechanistic basis for COSY’s coumarin synthase activity. Our structural analyses reveal an unconventional active-site configuration adapted to COSY’s specialized activity. Through mutagenesis studies and deuterium exchange experiments, we identify a unique proton exchange mechanism at the α-carbon of the o-hydroxylated trans-hydroxycinnamoyl-CoA substrates during the catalytic cycle of COSY. Quantum mechanical cluster modeling and molecular dynamics further support this key mechanism for lowering the activation energy of the rate-limiting trans-to-cis isomerization step in coumarin production. This study unveils an unconventional catalytic mechanism mediated by a BAHD-family enzyme, and sheds light on COSY’s evolutionary origin and its recruitment to coumarin biosynthesis in eudicots.

Identification and Validation of Cyclic Peptides with Mucin-Selective, Location-Specific Binding in the Gastrointestinal Tract

2026-03-17T03:10:02Z

Identification and Validation of Cyclic Peptides with Mucin-Selective, Location-Specific Binding in the Gastrointestinal Tract Subramanian, Deepak A; Chin, Austin; Shi, Yunhua; Liu, Gary W; Langer, Robert; Traverso, Giovanni Oral drug delivery is a widely preferred method of drug administration due to its ease of use and convenience for patients. Localization of drug release in the gastrointestinal (GI) tract is important to treat localized diseases and maximize drug absorption. However, achieving drug localization in the dynamic GI tract is challenging. To address this challenge, we leveraged the geographic diversity of the GI tract by targeting its mucus layers, which coat the epithelial surfaces. These layers, composed of mucin glycoproteins, are synthesized with unique chemical compositions and expressed in different regions, making them ideal targets for drug localization. In this article, we identify cyclic peptides that bind selectively to MUC2 (in the intestines) and MUC5AC (in the stomach), serving as targeting ligands to these regions of the GI tract. We demonstrate the effectiveness of these peptides through in vitro, ex vivo, and in vivo experiments, showing that incorporating these targeting ligands can increase binding and selectivity 2-fold to the desired regions, thus potentially overcoming challenges with localizing drug distribution in oral delivery. These results indicate that cyclic peptides can be used to localize drug cargoes at certain sites in the body compared to free drugs.

Pd-Catalyzed Amination of Base-Sensitive Five-Membered Heteroaryl Halides with Aliphatic Amines

2026-03-14T05:51:44Z

Pd-Catalyzed Amination of Base-Sensitive Five-Membered Heteroaryl Halides with Aliphatic Amines Reichert, Elaine C; Feng, Kaibo; Sather, Aaron C; Buchwald, Stephen L We report a versatile and functional-group-tolerant method for the Pd-catalyzed C–N cross-coupling of five-membered heteroaryl halides with primary and secondary amines, an important but underexplored transformation. Coupling reactions of challenging, pharmaceutically relevant heteroarenes, such as 2-H-1,3-azoles, are reported in good-to-excellent yields. High-yielding coupling reactions of a wide set of five-membered heteroaryl halides with sterically demanding α-branched cyclic amines and acyclic secondary amines are reported for the first time. The key to the broad applicability of this method is the synergistic combination of (1) the moderate-strength base NaOTMS, which limits base-mediated decomposition of sensitive five-membered heteroarenes that ultimately leads to catalyst deactivation, and (2) the use of a GPhos-supported Pd catalyst, which effectively resists heteroarene-induced catalyst deactivation while promoting efficient coupling, even for challenging and sterically demanding amines. Cross-coupling reactions between a wide variety of five-membered heteroaryl halides and amines are demonstrated, including eight examples involving densely functionalized medicinal chemistry building blocks.

Abiotic peptides as carriers of information for the encoding of small-molecule library synthesis

2026-03-14T05:51:45Z

Abiotic peptides as carriers of information for the encoding of small-molecule library synthesis Rössler, Simon L; Grob, Nathalie M; Buchwald, Stephen L; Pentelute, Bradley L Encoding small-molecule information in DNA has been leveraged to accelerate the discovery of ligands for therapeutic targets such as proteins. However, oligonucleotide-based encoding is hampered by inherent limitations of information stability and density. In this study, we establish abiotic peptides for next-generation information storage and apply them for the encoding of diverse small-molecule synthesis. The chemical stability of the peptide-based tag allows the use of palladium-mediated reactions to efficiently synthesize peptide-encoded libraries (PELs) with broad chemical diversity and high purity. We demonstrate the successful de novo discovery of small-molecule protein ligands from PELs by affinity selection against carbonic anhydrase IX and the oncogenic protein targets BRD4(1) and MDM2. Collectively, this work establishes abiotic peptides as carriers of information for the encoding of small-molecule synthesis, leveraged herein for the discovery of protein ligands.

Studying Regioisomer Formation in the Pd‐Catalyzed Fluorination of Cyclic Vinyl Triflates: Evidence for in situ Ligand Modification

2026-03-14T05:51:43Z

Studying Regioisomer Formation in the Pd‐Catalyzed Fluorination of Cyclic Vinyl Triflates: Evidence for in situ Ligand Modification Ye, Yuxuan; Kim, Seoung‐Tae; King, Ryan P; Baik, Mu‐Hyun; Buchwald, Stephen L Pd-catalyzed nucleophilic fluorination reactions are important methods for the synthesis of fluoroarenes and fluoroalkenes. However, these reactions can generate a mixture of regioisomeric products that are often difficult to separate. While investigating the Pd-catalyzed fluorination of cyclic vinyl triflates, we observed that the addition of a substoichiometric quantity of TESCF3 significantly improved the regioselectivity of the reaction. Herein, we report a combined experimental and computational study on the mechanism of this transformation focusing on the role of TESCF3. The poor regioselectivity of the reaction in the absence of additives results from the formation of LPd-cyclohexyne complexes (L=biaryl monophosphine ligand). When TESCF3 is added to the reaction mixture, the generation of the Pd-cyclohexyne complexes is diminished by an unexpected pathway involving the dearomatization of the ligand by nucleophilic attack from a trifluoromethyl anion (CF3−).

Room-Temperature Cu-Catalyzed Amination of Aryl Bromides Enabled by DFT-Guided Ligand Design

2026-03-14T05:51:46Z

Room-Temperature Cu-Catalyzed Amination of Aryl Bromides Enabled by DFT-Guided Ligand Design Kim, Seoung-Tae; Strauss, Michael J; Cabré, Albert; Buchwald, Stephen L Ullmann-type C–N coupling reactions represent an important alternative to well-established Pd-catalyzed approaches due to the differing reactivity and the lower cost of Cu. While the design of anionic Cu ligands, particularly those by Ma, has enabled the coupling of various classes of aryl halides and alkyl amines, most methods require conditions that can limit their utility on complex substrates. Herein, we disclose the development of anionic N1,N2-diarylbenzene-1,2-diamine ligands that promote the Cu-catalyzed amination of aryl bromides under mild conditions. Guided by DFT calculations, these ligands were designed to (1) increase the electron density on Cu, thereby increasing the rate of oxidative addition of aryl bromides, and (2) stabilize the active anionic CuI complex via a π-interaction. Under optimized conditions, structurally diverse aryl and heteroaryl bromides and a broad range of alkyl amine nucleophiles, including pharmaceuticals bearing multiple functional groups, were efficiently coupled at room temperature. Combined computational and experimental studies support a mechanism of C–N bond formation that follows a catalytic cycle akin to the well-explored Pd-catalyzed variants. Modification of the ligand structure to include a naphthyl residue resulted in a lower energy barrier to oxidative addition, providing a 30-fold rate increase relative to what is seen with other ligands. Collectively, these results establish a new class of anionic ligands for Cu-catalyzed C–N couplings, which we anticipate may be extended to other Cu-catalyzed C–heteroatom and C–C bond-forming reactions.

Stereoselective Synthesis of Trisubstituted Alkenes via Copper Hydride-Catalyzed Alkyne Hydroalkylation

2026-03-14T05:51:40Z

Stereoselective Synthesis of Trisubstituted Alkenes via Copper Hydride-Catalyzed Alkyne Hydroalkylation Kutateladze, Dennis A; Mai, Binh Khanh; Dong, Yuyang; Zhang, Yu; Liu, Peng; Buchwald, Stephen L Alkenes are ubiquitous in organic chemistry, yet many classes of alkenes remain challenging to access by current synthetic methodology. Herein, we report a copper hydride-catalyzed approach for the synthesis of Z-configured trisubstituted alkenes with high stereo- and regioselectivity via alkyne hydroalkylation. A DTBM-dppf-supported Cu catalyst was found to be optimal, providing a substantial increase in product yield compared to reactions conducted with dppf as the ligand. DFT calculations show that the DTBM substitution leads to the acceleration of alkyne hydrocupration through combined ground and transition state effects related to preventing catalyst dimerization and enhancing catalyst–substrate dispersion interactions, respectively. Alkyne hydroalkylation was successfully demonstrated with methyl and larger alkyl tosylate electrophiles to produce a variety of (hetero)aryl-substituted alkenes in moderate to high yields with complete selectivity for the Z stereochemically configured products. In the formation of the key C–C bond, computational studies revealed a direct SN2 pathway for alkylation of the vinylcopper intermediate with in situ-formed alkyl iodides.

Room‐Temperature Copper‐Catalyzed Etherification of Aryl Bromides

2026-03-13T03:07:34Z

Room‐Temperature Copper‐Catalyzed Etherification of Aryl Bromides Strauss, Michael J; Greaves, Megan E; Kim, Seoung‐Tae; Teijaro, Christiana N; Schmidt, Michael A; Scola, Paul M; Buchwald, Stephen L We disclose the development of a Cu-catalyzed C−O coupling method utilizing a new N1,N2-diarylbenzene-1,2-diamine ligand, L8. Under optimized reaction conditions, structurally diverse aryl and heteroaryl bromides underwent efficient coupling with a variety of alcohols at room temperature using an L8-based catalyst. Notably, the L8-derived catalyst exhibited enhanced activity when compared to the L4-based system previously disclosed for C−N coupling, namely the ability to functionalize aryl bromides containing acidic functional groups. Mechanistic studies demonstrate that C−O coupling utilizing L8 ⋅ Cu involves rate-limiting alkoxide transmetallation, resulting in a mechanism of C−O bond formation that is distinct from previously described Pd-, Cu-, or Ni-based systems. This lower energy pathway leads to rapid C−O bond formation; a 7-fold increase relative to what is seen with other ligands. The results presented in this report overcome limitations in previously described C−O coupling methods and introduce a new ligand that we anticipate may be useful in other Cu-catalyzed C-heteroatom bond-forming reactions.

CuH-Catalyzed Regio- and Enantioselective Formal Hydroformylation of Vinyl Arenes

2026-03-13T03:07:21Z

CuH-Catalyzed Regio- and Enantioselective Formal Hydroformylation of Vinyl Arenes Garhwal, Subhash; Dong, Yuyang; Mai, Binh Khanh; Liu, Peng; Buchwald, Stephen L A highly enantioselective formal hydroformylation of vinyl arenes enabled by copper hydride (CuH) catalysis is reported. Key to the success of the method was the use of the mild Lewis acid zinc triflate to promote the formation of oxocarbenium electrophiles through the activation of diethoxymethyl acetate. Using the newly developed protocol, a broad range of vinyl arene substrates underwent efficient hydroacetalization reactions to provide access to highly enantioenriched α-aryl acetal products in good yields with exclusively branched regioselectivity. The acetal products could be converted to the corresponding aldehydes, alcohols, and amines with full preservation of the enantiomeric purity. Density functional theory studies support that the key C–C bond-forming event between the alkyl copper intermediate and the oxocarbenium electrophile takes place with inversion of configuration of the Cu–C bond in a backside SE2-type mechanism.

Cu-Catalyzed Amination of Base-Sensitive Aryl Bromides and the Chemoselective N- and O-Arylation of Amino Alcohols

2026-03-13T03:07:37Z

Cu-Catalyzed Amination of Base-Sensitive Aryl Bromides and the Chemoselective N- and O-Arylation of Amino Alcohols Strauss, Michael J; Liu, Kaylee X; Greaves, Megan E; Dahl, Jakob C; Kim, Seoung-Tae; Wu, Yong-Jin; Schmidt, Michael A; Scola, Paul M; Buchwald, Stephen L We report a general and functional-group-tolerant method for the Cu-catalyzed amination of base-sensitive aryl bromides including substrates possessing acidic functional groups and small five-membered heteroarenes. The results presented herein substantially expand the scope of Cu-catalyzed C–N coupling reactions. The combination of L8, an anionic N1,N2-diarylbenzene-1,2-diamine ligand, along with the mild base NaOTMS leads to the formation of a stable yet reactive catalyst that resists deactivation from coordination to heterocycles or charged intermediates. This system enables the use of low catalyst and ligand loadings. Exploiting the differences in nucleophile deprotonation in C–O and C–N coupling reactions catalyzed by Cu·L8 we developed a method to chemoselectively N- and O-arylate a variety of amino alcohol substrates. Employing NaOt-Bu as the base resulted exclusively in C–O coupling when the amino alcohols featured primary alcohols and more hindered amines or aniline groups. Utilizing NaOTMS enabled the ability to override the steric-based selectivity of these reactions completely and exclusively promoted C–N coupling regardless of the structure of the amino alcohol. The ability to invert the observed chemoselectivity is distinct from previously described methods that require protecting group manipulations or rely entirely on steric effects to control reactivity. These results substantially improve the scope of Cu-catalyzed C–N coupling reactions using N1,N2-diarylbenzene-1,2-diamine ligands and introduce a new chemoselective method to arylate amino alcohols.

Copper-Catalyzed Amination of Aryl Chlorides under Mild Reaction Conditions

2026-03-13T03:07:29Z

Copper-Catalyzed Amination of Aryl Chlorides under Mild Reaction Conditions Ai, Han-Jun; Kim, Seoung-Tae; Liu, Cecilia; Buchwald, Stephen L We report a mild method for the copper-catalyzed amination of aryl chlorides. Key to the success of the method was the use of highly sterically encumbered N¹,N²-diaryl diamine ligands which resist catalyst deactivation, allowing reactions to proceed at significantly lower temperatures and with a broader scope than current protocols. A sequence of highly chemoselective C-N and C-O cross-coupling reactions were demonstrated, and mechanistic studies indicate that oxidative addition of the Cu catalyst to the aryl chlorides is rate-limiting. We anticipate that the design principles disclosed herein will help motivate further advances in Cu-catalyzed transformations of aryl chlorides.

Development of a Deactivation-Resistant Dialkylbiarylphosphine Ligand for Pd-Catalyzed Arylation of Secondary Amines

2026-03-13T03:07:27Z

Development of a Deactivation-Resistant Dialkylbiarylphosphine Ligand for Pd-Catalyzed Arylation of Secondary Amines Feng, Kaibo; Raguram, Elaine Reichert; Howard, James R; Peters, Ellyn; Liu, Cecilia; Sigman, Matthew S; Buchwald, Stephen L Despite the prevalence of N-heteroarenes in small-molecule pharmaceuticals, Pd-catalyzed C-N cross-coupling reactions of aryl halides and amines containing these rings remain challenging due to their ability to displace the supporting ligand via coordination to the metal center. To address this limitation, we report the development of a highly robust Pd catalyst supported by a new dialkylbiarylphosphine ligand, FPhos. The FPhos-supported catalyst effectively resists N-heteroarene-mediated catalyst deactivation to readily promote C-N coupling between a wide variety of Lewis-basic aryl halides and secondary amines, including densely functionalized pharmaceuticals. Mechanistic and structural investigations, as well as principal component analysis and density functional theory, elucidated two key design features that enable FPhos to overcome the limitations of previous ligands. First, the ligated Pd complex is stabilized through its conformational preference for the O-bound isomer, which likely resists coordination by N-heteroarenes. Second, 3',5'-disubstitution on the non-phosphorus-containing ring of FPhos creates the ideal steric environment around the Pd center, which facilitates binding by larger secondary amines while mitigating the formation of off-cycle palladacycle species.

Kinetic Modeling Enables Understanding of Off-Cycle Processes in Pd-Catalyzed Amination of Five-Membered Heteroaryl Halides

2026-03-13T03:07:30Z

Kinetic Modeling Enables Understanding of Off-Cycle Processes in Pd-Catalyzed Amination of Five-Membered Heteroaryl Halides Raguram, Elaine Reichert; Dahl, Jakob C; Jensen, Klavs F; Buchwald, Stephen L The mechanism of Pd-catalyzed amination of five-membered heteroaryl halides was investigated by integrating experimental kinetic analysis with kinetic modeling through predictive testing and likelihood ratio analysis, revealing an atypical productive coupling pathway and multiple off-cycle events. The GPhos-supported Pd catalyst, along with the moderate-strength base NaOTMS, was previously found to promote efficient coupling between five-membered heteroaryl halides and secondary amines. However, slight deviations from the optimal concentration, temperature, and/or solvent resulted in significantly lower yields, contrary to typical reaction optimization trends. We found that the coupling of 4-bromothiazole with piperidine proceeds through an uncommon mechanism in which the NaOTMS base, rather than the amine, binds first to the oxidative addition complex; the resulting OTMS-bound Pd species is the resting state. Formation of the Pd-amido complex via base/amine exchange was identified as the turnover-limiting step, unlike other reported catalyst systems for which reductive elimination is turnover-limiting. We determined that the amine-bound Pd complex, usually an on-cycle intermediate, is instead a reversibly generated off-cycle species, and that base-mediated decomposition of 4-bromothiazole is the primary irreversible catalyst deactivation pathway. Predictive testing and kinetic modeling were key to the identification of these off-cycle processes, providing insight into minor mechanistic pathways that are difficult to observe experimentally. Collectively, this report reveals the unique enabling features of the Pd-GPhos/NaOTMS system, implementing mechanistic insights to improve the yields of particularly challenging coupling reactions. Moreover, these findings highlight the utility of applying predictive tests to kinetic models for the rapid evaluation of mechanistic possibilities in small-molecule catalytic systems.

Characterization of the response of radiochromic film to quasi-monoenergetic x rays through a cross-calibration with image plates

2026-03-13T03:07:23Z

Characterization of the response of radiochromic film to quasi-monoenergetic x rays through a cross-calibration with image plates Buschmann, BI; Cufari, M; Vanderloo, N; Vargas, J; Foo, BC; DeVault, A; Dannhoff, SG; Evans, TE; Johnson, TM; Kunimune, JH; Lawrence, Y; Pearcy, JA; Reichelt, BL; Wink, CW; Russell, L; Gatu Johnson, M; Petrasso, RD; Frenje, JA Radiochromic film (RCF) and image plates (IPs) are both commonly used detectors in diagnostics fielded at inertial confinement fusion (ICF) and high-energy-density physics (HEDP) research facilities. Due to the intense x-ray background in all ICF/HEDP experiments, accurately calibrating the optical density of RCF as a function of x-ray dose, and the photostimulated luminescence per photon of IPs as a function of x-ray energy, is necessary for interpreting experimental results. Various measurements of the sensitivity curve of different IPs to x rays have been performed [Izumi et al., Proc. SPIE 8850, 885006 (2013) and Rosenberg et al., Rev. Sci. Instrum. 90(1), 013506 (2019)]; however, calibrating RCF is a tedious process that depends on factors such as the orientation in which the RCF is scanned in the film scanner and the batch of RCF used. These issues can be mitigated by cross-calibrating RCF with IPs to enable the use of IPs for the determination of dose on the RCF without scanning the RCF. Here, the first cross-calibration of RCF with IPs to quasi-monoenergetic titanium, copper, and molybdenum K-line x rays is presented. It is found that the IP-inferred dose rates on the RCF for the Ti and Mo x rays agree well with the measured dose rates, while the IP-inferred dose rate for the Cu x rays is larger than the measured dose rate by ∼2×. Explanations for this discrepancy and plans for future work are discussed.

Determination of the response for the National Ignition Facility particle time of flight (PTOF) detector using single particle counting

2026-03-13T03:07:36Z

Determination of the response for the National Ignition Facility particle time of flight (PTOF) detector using single particle counting Lawrence, Y; Reichelt, BL; Wink, CW; Rigon, G; Johnson, M Gatu; Li, CK; Frenje, JA The Particle Time of Flight (PTOF) detector is a chemical vapor deposition diamond-based detector used to measure bang times in low-yield (≲ 1015 neutrons) experiments at the National Ignition Facility (NIF) at Lawrence Livermore National Laboratory (LLNL). Historically, the impulse response for PTOF diamond detectors has been obtained from x-ray timing shots on the NIF and shots on the MegaRay pulsed electron accelerator at LLNL. The impulse response may alternatively be obtained using single particle interactions with the detector, at substantially less cost and higher frequency compared to NIF timing shots, which typically occur months apart. Here, the response of a PTOF detector setup is characterized by statistically averaging a large number of single particle waveforms. A high fidelity instrument response function can be constructed in this way. This is confirmed by comparison of the single particle counting-constructed response to the impulse response function measured for the same detector at LLNL’s MegaRay facility.

Development of a compact magnetic spectrometer for use at the OMEGA Laser Facility and the National Ignition Facility

2026-03-13T03:07:32Z

Development of a compact magnetic spectrometer for use at the OMEGA Laser Facility and the National Ignition Facility Pearcy, JA; Russell, L; Kabadi, NV; Johnson, TM; Adrian, PA; Gatu-Johnson, M; Casco, E; Palmisano, K; Gates, G; Burgett, T; Scott, M; Petrasso, RD; Li, CK; Frenje, J Measurement of proton spectra is an important diagnostic for a variety of high energy density physics experiments. Current diagnostics are either not designed to capture the spectrum of low-energy protons or are unsuitable for high debris experiments. To bridge the gap, a new CR-39 based compact magnetic spectrometer (MagSpec) has been developed to measure proton spectra in the 1–20 MeV energy range, with a particular focus on the low-energy (1–6 MeV) spectrum, for use in experiments at the OMEGA Laser Facility and the National Ignition Facility (NIF). In the MagSpec diagnostic, protons of different energies are dispersed as they pass through a magnetic field before impinging on a differentially filtered CR-39 surface, resulting in a spatial distribution of CR-39 tracks that corresponds to the energy spectrum. In this paper, we discuss details of the design and implementation of MagSpec on the NIF and OMEGA.

Temperature stabilization of a lab space at 10 mK-level over a day

2026-03-13T03:07:26Z

Temperature stabilization of a lab space at 10 mK-level over a day Fife, Dylan; Shin, Dong-Chel; Sudhir, Vivishek Temperature fluctuations over long time scales (≳ 1 h) are an insidious problem for precision measurements. In optical laboratories, the primary effect of temperature fluctuations is drifts in optical circuits over spatial scales of a few meters and temporal scales extending beyond a few minutes. We present a lab-scale environment temperature control system approaching 10 mK-level temperature instability across a lab for integration times above an hour and extending to a day. This is achieved by passive isolation of the laboratory space from the building walls using a circulating air gap and an active control system feeding back to heating coils at the outlet of the laboratory’s Heating-Ventilation-Air-Conditioning (HVAC) unit. These techniques together result in 20 dB suppression of the temperature power spectrum across the lab at 10−4 Hz—approaching the limit set by statistical coherence of the temperature field—and 10 mK Allan deviation around 15 °C after an hour of averaging, which is an order of magnitude better than any previous report for a full laboratory.

Incentives to Comply with the Minimum Wage in the United States and the United Kingdom

2026-03-13T03:07:19Z

Incentives to Comply with the Minimum Wage in the United States and the United Kingdom Stansbury, Anna There is substantial evidence of minimum wage non-compliance in the United States and the United Kingdom. In this article, the author compiles new, comprehensive data on the costs that minimum wage violators incur when non-compliance is detected. In both countries, the costs violators face are often little more than the money they saved by underpaying. To have an incentive to comply under existing penalty regimes, typical US firms would thus have to expect a 47% to 83% probability of detection by the Department of Labor (DOL), or a 25% probability of a successful Fair Labor Standards Act (FLSA) suit. In the United Kingdom, typical firms would have to expect a 44% to 56% probability of detection. Actual probabilities of detection are substantially lower than this for many firms and would likely remain so even with realistic increases in enforcement capacity. Improved enforcement alone is thus insufficient: Expected penalties must also substantially increase to ensure that most firms have an incentive to comply.

Non-Euclidean motion planning with graphs of geodesically convex sets

2026-03-13T03:07:12Z

Non-Euclidean motion planning with graphs of geodesically convex sets Cohn, Thomas; Petersen, Mark; Simchowitz, Max; Tedrake, Russ Computing optimal, collision-free trajectories for high-dimensional systems is a challenging and important problem. Sampling-based planners struggle with the dimensionality, whereas trajectory optimizers may get stuck in local minima due to inherent nonconvexities in the optimization landscape. The use of mixed-integer programming to encapsulate these nonconvexities and find globally optimal trajectories has recently shown great promise, thanks in part to tight convex relaxations and efficient approximation strategies that greatly reduce runtimes. These approaches were previously limited to Euclidean configuration spaces, precluding their use with mobile bases or continuous revolute joints. In this paper, we handle such scenarios by modeling configuration spaces as Riemannian manifolds, and we describe a reduction procedure for the zero-curvature case to a mixed-integer convex optimization problem. We further present a method for obtaining approximate solutions via piecewise-linear approximations that is applicable to manifolds of arbitrary curvature. We demonstrate our results on various robot platforms, including producing efficient collision-free trajectories for a PR2 bimanual mobile manipulator.

Extended state infrastructure power in an age of networked competition: The cases of Thailand and Taiwan

2026-03-13T03:07:28Z

Extended state infrastructure power in an age of networked competition: The cases of Thailand and Taiwan Stokols, Andrew; Kollar, Justin Scholars have highlighted the emergence of infrastructure as a key domain in the struggle over network centrality in what some call the ‘Second Cold War’ between the U.S. and China. We qualify this ‘infrastructural turn’ by drawing attention to the contingent nature of state infrastructural power as depending on key domestic firms that often serve as intermediaries between domestic infrastructure and global supply chains or international partners. Utilising empirical case studies based on field research conducted between 2021 and 2023 in Thailand and Taiwan, we analyse the ways in which state infrastructure power is exercised through strategic negotiation between national politics of the state and territorial investment decisions of multinational and major domestic firms within global supply chains. The study highlights how outcomes of state projects to foster connectivity or centrality in networks are shaped by contingent and sometimes ad-hoc coalitions between state agencies and domestic and multinational companies with their own interests and agency. In the case of Taiwan, the centrality of Taiwan Semiconductor Manufacturing Company (TSMC) to global supply chains makes it an important player amidst continued U.S.-China tension. In Thailand, CP Group’s connections to China have afforded it a role as an interlocutor between Thailand and China, allowing it to obtain state infrastructure contracts. Through comparative case studies the paper complicates both ‘globalist’ and methodologically nationalist perspectives on the ‘infrastructural turn’, and introduces the concept of ‘extended state infrastructural power’ to account for this complex, networked exercise of state authority.

Uncovering Patterns in Overdose Deaths: An Analysis of Spike Identification in Fatal Drug Overdose Data in Massachusetts, 2017-2023

2026-03-13T03:07:33Z

Uncovering Patterns in Overdose Deaths: An Analysis of Spike Identification in Fatal Drug Overdose Data in Massachusetts, 2017-2023 Lee, Hannah; Otero-Leon, Daniel; Dong, Huiru; Stringfellow, Erin J; Jalali, Mohammad S Objectives: Yearly rolling aggregate trends or rates are commonly used to analyze trends in overdose deaths, but focusing on long-term trends can obscure short-term fluctuations (eg, daily spikes). We analyzed data on spikes in daily fatal overdoses and how various spike detection thresholds influence the identification of spikes. Materials and Methods: We used a spike detection algorithm to identify spikes among 16 660 drug-related overdose deaths (from any drug) reported in Massachusetts’ vital statistics from 2017 through 2023. We adjusted the parameters of the algorithm to define spikes in 3 distinct scenarios: deaths exceeding 2 adjusted moving SDs above the 7-, 30-, and 90-day adjusted moving average. Results: Our results confirmed the on-the-ground observation that there are days when many more people die of overdoses than would be expected based on fluctuations due to differences among people alone. We identified spikes on 5.8% to 20.6% of the days across the 3 scenarios, annually, constituting 11.1% to 31.6% of all overdose deaths. The absolute difference in percentage points of days identified as spikes varied from 5.2 to 11.5 between 7- and 30-day lags and from 0 to 4.6 between 30- and 90-day lags across years. When compared with the adjusted moving average across the 3 scenarios, in 2017 an average of 3.9 to 5.5 additional deaths occurred on spike days, while in 2023 the range was 3.7 to 6.0. Practice Implications: A substantial percentage of deaths occurred annually on spike days, highlighting the need for effectively monitoring short-term overdose trends. Moreover, our study serves as a foundational analysis for future research into exogenous events that may contribute to spikes in overdose deaths, aiming to prevent future deaths.

Experiential learning amid disequilibrium: Attuning to student emotions

2026-03-13T03:07:24Z

Experiential learning amid disequilibrium: Attuning to student emotions O’Flanagan, Sinead E; Y Jester, Michellana Educators recognize the significant role emotions play in experiential learning (EL), particularly in how they support students through the inherent emotion work. However, the traditional design of experiential learning theory (ELT) in higher education (HE) often presupposes a stable environment, which overlooks the impact of unpredictable external factors on students’ emotions and learning. Despite its critical importance, emotion work in EL remains underexplored, with emotional dynamics often obscured or dismissed as isolated incidents. This study sheds light on the heightened emotional challenges that arise during periods of sustained disequilibrium, such as the COVID-19-induced restrictions. It provides novel insights into the dynamic interplay of emotions and learning progression within EL frameworks, drawing on perspectives from EL educators, advisors, and students. The research underscores the importance of emotion-focused dialogue, educator-student connection, and assimilating autonomy needs in EL amid disequilibrium. It also identifies often-neglected elements in EL frameworks, such as students “sharing struggles” or “valuing work efforts,” alongside educator strategies like “personal anchoring.” The findings contribute to ELT by proposing adaptive strategies that integrate emotion work into pedagogical frameworks, enhancing reflection and conceptualization practices, and extending ELT’s applicability across diverse educational and work-based management learning settings.

Report to the President for year ended June 30, 2025, Associate Vice President for Research Administration

2026-03-12T07:26:11Z

Report to the President for year ended June 30, 2025, Associate Vice President for Research Administration White, Anne This report contains the following sections: Overview, Research Administration Services, OSATT Core, Technology Licensing Office, Corporate Relations, Research Systems & Support, and Cost Analysis.

Insulin Delivery Pumps for Human Spaceflight: Steps Toward an Accessible Space Future

2026-03-12T07:25:04Z

Insulin Delivery Pumps for Human Spaceflight: Steps Toward an Accessible Space Future Horn, Kyle J; Hoffman, Jeffrey A Commercially available insulin pumps for treatment of diabetes mellitus are currently not qualified to operate in the space environment. This work rigorously tested the fluid delivery performance of a Tandem t:slim X2 insulin pump in both micro- and hypergravity during a parabolic microgravity research flight. The parabolic research flight environment serves as an analogue to the types of transient gravitational loadings experienced during human-led missions, which provides a foundation to expand testing to suborbital and orbital flights in addition to other extreme environmental tests for wilderness dependency. The results of the flight data showed no significant difference between fluid delivery performance at 0, 1, and 2g acceleration regimes, nor at the transitions between gravity environments. Recommendations are made for further experimentation and qualification tests before use in future spaceflight missions.

On the nonlinear Eshelby inclusion problem and its isomorphic growth limit

2026-03-12T07:24:57Z

On the nonlinear Eshelby inclusion problem and its isomorphic growth limit Bonavia, Joseph E; Chockalingam, S; Cohen, Tal In the late 1950s, Eshelby’s linear solutions for the deformation field inside an ellipsoidal inclusion and, subsequently, the infinite matrix in which it is embedded were published. The solutions’ ability to capture the behavior of an orthotropically symmetric shaped inclusion made it invaluable in efforts to understand the behavior of defects within, and the micromechanics of, metals and other stiff materials throughout the rest of the 20th century. Over half a century later, we wish to understand the analogous effects of microstructure on the behavior of soft materials, both organic and synthetic, but in order to do so, we must venture beyond the linear limit, far into the nonlinear regime. However, no solutions to these analogous problems currently exist for non-spherical inclusions. In this work, we present an accurate semi-inverse solution for the elastic field in an isotropically growing spheroidal inclusion embedded in an infinite matrix, both made of the same incompressible neo-Hookean material. We also investigate the behavior of such an inclusion as it grows infinitely large, demonstrating the existence of a non-spherical asymptotic shape and an associated asymptotic pressure. We call this the isomorphic limit, and the associated pressure the isomorphic pressure.

Evaluating Risk for Astronaut Involvement in In-Space Manufacturing: Analog Field Testing and Future Planetary Surface Procedures

2026-03-12T07:24:54Z

Evaluating Risk for Astronaut Involvement in In-Space Manufacturing: Analog Field Testing and Future Planetary Surface Procedures MacRobbie, Madelyn; Patel, Palak B. Introduction A key objective of the NASA Artemis program is to establish a sustained human presence on the Moon, along with its international and commercial partners. NASA aims to establish a lunar economy, increasing the need for infrastructure to support human habitation and facilitate growth. In-space manufacturing (ISM) coupled with in situ resource utilization (ISRU) can reduce launch mass and reduce the dependency on Earth resupply for long-term habitation, enabling rapid expansion. However, the space environment introduces unique challenges compared to Earth, such as the absence of an atmosphere, reduced gravity levels, and high consequences of human-machine interactions given the barrier to evacuating an astronaut injured in a manufacturing accident on the Moon, necessitating new safety standards for ISM processes. Methods This study proposes the application of a modified analytical hierarchy process (AHP) to identify high-risk aspects of crew procedures in molten regolith electrolysis (MRE) for both Earth-based analog testing and lunar production. Results The modified AHP assists in pinpointing areas needing hazard mitigation to protect crew members, enabling the improvement of safety standards for MRE in both environments. Conclusion Findings will inform the development of robust safety protocols for ISM, crucial for the success of NASA's Artemis missions and the broader goal of sustained human presence on the Moon and Mars.

Origins of Face Responses in the Human Cortex: fNIRS and fMRI Evidence From Infants

2026-03-12T07:25:03Z

Origins of Face Responses in the Human Cortex: fNIRS and fMRI Evidence From Infants Saxe, Rebecca; Kosakowski, Heather L In adults, cortical regions in the fusiform face area (FFA), superior temporal sulcus (STS), and medial prefrontal cortex (MPFC) respond selectively to faces but underlie distinct perceptual and social processes. When do each of these regions, and their distinctive functions, develop? We reviewed recent studies of awake human infants’ cortical responses to faces using functional near-infrared spectroscopy (fNIRS) and functional MRI (fMRI). The results converged and do not support a slow, sequential posterior-to-anterior development of face-selective responses. Instead, cortical face-selective responses arise very early and simultaneously in infancy and may reflect distinctively social processes from the start.

The Effect of Basketball Analytics Investment on National Basketball Association (NBA) Team Performance

2026-03-12T07:25:02Z

The Effect of Basketball Analytics Investment on National Basketball Association (NBA) Team Performance Wang, Henry; Sarker, Arnab; Hosoi, Anette In the National Basketball Association (NBA), basketball data and analytics is an area of significant financial investment for all 30 franchises, despite there being little quantitative evidence demonstrating analytics adoption actually improves team-level performance. This study seeks to measure the return on investment of analytics on NBA team success in a time of great demand for analytical front office personnel. Using a two-way fixed effects modeling approach, we identify the causal effect of analytics department headcounts on regular season wins using 12 years of season-level data for each team. We find a positive and statistically significant effect, suggesting clubs that invest more in analytics tend to outperform competitors when controlling for roster characteristics, injuries, difficulty of schedule, and team-specific and time-specific effects. This research contributes to the body of literature affirming the value of data analytics for organizational performance and supports current investments in analytics being made by NBA teams.

Workshop on Noninvasive Glucose Monitoring 2024

2026-03-12T07:25:00Z

Workshop on Noninvasive Glucose Monitoring 2024 Kang, Jeon Woong; Arnold, Mark A; Steenkamp, Devin; Tapsak, Mark A; Mäntele, Werner; Khang, Yoonho; Jue, Miyeon; So, Peter TC This first workshop on noninvasive glucose monitoring (NIGM) was held at the Massachusetts Institute of Technology (MIT) on October 30, 2024. Six invited speakers, representing industry, academia, and clinics, gave presentations that covered (1) an overview of the NIGM technologies, (2) the state of the art in NIGM technologies, such as near-infrared (NIR), mid-infrared (IR), photoacoustic, and Raman spectroscopies, (3) minimally invasive implantable continuous glucose monitoring (CGM) sensors, and (4) a clinician’s perspective on the impact of the current CGM devices for patient care.

The community test tube of American civilization: Burt and Ethel Aginsky’s Social Science Field Laboratory, 1939–47

2026-03-12T07:25:01Z

The community test tube of American civilization: Burt and Ethel Aginsky’s Social Science Field Laboratory, 1939–47 Kapsalakis, Lauren The Social Science Field Laboratory (SSFL, 1939–47), a field school in the Ukiah Valley that trained students in social scientific and anthropological methodology, sheds light on a period in anthropology when methods were shifting from objective empiricism to meaningful participation. As analytic tools for framing the study of society failed to keep pace with social change, sociopolitical trends inside and outside anthropology situated a valley in northern California as the opportune place to gather a sample of ‘American history in vitro’. Founded by Columbia-trained anthropologists Burt and Ethel Aginsky, the SSFL responded to trends inside and outside anthropology. As the Great Depression directed anthropologists’ attention to the study of practical, modern problems in complex American communities—such as race relations, immigration, modernization, and urbanization—funding agencies strengthened the relations between sociology and anthropology and encouraged the development of interdisciplinary approaches. The Aginskys conceived of the Ukiah Valley as a ‘community test-tube of American civilization’, where scientists from all disciplines ‘can come for a convenient sample of the United States, past and present’. In teaching students how to collect data in the field, the Aginskys pierced the widely held notion that ethnographic technique cannot be taught but must be experienced by the lone individual in the field.

Exploring How Organizational Actors Experience Evaluation and Its Influence: A Q-Methodological Study

2026-03-12T07:24:59Z

Exploring How Organizational Actors Experience Evaluation and Its Influence: A Q-Methodological Study Kelly, Catherine This article contributes to research on evaluation by examining how organizational actors respond to and use evaluation imposed on them within an evaluation system. Drawing on Henry and Mark's theory of evaluation influence, this study uses Q-methodology to explore how staff within English higher education providers experience evaluation and its influence on their widening participation practice and strategy decision-making. The experiences of organizational actors are examined and classified into four types: strategic practitioners, pragmatic practitioners, staff with indirect involvement in widening participation, and evaluation enthusiasts. Through analyzing these experiences, the findings illustrate the diverse ways organizational actors are influenced by evaluation within evaluation systems. To deepen our understanding of evaluation influence in the contexts of evaluation systems, this article recommends explicitly embedding organizational theories into future theories of evaluation influence and provides suggestions for future research on the topic.

Mapping the Caregiver Experience: Predicting Dimensions of Caregiver Strain Through Task-Based Profiles

2026-03-12T07:24:55Z

Mapping the Caregiver Experience: Predicting Dimensions of Caregiver Strain Through Task-Based Profiles Brady, Samantha; Ashebir, Sophia; D’Ambrosio, Lisa; Balmuth, Alexa; Felts, Adam; Lee, Chaiwoo Objective: Family caregiving is a prevalent, diverse, and often challenging experience. We develop caregiving activity profiles to better understand how sets of care-tasks contribute to various aspects of strain. Methods: Using diary data from a survey of 213 family caregivers in the U.S., we perform latent class analysis to group commonly occurring care-related tasks into activity profiles. We then use these classifications to predict physical, financial, and emotional strain. Main Findings: We identified 4 unique activity profiles based on a set of 36 daily caregiving activities performed. Activity profiles varied significantly across the three analyzed strain dimensions. Conclusion: Activity profiles present opportunities to better understand how caregiving tasks are related to specific types kinds of caregiving strain.

Your home is not a school: The limits of homeschooling as a political practice

2026-03-12T07:24:53Z

Your home is not a school: The limits of homeschooling as a political practice Pavel, Sonia Maria; Cynamon, Jeremy Kingston Homeschooling is on the rise. It appeals to very different perspectives and ideologies that tend not to have common ground, from classical conservative to radical progressive. But the justifications for the practice are weak. In this paper, we build a case against the “home school” as a political practice using the existing commitments of liberal, conservative, and democratic theories of education. Whether education should aim at the cultivation of children's autonomy, their formation as members of cultural communities, or their training as democratic citizens, there are reasons to doubt that the practice of homeschooling can fulfill our educational goals. As such, we argue that liberals, conservatives, and democrats each have their own motivations to oppose homeschooling as an institutional alternative to traditional schools. Through our critiques, we also advance a metatheoretical argument in favor of centering the aims of education in our philosophical and political debates.

Qualitative Assessment of Terrestrial Care Settings to Inform Self-sufficient Spaceflight Medical Care

2026-03-12T07:24:52Z

Qualitative Assessment of Terrestrial Care Settings to Inform Self-sufficient Spaceflight Medical Care Porter, Allison; Arquilla, Katya; Stankovic, Aleksandra Introduction Long communication latencies in exploration spaceflight will necessitate in situ resolution to medical problems. Integrating automation into the care paradigm can address challenges posed by resource gaps inherent to spaceflight operations. However, it is not clear what aspects of exploration care are most well suited for automation integration. Methods To probe the potential role of automation in spaceflight medicine, we began by decomposing the human-automation system to first characterize the work domain(s) of the human tasks. Using the lens of point-of-care ultrasound, we leveraged existing analogous Earth medical domains to conduct in situ observations in a hospital emergency department to understand how clinicians process contextual information to provide urgent care using ultrasound and semistructured interviews with specialists to identify key procedural information components for automation. Results This investigation allowed us to characterize the dynamic system surrounding a task that does not exist in its intended—currently inaccessible—use case (ie, point-of-care ultrasound on Mars) to guide future human-automation systems development. Conclusion We conclude that specific aspects of the care environment that influence the result of a task or process (“mediating factors”) from candidate work domains call for distinct, targeted guidance for automation support and are valuable in providing system developers with tunable automation level and implementation guidelines within and/or between those work domains. Such evidence-based design practice is directly translatable to automation assistance for medical providers in resource-limited environments as well as to any situation where a person's sensory processing, perception, decision making, or response selection could be aided by automation to accomplish a task.

Solutions and Challenges for Addressing Misinformation

2026-03-12T07:24:47Z

Solutions and Challenges for Addressing Misinformation Martel, Cameron; Rand, David G Research on mitigating the effects of misinformation has contributed to the development of multiple feasible interventions designed to reduce belief in, and sharing of, falsehoods. The authors review these interventions and discuss challenges and open questions for future research. First, they provide an overview of content-neutral and content-based interventions. Next, they discuss two practical challenges to deploying and assessing these interventions in the field: scalability and pushback against content moderation efforts due to perceived political bias. Finally, they highlight several open theoretical questions and common pitfalls of research on misinformation. In particular, they argue for critical evaluation of how interventions may be effective across different types of misinformative content, different key subpopulations, and different media and environmental contexts.

Atomic Transactions

2026-04-16T03:10:40Z

Atomic Transactions Lynch, Nancy; Merritt, Michael; Weihl, William; Fekete, Alan

A design of a low-pressure steam turbine

2026-03-11T03:05:17Z

A design of a low-pressure steam turbine Jones, Bradley. Thesis: B.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1910

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).

Cloud-chamber study of cosmic ray showers in lead plates

2026-03-11T03:02:21Z

Cloud-chamber study of cosmic ray showers in lead plates Fussell, Lewis. Thesis: Sc. D., Massachusetts Institute of Technology, Department of Electrical Engineering, 1938; Includes bibliographical references (leaves [113]-[118]).

Development of a high-speed light source suitable for photoelastic studies

2026-03-11T03:05:14Z

Development of a high-speed light source suitable for photoelastic studies Wyle, Frank S. Thesis: B.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1941; Includes bibliographical references (leaf 25).

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).

Manipulation and measurement of charge transfer kinetics at chemically modified electrodes

2026-03-11T03:02:10Z

Manipulation and measurement of charge transfer kinetics at chemically modified electrodes Lewis, Nathan S. (Nathan Saul) Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemistry, 1981; Includes bibliographical references.

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.

Double valves

2026-03-11T03:05:04Z

Double valves Faunce, Linus. Thesis: B.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1877

Recirculation through western boundary currents varies nonlinearly with the ocean basin's aspect ratio

2026-04-24T03:15:52Z

Recirculation through western boundary currents varies nonlinearly with the ocean basin's aspect ratio Gianchandani, Kaushal Recirculation gyres adjacent to western boundary currents (WBCs) in the ocean enhance the poleward transport of these currents. While it is well-established that the WBC in a barotropic ocean strengthens with increase in basin's aspect ratio (the meridional-to-zonal extent ratio), how intensity of the recirculation through the western boundary layer varies with this parameter remains unexplored. I address this using the non-dimensional form of the nonlinear, wind-driven Stommel–Munk model of westward intensification that comprises three parameters—the aspect ratio (δ), the damping coefficient (ϵ), and the β-Rossby number (Rβ). Here, ϵ is set by the ratio of Rayleigh friction coefficient (or eddy viscosity) to the meridional gradient of the Coriolis frequency and the basin's zonal dimension, while Rβ is proportional to wind stress amplitude and quantifies the strength of nonlinearity. In the weak-to-moderate nonlinearity limit (Rβ<∼ϵ), perturbation analysis reveals that recirculation varies concavely with aspect ratio, suggesting existence of an optimal aspect ratio (δopt) for which the recirculation is maximum and for typical values of ϵ (10−3−10−2), δopt follows the power-law relation δopt=4.3ϵ. Numerical simulations further validate the existence of δopt. For large ϵ (>5×10−3), the power-law predicts δopt for the numerical solutions rather accurately, but does not hold for smaller ϵ (2×10−3) due to increased importance of nonlinear terms. Nevertheless, the nonlinear variation in recirculation through the western boundary layer with aspect ratio is observed for all ϵ values and may contribute to the heterogeneous increase in the WBC's transport across different ocean basins in a warming climate.

Streamlining Physics Problem Generation to Support Physics Teachers in Using Generative Artificial Intelligence

2026-04-24T03:15:23Z

Streamlining Physics Problem Generation to Support Physics Teachers in Using Generative Artificial Intelligence El-Adawy, Shams; Liao, Isaac; Lad, Vedang; Abdelhafez, Mohamed; Dourmashkin, Peter The rapid advancement of large language models (LLMs) presents a unique opportunity for educators to find ways to include artificial intelligence (AI) in physics course design. By critically engaging with LLMs to help with the task of generating problems, physics teachers can not only model a potentially effective way to use LLMs for other teachers, but also showcase to students ways to productively engage with LLMs. This article presents a workflow with two different starting points to generate physics problems using ChatGPT 3.5. The first initialization involves interacting with ChatGPT in a conversational manner, guiding iterative problem creation by breaking tasks into smaller tasks. The second initialization harnesses ChatGPT’s generative abilities, aligning problem generation with established problem styles by instructing the model to emulate contexts from question banks. We discuss the implications of this workflow for other physics instructors exploring productive ways to incorporate the use of AI in their own course design.

Ion optical design of the magnetic proton recoil neutron spectrometer for the SPARC tokamak

2026-05-08T03:14:00Z

Ion optical design of the magnetic proton recoil neutron spectrometer for the SPARC tokamak Mackie, S; Wink, CW; Dalla Rosa, M; Berg, GPA; Ball, JL; Wang, X; Carmichael, J; Tinguely, RA; Rigamonti, D; Tardocchi, M; Raj, P; Frenje, J; Rice, J A magnetic proton recoil (MPR) neutron spectrometer is being designed for SPARC, a high magnetic field (BT = 12 T), compact (R0 = 1.85 m, a = 0.57 m) tokamak currently under construction in Devens, MA, USA. MPR neutron spectrometers are versatile tools for making high fidelity ab initio calibrated measurements of fusion neutron flux spectra and have been used to infer fusion power, ion temperature, fuel ion ratio, and suprathermal fuel populations at several high performance fusion experiments. The performance of an MPR neutron spectrometer is in large part determined by the design of the magnetic field, which disperses and focuses recoil protons. This article details the ion optical design of a high-resolution MPR neutron spectrometer, including the amelioration of image aberrations due to nonlinear effects. An optimized design is presented that achieves ion optical energy resolution δE/E < 1% and focal plane properties that enable straightforward integration with the hodoscope detector array.

Performance predictions of the SPARC x-ray crystal spectrometers for ion temperature and toroidal rotation measurements

2026-05-08T03:12:47Z

Performance predictions of the SPARC x-ray crystal spectrometers for ion temperature and toroidal rotation measurements Perks, C; Vezinet, D; Rice, JE; Reinke, ML SPARC will be outfitted with three systems of x-ray crystal spectrometer arrays. Two of these are designed using cylindrically bent crystals to achieve high spectral-resolution for ion temperature and toroidal velocity measurements via imaging He-like Kr and Ne-like Xe. The last acts as a spectral survey system to monitor Ne-like W and nearby H- and He-like emission from Cr, Fe, Co, Ni, and Cu. Line radiation intensities are calculated using the Flexible Atomic Code for atomic data and ColRadPy for collisional-radiative modeling, then convoluted with a Voigt line shape. Free–free, free-bound, and two-photon continuum radiation is also included. The ToFu code is used to perform volume-of-sight integration to produce synthetic detector images. In addition, presented is cross-validation performed using the XICSRT Monte Carlo ray-tracing code. Ion temperature and toroidal velocity profiles are reconstructed using ToFu via tomographic inversion.

Edge scanning reflectometry for density profile measurement on the SPARC tokamak

2026-05-08T03:12:35Z

Edge scanning reflectometry for density profile measurement on the SPARC tokamak Lin, Y; Nikolaeva, V; Hachmeister, D; Kowalski, E; Reinke, ML Edge scanning reflectometry (ESRL) on the SPARC tokamak aims to measure the electron density profile from the far scrape-off layer to the top of the typical H-mode pedestal and provide real-time data for plasma control. ESRL uses a standard frequency-modulated continuous wave technique from 18 to 90 GHz. By implementing both the O-mode and left-hand-cutoff X-mode, it covers densities from ∼4 × 1018 to ∼4 × 1020 m−3 at B0 ∼12 T. A voltage-controlled oscillator acts as the frequency sweep source. Phase-locked dielectric resonator oscillators and bandpass filters generate base signals ∼9–15 GHz. The signals are then frequency multiplied and amplified to reach the K (18–26 GHz), Ka (26–40 GHz), U (40–60 GHz), and E (60–90 GHz) bands. Multi-band signals are combined via the quasi-optical technique. ESRL plans to use oversized waveguides (∼20 m one-way) and a bi-static arrangement to minimize signal losses and distortions while allowing system flexibility. A COMSOL Multiphysics RF model in 2D has been set up to simulate the reflectometry process and help decide the layout of the horn antennas. Engineering analyses of the key parts of the system have been carried out in support of its preliminary design.

Neutronics simulations for the design of neutron flux monitors in SPARC

2026-04-24T03:15:03Z

Neutronics simulations for the design of neutron flux monitors in SPARC Wang, X; Gocht, R; Ball, J; Mackie, S; Panontin, E; Tinguely, RA; Raj, P; Holmes, I; Saltos, AA; Johnson, A; Grieve, A This paper presents the development and application of high-fidelity neutronic models of the SPARC tokamak for the design of neutron flux monitors (NFM) for application during plasma operations. NFMs measure the neutron flux in the tokamak hall, which is related to fusion power via calibration. We have explored Boron-10 gamma-compensated ionization chambers (ICs) and parallel-plate Uranium-238 fission chambers (FCs). We plan for all NFMs to be located by the wall in the tokamak hall and directly exposed to neutrons streaming through a shielded opening in a midplane port. Our simulations primarily use a constructive solid geometry-based OpenMC model based on the true SPARC geometry. The OpenMC model is benchmarked against a detailed CAD-based MCNP6 model. The B10 ICs are equipped with high-density polyethylene (HDPE) sleeves, borated HDPE housings, and borated aluminum covers to shield out scattered neutrons, optimize detector response levels, and make calibration robust against changes in the tokamak hall. The B10 neutron absorption branching ratio may cause the detectors’ responses to be non-linear to neutron flux >200 keV. However, our simulations unveil that, in the SPARC environment and with the proposed housings and sleeves, >99% of the detector responses are induced by <100 keV neutrons. U238’s insensitivity to slow neutrons makes this FC a promising candidate for direct fusion neutron measurements. Along with a borated HDPE sleeve, about 60% of the FCs’ responses are induced by direct neutrons.

Image plate multi-scan response to fusion protons in the range of 1–14 MeV

2026-04-24T03:15:51Z

Image plate multi-scan response to fusion protons in the range of 1–14 MeV Vanderloo, N; Cufari, M; Russell, L; Johnson, TM; Vargas, J; Foo, BC; Buschmann, BI; Dannhoff, SG; DeVault, A; Evans, TE; Kunimune, JH; Lawrence, Y; Pearcy, JA; Reichelt, BL; Wink, CW; Gatu Johnson, M; Petrasso, RD; Frenje, JA; Li, CK Image plates (IPs) are a quickly recoverable and reusable radiation detector often used to measure proton and x-ray fluence in laser-driven experiments. Recently, IPs have been used in a proton radiography detector stack on the OMEGA laser, a diagnostic historically implemented with CR-39, or radiochromic film. The IPs used in this and other diagnostics detect charged particles, neutrons, and x-rays indiscriminately. IPs detect radiation using a photo-stimulated luminescence (PSL) material, often phosphor, in which electrons are excited to metastable states by ionizing radiation. Protons at MeV energies deposit energy deeper into the IP compared with x rays below ∼20 keV due to the Bragg peak present for protons. This property is exploited to discriminate between radiation types. Doses of mono-energetic protons between 1.7 and 14 MeV are applied to IPs using the MIT linear electrostatic ion accelerator. This paper presents the results from consecutive scans of IPs irradiated with different proton energies. The PSL ratios between subsequent scans are shown to depend on proton energy, with higher energy protons having lower PSL ratios for each scan. This finding is separate from the known energy dependence in the absolute sensitivity of IPs. The results can be compared to complimentary work on x rays, showing a difference between protons and x rays, forging a path to discriminate between proton and x-ray fluence in mixed radiation environments.

A compact and portable gamma-ray spectrometer (GRASP) for inertial confinement fusion and basic science experiments

2026-05-08T03:13:22Z

A compact and portable gamma-ray spectrometer (GRASP) for inertial confinement fusion and basic science experiments Dannhoff, SG; Wink, CW; Mackie, S; Berg, GPA; Frenje, JA A compact and portable gamma-ray spectrometer has been designed to diagnose different components of the inertial confinement fusionrelevant γ-ray spectrum with energies between ∼3.7–17.9 MeV. The system is designed to be as compact as possible for convenient transportation and fielding in diagnostic ports on the OMEGA laser, the National Ignition Facility, and other photon-source facilities. The system consists of a conversion foil for Compton scattering in front of four magnetic spectrometer “arms,” each covering a different energy range and constructed out of cylindrical permanent magnet Halbach arrays. Monte Carlo simulations have been used to optimize and assess the performance of the conversion foil, and COSY INFINITY ion-optical simulations have been used to optimize the spectrometer magnets. The performance of the design is assessed for a simulated direct-drive γ-ray spectrum. Spanning its total γ-ray energy bandwidth and using a 1.7 mm thick boron conversion foil, the system’s total energy resolution and efficiency are ∼15.8%–4.5% and 5.4 × 10−7 –3.7 × 10−7 e − /γ, respectively, with room for improvement. Spectral γ-ray measurements will provide guidance to the inertial confinement fusion program toward achieving high-energy gain relevant to inertial fusion energy and enable new measurement capabilities for basic discovery science.

Characterization of the image plate multi-scan response to mono-energetic x-rays

2026-05-08T03:12:39Z

Characterization of the image plate multi-scan response to mono-energetic x-rays Cufari, M; Vanderloo, N; Buschmann, BI; DeVault, A; Foo, BC; Vargas, J; Dannhoff, SG; Evans, TE; Johnson, TM; Kunimune, J; Lawrence, Y; Pearcy, JA; Reichelt, BL; Russell, L; Wink, CW; Gatu Johnson, M; Petrasso, RD; Frenje, JA Image plates (IPs), or phosphor storage screens, are a technology employed frequently in inertial confinement fusion (ICF) and high energy density plasma (HEDP) diagnostics because of their sensitivity to many types of radiation, including, x rays, protons, alphas, beta particles, and neutrons. Prior studies characterizing IPs are predicated on the signal level remaining below the scanner saturation threshold. Since the scanning process removes some signal from the IP via photostimulated luminescence, repeatedly scanning an IP can bring the signal level below the scanner saturation threshold. This process, in turn, raises concerns about the signal response of IPs after an arbitrary number of scans and whether such a process yields, for example, a constant ratio of signal between the nth and n + 1st scan. Here, the sensitivity of IPs is investigated when scanned multiple times. It is demonstrated that the ratio of signal decay is not a constant with the number of scans and that the signal decay depends on the x-ray energy. As such, repeatedly scanning an IP with a mixture of signal types (e.g., x ray, neutron, and protons) enables ICF and HEDP diagnostics employing IPs to better isolate a particular signal type.

Microbially-enhanced dissolution of calcite in sinking marine particles

2026-04-24T03:15:02Z

Microbially-enhanced dissolution of calcite in sinking marine particles Borer, Benedict; Subhas, Adam V.; Hayden, Matthew G.; Woosley, Ryan J.; Babbin, Andrew R. Evidence for the shallow cycling of calcium carbonate in the global ocean is mounting, but the mechanisms driving the dissolution of thermodynamically stable polymorphs, like aragonite and calcite, in the surface ocean remain unconstrained. Here, we quantify how microbial metabolism creates acidic microenvironments in marine particles that enhance the local dissolution of calcite despite supersaturated conditions in bulk waters. A temporal decoupling of particle deoxygenation and acidification suggests that respiration-derived carbon dioxide is not the sole driver of the observed undersaturation. Rapid dissolution occurs in particles exhibiting bacterial growth, with rates exceeding abiotic dissolution at the same bulk saturation by more than an order of magnitude. We observe the highest particle-associated dissolution rates at intermediate settling velocities, indicating that a trade-off between elevated mass transfer due to settling and bacterial respiration governs the ensuing dissolution rates. Translation of our experiments to the water column suggests that microbially driven undersaturation in marine particles may dissolve sufficient calcite in the mesopelagic ocean to extend particle transit times by eliminating this vital ballast mineral, reducing the efficiency of organic carbon sequestration.

Process cost analysis of performance challenges and their mitigations in sodium-ion battery cathode materials

2026-03-10T03:07:47Z

Process cost analysis of performance challenges and their mitigations in sodium-ion battery cathode materials Munjal, Mrigi; Prein, Thorben; Ramadan, Mahmoud M.; Smith, Hugh B.; Venugopal, Vineeth; Rupp, Jennifer L.M.; Abate, Iwnetim I.; Olivetti, Elsa A.; Huang, Kevin J. The success of sodium-ion batteries (SIBs) hinges on mitigating underperformance in ways that are cost effective, manufacturable, and scalable. This work investigates interfacial, morphological, and bulk interventions to enhance the performance of layered metal oxide cathode active materials (CAMs) for SIBs. We mapped the full space of literature-reported SIB CAM challenges and their mitigations. We then estimated the manufacturing costs for a diverse and representative set of mitigation approaches. Adding sacrificial salts can be cost effective, given low materials costs and minimal process changes. By contrast, many methods are reported to tune CAM morphology. Several are likely challenging at scale due to process throughput and yield limitations. Finally, bulk modifications can mitigate the moisture sensitivity of some CAMs, a likely less costly route than expanding stringent atmosphere controls during manufacturing. We end by discussing the limits and promise of process cost analysis, given the current state of battery reporting in the literature.

Policy Analytics for Cybersecurity of Cyber-Physical Systems

2026-03-06T03:00:51Z

Policy Analytics for Cybersecurity of Cyber-Physical Systems Choucri, Nazli Mounting concerns about safety and security have resulted in an intricate ecosystem system of guidelines, compliance measures, directives and policy reports for cybersecurity of all critical infrastructure. The policy paradox is that the text form of policy documents is an impediment to the implementation of policies and directives and creates potentially powerful opportunity costs. As a general practice, guidelines, directives and policy documents are presented in text form, page-by-page and word-by-word all supported by figures, diagrams and tables as needed. By definition text obscures properties of both policy and system-target in terms of dynamic relationships, feedback, “drill-down”, leads and lags, and so forth. The challenge is to develop analytics for cybersecurity policy of cyber physical systems. We begin with constructing (a) a structured system model of the system, in order to (b) identify major policydefined system-wide parameters, (c) situate system vulnerabilities, (d) map security requirements to security objectives, and (e) advance research on how system properties respond to diverse policy controls for security of cyber physical systems. This Project addresses the hard problem of policy-governed secure collaboration related to cyberphysical security of critical infrastructure (focusing on a generic and fundamental feature, namely smart grid of electric power systems). The purpose is to (a) reduce, if not eliminate barriers to full understanding of policy text as transmitted by the source, (b) explore system-wide or targeted implications, (c) help contextualize generic directives for specific applications, and (d) facilitate contingency analysis, as needed. This Compilation is based on the Quarterly Research Reports submitted by MIT to the Cyber- Physical Systems Organization of Vanderbilt University. The Compilation is the first of several Reports highlighting the research process and products of the MIT Project on Policy Analytics for Cybersecurity of Cyber-Physical Systems. Gaurav Agarwal [a.k.a. Gaurav], MIT alumnus, served as Lead Researcher for the Proof-of-Concept case presented here.

Summary of the Fourth ALMA Phasing Project (APP) Commissioning and Science Verification Mission: 2016 April 3-8

2026-03-06T03:01:04Z

Summary of the Fourth ALMA Phasing Project (APP) Commissioning and Science Verification Mission: 2016 April 3-8 Matthews, Lynn D.; Crew, G. B.; Fish, Vincent The primary objectives of the fourth APP CSV campaign were twofold: (1) to execute Very Long Baseline Interferometry (VLBI) observing mode (VOM) observations using Schedule Blocks (SBs); (2) to carry out the first end-to-end testing of intercontinental VLBI sessions in both Bands 3 and 6. While intercontinental VLBI fringes with ALMA have already been obtained during previous CSV campaigns (Matthews & Crew 2016b, c), those sessions were not conducted in a manner identical to future VLBI science campaigns (i.e., they used manual execution of observing commands rather than SBs) and did not involve observations of a full suite of ALMA and VLBI calibrators. Secondary objectives of the fourth CSV mission included further development work on an ALMA Phasing System (APS) graphical user interface (GUI), additional testing of the fast phasing loop (under a wider variety of weather conditions), tests of the phasing system in Band 7 (in support of an ongoing North America ALMA Study award), and the training of ALMA staﬀ in the operation of the VOM and the APS hardware and software. This is a report of the activities of this campaign. This report was prepared to report on APP commissioning progress, provided as ALMA Technical Note #19.

Summary of the Third ALMA Phasing Project (APP) Commissioning and Science Verification Mission: 2015 July 28-August 3

2026-03-06T03:01:12Z

Summary of the Third ALMA Phasing Project (APP) Commissioning and Science Verification Mission: 2015 July 28-August 3 Matthews, Lynn D.; Crew, G. B. The primary objective for the third APP CSV campaign was to perform intercontinental Very Long Baseline Interferometry (VLBI) fringe tests between phased ALMA and remote stations in Band 3, Band 6, and (conditions permitting) Band 7. Secondary objectives included preparations for making the APS available to the community for ALMA Cycle 4 and training ALMA staff in the operation of the ALMA Phasing System (APS). This is a report of those activities. This report was prepared to report on APP commissioning progress, provided as ALMA Technical Note #18.

Summary of the Second ALMA Phasing Project (APP) Commissioning and Science Verification Mission: 2015 March 24-30

2026-03-06T03:01:14Z

Summary of the Second ALMA Phasing Project (APP) Commissioning and Science Verification Mission: 2015 March 24-30 Matthews, Lynn D.; Crew, G. B. The primary objective for the second APP CSV campaign was to test and characterize the phasing system, including the recent changes in the handling of the front-end delays. Secondary goals were to repeat the local VLBI test between ALMA and an antenna at the Operations Support Facility (OSF) (also attempted during the January mission) and to obtain a short VLBI recording on a calibrator source with ALMA and one or more stations operating as part of the Event Horizon Telescope (EHT) network, thus allowing demonstration of an intercontinental VLBI fringe. This is a report of the week's activities. This report was prepared to report on APP commissioning progress, provided as ALMA Technical Note #17.

Summary of the First ALMA Phasing Project (APP) Commissioning and Science Verification Mission: 2015 January 6-13

2026-03-06T03:01:08Z

Summary of the First ALMA Phasing Project (APP) Commissioning and Science Verification Mission: 2015 January 6-13 Matthews, Lynn D.; Crew, G. B. The first Commissioning and Science Verification (CSV) mission for the ALMA Phasing Project (APP) took place during the ALMA EOC Week from 2015 January 6-13. The formal commencement of APP CSV activities followed the provisional acceptance of the APP hardware during a formal review by JAO that took place on 2014 December 11. This is a report of activities during the week. This report was prepared to report on APP commissioning progress, provided as ALMA Technical Note #16.

ALMA North America Cycle 4 Study Project Final Report: Diversifying the Applications of the ALMA Phasing System

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ALMA North America Cycle 4 Study Project Final Report: Diversifying the Applications of the ALMA Phasing System Matthews, Lynn D.; Crew, G.; Hecht, M. H. The Atacama Millimeter/submillimeter Array (ALMA) Phasing Project (APP) produced the hardware and software modifications necessary to bring Very Long Baseline Interferometry (VLBI) capabilities to ALMA. The resulting VLBI observing mode was introduced to the science community in ALMA Cycle 4 (2017), and two VLBI science campaigns have now been carried out successfully at ALMA. The current Cycle 4 ALMA North America (NA) Study was proposed to lay the groundwork for a variety of enhancements to the ALMA Phasing System (APS) that were not within the scope of the original APP project. These include: (1) devising an improved method for the handling of baseband delays; (2) development of procedures for use of the APS on fainter astronomical sources than is presently possible; (3) development of data acquisition and correlation techniques to allow the APS to be used for spectral line VLBI experiments. These tasks were intended as preparatory steps for a future full-scale implementation project (if approved). Formal approval of this implementation work has now been granted and is funded through an ALMA Cycle 5 NA Development project known as APP “Phase 2” (APP-2). As a result, efforts to implement capabilities designed and explored under the current Cycle 4 Study, as well as a previous Cycle 3 Study award, are now underway. This report provides a status summary of Cycle 4 activities and outlines follow-on work that is continuing as part of the ongoing Cycle 5 Development efforts.

ALMA North America Cycle 3 Study Project Final Report: Extensions and Enhancements to the ALMA Phasing System

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ALMA North America Cycle 3 Study Project Final Report: Extensions and Enhancements to the ALMA Phasing System Matthews, L.; Crew, G.; Hecht, M. H. The Atacama Millimeter/submillimeter Array (ALMA) Phasing Project (APP) has successfully brought Very Long Baseline Interferometry (VLBI) to ALMA. Nine VLBI science projects were observed in 2017 during ALMA’s in augural VLBI campaign as part of Cycle 4. This marked the culmination of an international 5-year effort that involved both hardware and software contribu- tions from the APP Team to the ALMA Observatory. A Cycle 3 ALMA North America (NA) Study was proposed to enable ongoing support of VLBI at ALMA and the investigation of enhancements to the ALMA Phasing System (APS) that were not within the scope of the original APP project. These included: (1) an extension of phasing capabilities to the submilleter (Band 7); (2) an exploration of correlation techniques to compensate for the mismatch in sampling rates between ALMA and other VLBI stations; (3) prescriptions for optimization of ALMA baseband delay application; (4) defining and documenting data calibration and analysis pathways for experiments utilizing phased ALMA data. This report summarizes outcomes from the Cycle 3 Study. Work on the APP remains ongoing under a Cycle 4 Study award and will continue under a pending ALMA NA Cycle 5 Development Project that is expected to enable full implementation of the capabilities explored under the Cycle 3 and Cycle 4 Studies.

Pulsars, Magnetars, and Transients with Phased ALMA, Final Report

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Pulsars, Magnetars, and Transients with Phased ALMA, Final Report Cordes, James; Blackburn, Lindy; Chatterjee, Shami; Crew, Geoffrey; Devignes, Gregory; Doeleman, Shep; Kramer, Michael; Lazio, Joe; Liu, Kuo; Ransom, Scott The present study developed fast time-domain capability for the ALMA phased-array system that is needed for observations of compact objects in the Galactic center and elsewhere in the Galaxy. ALMA can provide unparalleled sensitivity to a spectral region that has been poorly explored for neutron stars. Observations at mm and sub-mm wavelengths have the potential for providing decisive observational constraints on emission processes from the magnetospheres of neutron stars. ALMA is also important for surveys for pulsars and transients in the Galactic center. This is a final report of the work performed.

Final Report: ALMA Phasing Project Augmentation

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Final Report: ALMA Phasing Project Augmentation Matthews, Lynn D. This report provides a summary of activities carried out under the NA ALMA Development Fund award to augment the National Science Foundation MRI award for the ALMA Beamformer proposal, performed as the ALMA Phasing Project.

MRI: Development of an ALMA Beamformer for Ultra High Resolution VLBI and High Frequency Phased Array Science

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MRI: Development of an ALMA Beamformer for Ultra High Resolution VLBI and High Frequency Phased Array Science Matthews, Lynn D. This is the final report covering the activities of the National Science Foundation MRI Award number 1126433.

Cycle 11 VLBI Acceptance: Delay Fix Final Report

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Cycle 11 VLBI Acceptance: Delay Fix Final Report Crew, Geoffrey B.; Matthews, Lynn D. This report presents details of changes to the VLBI software system made on the path to the Cycle 11 Acceptance. The principal new feature is the long-awaited "delay fix" to the APS that is presented in considerable detail here. This was proposed for a new ADF implementation project, APP2. The delay fix is a technology that allows the full 2-GHz continuum band to be used in active phasing, resulting results in a lower flux density limit for direct observation of science targets. It also allows a greater range of passive phasing targets to support weaker targets. Work on the delay fix began during Cycle 3 and is only now concluded in time for additional testing as desired in Cycle 11 and then full use in Cycle 12. Other than the delay fix, a new software device was delivered to support the new hydrogen maser, and there was the usual round of minor scripting updates in the SSR component. This report is written to present a largely self-contained history of the delay fix effort and its fruit, and it also covers the other, minor development items which are formally part of the Cycle 11 Acceptance. As the software phasing engine which supports this mode is also now the recommended engine for the APS, this document also serves to present relatively complete documentation on the final implementation of the SSR as well as TelCal sides of the APS. The underlying VOM remains as it was deployed and used in Cycle 4.

ObsMode2022 Cycle 10 Go/No-Go Report for VLBI Capabilities

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ObsMode2022 Cycle 10 Go/No-Go Report for VLBI Capabilities Matthews, Lynn D.; Crew, Geoff; Fish, Vincent; Messias, Hugo; Titus, Mike; Krichbaum, Thomas We present here a report on the Very Long Baseline Interferometry (VLBI) development efforts under consideration at ALMA as new offerings in Cycle 10. These activities are being carried out under the ALMA North America Development Project known as the ALMA Phasing Project Phase 3 (APP3). The two VLBI priorities previously identified for Cycle 10 by the ObsMode process are: (1) spectral line VLBI with flexible tuning; (2) panchromatic VLBI for spectral line and continuum (Bands 1, 3, 6 and 7, with provisions for extension to any other band). This document provides an overview of the development, testing, and readiness of these capabilities. Some updates on minor development efforts are also provided. This report was prepared for the formal acceptance of the software required for Cycle 10. Notionally it is ALMA Technical Note #25, but not published (yet) as such.

Cycle 8 (2021–2022) VLBI Delta Acceptance Report

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Cycle 8 (2021–2022) VLBI Delta Acceptance Report Crew, Geoff; Vila-Vilaro, Baltasar This report summarizes the acceptance process for VLBI which was carried out in 2021 as part of the normal Cycle 8 Acceptance and later, through the 2021–2022 preparations for the 2022 VLBI Campaigns. It reviews the hardware setup and checks that must be made at various times prior to any observations with VLBI peers. Then there is a suite of offline tests of the SB-generation that include observation simulation. There is also a suite of on-sky regression tests that exercise the ALMA Phasing System (APS). The final step of the acceptance has historically been the execution of a short “dress rehearsal” (DR) usually in January of the cycle year with the Event Horizon Telescope (EHT) to provide end-to-end validation of the system via fringes to remote peers. Collectively these tests establish that the science VLBI projects may proceed without issue. This report was prepared for the formal acceptance of the software required for Cycle 8. Notionally, it is ALMA Technical Note #24, but not published (yet) as such.

Design of a diamond-based in-vessel soft x-ray detector for the SPARC tokamak

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Design of a diamond-based in-vessel soft x-ray detector for the SPARC tokamak Normile, S; Vezinet, D; Perks, C; Bombarda, F; Verona-Rinati, G; Rice, JE; Verona, C; Raso, AM; Angelone, M The in-vessel silicon diode arrays that are used for soft x-ray detection in many tokamaks are sensitive to neutron damage, making them unsuitable for burning plasma devices such as SPARC. In such a device, the silicon diodes would need to be placed far from the plasma—limiting their field of view—or an alternative detector could be used. Here, we present the design of a camera containing an array of chemical vapor deposition single-crystal diamonds, which will be placed in the upper and lower port plugs of the SPARC tokamak with a large enough view of the poloidal cross section to enable tomographic inversion. The camera design presented here is optimized to provide a wide field of view of the poloidal cross section. Simulated plasma conditions are used to estimate the x-ray signal that this detector array will receive and to fine-tune the camera placement within the tokamak.

Development of the prototype for the SPARC hard X-ray monitor

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Development of the prototype for the SPARC hard X-ray monitor Panontin, E; Tinguely, RA; Hartwig, ZS; Saltos, AA; Vezinet, D; Rice, J The SPARC tokamak will be equipped with a hard X-ray (HXR) monitor system capable of measuring the bremsstrahlung emission from runaway electrons with photon energies in excess of about 100 keV. This diagnostic will detect the formation of runaway electron beams during plasma start-up and inform the plasma control system to terminate the discharge early to protect the machine. In this work, we present a 0D estimate of the HXR emission in SPARC during plasma start-up. Then we discuss the characterization of a prototype of the HXR monitor. The detector mounts a 1 × 1-in.2 LaBr3 inorganic scintillator coupled with a photomultiplier tube and has been tested with γ-ray sources to find its dynamic range. Finally, two possible modes of operation for spectroscopic and current mode measurements on SPARC are proposed.

Perspectives on pilot-wave hydrodynamics

2026-03-06T03:08:58Z

Perspectives on pilot-wave hydrodynamics Bush, John WM; Frumkin, Valeri; Sáenz, Pedro J We present a number of fresh perspectives on pilot-wave hydrodynamics, the field initiated in 2005 by Couder and Fort's discovery that millimetric droplets self-propelling along the surface of a vibrating bath can capture certain features of quantum systems. A recurring theme will be that pilot-wave hydrodynamics furnishes a classical framework for reproducing many quantum phenomena and allows one to rationalize such phenomena mechanistically, from a local realist perspective, obviating the need to appeal to quantum nonlocality. The distinction is drawn between hydrodynamic pilot-wave theory and its quantum counterparts, Bohmian mechanics, the Bohm–Vigier stochastic pilot-wave theory, and de Broglie's theory of the double-solution. Each of these quantum predecessors provide a valuable touchstone as we take the physical picture engendered in the walking droplets and extend it into the quantum realm via theoretical modeling. Emphasis is given to recent developments in the field, both experimental and conceptual, and to forecasting potentially fruitful new directions.

Automated transient grating spectroscopy mapping and signal control for large samples

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Automated transient grating spectroscopy mapping and signal control for large samples Weaver, Colin; Stapelberg, Myles; Short, Michael P; Wylie, Angus; Artalejo, Elena Botica We present developments for the mapping of large areas using transient grating spectroscopy (TGS) that allow for smoother, larger, autonomous measurements of material samples. The addition of a precise linear stage in the direction parallel to laser sampling coupled with signal optimizing control allows for hands free, self-correcting measurements. In addition, the simplification of the sample holding design to a form that is small enough to mount directly to the linear stage exhibits a straightforward, low-cost solution for automated TGS applications. This capability is demonstrated by taking large uninterrupted maps of gradient wafers, and the results are validated on calibrated tungsten samples and control TGS samples from gradient wafers.

Manipulating the duration of picoinjection controls the injected volume of individual droplets

2026-03-06T03:09:01Z

Manipulating the duration of picoinjection controls the injected volume of individual droplets Thakur, R.; Weitz, D. The ability to add reagents into droplets is required in many microfluidic workflows. Picoinjection can address this need; however, it is unable to control the injection volume for each individual droplet. Here, we present an improved picoinjection method that can inject controlled volumes into individual droplets. We achieve this by adjusting the injection duration for each picoinjection event. This improved picoinjection method can be used to create complex microfluidic workflows that are able to control the biochemical composition of individual droplets.

Multitask methods for predicting molecular properties from heterogeneous data

2026-03-06T03:08:49Z

Multitask methods for predicting molecular properties from heterogeneous data Fisher, KE; Herbst, MF; Marzouk, YM Data generation remains a bottleneck in training surrogate models to predict molecular properties. We demonstrate that multitask Gaussian process regression overcomes this limitation by leveraging both expensive and cheap data sources. In particular, we consider training sets constructed from coupled-cluster (CC) and density functional theory (DFT) data. We report that multitask surrogates can predict at CC-level accuracy with a reduction in data generation cost by over an order of magnitude. Of note, our approach allows the training set to include DFT data generated by a heterogeneous mix of exchange–correlation functionals without imposing any artificial hierarchy on functional accuracy. More generally, the multitask framework can accommodate a wider range of training set structures—including the full disparity between the different levels of fidelity—than existing kernel approaches based on Δ-learning although we show that the accuracy of the two approaches can be similar. Consequently, multitask regression can be a tool for reducing data generation costs even further by opportunistically exploiting existing data sources.

7.344 Cellular Metabolism and Cancer: Nature or Nurture?, Fall 2018

2026-03-09T18:17:48Z

7.344 Cellular Metabolism and Cancer: Nature or Nurture?, Fall 2018 Lau, Allison; Lien, Evan In this course we will explore how altered metabolism drives cancer progression. Students will learn (1) how to read, discuss, and critically evaluate scientific findings in the primary research literature, (2) how scientists experimentally approach fundamental issues in biology and medicine, (3) how recent findings have challenged the traditional “textbook” understanding of metabolism and given us new insight into cancer, and (4) how a local pharmaceutical company is developing therapeutics to target cancer metabolism in an effort to revolutionize cancer therapy.

7.343 Single-Molecule Imaging: Capturing Nanoscale Cellular Machines in Action, Fall 2021

2026-03-09T18:16:55Z

7.343 Single-Molecule Imaging: Capturing Nanoscale Cellular Machines in Action, Fall 2021 Kose, Hazal B. Did you know that we have approximately 2 meters of DNA packed in our cells, which are less than 10 μm diameter? Or that to replicate DNA it is copied at a rate of 70,000 basepairs per second by a cellular apparatus that coordinates at least six different enzymes? Or that microtubules form greater than 1 meter long “railways” upon which molecular machines transport cargo within nerve cells? In this course, we will explore how single-molecule imaging techniques capture the mega-cellular machines working in real-time. This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interactive setting. Many instructors of the Advanced Undergraduate Seminars are postdoctoral scientists with a strong interest in teaching.

7.342 Immune Cell Migration: On the Move in Response to Pathogens and Cancer Immunotherapy, Fall 2021

2026-03-09T18:16:00Z

7.342 Immune Cell Migration: On the Move in Response to Pathogens and Cancer Immunotherapy, Fall 2021 Fessenden, Timothy The mammalian immune system is sometimes called a “liquid organ,” capable of rapidly initiating and then resolving potent responses to pathogens at almost any location in the organism. What protein machinery drives immune cells’ rapid migration? How do cells make pathfinding decisions around barriers? How do they find rare pathogens or target cells in complex environments? This course will begin by examining the general immunological functions of two major immune cell types—T cells and dendritic cells. Through our readings and discussions, we will examine the connections between immunotherapy as an emerging treatment modality for a variety of cancers and the migration of immune cells. This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interactive setting. Many instructors of the Advanced Undergraduate Seminars are postdoctoral scientists with a strong interest in teaching.

7.341 Turning Evolutionary Dials: Directed Evolution Techniques for Climate Change and Beyond, Spring 2022

2026-03-09T18:15:19Z

7.341 Turning Evolutionary Dials: Directed Evolution Techniques for Climate Change and Beyond, Spring 2022 Kizer, Megan; Wilson, Robbie This course will cover the many ways in which we have realized evolution in the laboratory toward functional biomolecules, such as protein and nucleic-acid-based therapeutics, enzymes that catalyze production of synthetic drugs, and carbon-dioxide capture molecules to lessen the impact of climate change. Students will both become familiar with the field of directed molecular evolution and learn how to critically analyze primary research papers, design research experiments, and present data relating to molecular biology and evolution. The importance of directed evolution in biomedical and biotechnological careers, both academic and industrial, will be highlighted. This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interactive setting. Many instructors of the Advanced Undergraduate Seminars are postdoctoral scientists with a strong interest in teaching.

7.342 Synapse Remodeling in Health and Disease, Fall 2022

2026-03-09T18:14:38Z

7.342 Synapse Remodeling in Health and Disease, Fall 2022 Ordonez, Dalila; Boivin, Josiah Our brains are remarkably adaptable throughout our lives. Individual brain cells called neurons form synapses, sites of physical connection and communication between neurons, and then repeatedly rewire those connections in response to new experiences or to neuronal cell death caused by injury, disease, or aging. In this course, we will explore how neurons establish their synapses in the healthy brain during childhood and later in life, and how this process goes awry in disease states. More specifically, we will discuss how the brain forms its synapses early in life, stabilizes a subset of those synapses for long-term maintenance, and continues to add and remove synapses throughout life. We will then explore synapse dysfunction in diseases such as autism and Alzheimer’s disease, which involve abnormal increases or losses of synaptic connections, respectively. We will also consider synapse remodeling, a process of adding and removing synaptic connections to optimize our brain network, in the context of neuroinflammation, recovery from traumatic brain injury, and psychological trauma following prolonged stress. This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interactive setting. Many instructors of the Advanced Undergraduate Seminars are postdoctoral scientists with a strong interest in teaching.

7.342 How To Build An Animal: Cell Fate and Identity in Development and Disease, Fall 2017

2026-03-09T18:14:04Z

7.342 How To Build An Animal: Cell Fate and Identity in Development and Disease, Fall 2017 Blanton, Laura V In this course, we will explore how animals determine and maintain cell fate. We will discuss changes to DNA structure and packaging, special proteins (known as "master regulators") with the ability to alter cell fate via transcription, cell-cell signaling, and RNA localization. This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interactive setting. Many instructors of the Advanced Undergraduate Seminars are postdoctoral scientists with a strong interest in teaching.

7.341 DNA's Sister Does All the Work: The Central Roles of RNA in Gene Expression , Spring 2019

2026-03-09T18:13:29Z

7.341 DNA's Sister Does All the Work: The Central Roles of RNA in Gene Expression , Spring 2019 Fiszbein, Ana; Jens, Marvin This course will explore the current frontiers of the world of RNA biology with primary research papers to trace how the original odd detail sometimes leads to major discoveries. As we discuss the different transcripts and processing events that enable this exciting diversity of RNA functions, we invite you to read landmark papers with us, think critically, and ask new questions, as we marvel at the wonders of RNA. This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interactive setting. Many instructors of the Advanced Undergraduate Seminars are postdoctoral scientists with a strong interest in teaching.

7.341 The Microbiome and Drug Delivery: Cross-species Communication in Health and Disease, Spring 2018

2026-03-09T18:12:34Z

7.341 The Microbiome and Drug Delivery: Cross-species Communication in Health and Disease, Spring 2018 Beyzavi, Ali; Jimenez, Miguel There are more microbes permanently living in our gut than there are cells in the human body. This rich community of bacteria, fungi and viruses, called the microbiome, plays a central role in human health and disease. Recent research has linked this passenger community to nutrition, circadian rhythms, infectious disease, inflammatory disease, cancer, diabetes, arthritis and even immune system and nervous system development. How can we analyze such a complex system? Can we exploit the microbiome to improve human health? Can interactions with microbes be harnessed for drug delivery? In this course, we will learn to critically assess the primary scientific literature to find answers to these questions and learn to distinguish between correlation and causality. We will learn how mechanistic insights and emerging tools, such as synthetic biology and microfluidics, together are transforming microbiome research, and might lead to new types of therapeutics and drug delivery for improving human health. This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interactive setting. Many instructors of the Advanced Undergraduate Seminars are postdoctoral scientists with a strong interest in teaching.

7.341 Microbes at War: The Mechanisms That Drive Infectious Diseases, Fall 2022

2026-03-09T18:11:52Z

7.341 Microbes at War: The Mechanisms That Drive Infectious Diseases, Fall 2022 McLellan, Lisa How can a tick bite cause a meat allergy? And does cranberry juice do anything to help cure a urinary tract infection? To answer these and other questions, we are going to take a dive into the molecular world of microbes. In this class, we will use the primary research literature to explore the molecular interactions between pathogens and their hosts that allow microbes to cause infectious diseases. We will examine the factors that pathogens use to colonize a host and how the host response can impact the outcome of the infection. By the end of the class, students will have both developed critical scientific skills in evaluating scientific literature and an appreciation of the microbes influencing our lives and health every day. This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interactive setting. Many instructors of the Advanced Undergraduate Seminars are postdoctoral scientists with a strong interest in teaching.

7.341 Biomaterials and Devices for Disease Diagnosis and Therapy, Fall 2018

2026-03-09T18:11:08Z

7.341 Biomaterials and Devices for Disease Diagnosis and Therapy, Fall 2018 McHugh, Kevin; Beyzavi, Ali Students will learn about the use of biomaterials to create advanced diagnostic tools for detection of infectious and chronic diseases, restore insulin production to supplement lost pancreatic function in diabetes, provide cells with appropriate physical, mechanical, and biochemical cues to direct tissue regeneration, and enhance the efficacy of cancer immunotherapy. This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interactive setting. Many instructors of the Advanced Undergraduate Seminars are postdoctoral scientists with a strong interest in teaching.

7.342 The Seeds and the Soil: Roles of Tumor Heterogeneity and the Tumor Microenvironment in Cancer Metastasis, Fall 2020

2026-03-09T18:09:46Z

7.342 The Seeds and the Soil: Roles of Tumor Heterogeneity and the Tumor Microenvironment in Cancer Metastasis, Fall 2020 Lambert, Arthur; Zhang, Yun Metastatic disease is responsible for the vast majority of deaths associated with cancer, yet our understanding of how metastases arise is still developing. In this course, we will introduce various concepts and models that have been proposed to explain how cancer cells disseminate from a primary tumor to distant anatomical sites. We’ll learn about the critical factors that influence cancer metastasis frontiers through analysis and discussion of relevant primary research articles, with an emphasis on mechanisms of metastasis that can be applied across different cancer types. Students will gain a broad understanding of the field of cancer metastasis, including state-of-the-art techniques that are being used to address pressing questions in the field. This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interactive setting. Many instructors of the Advanced Undergraduate Seminars are postdoctoral scientists with a strong interest in teaching.

7.343 Microbial Megaproducers: Discovery, Biosynthesis, Engineering and Applications of Natural Products, Fall 2020

2026-03-09T18:08:41Z

7.343 Microbial Megaproducers: Discovery, Biosynthesis, Engineering and Applications of Natural Products, Fall 2020 Ulrich, Emily C; Hetrick, Kenton The natural world is a mega-factory of small molecules, peptides, fatty acids, phospholipids, and a host of other compounds, known as natural products (NPs). Immensely diverse in structure and function, NPs have strongly influenced how we treat infectious disease, cancer, pain, and a host of other conditions. Roughly half of the drugs that have been approved in the past 30 years are NPs, derivatives of NPs or NP-inspired. In this discussion-based course, we will delve into research on discovering NPs from producing organisms, investigating the biochemistry of NP production, and using synthetic biology to create NP derivatives—all with a particular emphasis on how genomic data guides and informs all these studies. This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interactive setting. Many instructors of the Advanced Undergraduate Seminars are postdoctoral scientists with a strong interest in teaching.

7.342 A Double-Edged Sword: Cellular Immunity in Health and Disease, Fall 2018

2026-03-09T18:09:08Z

7.342 A Double-Edged Sword: Cellular Immunity in Health and Disease, Fall 2018 Ma, Haiting Immune cells protect our bodies from both self-derived threats and exogenous pathogens, while keeping peace with normal cells and non-harmful commensal microbiota. They have various mechanisms to perform these tasks, a capacity that is essential for maintaining homeostasis. However, these same mechanisms can backfire, resulting in severe disorders such as immunodeficiency, chronic inflammation, allergy, degenerative diseases, and cancer. This course discusses the connections between normal physiology and disease by examining the developmental relationship between innate and adaptive immune cells as well as the functions and malfunctions of immune cells. The course familiarizes students with both basic biological principles (such as cell death and immune cell signaling) and clinical applications (such as immune checkpoint blockade). More generally, students learn to identify relevant primary research literature, critically evaluate experimental data, and reach their own conclusions based on primary data. This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interactive setting. Many instructors of the Advanced Undergraduate Seminars are postdoctoral scientists with a strong interest in teaching.

Automated Flow Synthesis of Artificial Heme Enzymes for Enantiodivergent Biocatalysis

2026-03-05T06:13:21Z

Automated Flow Synthesis of Artificial Heme Enzymes for Enantiodivergent Biocatalysis Fittolani, Giulio; Kutateladze, Dennis A; Loas, Andrei; Buchwald, Stephen L; Pentelute, Bradley L The remarkable efficiency with which enzymes catalyze small-molecule reactions has driven their widespread application in organic chemistry. Here, we employ automated fast-flow solid-phase synthesis to access catalytically active full-length enzymes without restrictions on the number and structure of noncanonical amino acids incorporated. We demonstrate the total syntheses of iron-dependent Bacillus subtilis myoglobin (BsMb) and sperm whale myoglobin (SwMb). The synthetic enzymes displayed excellent enantioselectivity and yield in carbene transfer reactions. Absolute control over enantioselectivity in styrene cyclopropanation was achieved using synthetic L- and D-BsMb mutants, which delivered each enantiomer of cyclopropane product in identical and opposite enantiomeric enrichment. BsMb mutants outfitted with noncanonical amino acids were used to facilitate detailed structure–activity relationship studies, revealing a previously unrecognized hydrogen-bonding interaction as the primary driver of enantioselectivity in styrene cyclopropanation. We anticipate that our approach will advance biocatalysis by providing reliable and rapid access to fully synthetic enzymes possessing noncanonical amino acids.

Development of a Ligand for Cu-Catalyzed Amination of Base-Sensitive (Hetero)aryl Chlorides

2026-03-05T06:13:11Z

Development of a Ligand for Cu-Catalyzed Amination of Base-Sensitive (Hetero)aryl Chlorides Ai, Han-Jun; Mai, Binh Khanh; Liu, Cecilia; Liu, Peng; Buchwald, Stephen L We report a new N1,N2-diarylbenzene-1,2-diamine ligand, L6, that supports a copper catalyst capable of coupling base-sensitive aryl chlorides and amines that were previously unsuccessful substrates for Cu-catalyzed C–N coupling. A detailed structure–activity relationship study, combined with density functional theory (DFT) calculations, was used to uncover two key structural features that contribute to the efficacy of the catalyst derived from L6. First, steric repulsion caused by a methyl substituent induces a conformational change that opens up additional space for ligand deprotonation and oxidative addition. Second, the trifluoromethyl groups create electrostatic interactions between the ligand and aryl chloride substrates that facilitate oxidative addition via through-space ligand–substrate interaction.

Ligand Design Enables Cu-Catalyzed Etherification of Aryl Bromides Using Mild Bases

2026-03-05T06:13:20Z

Ligand Design Enables Cu-Catalyzed Etherification of Aryl Bromides Using Mild Bases Strauss, Michael J; Greaves, Megan E; Kim, Seoung-Tae; Schmidt, Michael A; Scola, Paul M; Buchwald, Stephen L We report a Cu-catalyzed method for the efficient coupling of base-sensitive aryl bromides and alcohols utilizing a newly developed N1,N2-diarylbenzene-1,2-diamine ligand, L15. This ligand was developed to increase the Lewis acidity of the Cu center, thereby permitting the use of a substantially milder base (NaOTMS or NaOPh) relative to those required in a previous iteration of this methodology (NaOMe or NaOt-Bu). Under the optimized reaction conditions, several classes of previously incompatible aryl bromides were efficiently transformed, including base-sensitive heterocycles and those containing acidic functional groups. Kinetic analyses support that C–O coupling proceeds via a mechanism involving binding/deprotonation of alcohol nucleophiles, that the pKa of the base influences the overall rate law, and that substoichiometric quantities of strong base can be utilized to accelerate ligand activation and thereby increase the overall rate of the transformation.

Time-Resolved Line Shapes of Single Quantum Emitters via Machine Learned Photon Correlations

2026-03-05T06:13:24Z

Time-Resolved Line Shapes of Single Quantum Emitters via Machine Learned Photon Correlations Proppe, Andrew H; Lee, Kin Long Kelvin; Kaplan, Alexander EK; Ginterseder, Matthias; Krajewska, Chantalle J; Bawendi, Moungi G Solid-state single-photon emitters (SPEs) are quantum light sources that combine atomlike optical properties with solid-state integration and fabrication capabilities. SPEs are hindered by spectral diffusion, where the emitter's surrounding environment induces random energy fluctuations. Timescales of spectral diffusion span nanoseconds to minutes and require probing single emitters to remove ensemble averaging. Photon correlation Fourier spectroscopy (PCFS) can be used to measure time-resolved single emitter line shapes, but is hindered by poor signal-to-noise ratio in the measured correlation functions at early times due to low photon counts. Here, we develop a framework to simulate PCFS correlation functions directly from diffusing spectra that match well with experimental data for single colloidal quantum dots. We use these simulated datasets to train a deep ensemble autoencoder machine learning model that outputs accurate, noiseless, and probabilistic reconstructions of the noisy correlations. Using this model, we obtain reconstructed time-resolved single dot emission line shapes at timescales as low as 10 ns, which are otherwise completely obscured by noise. This enables PCFS to extract optical coherence times on the same timescales as Hong-Ou-Mandel two-photon interference, but with the advantage of providing spectral information in addition to estimates of photon indistinguishability. Our machine learning approach is broadly applicable to different photon correlation spectroscopy techniques and SPE systems, offering an enhanced tool for probing single emitter line shapes on previously inaccessible timescales.

Uncovering temperature-dependent exciton-polariton relaxation mechanisms in hybrid organic-inorganic perovskites

2026-03-05T06:13:18Z

Uncovering temperature-dependent exciton-polariton relaxation mechanisms in hybrid organic-inorganic perovskites Laitz, Madeleine; Kaplan, Alexander EK; Deschamps, Jude; Barotov, Ulugbek; Proppe, Andrew H; García-Benito, Inés; Osherov, Anna; Grancini, Giulia; deQuilettes, Dane W; Nelson, Keith A; Bawendi, Moungi G; Bulović, Vladimir Hybrid perovskites have emerged as a promising material candidate for exciton-polariton (polariton) optoelectronics. Thermodynamically, lowthreshold Bose-Einstein condensation requires efficient scattering to the polariton energy dispersion minimum, and many applications demand precise control of polariton interactions. Thus far, the primary mechanisms by which polaritons relax in perovskites remains unclear. In this work, we perform temperature-dependent measurements of polaritons in low-dimensional perovskite wedged microcavities achieving a Rabi splitting of _ΩRabi = 260 ± 5 meV. We change the Hopfield coefficients by moving the optical excitation along the cavity wedge and thus tune the strength of the primary polariton relaxation mechanisms in this material. We observe the polariton bottleneck regime and show that it can be overcome by harnessing the interplay between the different excitonic species whose corresponding dynamics are modified by strong coupling. This work provides an understanding of polariton relaxation in perovskites benefiting from efficient, material-specific relaxation pathways and intracavity pumping schemes from thermally brightened excitonic species. Springer Science and Business Media LLC

ObsMode2021 Cycle 9 Go/No-Go Report for VLBI Capabilities

2026-03-05T18:45:41Z

ObsMode2021 Cycle 9 Go/No-Go Report for VLBI Capabilities Matthews, Lynn D.; Crew, Geoff; Goddi, Ciriaco; Marti-Vidal, Ivan; Titus, Mike; Fish, Vincent; Wagner, Jan; Rottmann, Helge; Pridiprihora, Yurii; Krichbaum, Thomas; Liu, Kuo; Kramer, Michael We present a status summary of the primary Very Long Baseline Interferometry (VLBI) development efforts which are under consideration at ALMA as new offerings in Cycle 9. These activities are being carried out under the ALMA North America Development Project known as the ALMA Phasing Project Phase 2 (APP2). The two VLBI priorities previously identified for Cycle 9 by the ObsMode process are: (1) a submillimeter (Band 7) VLBI observing capability and (2) a prototype spectral line VLBI mode (in Band 3 only). This document provides an overview of the development, testing, and readiness of these capabilities. Updates on other APP2 development efforts of relevance for future cycles, including the phased-array mode offered for the first time in Cycle 8, are also provided. Warning: This report contains material that is considered proprietary to the Event Horizon Telescope Collaboration (EHTC). These results may not be publicly posted, cited, or shared in any form. They are presented here with permission from EHTC Management solely for the purpose of allowing an evaluation of ALMA's performance as a VLBI station for 345 GHz (Band 7) VLBI. This report also contains ALMA EOC (test) data which are subject to ALMA's standard policies on the use of such data. We are working to ensure that the EHTC follows the ALMA guidelines (Carpenter et al., 2019) in the appropriate publication and release of data to allow follow-up work for science. This report was prepared for the formal acceptance of the software required for Cycle 9. Notionally it is ALMA Technical Note #23, but not published (yet) as such.

A Self-Directed, Home-Like XR System for Sustained Intangible Cultural Heritage Practice: An Ikebana Case Study

2026-03-05T06:13:11Z

A Self-Directed, Home-Like XR System for Sustained Intangible Cultural Heritage Practice: An Ikebana Case Study Wu, Yu; Li, Manxueying; Mai, Gelei Sustained Intangible Cultural Heritage (ICH) practices for novices depend more on curiosity and creative agency than on procedural training. Yet, most extended reality (XR) systems for ICH emphasize guided instruction or exhibitions, limiting self-direction and continuity beyond the device. Using Ikebana as a case study, we present a self-directed, home-like virtual reality (VR) experience built with 3D Gaussian Splatting (3D GS) and natural hand tracking, complemented by an augmented reality (AR) revisiting feature that exports creations for real-world placement and sharing. In a study with 11 novices, pre-post questionnaires showed gains in interest, likelihood to continue offline, and understanding (p ≤.01). Interviews indicated that domestic realism reduced intimidation, natural gestures supported immersion, and AR revisiting extended reflection and engagement. We contribute (1) a home-like, self-directed XR design for ICH practice and (2) evidence that approachability, autonomy, and cross-reality continuity enhance motivation beyond the virtual world. VRCAI ’25, Macau, China

CS Ed. in Prisons and Jails: Evidence of Computer Programming Self-Efficacy Growth Across Multiple Course Offerings

2026-03-05T06:13:06Z

CS Ed. in Prisons and Jails: Evidence of Computer Programming Self-Efficacy Growth Across Multiple Course Offerings Fishberg, Andrew; Gaetz, Marisa; Nisser, Martin; Cafferty, Carole; Perlman, Lee; Soicher, Raechel N.; Long, Joshua Incarcerated students enrolled in education programs in prisons and jails experience a range of benefits, from reduced recidivism to improved psychosocial well-being. With respect to computer science education, little is still known about how courses impact incarcerated students' experiences, though recent work has explored fears and confidence of incarcerated students enrolled in computer science courses. Our work investigates incarcerated students' changes in self-efficacy over multiple iterations of four different classes. Our findings showed that all subscales of computer programming self-efficacy (algorithm, control, cooperation, debugging, and logic), but not generalized self-efficacy, were statistically significantly increased at the end of the courses relative to the beginning (p < 0.001, n = 36). A similar pattern of results across the full sample (n = 188) adds additional support for the veracity of the effects found in the subset of paired data. Additionally, we share students' qualitative data to add nuance to our findings and emphasize the importance of these educational experiences for incarcerated students' personal and professional development.

HyProf: A Profiler for Programming Students that Offers Hypotheses about Performance Bugs

2026-03-05T06:13:25Z

HyProf: A Profiler for Programming Students that Offers Hypotheses about Performance Bugs Dargan, Hope; Hartz, Adam; Miller, Robert Programming students often struggle to find and fix performance bugs in their code. To provide students additional performance debugging support, as well as expose them to profiling tools, we developed Hypothesis Profiler (HyProf). HyProf automatically profiles a slow student submission and produces a profile visualization suitable for learners. In addition to showing individual function and line times, HyProf shows details about the call graph, lines that made recursive calls or did not execute, and hypotheses about possible causes of slow performance, formulated by comparing the slow profile against fast submissions from other students. We deployed HyProf in a 400-student Python course and evaluated it through web logs, office hour observations, and surveys, which showed that 75% of respondents successfully used HyProf to find or fix a performance issue and 85% would recommend it to others. SIGCSE TS 2026, St. Louis, MO, USA

KANELÉ: Kolmogorov–Arnold Networks for Efficient LUT-based Evaluation

2026-03-05T06:13:27Z

KANELÉ: Kolmogorov–Arnold Networks for Efficient LUT-based Evaluation Hoang, Duc; Gupta, Aarush; Harris, Philip C Low-latency, resource-efficient neural network inference on FPGAs is essential for applications demanding real-time capability and low power. Lookup table (LUT)-based neural networks are a common solution, combining strong representational power with efficient FPGA implementation. In this work, we introduce KANELÉ, a framework that exploits the unique properties of Kolmogorov–Arnold Networks (KANs) for FPGA deployment. Unlike traditional multilayer perceptrons (MLPs), KANs employ learnable one-dimensional splines with fixed domains as edge activations, a structure naturally suited to discretization and efficient LUT mapping. We present the first systematic design flow for implementing KANs on FPGAs, co-optimizing training with quantization and pruning to enable compact, high-throughput, and low-latency KAN architectures. Our results demonstrate up to a 2700x speedup and orders of magnitude resource savings compared to prior KAN-on-FPGA approaches. Moreover, KANELÉ matches or surpasses other LUT-based architectures on widely used benchmarks, particularly for tasks involving symbolic or physical formulas, while balancing resource usage across FPGA hardware. Finally, we showcase the versatility of the framework by extending it to real-time, power-efficient control systems. FPGA ’26, Seaside, CA, USA

AI Séance: Recounts from designing artificial intelligence for transcendence, interpretive lenses and chance

2026-03-05T06:13:22Z

AI Séance: Recounts from designing artificial intelligence for transcendence, interpretive lenses and chance Schroeder, Hope; Smith, Amy; Epstein, Ziv As AI becomes a prism through which we reflect, see, and make sense of the world, the way we create creative, transcendent experiences around AI can shape our relationship to it. Drawing inspiration from the ritual structures of Spiritualist séances and creative art-making séances of Hilma af Klint, we present reflections from a series of participatory experiments we called AI Séances. These gatherings brought together artists, technologists, and spiritual practitioners to engage with generative models in contexts shaped by ritual, randomness, and collaborative interpretation. We found that creative production with AI can yield transcendent user experiences (TUX), different communities bring distinct interpretive lenses to AI outputs, and increased technical control can paradoxically diminish serendipity and transcendence. Through our experiences, we suggest that reclaiming interpretive agency over AI outputs in the creative and spiritual context, rather than treating models as machines that produce answers, opens up new avenues for critical and creative engagement with these technologies and is critical to preserving our humanity. The AI Séance offers a model for human-centered interaction with generative systems where magic lies not in the machine’s capabilities, but in our collective ability to create meaning.

2.008 Design and Manufacturing II, Spring 2003

2026-03-04T18:04:45Z

2.008 Design and Manufacturing II, Spring 2003 Dow, David; Sachs, Emanuel; Chun, Jung-Hoon; McAtamney, Patrick; Sarma, Sanjay Integration of design, engineering, and management disciplines and practices for analysis and design of manufacturing enterprises. Emphasis is on the physics and stochastic nature of manufacturing processes and systems, and their effects on quality, rate, cost, and flexibility. Topics include process physics and control, design for manufacturing, and manufacturing systems. Group project requires design and fabrication of parts using mass-production and assembly methods to produce a product in quantity.

2.008 Design and Manufacturing II, Spring 2004

2026-03-04T18:05:26Z

2.008 Design and Manufacturing II, Spring 2004 Chun, Jung-Hoon; Kim, Sang-Gook This course introduces you to modern manufacturing with four areas of emphasis: manufacturing processes, equipment/control, systems, and design for manufacturing. The course exposes you to integration of engineering and management disciplines for determining manufacturing rate, cost, quality and flexibility. Topics include process physics, equipment design and automation/control, quality, design for manufacturing, industrial management, and systems design and operation. Labs are integral parts of the course, and expose you to various manufacturing disciplines and practices.

NeuSE: Neural SE(3)-equivariant embedding for long-term object-based simultaneous localization and mapping

2026-03-05T06:13:16Z

NeuSE: Neural SE(3)-equivariant embedding for long-term object-based simultaneous localization and mapping Fu, Jiahui; Du, Yilun; Singh, Kurran; Tenenbaum, Joshua B; Leonard, John J We present NeuSE, a novel Neural SE(3)-Equivariant Embedding for objects, and illustrate how it supports object-based Simultaneous Localization and Mapping (SLAM) for consistent spatial understanding with long-term scene changes. NeuSE is a set of latent object embeddings created from partial object observations. It serves as a compact point cloud surrogate for complete object models, encoding the full shape, scale, and transform information about an object. In addition, the inferred latent code is both SE(3) and scale equivariant, enabling strong generalization to objects of both unseen sizes and different SE(3) poses. This makes NeuSE particularly effective in real-world scenarios where objects may vary in size or spatial configuration. With NeuSE, relative frame transforms can be directly derived from inferred latent codes. Our proposed SLAM paradigm, using NeuSE for object shape, size, and pose characterization, can operate independently or in conjunction with typical SLAM systems. It directly infers SE(3) camera pose constraints that are compatible with general SLAM pose graph optimization, while maintaining a lightweight, object-centric map that adapts to real-world changes. Our evaluation is conducted on synthetic and real-world sequences with changes in both controlled and uncontrolled settings, featuring multi-category objects of various shapes and sizes. Our approach demonstrates improved localization capability and change-aware mapping consistency when working either independently or as a complement to common SLAM pipelines.

TravelAgent: Generative agents in the built environment

2026-03-05T06:13:07Z

TravelAgent: Generative agents in the built environment Noyman, Ariel; Hu, Kai; Larson, Kent Understanding human behavior in the built environment is critical for designing highly-functional, human-centered urban spaces. Traditional approaches, such as manual observations, surveys, and simple simulations, often struggle to capture the complexity and nuance of real-world human behavior and experience. Here we introduce TravelAgent, a novel agentic simulation platform that models pedestrian navigation, activity, and human-like decision-making in the built environment. TravelAgent is proposed to help design teams and decision-makers understand how different users might experience diverse built environments under varying environmental conditions. TravelAgent integrates Generative Agents, multi-modal sensory inputs, and virtual environments, enabling agents to perceive, navigate, and interact with their surroundings, with tasks ranging from goal-oriented navigation to free exploration. We share analysis from 200 simulations with 3364 decision points and task completion rate of ∼80%, across diverse spatial layouts and agent archetypes. We present spatial, linguistic, and sentiment analysis, and show how agents react and experience their surroundings. Finally, we suggest TravelAgent as a new paradigm for designing, simulating, and understanding human experiences in urban environments.

Cleaning a dark matter detector: A case of ontological and normative elusiveness

2026-03-05T06:13:29Z

Cleaning a dark matter detector: A case of ontological and normative elusiveness de Swart, Jaco; Mol, Annemarie Laboratory sciences crucially depend on the cleanliness of experiments. But what is clean? In this article, we show that the salience of the valuation clean emerges through its relation to a particular ontological repertoire. Our case is the XENONnT experiment in the Gran Sasso Mountains of Italy, designed to detect dark matter in the form of hypothetical WIMPs (Weakly Interacting Massive Particles). In this experiment, dirt presents a significant disruption, as contaminations can mimic the signals of WIMPs, and electronegative molecules risk erasing such signals. The ideosyncratic cleanliness required makes the practice of cleaning the XENONnT detector exceedingly difficult. So far, the ontological question ‘do WIMPs exist?’ remains open, which means that the normative question ‘is the detector clean enough?’ cannot be answered either. In addition, more cleaning will make the detector sensitive to a background of unremovable neutrinos—hence irredeemably dirty. With the normative goal of a ‘clean detector’ out of reach, the ontological question ‘do WIMPs exist?’ is bound to remain open as well. Alternative experiments therefore hunt for different hypothetical dark matter candidates, with different equipment, requiring different kinds of cleanliness. At the same time, the XENONnT experiment must navigate tensions between its own cleanliness goals and rules meant to ensure the environmental cleanliness of the Gran Sasso National Park. Cleaning turns out to be dirty. This leads us to ask: Which goods deserve to be cherished, and, intertwined with that, which realities deserve to be cared for?

Multi-fidelity reinforcement learning for time-optimal quadrotor re-planning

2026-03-05T06:12:59Z

Multi-fidelity reinforcement learning for time-optimal quadrotor re-planning Ryou, Gilhyun; Wang, Geoffrey; Karaman, Sertac High-speed online trajectory planning for UAVs poses a significant challenge due to the need for precise modeling of complex dynamics while also being constrained by computational limitations. This paper presents a multi-fidelity reinforcement learning method (MFRL) that aims to effectively create a realistic dynamics model and simultaneously train a planning policy that can be readily deployed in real-time applications. The proposed method involves the co-training of a planning policy and a reward estimator; the latter predicts the performance of the policy’s output and is trained efficiently through multi-fidelity Bayesian optimization. This optimization approach models the correlation between different fidelity levels, thereby constructing a high-fidelity model based on a low-fidelity foundation, which enables the accurate development of the reward model with limited high-fidelity experiments. The framework is further extended to include real-world flight experiments in reinforcement learning training, allowing the reward model to precisely reflect real-world constraints and broadening the policy’s applicability to real-world scenarios. We present rigorous evaluations by training and testing the planning policy in both simulated and real-world environments. The resulting trained policy not only generates faster and more reliable trajectories compared to the baseline snap minimization method, but it also achieves trajectory updates in 2 ms on average, while the baseline method takes several minutes.

Designing Distribution Network Tariffs in the US with an Application to Increased Electric Vehicle Adoption

2026-03-05T06:13:23Z

Designing Distribution Network Tariffs in the US with an Application to Increased Electric Vehicle Adoption Turk, Graham; Schittekatte, Tim; Duenas-Martinez, Pablo; Joskow, Paul L; Schmalensee, Richard Time-of-use (TOU) tariffs that vary the cost per kWh to reflect wide variations in generation and wholesale market costs give incentives to shift all electric vehicle (EV) charging to low-price periods. As EV penetration increases, such tariffs would substantially raise the local kW demand in those low-priced periods, which eventually would lead to increasing network expansion costs. A straightforward way to mitigate this problem is to separate energy charges from network charges, with appropriate rate designs for each. This paper uses a realistic case study to investigate the implications of combining TOU energy charges with various network tariff designs in the face of increased EV penetration. Our results provide support for the adoption in the US of ex-ante subscribed capacity tariffs (subscription charges), which give consumers incentives to reduce their peak kW demands. Reducing costs of EV ownership (a priority for many US states) need not be pursued at the expense of broader affordability goals. JEL classification: L51, L94, L97, Q41, D40

Geopolitical ecologies of cloud capitalism: Territorial restructuring and the making of national computing power in the U.S. and China

2026-03-05T06:13:28Z

Geopolitical ecologies of cloud capitalism: Territorial restructuring and the making of national computing power in the U.S. and China Kollar, Justin; Stokols, Andrew As computing power becomes central to geopolitical rivalry, cloud infrastructure is increasingly framed as critical to national security, economic resilience and technological sovereignty. Current debates often focus on global competition – especially between the U.S. and China – highlighting strategic investments, export controls and infrastructure diplomacy abroad. Yet far less attention has been paid to the domestic territorial transformations that make such geopolitical projection possible. This paper argues that national strategies for AI and cloud dominance depend on the reorganization of land, energy and regulatory systems to sustain large-scale computation. Using a geopolitical ecology framework, we examine how the U.S. and China build national computing power as a strategic economic and military resource. In the U.S., cloud firms operate as state-aligned actors, drawing on fragmented regulatory authority, public subsidies and national security discourse to expand into rural and peri-urban regions. China pursues a more centralized strategy through its East Data, West Computing initiative, redistributing infrastructure to inland provinces under state-led development goals. Through comparative regional analysis, we show how domestic infrastructural expansion underpins geopolitical rivalry, producing new forms of territorial governance and socio-environmental inequality. Far from immaterial, the cloud is grounded in enclosure, extraction and the spatial foundations of techno-industrial power.

Unlikely Organizers: The Rise of Tech Worker Labor Activism

2026-03-05T06:13:09Z

Unlikely Organizers: The Rise of Tech Worker Labor Activism Tan, JS; Luka, Natalia; Mazo, Emily Tech workers—professionals in the technology industry, such as software engineers, product managers, and UX designers—are not normally associated with labor activism. Yet, since 2017, there has been a significant rise in workplace activism over “bread-and-butter” issues among this group. Using an original data set, the authors demonstrate how, in the case of tech workers, periods of intense workplace social activism preceded later periods of heightened labor activism. Regression analysis confirms that participation in social activism increases the likelihood of labor activism six months to one year later at the same company. Extending Rick Fantasia’s cultures of solidarity to professional workers, the authors highlight a new mechanism by which professionals engage in labor organizing: First, tech workers, guided by their professional interest in socially beneficial work, engage in workplace social activism. This action generates solidarity among employee-participants but also creates conflict with management and leads to the emergence of labor activism among professionals.

Fast detection of liver fibrosis with collagen-binding single-nanometer iron oxide nanoparticles via T1-weighted MRI

2026-04-23T03:15:51Z

Fast detection of liver fibrosis with collagen-binding single-nanometer iron oxide nanoparticles via T1-weighted MRI Zhang, Juanye; Ning, Yingying; Zhu, Hua; Rotile, Nicholas J; Wei, He; Diyabalanage, Himashinie; Hansen, Eric C; Zhou, Iris Y; Barrett, Stephen C; Sojoodi, Mozhdeh; Tanabe, Kenneth K; Humblet, Valerie; Jasanoff, Alan; Caravan, Peter; Bawendi, Moungi G SNIO–CBP, a single-nanometer iron oxide (SNIO) nanoparticle functionalized with a type I collagen-binding peptide (CBP), was developed as a T1-weighted MRI contrast agent with only endogenous elements for fast and noninvasive detection of liver fibrosis. SNIO–CBP exhibits 6.7-fold higher relaxivity compared to a molecular gadolinium-based collagen-binding contrast agent CM-101 on a per CBP basis at 4.7 T. Unlike most iron oxide nanoparticles, SNIO–CBP exhibits fast elimination from the bloodstream with a 5.7 min half-life, high renal clearance, and low, transient liver enhancement in healthy mice. We show that a dose of SNIO–CBP that is 2.5-fold lower than that for CM-101 has comparable imaging efficacy in rapid (within 15 min following intravenous injection) detection of hepatotoxin-induced liver fibrosis using T1-weighted MRI in a carbon tetrachloride–induced mouse liver injury model. We further demonstrate the applicability of SNIO–CBP in detecting liver fibrosis in choline-deficient L-amino acid-defined high-fat diet mouse model of nonalcoholic steatohepatitis. These results provide a platform with potential for the development of high relaxivity, gadolinium-free molecular MRI probes for characterizing chronic liver disease.

Quantum Shell in a Shell: Engineering Colloidal Nanocrystals for a High-Intensity Excitation Regime

2026-05-08T03:14:26Z

Quantum Shell in a Shell: Engineering Colloidal Nanocrystals for a High-Intensity Excitation Regime Harankahage, Dulanjan; Cassidy, James; Beavon, Jacob; Huang, Jiamin; Brown, Niamh; Berkinsky, David B; Marder, Andrew; Kayira, Barbra; Montemurri, Michael; Anzenbacher, Pavel; Schaller, Richard D; Sun, Liangfeng; Bawendi, Moungi G; Malko, Anton V; Diroll, Benjamin T; Zamkov, Mikhail Many optoelectronic processes in colloidal semiconductor nanocrystals (NCs) suffer an efficiency decline under high-intensity excitation. This issue is caused by Auger recombination of multiple excitons, which converts the NC energy into excess heat, reducing the efficiency and life span of NC-based devices, including photodetectors, X-ray scintillators, lasers, and high-brightness light-emitting diodes (LEDs). Recently, semiconductor quantum shells (QSs) have emerged as a promising NC geometry for the suppression of Auger decay; however, their optoelectronic performance has been hindered by surface-related carrier losses. Here, we address this issue by introducing quantum shells with a CdS-CdSe-CdS-ZnS core-shell-shell-shell multilayer structure. The ZnS barrier inhibits the surface carrier decay, which increases the photoluminescence (PL) quantum yield (QY) to 90% while retaining a high biexciton emission QY of 79%. The improved QS morphology allows demonstrating one of the longest Auger lifetimes reported for colloidal NCs to date. The reduction of nonradiative losses in QSs also leads to suppressed blinking in single nanoparticles and low-threshold amplified spontaneous emission. We expect that ZnS-encapsulated quantum shells will benefit many applications exploiting high-power optical or electrical excitation regimes.

Synthesis of Zwitterionic CsPbBr3 Nanocrystals with Controlled Anisotropy using Surface-Selective Ligand Pairs

2026-05-08T03:12:32Z

Synthesis of Zwitterionic CsPbBr3 Nanocrystals with Controlled Anisotropy using Surface-Selective Ligand Pairs Zhu, Hua; Kick, Matthias; Ginterseder, Matthias; Krajewska, Chantalle J; Šverko, Tara; Li, Ruipeng; Lu, Yongli; Shih, Meng‐Chen; Van Voorhis, Troy; Bawendi, Moungi G Mechanistic studies of the morphology of lead halide perovskite nanocrystals (LHP‐NCs) are hampered by a lack of generalizable suitable synthetic strategies and ligand systems. Here, the synthesis of zwitterionic CsPbBr3 NCs is presented with controlled anisotropy using a proposed “surface‐selective ligand pairs” strategy. Such a strategy provides a platform to systematically study the binding affinity of capping ligand pairs and the resulting LHP morphologies. By using zwitterionic ligands (ZwL) with varying structures, majority ZwL‐capped LHP NCs with controlled morphology are obtained, including anisotropic nanoplatelets and nanorods, for the first time. Combining experiments with density functional theory calculations, factors that govern the ligand binding on the different surface facets of LHP‐NCs are revealed, including the steric bulkiness of the ligand, the number of binding sites, and the charge distance between binding moieties. This study provides guidance for the further exploration of anisotropic LHP‐NCs.

Theory of Photoluminescence Spectral Line Shapes of Semiconductor Nanocrystals

2026-05-08T03:12:50Z

Theory of Photoluminescence Spectral Line Shapes of Semiconductor Nanocrystals Lin, Kailai; Jasrasaria, Dipti; Yoo, Jason J; Bawendi, Moungi; Utzat, Hendrik; Rabani, Eran Single-molecule photoluminescence (PL) spectroscopy of semiconductor nanocrystals (NCs) reveals the nature of exciton-phonon interactions in NCs. Understanding the homogeneous spectral line shapes and their temperature dependence remains an open problem. Here, we develop an atomistic model to describe the PL spectrum of NCs, accounting for excitonic effects, phonon dispersion relations, and exciton-phonon couplings. We validate our model using single-NC measurements on CdSe/CdS NCs from T = 4 to 290 K, and we find that the slightly asymmetric main peak at low temperatures is comprised of a narrow zero-phonon line (ZPL) and acoustic phonon sidebands. Furthermore, we identify the specific phonon modes that give rise to the optical phonon sidebands. At temperatures above 200 K, the spectral line width shows a stronger dependence upon the temperature, which we demonstrate to be correlated with higher order exciton-phonon couplings. We also identify the line width dependence upon reorganization energy, NC core sizes, and shell thicknesses.

Ultrafast dense DNA functionalization of quantum dots and rods for scalable 2D array fabrication with nanoscale precision

2026-05-08T03:13:52Z

Ultrafast dense DNA functionalization of quantum dots and rods for scalable 2D array fabrication with nanoscale precision Chen, Chi; Luo, Xin; Kaplan, Alexander EK; Bawendi, Moungi G; Macfarlane, Robert J; Bathe, Mark Scalable fabrication of two-dimensional (2D) arrays of quantum dots (QDs) and quantum rods (QRs) with nanoscale precision is required for numerous device applications. However, self-assembly–based fabrication of such arrays using DNA origami typically suffers from low yield due to inefficient QD and QR DNA functionalization. In addition, it is challenging to organize solution-assembled DNA origami arrays on 2D device substrates while maintaining their structural fidelity. Here, we reduced manufacturing time from a few days to a few minutes by preparing high-density DNA-conjugated QDs/QRs from organic solution using a dehydration and rehydration process. We used a surface-assisted large-scale assembly (SALSA) method to construct 2D origami lattices directly on solid substrates to template QD and QR 2D arrays with orientational control, with overall loading yields exceeding 90%. Our fabrication approach enables the scalable, high fidelity manufacturing of 2D addressable QDs and QRs with nanoscale orientational and spacing control for functional 2D photonic devices.

Rational Design of a Chemical Bath Deposition Based Tin Oxide Electron‐Transport Layer for Perovskite Photovoltaics

2026-03-25T03:16:31Z

Rational Design of a Chemical Bath Deposition Based Tin Oxide Electron‐Transport Layer for Perovskite Photovoltaics Lu, Yongli; Shih, Meng‐Chen; Tan, Shaun; Grotevent, Matthias J; Wang, Lili; Zhu, Hua; Zhang, Ruiqi; Lee, Joo‐Hong; Lee, Jin‐Wook; Bulović, Vladimir; Bawendi, Moungi G Chemical bath deposition (CBD) is widely used to deposit tin oxide (SnOx) as an electron-transport layer in perovskite solar cells (PSCs). The conventional recipe uses thioglycolic acid (TGA) to facilitate attachments of SnOx particles onto the substrate. However, nonvolatile TGA is reported to harm the operational stability of PSCs. In this work, a volatile oxalic acid (OA) is introduced as an alternative to TGA. OA, a dicarboxylic acid, functions as a chemical linker for the nucleation and attachment of particles to the substrate in the chemical bath. Moreover, OA can be readily removed through thermal annealing followed by a mild H2O2 treatment, as shown by FTIR measurements. Synergistically, the mild H2O2 treatment selectively oxidizes the surface of the SnOx layer, minimizing nonradiative interface carrier recombination. EELS (electron-energy-loss spectroscopy) confirms that the SnOx surface is dominated by Sn4+, while the bulk is a mixture of Sn2+ and Sn4+. This rational design of a CBD SnOx layer leads to devices with T85 ≈1500 h, a significant improvement over the TGA-based device with T80 ≈250 h. The champion device reached a power conversion efficiency of 24.6%. This work offers a rationale for optimizing the complex parameter space of CBD SnOx to achieve efficient and stable PSCs.

Reduced recombination via tunable surface fields in perovskite thin films

2026-05-08T03:14:08Z

Reduced recombination via tunable surface fields in perovskite thin films deQuilettes, Dane W; Yoo, Jason J; Brenes, Roberto; Kosasih, Felix Utama; Laitz, Madeleine; Dou, Benjia Dak; Graham, Daniel J; Ho, Kevin; Shi, Yangwei; Shin, Seong Sik; Ducati, Caterina; Bawendi, Moungi G; Bulović, Vladimir The ability to reduce energy loss at semiconductor surfaces through passivation or surface field engineering is an essential step in the manufacturing of efficient photovoltaic (PV) and optoelectronic devices. Similarly, surface modification of emerging halide perovskites with quasi-two-dimensional (2D) heterostructures is now ubiquitous to achieve PV power conversion efficiencies (PCEs) >25%, yet a fundamental understanding to how these treatments function is still generally lacking. Here we use a unique combination of depth-sensitive nanoscale characterization techniques to uncover a tunable passivation strategy and mechanism found in perovskite PV devices that were the first to reach the >25% PCE milestone. Namely, treatment with hexylammonium bromide leads to the simultaneous formation of an iodide-rich 2D layer along with a Br halide gradient that extends from defective surfaces and grain boundaries into the bulk three-dimensional (3D) layer. This interface can be optimized to extend the charge carrier lifetime to record values >30 μs and to reduce interfacial recombination velocities to values as low as <7 cm s−1.

Solution-phase sample-averaged single-particle spectroscopy of quantum emitters with femtosecond resolution

2026-04-24T03:15:32Z

Solution-phase sample-averaged single-particle spectroscopy of quantum emitters with femtosecond resolution Shi, Jiaojian; Shen, Yuejun; Pan, Feng; Sun, Weiwei; Mangu, Anudeep; Shi, Cindy; McKeown-Green, Amy; Moradifar, Parivash; Bawendi, Moungi G; Moerner, WE; Dionne, Jennifer A; Liu, Fang; Lindenberg, Aaron M The development of many quantum optical technologies depends on the availability of single quantum emitters with near-perfect coherence. Systematic improvement is limited by a lack of understanding of the microscopic energy flow at the single-emitter level and ultrafast timescales. Here we utilize a combination of fluorescence correlation spectroscopy and ultrafast spectroscopy to capture the sample-averaged dynamics of defects with single-particle sensitivity. We employ this approach to study heterogeneous emitters in two-dimensional hexagonal boron nitride. From milliseconds to nanoseconds, the translational, shelving, rotational and antibunching features are disentangled in time, which quantifies the normalized two-photon emission quantum yield. Leveraging the femtosecond resolution of this technique, we visualize electron–phonon coupling and discover the acceleration of polaronic formation on multi-electron excitation. Corroborated with theory, this translates to the photon fidelity characterization of cascaded emission efficiency and decoherence time. Our work provides a framework for ultrafast spectroscopy in heterogeneous emitters, opening new avenues of extreme-scale characterization for quantum applications.

Additive‐Free Oxidized Spiro‐MeOTAD Hole Transport Layer Significantly Improves Thermal Solar Cell Stability

2026-04-24T03:14:37Z

Additive‐Free Oxidized Spiro‐MeOTAD Hole Transport Layer Significantly Improves Thermal Solar Cell Stability Grotevent, Matthias J; Lu, Yongli; Šverko, Tara; Shih, Meng‐Chen; Tan, Shaun; Zhu, Hua; Dang, Tong; Mwaura, Jeremiah K; Swartwout, Richard; Beiglböck, Finn; Kothe, Linda; Bulović, Vladimir; Bawendi, Moungi G Perovskite solar cells are among the most promising new solar technologies, already surpassing polycrystalline silicon solar cell efficiencies. The stability of the highest efficiency devices at elevated temperature is, however, poor. These cells typically use Spiro‐MeOTAD as the hole transporting layer. It is generally believed that additives, required for enhancing electrical conductivity and optimizing energy level alignment, are responsible for the reduced stability—inferring that Spiro‐MeOTAD based hole transporting layers are intrinsically unstable. Here, a reliable noble metal free synthesis of Spiro‐MeOTAD (bis(trifluoromethane)sulfonimide)4 is presented which is used as the oxidizing agent. No additives are added to the partially oxidized Spiro‐MeOTAD hole‐transporting layer. Device efficiencies up to 24.2% are achieved. Electrical conductivity is largely developed by the first 1% oxidation. Further oxidation shifts the energy levels away from the vacuum level, which allows tuning of the energy level alignment without the use of additives—contradicting the current understanding of this system. Without additives, devices demonstrate significant improvement in stability at elevated temperatures up to 85 °C under one sun over 1400 h continuous illumination. The remaining degradation is pinpointed to ion migration and reactions in the perovskite layer which may be further suppressed with compositional engineering and adequate ion barrier layers.

Bright and Fast Emission from Robust Supramolecular J-Aggregate Nanostructures through Silica-Encapsulation

2026-04-24T03:15:53Z

Bright and Fast Emission from Robust Supramolecular J-Aggregate Nanostructures through Silica-Encapsulation Thanippuli Arachchi, Dimuthu H; Barotov, Ulugbek; Perkinson, Collin F; Šverko, Tara; Kaplan, Alexander EK; Bawendi, Moungi G We introduce a two-step silica-encapsulation procedure to optimize both the optical efficiency and structural robustness of 5,5',6,6'-tetrachloro-1,1'-diethyl-3,3'-di(4-sulfobutyl)-benzimidazolocarbocyanine (TDBC), a two-dimensional sheet-like J-aggregate. We report a fluorescence quantum yield of ∼98%, the highest quantum yield recorded for any J-aggregate structure at room temperature, and a fast, emissive lifetime of 234 ps. Silica, as an encapsulating matrix, provides optical transparency, chemical inertness, and robustness to dilution, while rigidifying the J-aggregate structure. Our in situ encapsulation process preserves the excitonic structure in TDBC J-aggregates, maintaining their light absorption and emission properties. The homogeneous silica coating has an average thickness of 0.5-1 nm around J-aggregate sheets. Silica encapsulation permits extensive dilutions of J-aggregates without significant disintegration into monomers. The narrow absorbance and emission line widths exhibit further narrowing upon cooling to 79 K, which is consistent with J-type coupling in the encapsulated aggregates. This silica TDBC J-aggregate construct signifies (1) a bright, fast, and robust fluorophore system, (2) a platform for further manipulation of J-aggregates as building blocks for integration with other optical materials and structures, and (3) a system for fundamental studies of exciton delocalization, transport, and emission dynamics within a rigid matrix.

Toward biophysical markers of depression vulnerability

2026-05-08T03:13:20Z

Toward biophysical markers of depression vulnerability Pinotsis, DA; Fitzgerald, S; See, C; Sementsova, A; Widge, AS A major difficulty with treating psychiatric disorders is their heterogeneity: different neural causes can lead to the same phenotype. To address this, we propose describing the underlying pathophysiology in terms of interpretable, biophysical parameters of a neural model derived from the electroencephalogram. We analyzed data from a small patient cohort of patients with depression and controls. Using DCM, we constructed biophysical models that describe neural dynamics in a cortical network activated during a task that is used to assess depression state. We show that biophysical model parameters are biomarkers, that is, variables that allow subtyping of depression at a biological level. They yield a low dimensional, interpretable feature space that allowed description of differences between individual patients with depressive symptoms. They could capture internal heterogeneity/variance of depression state and achieve significantly better classification than commonly used EEG features. Our work is a proof of concept that a combination of biophysical models and machine learning may outperform earlier approaches based on classical statistics and raw brain data.

Understanding Robustness and Generalization of Artificial Neural Networks Through Fourier Masks

2026-05-08T03:12:21Z

Understanding Robustness and Generalization of Artificial Neural Networks Through Fourier Masks Karantzas, Nikos; Besier, Emma; Ortega Caro, Josue; Pitkow, Xaq; Tolias, Andreas S.; Patel, Ankit B.; Anselmi, Fabio Despite the enormous success of artificial neural networks (ANNs) in many disciplines, the characterization of their computations and the origin of key properties such as generalization and robustness remain open questions. Recent literature suggests that robust networks with good generalization properties tend to be biased toward processing low frequencies in images. To explore the frequency bias hypothesis further, we develop an algorithm that allows us to learn modulatory masks highlighting the essential input frequencies needed for preserving a trained network's performance. We achieve this by imposing invariance in the loss with respect to such modulations in the input frequencies. We first use our method to test the low-frequency preference hypothesis of adversarially trained or data-augmented networks. Our results suggest that adversarially robust networks indeed exhibit a low-frequency bias but we find this bias is also dependent on directions in frequency space. However, this is not necessarily true for other types of data augmentation. Our results also indicate that the essential frequencies in question are effectively the ones used to achieve generalization in the first place. Surprisingly, images seen through these modulatory masks are not recognizable and resemble texture-like patterns.

Enhancement of Cyanobacterial Bloom Monitoring in Lake Taihu Using Dual Red-Edge Bands of GF-6/WFV: Multi-Dimensional Feature Combination and Extraction Accuracy Analysis

2026-04-24T03:14:23Z

Enhancement of Cyanobacterial Bloom Monitoring in Lake Taihu Using Dual Red-Edge Bands of GF-6/WFV: Multi-Dimensional Feature Combination and Extraction Accuracy Analysis Sun, Yunxiao; Zhang, Ruolin; Zhao, Chunhong; Meng, Qingyan; Sun, Zhenhui; Wang, Jialong; Wu, Jun; Wang, Yao; Gao, Decai; Guan, Huyi Cyanobacterial blooms pose a serious threat to freshwater ecosystems, necessitating accurate remote sensing monitoring. Although red-edge bands show potential in terrestrial monitoring, their multi-dimensional features (i.e., spectral, textural, and index-based characteristics) remain underutilized for aquatic blooms. This study leverages the dual red-edge bands (710 nm and 750 nm) of GF-6/WFV to enhance cyanobacterial bloom identification in Lake Taihu. Multi-temporal images from 2019–2023 were used to construct red-edge features in three dimensions: spectral (evaluated via adaptive band selection method) and Jeffries–Matusita–Bhattacharyya distance), texture (based on Gray Level Co-occurrence Matrix and principal component analysis), and indices (nine vegetation indices ranked by Random Forest importance). Twelve feature-combination schemes were designed and implemented with a Random Forest classifier. Results show that red-edge features consistently improve identification accuracy. Quantitatively, compared to the basic four-band (RGBN) combination, the 710 nm band improved spectral separability by an average of 9.63%, whereas the 750 nm band yielded a lower average improvement of 5.69%. Red-edge indices, especially the modified chlorophyll absorption reflectance index 1 (MCARI1) and normalized difference red-edge index (NDRE), exhibited higher importance than non-red-edge indices. All schemes incorporating red-edge features achieved mean overall accuracies of 92.8–94.9% and Kappa coefficients of 0.86–0.94, surpassing the basic four-band scheme. Among these features, red-edge indices contributed most significantly to accuracy gains, increasing the overall accuracy by an average of 0.36–6.06% and the Kappa coefficient by up to 0.06. The enhancement effect of the red-edge 710 nm band features was superior to that of the 750 nm band. This study demonstrates that multi-dimensional red-edge features effectively enhance the identification accuracy of cyanobacterial blooms and provides a methodological reference for operational GF-6 applications in water quality monitoring.

Biological Activity of Metal Complexes

2026-04-24T03:14:21Z

Biological Activity of Metal Complexes Sharma, Vinay K. Metal complexes play a fundamental role in biological systems and continue to attract sustained interest due to their remarkable potential in therapeutic, diagnostic, and biotechnological applications [1–8]. In recent years, the field of bioinorganic chemistry has advanced rapidly, driven by progress in coordination chemistry, spectroscopy, nanotechnology, and molecular biology [9–22]. These developments have enabled a deeper understanding of how metal ions and complexes interact with biomolecular targets and have opened new avenues for the rational design of metal-based agents for cancer therapy, antimicrobial treatment, imaging, and the study of metal-mediated biochemical processes [23–30].

Single Parameter Model for Galaxy Rotation Curves

2026-04-24T03:14:43Z

Single Parameter Model for Galaxy Rotation Curves Cisneros, Sophia N.; Ott, Rich; Crowley, Meagan; Roberts, Amy; Paz, Marcus One key piece of evidence for dark matter is the rotation-curve problem: the disagreement between measured galactic rotation curves and their luminous mass. A novel solution to this problem is presented here, in a model that predicts observed Doppler-shifted spectra based only on the luminous matter estimates and one free model parameter 𝛼. This model is applied to fit the rotation curves of the SPARC sample of 175 galaxies, yielding mass-to-light ratios, goodness of fit measurements, and 𝛼. The measured average 𝜒2 𝑟 =2.24 compares favorably with the Navarro-Frenk-White dark matter model’s average of 𝜒2 𝑟 =4.19 for the same data, and more galaxies are successfully fit by this model. The model provides a useful formulation linking luminous matter to the observed rotation curves, with the dark matter contribution to galaxies encoded in two transformation terms of the luminous mass. It also offers a lower-parameter characterization of the rotation curve problem, and a power law relationship between 𝛼 and galactic photometric quantities is observed, potentially removing the need for the free parameter.

Non-Clinical Safety of GRAd Vector-Based COVID-19 and HIV Vaccines Supports a Platform Regulatory Approach

2026-03-25T03:16:25Z

Non-Clinical Safety of GRAd Vector-Based COVID-19 and HIV Vaccines Supports a Platform Regulatory Approach Paalangara, Reji; Gohin, Stephanie; Menard, Alexis; Amy, Charlotte; Berrabah, Wahiba; Rogue, Alexandra; Getz, Matthew A.; Alrubayyi, Aljawharah; Battella, Simone; Raggioli, Angelo; Gentile, Michela; Di Rita, Anthea; Noto, Alessia; Miselli, Giuseppina; Grazioli, Fabiana; Napolitano, Federico; Sowcik, Dhurata; Soriani, Marco; Chmielewski, Benjamin; Molife, Lebohang Background/Objectives: The rapid development of safe and efficacious vaccines is often hindered by extensive, mandated non-clinical safety evaluations in animals. With the aim to provide scientific evidence supporting a “vaccine platform approach”, here we present the complete non-clinical studies for two investigational vaccines, GRAd-COV2 and GRAdHIVNE1, based on GRAd, a gorilla-derived group C adenoviral vector. Methods: The biodistribution of GRAd genomes following the intramuscular administration of the vaccines was assessed in rats by a sensitive qPCR method. Local tolerance and systemic toxic effects were evaluated in single- and repeated-dose toxicity studies in rabbits. Results: GRAd-COV2 and GRAdHIVNE1 were well-tolerated. Distribution was highly confined to the injection site and draining lymph nodes, and toxicity profile consisted of transient, non-adverse inflammatory responses, while the expected immune responses to the encoded antigens were successfully induced. Notably, both vaccines demonstrated a consistent safety profile despite transgene and backbone differences, comparable to other replication-defective adenoviral vectors. Conclusions: The established non-clinical safety profile of the GRAd platform provides a robust foundation for a more efficient and streamlined regulatory pathway. By leveraging this prior knowledge, future GRAd-based vaccines can achieve accelerated clinical development while fully adhering to the ethical principles of replacement, reduction, and refinement of animal use in research.

Design and User-Centered Field Evaluation of an Accessible Precision Irrigation Tool and Its Human–Machine Interaction on a Jordanian Farm

2026-04-24T03:15:46Z

Design and User-Centered Field Evaluation of an Accessible Precision Irrigation Tool and Its Human–Machine Interaction on a Jordanian Farm Van de Zande, Georgia D.; Sheline, Carolyn; Pratt, Shane R.; Winter V, Amos G. This work aims to demonstrate the successful, long-term human use of an automatic scheduling-manual operation (AS-MO) precision irrigation tool by farmers on a medium-scale Jordanian farm. Innovation in low-cost, accessible, and water-efficient irrigation technologies is critical as water resources become scarce, especially on resource-constrained farms in the drought-prone Middle East and North Africa (MENA) region. Prior work has shown that a proposed AS-MO decision support tool could bridge the gap between fully manual irrigation—a common practice on many MENA farms—and existing precision agriculture solutions, which are often too expensive or complex for medium-scale farmers to adopt. Recent developments have also demonstrated that the scheduling theory behind the proposed AS-MO tool uses up to 44% less water compared to fully manual irrigation. However, a functional design of the AS-MO tool has not been realized nor has it been demonstrated on a farm with farmer users. This work documents the detailed design of an AS-MO tool’s human–machine interaction (HMI) and validates the human execution of the tool in context. Through an 11-week case study conducted on a Jordanian farm, we show that farmers used a functional prototype of the AS-MO tool as intended. The functional tool prototype was designed to deliver a long-term AS-MO user experience to study participants. The prototype monitored local weather conditions, generated water-efficient schedules using an existing scheduling theory, and notified users’ phones when they should manually open or close valves. The irrigation practices of participants using the AS-MO prototype were measured, and participants demonstrated successful use of the tool. Users correctly confirmed 93% of the scheduled events using the tool’s HMI. Despite manual operation, a majority of confirmed irrigation event durations fell within 15% of the automatically scheduled durations; relative to the length of scheduled irrigation event durations, the medians of confirmed and scheduled durations were 102% and 88%, respectively. These results demonstrate the success of the tool’s decision support ability. Feedback from study participants can support the AS-MO tool’s next design iteration and can inform the development of other decision support systems designed for resource-constrained, medium-scale farms. This work presents an important step towards developing a precision irrigation tool that, if adopted at scale, could increase the adoption of water-efficient irrigation practices on resource-constrained farms that are not served by existing technology, improving sustainable agriculture in MENA.

The Effect of Genipin Matrix Augmentation on the Retention of Glycosaminoglycans in the Intervertebral Disc—A Pilot Study

2026-05-08T03:12:37Z

The Effect of Genipin Matrix Augmentation on the Retention of Glycosaminoglycans in the Intervertebral Disc—A Pilot Study Hedman, Thomas; Brown, Matthew; Slusarewicz, Pawel The degradation of intervertebral disc proteoglycans, including the loss or shortening of their hydrophilic glycosaminoglycan chains, causes a loss of disc hydration, leading to an increase in solid matrix stresses. This illustrates one aspect of the complex multifactorial relationship between tissue degradation and the resulting mechanical dysfunction. Genipin matrix augmentation has previously been evaluated with regard to its ability to improve mechanical properties of the disc, increasing joint stability and permeability. The study aim was to evaluate the ability of genipin augmentation to increase retention of glycosaminoglycans in disc specimens subjected to free swelling. Three different models were utilized: whole bovine caudal discs, partial annulus specimens from bovine, and human thoracic discs. Total glycosaminoglycan release to a surrounding bath was quantified using a modified dimethyl-methylene blue assay. Genipin solution injections reduced glycosaminoglycan loss by 44.0% in intact bovine discs compared to buffer-only controls (p = 0.027), by 75.8% in partial bovine annulus specimens (p = 0.0004), and by 51.9% in human annulus specimens (p = 0.017). The combination of increased permeability and glycosaminoglycans retention may produce beneficial effects on nutritional flow, diurnal irrigation, and reduction of matrix solid phase stress. Combining these effects with the ability to improve joint stability and augment tissue mechanical properties suggests this nano-scale device may be capable of arresting ongoing degeneration.

A Novel Recurrent Neural Network Framework for Prediction and Treatment of Oncogenic Mutation Progression

2026-04-24T03:15:36Z

A Novel Recurrent Neural Network Framework for Prediction and Treatment of Oncogenic Mutation Progression Parthasarathy, Rishab; Bhowmik, Achintya K. Despite significant medical advancements, cancer remains the second leading cause of death in the US, causing over 600,000 deaths per year. One emerging field, pathway analysis, is promising but still relies on manually derived wet lab data, which is time-consuming to acquire. This work proposes an efficient, effective, end-to-end framework for Artificial Intelligence (AI)-based pathway analysis that predicts both cancer severity and mutation progression in order to recommend possible treatments. The proposed technique involves a novel combination of time-series machine learning models and pathway analysis. First, mutation sequences were isolated from The Cancer Genome Atlas (TCGA) Database. Then, a novel preprocessing algorithm was used to filter key mutations by mutation frequency. This data was fed into a Recurrent Neural Network (RNN) that predicted cancer severity. The model probabilistically used the RNN predictions, information from the preprocessing algorithm, and multiple drug-target databases to predict future mutations and recommend possible treatments. This framework achieved robust results and Receiver Operating Characteristic (ROC) curves (a key statistical metric) with accuracies greater than 60%, similar to existing cancer diagnostics. In addition, preprocessing played a key role in isolating a few hundred key driver mutations per cancer stage, consistent with current research. Heatmaps based on predicted gene frequency were also generated, highlighting key mutations in each cancer. Overall, this work is the first to propose an efficient, cost-effective end-to-end framework for projecting cancer prognosis and providing possible treatments without relying on expensive, time-consuming wet lab work.

All-Pay Auctions with Different Forfeits

2026-03-25T03:16:28Z

All-Pay Auctions with Different Forfeits Kang, Benjamin; Unwin, James In an auction, each party bids a certain amount, and the one who bids the highest is the winner. Interestingly, auctions can also be used as models for other real-world systems. In an all-pay auction all parties must pay a forfeit for bidding. In the most commonly studied all-pay auction, parties forfeit their entire bid, and this has been considered as a model for expenditure on political campaigns. Here, we consider a number of alternative forfeits that might be used as models for different real-world competitions, such as preparing bids for defense or infrastructure contracts.

Spontaneous formation of robust two-dimensional perovskite phases

2026-03-03T03:07:04Z

Spontaneous formation of robust two-dimensional perovskite phases Tan, Shaun; Shih, Meng-Chen; Lu, Yongli; Choi, Seung-Gu; Dong, Yifan; Lee, Joo-Hong; Yavuz, Ilhan; Larson, Bryon W; Park, So Yeon; Kodalle, Tim; Zhang, Ruiqi; Grotevent, Matthias J; Lin, Yu-Kuan; Zhu, Hua; Bulović, Vladimir; Sutter-Fella, Carolin M; Park, Nam-Gyu; Beard, Matthew C; Lee, Jin-Wook; Zhu, Kai; Bawendi, Moungi G The two-dimensional on three-dimensional (2D/3D) perovskite bilayer heterostructure can improve the stability and performance of perovskite solar cells. We show that the 2D/3D perovskite stack in a device evolves dynamically during its end-of-life decomposition. Initially phase-pure 2D interlayers can evolve differently, resulting in different device stabilities. We show that a robust 2D interlayer can be formed using mixed solvents to regulate its crystallinity and phase purity. The resulting 2D/3D devices achieved 25.9% efficiency and had good durability, retaining 91% of their initial performance after 1074 hours at 85°C using maximum power point tracking.

Third-order photon correlations extract single-nanocrystal multiexciton properties in solution

2026-05-08T03:14:24Z

Third-order photon correlations extract single-nanocrystal multiexciton properties in solution Horowitz, Jonah R; Berkinsky, David B; Bendekgey, Henry C; Tye, Oliver J; Šverko, Tara; Shulenberger, Katherine E; Bawendi, Moungi G Colloidal semiconductor nanocrystals are considered promising materials for high-flux optical applications, including lasing, light-emitting diodes, biological imaging, and quantum optics. In high-flux applications, multiexcitons can significantly contribute to emission, influencing its brightness, spectral purity, and kinetics. As a result, understanding and controlling multiexciton emission in colloidal nanocrystal materials is of the utmost importance. In the past, single-nanocrystal photon correlation methods have been applied to understand biexciton and triexciton efficiencies, lifetimes, and spectra. While powerful, such methods suffer from user selection bias and require stable emission from single nanocrystals. To compensate for this shortcoming, second-order correlation methods were developed to extract sample-averaged biexciton properties from a solution of nanocrystals. Until now, however, the analogous third-order solution photon correlation methods remained unexplored. In this work, we present a pair of third-order photon correlation techniques to obtain the sample-averaged single-nanocrystal triexciton quantum yield and lifetime in a solution-phase experiment. These techniques derive from the relationship between the Poisson probability of nanocrystal photon absorption and the intrinsic probability of nanocrystal photon emission. We validate the theoretical background of these techniques by creating a numerical model to simulate the diffusion and emission of many nanocrystals in solution. Our simulations confirm that the average triexciton quantum yield and triexciton lifetime can be extracted from a solution of nanocrystals. These techniques will enable researchers to gain a better understanding of the fundamental multiexciton properties of colloidal nanocrystals.

Challenges of II‐VI and III‐V Blue Quantum Dot Light‐Emitting Diodes

2026-04-23T03:15:31Z

Challenges of II‐VI and III‐V Blue Quantum Dot Light‐Emitting Diodes Tan, Shaun; Horowitz, Jonah R; Tye, Oliver J; Bawendi, Moungi G Quantum dot light-emitting diodes (QD-LEDs) are electroluminescent devices where the emissive layer consists of inorganic colloidal quantum dots. Recent breakthroughs have enabled the development of bright and efficient blue-emitting QD-LEDs based on heavy metal-free compositions. However, challenges remain that hinder their practical application in electroluminescent displays and lighting technologies. The primary obstacle is their limited operational lifetimes which remain significantly below practical requirement standards, especially in comparison to the red- and green-emitting QD-LEDs. Another important issue is the low color purity and broad spectral linewidths of heavy metal-free blue quantum dot compositions. Additional problems include transient electroluminescent behaviors such as fluorescence intermittency and positive aging effects. This review examines the current understanding of the physical mechanisms underlying these challenges faced by blue QD-LEDs. Often, contradictory explanations are proposed to account for the same phenomenon. Here, potential interpretations are suggested that may help reconcile the conflicting reports. Recent advances are further examined that have contributed to the development of state-of-the-art blue QD-LEDs.

Impact of Processing Environment on Anti-Solvent Free FAPbI3 Films and Solar Cells

2026-04-24T03:15:06Z

Impact of Processing Environment on Anti-Solvent Free FAPbI3 Films and Solar Cells Wall, Elizabeth M; Lin, Yu‐Kuan; Bawendi, Moungi; Burlingame, Quinn C; Loo, Yueh‐Lin As perovskite solar cells approach commercialization, understanding the environmental sensitivities of perovskites during fabrication becomes increasingly important. In this work, the humidity-dependence of each deposition and annealing step in the anti-solvent-free two-step formamidinium lead iodide fabrication process is investigated in air and N2. In-situ grazing-incidence wide-angle X-ray scattering measurements during spin-coating indicate that humidity affects the formation and dynamics of intermediate phases in perovskite precursor films. These differences, and those induced by annealing in humidity, impact the structure, morphology, and composition of resultant perovskite films, though the initial performance of solar cells fabricated using these active layers is relatively insensitive to humidity across the range studied. In contrast, stability is maximized in devices with dry-processed active layers and those terminally annealed in humidity. Spin-coating of PbI2 is most environmentally sensitive—needle-like structures precipitate while spin-coating in 40% relative humidity leading to significantly reduced photovoltaic performance and device stability. Additionally, films and solar cells fabricated in air appear virtually identical to those fabricated in N2. Collectively, these results show that optimal performance and stability of two-step processed formamidinium lead iodide solar cells is achieved when fabricating active layers in a dry atmosphere or with some humidity during the final anneal.

Cognitive Reinforcement: Capturing Tacit Knowledge and Enhancing Expertise with a Biofeedback Interface for Visual Attention

2026-03-04T17:47:02Z

Cognitive Reinforcement: Capturing Tacit Knowledge and Enhancing Expertise with a Biofeedback Interface for Visual Attention Armengol-Urpi, Alexandre; Salazar-Gomez, Andres F.; Sinha, Pawan; Sarma, Sanjay E. Objective. Tacit or implicit knowledge refers to know-how that experts possess but often cannot articulate, codify, or explicitly transfer to others. This can present a significant challenge for learning, skill acquisition, and knowledge transfer across various domains, including those that rely on apprenticeships, craftsmanship, sports, and medical imaging diagnosis. This study explores whether expert tacit knowledge can be accessed and leveraged using an EEG and gaze-informed biofeedback interface to enhance expertise transfer and training. Approach. We designed an image classification task where novices were trained until they implicitly learned to classify images correctly, despite being unaware of which image regions or features guided their decisions. The task involved images with a hidden spatial asymmetry that even trained participants did not explicitly recognize. Using combined eye-tracking and EEG measures, we tracked both overt and covert visual attention to determine whether individuals unconsciously internalized this asymmetry during learning. We then investigated whether providing explicit gaze-informed feedback on their own implicit attention biases could further improve task performance of trained participants. Main Results. Our findings reveal that as participants became trained, their attention patterns —both overt and covert— consistently reflected an unconscious awareness of image asymmetry, with attention biased toward task-relevant image regions. Moreover, trained individuals who received explicit feedback derived from their own gaze behavior showed additional improvements in classification performance compared to an equally trained control group. Significance. These results open the door to novel uses of biofeedback interfaces to facilitate new forms of expertise transfer, training, and collective intelligence. By extracting and conveying tacit expert knowledge—ordinarily difficult to externalize—our interface enables its transmission to novices, trained individuals, or even machine learning systems. We refer to this process as cognitive reinforcement.

Higher Siegel-Weil formulae over function fields

2026-02-28T03:02:13Z

Higher Siegel-Weil formulae over function fields Mkrtchyan, Mikayel In their seminal work, Feng-Yun-Zhang introduced function field analogues of Kudla-Rapoport cycles for moduli spaces of unitary shtukas, and initiated the study of their intersection theory. They proved a higher Siegel-Weil formula in the case of non-degenerate Fourier coefficients, relating the degrees of these cycles to higher derivatives of Siegel-Eisenstein series. In this thesis, we generalize their result in two directions: we 1) prove a higher Siegel-Weil formula for unitary groups for corank-1 degenerate coefficients, and 2) introduce analogous cycles on moduli spaces of symplectic shtukas, and prove a higher Siegel-Weil formula for such cycles in the non-degenerate case, relating their degrees to derivatives of Siegel-Eisenstein series on split orthogonal groups.

A Diverse Array of Synthetic Strategies for Phosphorus Group Transfer Chemistry: From Phosphinidenes to Phosphates

2026-02-28T03:02:21Z

A Diverse Array of Synthetic Strategies for Phosphorus Group Transfer Chemistry: From Phosphinidenes to Phosphates Xin, Tiansi This thesis compiles the published scientific contributions of Tiansi Xin. Chapter 1 consists of a brief collection of eulogies from friends and colleagues, reflecting on his life and time at the Massachusetts Institute of Technology. The subsequent chapters describe the development of novel synthetic methods for the transfer of phosphorus-containing moieties, specifically metaphosphates and phosphinidenes. The work presented here has significant implications for both the fundamental understanding and practical advancement of synthetic inorganic and organic chemistry. Chapters 2 and 3 address the sustainable production and processing of phosphoruscontaining chemicals, focusing on mechanochemical methods to synthesize reduced phosphorus species while circumventing the need to access hazardous white phosphorus as an intermediate. In particular, Chapter 2 describes a solvent-free mechanochemical approach to producing phosphite (HPO₃²⁻) via hydride-mediated reduction of condensed phosphates. Using potassium hydride, a range of inorganic phosphate sources—including pyrophosphate, triphosphate, trimetaphosphate, fluorophosphate, and polyphosphate—were converted to phosphite in moderate to high yields. Mechanistic studies identified overreduction pathways leading to hypophosphite and other low-oxidation P-species. Chapter 3 similarly applies this mechanochemical approach to phosphorus–carbon bond formation, reporting the phosphorylation of acetylides with condensed phosphates to afford phosphonates. Biogenic polyphosphates were also shown to be viable precursors, a proof-of-concept to closing the modern phosphorus cycle using recycled inputs. These results demonstrate the possibility of accessing organophosphorus chemicals directly from condensed phosphates and may offer an opportunity toward a “greener” phosphorus industry. Chapters 4 and 5 shift focus to phosphinidene transfer chemistry and the synthesis of novel phosphorus-containing heterocycles. This expands on previously published studies from the Cummins group on the chemistry of dibenzo-7-phosphanorbornadiene “RPA” reagents. Chapter 4 reports the preparation and structural characterization of iron–phosphido complexes relevant to phosphinidene group transfer catalysis and describes the development of an improved catalytic system based on a simple diiron precursor (Fp₂), enabling efficient synthesis of phosphiranes from electron-deficient alkenes. The mechanism was thoroughly experimentally and computationally interrogated. Chapter 5 describes the novel synthesis of free, uncomplexed phosphet-2-ones via phosphinidene transfer to cyclopropenones, with experimental and theoretical studies supporting a mechanism involving ketene-derived intermediates and transformations to additional phosphorus heterocycles through subsequent reactions.

Host–Guest Complexation by β-Cyclodextrin Enhances the Solubility of an Esterified Protein

2026-02-27T04:16:04Z

Host–Guest Complexation by β-Cyclodextrin Enhances the Solubility of an Esterified Protein Cheah, Keith M; Jun, Joomyung V; Wittrup, K Dane; Raines, Ronald T The carboxyl groups of a protein can be esterified by reaction with a diazo compound, 2-diazo-2-(p-methylphenyl)-N,N-dimethylacetamide. This esterification enables the entry of the protein into the cytosol of a mammalian cell, where the nascent ester groups are hydrolyzed by endogenous esterases. The low aqueous solubility of the ensuing esterified protein is, however, a major practical challenge. Solubility screening revealed that β-cyclodextrin (β-CD) is an optimal solubilizing agent for esterified green fluorescent protein (est-GFP). Its addition can increase the recovery of est-GFP by 10-fold. α-CD, γ-CD, and cucurbit-7-uril are less effective excipients. 1H NMR titration experiments revealed that β-CD encapsulates the hydrophobic tolyl group of ester conjugates with Ka = 321 M–1. Combining l-arginine and sucrose with β-CD enables the nearly quantitative recovery of est-GFP. Thus, the insolubility of esterified proteins can be overcome with excipients.

Quantifying the Role of Kinematic and Behavioral Features in Driver-Pedestrian Interaction across Environments: An Inverse Reinforcement Learning Approach

2026-02-27T04:16:16Z

Quantifying the Role of Kinematic and Behavioral Features in Driver-Pedestrian Interaction across Environments: An Inverse Reinforcement Learning Approach Noonan, T Zach; Gershon, Pnina; Domeyer, Josh; Mehler, Bruce; Reimer, Bryan This study examined real-world driver-pedestrian encounters to identify key interaction features and assess how the importance of these features is mediated by protection afforded by the environment. Using inverse reinforcement learning, we estimated the utility functions to evaluate the relative importance of different aspects of the interaction for each road user and how they differ between undesignated (e.g., jaywalking) and designated (e.g., zebra crossings) crossings. Pedestrian pausing behavior and dynamic features like acceleration changes and time gaps were important at designated crossings, whereas undesignated crossings relied on distances and bidirectional gaze, highlighting reliance on non-verbal cues. This work builds on previous studies analyzing the role of environmental features on interaction, communication, and negotiation between drivers and pedestrians. Understanding driver-pedestrian communication and identifying the most important interaction features may enhance the design of effective and coordinated driver-pedestrian interaction strategies, especially in the context of automated driving systems.

Preoperative Function, Previous SERM Treatment, and Triple-Negative Tumor Status are Independently Associated With 3-Month Postoperative Function After Surgical Decompression of Metastatic Breast Cancer

2026-02-27T04:16:12Z

Preoperative Function, Previous SERM Treatment, and Triple-Negative Tumor Status are Independently Associated With 3-Month Postoperative Function After Surgical Decompression of Metastatic Breast Cancer Siraj, Layla; Duvall, Julia B.; Massaad, Elie; Fourman, Mitchell S.; Shin, John H. Background: The most common cancer in women worldwide, breast cancer most often metastasizes to the bone. Improved chemo- and radiotherapies and novel molecular therapies have prolonged survival in women with osseous metastatic breast cancer, but spinal metastases often cause cord compression that degrades their functional independence. Purpose: In women with breast cancer metastasized to the spine, we sought to (1) identify independent predictors of a functional deficit 3 months after surgical management and (2) assess the utility of existing metrics at highlighting patients at risk of a postoperative functional deficit. Methods: We performed a single-institution, retrospective analysis of 92 patients meeting our inclusion criteria between 2004 and 2021. Patients were classified by 3-month postoperative Eastern Cooperative Oncology Group (ECOG) scores into good/independent (ECOG 0 to 2) and poor/dependent (ECOG 3 to 5) functional outcome groups. Univariate and multivariate analyses were performed to identify patient and tumor factors associated with good vs. poor 3-month ECOG scores. Results: Preoperative use of selective estrogen receptor modulators (SERMs) was significantly associated with good postoperative functional outcomes. Poor preoperative function, the presence of visceral metastases at the time of surgery, and triple-negative primary or metastatic tumor status were independently associated with poor 3-month postoperative function. Host characteristics, sociodemographic factors, and indicators of surgical complexity, including estimated blood loss, front/back surgery, and corpectomy reconstruction, were not associated with 3-month ECOG score. A multivariate model including these significant univariate associations and normalized for patient demographics identified preoperative SERM use, poor preoperative function (ECOG score), and triple-negative primary or metastatic tumor status as independently associated with functional status 3 months after surgery. Conclusions: Our retrospective analysis found that preoperative SERM use was significantly associated with improved postoperative functional outcomes, while poor preoperative function and triple-negative tumor status were significantly associated with poor function 3 months after surgery. These factors may serve as indicators of function and independence after surgery for patients with metastatic breast cancer to the spine. Level of Evidence: Level IV: Prognostic Study

The Influence of Prior Semantic Knowledge in Noisy Channel Interpretation

2026-02-27T04:16:13Z

The Influence of Prior Semantic Knowledge in Noisy Channel Interpretation Chen, Sihan; Washington, Lia; Gibson, Edward How do comprehenders interpret semantically implausible sentences? Previous studies proposed a noisy-channel framework of sentence comprehension, where communication between a speaker and a comprehender happens in a noisy channel. The comprehender rationally adopts an interpretation of a sentence based on how likely the interpretation is (the semantic prior) and how likely is the interpretation corrupted into the perceived sentence because of noise (the likelihood). The theory predicted that comprehenders would be more likely to adopt a literal interpretation of an implausible sentence if their prior of implausible sentences were higher. To test this hypothesis, Gibson et al. manipulated the proportion of implausible test sentences in two sets of experiments, where participants read a number of sentences and answer a comprehension question following each sentence. Although their results supported the hypothesis, the experiment could be confounded (a) by participants’ adaptation effect (due to different experiment lengths) and (b) by different participants having different strategies to do the task (due to the between-subject design). In our study, we manipulated the semantic prior and controlled for these potential confounds. We found participants exposed to more implausible sentences were indeed more likely to interpret implausible sentences literally. Our results hence offer additional support for the noisy-channel framework.

The Politics of Engagement in Platform Governance

2026-02-27T04:16:11Z

The Politics of Engagement in Platform Governance Lewis, Becca; Christin, Angèle In recent years, the concept of user engagement has dominated debate over the governance of online platforms, and critics use the term to assign crass commercial interests to social media companies. We argue that social media engagement is a multifaceted ideal that serves both economic and ideological functions for platforms. We show how Facebook’s early leadership used the concept to reconcile the competing demands of expansion, revenue generation, and community-building. In doing so, they synthesized three distinct ideas: the Silicon Valley belief that network expansion correlated with network strength, the ad industry’s contention that media should promote emotional investment from viewers, and the academic claim that civic participation is the most important democratic virtue. Even as the contradictions that these claims yield have come to the foreground, the multiple logics of engagement have proven difficult to evade, and it continues to shape discussions of platform governance.

The Impact of Electrification and Partial Automation on Driver Speeding Behavior

2026-02-27T04:16:15Z

The Impact of Electrification and Partial Automation on Driver Speeding Behavior Gershon, Pnina; Noonan, T Zach; Lenneman, John As electric vehicles (EVs) and partial automation systems become increasingly prevalent, their impact on everyday driving behavior remains underexplored. This study utilizes real-world naturalistic data to examine how vehicle type, an electric versus an internal combustion engine (ICE), and the use of partial automation are associated with speeding behavior. Data were collected from 24 drivers over the course of a month each, comparing Tesla Model 3s with Autopilot (EV) and Cadillac CT6s with Super Cruise (ICE), covering about 38,000 miles of driving. Results indicate that EV drivers tended to speed for shorter durations on arterial roads but exhibited higher speeding magnitudes on residential and controlled access roads after their first week of driving. Notably, driving with partial automation, regardless of powertrain, was associated with significantly longer speeding durations and slightly greater speeding magnitudes compared to manual driving. These findings suggest that both electrification and automation contribute to evolving driver behaviors, changing speeding behavior in specific driving contexts. As drivers adapt to new vehicle technologies, understanding how these systems shape behavior is important. Insights from this study may inform the design of future in-vehicle systems and guide driver education strategies to promote safe driving practices in an evolving transportation landscape.

Cloud Capitalism and the AI Transition

2026-02-27T04:16:14Z

Cloud Capitalism and the AI Transition Tan, JS; Thelen, Kathleen This article explores the origins and implications of a new cloud business model that is powering the advance of AI. We document how this model emerged within a handful of the most dominant IT firms whose reach into all corners of the economy makes them a powerful node or “choke point” in the political economy as a whole. We then elaborate how the features of the cloud business model differ from the traditional platform model out of which it grew, as it evolved from asset-light to asset-heavy, from hierarchical organization to semivertical integration, from domination over to collaboration with partner firms, and from embracing consumer- to enterprise-facing strategies. A final section considers the technological, political, and distributional impacts of the rise of this new business model—showing how the current race to artificial general intelligence (AGI) has reinforced and accelerated its underlying dynamics (above all, intensifying the drive for scale and ever-greater asset intensity), analyzing the new techno-nationalist alliance between industry leaders and the state that the model's development has inspired, and considering the new power-distributional dynamics this model has produced.

Report to the President for year ended June 30, 2025, Vice President for Research

2026-02-27T04:17:20Z

Report to the President for year ended June 30, 2025, Vice President for Research Waitz, Ian A This report contains the following sections: Overview, Research Administration, Research Compliance, Office of Research Computing and Data, International Scholars Office, Postdoctoral Services, OVPR Administration, and Lab and Center Leadership.

Hexamethylbenzene Elimination Enables the Generation of Transient, Sterically Unhindered Multiply Bonded Boron Species

2026-02-27T04:16:10Z

Hexamethylbenzene Elimination Enables the Generation of Transient, Sterically Unhindered Multiply Bonded Boron Species Zhang, Chonghe; Dabringhaus, Philipp; Tra, Bi Youan E.; Gilliard, Robert J. Jr; Cummins, Christopher C. We present a method for the generation of boron-containing unsaturated small molecules via hexamethylbenzene elimination. The fragmentation precursors are obtained through bond insertion into phenyl boranorbornadiene (PhB(C6Me6), 1). Compound 1 undergoes 1,1-insertion with 2,6-xylyl isocyanide, affording a boron-doped bicyclo[2.2.2]octa-2,5-diene 2. Heating 2 in toluene results in the formation of a base-stabilized boraketenimine PhB(CNxyl)2 (i.e., borylene diisocyanide) as an intermediate via retro-Diels–Alder reaction. Surprisingly, PhB(CNxyl)2 dimerizes to give a boron-doped 6-membered ring (PhB)2C4(CNxyl)64. The reaction of 1 with trimethylamine N-oxide and phenyl azide yields triphenyl boroxine and a BN4 ring, respectively, implying the involvement of transient oxoborane (PhB[triple bond, length as m-dash]O) and iminoborane intermediates (PhB[triple bond, length as m-dash]NPh), respectively. Furthermore, boranorbornadiene also undergoes 2,3-insertion with mesityl isocyanate (MesNCO), affording a fused 6/5-membered heterocycle 11. This insertion profile is analogous to the insertion of phenyl azide into 1.

Mechanisms and Scale-up Potential of 3D Solar Interfacial-Evaporators

2026-02-27T04:16:06Z

Mechanisms and Scale-up Potential of 3D Solar Interfacial-Evaporators Zhang, James H.; Mittapally, Rohith; Oluwade, Abimbola; Chen, Gang Evaporation fluxes from porous evaporators under sunlight have been reported to exceed the solar-thermal limit, determined by relating the incoming solar energy to the latent and sensible heat of water, for applications in desalination and brine pond drying. Although flat two-dimensional (2D) evaporators exceeding the solar limit imply a non-thermal process, tall three-dimensional (3D) solar evaporators can exceed it by absorbing additional environmental heat into its cold sidewalls. Through modeling, we explain the physics and identify the critical heights in which a fin transitions from 2D to 3D evaporation and exceeds the solar-thermal limit. Our analyses illustrate that environmental heat absorption in 3D evaporators is determined by the ambient relative humidity and the airflow velocity. The model is then coarse-grained into a large-scale fin array device on the meters scale to analyze their scalability. We identify that these devices are unlikely to scale favorably in closed environment settings such as solar stills. Our modeling clearly illustrates the benefits and limitations of 3D evaporating arrays and pinpoints design choices in previous works that hinder the device's overall performance. This work illustrates the importance in distinguishing 2D from 3D evaporation for mechanisms underlying interfacial evaporation exceeding the solar-thermal limit.

Decarbonization Approaches for Ethylene Production: Comparative Techno-Economic and Life-Cycle Analysis

2026-02-27T04:16:09Z

Decarbonization Approaches for Ethylene Production: Comparative Techno-Economic and Life-Cycle Analysis Shin, Woojae; Lin, Bosong; Lai, Haoxiang; Ibrahima, Gasim; Zang, Guiyan Ethylene, a building block of the chemical industry, significantly contributes to global greenhouse gas (GHG) emissions, prompting interest in decarbonization approaches to align with recent carbon neutrality initiatives. This paper presents a comprehensive techno-economic analysis (TEA) and life cycle analysis (LCA) of GHG emissions, comparing conventional ethane-based ethylene plants with three decarbonization approaches. The study was conducted within the context of the U.S. average, with sensitivity analysis to identify key drivers affecting well-to-gate (WTG) GHG emissions and the levelized cost of ethylene (LCOE). The conventional plant exhibited a GHG emission of 869 kgCO2e per tonne-ethylene and a LCOE of $746 per tonne-ethylene. Substituting external natural gas fuels with grid or renewable electricity decreased the emissions to 806 and 717 kgCO2e per tonne-ethylene, respectively. The emissions of the grid-powered or renewable-powered electrically heated cracker that exports co-produced hydrogen to substitute conventional gray hydrogen were 1031 and −163 kgCO2e per tonne-ethylene, respectively. The application of CCS to purge gas showed 703 and 514 kgCO2e per tonne-ethylene emissions, respectively. The electric cracker showed lower emissions than the conventional plant below 380 kgCO2e per MW h electricity upstream, and at 60 kgCO2e per MW h, it achieved carbon neutrality. Regarding LCOE, when using a grid electricity source, no external natural gas, electric cracker, and adding CCS to purge gas showed $743, 833, and 771 per tonne-ethylene, respectively. When these plants adopt renewable electricity, their LCOEs will be $737, 746 and 757 per tonne-ethylene. Below $41.1 per MW h electricity price, the electric cracker had the lowest value among all cases. With hydrogen prices of $0.5–3.0 per kg-H2, the electric cracker's LCOE ranged from −$45(cost)–128(saving) per tonne-ethylene compared to the conventional concept.

Archerfish: A Retrofitted 3D Printer for High-throughput Combinatorial Experimentation via Continuous Printing

2026-02-27T04:16:03Z

Archerfish: A Retrofitted 3D Printer for High-throughput Combinatorial Experimentation via Continuous Printing Alexander E. Siemenn, Basita Das, Eunice Aissi, Fang Sheng, Lleyton Elliott, Blake Hudspeth, Marilyn Meyers, James Serdy and Tonio Buonassisi The maturation of 3D printing technology has enabled low-cost, rapid prototyping capabilities for mainstreaming accelerated product design. The materials research community has recognized this need, but no universally accepted rapid prototyping technique currently exists for material design. Toward this end, we develop Archerfish, a 3D printer retrofitted to dispense liquid with in situ mixing capabilities for performing high-throughput combinatorial printing (HTCP) of material compositions. Using this HTCP design, we demonstrate continuous printing throughputs of up to 250 unique compositions per minute, 100× faster than similar tools such as Opentrons that utilize stepwise printing with ex situ mixing. We validate the formation of these combinatorial “prototype” material gradients using hyperspectral image analysis and energy-dispersive X-ray spectroscopy. Furthermore, we describe hardware challenges to realizing reproducible, accurate, and precise composition gradients with continuous printing, including those related to precursor dispensing, mixing, and deposition. Despite these limitations, the continuous printing and low-cost design of Archerfish demonstrate promising accelerated materials screening results across a range of materials systems from nanoparticles to perovskites.

Borinine-FLP Ring Expansion: Isolation of Eight-Membered B-P Rings Bridged by µ2 Chalcogenide and Chloronium Ions

2026-02-27T04:15:52Z

Borinine-FLP Ring Expansion: Isolation of Eight-Membered B-P Rings Bridged by µ2 Chalcogenide and Chloronium Ions Frey, Nathan C.; Sarkar, Samir Kumar; Dickie, Diane A.; Molinoa, Andrew; Gilliard, Robert J. Jr Boron–phosphorus (B–P) frustrated Lewis pairs (FLPs) are an important class of compounds for activating various small molecules. Utilizing the ring expansion reactivity of 9-chloro-9-borafluorene, a borinine-based FLP was synthesized. Various five-membered main-group element heterocycles were obtained via the reaction of the FLP with Me3NO, S8, and Se. Subsequent reduction of these species yielded the ring-expanded compounds, each featuring bridging B–E–B (E = O, S, Se) bonds. Similarly, halide abstraction from the FLP with AgNTf2 led to the formation of a cationic ring-expanded compound with a bridging B–Cl–B motif. This motif constitutes one of the first examples of a boron-stabilized chloronium ion, as verified using in-depth bonding analysis methods. Mechanistic pathways for the reduction- and halide abstraction-mediated ring expansion reactions are proposed with the aid of density functional theory. Electronic structure computations were performed to determine the best representation of bonding interactions in each compound, suggesting phosophorus(V)–chalcogen double bonding and chalcogen–boron(III) dative interactions within the heterocycles.

High-resolution structure of Zn3(HOTP)2 (HOTP = hexaoxidotriphenylene), a three-dimensional conductive MOF

2026-02-27T04:16:01Z

High-resolution structure of Zn3(HOTP)2 (HOTP = hexaoxidotriphenylene), a three-dimensional conductive MOF Zhang, Kimberly J.; Chen, Tianyang; Oppenheim, Julius J.; Yang, Luming; Palatinus, Lukáš; Müller, Peter; Van Voorhisa, Troy; Dincă, Mircea Although two-dimensional (2D) electrically conducting metal–organic frameworks (cMOFs) have become prominent due to their numerous potential applications, their structures are often implied or assumed from rather crude powder X-ray diffraction data. Indeed, exceedingly few examples exist of atomic-level structural details coming from single crystal diffraction experiments. Most widely studied among cMOFs are materials based on triphenylene ligands, in particular M3(HOTP)2 (M = Cu, Zn) and [M3(HOTP)2][M3(HOTP)]2 (M = Mg, Ni, Co; H6HOTP = 2,3,6,7,10,11-hexahydroxytriphenylene), which are invariably described as 2D van der Waals materials with sheets of ligands connected by square planar or octahedral metal ions. Here, we employ electron diffraction to show that, unlike the Mg, Co, Ni, and Cu analogs, Zn3(HOTP)2 crystallizes into a three-dimensional network that is analogous to the structures of the lanthanide-based HOTP MOFs. Moreover, similar to the lanthanide frameworks, Zn3(HOTP)2 exhibits incommensurate modulation, likely originating from a frustration between the preferred π–π stacking distance and the Zn–O bond lengths, or from a Peierls distortion. This work reinforces the importance of employing single crystal diffraction measurements for the characterization of conductive MOFs, especially when trying to correlate electronic properties to structural details.

Intratumoral nanobody–IL-2 fusions that bind the tumor extracellular matrix suppress solid tumor growth in mice

2026-02-26T03:07:36Z

Intratumoral nanobody–IL-2 fusions that bind the tumor extracellular matrix suppress solid tumor growth in mice Lutz, Emi A; Jailkhani, Noor; Momin, Noor; Huang, Ying; Sheen, Allison; Kang, Byong H; Wittrup, K Dane; Hynes, Richard O Confining cytokine exposure to the tumors would greatly enhance cancer immunotherapy safety and efficacy. Immunocytokines, cytokines fused to tumor-targeting antibodies, have been developed with this intention, but without significant clinical success to date. A critical limitation is uptake by receptor-expressing cells in the blood, that decreases the dose at the tumor and engenders toxicity. Small-format immunocytokines, constructed with antibody fragments, are hypothesized to improve tumor specificity due to rapid systemic clearance. However, effective design criteria for small-format immunocytokines need further examination. Here, we engineer small interleukin-2 (IL-2) immunocytokines fused to nanobodies with nanomolar to picomolar affinities for the tumor-specific EIIIB domain of fibronectin (also known as EDB). Upon intravenous delivery into immunocompetent mice, such immunocytokines led to similar tumor growth delay as size-matched untargeted IL-2. Intratumoral (i.t.) delivery imparted improved survival dependent on affinity to EIIIB. I.t. administration offers a promising avenue to deliver small-format immunocytokines, given effective affinity for the tumor microenvironment.

Ablative radiotherapy improves survival but does not cure autochthonous mouse models of prostate and colorectal cancer

2026-02-26T03:07:27Z

Ablative radiotherapy improves survival but does not cure autochthonous mouse models of prostate and colorectal cancer Schmidt, Daniel R; Gramatikov, Iva Monique T; Sheen, Allison; Williams, Christopher L; Hurwitz, Martina; Dodge, Laura E; Holupka, Edward; Kiger, WS; Cornwall-Brady, Milton R; Huang, Wei; Mak, Howard H; Cormier, Kathleen S; Condon, Charlene; Dane Wittrup, K; Yilmaz, Ömer H; Stevenson, Mary Ann; Down, Julian D; Floyd, Scott R; Roper, Jatin; Vander Heiden, Matthew G Background Genetically engineered mouse models (GEMMs) of cancer are powerful tools to study mechanisms of disease progression and therapy response, yet little is known about how these models respond to multimodality therapy used in patients. Radiation therapy (RT) is frequently used to treat localized cancers with curative intent, delay progression of oligometastases, and palliate symptoms of metastatic disease. Methods Here we report the development, testing, and validation of a platform to immobilize and target tumors in mice with stereotactic ablative RT (SART). Xenograft and autochthonous tumor models were treated with hypofractionated ablative doses of radiotherapy. Results We demonstrate that hypofractionated regimens used in clinical practice can be effectively delivered in mouse models. SART alters tumor stroma and the immune environment, improves survival in GEMMs of primary prostate and colorectal cancer, and synergizes with androgen deprivation in prostate cancer. Complete pathologic responses were achieved in xenograft models, but not in GEMMs. Conclusions While SART is capable of fully ablating xenografts, it is unable to completely eradicate disease in GEMMs, arguing that resistance to potentially curative therapy can be modeled in GEMMs. Plain language summary Mice can be used to model the types of cancer seen in people to investigate the effects of cancer therapies, such as radiation. Here, we apply radiation therapy treatments that are able to cure cancer in humans to mice that have cancer of the prostate or colorectum. We show that the mice do not experience many side effects and that the tumours reduce in size, but in some cases show progression after treatment. Our study demonstrates that mice can be used to better understand how human cancers respond to radiation treatment, which can lead to the development of improved treatments and treatment schedules.

Anti–PD-1 and Extended Half-life IL2 Synergize for Treatment of Murine Glioblastoma Independent of Host MHC Class I Expression

2026-02-26T03:07:45Z

Anti–PD-1 and Extended Half-life IL2 Synergize for Treatment of Murine Glioblastoma Independent of Host MHC Class I Expression Tritz, Zachariah P; Ayasoufi, Katayoun; Wolf, Delaney M; Owens, Carley A; Malo, Courtney S; Himes, Benjamin T; Fain, Cori E; Goddery, Emma N; Yokanovich, Lila T; Jin, Fang; Hansen, Michael J; Parney, Ian F; Wang, Chensu; Moynihan, Kelly D; Irvine, Darrell J; Wittrup, K Dane; Diaz Marcano, Rosa M; Vile, Richard G; Johnson, Aaron J Glioblastoma (GBM) is the most common malignant brain tumor in adults, responsible for approximately 225,000 deaths per year. Despite preclinical successes, most interventions have failed to extend patient survival by more than a few months. Treatment with anti—programmed cell death protein 1 (anti–PD-1) immune checkpoint blockade (ICB) monotherapy has been beneficial for malignant tumors such as melanoma and lung cancers but has yet to be effectively employed in GBM. This study aimed to determine whether supplementing anti–PD-1 ICB with engineered extended half-life IL2, a potent lymphoproliferative cytokine, could improve outcomes. This combination therapy, subsequently referred to as enhanced checkpoint blockade (ECB), delivered intraperitoneally, reliably cures approximately 50% of C57BL/6 mice bearing orthotopic GL261 gliomas and extends median survival of the treated cohort. In the CT2A model, characterized as being resistant to CBI, ECB caused a decrease in CT2A tumor volume in half of measured animals similar to what was observed in GL261-bearing mice, promoting a trending survival increase. ECB generates robust immunologic responses, features of which include secondary lymphoid organ enlargement and increased activation status of both CD4 and CD8 T cells. This immunity is durable, with long-term ECB survivors able to resist GL261 rechallenge. Through employment of depletion strategies, ECB's efficacy was shown to be independent of host MHC class I–restricted antigen presentation but reliant on CD4 T cells. These results demonstrate ECB is efficacious against the GL261 glioma model through an MHC class I–independent mechanism and supporting further investigation into IL2-supplemented ICB therapies for tumors of the central nervous system.

Collagen-Anchored Interleukin-2 and Interleukin-12 Safely Reprogram the Tumor Microenvironment in Canine Soft-Tissue Sarcomas

2026-02-26T03:07:43Z

Collagen-Anchored Interleukin-2 and Interleukin-12 Safely Reprogram the Tumor Microenvironment in Canine Soft-Tissue Sarcomas Stinson, Jordan A; Sheen, Allison; Momin, Noor; Hampel, Jordan; Bernstein, Rebecca; Kamerer, Rebecca; Fadl-Alla, Bahaa; Samuelson, Jonathan; Fink, Elizabeth; Fan, Timothy M; Wittrup, K Dane Purpose: Cytokine therapies such as IL2 and IL12 suffer from impractically small therapeutic windows driven by their on-target, off-tumor activity, limiting their clinical potential despite potent antitumor effects. We previously engineered cytokines that bind and anchor to tumor collagen following intratumoral injection, and sought to test their safety and biomarker activity in spontaneous canine soft-tissue sarcomas (STS). Experimental Design: Collagen-binding cytokines were canine-ized to minimize immunogenicity and were used in a rapid dose-escalation study in healthy beagles to identify a maximum tolerated dose. Ten client-owned pet dogs with STS were then enrolled into trial, receiving cytokines at different intervals prior to surgical tumor excision. Tumor tissue was analyzed through IHC and NanoString RNA profiling for dynamic changes within treated tumors. Archived, untreated STS samples were analyzed in parallel as controls. Results: Intratumorally administered collagen-binding IL2 and IL12 were well tolerated by STS-bearing dogs, with only Grade 1/2 adverse events observed (mild fever, thrombocytopenia, neutropenia). IHC revealed enhanced T-cell infiltrates, corroborated by an enhancement in gene expression associated with cytotoxic immune function. We found concordant increases in expression of counter-regulatory genes that we hypothesize would contribute to a transient antitumor effect, and confirmed in mouse models that combination therapy to inhibit this counter-regulation can improve responses to cytokine therapy. Conclusions: These results support the safety and activity of intratumorally delivered, collagen-anchoring cytokines for inflammatory polarization of the canine STS tumor microenvironment. We are further evaluating the efficacy of this approach in additional canine cancers, including oral malignant melanoma. Translational Relevance Successful translation of novel cancer therapies could be accelerated through the inclusion of tumor models that accurately recapitulate natural evolution and malignant transformation processes operative in human tumor development. Spontaneous cancer in pet dogs provides an underutilized opportunity to assess the safety and activity of investigational cancer therapies in tumors that arise following years of immunoediting. Particularly for the evaluation of immunotherapies, canine tumors enable the assessment of clinical potential in the context of an experienced, and often senescent, immune background. Beyond efficacy, such evaluation provides meaningful insight into tumor resistance mechanisms that could influence eventual human clinical success. Herein, we characterize immune activities generated by intratumoral injections of engineered collagen-binding cytokines IL2 and IL12 into naturally occurring canine soft-tissue sarcomas, and demonstrate through comparative assessment in mouse tumors the differential learnings from each model and their combined role in guiding rational design of treatment combinations with greater expected efficacy.

Both intratumoral regulatory T cell depletion and CTLA-4 antagonism are required for maximum efficacy of anti-CTLA-4 antibodies

2026-02-26T03:07:38Z

Both intratumoral regulatory T cell depletion and CTLA-4 antagonism are required for maximum efficacy of anti-CTLA-4 antibodies Lax, Brianna M; Palmeri, Joseph R; Lutz, Emi A; Sheen, Allison; Stinson, Jordan A; Duhamel, Lauren; Santollani, Luciano; Kennedy, Alan; Rothschilds, Adrienne M; Spranger, Stefani; Sansom, David M; Wittrup, K Dane Anti-CTLA-4 antibodies have successfully elicited durable tumor regression in the clinic; however, long-term benefit is limited to a subset of patients for select cancer indications. The incomplete understanding of their mechanism of action has hindered efforts at improvement, with conflicting hypotheses proposing either antagonism of the CTLA-4:B7 axis or Fc effector-mediated regulatory T cell (Treg) depletion governing efficacy. Here, we report the engineering of a nonantagonistic CTLA-4 binding domain (b1s1e2) that depletes intratumoral Tregs as an Fc fusion. Comparison of b1s1e2-Fc to 9d9, an antagonistic anti-CTLA-4 antibody, allowed for interrogation of the separate contributions of CTLA-4 antagonism and Treg depletion to efficacy. Despite equivalent levels of intratumoral Treg depletion, 9d9 achieved more long-term cures than b1s1e2-Fc in MC38 tumors, demonstrating that CTLA-4 antagonism provided additional survival benefit. Consistent with prior reports that CTLA-4 antagonism enhances priming, treatment with 9d9, but not b1s1e2-Fc, increased the percentage of activated T cells in the tumor-draining lymph node (tdLN). Treg depletion with either construct was restricted to the tumor due to insufficient surface CTLA-4 expression on Tregs in other compartments. Through intratumoral administration of diphtheria toxin in Foxp3-DTR mice, we show that depletion of both intratumoral and nodal Tregs provided even greater survival benefit than 9d9, consistent with Treg-driven restraint of priming in the tdLN. Our data demonstrate that anti-CTLA-4 therapies require both CTLA-4 antagonism and intratumoral Treg depletion for maximum efficacy—but that potential future therapies also capable of depleting nodal Tregs could show efficacy in the absence of CTLA-4 antagonism.

Overcoming lung cancer immunotherapy resistance by combining nontoxic variants of IL-12 and IL-2

2026-02-26T03:07:35Z

Overcoming lung cancer immunotherapy resistance by combining nontoxic variants of IL-12 and IL-2 Horton, Brendan L; D’Souza, Alicia D; Zagorulya, Maria; McCreery, Chloe V; Abhiraman, Gita C; Picton, Lora; Sheen, Allison; Agarwal, Yash; Momin, Noor; Wittrup, K Dane; White, Forest M; Garcia, K Christopher; Spranger, Stefani Engineered cytokine-based approaches for immunotherapy of cancer are poised to enter the clinic, with IL-12 being at the forefront. However, little is known about potential mechanisms of resistance to cytokine therapies. We found that orthotopic murine lung tumors were resistant to systemically delivered IL-12 fused to murine serum albumin (MSA, IL12-MSA) because of low IL-12 receptor (IL-12R) expression on tumor-reactive CD8+ T cells. IL2-MSA increased binding of IL12-MSA by tumor-reactive CD8+ T cells, and combined administration of IL12-MSA and IL2-MSA led to enhanced tumor-reactive CD8+ T cell effector differentiation, decreased numbers of tumor-infiltrating CD4+ regulatory T cells, and increased survival of lung tumor-bearing mice. Predictably, the combination of IL-2 and IL-12 at therapeutic doses led to significant dose-limiting toxicity. Administering IL-12 and IL-2 analogs with preferential binding to cells expressing Il12rb1 and CD25, respectively, led to a significant extension of survival in mice with lung tumors while abrogating dose-limiting toxicity. These findings suggest that IL-12 and IL-2 represent a rational approach to combination cytokine therapy whose dose-limiting toxicity can be overcome with engineered cytokine variants.

Intratumoral aluminum hydroxide–anchored IL-12 drives potent antitumor activity by remodeling the tumor microenvironment

2026-02-26T03:07:32Z

Intratumoral aluminum hydroxide–anchored IL-12 drives potent antitumor activity by remodeling the tumor microenvironment Battula, Sailaja; Papastoitsis, Gregory; Kaufman, Howard L; Wittrup, K Dane; Schmidt, Michael M IL-12 is a potent cytokine that can promote innate and adaptive anticancer immunity, but its clinical development has been limited by toxicity when delivered systemically. Intratumoral (i.t.) administration can expand the therapeutic window of IL-12 and other cytokines but is in turn limited by rapid drug clearance from the tumor, which reduces efficacy, necessitates frequent administration, and increases systemic accumulation. To address these limitations, we developed an anchored IL-12 designated ANK-101, composed of an engineered IL-12 variant that forms a stable complex with the FDA-approved vaccine adjuvant aluminum hydroxide (Alhydrogel). Following i.t. administration of murine ANK-101 (mANK-101) in early intervention syngeneic mouse tumors, the complex formed a depot that was locally retained for weeks as measured by IVIS or SPECT/CT imaging, while unanchored protein injected i.t. was cleared within hours. One or 2 i.t. injections of mANK-101 induced single-agent antitumor activity across a diverse range of syngeneic tumors, including models resistant to checkpoint blockade at doses where unanchored IL-12 had no efficacy. Local treatment with mANK-101 further induced regressions of noninjected lesions, especially when combined with systemic checkpoint blockade. Antitumor activity was associated with remodeling of the tumor microenvironment, including prolonged IFN-γ and chemokine expression, recruitment and activation of T and NK cells, M1 myeloid cell skewing, and increased antigen processing and presentation. Subcutaneous administration of ANK-101 in cynomolgus macaques was well tolerated. Together, these data demonstrate that ANK-101 has an enhanced efficacy and safety profile and warrants future clinical development.

CD8+ T cell priming that is required for curative intratumorally anchored anti-4-1BB immunotherapy is constrained by Tregs

2026-02-26T03:07:40Z

CD8+ T cell priming that is required for curative intratumorally anchored anti-4-1BB immunotherapy is constrained by Tregs Palmeri, Joseph R; Lax, Brianna M; Peters, Joshua M; Duhamel, Lauren; Stinson, Jordan A; Santollani, Luciano; Lutz, Emi A; Pinney, William; Bryson, Bryan D; Dane Wittrup, K Although co-stimulation of T cells with agonist antibodies targeting 4-1BB (CD137) improves antitumor immune responses in preclinical studies, clinical success has been limited by on-target, off-tumor activity. Here, we report the development of a tumor-anchored ɑ4-1BB agonist (ɑ4-1BB-LAIR), which consists of a ɑ4-1BB antibody fused to the collagen-binding protein LAIR. While combination treatment with an antitumor antibody (TA99) shows only modest efficacy, simultaneous depletion of CD4+ T cells boosts cure rates to over 90% of mice. Mechanistically, this synergy depends on ɑCD4 eliminating tumor draining lymph node regulatory T cells, resulting in priming and activation of CD8+ T cells which then infiltrate the tumor microenvironment. The cytotoxic program of these newly primed CD8+ T cells is then supported by the combined effect of TA99 and ɑ4-1BB-LAIR. The combination of TA99 and ɑ4-1BB-LAIR with a clinically approved ɑCTLA-4 antibody known for enhancing T cell priming results in equivalent cure rates, which validates the mechanistic principle, while the addition of ɑCTLA-4 also generates robust immunological memory against secondary tumor rechallenge. Thus, our study establishes the proof of principle for a clinically translatable cancer immunotherapy.

Estimating Isotope Shifts for $^{227}$Th and $^{229}$Th in Th$^{3+}$: 5F$_{5/2} \rightarrow$ 6D$_{5/2}$ Transition

2026-02-26T03:01:06Z

Estimating Isotope Shifts for $^{227}$Th and $^{229}$Th in Th$^{3+}$: 5F$_{5/2} \rightarrow$ 6D$_{5/2}$ Transition Lam, P. Y. Ian; MohanMurthy, Prajwal \textbf{Motivation:} Accurate spectroscopic information for isotopes of $^{224-232}$Th are critical for experimental programs investigating such thorium isotopes as candidates for next-generation nuclear optical clocks and as platforms for searches of symmetry-violating effects. Direct experimental data on isotope shifts in the $\text{Th}^{3+}:5F_{5/2} \to 6D_{5/2}$ line at $690$ nm are sparse, with measurements available only for $^{229}\text{Th}$ and $^{230}\text{Th}$ relative to the reference isotope $^{232}\text{Th}$.\\ \textbf{Method:} To address this gap, we employed a King-plot analysis comparing the well-characterized isotope shifts of the $\text{Th}^{+}$ transition at $583.9$ nm to the limited data available for the $690$ nm transition of $\text{Th}^{3+}$. Using nuclear structure information on mean-square charge radii and nuclear quadrupole deformations, we extracted the field-shift constant $F_{690}$ and mass-shift constant $M_{690}$ for the $690$ nm transition. We subsequently calculated the missing isotope shifts by incorporating published values of $\delta\langle r^2\rangle$ where available and estimating $\delta\langle r^2\rangle$ for unmeasured isotopes using nuclear quadrupole deformation coefficients $\beta_2$ from the FRDM model.\\ \textbf{Key Results:} The calculated isotope shifts for the $\text{Th}^{3+}:5F_{5/2} \to 6D_{5/2}$ transition relative to $690~$nm transition of $^{232}\text{Th}$ are: \begin{align} \delta\nu^{224,232}_{690} &= -29296(5585) \text{MHz} \nonumber\\ \delta\nu^{225,232}_{690} &= -25840(4930) \text{MHz} \nonumber\\ \delta\nu^{226,232}_{690} &= -22113(4219) \text{MHz} \nonumber\\ \delta\nu^{227,232}_{690} &= -18631(6238) \text{MHz} \nonumber\\ \delta\nu^{228,232}_{690} &= -14970(6181) \text{MHz} \nonumber\\ \delta\nu^{231,232}_{690} &= -3742(715) \nonumber\text{MHz} \end{align} Our results for the isotopes of $^{227,228}$Th use experimental $\delta\langle r^2\rangle$ values, whereas the remaining ones use theoretical values of $\delta\langle r^2\rangle$ calculated from quadrupole deformation coefficients. These results provide essential spectroscopic data for future precision measurements with thorium isotopes in various ionized states. This work is supported by BNL award #460913, a Phi Kappa Phi Fellowship, and generous support from Prof. R. P. Redwine and MIT LNS.

Targeting of the CD161 inhibitory receptor enhances T-cell–mediated immunity against hematological malignancies

2026-02-26T03:07:30Z

Targeting of the CD161 inhibitory receptor enhances T-cell–mediated immunity against hematological malignancies Alvarez Calderon, Francesca; Kang, Byong H; Kyrysyuk, Oleksandr; Zheng, Shiwei; Wang, Hao; Mathewson, Nathan D; Luoma, Adrienne M; Ning, Xiaohan; Pyrdol, Jason; Cao, Xuan; Suvà, Mario L; Yuan, Guo-Cheng; Wittrup, K Dane; Wucherpfennig, Kai W The CD161 inhibitory receptor is highly upregulated by tumor-infiltrating T cells in multiple human solid tumor types, and its ligand, CLEC2D, is expressed by both tumor cells and infiltrating myeloid cells. Here, we assessed the role of the CD161 receptor in hematological malignancies. Systematic analysis of CLEC2D expression using the Cancer Cell Line Encyclopedia revealed that CLEC2D messenger RNA was most abundant in hematological malignancies, including B-cell and T-cell lymphomas as well as lymphocytic and myelogenous leukemias. CLEC2D protein was detected by flow cytometry on a panel of cell lines representing a diverse set of hematological malignancies. We, therefore, used yeast display to generate a panel of high-affinity, fully human CD161 monoclonal antibodies (mAbs) that blocked CLEC2D binding. These mAbs were specific for CD161 and had a similar affinity for human and nonhuman primate CD161, a property relevant for clinical translation. A high-affinity CD161 mAb enhanced key aspects of T-cell function, including cytotoxicity, cytokine production, and proliferation, against B-cell lines originating from patients with acute lymphoblastic leukemia, diffuse large B-cell lymphoma, and Burkitt lymphoma. In humanized mouse models, this CD161 mAb enhanced T-cell–mediated immunity, resulting in a significant survival benefit. Single cell RNA-seq data demonstrated that CD161 mAb treatment enhanced expression of cytotoxicity genes by CD4 T cells as well as a tissue-residency program by CD4 and CD8 T cells that is associated with favorable survival outcomes in multiple human cancer types. These fully human mAbs, thus, represent potential immunotherapy agents for hematological malignancies.

Upcycling spent medium-Ni cathodes via novel liquified salts sourcing

2026-02-26T03:07:25Z

Upcycling spent medium-Ni cathodes via novel liquified salts sourcing Yoon, Moonsu; Park, Jin-Sung; Chen, Weiyin; Huang, Yimeng; Dai, Tao; Lee, Yumin; Shin, Jungmin; Lee, Seungmi; Kim, Yongil; Lee, Dongsoo; Shin, Daiha; Cho, Jaephil; Dong, Yanhao; Li, Ju The rapid growth in lithium-ion battery technology underscores the urgent need for sustainable recycling to address the environmental and economic challenges of battery waste. This study introduces a liquified-salts-assisted upcycling approach to transform spent medium-Ni cathodes into high-performance single-crystalline Ni-rich cathodes. Utilizing the LiOH–LiNO3–Ni(NO3)2·6H2O eutectic, this method leverages planetary centrifugal mixing to create a liquid-like environment for accelerated elemental diffusion and microstructural refinement. The in situ liquefaction of these salts ensures seamless precursor integration, achieving compositional uniformity and minimizing impurity formation. Compared to conventional solid-state methods, our method significantly suppresses rock-salt phase formation, and improves electrochemical performance with superior cycling stability and rate capability. The environmental and economic advantages of our approach highlight its potential to reduce greenhouse gas emissions and energy consumption. This scalable, energy-efficient strategy provides a transformative solution for battery waste management, paving the way for the sustainable production of next-generation cathode materials.

CLN-617 Retains IL2 and IL12 in Injected Tumors to Drive Robust and Systemic Immune-Mediated Antitumor Activity

2026-02-26T03:07:34Z

CLN-617 Retains IL2 and IL12 in Injected Tumors to Drive Robust and Systemic Immune-Mediated Antitumor Activity Mehta, Naveen K; Rakhra, Kavya; Meetze, Kristan A; Li, Bochong; Momin, Noor; Chang, Jason YH; Wittrup, K Dane; Baeuerle, Patrick A; Michaelson, Jennifer S Despite clinical evidence of antitumor activity, the development of cytokine therapies has been hampered by a narrow therapeutic window and limited response rates. Two cytokines of high interest for clinical development are interleukin 2 (IL2) and interleukin 12 (IL12), which potently synergize to promote the activation and proliferation of T cells and NK cells. However, the only approved human IL2 therapy, Proleukin, is rarely used in the clinic due to systemic toxicities, and no IL12 product has been approved to date due to severe dose-limiting toxicities. Here, we describe CLN-617, a first-in-class therapeutic for intratumoral (IT) injection that co-delivers IL2 and IL12 on a single molecule in a safe and effective manner. CLN-617 is a single-chain fusion protein comprised of IL2, leukocyte-associated immunoglobulin-like receptor 2 (LAIR2), human serum albumin (HSA), and IL12. LAIR2 and HSA function to retain CLN-617 in the treated tumor by binding collagen and increasing molecular weight, respectively. We found that IT administration of a murine surrogate of CLN-617, mCLN-617, eradicated established treated and untreated tumors in syngeneic models, significantly improved response to anti-PD1 checkpoint therapy, and generated a robust abscopal response dependent on cellular immunity and antigen cross-presentation. CLN-617 is being evaluated in a clinical trial in patients with advanced solid tumors (NCT06035744).

Porous Organic Materials-Based Atomically Dispersed Metal Electrocatalysts

2026-02-26T03:07:23Z

Porous Organic Materials-Based Atomically Dispersed Metal Electrocatalysts Zhang, Hao; Wang, Suwen; Lv, Enmin; Qi, Menghui; He, Chengchao; Dong, Xinglong; Qiu, Jieshan; Wang, Yong; Wen, Zhenhai The transition to renewable energy sources and the need for efficient energy conversion technologies have led to the development of various types of catalysts, among which atomically dispersed metal catalysts (ADMCs) supported by porous organic materials (POMs) have attracted attention for their high catalytic efficiency and stability. This review focuses on the development and application of ADMCs supported by POMs, such as MOFs, COFs, and HOFs, which offer catalytic performance due to their high atomic utilization, stability, and selectivity. This paper systematically explores various strategies for synthesizing ADMCs, including the use of organic linkers, metal nodes, and pore spaces within POMs to stabilize metal atoms and prevent aggregation. Key applications highlighted include energy conversion and storage technologies, such as fuel cells, water splitting, CO2 reduction and nitrogen reduction, where ADMCs demonstrate the potential to replace noble metals. Despite the progress, challenges remain in achieving high metal loading, long-term stability, and cost-effective large-scale production. This study underscores the importance of advanced characterization techniques and computational models to deepen the understanding of ADMCs’ catalytic mechanisms and guide future material design, paving the way for their broader application in sustainable energy technologies.

Quantifying Mining Requirement and Waste for Energy Sustainability

2026-02-26T03:07:18Z

Quantifying Mining Requirement and Waste for Energy Sustainability Ermakova, Dinara; Sen, Drishti; Wainwright, Haruko; Bae, Jin Whan; Chene, Lisha; Vujic, Jasmina This study demonstrates the life-cycle assessment of different energy sources-coal, natural gas, solar, wind, nuclear, and hydro-particularly focused on mining activities and waste per given electricity capacity and generation. It also includes carbon dioxide emissions generated during the transportation of raw materials to build and operate electricity generating systems and their environmental impacts in the US from 2023 to 2050. We identify the raw material and metal requirements for the U.S.-based typical systems in each energy type and synthesize datasets on typical ore fraction and material recycling factors, while taking into account the capacity factor of the power plants. We then compute the total mass and volume of material requirements and waste mass and volume for the front-end (i.e., mining, material needed for construction), operation (i.e., fuel, maintenance), and back-end (i.e., decommissioning) activities. The key findings are that (1) the energy transition from fossil fuel to low-carbon energy sources would reduce mining waste as well as the shipping carbon footprint; (2) the difference in capacity and actual electricity generation is significant for the life-cycle assessment due to low capacity factors of solar and wind energy; (3) several key metals with low abundance or high requirements dominate mining waste, which highlights the need for recycling and establishing a circular economy; (4) mining of critical minerals becomes important during the clean energy transition and (5) nuclear energy generates least waste and contributes least to shipping emissions among the low-carbon sources due to the high energy density and capacity factor and the small mass of materials it requires. Although the waste mass may not necessarily be equal to the environmental impact due to different waste isolation technologies, we aim to highlight the importance of considering mining and decommissioning waste, which are often ignored but important for accounting for the environmental impacts and addressing energy justice issues.

The Scholarly Knowledge Ecosystem: Challenges and Opportunities for the Field of Information

2026-02-26T03:07:41Z

The Scholarly Knowledge Ecosystem: Challenges and Opportunities for the Field of Information Altman, Micah; Cohen, Philip N The scholarly knowledge ecosystem presents an outstanding exemplar of the challenges of understanding, improving, and governing information ecosystems at scale. This article draws upon significant reports on aspects of the ecosystem to characterize the most important research challenges and promising potential approaches. The focus of this review article is the fundamental scientific research challenges related to developing a better understanding of the scholarly knowledge ecosystem. Across a range of disciplines, we identify reports that are conceived broadly, published recently, and written collectively. We extract the critical research questions, summarize these using quantitative text analysis, and use this quantitative analysis to inform a qualitative synthesis. Three broad themes emerge from this analysis: the need for multi-sectoral cooperation and coordination, for mixed methods analysis at multiple levels, and interdisciplinary collaboration. Further, we draw attention to an emerging consensus that scientific research in this area should by a set of core human values.

Investigation of L2/Ln Pragmatic Competence: Its Core and Route Map

2026-02-26T03:07:44Z

Investigation of L2/Ln Pragmatic Competence: Its Core and Route Map Mao, Tiaoyuan How to use language properly and acquire the capacity for language use has become the focus of linguists and philosophers for centuries. Therefore, pragmatic competence underlying language use arouses enormous interests of language acquisition practitioners. This study reveals the core properties of various models or theories of pragmatic competence, such as the communicative componential models, the form-function mapping proposal of the functionalist, the tripartite cognitive model, and the current integrated model of pragmatic competence. The common core includes (but not limited to) integration of thought and communication, one uniform pragmatic mechanism, dynamic form-function mapping, and complementarity between grammatical and pragmatic competences. With the findings as a departure, a brief outline for further investigation of pragmatic competence is proposed finally, including pathological and neurobiological examination of pragmatic competence.

Investigating Structural Biophysical Features for Antigen-Binding Fragment Crystallization via Machine Learning

2026-02-26T03:07:12Z

Investigating Structural Biophysical Features for Antigen-Binding Fragment Crystallization via Machine Learning Chattaraj, Krishna Gopal; Ferreira, Joana; Myerson, Allan S.; Trout, Bernhardt L. Antibody-based therapeutics continue to be an important pharmaceutical development modality. Crystallization of antibodies is important for structural characterization, but in addition has the potential for use as a separation method and for use as a dosage form. Nevertheless, bringing about controlled crystallization of an antibody remains a challenging task due to its large size, high degree of segmental flexibility, and the intricacy of all the occurring interactions (e.g., protein–protein interactions, protein–solvent interactions, etc.). Methods to predict important contact sites could help to develop such crystallization methods. However, limited data and understanding have hitherto not allowed the development of such robust methods. This study employs machine learning combined with in silico modelling of crystal structures using available experimental structures to identify the crucial physicochemical features necessary for successful antibody crystallization in an attempt to remedy that gap. The developed method can with good accuracy distinguish crystal-site residues from non-crystal-site residues. A set of 510 descriptors is utilized to characterize each residue, which is treated as a distinct data point. Moreover, new algorithms have been developed to design novel descriptors that improve the model's predictive capabilities. Fragment antigen-binding (Fab) regions are investigated due to the scarcity of full-length monoclonal antibodies (mAbs) crystal structures. The current findings show that the extreme gradient boosting (XGBoost) algorithm effectively identifies crystal site residues, as evidenced by an AUPRC value that is more than 3-fold higher than that of the baseline model. The top-ranked descriptors indicate that crystal-site residues are primarily characterized by solvent-exposed residues with high spatial aggregation propensity (SAP), signifying hydrophobic patches, and their immediate surface-exposed neighbors. Moreover, these high SAP residues are often surrounded by other solvent-exposed residues that are either polar, charged, or both. In contrast, residues not involved in crystal interfaces generally lack these essential features, though some might be excluded due to specific crystal lattice arrangements. Additionally, reducing the feature set from 510 to the top 15% in the XGBoost model yields similar performance while significantly simplifying the model.

Modeling the Role of Supramolecular Clustering in Multivalent Assembly

2026-02-26T03:07:14Z

Modeling the Role of Supramolecular Clustering in Multivalent Assembly Sbalbi, Nicholas; Petrov, Artem; Sass, Jacob; Ye, Matthew; Alexander-Katz, Alfredo; Macfarlane, Robert J. In self-assembled systems, a combination of multiple weak supramolecular interactions is often utilized to enable strong yet reversible binding. When modeling the behavior of these multivalent interfaces, it is commonly assumed that binding pairs are independent, i.e., the probability of a pair being bound is unaffected by the bound state of neighboring pairs. Inspired by recent experimental work, we report that for a variety of systems this assumption may not hold, leading to the formation of clusters at the binding interface. Through a series of analytical and numerical models of end-functionalized brushes, we reveal the role of cluster size on binding thermodynamics, detail how entropic contributions from polymer chains provide tunable control of cluster size, and provide predictions for cluster size as a function of system architecture. Investigation of these models yields surprising results: within the melting window, the enthalpy of binding of multivalent interfaces is predicted to depend only on cluster size and not on the overall valency of the multivalent system. Moreover, clustering is predicted to be significant even in systems with only weak dipole and dispersion interactions between neighboring groups. Combined, this work brings to light the potential impacts of clustering on multivalent self-assembly, providing theoretical justification for previous experimental observations and paving the way for future work in this area.

Elucidating the effect of Fe substitution on structural and redox stability of Na2Mn3O7

2026-02-26T03:07:21Z

Elucidating the effect of Fe substitution on structural and redox stability of Na2Mn3O7 Smith, Hugh B.; Lee, Gi-Hyeok; Kumar, Bachu Sravan; Penn, Aubrey N.; Venturi, Victor; Gao, Yifan; Davis, Ryan C.; Stone, Kevin Hunter; Hunt, Adrian; Waluyo, Iradwikanari; Stavitski, Eli; Yangb, Wanli; Abate, Iwnetim I. Sodium-ion batteries have the potential to meet the growing demand for energy storage due to their low costs stemming from natural resource abundances, but their cathode energy densities must be improved to be comparable to those of lithium-ion batteries. One strategy is accessing high voltage capacity through high-valent redox reactions. Such reactions usually cause instability in cathode materials, but Na2Mn3O7 (NMO) has demonstrated excellent performance and reversibility in the high-valent regime due to its unique lattice structure with ordered Mn vacancies. This work expands the universality of the ordered vacancy as a design principle and increases the material candidates with such exceptional electrochemical behavior. Our approach involves synergizing cationic ordered vacancies with tunable metal–ligand hybridization through partial metal substitution. In particular, we successfully incorporated Fe3+ for Mn4+ in NMO to make Na2.25Mn2.75Fe0.25O7 and achieved improved high-valent redox behavior. Fe substitution leads to larger specific capacities (171 vs. 159 mA h g−1 first cycle), enhanced cycle stability (97 vs. 60 mA h g−1 after 50 cycles), and superior rate performance. This study lays the foundation for developing new cathode materials with stable high-valent redox through substitution of redox-active transition metals by employing cationic ordered vacancies and partial transition metal substitution as design principles in tandem.

Progress in Computational Methods and Mechanistic Insights on the Growth of Carbon Nanotubes

2026-02-26T03:07:16Z

Progress in Computational Methods and Mechanistic Insights on the Growth of Carbon Nanotubes Wang, Linzheng; Tricard, Nicolas; Chen, Zituo; Deng, Sili Carbon nanotubes (CNTs), as a promising nanomaterial with broad applications across various fields, are continuously attracting significant research attention. Despite substantial progress in understanding their growth mechanisms, synthesis methods, and post-processing techniques, two major goals remain challenging: achieving property-targeted growth and efficient mass production. Recent advancements in computational methods driven by increased computational resources, the development of platforms, and the refinement of theoretical models, have significantly deepened our understanding of the mechanisms underlying CNT growth. This review aims to comprehensively examine the latest computational techniques that shed light on various aspects of CNT synthesis. The first part of this review focuses on progress in computational methods. Beginning with atomistic simulation approaches, we introduce the fundamentals and advancements in density functional theory (DFT), molecular dynamics (MD) simulations, and kinetic Monte Carlo (kMC) simulations. We discuss the applicability and limitations of each method in studying mechanisms of CNT growth. Then, the focus shifts to multiscale modeling approaches, where we demonstrate the coupling of atomic-scale simulations with reactor-scale multiphase flow models. Given that CNT growth inherently spans multiple temporal and spatial scales, the development and application of multiscale modeling techniques are poised to become a central focus of future computational research in this field. Furthermore, this review emphasizes the growing role played by machine learning in CNT growth research. Compared with traditional physics-based simulation methods, data-driven machine learning approaches have rapidly emerged in recent years, revolutionizing research paradigms from molecular simulation to experimental design. In the second part of this review, we highlight the latest advancements in CNT growth mechanisms and synthesis methods achieved through computational techniques. These include novel findings across fundamental growth stages, i.e., from nucleation to elongation and ultimately termination. We also examine the dynamic behaviors of catalyst nanoparticles and chirality-controlled growth processes, emphasizing how these insights contribute to advancing the field. Finally, in the concluding section, we propose future directions for advancements of computational approaches toward deeper understanding of CNT growth mechanisms and better support of CNT manufacturing.

Superthermal Solar Interfacial Evaporation is not due to Reduced Latent Heat of Water

2026-02-26T03:07:09Z

Superthermal Solar Interfacial Evaporation is not due to Reduced Latent Heat of Water Zhang, James H.; Mittapally, Rohith; Lva, Guangxin; Chen, Gang To explain reported solar interfacial-evaporation rates from porous materials beyond an apparent 100% efficiency using the thermal evaporation mechanism, many publications hypothesize that intermediate water inside porous materials has a reduced latent heat. Key supporting evidence is that water-only surfaces have lower natural evaporation rates than porous evaporators, with the ratio of the two rates taken as the latent heat reduction. Through simulations and experiments, we study natural evaporation of water and show that reported differences in evaporation rates between porous materials and water are likely due to experimental error from recessed evaporating surfaces. A few millimeter recession of the water surface relative to the container lip can drop evaporation rates by over 50% due to a stagnant air layer, suggesting that the comparative experiments are prone to error. Furthermore, in the reduced latent heat picture, interfacial cooling must occur at the porous sample–water interface due to the enthalpy difference between bulk water and intermediate water. Our transport modeling shows that reduced latent heat cannot explain superthermal evaporation and that new mechanistic directions need to be pursued.

Generative design and molecular mechanics characterization of silk proteins based on unfolding behavior

2026-02-26T03:07:20Z

Generative design and molecular mechanics characterization of silk proteins based on unfolding behavior Lu, Wei; Buehler, Markus J. Spider silk exhibits exceptional mechanical properties, biocompatibility, and biodegradability, making it a promising material for bioengineered applications. However, the complexity and diversity of silk proteins, coupled with limited experimental data, have hindered the rational design of silk-based biomaterials. Furthermore, the mechanobiology of these proteins and their impact on silk fiber properties remain underexplored. In this study, we introduce a series of novel silk protein sequences and characterize their nonlinear unfolding behavior and mechanical properties through molecular dynamics (MD) simulations. Focusing on major ampullate spidroin (MaSp) silk proteins, we curate a dataset that integrates experimentally acquired sequences with synthetic sequences generated by SilkomeGPT, a generative model for silk-inspired proteins. Structural predictions are performed using OmegaFold, from which high-fidelity regions are extracted and analyzed. Their unfolding responses are assessed via implicit all-atom MD simulations, enabling characterization of their mechanical behavior. This computationally efficient framework facilitates the rational design of spider silk proteins by linking atomistic and sequence features to larger-scale properties. The developed dataset systematically captures structural uncertainties, while simulations provide atomic-level insights into how protein mechanics contribute to fiber properties, advancing the mechanobiological understanding of spider silk and supporting diverse applications in biomaterials design.

Tumor-Localized Interleukin-2 and Interleukin-12 Combine with Radiation Therapy to Safely Potentiate Regression of Advanced Malignant Melanoma in Pet Dogs

2026-02-25T07:11:58Z

Tumor-Localized Interleukin-2 and Interleukin-12 Combine with Radiation Therapy to Safely Potentiate Regression of Advanced Malignant Melanoma in Pet Dogs Stinson, Jordan A; Barbosa, Matheus Moreno P; Sheen, Allison; Momin, Noor; Fink, Elizabeth; Hampel, Jordan; Selting, Kim A; Kamerer, Rebecca L; Bailey, Keith L; Wittrup, Karl D; Fan, Timothy M Purpose: Cytokines IL2 and IL12 exhibit potent anticancer activity but suffer a narrow therapeutic window due to off-tumor immune cell activation. Engineering cytokines with the ability to bind and associate with tumor collagen after intratumoral injection potentiated response without toxicity in mice and was previously safe in pet dogs with sarcoma. Here, we sought to test the efficacy of this approach in dogs with advanced melanoma. Patients and Methods: This study examined 15 client-owned dogs with histologically or cytologically confirmed malignant melanoma that received a single 9-Gy fraction of radiotherapy, followed by six cycles of combined collagen-anchored IL2 and IL12 therapy every 2 weeks. Cytokine dosing followed a 3 + 3 dose escalation design, with the initial cytokine dose chosen from prior evaluation in canine sarcomas. No exclusion criteria for tumor stage or metastatic burden, age, weight, or neuter status were applied for this trial. Results: Median survival regardless of the tumor stage or dose level was 256 days, and 10/13 (76.9%) dogs that completed treatment had CT-measured tumor regression at the treated lesion. In dogs with metastatic disease, 8/13 (61.5%) had partial responses across their combined lesions, which is evidence of locoregional response. Profiling by NanoString of treatment-resistant dogs revealed that B2m loss was predictive of poor response to this therapy. Conclusions: Collectively, these results confirm the ability of locally administered tumor-anchored cytokines to potentiate responses at regional disease sites when combined with radiation. This evidence supports the clinical translation of this approach and highlights the utility of comparative investigation in canine cancers.

Bivalent target-binding bioPROTACs induce potent degradation of oncogenic SHP2

2026-02-25T07:11:56Z

Bivalent target-binding bioPROTACs induce potent degradation of oncogenic SHP2 Hoffman, Megan; Krum, David; Wittrup, K Dane Targeted protein degradation is an emergent and rapidly evolving therapeutic strategy. In particular, biologics-based targeted degradation modalities (bioPROTACs) are relatively under explored compared to small molecules. Here, we investigate how target affinity, cellular localization, and valency of bioPROTACs impact efficacy of targeted degradation of the oncogenic phosphatase src-homology 2 containing protein tyrosine phosphatase-2 (SHP2). We identify bivalent recruitment of SHP2 by bioPROTACs as a broadly applicable strategy to improve potency. Moreover, we demonstrate that SHP2-targeted bioPROTACs can effectively counteract gain-of-function SHP2 mutants present in cancer, which are otherwise challenging to selectively target with small molecule constructs. Overall, this study demonstrates the utility of bioPROTACs for challenging targets, and further explicates design principles for therapeutic bioPROTACs.

Local delivery of cell surface-targeted immunocytokines programs systemic antitumor immunity

2026-02-25T07:11:50Z

Local delivery of cell surface-targeted immunocytokines programs systemic antitumor immunity Santollani, Luciano; Maiorino, Laura; Zhang, Yiming J; Palmeri, Joseph R; Stinson, Jordan A; Duhamel, Lauren R; Qureshi, Kashif; Suggs, Jack R; Porth, Owen T; Pinney, William; Msari, Riyam Al; Walsh, Agnes A; Wittrup, K Dane; Irvine, Darrell J Systemically administered cytokines are potent immunotherapeutics but can cause severe dose-limiting toxicities. To overcome this challenge, cytokines have been engineered for intratumoral retention after local delivery. However, despite inducing regression of treated lesions, tumor-localized cytokines often elicit only modest responses at distal untreated tumors. In the present study, we report a localized cytokine therapy that safely elicits systemic antitumor immunity by targeting the ubiquitous leukocyte receptor CD45. CD45-targeted immunocytokines have lower internalization rates relative to wild-type counterparts, leading to sustained downstream cis and trans signaling between lymphocytes. A single intratumoral dose of αCD45-interleukin (IL)-12 followed by a single dose of αCD45-IL-15 eradicated treated tumors and untreated distal lesions in multiple syngeneic mouse tumor models without toxicity. Mechanistically, CD45-targeted cytokines reprogrammed tumor-specific CD8+ T cells in the tumor-draining lymph nodes to have an antiviral transcriptional signature. CD45 anchoring represents a broad platform for protein retention by host immune cells for use in immunotherapy.

Tumor Integrin-Targeted Glucose Oxidase Enzyme Promotes ROS-Mediated Cell Death that Combines with Interferon Alpha Therapy for Tumor Control

2026-02-25T07:12:00Z

Tumor Integrin-Targeted Glucose Oxidase Enzyme Promotes ROS-Mediated Cell Death that Combines with Interferon Alpha Therapy for Tumor Control Stinson, Jordan A; Sheen, Allison; Lax, Brianna M; Yang, Grace N; Duhamel, Lauren; Santollani, Luciano; Fink, Elizabeth; Palmeri, Joseph R; Wittrup, Karl Dane Although heightened intratumoral levels of reactive oxygen species (ROS) are typically associated with a suppressive tumor microenvironment, under certain conditions ROS contribute to tumor elimination. Treatment approaches, including some chemotherapy and radiation protocols, increase cancer cell ROS levels that influence their mechanism of cell death and subsequent recognition by the immune system. Furthermore, activated myeloid cells rapidly generate ROS upon encounter with pathogens or infected cells to eliminate disease, and recently, this effector function has been noted in cancer contexts as well. Collectively, ROS-induced cancer cell death may help initiate adaptive antitumor immune responses that could synergize with current approved immunotherapies, for improved control of solid tumors. In this work, we explore the use of glucose oxidase, an enzyme which produces hydrogen peroxide, a type of ROS, to therapeutically mimic the endogenous oxidative burst from myeloid cells to promote antigen generation within the tumor microenvironment. We engineer the enzyme to target pan-tumor-expressed integrins both as a tumor-agnostic therapeutic approach and as a strategy to prolong local enzyme activity following intratumoral administration. We found the targeted enzyme potently induced cancer cell death and enhanced cross-presentation by dendritic cells in vitro and further combined with interferon alpha for long-term tumor control in murine MC38 tumors in vivo. Optimizing the single-dose administration of this enzyme overcomes limitations with immunogenicity noted for other prooxidant enzyme approaches. Overall, our results suggest ROS-induced cell death can be harnessed for tumor control and highlight the potential use of designed enzyme therapies alongside immunotherapy against cancer.

Yeast as a tool for exploring disulfide-rich peptides

2026-02-25T07:12:01Z

Yeast as a tool for exploring disulfide-rich peptides Yap, Kuok; Porth, Owen T; Xie, Jing; Wang, Conan K; Durek, Thomas; Wittrup, K Dane; Craik, David J Cyclic disulfide-rich peptides have become increasingly popular in drug development because their structures enhance molecular stability and allow for mutagenesis to introduce non-native functions. This review focuses on yeast-based platform technologies and their utility in advancing cyclic disulfide-rich peptides as drug modalities and for large-scale biomanufacturing. These technologies include yeast surface display which facilitates the screening of large libraries to develop peptide binders with strong affinity and selectivity for protein targets, while maintaining the innate high stability of the peptide scaffold via protease-based selection pressure. We also describe a recently developed platform that leverages yeast’s ability to secrete correctly folded disulfide-rich peptides while simultaneously displaying peptide or protein tags on their surfaces. In combination with microfluidics technology, the platform creates single-cell yeast-in-droplets reactors, enabling the screening of large libraries based on functional output rather than solely on binding affinity. After identifying cyclic peptide candidates through library-based discovery, these candidates can be produced using a versatile yeast-based bioproduction platform. Traditionally, cyclic disulfide-rich peptides are produced through solid-phase synthesis, a method that generates significant amounts of toxic waste. In contrast, yeast-based bioproduction offers an environmentally sustainable alternative. It has the capability to produce structurally distinct peptides with minimal adjustments and is easily scalable using microbial fermenters, making it an ideal choice for large-scale production.

Aligning supply chain design for boosting resilience

2026-02-25T07:12:02Z

Aligning supply chain design for boosting resilience Sáenz, María Jesús; Revilla, Elena; Acero, Beatriz Many researchers have analyzed the effect of disruptive events, such as natural disasters and economic and market forces, on global supply chains. However, there is a lack of consensus on delineating a universal collection of supply chain risk management practices that will help companies operate in a global market with large-scale disruptions. In this article, we present an analysis, in conjunction with a worldwide online survey, based on successful global brands and their supply chains. We propose a framework that deploys the dynamics of building supply chain resilience, first linking the design of the supply chain portfolio (local versus global scope, as well as strategic responsiveness versus cost reduction) with supply chain vulnerabilities (external versus internal). We describe the transition between different supply chain structures as a way of coping with disruptions and thus proactively developing resilience. In this article, we introduce both a supply chain risk management approach and the reactive-by-deployment mode, as illustrated by successful global company examples.

Pennies, Penny Pools

2026-02-25T03:01:14Z

Pennies, Penny Pools Bucciarelli, Louis On Feburay 25, 2025, our president ordered the US Treasury to cease minting pennies. In this essay, I recount how members in Congress have tried to legislate the same - calling for rounding to the nearest nickel - but have not succeeded, some fearing that prevailing price structures (e.g., $xx.89) would leave customers on the short end of the stick over time with rounding up (customer loses) being more prevalent than rounding down (customer wins). I analyze the situation, then suggest how a “penny pool” might be used by retailers to ease the transition to a just and fair penny-less society.

Directed evolution-based discovery of ligands for in vivo restimulation of chimeric antigen receptor T cells

2026-04-24T03:15:27Z

Directed evolution-based discovery of ligands for in vivo restimulation of chimeric antigen receptor T cells Grzywa, Tomasz M; Neeser, Alexandra; Ramasubramanian, Ranjani; Romanov, Anna; Tannir, Ryan; Mehta, Naveen K; Cossette, Benjamin; Morgan, Duncan M; Goncalves, Beatriz; Sukaj, Ina; Bergaggio, Elisa; Kadauke, Stephan; Myers, Regina M; Paruzzo, Luca; Ghilardi, Guido; Cozzone, Austin; Schuster, Stephen J; Frey, Noelle; Zhang, Libin; Yousefpour, Parisa; Abraham, Wuhbet; Suh, Heikyung; Ruella, Marco; Grupp, Stephan A; Chiarle, Roberto; Wittrup, K Dane; Ma, Leyuan; Irvine, Darrell J Chimeric antigen receptor (CAR) T cell therapy targeting CD19 elicits remarkable clinical efficacy in B cell malignancies, but many patients relapse owing to failed expansion and/or progressive loss of CAR-T cells. We recently reported a strategy to potently restimulate CAR-T cells in vivo, enhancing their functionality by administration of a vaccine-like stimulus comprised of surrogate peptide ligands for a CAR linked to a lymph node-targeting amphiphilic PEG-lipid (amph-vax). Here we demonstrate a general strategy to discover and optimize peptide mimotopes enabling amph-vax generation for any CAR. We use yeast surface display to identify peptide binders to FMC63 (the scFv used in clinical CD19 CARs), which are then subsequently affinity matured by directed evolution. CAR-T vaccines using these optimized mimotopes triggered marked expansion and memory development of CD19 CAR-T cells in both syngeneic and humanized mouse models of B-acute lymphoblastic leukaemia/lymphoma, and enhanced control of disease progression compared with CD19 CAR-T-only-treated mice. This approach enables amph-vax boosting to be applied to any clinically relevant CAR-T cell product.

Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics

2026-04-14T03:39:12Z

Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics Lai, Pin-Kuang; Gallegos, Austin; Mody, Neil; Sathish, Hasige A; Trout, Bernhardt L Machine learning has been recently used to predict therapeutic antibody aggregation rates and viscosity at high concentrations (150 mg/ml). These works focused on commercially available antibodies, which may have been optimized for stability. In this study, we measured accelerated aggregation rates at 45°C and viscosity at 150 mg/ml for 20 preclinical and clinical-stage antibodies. Features obtained from molecular dynamics simulations of the full-length antibody and sequences were used for machine learning model construction. We found a k-nearest neighbors regression model with two features, spatial positive charge map on the CDRH2 and solvent-accessible surface area of hydrophobic residues on the variable fragment, gives the best performance for predicting antibody aggregation rates (r = 0.89). For the viscosity classification model, the model with the highest accuracy is a logistic regression model with two features, spatial negative charge map on the heavy chain variable region and spatial negative charge map on the light chain variable region. The accuracy and the area under precision recall curve of the classification model from validation tests are 0.86 and 0.70, respectively. In addition, we combined data from another 27 commercial mAbs to develop a viscosity predictive model. The best model is a logistic regression model with two features, number of hydrophobic residues on the light chain variable region and net charges on the light chain variable region. The accuracy and the area under precision recall curve of the classification model are 0.85 and 0.6, respectively. The aggregation rates and viscosity models can be used to predict antibody stability to facilitate pharmaceutical development.

Enhanced O-glycosylation site prediction using explainable machine learning technique with spatial local environment

2026-04-14T03:39:12Z

Enhanced O-glycosylation site prediction using explainable machine learning technique with spatial local environment Hong, Seokyoung; Chattaraj, Krishna Gopal; Guo, Jing; Trout, Bernhardt L; Braatz, Richard D Motivation: The accurate prediction of O-GlcNAcylation sites is crucial for understanding disease mechanisms and developing effective treatments. Previous machine learning (ML) models primarily relied on primary or secondary protein structural and related properties, which have limitations in capturing the spatial interactions of neighboring amino acids. This study introduces local environmental features as a novel approach that incorporates three-dimensional spatial information, significantly improving model performance by considering the spatial context around the target site. Additionally, we utilize sparse recurrent neural networks to effectively capture sequential nature of the proteins and to identify key factors influencing O-GlcNAcylation as an explainable ML model. Results: Our findings demonstrate the effectiveness of our proposed features with the model achieving an F1 score of 28.3%, as well as feature selection capability with the model using only the top 20% of features achieving the highest F1 score of 32.02%, a 1.4-fold improvement over existing PTM models. Statistical analysis of the top 20 features confirmed their consistency with literature. This method not only boosts prediction accuracy but also paves the way for further research in understanding and targeting O-GlcNAcylation. Availability and implementation: The entire code, data, features used in this study are available in the GitHub repository: https://github.com/ pseokyoung/o-glcnac-

Leveraging microtopography to pattern multi-oriented muscle actuators

2026-02-20T03:08:08Z

Leveraging microtopography to pattern multi-oriented muscle actuators Rossy, Tamara; Schwendeman, Laura; Kohli, Sonika; Bawa, Maheera; Umashankar, Pavankumar; Habba, Roi; Tchaicheeyan, Oren; Lesmanbc, Ayelet; Raman, Ritu Engineering skeletal muscle tissue with precisely defined alignment is of significant importance for applications ranging from drug screening to biohybrid robotics. Aligning 2D contractile muscle monolayers, which are compatible with high-content imaging and can be deployed in planar soft robots, typically requires micropatterned cues. However, current protocols for integrating microscale topographical features in extracellular matrix hydrogels require expensive microfabrication equipment and multi-step procedures involving error-prone manual handling steps. To address this challenge, we present STAMP (simple templating of actuators via micro-topographical patterning), an easily accessible and cost-effective one-step method to pattern microtopography of various sizes and configurations on the surface of hydrogels using reusable 3D printed stamps. We demonstrate that STAMP enables precisely controlling the alignment of mouse and human skeletal muscle fibers without negatively impacting their maturation or function. To showcase the versatility of our technique, we designed a planar soft robot inspired by the iris, which leverages spatially segregated regions of concentric and radial muscle fibers to control pupil dilation. Optogenetic skeletal muscle fibers grown on a STAMPed iris substrates formed a multi-oriented actuator, and selective light stimulation of the radial and concentric fibers was used to control the function of the iris, including pupil constriction. Computational modeling of the biohybrid robot as an active bilayer matched experimental outcomes, showcasing the robustness of our STAMP method for designing, fabricating, and testing planar biohybrid robots capable of complex multi-DOF motion.

Enhancing Spray Retention Using Cloaked Droplets to Reduce Pesticide Pollution

2026-02-20T03:08:05Z

Enhancing Spray Retention Using Cloaked Droplets to Reduce Pesticide Pollution Jayaprakash, Vishnu; Rufer, Simon; Panata, Sreedath; Varanasi, Kripa K. Enhancing agrochemical spray retention on plant surfaces would have tremendous benefits to global health and the environment. The bouncing of sprayed pesticide droplets from hydrophobic leaves is a major source of water and soil pollution, and the resultant overuse of pesticides is a human health hazard and a financial burden for farmers. Here we report on the development of sustainable agricultural sprays consisting of cloaked droplets that significantly enhance droplet retention on plant surfaces. By leveraging wetting dynamics, we create cloaked droplets that consist of an ultra-thin food and environmentally safe oil layer (<1% by volume) that encapsulates water droplets. We develop a fundamental understanding of the dynamics of cloaked droplet impact and retention on superhydrophobic surfaces. Using high-speed imaging, we capture how the oil cloak transforms into a wetting ridge that pins the droplets and suppresses their rebound. We span a wide range of impact conditions, oils, oil viscosities, and oil volume fractions to demonstrate the robustness of the approach. By considering a balance of kinetic energy, the work of adhesion, and viscous dissipation in this four-phase system, we develop a physical model that allows us to establish a regime map for rebound suppression. Finally, these findings are implemented into a prototype sprayer which leads to a ∼5-fold reduction in spray waste on crop leaves. We believe that our spray approach can greatly reduce agrochemical pollution as well as pesticide and surfactant usage.

Total Synthesis and 13C NMR Revision of Nagelamide C

2026-02-20T03:08:04Z

Total Synthesis and 13C NMR Revision of Nagelamide C Tong, Guanghu; Nguyen, Long V.; Jamison, Timothy F. Nagelamide C (1), a dimeric pyrrole–imidazole alkaloid, exhibits antimicrobial and antibacterial activities. We demonstrate herein the first total synthesis of nagelamide C. This concise work was enabled by a series of significant transformations featuring: an imidazole benzylic Wittig olefination, a site selective bromination, and a regioselective trans-hydrostannylation/Stille coupling to construct a unique trisubstituted olefin. In addition, we show the original 13C NMR data of nagelamide C to be in error and revise the data.

Symmetry-Constrained Generation of Diverse Low-Bandgap Molecules with Monte Carlo Tree Search

2026-02-20T03:08:03Z

Symmetry-Constrained Generation of Diverse Low-Bandgap Molecules with Monte Carlo Tree Search Subramanian, Akshay; Damewood, James; Nam, Juno; Greenman, Kevin P.; Singhal, Avni P.; Gómez-Bombarelli, Rafael Organic optoelectronic materials are a promising avenue for next-generation electronic devices due to their solution processability, mechanical flexibility, and tunable electronic properties. In particular, near-infrared (NIR) sensitive molecules have unique applications in night-vision equipment and biomedical imaging. Molecular engineering has played a crucial role in developing non-fullerene acceptors (NFAs) such as the Y-series molecules, which feature a rigid fused-ring electron donor core flanked by electron-deficient end groups, leading to strong intramolecular charge-transfer and extended absorption into the NIR region. However, systematically designing molecules with targeted optoelectronic properties while ensuring synthetic accessibility remains a challenge. To address this, we leverage structural priors from domain-focused, patent-mined datasets of organic electronic molecules using a symmetry-aware fragment decomposition algorithm and a fragment-constrained Monte Carlo Tree Search (MCTS) generator. Our approach generates candidates that retain symmetry constraints from the patent dataset, while also exhibiting red-shifted absorption, as validated by TD-DFT calculations.

Molecular analysis and design using generative artificial intelligence via multi-agent modeling

2026-02-20T03:08:09Z

Molecular analysis and design using generative artificial intelligence via multi-agent modeling Stewart, Isabella; Buehler, Markus J. We report the use of a multiagent generative artificial intelligence framework, the X-LoRA-Gemma large language model (LLM), to analyze, design and test molecular design. The X-LoRA-Gemma model, inspired by biological principles and featuring 7 billion parameters, dynamically reconfigures its structure through a dual-pass inference strategy to enhance its problem-solving abilities across diverse scientific domains. The model is used to first identify molecular engineering targets through a systematic human–AI and AI–AI self-driving multi-agent approach to elucidate key targets for molecular optimization to improve interactions between molecules. Next, a multi-agent generative design process is used that includes rational steps, reasoning and autonomous knowledge extraction. Target properties of the molecule are identified either using a principal component analysis (PCA) of key molecular properties or sampling from the distribution of known molecular properties. The model is then used to generate a large set of candidate molecules, which are analyzed via their molecular structure, charge distribution, and other features. We validate that as predicted, increased dipole moment and polarizability is indeed achieved in the designed molecules. We anticipate an increasing integration of these techniques into the molecular engineering workflow, ultimately enabling the development of innovative solutions to address a wide range of societal challenges. We conclude with a critical discussion of challenges and opportunities of the use of multi-agent generative AI for molecular engineering, analysis and design.

Automated fast-flow synthesis of the immune checkpoint receptors PD-1 and PD-L1

2026-02-20T03:08:10Z

Automated fast-flow synthesis of the immune checkpoint receptors PD-1 and PD-L1 Fittolani, Giulio; Callahan, Alex J.; Loas, Andrei; Pentelute, Bradley L. Programmed cell death protein 1 (PD-1) and programmed cell death ligand 1 (PD-L1) are key targets for cancer therapy. Here, we use automated fast-flow peptide synthesis (AFPS) to rapidly produce these challenging β-sheet-rich proteins in their active forms following oxidative refolding protocols. The methods presented here provide rapid access to synthetic, air-stable mutants of PD-1 and PD-L1 in which L-methionine residues are substituted with L-norleucine, potentially enabling investigation of post-translational modifications and mirror-image analogs for drug discovery.

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

Structural geology of Eastern Massachusetts

2026-02-20T03:02:14Z

Structural geology of Eastern Massachusetts Ilsley, Ralph, 1896- Thesis: Sc. D., Massachusetts Institute of Technology, Department of Geology, 1934; Vita.

The design of a hydraulic draft gear for railway passenger cars

2026-02-20T03:05:00Z

The design of a hydraulic draft gear for railway passenger cars Pearson, Harry L.; McGrady, Charles T. Thesis: B.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1922; Includes bibliographical references.

A study of "Chu-Ma" as a textile fiber

2026-02-20T03:04:57Z

A study of "Chu-Ma" as a textile fiber Chou, Cheng Yu, 1901-; Hsueh, Tsu Kang. Thesis: B.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1924

The transportation decision making process in metropolitan Boston

2026-02-20T03:04:52Z

The transportation decision making process in metropolitan Boston Zinner, Richard Mark. Thesis: B.S., Massachusetts Institute of Technology, Department of Political Science, 1967; One unnumbered page inserted.; Bibliography: leaf 74.

Oscillographic presentation of impedances on the reflection-coefficient plane

2026-02-20T03:04:47Z

Oscillographic presentation of impedances on the reflection-coefficient plane Eckhart, Myron.; Fowler, Earl Bealle. Thesis: B.S., Massachusetts Institute of Technology, Department of Electrical Engineering, 1949

Radiation transfer in massive binary x-ray systems

2026-02-20T03:02:25Z

Radiation transfer in massive binary x-ray systems Lewis, Wayne Lloyd. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 1991; Includes bibliographical references (leaves 167-173).

Laser induced photoionization of helium

2026-02-20T03:04:45Z

Laser induced photoionization of helium Lewis, Wayne Lloyd. Thesis: B.S., Massachusetts Institute of Technology, Department of Physics, 1980; Includes bibliographical references.

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.

Petrography and geology of the Shoshone mining region in northwestern Wyoming

2026-02-20T03:04:35Z

Petrography and geology of the Shoshone mining region in northwestern Wyoming Benedict, P. C. (Platt Carrico), 1900-1969. Thesis: B.S., Massachusetts Institute of Technology, Department of Geology, 1922

GeoXCP: uncertainty quantification of spatial explanations in explainable AI

2026-02-20T03:08:01Z

Open-source device for high sensitivity magnetic particle spectroscopy, relaxometry, and hysteresis loop tracing

2026-02-19T04:36:04Z

Open-source device for high sensitivity magnetic particle spectroscopy, relaxometry, and hysteresis loop tracing Mattingly, E.; Barksdale, A. C.; Śliwiak, M.; Chacon-Caldera, J.; Mason, E. E.; Wald, L. L. Magnetic nanoparticles (MNPs) are used extensively across numerous disciples, with applications including Magnetic Particle Imaging (MPI), targeted hyperthermia, deep brain stimulation, immunoassays, and thermometry. The assessment of MNPs, especially those being designed for MPI, is performed with magnetic particle spectrometers, relaxometers, loop tracers, or similar devices. Despite the many applications and the need for particle assessment, there are few consolidated resources for designing or building such a MNP assessment system. Here, we describe the design and performance of an open-source device capable of spectroscopy, relaxometry, and loop tracing. We show example measurements from the device and quantify the detection sensitivity by measuring a dilution series of Synomag-D 70 nm (from 0.5 mg Fe/ml to 7 ng Fe/ml) with a 10 mT drive field at 23.8 kHz. The device measures 260 pg Fe with SNR = 1 and 1.3 ng at SNR = 5 in spectroscopy mode in under one second of measurement time. The system has a dynamic range of 60 μg to 260 pg Fe without changing the hardware configuration. As an example application, we characterize Synomag-D’s relaxation time constant for drive fields 2–18 mT and compare the magnetization responses of two commonly used MNPs.

Precise Fermi level engineering in a topological Weyl semimetal via fast ion implantation

2026-02-19T04:36:09Z

Precise Fermi level engineering in a topological Weyl semimetal via fast ion implantation Mandal, Manasi; Chotrattanapituk, Abhijatmedhi; Woller, Kevin; Wu, Lijun; Xu, Haowei; Hung, Nguyen Tuan; Mao, Nannan; Okabe, Ryotaro; Boonkird, Artittaya; Nguyen, Thanh; Drucker, Nathan C; Chen, Xiaoqian M; Momiki, Takashi; Li, Ju; Kong, Jing; Zhu, Yimei; Li, Mingda The precise controllability of the Fermi level is a critical aspect of quantum materials. For topological Weyl semimetals, there is a pressing need to fine-tune the Fermi level to the Weyl nodes and unlock exotic electronic and optoelectronic effects associated with the divergent Berry curvature. However, in contrast to two-dimensional materials, where the Fermi level can be controlled through various techniques, the situation for bulk crystals beyond laborious chemical doping poses significant challenges. Here, we report the milli-electron-volt (meV) level ultra-fine-tuning of the Fermi level of bulk topological Weyl semimetal tantalum phosphide using accelerator-based high-energy hydrogen implantation and theory-driven planning. By calculating the desired carrier density and controlling the accelerator profiles, the Fermi level can be experimentally fine-tuned from 5 meV below, to 3.8 meV below, to 3.2 meV above the Weyl nodes. High-resolution transmission electron microscopy reveals the crystalline structure is largely maintained under irradiation, while electrical transport indicates that Weyl nodes are preserved and carrier mobility is also largely retained. Our work demonstrates the viability of this generic approach to tune the Fermi level in semimetal systems and could serve to achieve property fine-tuning for other bulk quantum materials with ultrahigh precision.

A facility for cryogenic ion irradiation and in situ characterization of rare-earth barium copper oxide superconducting tapes

2026-02-19T04:36:11Z

A facility for cryogenic ion irradiation and in situ characterization of rare-earth barium copper oxide superconducting tapes Devitre, AR; Fischer, DX; Woller, KB; Clark, BC; Short, MP; Whyte, DG; Hartwig, ZS Superconducting magnets based on Rare Earth Barium Copper Oxides (REBCO) offer transformative capabilities in the fields of fusion energy, high energy physics, and space exploration. A challenge shared by these applications is the limited lifetime of REBCO due to radiation damage sustained during operation. Here we present a new ion-beam facility that enables simultaneous cryogenic irradiation and in situ characterization of commercial REBCO tapes. The ion source provides spatially uniform fluxes up to 1018 protons/m2s with kinetic energies up to 3.4 MeV, in addition to helium and higher-Z species. Using this facility, we can induce uniform damage profiles in the first 10–20 µm of REBCO tapes with less than 0.25 appm of hydrogen implanted in REBCO after a dose of 1020 protons/m2. The tape can be held between 20 and 300 K with an accuracy of ±0.1 K and is connected to a four-point probe measuring the critical current, Ic, and critical temperature, Tc, before, during, and after irradiation with transport current ranging from 100 nA to 100 A, and a typical voltage noise less than 0.1 μV. These capabilities are presently used to study the effect of irradiation temperature on REBCO performance change during and after proton bombardment, to assess the possibility of Ic and Tc recovery after irradiation through thermal annealing, and to explore the instantaneous and recoverable suppression of Ic and Tc observed during irradiation.

High temperature stability of regrown and alloyed Ohmic contacts to AlGaN/GaN heterostructure up to 500 °C

2026-02-19T04:36:06Z

High temperature stability of regrown and alloyed Ohmic contacts to AlGaN/GaN heterostructure up to 500 °C Niroula, John; Xie, Qingyun; Rajput, Nitul S; Darmawi-Iskandar, Patrick K; Rahman, Sheikh Ifatur; Luo, Shisong; Palash, Rafid Hassan; Sikder, Bejoy; Yuan, Mengyang; Yadav, Pradyot; Micale, Gillian K; Chowdhury, Nadim; Zhao, Yuji; Rajan, Siddharth; Palacios, Tomás This Letter reports the stability of regrown and alloyed Ohmic contacts to AlGaN/GaN-on-Si high electron mobility transistors (HEMTs) for high temperature applications up to 500 °C. Transfer length method (TLM) measurements from 25 to 500 °C in air show that the regrown contacts appear to be stable up to 500 °C during short term (approximately 1 h) testing, while alloyed contacts appear to decrease in contact resistance from 300 to 500 °C though increases in the error bounds due to increase sheet resistance make it difficult to conclude definitely. Additionally, longer term testing shows both technologies remain stable at least up to 48 h at 500 °C, after which the large increase in sheet resistance makes the measurement uncertainty too large to conclude definitively. Advanced microscopy images indicate both the regrown and alloyed contact regions remain structurally intact after prolonged high temperature exposure with no visible degradation in crystallinity or metal composition.

Optical-pump–terahertz-probe spectroscopy in high magnetic fields with kHz single-shot detection

2026-02-19T04:36:14Z

Optical-pump–terahertz-probe spectroscopy in high magnetic fields with kHz single-shot detection Dastrup, Blake S; Miedaner, Peter R; Zhang, Zhuquan; Nelson, Keith A We demonstrate optical pump–THz probe (OPTP) spectroscopy with a variable external magnetic field (0–9 T), in which the time-dependent THz signal is measured by echelon-based single-shot detection at a repetition rate of 1 kHz. The method reduces data acquisition times by more than an order of magnitude compared to conventional electro-optic sampling using a scanning delay stage. The approach illustrates the wide applicability of the single-shot measurement approach to non-equilibrium systems that are studied through OPTP spectroscopy, especially in cases where parameters such as magnetic field strength (B) or other experimental parameters are varied. We demonstrate the capabilities of our measurement by performing cyclotron resonance experiments in bulk silicon, where we observe B-field-dependent carrier relaxation and distinct relaxation rates for different carrier types. We use a pair of economical linear array detectors to measure 500 time points on each shot, offering an equivalent performance to camera-based detection with possibilities for higher repetition rates.

Validation of the OpenMC Code for Fusion Applications: The FNG-Streaming Benchmark Case

2026-02-19T04:36:08Z

Validation of the OpenMC Code for Fusion Applications: The FNG-Streaming Benchmark Case Segantin, Stefano; Ebiwonjumi, Bamidele; Peterson, Ethan In this work, we benchmark OpenMC against the FNG-ITER streaming experiment. FNG-ITER streaming, a high-quality experiment carried out at the ENEA laboratories in Frascati, Italy, was initially included in SINBAD (Shielding Integral Benchmark Archive and Database). More recently, the benchmark was included in the Compilation of Nuclear Data Experiments for Radiation Characterization as well. It consists of a neutron shielding experiment with a rather complex geometry that constitutes an appropriate validation study for the use of weight windows within OpenMC. Measurements include flux detection via four different types of activation foils divided into three batches and a set of thermoluminescent detectors for nuclear heating. The OpenMC results are in very good agreement with those of MCNP and the experimental measurements, with the majority of the discrepancies within the combined statistical error and experimental uncertainty (less than 10% computed measured discrepancy).

Use of Bayesian decision analysis to maximize value in patient-centered randomized clinical trials in Parkinson’s disease

2026-02-19T04:36:13Z

Use of Bayesian decision analysis to maximize value in patient-centered randomized clinical trials in Parkinson’s disease Chaudhuri, Shomesh E; Ben Chaouch, Zied; Hauber, Brett; Mange, Brennan; Zhou, Mo; Christopher, Stephanie; Bardot, Dawn; Sheehan, Margaret; Donnelly, Anne; McLaughlin, Lauren; Caldwell, Brittany; Benz, Heather L; Ho, Martin; Saha, Anindita; Gwinn, Katrina; Sheldon, Murray; Lo, Andrew W A fixed one-sided significance level of 5% is commonly used to interpret the statistical significance of randomized clinical trial (RCT) outcomes. While it is necessary to reduce the false positive rate, the threshold used could be chosen quantitatively and transparently to specifically reflect patient preferences regarding benefit–risk tradeoffs as well as other considerations. How can patient preferences be explicitly incorporated into RCTs in Parkinson’s disease (PD), and what is the impact on statistical thresholds for device approval? In this analysis, we apply Bayesian decision analysis (BDA) to PD patient preference scores elicited from survey data. BDA allows us to choose a sample size (𝑛) and significance level (𝛼) that maximizes the overall expected value to patients of a balanced two-arm fixed-sample RCT, where the expected value is computed under both null and alternative hypotheses. For PD patients who had previously received deep brain stimulation (DBS) treatment, the BDA-optimal significance levels fell between 4.0% and 10.0%, similar to or greater than the traditional value of 5%. Conversely, for patients who had never received DBS, the optimal significance level ranged from 0.2% to 4.4%. In both of these populations, the optimal significance level increased with the severity of the patients’ cognitive and motor function symptoms. By explicitly incorporating patient preferences into clinical trial designs and the regulatory decision-making process, BDA provides a quantitative and transparent approach to combine clinical and statistical significance. For PD patients who have never received DBS treatment, a 5% significance threshold may not be conservative enough to reflect their risk-aversion level. However, this study shows that patients who previously received DBS treatment present a higher tolerance to accept therapeutic risks in exchange for improved efficacy which is reflected in a higher statistical threshold.

Choice denied: impact of income and credit-based tenant screening on the Housing Choice Voucher program

2026-02-19T04:35:56Z

Choice denied: impact of income and credit-based tenant screening on the Housing Choice Voucher program So, Wonyoung; Gade, Anisha; Hangen, Forrest The Housing Choice Voucher program supports over 2.5 million households by subsidizing rent payments within the private housing market. However, challenges arise due to exclusionary practices, undermining the program’s goal of ‘choice.’ Tenant screening practices have been critical in exacerbating these challenges, yet their impact remains understudied. Drawing on tenant screening criteria documents from property management websites and the Survey of Consumer Finances, this study finds that while voucher holders generally meet rent-to-income thresholds due to the subsidies—keeping their rent burden relative to their income, they still face barriers related to credit scores, bankruptcy history, and debt. These criteria, which apply to both voucher and non-voucher renters, may exclude approximately one in ten voucher holders, despite the guaranteed portion of rent covered by public assistance. These findings show an urgent need for policy interventions to the potential exclusionary impacts of tenant screening services.

AgentNexus: Accelerating AI Agent Development and Enhancing Interoperability with MCP

2026-02-18T03:09:03Z

AgentNexus: Accelerating AI Agent Development and Enhancing Interoperability with MCP Yae, Jung; Hamilton, Lei The DoD faces significant challenges in its pursuit of AI superiority, as disparate data and development platforms create redundant efforts and limit interoperability. Additionally, existing DoD systems are ill-equipped to handle the recent paradigm shift toward agentic AI, which requires modern standards and tools. To address these gaps, this paper introduces AgentNexus, an application designed to streamline the development, deployment, and servicing of AI agents. AgentNexus provides an application featuring an advanced agents processing backend, a scalable service layer, and an intuitive user interface. It provides pre-built toolkits, sophisticated RAG pipeline, and MCP for enhanced interoperability. The successful development of an Education Assistant agent validates the application’s capacity to support the rapid implementation of multi-agent workflows. By fostering a collaborative and standardized environment, AgentNexus mitigates critical barriers of interoperability and duplicated effort, accelerating the delivery of multi-agent AI to warfighters.

Intelligent C-17 Load Planning for Flight Optimization

2026-02-18T03:08:58Z

Intelligent C-17 Load Planning for Flight Optimization McAlister, Catherine; Jones, Mathew; McConville, Sean C-17 Globemaster III cargo capacity is significantly underutilized, with many sorties transporting only a few pallets despite the aircraft’s 170,900-pound payload capability. Historical flight data analysis reveals inefficient scheduling practices that increase operational costs, crew workload, and overall negatively effect mission capability. This paper details the development of an AI-powered optimization model to improve C-17 cargo utilization and reduce required flight operations. We analyzed historical C-17 transportation data and created both traditional optimization algorithms and predictive AI models to determine optimal flight scheduling for 3-week operational periods. The AI model achieved 97.9% accuracy in predicting optimal flight count requirements and 89.3% accuracy in predicting optimal flight assignment for specific cargo, representing a 23% reduction in total flights and a 15% increase in average cargo utilization. These results demonstrate that data-driven flight scheduling can significantly improve C-17 operational efficiency, reduce costs across the airlift community, and enabling additional time towards advanced training, contingency support, and critical warfighter operations, ultimately increasing the lethality and readiness of the Department of Defense.

Securing Intelligence: The Strategic Necessity of Air-Gapped AI Systems in the Age of Cloud-Based LLMs

2026-02-18T03:09:05Z

Securing Intelligence: The Strategic Necessity of Air-Gapped AI Systems in the Age of Cloud-Based LLMs Viggh, Herbert; Tsagaratos, Jennifer The increasing use of large language models (LLMs) in applications, from military strategy to customer service, raises concerns about data sovereignty, security, and privacy. Cloudbased API models, created by companies such as OpenAI, pose significant risks due to training data exposure and prompt injection attacks, which can compromise sensitive information and hidden biases that could influence reporting or executive decision-making processes. Real-world incidents, such as the leakage of Samsung’s proprietary source code through ChatGPT, highlight the dangers of relying on cloud providers with complete visibility into client queries. Furthermore, data localization laws and regulations, such as the General Data Protection Regulation (GDPR), underscore the risks associated with outsourcing intelligence and decision support systems to foreign entities. Airgapped AI solutions, which run on isolated networks disconnected from the outside world, offer a secure alternative for sensitive environments such as national defense, research laboratories, and critical infrastructure. By maintaining control over AI processes, organizations can ensure information safety, comply with regulations, and mitigate risks associated with cloud-based AI infrastructure, ultimately safeguarding their data integrity, privacy, and operational independence.

RAIMOND Requirements AI for Military Operational Needs Development

2026-02-18T03:09:07Z

RAIMOND Requirements AI for Military Operational Needs Development Garcia, Fabio; Steilberg, Jackson The Joint Capabilities Integration and Development System (JCIDS) was created as a means to overhaul military procurement processes. Ideally, the requirements development process is meant to take a total of 2-4 years from concept to manufacturing. However the actual length of concept development is much longer. As a result, technologies that are conceptualized through the analytical process often enter the acquisition too late to need for the warrior. To reduce the lengthy timeline in requirements development, we used Large Language Models (LLMs) to conduct the necessary research and synthesize documents that abide by strict JCIDS guidelines. Prompt engineering can achieve these results as a proof of concept. However, the output responses lack the content length and depth necessary to pass through the requirements validation process. Therefore, a combination of agentic workflows, prompt engineering, and sufficient context is needed to achieve the desired outcomes. This project utilizes a novel framework to derive Capabilities Based Assessments (CBAs) at an approximate 80 percent readiness level requiring the final steps of validation and verification by subject matter experts.

Machine Learning for the Enhancement of Adaptive Optics

2026-02-18T03:09:05Z

Machine Learning for the Enhancement of Adaptive Optics Hall, Robert; Chen, Justin Optical systems (telescopes, lasers, microscopes, etc.) have degraded performance over long distances due to scintillation caused by Earth’s atmosphere, where adaptive optics (AO) is often used to enhance its signal-to-noise (SNR) ratio or image quality. Astronomers have found success in laser-based adaptive optics where they survey the atmosphere with a laser and subtract its effects on the resultant image. Although effective in most cases, these systems can be extremely costly, are computationally intensive in real time, and fall short in some edge cases. We propose an autoencoder/ decoder and a generalized sequence to sequence model (LSTM) as a cost-effective method to off-load computational complexity from real time and enhance performance in edge cases. This study utilizes four simulated datasets of wavefront sensor frames for a variety of atmospheric conditions, done in collaboration with MIT Lincoln Laboratory [1]–found auto-encoding performance just shy of traditional methodology and found LSTM performance that predicts well the general shape on the WFS, but suffers from scaling issues.

From Hype to Reality: Real-World Lessons and Recommendations for AI in Military Applications

2026-02-18T03:09:06Z

From Hype to Reality: Real-World Lessons and Recommendations for AI in Military Applications Lynch, Joshua; Niss, Laura The current use cases, limitations, and future capacity of large language models (LLMs) as assistants to military personnel remain an open question. This paper presents a case study of an Airman’s interaction with and trust calibration of LLMs over three months, both as an everyday assistant and for development of ROMAD-AI, a tactical military application. Through intuitive, AI-generated software development, an approach relying on iterative code generation through natural language prompting of LLMs from a technical novice rather than human generated programming from a technical expert, the research reveals significant gaps between industry curated AI capability demonstrations and operational reality, requiring systematic trust calibration and realistic scope management. Outcomes are analyzed through operational and technical expertise perspectives to provide practical guidance for both military service members seeking effective AI integration and researchers developing military-focused AI systems.

Large Language Models and Defense Strategy: Escalation Risks and National Security Challenges

2026-02-18T03:09:04Z

Large Language Models and Defense Strategy: Escalation Risks and National Security Challenges Hou, Jonathan; Lax, Edwin This literature review examines the strategic vulnerabilities posed by Large Language Models (LLMs) in military and national security contexts. It synthesizes recent research on their propensity for escalatory reasoning, cultural misalignment, semantic manipulation, and dual-use ambiguity. Findings from conflict s imulations a nd c oalition p lanning m odels reveal how LLMs may default to aggressive or biased outputs under ambiguity. These tendencies threaten alliance cohesion, distort decision-making, and undermine trust in AI-enabled operations. The review concludes by advocating for safeguards such as culturally calibrated training, rigorous output verification, a nd the integration of human-AI intermediaries to prevent destabilizing outcomes.

Synchronization-Aware Diffusion Models for Intra-Family RF Signal Classification

2026-02-18T03:09:02Z

Synchronization-Aware Diffusion Models for Intra-Family RF Signal Classification Hayden, Hunter; Botero, Joey Classification of radio frequency (RF) signals in the presence of channel-induced synchronization errors remains a critical challenge in spectrum awareness systems. Traditional classification pipelines generally rely on fixed synchronization algorithms or assume aligned signals, which limits robustness under real world timing, phase, and frequency distortions. We introduce SyncDiff, a novel encoder-only diffusion model architecture that predicts synchronization parameters through iterative denoising steps prior to classification. By replacing conventional synchronization algorithms with a learned datadriven correction mechanism, our approach enables adaptive signal alignment based on current channel distortions in unsynchronized input data. SyncDiff employs a UNet based encoder to refine synchronization parameters across multiple inference steps, dynamically reducing channel-induced alignment errors while preserving the inherit modulation specific characteristics that allow these signals to be discriminable. Evaluations of the RadioML2018 RF standard benchmark data set [1] demonstrates improved classification accuracy across varying SNRs, modulation schemes and synchronization impairments. Our findings highlight the potential of diffusion-based synchronization learning to improve downstream RF classification without reliance on expert-engineered synchronization routines.

Three-Dimensional Full-Core BEAVRS Using OpenMOC with Transport Equivalence

2026-02-18T03:07:45Z

Three-Dimensional Full-Core BEAVRS Using OpenMOC with Transport Equivalence Giudicelli, G; Forget, B; Smith, K Using an optimized implementation of the three-dimensional (3D) method of characteristics for neutron transport, along with a novel equivalence method for transport calculations that was designed to correct self-shielding errors from neglecting the angular dependence of resonant group absorption, a 3D full-core light water reactor hybrid stochastic-deterministic eigenvalue calculation was achieved. This paper presents the optimizations developed and compares the transport solutions obtained. For the statepoint, run times near 10 000 CPU hours are achieved—improving on previous works by an order of magnitude—with near 1% error on pin fission to 238U capture ratios and a few dozen pcms on the eigenvalue.

Considering a US-Supported Self-Defense Option for Taiwan

2026-02-18T03:07:50Z

Considering a US-Supported Self-Defense Option for Taiwan Glaser, Charles L. There is wide agreement that Taiwan is the most dangerous issue dividing the United States and China. China believes Taiwan is part of its homeland, views unification with Taiwan as a core interest, and is determined to gain full control of the island. China continues to prefer peaceful unification, but explicitly retains the option of using military forces to achieve unification and seeks to use the threat of military force to strengthen its negotiating hand. Current US policy includes an ambiguous commitment to defend Taiwan if attacked or severely coerced by China—it leaves open whether and how the United States would respond. In addition, the United States provides Taiwan with weapons to improve its ability to defend itself. The United States is pressing Taiwan to deploy smaller mobile weapons that would increase the survivability and lethality of its forces; these forces would support a “porcupine strategy” that makes Taiwan harder to invade and conquer and would, at a minimum, provide time for US forces to arrive to aid Taiwan’s defense.

Needs, Wants . . . and Excuses: What Executives Can Learn from Zig Ziglar About Working with Universities

2026-02-18T03:07:49Z

Needs, Wants . . . and Excuses: What Executives Can Learn from Zig Ziglar About Working with Universities Wright, Randall S. Zig Ziglar was a famous sales trainer, motivational speaker, and author on salesmanship. When he died on November 28, 2012, Kevin Kruse (Citation2024)—best-selling author of Emotional Intelligence: 52 Strategies, coach to Fortune 500 CEOs, Marine Corps generals, and Silicon Valley entrepreneurs—wrote this in Forbes: “Zig Ziglar died today at age 86. A World War II veteran, Zig Ziglar became the top salesperson in several organizations before striking out on his own as a motivational speaker and trainer. With a Southern charm and lessons grounded in Christianity, Ziglar wrote over two dozen books and amassed a following of millions who were encouraged by his lessons for success.”

Computational studies of electric field effects in CO2 methanation on Ni metal surfaces

2026-02-18T03:07:48Z

Computational studies of electric field effects in CO2 methanation on Ni metal surfaces Wakamatsu, Katsuhiro; Yasuda, Takaaki; Aratani, Masato; Ogura, Teppei Non-Faradaic electrochemical modification of catalytic activity (NEMCA) with an electric field (EF) has attracted attention as one of the methods to improve catalyst performance. However, this activation mechanism is not still clear. In this study, we focused on the NEMCA mechanism in CO2 methanation on Ni metal catalyst with solid oxide electrolysis cell (SOEC) and calculated two possible effects of the NEMCA mechanism; direct EF applications and oxygen atom co-adsorptions, using the density functional theory calculations and detailed kinetic simulations. Compared with these effects in terms of kinetic energy changes in the rate-determining steps, it has been revealed that the spillover effect of lattice oxygen toward the catalyst surface is dominant in the NEMCA mechanism. Also, we have found that overall CO2 methanation is promoted in SOEC mode with oxygen atom co-adsorptions in both cases of Ni flat and step sites.

Equilibrium configurations of line arrays with respect to the deviatoric mean drift forces

2026-02-18T03:07:47Z

Equilibrium configurations of line arrays with respect to the deviatoric mean drift forces Tokić, Grgur; Yue, Dick KP Monochromatic waves incident on an array of structures give rise to nonlinear, time-constant mean drift forces (MDFs). These forces depend on the array's spatial configuration; their magnitude and the direction is, in general, different for every structure in the array. If the spatial configuration of an array is not fixed, as is the case in arrays of individually anchor-moored structures, the time-constant differences in MDF on individual bodies can lead to a change in spatial configuration, which could, in turn, significantly affect both the first-order, time-harmonic response of the array, as well as the downwave component of the MDF. Here, we explore the dependency of these deviatoric forces on array configurations and on the frequency of the incident monochromatic waves. We consider configurations of line arrays (consisting of 2–5 vertical circular cylinders) that are described by 1 or 2 parameters, and we focus on the along-array component of deviatoric forces. Using multiple scattering computational simulations, we identify the array configurations in which the deviatoric drift forces are zero, and we discuss the stability of these equilibrium configurations with respect to class-preserving configuration perturbations. Both stable and unstable equilibria exist, but the relative number of unstable equilibria grows as the number of degrees of freedom of the configuration perturbations increases. Interestingly, the stable configurations experience a generally lower downwave mean drift force on the entire array than the unstable ones. Overall, the variations in the deviatoric and the downwave MDFs between equilibria are significant (on the order of the isolated body MDF).

1000-MW CSP with 100-gigawatt-hour crushed-rock heat storage to replace dispatchable fossil-fuel electricity

2026-02-18T03:07:41Z

1000-MW CSP with 100-gigawatt-hour crushed-rock heat storage to replace dispatchable fossil-fuel electricity Forsberg, Charles We are developing 100-GWh heat-storage systems for use with 1000-MW Concentrated Solar Power (CSP) and nuclear reactor systems with capital cost goals of several dollars per kWh of heat storage—a factor of 50 under lithium ion batteries per unit of electricity. The capabilities of a 100-GWh heat storage system are similar to the Tennessee Valley Authority Raccoon Mountain pumped hydro facility that can provide 1652 MW(e) for 22 hours to address daily to weekly storage. The low capital cost of the Crushed Rock Ultra-large Stored Heat (CRUSH) system is only possible in large-capacity systems; thus, the CSP system average 24/7 heat inputs may exceed 1000 MW to match the heat storage capacity. Hot oil or nitrate salt is pumped from multiple solar farms or towers to the central CRUSH system and associated power block. The peak power block output may be 2 to 4 times average output with large economics of scale relative to the smaller power blocks associated with existing CSP systems. The cost savings from the large storage and the power block exceed the cost of hot oil or hot nitrate salt insulated pipelines over 10+ kilometers. The heat is stored in crushed rock in piles 20 m high and up to 250 m by 250 m on a side within an insulated floor and building structure. The sides of the rock pile are sloped rock that allow rock expansion and contraction with temperature without generating mechanical forces against walls. Heat is transferred from CSP to the crushed rock and then to the power cycle using (1) heat transfer oils for lower-temperature power systems to 400°C or (2) nitrate salts for higher-temperature power systems to 600°C. In charging mode, hot heat transfer fluid is sprayed over crushed rock and drains through the rock to the collection pans at the bottom to be reheated. Sections of rock are heated sequentially. In discharge mode cold heat transfer fluid is sprayed over crushed rock and drains through the rock to the collection pans below to deliver hot fluid to the power cycle. Heat storage costs are minimized by three features. Crushed rock is the lowest-cost storage material. The large building size minimizes the surface-to-volume ratio and thus building, insulation and foundation costs. The inventory and thus cost of oil and nitrate salt is minimized by using these fluids to transfer heat from CSP collectors to storage and then to the power block—but not for heat storage.

The way forward: The path to monolithic additive manufacture of lower hybrid current drive launchers

2026-02-18T03:07:38Z

The way forward: The path to monolithic additive manufacture of lower hybrid current drive launchers Seltzman, AH; Wukitch, SJ Additive Manufacturing (AM) is a key enabling technology for the rapid production of complex radio-frequency (RF) structures used in lower hybrid current drive (LHCD) launchers. Glenn Research Copper 84 (GRCop-84), a Niobium Chromide (Cr2Nb) 8 at. % Cr, 4 at. % Nb precipitation hardened alloy, is suitable for AM with Laser Powder Bed Fusion (L-PBF), achieving 99.5% density, Ra=3-4 µm surface roughness, yield strength of 470 MPa and an ultimate tensile strength (UTS) of 710 MPa in as-printed condition. AM of a high field side (HFS) lower LHCD launcher from GRCop-84 alloy demonstrated several critical advancements in AM of RF launchers. Waveguides with a pentagonal cross-section were designed to support the top internal waveguide surface with 45-degree chamfers from the sidewall, eliminating collapse of the ceiling, while maintaining RF properties near identical to a rectangular cross section. Hot Isostatic Pressing (HIPing) consolidated residual voids within the material, increasing density from 99.5% to 100%. Chemical-Mechanical Polishing (CMP) reduced residual surface roughness from the L-PBF process to Ra=0.1 µm / Rq=0.4 µm to lower RF losses. Advancements in L-PBF for the AM of copper alloys have increased the maximum build volume from 250x250x300mm on the Concept Laser M2 printer to 400x400x400mm on the EOS M400 printer. This increased build volume now enables monolithic AM of complete LHCD launchers with integrated cooling channels that eliminate the time-consuming laser welding assembly of launcher segments previously required by the smaller build volume.

Particle-in-cell simulations of parasitic electrostatic wave excitation in the ion cyclotron range of frequencies and high harmonic fast wave regimes

2026-02-18T03:07:31Z

Particle-in-cell simulations of parasitic electrostatic wave excitation in the ion cyclotron range of frequencies and high harmonic fast wave regimes Diab, Raymond; Baek, Seung-Gyou; Bonoli, Paul; Jenkins, Thomas G; Ono, Masayuki; Smithe, David Using the open-source code SMILEI [J. Derouillat et al., Comput. Phys. Commun. 222, 351-373 (2018)], we perform one-dimensional full-f particle-in-cell (PIC) simulations of parasitic electrostatic wave excitation in the Ion Cyclotron Range of Frequencies (ICRF) and High Harmonic Fast Wave (HHFW) regimes in an inhomogeneous plasma. We first study direct coupling from the fast wave to electrostatic waves at the lower hybrid (LH) resonance (S=0). In the ICRF regime, we show that the fast wave can couple to the Ion Bernstein Wave (IBW), which propagates beyond the LH resonance layer. On the other hand, in the HHFW regime, no direct coupling to the IBW is observed, but electrostatic waves, likely to be Hot Ion Plasma Waves (HIPW or HPW), are seen on the low-density side of the LH resonance layer. The coupling efficiency to electrostatic waves is seen to increase with ion temperature. Parametric decay instabilities (PDIs) are then investigated in both regimes. In the ICRF regime, both resonant and non-resonant decay channels are observed and compared with theory. In the HHFW regime, we observe multiple sidebands separated by the ion cyclotron frequency, as measured experimentally on NSTX [J. R. Wilson et al., AIP Conf. Proc. 787, 66 (2005)]. The nature of these waves is discussed. Perpendicular ion heating is also found in the region where PDIs occur, consistent with experimental observations.

Towards fast, accurate predictions of RF simulations via data-driven modeling: Forward and lateral models

2026-02-18T03:07:43Z

Towards fast, accurate predictions of RF simulations via data-driven modeling: Forward and lateral models Wallace, GM; Bai, Z; Bertelli, N; Bethel, EW; Perciano, T; Shiraiwa, S; Wright, JC Three machine learning techniques (multilayer perceptron, random forest, and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. A single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time to complete, which is acceptable for many purposes, but too slow for integrated modeling and real-time control applications. More accurate simulations with fast electron diffusion are even slower, requiring multiple hours of run time with parallel processing. The machine learning models use a database of 16,000+ GEN-RAY/CQL3D simulations for training, validation, and testing. Latin hypercube sampling methods implemented in πScope ensure that the database covers the range of 9 input parameters (ne0, Te0, Ip, Bt, R0, n∥︀, Ze f f, Vloop, PLHCD) with sufficient density in all regions of parameter space. The surrogate models reduce the computation time from minutes-hours to ms with high accuracy across the input parameter space. Data-driven surrogate models also allow for solving inverse and “lateral” problems. A surrogate model for the inverse problem maps from a desired current drive or power deposition profile to a set of input parameters that would result in such a profile, while a surrogate model for the lateral problem maps from a measured experimental quantity such as hard x-ray emission to a current drive or power deposition profile. The πScope database creation workflow is flexible and applicable to other RF simulation codes such as TORIC.

Decrypting the mechanisms of wicking and evaporation heat transfer on micro-pillars during the pool boiling of water using high-resolution infrared thermometry

2026-02-18T03:07:40Z

Decrypting the mechanisms of wicking and evaporation heat transfer on micro-pillars during the pool boiling of water using high-resolution infrared thermometry Wang, Chi; Rahman, Md Mahamudur; Bucci, Matteo Surfaces with micrometer-scale pillars have shown great potential in delaying the boiling crisis and enhancing the critical heat flux (CHF). However, physical mechanisms enabling this enhancement remain unclear. This knowledge gap is due to a lack of diagnostics that allow elucidating how micro-pillars affect thermal transport phenomena on the engineered surface. In this study, for the first time, we are able to measure time-dependent temperature and heat flux distributions on a boiling surface with engineered micro-pillars using infrared thermometry. Using these data, we reveal the presence of an intra-pillar liquid layer, created by the nucleation of bubbles and partially refilled by capillary effects. However, contrarily to conventional wisdom, the energy removed by the evaporation of this liquid cannot explain the observed CHF enhancement. Yet, predicting its dry out is the key to delaying the boiling crisis. We achieve this goal using simple analytic models and demonstrate that this process is driven by conduction effects in the boiling substrates and, importantly, in the intra-pillar liquid layer itself. Importantly, these effects also control the wicking flow rate and its penetration length. The boiling crisis occurs when, by coalescing, the size of the intra-pillar liquid layer becomes too large for the wicking flow to reach its innermost region. Our study reveals and quantifies unidentified physical aspects, key to the performance optimization of boiling surfaces for cooling applications.

APP2 Status Summary: Proposed New VLBI Capabilities for Cycle 8

2026-03-19T03:15:26Z

APP2 Status Summary: Proposed New VLBI Capabilities for Cycle 8 Matthews, Lynn D.; Crew, Geoffrey B. This document contains material regarding the new capabilities proposed for ALMA Cycle 8. These included a passive phasing mode (for weaker sources), an ALMA Band 3 pulsar observing mode, a prototype spectral line mode, also for Band 3, and minor improvements to the code for operational reasons. In the event, the first two were accepted for a Cycle 8 that was delayed by the COVID-19 pandemic. The spectral line capability was deferred until Cycle 9. In response to reviewer questions, a modified figure was prepared and presented as an addendum. Both documents are presented here as a single PDF. This report was prepared for the formal acceptance of the software required for Cycle 8. Notionally it is ALMA Technical Note #21, but not (yet) published as such.

Final Report: Enabling New VLBI Science with the ALMA Phasing System - Phase 3 (APP3): An ALMA North America Development Project

2026-03-04T18:34:05Z

Final Report: Enabling New VLBI Science with the ALMA Phasing System - Phase 3 (APP3): An ALMA North America Development Project Matthews, Lynn D.; Crew, Geoffrey B. Executive Summary: This document provides a summary of activities undertaken as part of the ALMA North America Development Project “Enabling New VLBI Science with the ALMA Phasing System - Phase 3 (APP3)”, whose period of performance ex- tended from January 17, 2022 to July 16, 2024. The successful completion of this project has resulted in the introduction of flexible tuning for ALMA very long baseline interfer- ometry (VLBI) operations, a fully flexible spectral line VLBI observing mode, and the enabling of panchromatic VLBI, allowing ALMA in principle to operate as a phased array in any available receiver band. The project also carried out a series of activities aimed at maintaining and optimizing existing VLBI infrastructure and provided training to staff at the Joint ALMA Observatory (JAO) to enable a transition to autonomous VLBI observing. A video feature and accompanying news article were produced near the conclusion of this project to make the results accessible to a broader audience. This report was prepared as a final report on the activities undertaken under the NA Development program mentioned in the abstract.

Open Access Task Force - Implementation Team Progress Report

2026-02-14T03:08:05Z

Open Access Task Force - Implementation Team Progress Report Bebergal, Peter; Bourg, Chris; Dunn, Katharine H.; Nurnberger, Amy; Pierce, Marianna; Shirer, Karen; Weeramuni, Lindsey; Wilcoxson, Jaren D. This report outlines the progress to date of the Open Access Task Force Implementation Team (OATF-IT), first convened in December 2019 to prioritize, shepherd, and support the final recommendations (October 2019) of the MIT-wide Ad Hoc Task Force on Open Access to MIT’s Research (OATF). The OATF launched in 2017 to update and revise the Institute’s policies and practices around open publications, data, educational materials, and software.

Recommendations of the MIT Ad Hoc Task Force on Open Access to MIT's Research

2026-02-14T03:08:04Z

Recommendations of the MIT Ad Hoc Task Force on Open Access to MIT's Research Abelson, Harold; Bourg, Chris; Bebergal, Peter; Bond, Robert A.; Cheng, Herng Yi; Chuang, Isaac L.; Cummins, Christopher C; Fitzgerald, Deborah K; Jarzombek, Mark; Lindsay, Nick; Pollard, Tom Joseph; Reid, Jack; Shirer, Karen; Trout, Bernhardt L.; Vander Heiden, Matthew G.; von Hippel, Eric A; Wilcoxson, Jaren D.

Regulating Wait-Driven Requests in Queues

2026-04-14T03:20:27Z

Regulating Wait-Driven Requests in Queues Freund, Daniel; Hausman, David; Weng, Wentao The study of rational queueing has a long and distinguished history focused on individuals' preference to avoid waiting. Surprisingly, there are settings in which some potential arrivals (which we also refer to as requests) derive utility from waiting and disutility from service. Our primary example is the U.S. affirmative asylum process. In this context, applicants obtain a work permit while waiting for an asylum interview; hence, if the (expected) wait is long enough, then even an applicant who knows that their application will be denied and lead to deportation proceedings, may find it in their interest to apply and thus benefit from legally working during the wait. Similar dynamics could occur in other settings like content moderation in social networks. The common thread of these examples is the potentially self-exciting queue: when wait times are long, many arrivals are incentivized to join, and wait times become even longer. However, the system designer usually wants to avoid a large backlog. Indeed, the US Citizenship and Immigration Services (USCIS) mostly schedules asylum interviews in a Last-In-First-Out (LIFO) manner with the explicit goal of dissuading applicants with non-meritorious cases trying to exploit the long backlog. Despite this interesting scheduling choice in practice, and the potential prevalence of similar settings in other applications, the existing literature on rational queueing lacks frameworks to study the impact of wait-driven requests. Motivated by this gap in the literature, we formalize a dynamical system where in each round, a given scheduling policy and a realized request rate determine the wait time distribution in a fluid queueing system. Observing the expected benefit from waiting in one round, requests update their decisions, setting the request rate for the next round. Assuming a concave benefit function from waiting, alongside general conditions, we prove that, for minimizing the backlog, LIFO is most effective while First-In-First-Out (FIFO) is least effective among all work-conserving policies. Moreover, we show that the dynamical system exhibits metastability: for either FIFO or LIFO, the system converges to either a zero-wait or a congested equilibrium. Although some asylum practitioners support the use of LIFO, critics often admonish the real-world use of LIFO for its failure to maintain FIFO's order fairness: earlier requests should get earlier service. Our results demonstrate this trade-off between LIFO and FIFO. But we also show limitations of hybrid policies, which probabilistically follow either LIFO or FIFO, in navigating the trade-off between LIFO's efficiency and FIFO's fairness. Our work formalizes the concept of order fairness in queueing systems with abandonment and demonstrates that hybrid policies can be Pareto-dominated by LIFO: they may have both longer backlog and worse order fairness. Finally, we use real-world data on the scheduling of affirmative asylum applications to evaluate the change in fairness over the past 20 years under different policies. EC ’25, July 7–10, 2025, Stanford, CA, USA

DeepSeek Inside: Origins, Technology, and Impact

2026-04-14T03:20:37Z

DeepSeek Inside: Origins, Technology, and Impact Cusumano, Michael The release of DeepSeek V3 and R1 in January 2025 caused steep declines in the stock prices of companies that provide generative artificial intelligence (GenAI) infrastructure technology and datacenter services. These two large language models (LLMs) came from a little-known Chinese startup with approximately 200 employees compared to at least 3,500 employees for industry-leader OpenAI. DeepSeek seemed to have developed this powerful technology much more cheaply than previously thought possible. If true, DeepSeek had the potential to disrupt the economics of the entire GenAI ecosystem and the dominance of U.S. companies ranging from OpenAI to Nvidia.

Density-Dependent Graph Orientation and Coloring in Scalable MPC

2026-04-14T03:20:40Z

Density-Dependent Graph Orientation and Coloring in Scalable MPC Ghaffari, Mohsen; Grunau, Christoph This paper presents massively parallel computation (MPC) algorithms in the strongly sublinear memory regime (aka, scalable MPC) for orienting and coloring graphs as a function of its subgraph density. Our algorithms run in poly(log log n) rounds and compute an orientation of the edges with maximum outdegree O (α log log n) as well as a coloring of the vertices with O (α log log n) colors. Here, α denotes the density of the densest subgraph. Our algorithm's round complexity is notable because it breaks the [EQUATION] barrier, which applied to the previously best known density-dependent orientation algorithm [Ghaffari, Lattanzi, and Mitrovic ICML'19] and is common to many other scalable MPC algorithms. PODC ’25, Huatulco, Mexico

Privacy-Preserving Mechanisms for Coordinating Airspace Usage in Advanced Air Mobility

2026-04-14T03:20:23Z

Privacy-Preserving Mechanisms for Coordinating Airspace Usage in Advanced Air Mobility Maheshwari, Chinmay; Mendoza, Maria; Tuck, Victoria; Su, Pan-Yang; Qin, Victor; Seshia, Sanjit; Balakrishnan, Hamsa; Sastry, Shankar Advanced Air Mobility (AAM) operations are expected to transform air transportation while challenging current air traffic management practices. By introducing a novel market-based mechanism, we address the problem of on-demand allocation of capacity-constrained airspace to AAM vehicles with heterogeneous and private valuations. We model airspace and air infrastructure as a collection of contiguous regions (or sectors) with constraints on the number of vehicles that simultaneously enter, stay, or exit each region. Vehicles request access to airspace with trajectories spanning multiple regions at different times. We use the graph structure of our airspace model to formulate the allocation problem as a path allocation problem on a time-extended graph. To ensure that the cost information of AAM vehicles remains private, we introduce a novel mechanism that allocates each vehicle a budget of "air-credits" (an artificial currency) and anonymously charges prices for traversing the edges of the time-extended graph. We seek to compute a competitive equilibrium that ensures that: (i) capacity constraints are satisfied, (ii) a strictly positive resource price implies that the sector capacity is fully utilized, and (iii) the allocation is integral and optimal for each AAM vehicle given current prices, without requiring access to individual vehicle utilities. However, a competitive equilibrium with integral allocations may not always exist. We provide sufficient conditions for the existence and computation of a fractional-competitive equilibrium, where allocations can be fractional. Building on these theoretical insights, we propose a distributed, iterative, two-step algorithm that: 1) computes a fractional competitive equilibrium, and 2) derives an integral allocation from this equilibrium. We validate the effectiveness of our approach in allocating trajectories for the emerging urban air mobility service of drone delivery.

Meschers: Geometry Processing of Impossible Objects

2026-04-14T03:20:38Z

Meschers: Geometry Processing of Impossible Objects Dodik, Ana; Yu, Isabella; Chandra, Kartik; Ragan-Kelley, Jonathan; Tenenbaum, Joshua; Sitzmann, Vincent; Solomon, Justin Impossible objects, geometric constructions that humans can perceive but that cannot exist in real life, have been a topic of intrigue in visual arts, perception, and graphics, yet no satisfying computer representation of such objects exists. Previous work embeds impossible objects in 3D, cutting them or twisting/bending them in the depth axis. Cutting an impossible object changes its local geometry at the cut, which can hamper downstream graphics applications, such as smoothing, while bending makes it difficult to relight the object. Both of these can invalidate geometry operations, such as distance computation. As an alternative, we introduce Meschers, meshes capable of representing impossible constructions akin to those found in M.C. Escher's woodcuts. Our representation has a theoretical foundation in discrete exterior calculus and supports the use-cases above, as we demonstrate in a number of example applications. Moreover, because we can do discrete geometry processing on our representation, we can inverse-render impossible objects. We also compare our representation to cut and bend representations of impossible objects.

Learning-Augmented Competitive Algorithms for Spatiotemporal Online Allocation with Deadline Constraints

2026-04-14T03:20:40Z

Learning-Augmented Competitive Algorithms for Spatiotemporal Online Allocation with Deadline Constraints Lechowicz, Adam; Christianson, Nicolas; Sun, Bo; Bashir, Noman; Hajiesmaili, Mohammad; Wierman, Adam; Shenoy, Prashant We introduce and study spatiotemporal online allocation with deadline constraints (SOAD), a new online problem motivated by emerging challenges in sustainability and energy. In SOAD, an online player completes a workload by allocating and scheduling it on the points of a metric space (X, d) while subject to a deadline T. At each time step, a service cost function is revealed that represents the cost of servicing the workload at each point, and the player must irrevocably decide the current allocation of work to points. Whenever the player moves this allocation, they incur a movement cost defined by the distance metric d(•, •) that captures, e.g., an overhead cost. SOAD formalizes the open problem of combining general metrics and deadline constraints in the online algorithms literature, unifying problems such as metrical task systems and online search. We propose a competitive algorithm for SOAD along with a matching lower bound establishing its optimality. Our main algorithm, ST-CLIP, is a learning-augmented algorithm that takes advantage of predictions (e.g., forecasts of relevant costs) and achieves an optimal consistency-robustness trade-off. We evaluate our proposed algorithms in a simulated case study of carbon-aware spatiotemporal workload management, an application in sustainable computing that schedules a delay-tolerant batch compute job on a distributed network of data centers. In these experiments, we show that ST-CLIP substantially improves on heuristic baseline methods. SIGMETRICS Abstracts ’25, Stony Brook, NY, USA

Faraday Cage Estimation of Normals for Point Clouds and Ribbon Sketches

2026-04-14T03:20:47Z

Faraday Cage Estimation of Normals for Point Clouds and Ribbon Sketches Scrivener, Daniel; Cui, Daniel; Coldren, Ellis; Abulnaga, Mazdak; Bessmeltsev, Mikhail; Chien, Edward We propose a novel method (FaCE) for normal estimation of unoriented point clouds and VR ribbon sketches that leverages a modeling of the Faraday cage effect. Input points, or a sampling of the ribbons, form a conductive cage and shield the interior from external fields. The gradient of the maximum field strength over external field scenarios is used to estimate a normal at each input point or ribbon. The electrostatic effect is modeled with a simple Poisson system, accommodating intuitive user-driven sculpting via the specification of point charges and Faraday cage points. On inputs sampled from clean, watertight meshes, our method achieves comparable normal quality to existing methods tailored for this scenario. On inputs containing interior structures and artifacts, our method produces superior surfacing output when combined with Poisson Surface Reconstruction. In the case of ribbon sketches, our method accommodates sparser ribbon input while maintaining an accurate geometry, allowing for greater flexibility in the artistic process. We demonstrate superior performance to an existing approach for surfacing ribbon sketches in this sparse setting.

Draft Recommendations of the MIT Ad Hoc Faculty Task Force on Open Access to MIT's Research

2026-02-14T03:08:03Z

Draft Recommendations of the MIT Ad Hoc Faculty Task Force on Open Access to MIT's Research Abelson, Harold; Bourg, Chris; Bebergal, Peter; Bond, Robert A.; Cheng, Herng Yi; Chuang, Isaac L.; Cummins, Christopher C; Fitzgerald, Deborah K; Jarzombek, Mark; Lindsay, Nick; Pollard, Tom Joseph; Reid, Jack; Shirer, Karen; Trout, Bernhardt L.; Vander Heiden, Matthew G.; von Hippel, Eric A; Wilcoxson, Jaren D.; Finnie, Ellen; Dunn, Katharine H.

Networking Systems for Video Anomaly Detection: A Tutorial and Survey

2026-04-14T03:20:25Z

Networking Systems for Video Anomaly Detection: A Tutorial and Survey Liu, Jing; Liu, Yang; Lin, Jieyu; Li, Jielin; Cao, Liang; Sun, Peng; Hu, Bo; Song, Liang; Boukerche, Azzedine; Leung, Victor The increasing utilization of surveillance cameras in smart cities, coupled with the surge of online video applications, has heightened concerns regarding public security and privacy protection, which propelled automated Video Anomaly Detection (VAD) into a fundamental research task within the Artificial Intelligence (AI) community. With the advancements in deep learning and edge computing, VAD has made significant progress and advances synergized with emerging applications in smart cities and video internet, which has moved beyond the conventional research scope of algorithm engineering to deployable Networking Systems for VAD (NSVAD), a practical hotspot for intersection exploration in the AI, IoVT, and computing fields. In this article, we delineate the foundational assumptions, learning frameworks, and applicable scenarios of various deep learning-driven VAD routes, offering an exhaustive tutorial for novices in NSVAD. In addition, this article elucidates core concepts by reviewing recent advances and typical solutions and aggregating available research resources accessible at https://github.com/fdjingliu/NSVAD. Lastly, this article projects future development trends and discusses how the integration of AI and computing technologies can address existing research challenges and promote open opportunities, serving as an insightful guide for prospective researchers and engineers.

Analysis and Performance Evaluation of Blockchain Consensus Mechanisms for Network Sharing

2026-04-14T03:20:35Z

Analysis and Performance Evaluation of Blockchain Consensus Mechanisms for Network Sharing Zeydan, Engin; MANGUES-BAFALLUY, JOSEP; Arslan, Suayb; Turk, Yekta; Antevski, Kiril The growing demand for mobile data services has made it necessary to find efficient and cost-effective ways to share networks. Blockchain technology is a promising solution to the challenges of network sharing, such as interoperability, trust, and accountability. This paper presents a comprehensive classification and categorization of blockchain-based network sharing scenarios, highlighting their advantages and limitations. Seven network sharing scenarios are identified, ranging from centralized network sharing to fully decentralized spectrum sharing. The suitability of some selected blockchain consensus algorithms (namely Proof-of-Work (PoW) with Ethereum, Proof-of-Authority (PoA) with Ethereum, Practical Byzantine Fault Tolerance (PBFT) with Tendermint and Proof-of-Stake (PoS) with Cosmo) is assessed for selected scenarios through extensive evaluations. This paper also identifies gaps and opportunities in blockchain-based network sharing solutions, and presents future research directions.

MapTune: Versatile ASIC Technology Mapping via Reinforcement Learning Guided Library Tuning

2026-04-14T03:20:28Z

MapTune: Versatile ASIC Technology Mapping via Reinforcement Learning Guided Library Tuning Liu, Mingju; Robinson, Daniel; Li, Yingjie; Maximilian Kuehn, Johannes; Liang, Rongjian; Ren, Haoxing; Yu, Cunxi Technology mapping involves mapping logical circuits to a library of cells. Traditionally, the full technology library is used, leading to a large search space and potential overhead. Motivated by randomly sampled technology mapping case studies, we propose a MapTune framework that addresses this challenge by utilizing reinforcement learning to make design-specific choices during cell selection. By learning from the environment, MapTune refines the cell selection process, resulting in a reduced search space and potentially improved mapping quality. The effectiveness of MapTune is evaluated on a wide range of benchmarks, different technology libraries, and various technology mappers. The experimental results demonstrate that MapTune achieves higher mapping accuracy and reduces delay/area across diverse circuit designs, technology libraries, and mappers. The paper also discusses the Pareto-Optimal exploration and confirms the perpetual delay-area trade-off. Conducted on benchmark suites ISCAS 85/89, ITC/ISCAS 99, VTR8.0, and EPFL benchmarks, the post-technology mapping and post-sizing quality-of-results (QoR) have been significantly improved, with average Area-Delay Product (ADP) improvement of 16.56\% among all different exploration settings in MapTune. The improvements consistently remained for four different technologies (7nm, 45nm, 130nm, and 180 nm) with various mappers from both state-of-the-art open-source and commercial synthesis tools.

A Two-Stage Approach to Improve Poverty Mapping Spatial Resolution

2026-04-14T03:38:39Z

A Two-Stage Approach to Improve Poverty Mapping Spatial Resolution Salas, Joaquín; Zea-Ortiz, Marivel; Vera, Pablo; Wood, Danielle Global extreme poverty has fallen dramatically over the past two centuries, yet hundreds of millions remain impoverished, underscoring the need for scalable monitoring tools. In Mexico, poverty metrics are available only sporadically in terms of time and space (e.g., every 5 years at the municipal level), making it difficult for decision-makers to access reliable, up-to-date, and sufficiently detailed information, highlighting the need for higher-resolution, timely methods. To address this problem, we propose a two-stage approach that combines socioeconomic and Earth Observations-based data. Initially, a machine learning model maps census variables to official poverty indicators belonging to a multidimensional model, yielding fine-scale poverty estimates. A census-based model trained with eXtreme Gradient Boosting (XGBoost) achieved a determination coefficient (𝑅2) of approximately 0.842, indicating strong agreement with official poverty figures and providing high-resolution proxies. Afterward, we use features based on remote observations to predict these poverty estimates at a 469 m grid scale. In this case, advanced foundation models outperformed other machine learning (ML) approaches, achieving an 𝑅2 of 0.683. While foundation models enable more accurate, fine-scale poverty mapping and could accelerate poverty assessments, their use comes at a heavy price in terms of carbon emissions.

Effectiveness of a Participatory Voice Intervention on Psychological Well-Being Among Warehouse Workers: Results From the Fulfillment Center Intervention Study, United States, 2021‒2023

2026-04-14T03:38:43Z

Effectiveness of a Participatory Voice Intervention on Psychological Well-Being Among Warehouse Workers: Results From the Fulfillment Center Intervention Study, United States, 2021‒2023 Siebach, Kirsten F.; Diaz-Linhart, Yaminette; Kubzansky, Laura D.; Berkman, Lisa F.; Wang, Molin; Ge, Lin; Kowalski, Alexander M.; Rahmandad, Hazhir; Kelly, Erin L. Objectives. To examine whether a novel workplace intervention designed to increase worker voice can reduce psychological distress and improve emotional vitality at 6- and 12-months follow-up. Methods. We conducted a cluster-randomized trial in 16 fulfillment centers throughout the United States between 2021-2023. Data were collected at three time points; 2813 workers participated in at least one survey. Treated fulfillment centers established a new, participatory committee called the Health and Well-Being Committee (HaWC). We compared differences in psychological distress and emotional vitality and explored differential treatment effects by gender. Results. At baseline, moderate or severe psychological distress was 51%. Intervention sites had lower average psychological distress at the 6-month follow-up compared to control sites, with no significant differences at 12-month follow-up. Gender moderation analyses suggest the HaWC was particularly effective in reducing psychological distress among men at 6-month follow-up. Conclusions. Our findings suggest that opportunities for workers to share concerns to a committee of their peers tasked with identifying solutions can support mental health. Our study contributes important experimental evidence on workplace interventions that improve the well-being of low-wage U.S. populations.

ML Prediction Models to Identify Novel Beyond Visual Range Tactics and Error Analysis for DARPA AIR Agents

2026-02-13T05:07:40Z

ML Prediction Models to Identify Novel Beyond Visual Range Tactics and Error Analysis for DARPA AIR Agents Li, William; Castor, Jeremy This paper investigates the utility of using machine learning models to predict the outcome of simulated 2 vs. 2 Tactical Intercept engagements flown by autonomous agents in support of the DARPA Artificial Intelligence Reinforcements (AIR) program. We investigated the performance of four models: Feed Forward Neural Network, Random Forest, Extreme Gradient Boost, and Long Short Term Memory (LSTM). We examined their ability to successfully predict the outcomes of simulated engagements, tactical errors, and the execution of novel game plans by autonomous agents. The models were trained on 53 features pertaining to the agents including distance between aircraft, altitude, speed, missile availability, and other eventbased features from simulated runs. The LSTM model had the best performance towards the beginning of a run and was able to predict the correct winner with 87.8% accuracy only one minute into a run while the XGBoost model achieved the best overall performance with a 91.7% classification accuracy and an R² of 0.712. The XGB model was also able to correctly predict the winner of 84.7% of the runs after only seven minutes into the simulated engagement. These results demonstrate the utility and need for further investigation into other ML models potential to identify unique attributes and predictive analysis of more complex multi-agent scenarios that include additional criteria such as varying rules of engagement, incorporating acceptable levels of risk as well as other requirements fighter pilots must take into account during offensive and defensive operations needed to gain air superiority and support the objectives of the Joint Forces Commander.

Open Access at MIT and Beyond: A White Paper of the MIT Ad Hoc Task Force on Open Access to MIT's Research

2026-02-13T04:26:28Z

Open Access at MIT and Beyond: A White Paper of the MIT Ad Hoc Task Force on Open Access to MIT's Research Dunn, Katharine H.; Abelson, Harold; Bourg, Chris; Finnie, Ellen

Enabling New Science with the ALMA Phasing System - Phase 2 (APP2): An ALMA North America Development Project

2026-03-04T18:32:50Z

Enabling New Science with the ALMA Phasing System - Phase 2 (APP2): An ALMA North America Development Project Matthews, L. D.; Crew, G. B. This document provides a summary of activities undertaken as part of the Cycle 5 ALMA North America Development Project “Enabling New Science with the ALMA Phasing System - Phase 2 (APP2)”, whose period of performance extended from January 1, 2018 to August 31, 2024. APP2 provided a series of enhancements to ALMA’s very long baseline interferometry (VLBI) and phased array capabilities, leading to the introduction of submillimeter (Band 7) phasing and VLBI capabilities, a passive phasing mode, a Phased Array (pulsar) observing mode, a prototype spectral line VLBI capability, an improved method of handling baseband delays, and a number of other minor system enhancements. This report was prepared as a final report on the activities undertaken under the NA Development program mentioned in the abstract.

Mechanisms of Interaction Between Hydraulic and Natural Fractures in Shale Rocks

2026-02-13T03:10:59Z

Mechanisms of Interaction Between Hydraulic and Natural Fractures in Shale Rocks Arzuaga García, Ignacio Martín Understanding the interaction between hydraulically induced fractures and pre-existing natural fractures in geologic formations is key for optimizing subsurface energy systems that rely on fluid injection into fractured rocks. These include Enhanced Geothermal Systems (EGS), CO₂ sequestration, hydrogen storage in depleted reservoirs, unconventional oil and gas development in shale formations, and nuclear waste disposal, among others. In all these applications, controlling fracture propagation and interaction is essential for ensuring operational efficiency, safety, and long-term integrity of the system. This thesis presents a comprehensive experimental and theoretical investigation of hydraulic fracture (HF) interactions with natural fractures (NFs), using Opalinus Clayshale as a representative anisotropic material. The experimental work involved a series of hydraulic fracturing tests on Opalinus Clayshale specimens under controlled quasi-true-triaxial stress conditions, comparing normal and dried states. Novel monitoring techniques, including high-resolution imaging, high-speed video, acoustic emissions (AE), and pressure tracking, were employed to capture the fracturing process in real-time. Three dominant interaction modes (Crossing, Arrest, and Opening) were systematically characterized and linked to key parameters, including stress ratio, fracture geometry, and injection rates. A critical stress ratio (σ₁/σ₃) of approximately 20 was identified as the threshold for achieving fracture crossing under our experimental conditions: cohesionless, “open” natural fractures, with a low viscosity injection fluid, in a toughness-dominated regime. In dried specimens, high flaw pressurization rates were necessary to overcome matrix fluid loss and achieve crossing. To complement and interpret the experimental results, existing theoretical models were reviewed and implemented. Furthermore, a simplified version of the OpenT model (Chuprakov et al., 2014) was developed and applied for Opalinus Clayshale, incorporating stress, energy, friction, and permeability effects. By integrating laboratory results with theoretical frameworks, this thesis offers an integral approach to predictive understanding of fracture propagation in naturally fractured rocks, stating that not only the characteristics of the discontinuity or the far-field stresses involved in the process are important in determining the mechanism of interaction, but also the dynamic energy balance at the fracture tip, which is influenced by injection rate, fluid viscosity, and discontinuity properties. Overall, this thesis bridges the gap between laboratory experiments and theoretical models, advancing a more comprehensive understanding of fracture propagation in naturally fractured media. The findings highlight the importance of considering both mechanical and hydraulic parameters, particularly in low-viscosity, toughness-dominated regimes, for accurately predicting fracture behavior.

Satellite Drag and Sustainable Space Operations in a Dynamic Thermosphere

2026-02-13T03:11:02Z

Satellite Drag and Sustainable Space Operations in a Dynamic Thermosphere Parker, William E. Earth’s orbit has become increasingly congested and contested in recent years. The surge in launched payloads, combined with satellite failures, explosions, and collisions, has contributed to a large and growing population of orbital debris objects that can remain in orbit for decades, centuries, or longer. Meanwhile, decreasing launch costs and maturing satellite technology have created conditions favorable for rapid commercialization across orbital regimes, especially in low Earth orbit (LEO). Today, a small number of commercial entities operate the large majority of the world’s active satellites as part of proliferated LEO constellations. Sustaining productive activity in an increasingly crowded orbital environment has made satellite conjunction assessment and collision avoidance essential for safe operations. These efforts require not just accurate trajectory predictions, but also credible estimates of uncertainty. In LEO, variability in atmospheric drag is by far the dominant source of propagation error, often leading to deviations of several kilometers per day due to unpredictable solar and geomagnetic activity. Even over short timescales, trajectory prediction is challenging because existing forecasts exhibit limited predictive skill. Although forecast errors are often non-Gaussian and heteroscedastic, operational products are generally presented as deterministic, and atmospheric models rarely provide rigorous uncertainty characterization. This work introduces a new approach for probabilistic satellite drag modeling based on historical correlations between space weather drivers and satellite dynamics. Unlike traditional methods, it models satellite behavior directly without reconstructing thermospheric mass density or requiring detailed knowledge of satellite properties such as the ballistic coefficient. This end-to-end strategy offers substantial computational and operational advantages for many space domain awareness tasks. Capturing both trajectory predictions and their associated uncertainty is critical for enabling informed collision avoidance decisions, particularly during geomagnetic storms when current infrastructure frequently fails. Because the orbital lifetime of debris objects can exceed hundreds of years, population dynamics in space critically depend on long-term variability in the composition of Earth’s thermosphere. Rising concentrations of carbon dioxide and other greenhouse gases have caused warming in the troposphere but cooling and contraction in the upper atmosphere. This contraction decreases atmospheric density in LEO, reducing drag and extending the orbital lifetime of debris objects. Longer-lived debris populations pose a persistent collision hazard for all active satellites as long as they remain in orbit. Even natural events, such as a prolonged grand solar minimum, could further reduce thermospheric density and contribute to longer debris lifetime in LEO. With little ability to predict such an event, it is necessary to understand the potential consequences and to identify strategies that enable the continued safe and productive use of LEO. This work models the impact of such long-term environmental changes on limits for sustainable satellite deployments. LEO is a finite respource increasingly at risk of overexploitation. Conserving it and sharing it fairly requires that we first understand its fundamental capacity and our current occupation of that capacity. Some metrics have been proposed to measure the satellite carrying capacity of Earth’s orbit, but none have previously accounted for the potential influence of a changing space climate. This work develops new methods for defining carrying capacity as a common currency, enabling clear constraint-driven thresholds on activity and a better understanding of how existing and proposed missions consume available capacity. These new metrics provide insight into how environmental variability may affect the long-term sustainability of operations in LEO. Respecting and understanding this influence that the natural environment has on our collective ability to operate spacecraft in LEO is critical to preventing the overexploitation of this regime and protecting it for future generations.

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.

Essays in Financial Economics and Econometrics

2026-02-13T03:10:50Z

Essays in Financial Economics and Econometrics Orestes, Victor M. This thesis comprises three essays in finance and econometrics. The first two essays focus on the role of credit access and liquidity in shaping real firm outcomes. The first essay examines the transmission of modern monetary policy through corporate asset markets. Exploiting quasi-experimental variation in the Central Bank of Brazil’s collateral framework and implementing a novel dynamic regression discontinuity design, it shows that monetary policy can ease expected future borrowing constraints, reduce firms’ precautionary cash holdings, and stimulate employment. The second essay analyzes how receivables financing through factoring helps firms smooth cash flows. Using a shift-share instrument and matched administrative data, it finds that cheaper liquidity leads firms to rely more on permanent labor. The third essay develops a new method for distributional inference—nonparametric quantile mixture models. This framework can be applied to financial settings such as tail risk estimation and density forecasting, as well as to causal inference when the objective is to estimate the distributional effects of interventions. It is used here to quantify the heterogeneous wage effects of a major environmental disaster. The first chapter (joint with Luis Alvarez and Thiago Christiano Silva) studies how modern monetary policy tools, which increasingly operate through corporate asset markets, affect real firm outcomes. We exploit quasi-experimental variation from the inclusion of specific corporate debt instruments in the Central Bank of Brazil’s collateral framework and implement a novel dynamic regression discontinuity design. We find that eligibility increases firms’ debt issuance, modestly lowers spreads, and reduces cash holdings, reflecting a decline in precautionary savings. These effects translate into higher employment and greater supply chain liquidity. We interpret the mechanism through the lens of segmented financial markets: by relaxing firms’ expected future borrowing constraints, the policy acts as a persistent borrowing subsidy and liquidity injection. This encourages firms to reduce cash hoarding and expand production. Using a semi-structural framework calibrated to our reduced-form estimates, we find that an induced 0.8% borrowing subsidy leads to a 1% increase in debt issuance, a 0.2% reduction in cash holdings, and a 0.4% increase in the wage bill. The second chapter (joint with Thiago Christiano Silva and Henry Zhang) shows that firms experience large increases in sales and purchases after receiving cheaper liquidity. We focus on factoring, defined as the supplier-initiated sale of receivables. In Brazil, receivables funds (FIDCs) securitize receivables for institutional investors. By assembling a novel transaction-level dataset of factoring with other credit operations for all registered firms and FIDCs, we construct a shift-share instrument for factoring financing supply based on FIDC flows. We then use a novel combination of electronic payments, trade credit, and employer-employee matched data to estimate the impacts. A flow-induced increase in receivables demand reduces firms’ factoring interest rate. In response, firms demand more permanent labor and less temporary labor. In our model, these effects arise from factoring’s purpose of reducing cash inflow volatility, helping firms match inflows to outflows, which firms otherwise achieve at an efficiency cost through substitution across labor types. The third chapter (joint with Luis Alvarez) introduces nonparametric quantile mixture models as a computationally convenient and flexible alternative to standard nonparametric density mixtures, which are widely used in Statistics and Econometrics but face significant computational and inferential challenges. We propose a sieve estimator based on a generalized method of L-moments and develop a full inferential theory. In doing so, we contribute to the statistical literature by extending a numerical bootstrap method to high-dimensional settings. As a direct application of our theory, we provide the first inference method for the distributional synthetic controls of Gunsilius (2023), a novel tool for counterfactual analysis that previously lacked formal inference procedures. We apply this method to evaluate the effects of the Brumadinho dam collapse—a large-scale environmental disaster—on the local wage distribution. The results reveal substantial heterogeneity across the distribution, with evidence of displacement effects in which median-paying jobs are replaced by lower-wage contracts. JEL Codes: C1, E4, E5, G2, G3

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.

Signaling at the Tumor-Immune Interface in Glioblastoma

2026-02-13T03:10:43Z

Signaling at the Tumor-Immune Interface in Glioblastoma D'Souza, Alicia D. Glioblastoma (GBM) is a devastating brain cancer, and the standard of care has not changed in over 20 years. GBM tumors are composed of a milieu of cancer cells and innate immune cells, which are co-opted by the cancer cells to promote an anti-inflammatory environment. Despite tremendous success in immunotherapy in several cancers over the past 10 years, immunotherapies have failed to show efficacy in GBM. A systems biology approach to characterizing temporal changes in tumor-immune interface of glioblastoma could illuminate new strategies to activate an anti-tumor immune response by examining changes in cell signaling and antigen presentation. In the first part of my thesis, I investigated how macrophages alter their phenotype in response to tumor co-culture and how these changes are reflected at the level of the phosphoproteome. To characterize signaling changes in distinct cell populations during co-culture, I developed a method to preserve and analyze cell-type-specific signaling using fixation. This approach enables phosphoproteomic profiling of two interacting cell types, capturing dynamic signaling events with cell-type resolution. I applied this method to study co-cultures of glioblastoma (GBM) cells and primary human macrophages. When cultured together, GBM cells induced an anti-inflammatory, immunosuppressive phenotype in macrophages, mirroring features observed in the glioblastoma tumor microenvironment. Phosphoproteomic analysis revealed that this phenotypic shift was accompanied by distinct signaling alterations in macrophages, including the upregulation of ABL kinase activity. To test this finding, I treated macrophages with an ABL kinase inhibitor and observed a reduction in the anti-inflammatory phenotype, suggesting that ABL signaling plays a role in supporting immunosuppressive macrophage polarization. Furthermore, in a mouse model of GBM, treatment with an ABL kinase inhibitor led to a reduction in the abundance of anti-inflammatory macrophages within the tumor and was associated with a modest extension of survival. In the second part, I examined changes in antigen presentation and signaling in glioblastoma tumors in response to treatment with an oncolytic virus (OV). In patient derived tumor (PDX) models in mice, mice treated with OV have increased antigen presentation, pointing to the use of OV therapy to reshape the tumor micro-environment to a more inflammatory state. Finally, tissue obtained from serial biopsies of GBM patients treated with OV shows an increase in antigen presentation and both Class I and Class II MHC protein expression. We also observed an increase in interferon alpha and interferon gamma signaling pathways as well as early induction of apoptotic pathways. These findings highlight the role of therapeutics in altering the tumor microenvironment and potentially priming it for combination immunotherapies. This thesis explores the dynamic nature of the tumor and immune compartments in glioblastoma and underscores how therapies can act on the immune compartment to promote anti-tumor activity.

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.

Intercellular flow-mediated force relaxation measurement on the three-dimensional multicellular tissue

2026-02-13T03:10:55Z

Intercellular flow-mediated force relaxation measurement on the three-dimensional multicellular tissue Liu, Fan Three-dimensional (3D) multicellular tissues are prevailing over 2D monolayer or single cells; their mechanical properties like stiffness, surface tension, and viscosity have been shown to relate to diseases like fibrosis or tumor metastasis. Multicellular tissues have been traditionally modeled as a viscoelastic material due to their apparent shape rearrangement, which hardly considers the internal structure, including the extracellular matrix (ECM) and resulting intercellular water flow. These intercellular communications usually provide significant information on diseases such as tumor invasion, but immediate supporting evidence of this behavior is lacking. In this work, we investigate the bulk response of 3D multicellular tissues due to such intercellular flows and explore the related mechanism through a tailored micro-mechanics platform. Firstly, we design and establish a micro-mechanics platform based on the parallel plate compression (PPC) method. We adopt a precise micro-balance as the sensor to detect the force variation of the sample during compression. A piezo linear stage is incorporated to exert such tiny vertical displacement. Besides, a lateral microscope is designed to monitor the compression process instantaneously. This platform has proved to be applicable to various samples, including hydrogels, cell spheroids, and natural tissues or organs. Then, we propose the critical criterion, the size dependency of force relaxation time, to distinguish a material's properties, i.e., viscoelasticity and poroelasticity. For poroelastic material, the force relaxation is due to water redistribution; hence, the speed highly depends on the sample sizes. In contrast, for viscoelastic material, it is determined by the bulk material properties, thus independent of the size. We theoretically verify this criterion via Abaqus simulation and experimentally on classic poro-/visco-elastic materials with various dimensions. Next, we apply the size-dependency criterion on the 3D multicellular tissues to distinguish the poro-/visco-elasticity in this biomaterial. We take the PPC on multiple cell spheroids with different sizes through the platform. It is observed that the force relaxation times are linearly proportional to the size of all tested cell lines, demonstrating poroelasticity in our experimental time range. Intriguingly, we take tests on the natural organs of the mouse islets and find such linear correlation as well. Hence, both cultured spheroids and natural tissues are poroelastic. Finally, we explore the mechanism determining the poroelasticity inside the 3D multicellular tissues. By inhibiting the cell-cell junctions, we demonstrate the intercellular water flow through the extracellular gaps dominates this poroelastic force relaxation in the biomaterial. Further experiments show that the stiffness of the structure and the extracellular gaps inside the 3D multicellular tissues couple to contribute to the intercellular water flow, i.e., the stiffer the structure and/or the larger the gaps, the faster the water flows, thus quicker the force decays after compression. These findings highlight the fundamental role of intercellular water flow in the mechanical properties of 3D multicellular tissues. The designed micro-mechanics platform is also beneficial to research at the tissue level with micro-newton forces owing to the development of artificial organoids for early disease diagnosis and treatment.

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.

Language Comprehension, Production, and Reasoning in Humans and Neural Language Models

2026-02-13T03:10:48Z

Language Comprehension, Production, and Reasoning in Humans and Neural Language Models Eisape, Tiwalayo How closely do neural language models mirror human language processing, and what can this alignment teach us about cognition? This dissertation presents convergent evidence in comprehension, production, and reasoning that neural language models (LMs) can serve as productive instruments for understanding naturalistic human language use at scale. Studies 1-2 examine comprehension with complementary methods. First, Cloze Distillation—a novel method for aligning models with human next-word predictions—improves both language modeling and reading time prediction, demonstrating that LMs and humans make distinct, complementary predictions. Second, new methods for identifying syntactic information in LM hidden states demonstrate that models learn to implicitly represent incremental syntactic state. These probes also enable targeted interventions, allowing us to manipulate representations to resolve (or induce) temporary misinterpretations, confirming mechanistic understanding. While these studies demonstrate prediction’s role in comprehension, a complete account requires examining whether these mechanisms also shape how humans produce language in real-time. Study 3 analyzes a massive corpus of 2.3 million competitive typing events from TypeRacer.com, uncovering the first evidence of in-context predictability effects in this domain of production. Finally, Study 4 compares human and LM reasoning systematically—LMs achieve higher syllogistic reasoning accuracy than humans while still replicating several fine-grained human-like error patterns that are orthogonal to logical accuracy, including premise ordering effects. These converging findings reveal prediction as a fundamental mechanism in comprehension, production, and reasoning in both humans and LMs. While models achieve this through statistical learning rather than specialized cognitive architecture—often outperforming humans yet replicating their systematic biases—this alignment supports predictive processing theories of cognition. This work establishes LMs as scalable cognitive laboratories that can complement traditional experiments, and contributes psycholinguistically principled methods for understanding and controlling LMs.

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.

Product architectures for solar-powered drip irrigation (SPDI) systems in the Middle East and North Africa

2026-02-13T03:10:44Z

Product architectures for solar-powered drip irrigation (SPDI) systems in the Middle East and North Africa Grant, Fiona R. To feed the growing global population, agriculture production must be intensified using existing land and resources. Sustainable agriculture intensification is particularly important in the Middle East and North Africa (MENA), the most water-stressed region in the world. Solar-powered drip irrigation (SPDI) has the potential to increase water use efficiency and reduce fossil fuel use for irrigation. Despite these benefits, SPDI adoption is limited by its high investment cost and the misalignment between farmers' risk tolerance and broader sustainability goals. Past work has explored three areas of SPDI innovation: low-pressure drip emitters, system cost optimization, and precision irrigation control. This thesis integrates previous innovations in an end-to-end design process to generate SPDI architectures that are accessible to resource-constrained farmers. A market study was conducted to understand farmers' priorities and constraints and articulate SPDI value propositions for the target users. Stakeholder surveys were conducted in Jordan and Morocco for farms ranging from 1–130 hectares. Three market segments were identified, grouping farmers who face similar economic and knowledge barriers. While farmers generally prioritized irrigation reliability and low system costs, the observed variety in farm size, production volume, and technical expertise suggested that SPDI architectures must be tailored to each market segment. This thesis proposes an energetic framework that captures system parametric relationships to identify feasible SPDI design trade-offs. The optimized solar power systems were 14%–80% less expensive than conventionally-sized designs. Despite significant changes to the hydraulic operating parameters, the proposed SPDI architectures were as reliable as existing systems. For farms with long irrigation times, it was optimal to pair low-pressure drip emitters with an irrigation schedule that tracks the daily solar profile, termed “solar profile matching” (SPM), to maximize direct solar power use. The SPM schedule reduced system cost by minimizing the battery capacity. An economic analysis demonstrated that the optimal SPDI designs could be made cost-competitive with grid power through SPDI retrofit subsidies, which some local governments already support. Researchers and industry professionals could use the energetic framework and techno-economic analysis presented in this thesis to inform system design and policy decisions and promote SPDI adoption. Finally, this work created guidelines for designing a precision irrigation controller in resource-constrained markets. A controller was conceptualized to implement the SPDI-SPM architecture. The controller functional requirements and design specifications were iteratively defined with stakeholders, and a prototype was tested on two farms in the MENA region. The controller reduced water and energy use by up to 44% and 43%, respectively, while maintaining crop yield. However, the controller relied on battery power to execute the irrigation schedule. A yield loss sensitivity analysis found that using 72%–79% of the available solar energy on average, an increase of about 40% from the experiment SPM schedules, would have been sufficient to reliably irrigate with solar alone. The results suggest that, with software modifications, the proposed controller could eliminate the need for a battery and enable low-cost SPDI systems. If adopted, the proposed controller could make sustainable irrigation practices more accessible to farmers.

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.

Topics in Geometric Machine Learning

2026-02-13T03:10:46Z

Topics in Geometric Machine Learning Tahmasebi, Behrooz Recent advances and the widespread adoption of neural networks have revolutionized machine learning and artificial intelligence. These developments demand learning paradigms capable of processing data from diverse applications and sources. In structured domains such as molecules, graphs, sets, and 3D objects, as well as fields such as drug discovery, materials science, and astronomy, models must account for data structures. The emerging field of geometric machine learning has gained attention for enabling neural networks to handle geometric structures, unlocking novel solutions across scientific disciplines. Despite recent advances, theoretical gaps remain. This thesis aims to address these gaps by studying the benefits and limitations of leveraging geometric structures and symmetries in data. We explore sample complexity, generalization bounds, hypothesis testing for the presence of symmetries in data, time complexity of learning under symmetries, and regularization and optimization in symmetric settings. The goal is to build a robust theoretical framework that validates recent successes and sheds light on unexplored aspects, fostering future progress in geometric machine learning.

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.

Chemical exposures in drinking water: contaminant analysis and physicochemical behavior

2026-02-13T03:10:41Z

Chemical exposures in drinking water: contaminant analysis and physicochemical behavior Bugher, Nicolette A. Environmental chemical exposures pose an understudied risk to human health. The quality and accessibility of data on occurrence in the environment and physicochemical behavior of industrial chemicals are integral for accurate exposure risk assessment. In this dissertation, analytical chemistry techniques were developed and leveraged to characterize human exposures to contaminants in drinking water and improve methods for assessing such risks. The occurrence of organic industrial pollutants in domestic well waters was investigated, with a particular focus on the impacts of region-specific industrial activity (e.g., hydraulic fracturing), legacy pollution sites (e.g., Superfund sites), and geochemistry. The exposure risk to water contaminants of domestic well users was further interrogated by evaluating trends in contaminant concentrations resulting from the implementation and maintenance of in-home water treatment devices. The results show widespread, low-dose mixtures of organic pollutants, where the efficacy of removal by in-home water treatment varied by water contaminant class and maintenance frequency. Additionally, analytical methods were optimized to quantify a group of organic water contaminants (i.e., probable carcinogens, N-nitrosamines), improving method sensitivity and critically identifying false-positive interferences. Finally, methods were evaluated and deployed for the determination of physicochemical properties of N-nitrosamines. The results of which demonstrate gaps in existing experimental data, provide a valuable methodological intercomparison (two experimental and two computational approaches), and contribute novel partitioning data. This dissertation addresses gaps in occurrence data, analytical method sensitivity, and reliability of physicochemical parameters for risk assessment. The combination of method development and implementation enables the study of exposures to water contaminant mixtures at health-relevant concentrations, representative of prevalent exposure pathways.

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.

Optimizing Data Layouts for Evolving Cloud Table Storage

2026-02-13T03:10:39Z

Optimizing Data Layouts for Evolving Cloud Table Storage Sudhir, Sivaprasad Modern data analytics platforms increasingly adopt disaggregated architectures, storing data in cost-effective cloud object stores. While this approach enables a clean separation of concerns, allowing each layer to be independently managed and scaled, it introduces significant performance bottlenecks due to expensive data movement. Effective data layouts, which organize data to minimize unnecessary data reads, are thus critical to achieving high query performance. However, existing techniques typically rely on manually specified layouts, collect limited metadata, or lack mechanisms to dynamically adapt to changing data and workloads. This thesis investigates adaptive, metadata-rich, expressive data layouts for cloud table storage. First, we introduce Pando, a correlation-aware layout technique that leverages rich metadata on query predicates to significantly improve data skipping. Next, we propose CopyRight, a partial replication strategy that selectively replicates subsets of data and optimizes each replica differently, efficiently serving heterogeneous query patterns. Finally, we describe Self-Organizing Data Containers (SDCs), a practical table storage layer for the cloud that incrementally reorganizes complex data layouts based on changes in data and workload distributions.

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.

Quantum information science and underground facilities

2026-04-14T03:39:00Z

Quantum information science and underground facilities Formaggio, Joseph A As both nuclear physics and particle physics involve the quantum interactions of many sub-atomic particles, there has always existed a strong interplay between these fields and the study of quantum physics and quantum information systems (QIS). This interplay has accelerated in recent years, particularly with the emergence of new, highly sensitive technologies, nascent access to quantum computing environments at the O(10)-O(100)-bit scale, and the use of coherence and entanglement to enhance sensitivity to novel and exotic phenomena. One unusual area of interplay between the two disciplines that has recently emerged is the role of background radiation and background mitigation on highly sensitive systems such as qubits.

Reduced-order model to predict thermal conductivity of dimensionally confined materials

2026-04-14T03:38:54Z

Reduced-order model to predict thermal conductivity of dimensionally confined materials Hosseini, S Aria; Greaney, Alex; Romano, Giuseppe Predicting nanoscale thermal transport in dielectrics requires models, such as the Boltzmann transport equation (BTE), that account for phonon boundary scattering in structures with complex geometries. Although the BTE has been validated against several key experiments, its computational expense limits its applicability. Here, we demonstrate the use of an analytic reduced-order model for predicting the thermal conductivity in dimensionally confined materials, i.e., monolithic and porous thin films, and rectangular and cylindrical nanowires. The approach uses the recently developed “Ballistic Correction Model,” which accounts for materials' full distribution of phonon mean-free-paths. The model is validated against BTE simulations for a selection of base materials, obtaining excellent agreement. By furnishing a precise yet easy-to-use prediction of thermal transport in nanostructures, our work strives to accelerate the identification of materials for energy-conversion and thermal-management applications.

Parametric decay instabilities driven by high power helicon waves in DIII-D

2026-04-14T03:38:53Z

Parametric decay instabilities driven by high power helicon waves in DIII-D Porkolab, M; Pinsker, RI; DeGrandchamp, GH; Baek, SG; Compernolle, B Van; Denk, S; Petty, CC; Tang, SX; Thome, KE High power helicon waves (whistler or very high harmonic fast lower hybrid waves) at a frequency of 476 MHz are being tested for efficient off-axis current drive on DIII-D with the goal of demonstrating profile control in AT plasmas [1-4]. In agreement with earlier theoretical predictions, strong Parametric Decay Instability (PDI) has been observed at injected RF power levels in the range of 0.05-0.5 MW with corresponding electric fields of 10-30 kV/m [5,6]. The dominant driver of the PDI is the E×B and the polarization drift velocity which can drive ion cyclotron quasi-modes and lower hybrid (or IBW) sideband waves unstable [5,6]. Initial experimental results have been obtained with powers up to 0.3 MW showing evidence of strong PDI measured with high-frequency one-turn magnetic probes located at both the outboard and the inboard wall at frequencies set by the usual selection rules [7,8]. Here we review the appropriate analytic formulation to predict such instabilities and present numerical evaluation of frequencies and growth rates relevant to DIII-D plasma parameters. We also assess the convective thresholds for the PDIs, and compared them with experimental observations.

Experimental footprints of a water-rich depletion layer in the Herschel–Bulkley pipe flow of solidifying polyelectrolytes

2026-04-14T03:38:58Z

Experimental footprints of a water-rich depletion layer in the Herschel–Bulkley pipe flow of solidifying polyelectrolytes Nazari, B.; Moghimi, E.; Bousfield, D. W. A fundamental understanding of the transition from fluid-like to gel-like behavior is critical for a range of applications including personal care, pharmaceuticals, food products, batteries, painting, biomaterials, and concrete. The pipe flow behavior of a Herschel–Bulkley fluid is examined by a combination of rheology, ultrasound imaging velocimetry, and pressure measurements together with modeling. The system is a solution of 0.50 wt. % polyelectrolytes of sulfated polysaccharides in water that solidifies on cooling. Fluids with different ionic strengths were pumped at various rates from a reservoir at 80 °C into a pipe submerged in a bath maintained at 20 °C. The fluid velocity, pressure drop ΔP, and temperature were monitored. The same quantities were extracted by solving continuity, energy, and momentum equations. Moreover, the modeling results demonstrate that the local pressure gradient along the pipe dPdx|x is related to the local yield stress near the pipe wall τywall|x, which explains the variations of dPdx|x along the pipe. Experimental results show much lower values for ΔP compared to those from modeling. This discrepancy is exacerbated at higher ionic strengths and smaller flow rates, where fluid shows a higher degree of solidification. The tabulated experimental ΔP data against the solidification onset length Lonset (where the fluid is cool enough to solidify) along with the ultrasound imaging velocimetry associate these discrepancies between experiments and models to a depletion layer of ∼1 μm, reflecting the lubrication effects caused by the water layer at the wall.

Radiation pressure of radio frequency waves on turbulence in edge plasmas

2026-04-14T03:39:05Z

Radiation pressure of radio frequency waves on turbulence in edge plasmas Ram, Abhay K; Hizanidis, Kyriakos The scattering of radio frequency (RF) waves – lower hybrid and helicon waves – by a single cylindrical filament, embedded in a background plasma, is studied using a full-wave analytical theory. While a filament can affect the propagation of RF waves, the radiation force exerted by the waves can influence the filament. The force on a filament is determined using the Maxwell stress tensor. The radiation force can either pull the filament towards the RF source or push it away. The radiation force, in the two frequency ranges, is large enough to impact the motion of a filament and could be measured experimentally. Consequently, it may be possible to modify the edge turbulence using RF waves.

Replies to Moran, Gallois, and Bar-On and Johnson

2026-04-14T03:39:04Z

Replies to Moran, Gallois, and Bar-On and Johnson Byrne, Alex I am very grateful to Dorit Bar-On, Drew Johnson, André Gallois, and Dick Moran for their thoughtful commentaries. Bar-On, Gallois, and Moran are discussed extensively in Transparency and Self-Knowledge (hereafter T&SK), and their work has been an important source of inspiration for my own. In order to make my contribution to this symposium reasonably compact, I have not attempted to reply to every single point. (One especially notable omission is the alternative account of self-knowledge sketched by Bar-On and Johnson.) Instead, I have concentrated on the main objections.

The end of MAD? Technological innovation and the future of nuclear retaliatory capabilities

2026-04-14T03:38:56Z

The end of MAD? Technological innovation and the future of nuclear retaliatory capabilities Glaser, Charles L. This article motivates the special issue, explaining the new debate over whether emerging technologies – including small satellites, machine learning, cyber weapons, and quantum technologies – will enable major powers to undermine each others’ nuclear retaliatory capabilities. The first article analyzes key relevant emerging technologies. Following articles explore how emerging technologies will influence the vulnerability of mobile missiles, ballistic missile submarines, and nuclear command-and-control; and the ability of missile defenses against intercontinental range missiles. The final article explores China’s views on the requirements of nuclear deterrence. Overall, the articles suggest that U.S. prospects for achieving a damage-limitation capability are poor and declining.

Prototyping longevity services: Tech-driven or human-assisted service?

2026-04-14T03:39:02Z

Prototyping longevity services: Tech-driven or human-assisted service? Lee, Sheng-Hung; Coughlin, Joseph F; Yang, Maria The study investigates the design of longevity services through an experimental comparison of tech-driven and human-assisted service encounters, focusing on six key features: learnability, efficiency, safety, trustworthiness, confidence, and satisfaction. The controlled experiment, which involved 12 gender-balanced participants from Boston, USA, employed four qualitative methods, including surveys, the Think-aloud technique, semi-structured interviews, and transcript analysis supported by computer-assisted qualitative data analysis software (CAQDAS) and its AI-empowered coding function. The study concluded with two insights: 1. Tech-driven services can improve safety, trust, confidence, and satisfaction; and 2. both service encounters are context-sensitive, shaped by participants’ demographics, personality, culture, and environmental factors. Although the small sample size limits the study’s generalizability, the participants’ stories and perceptions offered valuable insights into their implicit needs and subtle behaviors in learning, experiencing, and addressing sensitive, private, and vulnerable topics related to longevity planning.

The Iraq Petroleum Company’s Infrastructure of “Desert Control” during the British Mandate in the Middle East

2026-04-14T03:38:51Z

The Iraq Petroleum Company’s Infrastructure of “Desert Control” during the British Mandate in the Middle East Freeman, Margaret This article discusses the infrastructure of the Iraq Petroleum Company (IPC) in the interwar British Mandatory Middle East as belonging to a larger British imperial project for “desert control” through architecture. Britain’s so-called “desert control” was, more accurately, a programme for control over the pastoralist Bedouin tribespeople who were the primary inhabitants of the Mandatory territories’ desert zones. This article identifies the two pillars of Britain’s “desert control” strategy: the use of Bedouin police forces, and the architectural annexation and restriction of water resources from Bedouin tribes. It argues that Mandate Britain’s “desert control” programme was replicated and adapted by the IPC for its own needs to protect its commercial infrastructural investment, the Iraq–Mediterranean Pipeline, in the British Mandatory territories. It compares two building typologies, the Mandate’s “desert outposts” and the IPC pipeline’s pumping stations, as sites where the Bedouin were alternately welcomed into and excluded from imperial and commercial projects in the interest of controlling them.

Surrogate modelling of surface roughness for asphalt pavements using artificial neural networks: a mechanistic-empirical approach

2026-04-14T03:38:49Z

Surrogate modelling of surface roughness for asphalt pavements using artificial neural networks: a mechanistic-empirical approach Li, Haoran; AzariJafari, Hessam; Kirchain, Randolph; Santos, João; Khazanovich, Lev Pavement surface smoothness (or roughness) is crucial for traffic safety, driving comfort, and fuel efficiency. As a widely applied roughness indicator, an accurate forecasting of the International Roughness Index (IRI) and its deterioration is essential for the design, maintenance, and management of asphalt pavements. Previous studies have used field measurement data or AASHTOWare Pavement ME Design simulations for the development of machine learning (ML) models to streamline the IRI modelling. However, these models frequently lack the accuracy and robustness of the measurement data or high-fidelity computational simulations they are intended to surrogate. To address this issue, we employed a new adaptive sampling technique to generate an informative yet efficient pavement damage database from Pavement ME simulations. Utilising Artificial Neural Networks (ANNs), we engineered two types of surrogate ML models: (a) Model I, an ANN-based IRI predictive model, and (b) Model II, a hybrid model combining ANN-based predictions of rutting, fatigue damage, and thermal cracking with closed-form relationships between these indicators and IRI. Our findings show that Model II outperforms Model I in IRI modelling accuracy both globally and locally. Moreover, Model II matches IRI simulations of Pavement ME while providing enhanced efficiency and adaptability to a broader spectrum of design considerations.

Honoring practices of community-based educators: lessons learned from the collaborative design of a creative mobile app

2026-04-14T03:38:41Z

Honoring practices of community-based educators: lessons learned from the collaborative design of a creative mobile app Rusk, Natalie; Jain, Rupal; Martin, Caitlin K.; Roque, Ricarose; Freitas, João Adriano; Molaodi, Linford This paper shares reflections and stories from a collaborative design process between the Lifelong Kindergarten group at the MIT Media Lab and a global network of community-based educators to develop a creative coding app called OctoStudio, which supports children and families to create and share interactive projects on mobile devices. The app design is grounded in practices that community-based educators who are primarily from the Global South have developed around strengths, needs, and interests of children and their communities, as well as constraints and affordances of local infrastructure. We use the lens of minimal computing – which focuses on community context and constraints in decisions about technology – to describe our collaborative work on OctoStudio. We describe trade-offs involved in the design decisions, and highlight insights from the process of collaboration to develop tools and practices that are more responsive and meaningful to communities who are often excluded from design decisions that impact them.

Expected Constant Round Byzantine Broadcast under Dishonest Majority

2026-02-12T03:06:52Z

Expected Constant Round Byzantine Broadcast under Dishonest Majority Wan, Jun; Xiao, Hanshen; Shi, Elaine; Devadas, Srinivas Byzantine Broadcast (BB) is a central question in distributed systems, and an important challenge is to understand its round complexity. Under the honest majority setting, it is long known that there exist randomized protocols that can achieve BB in expected constant rounds, regardless of the number of nodes n. However, whether we can match the expected constant round complexity in the corrupt majority setting --- or more precisely, when f > n/2 --- remains unknown, where f denotes the number of corrupt nodes. In this paper, we are the first to resolve this long-standing question. We show how to achieve BB in expected O((n/(n-f))2) rounds. Our results hold under a weakly adaptive adversary who cannot perform ``after-the-fact removal' of messages already sent by a node before it becomes corrupt. We also assume trusted setup and the Decision Linear (DLIN) assumption in bilinear groups.

Minimum Plane Bichromatic Spanning Trees

2026-02-12T03:06:50Z

Minimum Plane Bichromatic Spanning Trees Akitaya, Hugo; Biniaz, Ahmad; Demaine, Erik; Kleist, Linda; Stock, Frederick; T?th, Csaba D. For a set of red and blue points in the plane, a minimum bichromatic spanning tree (MinBST) is a shortest spanning tree of the points such that every edge has a red and a blue endpoint. A MinBST can be computed in O(n log n) time where n is the number of points. In contrast to the standard Euclidean MST, which is always plane (noncrossing), a MinBST may have edges that cross each other. However, we prove that a MinBST is quasi-plane, that is, it does not contain three pairwise crossing edges, and we determine the maximum number of crossings. Moreover, we study the problem of finding a minimum plane bichromatic spanning tree (MinPBST) which is a shortest bichromatic spanning tree with pairwise noncrossing edges. This problem is known to be NP-hard. The previous best approximation algorithm, due to Borgelt et al. (2009), has a ratio of O(sqrt(n)). It is also known that the optimum solution can be computed in polynomial time in some special cases, for instance, when the points are in convex position, collinear, semi-collinear, or when one color class has constant size. We present an O(log n)-factor approximation algorithm for the general case.

Nested Dissection Meets IPMs: Planar Min-Cost Flow in Nearly-Linear Time

2026-02-12T03:07:19Z

Nested Dissection Meets IPMs: Planar Min-Cost Flow in Nearly-Linear Time Dong, Sally; Gao, Yu; Goranci, Gramoz; Lee, Yin Tat; Sachdeva, Sushant; Peng, Richard; Ye, Guanghao We present a nearly-linear time algorithm for finding a minimum-cost flow in planar graphs with polynomially bounded integer costs and capacities. The previous fastest algorithm for this problem is based on interior point methods (IPMs) and works for general sparse graphs in O(n1.5 polylog n)) time [Daitch-Spielman, STOC'. Intuitively, ?(n1.5) is a natural runtime barrier for IPM-based methods, since they require ?n iterations, each routing a possibly-dense electrical flow. To break this barrier, we develop a new implicit representation for flows based on generalized nested dissection [Lipton-Rose-Tarjan, SINUM'79] and approximate Schur complements [Kyng-Sachdeva, FOCS'. This implicit representation permits us to design a data structure to route an electrical flow with sparse demands in roughly ?n update time, resulting in a total runtime of O(n polylog n). Our results immediately extend to all families of separable graphs.

Enhancing Electric Vehicle Security and Privacy through Decentralized Identity Management

2026-02-12T03:06:49Z

Enhancing Electric Vehicle Security and Privacy through Decentralized Identity Management Aydeger, Abdullah; Zeydan, Engin; Mangues-Bafalluy, Josep; Arslan, Suayb; Turk, Yekta In the next decade, electric vehicles (EVs) are expected to contribute to reducing climate change and transforming road mobility significantly. However, the security and privacy of EV charging systems present considerable challenges that need to be addressed. This paper introduces a novel approach by integrating blockchain-based self-sovereign identity (SSI) to enhance the security and privacy of EV charging systems. By leveraging decentralized and immutable nature of blockchain, the proposed SSI framework can ensure secure and private data exchanges between EVs, charging stations, and backend systems. This three-way integration addresses the vulnerabilities identified in existing EV charging methods, such as conductive, inductive, and battery swapping, and complies with cybersecurity regulations like UNECE R155. This paper provides a comprehensive analysis, practical case study, and evaluation of the security and privacy enhancements achieved through the proposed SSI framework, offering valuable insights for industry professionals and researchers. We have conducted extensive end-to-end testing to evaluate the performance of our blockchain-based SSI framework in the EV charging ecosystem, focusing on identity verification, credential management and service orchestration. The results show that the system enables fast wallet creation, efficient metadata retrieval and low-latency service deployment, ensuring seamless identity management and service orchestration.

Local Distributed Rounding: Generalized to MIS, Matching, Set Cover, and Beyond

2026-02-12T03:06:46Z

Local Distributed Rounding: Generalized to MIS, Matching, Set Cover, and Beyond Faour, Salwa; Ghaffari, Mohsen; Grunau, Christoph; Kuhn, Fabian; Rozho?, V?clav We develop a general deterministic distributed method for locally rounding fractional solutions of graph problems for which the analysis can be broken down into analyzing pairs of vertices. Roughly speaking, the method can transform fractional/probabilistic label assignments of the vertices into integral/deterministic label assignments for the vertices, while approximately preserving a potential function that is a linear combination of functions, each of which depends on at most two vertices (subject to some conditions usually satisfied in pairwise analyses). The method unifies and significantly generalizes prior work on deterministic local rounding techniques [Ghaffari, Kuhn FOCS'21; Harris FOCS'19; Fischer, Ghaffari, Kuhn FOCS'17; Fischer DISC' to obtain polylogarithmic-time deterministic distributed solutions for combinatorial graph problems. Our general rounding result enables us to locally and efficiently derandomize a range of distributed algorithms for local graph problems, including maximal independent set (MIS), maximum-weight independent set approximation, and minimum-cost set cover approximation.

Feasibility Study on Heat Pipes for Radio Frequency Antennas

2026-02-12T03:07:33Z

Feasibility Study on Heat Pipes for Radio Frequency Antennas Jung, Minuk; Watterson, Amy; Wallace, Gregory M The applicability of a heat pipe is investigated for the cooling of radio frequency antennas in fusion reactors operating at high temperatures. A heat pipe is a passive cooling device that transfers a large amount of heat through the liquid-vapor phase change and pumps the working fluid by the surface tension of the wick structure without moving parts. As the heat pipe is expected to operate near 1000 K, refractory metals or ceramics should be used for wall materials, and liquid metals are primarily considered as the working fluid. However, liquid metals are electrically conductive, and the strong magnetic field perpendicular to the flow direction imposes significant magnetohydrodynamic (MHD) flow resistance in addition to viscous friction, which impairs heat transfer performance. Since a strong magnetic field is inevitable in magnetic confinement fusion reactors, materials with low electrical conductivity should be applied to wall coatings to reduce the MHD effect. Heat flux limitations at a magnetic field of 10 T and a condenser coolant temperature of 773 K are estimated using COMSOL multiphysics, which can capture the fully developed MHD wick flow, laminar/turbulent vapor flow, and heat transfer simultaneously. For simplicity, the generic heat pipe geometry of a straight horizontal cylinder with a length of 2 ft (0.6096 m) is employed. Optimal geometrical parameters are evaluated to meet radial evaporator/condenser heat fluxes greater than 0.1 MW/m2, even under a strong MHD effect.

Regional incidence and persistence of high-growth firms: testing ideas from the entrepreneurial ecosystems literature

2026-02-12T03:07:25Z

Regional incidence and persistence of high-growth firms: testing ideas from the entrepreneurial ecosystems literature Coad, Alex; Domnick, Clemens; Santoleri, Pietro; Srhoj, Stjepan Policymakers and scholars often assume that a higher incidence of high-growth firms (HGFs) is synonymous with vibrant regional economic dynamics, and that HGF shares are persistent over time as entrepreneurial ecosystems (EEs) have slowly changing features. In this paper we test these hypotheses, which are deeply rooted in the EE literature. Results do not provide strong support for the hypothesis that more developed regions feature higher HGF shares. We do find evidence consistent with HGF shares displaying persistency over time. However, we show that more developed regions do not have higher persistence in their HGF shares, and that the strength in persistence does not increase across the HGFs distribution, which does not support path-dependency as the main mechanism behind the observed persistence. Overall, we call for a more nuanced interpretation of both regional HGF shares and the EEs literature.

Knives out: response to critics

2026-02-12T03:07:34Z

Knives out: response to critics Khoo, Justin Writing a book can feel like a solitary endeavor. You labor for (in my case) years, sometimes talking about parts of the project with others, but mostly toiling alone to work out the consequences of commitments you made months and years prior. I'm grateful for the opportunity to engage with three brilliant interlocutors about these ideas, which for so long seemed to matter to no one besides myself (and maybe my publisher).

Toward Ontological Alignment: Coordinating Student Ideas with the Representational System of a Computational Modeling Unit for Science Learning

2026-02-12T03:07:10Z

Toward Ontological Alignment: Coordinating Student Ideas with the Representational System of a Computational Modeling Unit for Science Learning Wagh, Aditi; Rosenbaum, Leah F.; Fuhrmann, Tamar; Eloy, Adelmo; Blikstein, Paulo; Wilkerson, Michelle Computational modeling tools present unique opportunities and challenges for student learning. Each tool has a representational system that impacts the kinds of explorations students engage in. Inquiry aligned with a tool’s representational system can support more productive engagement toward target learning goals. However, little research has examined how teachers can make visible the ways students’ ideas about a phenomenon can be expressed and explored within a tool’s representational system. In this paper, we elaborate on the construct of ontological alignment—that is, identifying and leveraging points of resonance between students’ existing ideas and the representational system of a tool. Using interaction analysis, we identify alignment practices adopted by a science teacher and her students in a computational agent-based modeling unit. Specifically, we describe three practices: (1) Elevating student ideas relevant to the tool’s representational system; (2) Exploring and testing links between students’ conceptual and computational models; and (3) Drawing on evidence resonant with the tool’s representational system to differentiate between theories. Finally, we discuss the pedagogical value of ontological alignment as a way to leverage students’ ideas in alignment with a tool’s representational system and suggest the presented practices as exemplary ways to support students’ computational modeling for science learning.

Stand Up and Split. Desiring Desertion in Jean Giono and Emmanuelle Lambert

2026-02-12T03:07:22Z

Stand Up and Split. Desiring Desertion in Jean Giono and Emmanuelle Lambert Perreau, Bruno To face the powers that be, contemporary French writer Virginie Despentes proposes a straightforward solution: “stand up and split!” But where to go and with whom? How do we stop the proliferation of contested norms if we clear the decks? In a context of ecological crisis, desiring desertion is not rare even if we have only one world to inhabit. This article analyzes the desire to desert from two texts: Le Déserteur et autres récits (Citation1966 [1973]) by Jean Giono and La Désertion (Citation2018a) by Emmanuelle Lambert. It demonstrates that desertion does not make a clean sweep of the past but rather accepts the desert at the heart of existence. That is, both presence and disappearance.

Fuel Behavior Implications of Reactor Design Choices in Pressurized Water SMRs

2026-02-12T03:07:18Z

Fuel Behavior Implications of Reactor Design Choices in Pressurized Water SMRs Halimi, Assil; Shirvan, Koroush Small pressurized water reactors can feature boron-free operation, natural circulation mode, reduced-height assemblies, and/or long refueling cycles. This paper attempts to explore core design optimization for each of these design evolutions. In consequence, five core design layouts are developed incorporating boron-free operation with continuous control rod insertion, natural circulation with low burnup/low power density design, natural circulation with high burnup/low power density design, forced circulation with standard core power density design, and forced circulation with high power density design. These cores’ performance is compared to a standard four-loop pressurized water reactor. The design process aims to improve the fuel cycle cost under safety constraints through core design optimization using the CASMO4E/SIMULATE3 reactor physics codes and the FRAPCON4.1 fuel performance assessment tool. Core modeling assumes standard 17×17 PWR fuel assemblies loaded with low enriched uranium up to 5 wt% or low enriched uranium plus (i.e. below 10 wt% enrichment) pellets with gadolinium oxide as the burnable poison. Satisfactory core and fuel performances are obtained for all the designed cores under steady state and considered overpower transients. For low power density operation, long cycle lengths are achieved reaching 2.5-year and 5-year cycles, and peak rod-average burnup is pushed to 83 MWd/kgU. Other cycle lengths are maintained at 18 months. Boron-free operation exhibits the ability to achieve longer cycle lengths at the cost of higher peaking factors leading to high local power and fuel temperatures, which prevents sizable power uprates and is deemed uneconomical. Fuel assembly height reduction allows coolant velocity retrofit, which enables higher core power density without violating the structural integrity of the fuel assembly. As a result, a core power density of 123 kW/L is reached where total cladding hoop strain becomes the limiting parameter.

Shades of authoritarian digital sovereignty: divergences in Russian and Chinese data localisation regimes

2026-02-12T03:07:21Z

Shades of authoritarian digital sovereignty: divergences in Russian and Chinese data localisation regimes Khasanova, Liliya; Tai, Katharin The concept of sovereignty is now referred to in cyberspace-related policy by a range of governments, both authoritarian and democratic. At the same time, the most prominent proponents of state – or sovereignty-centric models of internet governance are Russia and China, whose positions are often characterised as a shared ‘Sino-Russian’ model. This paper subjects this idea of a shared Sino-Russian approach to empirical scrutiny by conducting a comparative analysis of rules, regulations and policies on data localisation in both countries. By delimiting the research question to regulations on data localisation and cross-border data transfers in both countries, we identify an important set of similarities and differences between the Russian and Chinese approaches. They share some features associated with authoritarian regimes, such as uncertainty around the selective enforcement of broadly formulated rules and a centralised assessment of outbound data transfers. However, we also find significant differences in the level of institutional centralisation, degrees of responsiveness within the policymaking process, and economic logics driving data localisation and cross-border transfer regulations. Based on these findings, we argue that despite a perception that Russia and China adhere to a similar model of authoritarian digital sovereignty, there are significant disparities in their data localisation regimes.

Race, profit, and algorithms: Neighborhood-level analysis of iBuyers’ profit margin

2026-02-12T03:07:08Z

Race, profit, and algorithms: Neighborhood-level analysis of iBuyers’ profit margin So, Wonyoung iBuyers are firms that use automated valuation models (AVMs), streamline home buying processes, and provide all-cash offers to purchase homes. Although the previous literature has explored the roles and limitations of iBuyers in the housing market, empirical research on the racial implications of these algorithmic home buying processes remains understudied. Using a spatial lag model, this study shows the spatial clustering of iBuyer profit margins, that iBuyers gain more profits when they resell to individuals than institutions, and that some iBuyers have a statistically significant correlation between their profit margins and the proportion of marginalized racial groups within a census tract, while controlling for individual housing characteristics, neighborhood housing quality and demand, and neighborhood amenities and socioeconomic factors. These findings suggest that the more adeptly iBuyers can forecast housing values, the greater the potential to maximize profits from homeowners in communities of color. Consequently, this research contributes to the understanding of how technological mechanisms operate within a purportedly race-neutral framework and advocates for the development and deployment of algorithmic systems guided by the principles of antisubordination, rather than relying solely on notions of “fairness” and anticlassification.

Powering Through the Turn: Finding Time for Concept Exploration Before Industry Stagnation

2026-02-12T03:07:29Z

Powering Through the Turn: Finding Time for Concept Exploration Before Industry Stagnation Noble, Connery; Cameron, Bruce G This study examines how the tension between exploration and exploitation affects early-stage development within the engineering teams of large corporations. Using survey data collected from over 900 system engineers and managers, it was observed that exploration decreased as an organization’s market growth declined, but dire market projections prompted a refocus on exploration. In addition, engineers routinely desire more concept exploration time than they perceive that they have available. The authors argue that engineering teams should more intentionally consider their innovation strategy, and that companies with stagnant market growth should invest in concept exploration before they get to a period of market decline.

Vital Biodiversity Systems: A Companion Paper

2026-02-12T03:01:00Z

Vital Biodiversity Systems: A Companion Paper Westerlaken, Michelle; Bischoff, Amanda; Mertens, Krishen; Pertusa, Alejandro Regenerative and diverse ecosystems are essential to living futures. Healthy ecosystems are more resilient to climate change and are better able to absorb and store carbon. Communities and corporations worldwide are currently establishing how environmental data can best support these processes. This Companion Paper provides the rationale for the Design Brief and synthesizes findings from four years of research across academia, corporate sustainability teams, and community stakeholders. It argues that biodiversity data systems are not neutral repositories but designed artefacts that embed assumptions and values. To redirect innovation, the paper supports the Brief by expanding on its key design principles, criteria, constraints, and propositions that together chart a pathway for ‘vital biodiversity systems’: platforms that embed the aliveness of the ecosystems they mediate.

Molecular dynamics simulations and structural bioinformatics of bacterial integral alpha-helical membrane enzymes and their AlphaFold2-predicted water-soluble QTY analogues

2026-02-12T03:07:30Z

Molecular dynamics simulations and structural bioinformatics of bacterial integral alpha-helical membrane enzymes and their AlphaFold2-predicted water-soluble QTY analogues Sajeev-Sheeja, Akash; Karagöl, Alper; Karagöl, Taner; Zhang, Shuguang The study of integral membrane proteins has long been challenging because of their poor solubility in aqueous environments. We previously used QTY code to enhance the hydrophilicity in alpha-helices, beta-barrels, and monoclonal antibodies by systematically pairwise replacing the hydrophobic amino acids L (leucine) to Q (glutamine), V(valine)/I(isoleucine) to T (threonine), and F (phenylalanine) to Y (tyrosine). The superposed AlphaFold2-predicted structures of alpha-helical transmembrane enzyme variants with >41% amino acid substitutions displayed remarkable similarity to native structures (RMSD 0.3Å-0.7 Å). We conducted molecular dynamics (MD) simulations, which revealed that, even in the absence of a lipid bilayer, the QTY-modified enzymes retained stable dynamics comparable to their membrane-bound forms. Root mean square fluctuation (RMSF) values remained below 2 Å across the transmembrane and core regions, and residue-wise root mean square deviation (RMSD) values were minimal (<3 Å), indicating that the structural integrity of the protein core was largely preserved. These results suggest that the QTY variants, designed for soluble environments, effectively mimic the stability and conformational rigidity of natural membrane-bound enzymes. Our findings show that the QTY code is a simple method for designing water-soluble membrane protein enzymes in different biological scenarios, and it may encourage further experiments to validate our structural bioinformatics research.

City of ‘social saints’: the role of place in driving impact entrepreneurship in Turin, Italy

2026-02-12T03:07:27Z

City of ‘social saints’: the role of place in driving impact entrepreneurship in Turin, Italy Burke, Mary Kathleen; Sydow, Alisa; Torchia, Daniel; Corazza, Laura This paper theorizes impact entrepreneurship (IE) in relation to place by examining dynamics at the individual, community, and organizational levels. While existing IE literature emphasizes entrepreneurship aimed at addressing grand challenges, it often adopts an aggregate view that overlooks how locally embedded entrepreneurs access and mobilize social and economic resources. We introduce a novel, multidimensional framework to show how sense of place, community embeddedness and IE interrelate to shape approaches to current social/environmental challenges. Adopting a qualitative approach, this paper investigates how different actors in Turin, Italy, contribute to IE through building on a legacy of social sector institutions. We find that individuals identifying with a place-based vocation of social impact find communities with a shared volition to work together and across organizations. We contribute to understanding how individuals’ senses of place can be leveraged into wider community efforts to support IE in the region. The paper advances the IE concept to account for the individual perspectives influencing local organizing practices and visions for IE rooted in place.

Plane Delivery: Towards a Physical Grammar for Large-Scale Digital Fabrication

2026-02-12T03:07:12Z

Plane Delivery: Towards a Physical Grammar for Large-Scale Digital Fabrication Sass, Lawrence There will come a day when computers and robots will participate regularly in designing, fabricating, and delivering homes as customized kits of parts (Sass Citation2008). They will not replace builders. Instead, one possible future is where computers and robots operate as intelligent assistants, discovering, reasoning, and inferring the best solutions using large language models (LLMs). This language will be vector-based on points, lines, and planes of the type Stiny described (Stiny Citation2006). A standard design and builder language is a first step towards automation. The proposed system is of a Lego-style approach to physical house production, used to manage costs, enhance design variety, improve design quality, and, most importantly, facilitate building.

Techno-statecraft and industrial strategy: semiconductor development in Arizona

2026-02-12T03:07:24Z

Techno-statecraft and industrial strategy: semiconductor development in Arizona Kollar, Justin The resurgence of U.S. industrial strategy discourse is not a centralised return of the state but a territorially fragmented form of techno-statecraft. This article analyzes Arizona's semiconductor expansion as a case in which subnational actors – agencies, utilities, universities, and developers – mobilise infrastructure, land-use policy, and regulatory coordination to attract global capital. Rather than a coherent national plan, Arizona's strategy reflects speculative governance oriented toward risk absorption and territorial readiness. The article situates this conjuncture within longer histories of militarised growth and infrastructural overbuild, contributing to debates on state capitalism, industrial strategy, and the spatial politics of techno-industrial transformation.

Ideology, Equity, and Structure: Comments on Tzu-wei Hung’s ‘Equity and Marxist Buddhism’

2026-02-12T03:07:35Z

Ideology, Equity, and Structure: Comments on Tzu-wei Hung’s ‘Equity and Marxist Buddhism’ Haslanger, Sally In his essay, ‘Equity and Marxist Buddhism’, Tzu-wei Hung argues that Marxist Buddhism brings a commitment to social justice together with a distinctive form of virtue theory. In my commentary, I raise several questions from a Marxian perspective: (1) Might it be argued that Marxist Buddhism is (in the critical sense) ideological (similar to religion) because the spiritual goal of ‘transcendence’ distracts us from the need to fight for emancipation? (2) Can justice as equity be achieved by promoting individual altruism? (3) Aren’t both mainstream accounts of justice and Marxist Buddhism aspirational and so need to rely on non-ideal theory to achieve justice?

A data-driven and context-aware approach for demand forecasting in the beverage industry

2026-02-12T03:07:07Z

A data-driven and context-aware approach for demand forecasting in the beverage industry Ma, Benedict Jun; Jackson, Ilya; Huang, Maggie; Villegas, Sebastian; Macias-Aguayo, Jaime Accurate demand forecasting is essential for logistics and supply chain management as it enables efficient inventory planning, reduces operational costs, and ensures high service levels across the network. However, in practice, diverse demand patterns of items make this task challenging, and a one-size-fits-all forecasting approach is inadequate. This paper proposes a data-driven and context-aware forecasting framework and tests it by using both endogenous data from a large private-label beverage manufacturer and exogenous features (such as holidays and temperature). Our method begins by classifying SKUs based on demand volume, volatility, and intermittency, and then refining the derived clusters by taking volume distribution into account. Totally, we obtain four distinct clusters, which are (i) stable and high volume, (ii) stable with low volume, (iii) erratic and intermittent, and (iv) lumpy. To explore the appropriate forecasting models for different demand patterns, we employ statistical models (exponential smoothing, ARIMA, and Croston), machine learning models (XGBoost), deep learning models (TiDE and N-BEATS), and even qualitative approaches such as collaborative planning, forecasting, and replenishment (CPFR). Our experimental results suggest which forecasting models are recommended for each demand pattern, and insightful implications are provided for the managers.

Adversarial Network Optimization under Bandit Feedback: Maximizing Utility in Non-Stationary Multi-Hop Networks

2026-02-11T04:36:10Z

Adversarial Network Optimization under Bandit Feedback: Maximizing Utility in Non-Stationary Multi-Hop Networks Dai, Yan; Huang, Longbo Stochastic Network Optimization (SNO) concerns scheduling in stochastic queueing systems and has been widely studied in network theory. Classical SNO algorithms require network conditions to be stationary w.r.t. time, which fails to capture the non-stationary components in increasingly many real-world scenarios. Moreover, most existing algorithms in network optimization assume perfect knowledge of network conditions before decision, which again rules out applications where unpredictability in network conditions presents. Motivated by these issues, this paper considers Adversarial Network Optimization (ANO) under bandit feedback. Specifically, we consider the task of i) maximizing some unknown and time-varying utility function associated with scheduler's actions, where ii) the underlying network topology is a non-stationary multi-hop network whose conditions change arbitrarily with time, and iii) only bandit feedback (the effect of actually deployed actions) is revealed after decision-making. We propose the UMO2 algorithm, which does not require any pre-decision knowledge or counterfactual feedback, ensures network stability, and also matches the utility maximization performance of any ''mildly varying'' reference policy up to a polynomially decaying gap. To our knowledge, no previous algorithm can handle multi-hop networks or achieve utility maximization guarantees in ANO problems with bandit feedback, whereas ours is able to do both. Technically, our method builds upon a novel integration of online learning techniques into the Lyapunov drift-plus-penalty method. Specifically, we propose meticulous analytical techniques to jointly balance online learning and Lyapunov arguments, which is used to handle the complex inter-dependency among queues in multi-hop networks. To tackle the learning obstacles due to potentially unbounded queue sizes and negative queue differences, we design a new online linear optimization algorithm that automatically adapts to the unknown (potentially negative) loss magnitudes. Finally, we also propose a bandit convex optimization algorithm with novel queue-dependent learning rate scheduling that suites drastically varying queue lengths in utility maximization. Our new insights and techniques in online learning can also be of independent interest. SIGMETRICS Abstracts ’25, Stony Brook, NY, USA

IDAT: A Multi-Modal Dataset and Toolkit for Building and Evaluating Interactive Task-Solving Agents

2026-02-11T04:36:14Z

IDAT: A Multi-Modal Dataset and Toolkit for Building and Evaluating Interactive Task-Solving Agents Mohanty, Shrestha; Arabzadeh, Negar; Tupini, Andrea; Sun, Yuxuan; Skrynnik, Alexey; Zholus, Artem; C?t?, Marc-Alexandre; Kiseleva, Julia Seamless interaction between AI agents and humans using natural language remains a key goal in AI research. This paper addresses the challenges of developing interactive agents capable of understanding and executing grounded natural language instructions through the IGLU competition. Despite advancements, challenges such as a scarcity of appropriate datasets and the need for effective evaluation platforms persist. We introduce a scalable data collection tool for gathering interactive grounded language instructions within a Minecraft-like environment, resulting in a Multi-Modal dataset with around 9,000 utterances and over 1,000 clarification questions. Additionally, we present a Human-in-the-Loop interactive evaluation platform for qualitative analysis and comparison of agent performance through multi-turn communication with human annotators. We offer to the community these assets referred to as IDAT (IGLU Dataset And Toolkit) which aim to advance the development of intelligent, interactive AI agents and provide essential resources for further research. SIGIR ’25, Padua, Italy

A Theory to Estimate, Bound, and Manage Systemic Cyber-Risk

2026-02-11T04:36:11Z

A Theory to Estimate, Bound, and Manage Systemic Cyber-Risk Pal, Ranjan; Duan, Konnie; Sequeira, Rohan The market to manage critical infrastructure cyber-risks using cyber insurance (CI) has been growing steadily (but not fast enough) as it is still skeptical of the extent of economic and societal impact of systemic risk across networked supply chains in interdependent IT-driven enterprises. To demystify this skepticism, we first study in this paper the role of (a) the statistical nature of multiple enterprise cyber-risks contributing to aggregate supply chain risk and (b) the graph structure of the underlying enterprise supply chain network, in the statistical spread of aggregate cyber-risk. We provide statistical tail bounds on the aggregate cyber-risk that a risk managing firm such as a cyber insurer is exposed to in a supply chain. Subsequently, we study the problem of aggregate cyber-risk management by cyber re-insurance firms via portfolio design to optimally diversify aggregate/systemic cyber-risk sourced from multiple CIs insuring enterprises on a supply chain. We propose the first mathematical framework for re-insurers to test the operational sustainability of systemic cyber-risk diversification portfolios with respect to the standard Value-at-Risk (VaR) metric for general aggregate cyber risk distributions. We also propose a statistical copula methodology to make systemic cyber-risk portfolio diversification sustainable for re-insurers in scenarios where the sustainability test fails. We validate our theory via Monte Carlo simulations. SIGSIM-PADS ’25, Santa Fe, NM, USA

14.41 Public Finance and Public Policy, Fall 2010

2026-02-10T18:32:40Z

14.41 Public Finance and Public Policy, Fall 2010 Gruber, Jonathan Explores the role of government in the economy, applying tools of basic microeconomics to answer important policy questions such as government response to global warming, school choice by K-12 students, Social Security versus private retirement savings accounts, government versus private health insurance, setting income tax rates for individuals and corporations.

Dynamic Incentive Allocation for City-Scale Deep Decarbonization

2026-02-11T04:35:56Z

Dynamic Incentive Allocation for City-Scale Deep Decarbonization Sitaraman, Anupama; Lechowicz, Adam; Bashir, Noman; Liu, Xutong; Hajiesmaili, Mohammad; Shenoy, Prashant Greenhouse gas emissions from the residential sector represent a large fraction of global emissions and must be significantly curtailed to achieve ambitious climate goals. To stimulate the adoption of relevant technologies such as rooftop PV and heat pumps, governments and utilities have designed incentives that encourage adoption of decarbonization technologies. However, studies have shown that many of these incentives are inefficient since a substantial fraction of spending does not actually promote adoption. Further, these incentives are not equitably distributed across socioeconomic groups. In this paper, we present a novel data-driven approach that adopts a holistic, emissions-based, and city-scale perspective on decarbonization. We propose an optimization model that dynamically allocates a total incentive budget to households to directly maximize the resultant carbon emissions reduction -- this is in contrast to prior work, which focuses on metrics such as the number of new installations. We leverage techniques from the multi-armed bandits problem to estimate human factors, such as a household's willingness to adopt new technologies given a certain incentive. We apply our proposed dynamic incentive framework to a city in the Northeast U.S., using real household energy data, grid carbon intensity data, and future price scenarios. We compare our learning-based technique to two baselines, one "status-quo' baseline using incentives offered by a state and utility, and one simple heuristic baseline. With these baselines, we show that our learning-based technique significantly outperforms both the status-quo baseline and the heuristic baseline, achieving up to 37.88% higher carbon reductions than the status-quo baseline and up to 28.76% higher carbon reductions compared to the heuristic baseline. Additionally, our incentive allocation approach is able to achieve significant carbon reduction even in a broad set of environments, with varying values for electricity and gas prices, and for carbon intensity of the grid. Finally, we show that our framework can accommodate equity-aware constraints to preserve an equitable allocation of incentives across socioeconomic groups while achieving 83.34% of the carbon reductions of the optimal solution on average.

The Economics of Large Language Models: Token Allocation, Fine-Tuning, and Optimal Pricing

2026-02-11T04:36:05Z

The Economics of Large Language Models: Token Allocation, Fine-Tuning, and Optimal Pricing Bergemann, Dirk; Bonatti, Alessandro; Smolin, Alex We develop an economic framework to analyze the optimal pricing and product design of Large Language Models (LLM). Our framework captures several key features of LLMs: variable operational costs of processing input and output tokens; the ability to customize models through fine-tuning; and high-dimensional user heterogeneity in terms of task requirements and error sensitivity. In our model, a monopolistic seller offers multiple versions of LLMs through a menu of products. The optimal pricing structure depends on whether token allocation across tasks is contractible and whether users face scale constraints. When it is possible to contract on the entire assignment of tokens to tasks, the seller's problem ("Token Allocations") is an infinite-dimensional screening problem, which is well-known to be difficult. We are nonetheless able to make progress in two important classes of environments: binary environment and two dimensional value-scale heterogeneity, in which case users with similar aggregate value-scale characteristics choose similar levels of fine-tuning and token consumption. When only the total number of tokens is contractible ("Token Packages"), we leverage the tractability of a constant elasticity of substitution framework to drastically simplify the problem: the buyer's type-a function mapping each task to a value of precision- is an index. This index for the value of precision allows the seller to solve a one-dimensional screening problem. The optimal mechanism can be implemented through menus of two-part tariffs, with higher markups for more intensive users. Our results rationalize observed industry practices such as tiered pricing based on model customization and usage levels. EC ’25, July 7–10, 2025, Stanford, CA, USA

Alternates, Assemble! Selecting Optimal Alternates for Citizens’ Assemblies

2026-02-11T04:36:09Z

Alternates, Assemble! Selecting Optimal Alternates for Citizens’ Assemblies Assos, Angelos; Baharav, Carmel; Flanigan, Bailey; Procaccia, Ariel Citizens' assemblies are an increasingly influential form of deliberative democracy, where randomly selected people discuss policy questions. The legitimacy of these assemblies hinges on their representation of the broader population, but participant dropout often leads to an unbalanced composition. In practice, dropouts are replaced by preselected alternates, but existing methods do not address how to choose these alternates. To address this gap, we introduce an optimization framework for alternate selection. Our algorithmic approach, which leverages learning-theoretic machinery, estimates dropout probabilities using historical data and selects alternates to minimize expected misrepresentation. Our theoretical bounds provide guarantees on sample complexity (with implications for computational efficiency) and on loss due to dropout probability mis-estimation. Empirical evaluation using real-world data demonstrates that, compared to the status quo, our method significantly improves representation while requiring fewer alternates. EC ’25, July 7–10, 2025, Stanford, CA, USA

Does Firm Size Influence the Collection of Sensitive Data?: A Study of Child-Orientated Apps

2026-02-11T04:36:07Z

Does Firm Size Influence the Collection of Sensitive Data?: A Study of Child-Orientated Apps Cecere, Grazia; Tucker, Catherine; Lefrere, Vincent How does firm size affect the privacy protections offered to customers? On the one hand, it could be that larger firms use their size to amass more data. On the other hand, smaller firms may be less careful in their data protection practices, because they have a different perception of risk. Using data from the Google Play Store over a three-year period, we explore this empirical question in the U.S. children's app market. Our findings indicate that larger app developers consistently implement stronger privacy protections, requesting less sensitive data compared to smaller developers. These results hold across empirical approaches, including instrumental variables and the propensity-score matching approach. Additionally, our analysis shows that mergers between developers and sudden increases in size of the user-bases of the product are associated with reduced data collection. We show that newly created and updated apps produced by large developers collect less data compared to existing apps. Our findings indicate a trend toward standardized privacy practices across different national regulatory regimes. This research highlights the potential for growth-driven improvements in data privacy practices among app developers, regardless of their regulatory context. EC ’25, July 7–10, 2025, Stanford, CA, USA

Inertial Coordination Games

2026-02-11T04:36:06Z

Inertial Coordination Games Koh, Andrew; Li, Ricky; Uzui, Kei Coordination lies at the heart of many economic phenomena. A well-known example is currency crises, in which traders decide whether to launch a speculative attack. On one hand, shocks to the currency's fundamentals can propagate: as more traders attack, the central bank's foreign reserves are depleted, which in turn encourages further attacks as traders seek to exploit a weakening currency. On the other hand, shocks can also fizzle out: traders may quickly learn that the central bank's balance sheet is strong, causing pessimism to dissipate and attacks to subside. When do shocks propagate, and when do they fizzle out? In particular, how do these outcomes depend on the speed at which traders learn about the fundamental? Motivated by these questions, we propose a model of inertial coordination games—dynamic coordination games where players repeatedly decide whether to take a risky action. The payoff from this risky action depends on (i) a persistent fundamental; and (ii) an endogenous component that depends on others' past play. Players receive private signals about the persistent fundamental over time and form beliefs about the current state. Notably, the current state depends on past play, which in turn depends on past beliefs about play yet farther back into the past. Thus, expectations about histories shape behavior in the present which, in turn, drives the evolution of future states and future play. Our main result develops a tight connection between the speed of learning and limit aggregate play: the risk-dominant action is played in the limit if and only if posterior precisions grow sub-quadratically. This has sharp implications for the long-run propagation of shocks. With slow (sub-quadratic) learning, limit play exhibits history independence: initial shocks have no lasting effect, and limit play is determined solely by fundamentals. By contrast, with fast (super-quadratic) learning, limit play is history dependent: initial shocks can be self-fulfilling, and whether they propagate depends jointly on fundamentals, the size of the shock, and the speed of learning. Our results offer a novel perspective on whether 'history' or 'expectations' shape long-run coordination outcomes: in our model, expectations about histories is what matters for whether self-fulfilling spirals occur. Finally, we show that the speed of learning also shapes the path of play, focusing on the case of sub-quadratic learning. When signals are precise, aggregate play exhibits a sudden transition from nearly all players choosing the non-risk-dominant action to nearly all players choosing the risk-dominant action. In contrast, when signals are noisy, the transition is gradual, with the share of players choosing the risk-dominant action increasing gradually over time. This suggests that "spikes" in aggregate behavior (such as a sudden and massive sell-off of a currency) can be consistent with transition to limit equilibrium play, and need not indicate an "equilibrium shift." EC ’25, July 7–10, 2025, Stanford, CA, USA

Galley: Modern Query Optimization for Sparse Tensor Programs

2026-02-11T04:36:13Z

Galley: Modern Query Optimization for Sparse Tensor Programs Deeds, Kyle; Ahrens, Willow; Balazinska, Magdalena; Suciu, Dan The tensor programming abstraction is a foundational paradigm which allows users to write high performance programs via a high-level imperative interface. Recent work on sparse tensor compilers has extended this paradigm to sparse tensors (i.e., tensors where most entries are not explicitly represented). With these systems, users define the semantics of the program and the algorithmic decisions in a concise language that can be compiled to efficient low-level code. However, these systems still require users to make complex decisions about program structure and memory layouts to write efficient programs. This work presents .Galley, a system for declarative tensor programming that allows users to write efficient tensor programs without making complex algorithmic decisions. Galley is the first system to perform cost based lowering of sparse tensor algebra to the imperative language of sparse tensor compilers, and the first to optimize arbitrary operators beyond Σ and *. First, it decomposes the input program into a sequence of aggregation steps through a novel extension of the FAQ framework. Second, Galley optimizes and converts each aggregation step to a concrete program, which is compiled and executed with a sparse tensor compiler. We show that Galley produces programs that are 1-300x faster than competing methods for machine learning over joins and 5-20x faster than a state-of-the-art relational database for subgraph counting workloads with a minimal optimization overhead.

Virtualizing Cloud Data Infrastructures with BRAD

2026-02-11T04:36:16Z

Virtualizing Cloud Data Infrastructures with BRAD Yu, Geoffrey; Wu, Ziniu; Kossmann, Ferdi; Li, Tianyu; Markakis, Markos; Ngom, Amadou; Zhang, Sophie; Kraska, Tim; Madden, Samuel Organizations usually manage their data using multiple specialized cloud database engines (e.g., Aurora, BigQuery, etc.). However, designing and managing multi-engine infrastructures is hard; there can be many designs, each with different performance and costs. Changing the design afterwards (e.g., due to growth) is even more challenging since application code usually ends up tightly coupled to the engines. We propose data infrastructure virtualization. The key idea is to declare a set of virtual database engines (VDBEs), which specify an engine's application-facing properties (e.g., query interface, performance) and its tables, but do not prescribe a concrete engine. An automated planner then decides how to best realize the VDBEs onto physical engines based on the workload. Clients connect to VDBE endpoints and are oblivious to the underlying physical engines-allowing for seamless infrastructure changes. We implemented VDBEs and an automated planner in BRAD: the first data infrastructure virtualization runtime. Our demo will showcase VDBEs and BRAD's automated planner under different workloads. SIGMOD-Companion ’25, Berlin, Germany

An Efficient Subcritical Multiplication Mode for Monte Carlo Solvers

2026-02-11T04:36:46Z

An Efficient Subcritical Multiplication Mode for Monte Carlo Solvers Forget, Benoit This paper presents an efficient Monte Carlo mode for simulating subcritical systems with external sources. While solving these systems as a fixed source is possible, the length of the histories grows significantly as the system nears criticality, making run time significant. Instead, a hybrid method is proposed that leverages the traditional eigensolver while including elements of the external source. The method builds on prior work,but proposes an approach that maintains the size of the source bank and also provides a natural way of scaling tallies with the true multiplication factor. The method is demonstrated on a subcritical sphere with varying point source position and energy spectrum, as well as an approach to criticality problem. The results demonstrate good agreement with the fixed-source mode, with much improved particle tracking rates for near-critical problems.

Afterword: Reflections from Afar, with Hope for our Collective Future

2026-02-11T04:36:44Z

Afterword: Reflections from Afar, with Hope for our Collective Future Henderson, Diana E Appearing in the wake of a decade of rapid growth in Indian screen Shakespeare as an academic subspeciality, ‘Adapting Shakespearean Romance in Indian Cinema’ reveals how cross-cultural comparison and attention to popular reception can profitably modify inherited critical assumptions for all Shakespeare’s readership. Taking ‘romance’ as a key term, this afterword considers the possibilities and potential problems of recasting its dominant meaning as thematic, focusing on modern love, rather than as a dramatic subgenre. In a time of increasing political censorship and existential threats to gender studies, greater engagement and exchange between those in other areas of Shakespeare studies with this rich cinematic corpus and its aligned subfield of cross-disciplinary criticism provides reasons for hope and renewed community.

Resolving the Contested Future of the GSEs: The Stakes Are High

2026-02-11T04:36:37Z

Resolving the Contested Future of the GSEs: The Stakes Are High Golding, Edward; Wachter, Susan Seventeen years after entering conservatorship, Fannie Mae and Freddie Mac remain central to the future of U.S. housing finance. This paper evaluates the feasibility of their exit from conservatorship without Congressional action, assessing repayment of the federal bailout, capital adequacy under current regulatory frameworks, and the durability of structural reforms. It puts forth a utility model that preserves liquidity, affordability, and mission alignment while mitigating risks of increased mortgage costs. Treasury mechanisms—including commitment fees and stock warrant monetization—are examined as tools to support affordable housing and fulfill charter mandates. A carefully structured exit, supported by robust oversight and capital standards, can balance adequate financial returns with public purpose. A regulatory framework that maintains stable lending standards and pricing over the business cycle is essential to reducing investor-required returns and enhancing affordability, thereby resolving the contested future of the GSEs.

Stabilizing far-from-equilibrium (Mo,Ti)S2 thin films by metal sulfurization at reduced temperature

2026-02-11T04:36:42Z

Stabilizing far-from-equilibrium (Mo,Ti)S2 thin films by metal sulfurization at reduced temperature Li, Yifei; Reidy, Kate; Penn, Aubrey; Lee, Seng Huat; Wang, Baoming; Ye, Kevin; Mao, Zhiqiang; Ross, Frances M; Jaramillo, R We report the synthesis of large-area, high-Ti-content, Mo1−xTixS2 alloy thin films in the 2H phase at temperature as low as 500 °C using a scalable two-step method of metal film deposition, followed by sulfurization in H2S. Film processing at higher temperature accelerates Ti segregation, film coarsening, and the formation of TiS2 in the 1T phase. Crystal growth at higher temperature results in the formation of multiple binary sulfide phases, in agreement with the equilibrium phase diagram. Making highly metastable, smooth, and uniform single-phase alloy films, therefore, hinges on developing low-temperature processing. Our results are relevant to the development of technologies based on designer transition metal dichalcogenide alloys, including in photonic integrated circuits and gas sensing.

Experimental study of lower hybrid wave power absorption on EAST

2026-02-11T04:36:40Z

Experimental study of lower hybrid wave power absorption on EAST Baek, S-G; Li, MH; Bonoli, PT; Ding, BJ; Wallace, GM; Chen, JL; Duan, YM; Gong, XZ; Qian, JP; Wang, L; Yang, H; Zang, Q; Zhang, JY; Zhang, XJ Lower hybrid power absorption analysis is presented on the EAST high-density plasmas using the power modulation technique. The change in the plasma and magnetic energy is monitored to evaluate the power absorption coefficient by linearizing the change for the first 10 msec for the given input power. The power absorption coefficient evaluated is approximately 0.44 (0.35) for 4.6 GHz (2.45 GHz) at n̄e = 3.5x1019 m-3 GENRAY/CQL3D current drive modeling suggests a combination of antenna spectrum, accessibility, and edge losses could primarily be responsible for the observed level of power absorption. Evidence of first-pass parasite LH power flow causing impurity sputtering is also reported, suggesting a need for optimum power coupling. Implications of the experimental findings are discussed.

Limiting role of dislocations in high-current AlGaN/GaN hot electron transistors

2026-02-11T04:36:38Z

Limiting role of dislocations in high-current AlGaN/GaN hot electron transistors Daulton, J. W.; Molnar, R. J.; Brinkerhoff, J. A.; Weir, T. J.; Hollis, M. A.; Zaslavsky, A. III-nitride-based hot electron transistors (HETs) hold significant promise as high-speed, high-power devices. In our previous work, we demonstrated high current density and common-emitter gain at room temperature. Here, we measure multiple devices at cryogenic temperatures, extending the Gummel characteristics past the onset of intervalley scattering at 77 K. We demonstrate a Gummel current gain of 4.7 at a collector current density of 2.6 MA/cm2 at 77 K as well as a peak current density exceeding 3 MA/cm2. From these data, we determine that dislocation-associated inhomogeneities play a limiting role in AlGaN/GaN HETs, controlling the current gain, density, knee voltage, and base-collector leakage. A comparison of two nominally identical devices suggests that even a modest reduction in dislocation density would result in a substantial improvement in HET performance.

SeerCuts: Explainable Attribute Discretization

2026-02-10T03:07:40Z

SeerCuts: Explainable Attribute Discretization Lai, Eugenie; Croitoru, Inbal; Bitton, Noam; Shalem, Ariel; Youngmann, Brit; Galhotra, Sainyam; Rezig, El Kindi; Cafarella, Michael This demonstration showcases SeerCuts - a tool that suggests useful and semantically meaningful discretization strategies (partitions) for numerical attributes. SeerCuts is a generic, interactive framework where users specify attributes to discretize and their utility measure for a downstream task of choice. It uses GPT-4o to assess the semantic meaningfulness of candidate partitions and employs an efficient search strategy to explore the vast space of discretization options. With hierarchical clustering to group related partitions and a multi-armed bandit policy to identify useful partitions with only a few samples, SeerCuts quickly finds meaningful and useful partitions. In the demo, we will provide an overview of SeerCuts and allow the audience to explore various datasets and tasks, including data visualization and comprehensive modeling. The users will be able to evaluate how SeerCuts identifies meaningful discretization strategies and compare the tradeoff between different discretization options. SIGMOD-Companion ’25, Berlin, Germany

PalimpChat: Declarative and Interactive AI analytics

2026-02-10T03:07:41Z

PalimpChat: Declarative and Interactive AI analytics Liu, Chunwei; Vitagliano, Gerardo; Rose, Brandon; Printz, Matthew; Samson, David Andrew; Cafarella, Michael Thanks to the advances in generative architectures and large language models, data scientists can now code pipelines of AI operations to process large collections of unstructured data. Recent progress has seen the rise of declarative AI frameworks (e.g., Palimpzest, Lotus, and DocETL) to build optimized and increasingly complex pipelines, but these systems often remain accessible only to expert programmers. In this demonstration, we present PalimpChat, a chat-based interface to Palimpzest that bridges this gap by letting users create and run sophisticated AI pipelines through natural language alone. By integrating Archytas, a ReAct-based reasoning agent, and Palimpzest's suite of relational and LLM-based operators, PalimpChat provides a practical illustration of how a chat interface can make declarative AI frameworks truly accessible to non-experts. Our demo system is publicly available online. At SIGMOD'25, participants can explore three real-world scenarios-scientific discovery, legal discovery, and real estate search-or apply PalimpChat to their own datasets. In this paper, we focus on how PalimpChat, supported by the Palimpzest optimizer, simplifies complex AI workflows such as extracting and analyzing biomedical data. SIGMOD-Companion ’25, Berlin, Germany

CauSumX: Summarized Causal Explanations For Group-By-Average Queries

2026-02-10T03:07:39Z

CauSumX: Summarized Causal Explanations For Group-By-Average Queries Levy, Nativ; Cafarella, Michael; Gilad, Amir; Roy, Sudeepa; Youngmann, Brit Group-by-average SQL queries are a cornerstone of data analysis, often employed to uncover patterns and trends within datasets. However, interpreting the results of these queries can be challenging and time-intensive, particularly when working with large, high-dimensional datasets. Automating the generation of explanations for such queries can greatly enhance analysts' ability to derive meaningful insights while reducing human effort. Effective explanations must balance succinctness and depth, offering insights into different patterns across aggregate results, while crucially reflecting cause-effect relationships rather than mere correlations. This ensures that users can make informed, data-driven decisions grounded in reality. In this demonstration, we present CauSumX, a system that produces concise and causal explanations for group-by-average queries. Leveraging background causal knowledge, CauSumX identifies the key causal factors driving variations in the outcome variable across different groups. The system employs an efficient algorithm based on a recently published paper. We will demonstrate the utility of CauSumX for generating useful summarized causal explanations by interacting with the SIGMOD'25 participants, who will act as data analysts aiming to explain their query results. SIGMOD-Companion ’25, Berlin, Germany

CausaLens: A System for Summarizing Causal DAGs

2026-02-10T03:07:04Z

CausaLens: A System for Summarizing Causal DAGs Chen, Noam; Zeng, Anna; Cafarella, Michael; Kenig, Batya; Markakis, Markos; Mishali, Oren; Youngmann, Brit; Salimi, Babak Causal inference aids researchers in discovering causal relationships, leading to scientific insights. Pearl's causal model uses causal DAGs to estimate causal effects, so DAG correctness is essential for reliable causal conclusions. However, for high-dimensional data, the causal DAGs are often complex beyond human verifiability. Graph summarization is a logical next step, but current methods for general-purpose graph summarization are inadequate for causal DAG summarization, as they are not designed to preserve causal information. In this demonstration, we present a system called CausaLens that summarizes a given causal DAG and balances graph simplification for better understanding and retention of essential causal information for reliable inference directly on the summary DAG. We illustrate that causal inference on the summary DAG is more robust to misspecification in the initial causal DAG compared to performing inference directly on the initial causal DAG, thereby enhancing the robustness of causal inference. We will demonstrate the utility of CausaLens for generating useful summary causal DAGs by interacting with the SIGMOD'25 participants, who will act as data analysts aiming to perform causal analysis on high dimensional datasets. SIGMOD-Companion ’25, Berlin, Germany

First Workshop on Novel Optimizations for Visionary AI Systems (NOVAS)

2026-02-10T03:06:46Z

First Workshop on Novel Optimizations for Visionary AI Systems (NOVAS) Vitagliano, Gerardo; Liu, Chunwei; Cao, Lei; Sun, Huan; Papotti, Paolo The first NOVAS workshop (Novel Optimizations for Visionary AI Systems) is aimed at hosting novel work at the intersection between artificial intelligence and data management. This area has emerged with the rise of transformer-based architectures, which have revolutionized data processing across modalities. While these models benefit from massive pre-training and large-context inference, there are significant challenges related to scalability, determinism, and resource constraints. These issues-long studied in the data management community-have sparked a convergence between generative AI and traditional database research. The workshop will be held on June 22nd, in conjunction with SIGMOD/PODS 2025. The workshop solicits regular and short papers on topics including hardware and execution optimizations, high-level programming abstractions, integration of LLMs with relational databases, and new transformer architectures for structured data. By bridging together the different communities of machine learning, data systems, and information retrieval, NOVAS aims at becoming the venue to discuss, share ideas and early results, and spark new research collaborations for the next-generation of data-driven AI systems. SIGMOD-Companion ’25, Berlin, Germany

Data-Efficient Discovery of Hyperelastic TPMS Metamaterials with Extreme Energy Dissipation

2026-02-10T03:07:38Z

Data-Efficient Discovery of Hyperelastic TPMS Metamaterials with Extreme Energy Dissipation Perroni-Scharf, Maxine; Ferguson, Zachary; Butruille, Thomas; Portela, Carlos; Konakovi? Lukovi?, Mina Triply periodic minimal surfaces (TPMS) are a class of metamaterials with a variety of applications and well-known primitive morphologies. We present a new method for discovering novel microscale TPMS structures with exceptional energy-dissipation capabilities, achieving double the energy absorption of the best existing TPMS primitive structure. Our approach employs a parametric representation, allowing seamless interpolation between structures and representing a rich TPMS design space. As simulations are intractable for efficiently optimizing microscale hyperelastic structures, we propose a sample-efficient computational strategy for rapid discovery with limited empirical data from 3D-printed and tested samples that ensures high-fidelity results. We achieve this by leveraging a predictive uncertainty-aware Deep Ensembles model to identify which structures to fabricate and test next. We iteratively refine our model through batch Bayesian optimization, selecting structures for fabrication that maximize exploration of the performance space and exploitation of our energy-dissipation objective. Using our method, we produce the first open-source dataset of hyperelastic microscale TPMS structures, including a set of novel structures that demonstrate extreme energy dissipation capabilities, and show several potential applications of these structures. SIGGRAPH Conference Papers ’25, Vancouver, BC, Canada

Splat and Replace: 3D Reconstruction with Repetitive Elements

2026-02-10T03:07:03Z

Splat and Replace: 3D Reconstruction with Repetitive Elements Violante, Nicolas; Meuleman, Andr?as; Gauthier, Alban; Durand, Fredo; Groueix, Thibault; Drettakis, George We leverage repetitive elements in 3D scenes to improve novel view synthesis. Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have greatly improved novel view synthesis but renderings of unseen and occluded parts remain low-quality if the training views are not exhaustive enough. Our key observation is that our environment is often full of repetitive elements. We propose to leverage those repetitions to improve the reconstruction of low-quality parts of the scene due to poor coverage and occlusions. We propose a method that segments each repeated instance in a 3DGS reconstruction, registers them together, and allows information to be shared among instances. Our method improves the geometry while also accounting for appearance variations across instances. We demonstrate our method on a variety of synthetic and real scenes with typical repetitive elements, leading to a substantial improvement in the quality of novel view synthesis. SIGGRAPH Conference Papers ’25, Vancouver, BC, Canada

Variational Elastodynamic Simulation

2026-02-10T03:07:26Z

Variational Elastodynamic Simulation Mattos Da Silva, Leticia; Sell?n, Silvia; Pacheco-Tallaj, Natalia; Solomon, Justin Numerical schemes for time integration are the cornerstone of dynamical simulations for deformable solids. The most popular time integrators for isotropic distortion energies rely on nonlinear root-finding solvers, most prominently, Newton’s method. These solvers are computationally expensive and sensitive to ill-conditioned Hessians and poor initial guesses; these difficulties can particularly hamper the effectiveness of variational integrators, whose momentum conservation properties require reliable root-finding. To tackle these difficulties, this paper shows how to express variational time integration for a large class of elastic energies as an optimization problem with a “hidden” convex substructure. This hidden convexity suggests uses of optimization techniques with rigorous convergence analysis, guaranteed inversion-free elements, and conservation of physical invariants up to tolerance/numerical precision. In particular, we propose an Alternating Direction Method of Multipliers (ADMM) algorithm combined with a proximal operator step to solve our formulation. Empirically, our integrator improves the performance of elastic simulation tasks, as we demonstrate in a number of examples. SIGGRAPH Conference Papers ’25, Vancouver, BC, Canada

Strain-Tunable Thermal Conductivity in Largely Amorphous Polyolefin Fibers via Alignment-Induced Vibrational Delocalization

2026-02-10T03:08:05Z

Strain-Tunable Thermal Conductivity in Largely Amorphous Polyolefin Fibers via Alignment-Induced Vibrational Delocalization Developing fast, reversible, and recyclable thermal switches is essential to advance adaptive thermal management. Here, we present a strain-tunable thermal switch based on largely amorphous olefin block copolymer (OBC) fibers, achieving a continuous switching ratio above 2 over 1000 cycles, as well as very short response times below 0.22 s. Using Raman spectroscopy, we quantify vibrational delocalization with increasing strain and demonstrate its direct connection to the observed thermal conductivity changes. We show that unlike prior assumptions linking propagating heat carriers primarily to crystalline domains, alignment in amorphous systems can enable phonon-like modes that dominate transport. To our best knowledge, this work is the first to experimentally probe vibrational delocalization using Raman spectroscopy and to demonstrate that alignment alone can govern the dominant carrier in disordered polymers. These findings establish design strategies for fatigue-resistant, high-performance, and recyclable polymer thermal switches for advanced thermal energy transport applications.

Introducing synchromodality: One missing link between transportation and supply chain management

2026-02-10T03:07:55Z

Introducing synchromodality: One missing link between transportation and supply chain management Acero, Beatriz; Saenz, Maria Jesus; Luzzini, Davide This study develops and tests the synchromodality construct, a novel supply chain concept that integrates the flexible use of different transport modes based on real-time information. At a time when global supply chains are complex and subject to uncertainty, synchromodality has emerged at the forefront of research and practice as a tool to ensure efficient delivery performance and thus supply chain competitiveness. Despite synchromodality is attracting the attention of leading companies and policy makers, only scholars within the transport research community have engaged with the topic so far. We believe a supply chain management perspective is missing, but essential, to develop the full potential of synchromodality. Our study shows that synchromodality capabilities encapsulate four key elements: visibility, integration, multi-modal transport, and flexibility. Thanks to a three-stage research approach exploiting multiple methods, this study conceptualizes, develops, and validates the first synchromodality measurement model, which reflects the multidimensional nature of the concept. We hope to set the stage for a number of potential future research opportunities that can explore synchromodality implementation and outcomes.

Shape Space Spectra

2026-02-10T03:07:07Z

Shape Space Spectra Chang, Yue; Benchekroun, Otman; Chiaramonte, Maurizio M.; Chen, Peter Yichen; Grinspun, Eitan Eigenanalysis of differential operators, such as the Laplace operator or elastic energy Hessian, is typically restricted to a single shape and its discretization, limiting reduced order modeling (ROM). We introduce the first eigenanalysis method for continuously parameterized shape families. Given a parametric shape, our method constructs spatial neural fields that represent eigen-functions across the entire shape space. It is agnostic to the specific shape representation, requiring only an inside/outside indicator function that depends on shape parameters. Eigenfunctions are computed by minimizing a variational principle over nested spaces with orthogonality constraints. Since eigenvalues may swap dominance at points of multiplicity, we jointly train multiple eigenfunctions while dynamically reordering them based on their eigenvalues at each step. Through causal gradient filtering, this reordering is reflected in backpropagation. Our method enables applications to operate over shape space, providing a single ROM that encapsulates vibration modes for all shapes, including previously unseen ones. Since our eigenanalysis is differentiable with respect to shape parameters, it facilitates eigenfunction-aware shape optimization. We evaluate our approach on shape optimization for sound synthesis and locomotion, as well as reduced-order modeling for elastodynamic simulation.

Dynamic Mesh Processing on the GPU

2026-04-14T03:20:37Z

Dynamic Mesh Processing on the GPU Mahmoud, Ahmed H.; Porumbescu, Serban D.; Owens, John D. We present a system for dynamic triangle mesh processing entirely on the GPU. Our system features an efficient data structure that enables rapid updates to mesh connectivity and attributes. By partitioning the mesh into small patches, we process all dynamic updates for each patch within the GPU's fast shared memory. This approach leverages speculative processing for conflict handling, minimizing rollback costs, maximizing parallelism, and reducing locking overhead. Additionally, we introduce a new programming model for dynamic mesh processing. This model provides concise semantics for dynamic updates, abstracting away concerns about conflicting updates during parallel execution. At the core of our model is the cavity operator, a general mesh update operator that facilitates any dynamic operation by removing a set of mesh elements and inserting new ones into the resulting void. We applied our system to various GPU applications, including isotropic remeshing, surface tracking, mesh decimation, and Delaunay edge flips. On large inputs, our system achieves an order-of-magnitude speedup compared to multi-threaded CPU solutions and is more than two orders of magnitude faster than state-of-the-art single-threaded CPU solutions. Furthermore, our data structure outperforms state-of-the-art GPU static data structures in terms of both speed and memory efficiency.

Exciton Fine Structure in 2D Perovskites: The Out‐of‐Plane Excitonic State

2026-04-14T03:38:37Z

Exciton Fine Structure in 2D Perovskites: The Out‐of‐Plane Excitonic State Posmyk, Katarzyna; Dyksik, Mateusz; Surrente, Alessandro; Maude, Duncan K; Zawadzka, Natalia; Babiński, Adam; Molas, Maciej R; Paritmongkol, Watcharaphol; Mączka, Mirosław; Tisdale, William A; Plochocka, Paulina; Baranowski, Michał 2D Ruddlesden-Popper metal-halide perovskites feature particularly strong excitonic effects, making them a fascinating playground for studying exciton physics. A complete understanding of the properties of this quasi-particle is crucial to fully exploit the tremendous potential of 2D perovskites (2DP) in light emission applications. Despite intense investigations, some of the exciton properties remain elusive to date, for example, the energy-ordering of the exciton states within the so-called fine structure manifold. Using optical spectroscopy, it demonstrates that in the archetypical 2DP (PEA)2PbI4, in contradiction to theoretical predictions, the energy of the bright out-of-plane exciton state is higher than that of two in-plane states. Having elucidated the order of exciton fine structure, it determines the g-factor of the dark exciton transition, together with the values of the electron and hole g-factors in the direction parallel to the c-axis of the crystal. In this way, it provides for the first time, a complete picture of the exciton fine structure in (PEA)2PbI4 2DP.

Discovery of enhanced lattice dynamics in a single-layered hybrid perovskite

2026-04-14T03:38:36Z

Discovery of enhanced lattice dynamics in a single-layered hybrid perovskite Zhang, Zhuquan; Zhang, Jiahao; Liu, Zi-Jie; Dahod, Nabeel S; Paritmongkol, Watcharaphol; Brown, Niamh; Stollmann, Alexia; Lee, Woo Seok; Chien, Yu-Che; Dai, Zhenbang; Nelson, Keith A; Tisdale, William A; Rappe, Andrew M; Baldini, Edoardo Layered hybrid perovskites exhibit emergent physical properties and exceptional functional performances, but the coexistence of lattice order and structural disorder severely hinders our understanding of these materials. One unsolved problem regards how the lattice dynamics are affected by the dimensional engineering of the inorganic frameworks and their interaction with the molecular moieties. Here, we address this question by using a combination of spontaneous Raman scattering, terahertz spectroscopy, and molecular dynamics simulations. This approach reveals the structural dynamics in and out of equilibrium and provides unexpected observables that differentiate single- and double-layered perovskites. While no distinct vibrational coherence is observed in double-layered perovskites, an off-resonant terahertz pulse can drive a long-lived coherent phonon mode in the single-layered system. This difference highlights the dramatic change in the lattice environment as the dimension is reduced, and the findings pave the way for ultrafast structural engineering and high-speed optical modulators based on layered perovskites.

Bright Excitonic Fine Structure in Metal-Halide Perovskites: From Two-Dimensional to Bulk

2026-04-14T03:39:08Z

Bright Excitonic Fine Structure in Metal-Halide Perovskites: From Two-Dimensional to Bulk Posmyk, Katarzyna; Zawadzka, Natalia; Łucja Kipczak; Dyksik, Mateusz; Surrente, Alessandro; Maude, Duncan K; Kazimierczuk, Tomasz; Babiński, Adam; Molas, Maciej R; Bumrungsan, Wakul; Chooseng, Chanisara; Paritmongkol, Watcharaphol; Tisdale, William A; Baranowski, Michał; Plochocka, Paulina The optical response of two-dimensional (2D) perovskites, often referred to as natural quantum wells, is primarily governed by excitons, whose properties can be readily tuned by adjusting the perovskite layer thickness. We have investigated the exciton fine structure splitting in the archetypal 2D perovskite (PEA)2(MA)n−1PbnI3n+1 with varying numbers of inorganic octahedral layers n = 1, 2, 3, and 4. We demonstrate that the in-plane excitonic states exhibit splitting and orthogonally oriented dipoles for all confinement regimes. The evolution of the exciton states in an external magnetic field provides further insights into the g-factors and diamagnetic coefficients. With increasing n, we observe a gradual evolution of the excitonic parameters characteristic of a 2D to three-dimensional transition. Our results provide valuable information concerning the evolution of the optoelectronic properties of 2D perovskites with the changing confinement strength.

Persistent enhancement of exciton diffusivity in CsPbBr3 nanocrystal solids

2026-04-14T03:38:35Z

Persistent enhancement of exciton diffusivity in CsPbBr3 nanocrystal solids Shcherbakov-Wu, Wenbi; Saris, Seryio; Sheehan, Thomas John; Wong, Narumi Nagaya; Powers, Eric R; Krieg, Franziska; Kovalenko, Maksym V; Willard, Adam P; Tisdale, William A In semiconductors, exciton or charge carrier diffusivity is typically described as an inherent material property. Here, we show that the transport of excitons among CsPbBr3 perovskite nanocrystals (NCs) depends markedly on how recently those NCs were occupied by a previous exciton. Using transient photoluminescence microscopy, we observe a striking dependence of the apparent exciton diffusivity on excitation laser power that does not arise from nonlinear exciton-exciton interactions or thermal heating. We interpret our observations with a model in which excitons cause NCs to transition to a long-lived metastable configuration that markedly increases exciton transport. The exciton diffusivity observed here (>0.15 square centimeters per second) is considerably higher than that observed in other NC systems, revealing unusually strong excitonic coupling between NCs. The finding of a persistent enhancement in excitonic coupling may help explain other photophysical behaviors observed in CsPbBr3 NCs, such as superfluorescence, and inform the design of optoelectronic devices.

All-Perovskite Multicomponent Nanocrystal Superlattices

2026-04-14T03:38:33Z

All-Perovskite Multicomponent Nanocrystal Superlattices Sekh, Taras V; Cherniukh, Ihor; Kobiyama, Etsuki; Sheehan, Thomas J; Manoli, Andreas; Zhu, Chenglian; Athanasiou, Modestos; Sergides, Marios; Ortikova, Oleksandra; Rossell, Marta D; Bertolotti, Federica; Guagliardi, Antonietta; Masciocchi, Norberto; Erni, Rolf; Othonos, Andreas; Itskos, Grigorios; Tisdale, William A; Stöferle, Thilo; Rainò, Gabriele; Bodnarchuk, Maryna I; Kovalenko, Maksym V Nanocrystal superlattices (NC SLs) have long been sought as promising metamaterials, with nanoscale-engineered properties arising from collective and synergistic effects among the constituent building blocks. Lead halide perovskite (LHP) NCs come across as outstanding candidates for SL design, as they demonstrate collective light emission, known as superfluorescence, in single- and multicomponent SLs. Thus far, LHP NCs have only been assembled in single-component SLs or coassembled with dielectric NC building blocks acting solely as spacers between luminescent NCs. Here, we report the formation of multicomponent LHP NC-only SLs, i.e., using only CsPbBr3 NCs of different sizes as building blocks. The structural diversity of the obtained SLs encompasses the ABO6, ABO3, and NaCl structure types, all of which contain orientationally and positionally locked NCs. For the selected model system, the ABO6-type SL, we observed efficient NC coupling and Förster-like energy transfer from strongly confined 5.3 nm CsPbBr3 NCs to weakly confined 17.6 nm CsPbBr3 NCs, along with characteristic superfluorescence features at cryogenic temperatures. Spatiotemporal exciton dynamics measurements reveal that binary SLs exhibit enhanced exciton diffusivity compared to single-component NC assemblies across the entire temperature range (from 5 to 298 K). The observed coherent and incoherent NC coupling and controllable excitonic transport within the solid NC SLs hold promise for applications in quantum optoelectronic devices.

Electrical manipulation of dissipation in microwave photon–magnon hybrid system through the spin Hall effect

2026-04-14T03:39:09Z

Electrical manipulation of dissipation in microwave photon–magnon hybrid system through the spin Hall effect Hou, Justin T; Chou, Chung-Tao; Han, Jiahao; Fan, Yabin; Liu, Luqiao Hybrid dynamic systems combine advantages from different subsystems for realizing information processing tasks in both classical and quantum domains. However, the lack of controlling knobs in tuning system parameters becomes a severe challenge in developing scalable, versatile hybrid systems for useful applications. Here, we report an on-chip microwave photon–magnon hybrid system where the dissipation rates and the coupling cooperativity can be electrically influenced by the spin Hall effect. Through magnon–photon coupling, the linewidths of the resonator photon mode and the hybridized magnon polariton modes are effectively changed by the spin injection into the magnetic wires from an applied direct current, which exhibit different trends in samples with low and high coupling strengths. Moreover, the linewidth modification by the spin Hall effect shows strong dependence on the detuning of the two subsystems, in contrast to the classical behavior of a standalone magnonic device. Our results point to a direction of realizing tunable, on-chip, scalable magnon-based hybrid dynamic systems, where spintronic effects provide useful control mechanisms.

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

2026-02-06T03:00:48Z

Human Factors Observations in Flightcrew Response to System Failure Events in Transport Category Aircraft from 2000 to 2024 Perez Gago, Cecilia; Hansman, R. John

When Will (Game) Wars End?

2026-04-14T03:38:36Z

When Will (Game) Wars End? Bhatia, Manan; Chin, Byron; Mani, Nitya; Mossel, Elchanan We study several variants of the classical card game war. As anyone who has played this game knows, the game can take some time to terminate, but it usually does. Here, we analyze a number of asymptotic variants of the game, where the number of cards is n, and show that all have an expected termination time of order 𝑛2. This is the same expected termination time as the game where at each turn a fair coin toss decides which player wins a card, known as Gambler’s Ruin and was studied by Blaise Pascal, Pierre de Fermat and others in the seventeenth century.

Semiconductor-free, monolithically 3D-printed logic gates and resettable fuses

2026-04-14T03:38:39Z

Semiconductor-free, monolithically 3D-printed logic gates and resettable fuses Cañada, Jorge; Velásquez-García, Luis Fernando Additive manufacturing has the potential to enable the inexpensive, single-step fabrication of fully functional electromechanical devices. However, while the 3D printing of mechanical parts and passive electrical components is well developed, the fabrication of fully 3D-printed active electronics, which are the cornerstone of intelligent devices, remains a challenge. Existing examples of 3D-printed active electronics show potential but lack integrability and accessibility. This work reports the first active electronics fully 3D-printed via material extrusion, i.e. one of the most accessible and versatile additive manufacturing processes. The technology is proof-of-concept demonstrated through the implementation of the first fully 3D-printed, semiconductor-free, solid-state logic gates, and the first fully 3D-printed resettable fuses. The devices take advantage of a positive temperature coefficient phenomenon found to affect narrow traces of 3D-printed copper-reinforced, polylactic acid. Although the reported devices don’t perform competitively against semiconductor-enabled integrated circuits, the customisability and accessibility intrinsic to material extrusion additive manufacturing make this technology promisingly disruptive. This work serves as a steppingstone for the semiconductor-free democratisation of electronic device fabrication and is of immediate relevance for the manufacture of custom, intelligent devices far from traditional manufacturing centres.

Rapid large-scale building damage level classification after earthquakes using deep learning with Lidar and satellite optical data

2026-04-14T03:39:10Z

Rapid large-scale building damage level classification after earthquakes using deep learning with Lidar and satellite optical data Liu, Chang; Ge, Linlin; Bai, Ting In post-earthquake scenarios, the swift assessment of building damage levels is pivotal for efficient emergency response and recovery planning. Nevertheless, conventional in-situ damage evaluations consume time. Current satellite-based deep learning methods save time but often lack detail, usually classifying damage as either collapsed or intact. This two-level information is not enough for rescue or recovery planning. Light Detection and Ranging (Lidar)-based deep learning methods, which provide three-dimensional (3D) information, could address this issue of damage details. Therefore, this paper proposes a deep learning-based building damage level classification method using both Lidar and satellite data. The proposed method classifies damage into four levels, including no/minor damage, partially collapsed, totally collapsed, and story failure. The developed network builds upon RandLA-Net, incorporating surface normal vectors to enhance accuracy. A colourised Lidar dataset was created for the network. The network underscores the advantage of incorporating surface normal information. A framework is also proposed based on the damage level outcomes of the developed network, which aids in emergency response efforts. Consequently, this paper demonstrates the practical utility of deep learning networks in rapidly assessing detailed building damage levels after earthquakes. Its practical contribution is guiding decision-making during the critical phases of post-earthquake response and recovery.

Cortical somatostatin innervation follows a unique experience-independent developmental trajectory

2026-04-14T03:38:32Z

Cortical somatostatin innervation follows a unique experience-independent developmental trajectory Boivin, Josiah R; Schmerl, Bettina; Martin, Kendyll B; Lee, Chia-Fang; Nedivi, Elly Despite the critical role of inhibition in regulating developmental plasticity, there are significant gaps in our understanding of inhibitory synapse development, particularly for the vast majority of inhibitory synapses that reside on dendrites. Dendritic inhibitory synapses, canonically arising from somatostatin (SST)-expressing neurons, are challenging to detect electrophysiologically and difficult to visualize without a molecular tag. Here, we integrate a genetic synapse labeling strategy with epitope-preserving magnified analysis of proteome (eMAP), a combination of tissue expansion and clearing, to reveal the development of SST innervation in the primary visual cortex of male and female mice. Unlike excitatory innervation, which follows a deep to shallow progression and undergoes pruning, we find that SST bouton formation occurs simultaneously across all cortical layers and is not subject to a period of net pruning. SST bouton and synapse formation occur most dramatically in the days following eye opening and during the opening of the critical period for ocular dominance plasticity. Yet, despite a coincidence with these visual milestones, neither SST bouton nor synapse formation depend on visual experience. This is in contrast to excitatory and non-SST inhibitory synapses, whose development has been shown to depend heavily on visual experience. Thus, SST cortical innervation follows a unique developmental trajectory that is independent of sensory experience and is optimally timed to regulate processes that are fundamental to cortical circuit maturation. Significance statement During development, neurons form extensive synaptic connections while maintaining a delicate balance of excitation and inhibition. It is critical to understand how different subpopulations of synapses form during development, because perturbations in this precisely coordinated process can cause neurodevelopmental disorders. Here, we reveal at unprecedented resolution the development of cortical inhibitory innervation from somatostatin-expressing neurons, which canonically target dendrites. We show that somatostatin neurons follow different rules than other cell types during development, and somatostatin innervation is well-timed to contribute to developmental processes that are central to healthy cortical function. Our results provide new insights on how somatostatin neurons, a critically influential cell type, integrate into cortical circuitry during development.

Neruda through copper-coloured glasses: the role of place attachment in the embeddedness of Chilean entrepreneurship

2026-04-14T03:38:38Z

Neruda through copper-coloured glasses: the role of place attachment in the embeddedness of Chilean entrepreneurship Burke, M. Kathleen; Conley, Mark A.; Jack, Sarah L. Despite scholarly interest in how emotional and instrumental place attachments motivate entrepreneurship, the influences on embeddedness remain underexplored. Building on the notion that entrepreneurship becomes embedded in a locality, we argue that this process is packed with place-based interpretations of the material and imagined reality. Engaging with the empirical setting of Chile, the world’s largest copper producer, we embark on a study examining the interactions between the place attachment, embeddedness and natural resource-based entrepreneurship. We uncover these interactions through analysing several works of poetry by Nobel laureate Pablo Neruda, which focus on the diverging place attachment styles between local and multinational agents. Through reflecting on the poems, we show how historical changes within the Chilean mining industry and broader societal changes are visible in Neruda’s imagery of place attachments, emotions and concerns for local conditions. We problematize embeddedness and entrepreneurship through illuminating the place attachments shaping local actors’ entrepreneurial imagination, thus contributing to knowledge about being embedded in natural resource-based entrepreneurship contexts. We provide new insights into how place attachment can evolve alongside different forms of embedded entrepreneurship.

Human-centric manufacturing culture: a research study of MedTech manufacturers in Ireland

2026-04-14T03:39:07Z

Human-centric manufacturing culture: a research study of MedTech manufacturers in Ireland Rhodes, Donna H; Cuddy, Sara; Jeffers, Malcolm; O’Rourke, Fiona Digital manufacturing is rapidly evolving; however, this transformation is predominantly technology centric. Human-centric manufacturing shifts the paradigm for the digital manufacturing enterprise towards a human focus to realising its envisioned digital future. In that context, Digital Manufacturing Ireland (DMI), Ireland’s expert body for driving digital adoption across manufacturing, initiated a research study in collaboration with two research partners, MIT and IAAE, in support of this important focus for future manufacturing. This paper discusses results of the DMI 2023 human-Centric Manufacturing Culture Study, which engaged manufacturing leaders from 11 MedTech companies with major manufacturing sites in Ireland. Overall findings are discussed, with a focus on 12 emergent themes grouped in four categories: imperatives, values, strategies, and practices. Planned collaboration initiatives and anticipated future research are described. This paper also highlights considerations regarding new thinking needed by manufacturing leaders, along with recommendations as to what leaders can begin to do differently.

Decomposition of Frobenius pushforwards of line bundles on wonderful compactifications

2026-04-14T03:38:31Z

Decomposition of Frobenius pushforwards of line bundles on wonderful compactifications Cai, Merrick; Krylov, Vasily De Concini and Procesi introduced varieties known as wonderful compactifications, which are smooth projective compactifications of semisimple adjoint groups G. We study the Frobenius pushforwards of line bundles on the wonderful compactifications, and in particular we decompose them into a direct sum of vector subbundles and explicitly describe the ranks. We are especially interested in when these subbundles are line bundles, and in the case of 𝐺=𝖯𝖲𝖫𝑛, we offer lower bounds on the multiplicities (as direct summands) for these line bundles.

BrepDiff: Single-Stage B-rep Diffusion Model

2026-04-14T03:20:22Z

BrepDiff: Single-Stage B-rep Diffusion Model Lee, Mingi; Zhang, Dongsu; Jambon, Cl?ment; Kim, Young Min The Boundary Representation (B-rep) is a widely used 3D model representation of most consumer products designed with CAD software. However, its highly irregular and sparse set of relationships poses significant challenges for designing a generative model tailored to B-reps. Existing approaches use multi-stage approaches to satisfy the complex constraints sequentially. As a result, the final geometry cannot incorporate user edits due to the non-deterministic dependencies between cascaded stages. In contrast, we propose BrepDiff, a single-stage diffusion model for B-rep generation. We present a masked UV grid representation consisting of structured point samples from faces, serving as input for a diffusion transformer. By introducing an asynchronous and shifted noise schedule, we improve the training signal, enabling the diffusion model to better capture the distribution of UV grids. The explicitness of our masked UV grid representation enables users to intuitively understand and freely design surface geometry without being constrained by topological validity. The interconnectivity can be derived from the face layout, which is later processed into a valid solid volume during post-processing. Our approach achieves performance on par with state-of-the-art cascaded models while offering complex and diverse manipulations of geometry and topology, such as shape completion, merging, and interpolation. SIGGRAPH Conference Papers ’25, Vancouver, BC, Canada

SwiftSketch: A Diffusion Model for Image-to-Vector Sketch Generation

2026-04-14T03:20:16Z

SwiftSketch: A Diffusion Model for Image-to-Vector Sketch Generation Arar, Ellie; Frenkel, Yarden; Cohen-Or, Daniel; Shamir, Ariel; Vinker, Yael Recent advancements in large vision-language models have enabled highly expressive and diverse vector sketch generation. However, state-of-the-art methods rely on a time-consuming optimization process involving repeated feedback from a pretrained model to determine stroke placement. Consequently, despite producing impressive sketches, these methods are limited in practical applications. In this work, we introduce SwiftSketch, a diffusion model for image-conditioned vector sketch generation that can produce high-quality sketches in less than a second. SwiftSketch operates by progressively denoising stroke control points sampled from a Gaussian distribution. Its transformer-decoder architecture is designed to effectively handle the discrete nature of vector representation and capture the inherent global dependencies between strokes. To train SwiftSketch, we construct a synthetic dataset of image-sketch pairs, addressing the limitations of existing sketch datasets, which are often created by non-artists and lack professional quality. For generating these synthetic sketches, we introduce ControlSketch, a method that enhances SDS-based techniques by incorporating precise spatial control through a depth-aware ControlNet. We demonstrate that SwiftSketch generalizes across diverse concepts, efficiently producing sketches that combine high fidelity with a natural and visually appealing style. SIGGRAPH Conference Papers ’25, Vancouver, BC, Canada

Lifting the Winding Number: Precise Discontinuities in Neural Fields for Physics Simulation

2026-04-14T03:20:18Z

Lifting the Winding Number: Precise Discontinuities in Neural Fields for Physics Simulation Chang, Yue; Liu, Mengfei; Wang, Zhecheng; Chen, Peter Yichen; Grinspun, Eitan Cutting thin-walled deformable structures is common in daily life, but poses significant challenges for simulation due to the introduced spatial discontinuities. Traditional methods rely on mesh-based domain representations, which require frequent remeshing and refinement to accurately capture evolving discontinuities. These challenges are further compounded in reduced-space simulations, where the basis functions are inherently geometry- and mesh-dependent, making it difficult or even impossible for the basis to represent the diverse family of discontinuities introduced by cuts. Recent advances in representing basis functions with neural fields offer a promising alternative, leveraging their discretization-agnostic nature to represent deformations across varying geometries. However, the inherent continuity of neural fields is an obstruction to generalization, particularly if discontinuities are encoded in neural network weights. We present Wind Lifter, a novel neural representation designed to accurately model complex cuts in thin-walled deformable structures. Our approach constructs neural fields that reproduce discontinuities precisely at specified locations, without “baking in” the position of the cut line. To achieve this, we augment the input coordinates of the neural field with the generalized winding number of any given cut line, effectively lifting the input from two to three dimensions. Lifting allows the network to focus on the easier problem of learning a 3D everywhere-continuous volumetric field, while a corresponding restriction operator enables the final output field to precisely resolve strict discontinuities. Crucially, our approach does not embed the discontinuity in the neural network’s weights, opening avenues to generalization of cut placement. Our method achieves real-time simulation speeds and supports dynamic updates to cut line geometry during the simulation. Moreover, the explicit representation of discontinuities makes our neural field intuitive to control and edit, offering a significant advantage over traditional neural fields, where discontinuities are embedded within the network’s weights, and enabling new applications that rely on general cut placement. SIGGRAPH Conference Papers ’25, Vancouver, BC, Canada

Making Concurrent Hardware Verification Sequential

2026-04-14T03:38:44Z

Making Concurrent Hardware Verification Sequential Bourgeat, Thomas; Liu, Jiazheng; Chlipala, Adam; Arvind Compared to familiar hardware-description languages like Verilog, rule-based languages like Bluespec offer opportunities to import modularity features from software programming. While Verilog modules are about connecting wires between submodules, Bluespec modules resemble objects in object-oriented programming, where interactions with a module occur only through calls to its methods. However, while software objects can typically be characterized one method at a time, the concurrent nature of hardware makes it essential to consider the repercussions of invoking multiple methods simultaneously. Prior formalizations of rule-based languages conceptualized modules by describing their semantics considering arbitrary sets of simultaneous method calls. This internalized concurrency significantly complicates correctness proofs. Rather than analyzing methods one-at-a-time, as is done when verifying software object methods, validating the correctness of rule-based modules necessitated simultaneous consideration of arbitrary subsets of method calls. The result was a number of proof cases that grew exponentially in the size of the module’s API. In this work, we side-step the exponential blowup through a set of judicious language restrictions. We introduce a new Bluespec-inspired formal language, Fjfj, that supports sequential characterization of modules, while preserving the concurrent hardware nature of the language. We evaluated Fjfj by implementing it in Coq, proving the key framework principle: the refinement theorem. We demonstrated Fjfj’s expressivity via implementation and verification of three examples: a pipelined processor, a parameterized crossbar, and a network switch.

Lilo: A Higher-Order, Relational Concurrent Separation Logic for Liveness

2026-04-14T03:20:20Z

Lilo: A Higher-Order, Relational Concurrent Separation Logic for Liveness Lee, Dongjae; Lee, Janggun; Yoon, Taeyoung; Cho, Minki; Kang, Jeehoon; Hur, Chung-Kil Concurrent separation logic (CSL) has excelled in verifying safety properties across various applications, yet its application to liveness properties remains limited. While existing approaches like TaDA Live and Fair Operational Semantics (FOS) have made significant strides, they still face limitations. TaDA Live struggles to verify certain classes of programs, particularly concurrent objects with non-local linearization points, and lacks support for general liveness properties such as "good things happen infinitely often". On the other hand, FOS’s scalability is hindered by the absence of thread modular reasoning principles and modular specifications. This paper introduces Lilo, a higher-order, relational CSL designed to overcome these limitations. Our core observation is that FOS helps us to maintain simple primitives for our logic, which enable us to explore design space with fewer restrictions. As a result, Lilo adapts various successful techniques from literature. It supports reasoning about non-terminating programs by supporting refinement proofs, and also provides Iris-style invariants and modular specifications to facilitate modular verification. To support higher-order reasoning without relying on step-indexing, we develop a technique called stratified propositions inspired by Nola. In particular, we develop novel abstractions for liveness reasoning that bring these techniques together in a uniform way. We show Lilo’s scalability through case studies, including the first termination-guaranteeing modular verification of the elimination stack. Lilo and examples in this paper are mechanized in Coq.

Stochastic Lazy Knowledge Compilation for Inference in Discrete Probabilistic Programs

2026-04-14T03:20:24Z

Stochastic Lazy Knowledge Compilation for Inference in Discrete Probabilistic Programs Bowers, Maddy; Lew, Alexander K.; Tenenbaum, Joshua B.; Solar-Lezama, Armando; Mansinghka, Vikash K. We present new techniques for exact and approximate inference in discrete probabilistic programs, based on two new ways of exploiting lazy evaluation. First, we show how knowledge compilation, a state-of-the art technique for exact inference in discrete probabilistic programs, can be made lazy, enabling asymptotic speed-ups. Second, we show how a probabilistic program’s lazy semantics naturally give rise to a division of its random choices into subproblems, which can be solved in sequence by sequential Monte Carlo with locally-optimal proposals automatically computed via lazy knowledge compilation. We implement our approach in a new tool, Pluck, and evaluate its performance against state-of-the-art approaches to inference in discrete probabilistic languages. We find that on a suite of inference benchmarks, lazy knowledge compilation can be faster than state-of-the-art approaches, sometimes by orders of magnitude.

Probabilistic Programming with Vectorized Programmable Inference

2026-04-14T03:38:27Z

Probabilistic Programming with Vectorized Programmable Inference Becker, McCoy R.; Huot, Mathieu; Matheos, George; Wang, Xiaoyan; Chung, Karen; Smith, Colin; Ritchie, Sam; Saurous, Rif A.; Lew, Alexander K.; Rinard, Martin C.; Mansinghka, Vikash K. We present GenJAX, a new language and compiler for vectorized programmable probabilistic inference. GenJAX integrates the vectorizing map (vmap) operation from array programming frameworks such as JAX into the programmable inference paradigm, enabling compositional vectorization of features such as probabilistic program traces, stochastic branching (for expressing mixture models), and programmable inference interfaces for writing custom probabilistic inference algorithms. We formalize vectorization as a source-to-source program transformation on a core calculus for probabilistic programming ($\gen$), and prove that it correctly vectorizes both modeling and inference operations. We have implemented our approach in \href{https://github.com/probcomp/genjax}{the GenJAX language and compiler}, and have empirically evaluated this implementation on several benchmarks and case studies. Our results show that our implementation supports a wide and expressive set of programmable inference patterns and delivers performance comparable to hand-optimized JAX code.

Waste-Efficient Work Stealing

2026-04-14T03:38:26Z

Waste-Efficient Work Stealing Singer, Kyle; Agrawal, Kunal; Schardl, Tao B. Although randomized work stealing is effective at automatically load-balancing task-parallel programs, it can waste computational resources when scheduling programs that lack sufficient parallelism to use all available threads. For such programs, threads will waste cycles attempting to steal parallel tasks when none are available. This waste can reduce the machine’s efficiency by wasting computational resources and energy and needlessly burdening the operating system. This paper introduces WEWS, a simple, practical, and provably efficient extension to randomized work stealing that mitigates waste. WEWS dynamically adjusts the number of active threads to reduce the waste of randomized work stealing. WEWS executes a parallel computation with the same asymptotic running time as traditional randomized work stealing while bounding the waste to O(min{PT∞, T1 + P2}) instructions. WEWS also follows the work-first principle to perform well in practice. WEWS requires no special support from the operating system or hardware, which simplifies its implementation. We implemented WEWS within the OpenCilk runtime system and compared it to other common waste-mitigation strategies. Across 10 parallel benchmarks, we find that WEWS has minimal impact on parallel running times while, on programs with limited parallelism, substantially reducing waste. PPoPP ’26, Sydney, NSW, Australia

UniTe: A Universal Tensor Abstraction for Capturing Spatial Relationships

2026-04-14T03:38:28Z

UniTe: A Universal Tensor Abstraction for Capturing Spatial Relationships Ray, Jessica; Collin, Teodoro; Sze, Vivienne; Reuther, Albert; Amarasinghe, Saman Tensors are an integral part of numerous domains, and while significant effort has been put into the design of tensor data structures in isolation, little attention has been paid to the relationships that exist across tensors and how this affects their representation and use. In this paper, we focus on spatial relationships across tensors in a program, where such tensors are defined relative to a common reference coordinate system. These relationships are complicated by the fact that the tensors may differ in their representations, such as having variations in their axes, spacings, origins, and overall shape. Due to the lack of existing abstractions and language support for these types of tensor semantics, users are currently forced to manually perform the bookkeeping necessary to account for these varying relationships and representations. Unfortunately, we cannot rely on a simple library to capture these relationships, as computations on these types of tensors often happen at the innermost levels of programs; we find that the overheads associated with an unoptimized implementation quickly accumulate, leading to performance up to nearly 65x slower than a reference C implementation on a series of image and video compression benchmarks. In this paper, we introduce the novel UniTe abstraction, which captures spatial relationships across all such tensors in a program. We also introduce two domain-specific languages and optimizing compilers, CoLa for Python and SHiM for C/C++, built off of UniTe. Both CoLa and SHiM provide users an intuitive set of tensor primitives based on spatial relationships, hiding the complexity that goes into maintaining the tensors and computing accesses across them. In addition, we discuss the optimizations necessary to remove the associated abstraction overhead, and describe their implementations. On the benchmarks, we show that both CoLa and SHiM successfully remove the overheads, achieving performance parity with existing C implementations.

Triplet Exciton Sensitization of Silicon Mediated by Defect States in Hafnium Oxynitride

2026-04-14T03:38:48Z

Triplet Exciton Sensitization of Silicon Mediated by Defect States in Hafnium Oxynitride Nagaya, Narumi; Alexiu, Alexandra; Perkinson, Collin F; Nix, Oliver M; Koh, Dooyong; Bawendi, Moungi G; Tisdale, William A; Van Voorhis, Troy; Baldo, Marc A Singlet exciton fission has the potential to increase the efficiency of crystalline silicon solar cells beyond the conventional single junction limit. Perhaps the largest obstacle to achieving this enhancement is uncertainty about energy coupling mechanisms at the interfaces between silicon and exciton fission materials such as tetracene. Here, the previously reported silicon‐hafnium oxynitride‐tetracene structure is studied and a combination of magnetic‐field‐dependent silicon photoluminescence measurements and density functional theory calculations is used to probe the influence of the interlayer composition on the triplet transfer process across the hafnium oxynitride interlayer. It is found that hafnium oxide interlayers do not show triplet exciton sensitization of silicon, and that nitrogen content in hafnium oxynitride layers is correlated with enhanced sensitization. Calculation results reveal that defects in hafnium oxynitride interlayers with higher nitrogen content introduce states close to the band‐edge of silicon, which can mediate the triplet exciton transfer process. Some defects introduce additional deleterious mid‐gap states, which may explain observed silicon photoluminescence quenching. These results show that band‐edge states can mediate the triplet exciton transfer process, potentially through a sequential charge transfer mechanism.

Layered Metal–Organic Chalcogenides: 2D Optoelectronics in 3D Self-Assembled Semiconductors

2026-04-14T03:39:01Z

Layered Metal–Organic Chalcogenides: 2D Optoelectronics in 3D Self-Assembled Semiconductors Paritmongkol, Watcharaphol; Feng, Zhifu; Refaely-Abramson, Sivan; Tisdale, William A; Kastl, Christoph; Maserati, Lorenzo Molecular self-assembly offers an effective and scalable way to design nanostructured materials with tunable optoelectronic properties. In the past 30 years, organic chemistry has delivered a plethora of metal-organic structures based on the combination of organic groups, chalcogens, and a broad range of metals. Among these, several layered metal-organic chalcogenides (MOCs)─including "mithrene" (AgSePh)─recently emerged as interesting platforms to host 2D physics embedded in 3D crystals. Their combination of broad tunability, easy processability, and promising optoelectronic performance is driving a renewed interest in the more general material group of "low-dimensional" hybrids. In addition, the covalent MOC lattice provides higher stability compared with polar materials in operating devices. Here, we provide a perspective on the rise of 2D MOCs in terms of their synthesis approaches, 2D quantum confined exciton physics, and potential future applications in UV and X-ray photodetection, chemical sensors, and electrocatalysis.

Exciton fission enhanced silicon solar cell

2026-04-14T03:38:50Z

Exciton fission enhanced silicon solar cell Nagaya, Narumi; Lee, Kangmin; Perkinson, Collin F; Li, Aaron; Lee, Youri; Zhong, Xinjue; Lee, Sujin; Weisburn, Leah P; Wang, Janet Z; Baikie, Tomi K; Bawendi, Moungi G; Van Voorhis, Troy; Tisdale, William A; Kahn, Antoine; Seo, Kwanyong; Baldo, Marc A While silicon solar cells dominate global photovoltaic energy production, their continued improvement is hindered by the single-junction limit. One potential solution is to use molecular singlet exciton fission to generate two electrons from each absorbed high-energy photon. We demonstrate that the long-standing challenge of coupling molecular excited states to silicon solar cells can be overcome using sequential charge transfer. Combining zinc phthalocyanine, aluminum oxide, and a shallow junction crystalline silicon microwire solar cell, the peak charge generation efficiency per photon absorbed in tetracene is (138% ± 6%), comfortably surpassing the quantum efficiency limit for conventional silicon solar cells and establishing a new, scalable approach to low-cost, high-efficiency photovoltaics.

1D Silver Organochalcogenide Semiconductors: Color Tunable Luminescence, Polarized Emission, and Long-Range Exciton Diffusion

2026-04-14T03:38:56Z

1D Silver Organochalcogenide Semiconductors: Color Tunable Luminescence, Polarized Emission, and Long-Range Exciton Diffusion Sakurada, Tomoaki; Pathoor, Nithin; Matsumoto, Takuma; Khamlue, Rattapon; Chatsiri, Petcharaphorn; Valenta, Jan; Kawamoto, Tadashi; Omagari, Shun; Tisdale, William A; Paritmongkol, Watcharaphol; Cho, Yeongsu; Vacha, Martin Metal organochalcogenides (MOCs) represent a promising class of organic-inorganic hybrid semiconductors with unique light-matter interactions. Their hybrid nature enables extensive structural and optoelectronic tunability via ligand engineering. In this study, we systematically modulated the electronic properties of ligands using Cl and Me functional groups, achieving precise control over the optoelectronic properties of Ag-based MOCs. Structural analysis revealed that these MOCs adopt a one-dimensional (1D) chain structure with organic ligands surrounding a Ag-chalcogen core. Density functional theory (DFT) calculations demonstrated that MOCs exhibit characteristics of 1D semiconductors with strongly dispersive conduction and valence bands aligned along the crystal rod directions. Experimentally, the MOCs displayed bright luminescence, with peaks centered between 560 and 690 nm. The substitution of Cl with Me groups in the benzene ligands induced a red shift in both absorption and photoluminescence, corroborated by experimental and theoretical analyses. Further optical measurements indicated that the emission from the MOCs is strongly polarized along the chain directions. Notably, Se-based MOCs exhibited enhanced exciton diffusivity along the chain axis with a diffusion length of 130 nm, which is among the highest reported for covalent systems. The observed trend in carrier diffusivity among individual compounds is attributed to differences in the effective masses of the carriers, as determined by DFT calculations. Our findings offer valuable insights into the systematic structural and property tuning of hybrid semiconductors and highlight the unique characteristics of the 1D MOC family.

Excitonic Anisotropy in Single‐Crystalline 2D Silver Phenylchalcogenides

2026-04-14T03:38:55Z

Excitonic Anisotropy in Single‐Crystalline 2D Silver Phenylchalcogenides Lee, Woo Seok; Cho, Yeongsu; Posmyk, Katarzyna; Peksa, Paulina; Dyksik, Mateusz; Samulewicz, Nicholas; Plochocka, Paulina; Baranowski, Michał; Kulik, Heather J; Tisdale, William A 2D materials exhibiting in‐plane anisotropy enable new applications in directional energy transport and polarized optical response. Silver phenylchalcogenides (AgEPh) – including mithrene (AgSePh), tethrene (AgTePh), and thiorene (AgSPh) – represent an exciting new addition to this family, with optical response spanning the visible to near‐UV. Here, excitonic anisotropy is predicted and characterized in this family of materials using a combination of ab initio theory and optical micro‐spectroscopy of single‐crystalline flakes. Using density functional theory and GW with the Bethe–Salpeter equation calculations, it is revealed that all AgEPh compounds exhibit anisotropic electronic band structure and host multiple delocalized excitons with in‐plane anisotropy. Room‐temperature polarization‐resolved optical micro‐spectroscopy shows that orthogonally polarized excitons with similar energy lead to nearly isotropic absorption in AgSPh, whereas energy separation between excitonic resonances in AgSePh and AgTePh leads to strong absorption and emission anisotropy. Cryogenic reflectance micro‐spectroscopy further reveals exciton fine structure in AgSePh, reconciling the discrepancies between room‐temperature experiments and theoretical predictions. Finally, it is demonstrated that the optical response of thicker AgEPh crystals is influenced by photonic effects arising from finite crystal size. Overall, this work advances the understanding of the relationship between anisotropic structure, composition, and excitonic properties in AgEPh, providing a foundation for technological integration.

Revolutionize cold chain: an AI/ML driven approach to overcome capacity shortages

2026-04-14T03:39:04Z

Revolutionize cold chain: an AI/ML driven approach to overcome capacity shortages Jackson, Ilya; Namdar, Jafar; Saénz, Maria Jesús; Elmquist III, Richard Augustus; Dávila Novoa, Luis Rodrigo This research investigates how Artificial Intelligence (AI) and Machine Learning (ML) forecasting methodologies can be leveraged for cold chain capacity planning, specifically utilising Prophet and Seasonal Autoregressive Integrated Moving Average parametrised through grid search. In collaboration with Americold, the world's second-largest refrigerated logistic service provider, the study explores the challenges and opportunities in applying AI/ML techniques to complex operations covering 385 customers and a capacity of 73,296 pallet positions. We train and test several AI/ML and traditional statistical models using extensive data for every customer over 3.5 years. Based on the results, MAPE of 5.28% was achieved on the whole site level, and SARIMA outperformed ML models in most cases. Next, we show that developing and applying a Customer Segmentation Matrix has enabled more accurate forecasting and planning across various customer segments, addressing the issue of forecasting inaccuracies. This approach effectively improves forecasting inaccuracies, underscoring the significance of tailoring AI/ML models for demand forecasting within the cold-chain industry. Ultimately, this research presents an AI-driven approach that transcends mere forecasting, offering a practical pathway to manage capacity in light of the constraints.

Beyond binary group categorization: towards a dynamic view of human groups

2026-04-14T03:38:50Z

Beyond binary group categorization: towards a dynamic view of human groups Kish Bar-On, Kati Society is a composite of interacting people and groups. These groups play a significant role in maintaining social status, establishing group identity and social identity, and enforcing norms. As such, groups are essential for understanding human behavior. Nevertheless, the study of groups in everyday group life yields many diverse and sometimes contradicting theories of group behavior, and researchers tend to agree that we have yet to understand the emergence of groups out of aggregates of individuals. The current paper aims to shed new light on the convoluted interrelation between groups and individuals by focusing on individuals’ social identities and group categorization. It does so by exploring the dynamic nature of the self and its implications on identity and group membership, and introducing a framework recognizing the fluidity of groups and group categorization. Incorporating historical insights with contemporary theories, this paper argues for a flexible understanding of group dynamics that surpasses rigid in-group and out-group classifications, proposing instead that group affiliations exist along a continuum that reflects the ever-changing social landscape.

Reparative Urban Science: Challenging the Myth of Neutrality and Crafting Data-Driven Narratives

2026-04-14T03:38:53Z

Reparative Urban Science: Challenging the Myth of Neutrality and Crafting Data-Driven Narratives So, Wonyoung I offer how urban planning should approach technology within the context of systemic racism, advocating for a reparative approach to address the issues of urban technology perpetuating today’s racial inequality and hindering efforts to redress historical oppression. I identify three mechanisms – formalization, context removal and legitimization, and penalization and extraction – that illustrate how urban technology perpetuates historical inequalities, often penalizing marginalized groups under the pretext of neutrality and fairness. Then, I discuss methodologies of reparative urban science, aiming to use urban technology to challenge race-neutral ideologies and create data-driven narratives for reparations.

What determines EV architecture? An analysis of the most influential battery electric vehicle design decisions from market data

2026-04-14T03:38:52Z

What determines EV architecture? An analysis of the most influential battery electric vehicle design decisions from market data Khan, Mumin; Cameron, Bruce The penetration and variety of Battery Electric Vehicles (BEVs) in the automotive sector have been growing rapidly. While there is substantial research on hybrid ICE-battery vs. battery-only choices, little work has examined whether a dominant design for BEVs is emerging, as predicted by the innovation literature. This study provides a comprehensive exploration of BEV architectures, examining the influence of individual architectural decisions on vehicle performance and market prevalence. This study utilizes multivariate linear regression to analyze a curated dataset of global BEV models from 2022 and 2023, focusing on candidate architectural decisions such as battery cathode composition, battery voltage choice, number of motors, and drive layout. Our research aims to identify potential dominant designs by assessing their impact on performance metrics. The analysis then leverages statistical tools to evaluate the correlation between these architectural decisions and vehicle performance, using range as a primary indicator of consumer appeal. Findings from this research indicate significant variance in the adoption of specific BEV architectures, suggesting that the market has not yet consolidated down to a dominant design. We observe, however, that range is most strongly influenced by the architectural decisions for battery capacity, drive type, and motor type.

Chemical and Chemical-Mechanical Polishing of Surface Roughness on L-PBF/GRCop-42 Cu-Cr-Nb Additive Manufactured 10-GHz RF Structures

2026-04-14T03:38:46Z

Chemical and Chemical-Mechanical Polishing of Surface Roughness on L-PBF/GRCop-42 Cu-Cr-Nb Additive Manufactured 10-GHz RF Structures Seltzman, AH; Wukitch, SJ Laser-based powder bed fusion (L-PBF) allows additive manufacture (AM) of lower hybrid current drive (LHCD) radio-frequency (RF) launchers from Glenn Research Copper, a Cr2Nb precipitation-hardened alloy (GRCop-42) in configurations unachievable with conventional machining. Rough surfaces in AM components increase RF losses and lead to arcing in high-power vacuum RF applications. Chemical polishing, chemical-mechanical polishing, or a combination of both were utilized to planarize the internal surfaces of RF structures, resulting in surface roughness as low as Ra = 0.2 µm. Refinement in polishing techniques now enables GRCop-42 alloys (4 at. % Cr, 2 at. % Nb) to achieve similar surface roughness to GRCop-84 (8 at. % Cr, 4 at. % Nb) and equivalent cavity losses to extruded oxygen-free copper waveguides at 10 GHz.

The Name of Moses in an Egyptian Context—A Hypothetical Etymology

2026-04-14T03:38:58Z

The Name of Moses in an Egyptian Context—A Hypothetical Etymology Adair, Aaron The etymological origins of the name “Moses” have been unclear, but an Egyptian candidate is the most likely hypothesis. In this article, a new proposal is given that finds the best candidate as the Demotic word, mšꜥ, but only in the late Persian period or later would it fit the Hebrew Mōše. Evidence from Greek orthography and testimony from Manetho provide a stronger basis for this proposal over prior candidates. However, this results in a Hellenistic-era inclusion of “Moses” into the Exodus narrative.

Making Sense of Models: Connecting Science and Math Through Decoding and Modifying Computational Models

2026-04-14T03:38:41Z

Making Sense of Models: Connecting Science and Math Through Decoding and Modifying Computational Models Lee, Irene A.; Sagartz, Mary; Meyer, Patricia; Anderson, Emma The Making Sense of Models (MSM) curriculum was designed to bridge math and science learning through agent-based modeling and rich computational thinking investigations that do not require teaching computer programming in middle school classrooms. The MSM curriculum supports students in the NGSS skill of reasoning about how and why a phenomenon happens. After developing decoding skills, students are able to assess the validity of a model based on comparing mechanisms in the model to what they learned about the phenomenon being modeled. In this article, the authors describe the decoding approach and how the MSM curriculum supports students’ ability to reason about scientific models and the real world.

Cycle 7 VLBI Acceptance Report

2026-03-05T18:39:37Z

Cycle 7 VLBI Acceptance Report Crew, Geoff This report summarizes the acceptance process for VLBI which was carried out in early 2020 in preparation for the 2020 VLBI Campaigns. Even though the ALMA operations are suspended, the EHTC campaign has been cancelled, and the GMVA is going forwards without ALMA, it is still a useful exercise to report on the Acceptance testing that was done. This is especially true since (for a variety of reasons) the testing was more extensive this past January. It has also been several years since the initial Acceptance of VLBI for Cycle 4, and new features are finally to become available in Cycle 8, so it is reasonable to capture the state of things at this time. Going forward, it has been suggested as desirable for the Acceptance be added to the normal ALMA Acceptance process. This report thus serves to detail the sort of checks that can and should be made in the future. Some Action items are also noted for the near term. On the bright side, the system was totally ready. This report reviews the setup and on-site checks that can be made in a stand-alone ( no co-observing peers) mode. Then we present results from four VLBI sessions. For each, a CASA reduction is compared with a reduction of the VLBI data which (in three of the four cases) are correlated with participating EHTC sites. This report was prepared for the formal acceptance of the software required for ALMA Observing Cycle 7. Notionally it is ALMA Technical Note #20, but not published (yet) as such.

Thermal analog computing: Application to matrix-vector multiplication with inverse-designed metastructures

2026-02-04T03:08:34Z

Thermal analog computing: Application to matrix-vector multiplication with inverse-designed metastructures Silva, Caio; Romano, Giuseppe The rising computational demand of modern workloads has renewed interest in energy-efficient paradigms, such as neuromorphic and analog computing. A fundamental operation in these systems is matrix-vector multiplication (MVM), ubiquitous in signal processing and machine learning. Here, we demonstrate MVM using inverse-designed metastructures that exploit heat conduction as the signal carrier. The proposed approach is based on a generalization of effective thermal conductivity to systems with multiple input and output ports: The input signal is encoded as a set of applied temperatures, while the output is represented by the power collected at designated terminals. The metastructures are obtained via density-based topology optimization, enabled by a differentiable thermal transport solver and automatic differentiation, achieving an accuracy greater than 99% in most cases across a pool of matrices with dimensions 2 ×2 and 3 ×3. We apply this methodology—termed thermal analog computing—to realize matrices relevant to practical tasks, including the discrete Fourier transform and convolutional filters. These findings open avenues for analog information processing in thermally active environments, including temperature-gradient sensing in microelectronics and thermal control systems.

When competition becomes contagious: Strategic arms racing spillovers, alliance politics, and the Sino-American nuclear competition

2026-02-04T03:08:48Z

When competition becomes contagious: Strategic arms racing spillovers, alliance politics, and the Sino-American nuclear competition Seitz, Samuel M.; Ji, Elliot S. The development of new conventional counterforce systems and improved missile defence systems enables non-nuclear states to directly influence the strategic nuclear balance. These dynamics increase the possibility of strategic arms racing spillovers, where arms racing in one dyad yields capabilities that threaten third parties’ arsenals and thus creates a type of security dilemma. It also increases the risk of non-nuclear allies entrapping their nuclear patrons in strategic arms racing. We illustrate this argument via the case of North and South Korea’s arms racing.

Building communities of critical inquiry in the language classroom

2026-02-04T03:08:44Z

Building communities of critical inquiry in the language classroom Dessein, Eva; Ledford, Julian A Addressing issues of power, difference, and social stratification is essential in language education, where systemic inequities shape classroom experiences. This study examines the design and impact of three targeted modules implemented in beginner and intermediate French courses at two U.S. institutions. Grounded in critical pedagogical principles, the modules focused on language and power, inclusive language practices, and cultural and intercultural awareness. They aimed to foster critical inquiry through individual reflection and engagement with socially relevant topics. Analysis of student reflections and survey responses indicates that the modules supported learners in critically examining how language reinforces or challenges inequities, particularly in relation to gender biases and colonial legacies. Students reported increased awareness of linguistic hierarchies, a stronger sense of agency, and deeper reflection on language’s sociopolitical dimensions. The modules also encouraged engagement with inclusive language and cultural diversity. While the interventions promoted critical awareness and personal growth, findings point to limited peer interaction and community-building. This suggests a need for more structured opportunities for dialogic learning. Overall, the study highlights the transformative potential of critical pedagogy in language education and the importance of designing inclusive curricula that prepare students to reflect on and challenge systemic inequities.

Managing technology-related disruptions and vulnerabilities in highly automated warehouse systems: an integrative review and research agenda

2026-02-04T03:08:41Z

Managing technology-related disruptions and vulnerabilities in highly automated warehouse systems: an integrative review and research agenda Rodríguez-García, Miguel; Kembro, Joakim Hans; Betts, Kellen; Ponce-Cueto, Eva Recent technological developments in warehousing have introduced new risks. This paper presents an integrative review that combines insights from highly automated warehouse systems (HAWS) and risk management, providing a comprehensive understanding of technology-related warehouse disruptions and vulnerabilities. We identify five major disruptions that can affect HAWS: cyberattacks, technology sabotage, technology failures, power and network outages, and human-machine interaction issues. Moreover, we identify 48 technology-related vulnerabilities across all disruptions. In particular, HAWS have become vulnerable to cyberattacks due to the increasing number of warehouse technology suppliers, greater complexity of multi-robot networks such as AMRs, reliance on cloud-based systems, and cascading effect of cyberattacks due to higher levels of interconnectivity in HAWS networks. Our review also shows that risk management strategies in HAWS are unevenly covered in the literature. In response, we propose a research agenda with 17 pathways aimed at enhancing prevention, detection, mitigation, and recovery strategies for HAWS. Managers also benefit from the identified disruptions and vulnerabilities, as they serve as a reference point for understanding their specific technology-related risks in HAWS. In addition, managers can use our review of current risk management practices as a benchmark and our research agenda to think about areas that they could develop further.

Preliminary Investigation of Gamma Radiation on the Chemical and Physical Characteristics of an Organic Coolant

2026-02-04T03:08:46Z

Preliminary Investigation of Gamma Radiation on the Chemical and Physical Characteristics of an Organic Coolant Vasquez, Angel; Seshadri, Arunkumar; Shirvan, Koroush; Buongiorno, Jacopo Organic-cooled reactor concepts offer potential advantages over traditional light water reactors, including operation at elevated temperatures and reduced pressures. However, radiation-induced degradation of organic coolants remains a critical concern requiring thorough investigation. This study examines the effects of gamma irradiation (1-MGy dose) on Dowtherm A (27% biphenyl, 73% diphenyl ether) under varying atmospheric conditions (ambient air versus argon) and temperatures (room temperature versus 250°C). Chemical characterization using Fourier transform infrared spectroscopy, ultraviolet-visible spectroscopy (UV-Vis), and gas chromatography-mass spectrometry revealed the formation of higher molecular weight byproducts, including terphenyls and quaterphenyls, along with notable biphenyl degradation. Physical property measurements using differential scanning calorimetry, rheometry, and thermal conductivity analysis demonstrated significant changes in the thermophysical properties, including decreased heat capacity and viscosity, with increased thermal conductivity observed under argon irradiation conditions. Pronounced photodarkening occurred in all the irradiated samples, with atmospheric conditions significantly influencing degradation pathways. UV-Vis analysis indicated that oxygen presence during irradiation suppresses certain chromophoric species formation. These findings provide crucial insights into radiation-induced degradation mechanisms and their impact on coolant performance, informing future organic coolant system design and optimization strategies for advanced reactor applications.

Housing data politics in the United States: Inequitable open data, informal networks, and strategic neutrality

2026-02-04T03:08:43Z

Housing data politics in the United States: Inequitable open data, informal networks, and strategic neutrality Aizman, Asya; So, Wonyoung; Navalkha, Chenab; D’Ignazio, Catherine Open housing data—property transactions, eviction filings, 311 complaints, and rental registries—have been a crucial resource for policymaking and real estate professionals. Meanwhile, housing data actors increasingly collect, analyze, and use data to address housing inequality, including efforts related to eviction prevention and land use reform, among others. This paper examines the motivations and practices of grassroots and institutional housing data actors. From a field scan of 67 entities engaged in housing data work across 12 U.S. states and 18 municipalities, we conducted 18 in-depth interviews to explore how housing data actors operate, their political goals, and data processes. We put forward a two‑axis framework that positions housing data actors according to their organizational structure (institutional/grassroots) and their stated data ideology (neutral/political). This framework contributes to understanding how different actors navigate complex issues such as embedded power dynamics and ethics in housing data. This two-axis view supplies a vocabulary for tracing how normative commitments and material constraints shape housing data pipelines and, ultimately, housing outcomes across the broader housing information ecosystem.

Unconstrained Sovereignty: Delegation of Authority and Reversibility

2026-02-04T03:08:49Z

Unconstrained Sovereignty: Delegation of Authority and Reversibility Grinberg, Mariya The concept of sovereignty shapes our understanding of the world. Yet our current understanding of sovereignty conflates delegation of authority with loss of sovereignty. Delegation is relatively cheap, quick, and leads to an assured outcome; it’s an affirmation of sovereignty. Use of force, however, is required to regain lost sovereignty. I propose a definition of sovereignty that draws a clear distinction between sovereignty and delegated authority. Adopting this definition shows that sovereignty applies across time and space, it is indivisible, institutions do not place permanent constraints on supreme authority, and popular sovereignty is not a well-grounded concept.

Concurrent Balanced Augmented Trees

2026-02-03T05:03:30Z

Concurrent Balanced Augmented Trees Wrench, Evan; Singh, Ajay; Roh, Younghun; Fatourou, Panagiota; Jayanti, Siddhartha; Ruppert, Eric; Wei, Yuanhao Augmentation makes search trees tremendously more versatile, allowing them to support efficient aggregation queries, order-statistic queries, and range queries in addition to insertion, deletion, and lookup. In this paper, we present the first lock-free augmented balanced search tree supporting generic augmentation functions. Our algorithmic ideas build upon a recent augmented unbalanced search tree presented by Fatourou and Ruppert [DISC, 2024]. We implement both data structures, solving some memory reclamation challenges in the process, and provide an experimental performance analysis of them. We also present optimized versions of our balanced tree that use delegation to achieve better scalability and performance (by more than 2x in most workloads). Our experiments show that our augmented balanced tree completes updates 2.2 to 30 times faster than the unbalanced augmented tree, and outperforms unaugmented trees by up to several orders of magnitude on 120 threads. PPoPP ’26, Sydney, NSW, Australia

Building Intelligent Agents with Neuro-Symbolic Concepts

2026-02-03T05:03:25Z

Building Intelligent Agents with Neuro-Symbolic Concepts Mao, Jiayuan; Tenenbaum, Joshua; Wu, Jiajun This article presents a concept-centric paradigm for building agents that can learn continually and reason flexibly. The concept-centric agent utilizes a vocabulary of neuro-symbolic concepts. These concepts, such as object, relation, and action concepts, are grounded on sensory inputs and actuation outputs. They are also compositional, allowing for the creation of novel concepts through their structural combination. To facilitate learning and reasoning, the concepts are typed and represented using a combination of symbolic programs and neural network representations. Leveraging such neuro-symbolic concepts, the agent can efficiently learn and recombine them to solve various tasks across different domains, ranging from 2D images, videos, 3D scenes, and robotic manipulation tasks. This concept-centric framework offers several advantages, including data efficiency, compositional generalization, continual learning, and zero-shot transfer.

Foundational Verification of Running-Time Bounds for Interactive Programs

2026-02-03T05:03:33Z

Foundational Verification of Running-Time Bounds for Interactive Programs Tockman, Andy; Singh, Pratap; Erbsen, Andres; Gruetter, Samuel; Chlipala, Adam Some important domains of software demand concrete bounds on how long functions may run, for instance for real-time cyberphysical systems where missed deadlines may damage industrial machinery. Such programs may interact with external devices throughout execution, where time deadlines ought to depend on, for instance, sensor readings (e.g. we only scramble to close a valve immediately when a sensor reports that a tank is about to overflow). We present the first software-development toolchain that delivers first-principles proofs of meaningful time bounds for interactive machine code, while allowing all per-application programming and verification to happen at the source-code level. We allow C-like programs to be proved against separation-logic specifications that also constrain their running time, and such proofs are composed with verification of a compiler to RISC-V machine code. All components are implemented and proved inside the Rocq proof assistant, producing final theorems whose statements depend only on machine-language formal semantics and some elementary specification constructions for describing running time. As a capstone case study, we extended a past verification (of a real microcontroller-based cyberphysical system) to bound time between arrival of network packets and actuation of an attached device. CPP ’26, Rennes, France

Network-RBV for Critical Minerals: How Standards, Permits, and Licensing Shape Midstream Bottlenecks

2026-02-03T05:04:11Z

Network-RBV for Critical Minerals: How Standards, Permits, and Licensing Shape Midstream Bottlenecks Kegenbekov, Zhandos; Alipova, Alima; Jackson, Ilya Critical mineral supply chains underpin electric mobility, power electronics, clean hydrogen, and advanced manufacturing. Drawing on the resource-based view (RBV), the relational view, and dynamic capabilities, we conceptualize advantage not as ownership of ore bodies but as orchestration of multi-tier resource systems: upstream access, midstream processing know-how, standards and permits, and durable inter-organizational ties. In a world of high concentration at key stages (refining, separation, engineered materials), full “decoupling” is economically costly and technologically constraining. We argue for structured cooperation among the United States, European Union, China, and other producers and consumers, combined with selective domestic capability building for bona fide security needs. Methodologically, we conduct a structured conceptual synthesis integrating RBV, relational view, dynamic capabilities, and network-of-network research, combined with a structured comparative policy analysis of U.S./EU/Chinese instruments anchored in official documents. We operationalize the argument via technology–material dependency maps that identify midstream bottlenecks and the policy/standard levers most likely to expand qualified, compliant capacity.

REEV SENSE IMUs for Spatiotemporal Gait Analysis in Post-Stroke Patients: Validation Against Optical Motion Capture

2026-02-03T05:04:02Z

REEV SENSE IMUs for Spatiotemporal Gait Analysis in Post-Stroke Patients: Validation Against Optical Motion Capture Marsan, Thibault; Clauzade, Sacha; Zhang, Xiang; Grandin, Nicolas; Urman, Tatiana; Linton, Evan; Sibachir, Samy; Ricciardi, Catherine E.; Temporelli, Robin Objective gait assessment is essential for post-stroke rehabilitation monitoring, yet optical motion capture systems remain inaccessible to most clinical settings due to cost and infrastructure constraints. This study assessed the validity of the REEV SENSE IMU for measuring spatiotemporal gait parameters in post-stroke individuals and evaluated assistive device effects on measurement accuracy. Twenty chronic post-stroke participants were enrolled, and fourteen completed the study (ten without an assistive device, four using a cane) after applying pre-defined exclusion criteria (walking speed <0.28 m/s, n = 6). Participants walked at self-selected speed while simultaneously being recorded by REEV SENSE IMUs and optical motion capture. Spatiotemporal parameters from matched heel strikes were compared using intraclass correlation coefficients (ICC), mean relative error (MRE), and Bland–Altman analysis. Temporal parameters demonstrated excellent reliability: contact time (ICC 0.96–0.99, MRE 2.77–5.45%), stride duration (ICC 0.95–0.99, MRE 2.57–2.62%), and cadence (ICC 0.98–0.99, MRE 1.80–1.93%). Spatial parameters showed greater variability, with stride length degrading substantially in slow-walking conditions (Cane group: ICC 0.76, MRE 8.60%). REEV SENSE provides reliable temporal parameter measurement comparable to commercial systems, positioning it as a practical tool for clinical gait monitoring in post-stroke rehabilitation. However, spatial parameter accuracy requires cautious interpretation in slow-walking regimes, necessitating independent validation when clinical decisions depend on precise stride length estimates.

Who Am I? Eyebrow Follicles Minimize Donor-Derived DNA for Germline Testing After Hematopoietic Stem Cell Transplantation

2026-02-03T05:03:59Z

Who Am I? Eyebrow Follicles Minimize Donor-Derived DNA for Germline Testing After Hematopoietic Stem Cell Transplantation Mertens, Matthias; Sadlo, Mona; Kühl, Jörn-Sven; Metzeler, Klaus; Zschenderlein, Louisa; Edelmann, Jeanett; Lehmann, Claudia; Thull, Sarah; Karakaya, Mert; Velmans, Clara; Tumewu, Theresa; Böhme, Matthias; Klötzer, Christina; Weigert, Anne; Vucinic, Vladan; Hentschel, Julia; Mertens, Mareike Germline genetic testing plays a critical role in diagnosing inherited predispositions and increasingly guides therapeutic and surveillance choices—but becomes technically challenging after allogeneic hematopoietic stem cell transplantation (HSCT), when donor-derived DNA contaminates host tissues. To address this, we compared donor-derived DNA across three accessible tissues—buccal swab, nail, and eyebrow follicles—in recipients after hematopoietic stem cell transplantation using two orthogonal assays (34-SNP next-generation sequencing and a 27-marker short tandem repeat panel) and modeled clinical covariates that influence chimerism. Eyebrow follicles showed consistently low donor DNA (median 1% by NGS; 3% by STR) whereas buccal swabs and nails carried substantially higher donor fractions (+25 and +22 percentage points versus eyebrow, respectively; both p < 0.01). Across methods, STR yielded on average ≈6 percentage points higher donor fractions than NGS at low-level chimerism. Several transplant covariates correlated with chimerism: matched-related donors and a perfect HLA match (10/10) were each associated with lower donor DNA (≈12–14 and 15–20 percentage points, respectively); longer times since hematopoietic stem cell transplantation correlated with lower levels for nail samples, and donor–recipient sex match correlated with higher donor DNA (~7–8 percentage points). Even low-level chimerism can distort germline variant interpretation. We propose a pragmatic protocol for post-hematopoietic stem cell transplantation germline testing that prioritizes eyebrow follicles as the default tissue. An SNP-based quality control assay is used to flag unsafe donor fractions (≥ 5–10%) before comprehensive germline analysis, reducing the risk that chimeric donor DNA distorts germline variant interpretation.

The effect of settlements on the stresses in building frames

2026-02-03T04:59:35Z

The effect of settlements on the stresses in building frames Granberg, Robert J. Thesis: B.S., Massachusetts Institute of Technology, Department of Building Engineering and Construction, 1935; Includes bibliographical references.

Irradiation grafting of styrene onto dacron fibers and films

2026-02-03T04:59:32Z

Irradiation grafting of styrene onto dacron fibers and films Schnetzer, L. J.; Hendren, J. W. Thesis: B.S., Massachusetts Institute of Technology, Department of Chemical Engineering, 1959; Includes bibliographical references (leaves 43-44).

An investigation of sound transmission irregularity in a one dimensional enclosure

2026-02-03T04:59:29Z

An investigation of sound transmission irregularity in a one dimensional enclosure Foster, Isaac C. Thesis: B.S., Massachusetts Institute of Technology, Department of Physics, 1949

Deposition and characterization of very low pressure CVD silicon/silicon-germanium heteroepitaxial structures

2026-02-03T03:48:14Z

Deposition and characterization of very low pressure CVD silicon/silicon-germanium heteroepitaxial structures Tsai, Curtis. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 1992; Includes bibliographical references (leaves 135-146).

An experimental study of the law of parity conservation in electromagnetic interactions.

2026-02-03T03:48:30Z

An experimental study of the law of parity conservation in electromagnetic interactions. Hegblom, Edwin Richard. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 1965

Analysis of a dynamic sales call policy model.

2026-02-03T04:59:26Z

Analysis of a dynamic sales call policy model. Karash, Richard Ivan. Thesis: B.S., Massachusetts Institute of Technology, Department of Physics, 1968; Bibliography: leaf 97.

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.

Comparative tests of the Boston Elevated Co's surface cars

2026-02-03T04:59:24Z

Comparative tests of the Boston Elevated Co's surface cars Jones, Philip C.; Katsainos, Nicholas M. Thesis: B.S., Massachusetts Institute of Technology, Department of Electrical Engineering, 1912

Rules for ring closure and aspects of organolithium chemistry

2026-02-03T03:48:24Z

Rules for ring closure and aspects of organolithium chemistry Dupont, William Alan. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemistry, 1980; Vita.; Includes bibliographical references.

Dracut nickel ore ; Geology and concentration, ore no. 2592

2026-02-03T04:58:59Z

Dracut nickel ore ; Geology and concentration, ore no. 2592 Burton, Eugene.; Spalding, William Livingston. Thesis: B.S., Massachusetts Institute of Technology, Department of Mining Engineering and Metallurgy, 1905

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).

Quantum Circuits Are Just a Phase

2026-02-03T05:03:32Z

Quantum Circuits Are Just a Phase Heunen, Chris; Lemonnier, Louis; McNally, Christopher; Rice, Alex Quantum programs today are written at a low level of abstraction---quantum circuits akin to assembly languages - and the unitary parts of even advanced quantum programming languages essentially function as circuit description languages. This state of affairs impedes scalability, clarity, and support for higher-level reasoning. More abstract and expressive quantum programming constructs are needed. To this end, we introduce a simple syntax for generating unitaries from "just a phase"; we combine a (global) phase operation that captures phase shifts with a quantum analogue of the "if let" construct that captures subspace selection via pattern matching. This minimal language lifts the focus from gates to eigendecomposition, conjugation, and controlled unitaries; common building blocks in quantum algorithm design. We demonstrate several aspects of the expressive power of our language in several ways. Firstly, we establish that our representation is universal by deriving a universal quantum gate set. Secondly, we show that important quantum algorithms can be expressed naturally and concisely, including Grover's search algorithm, Hamiltonian simulation, Quantum Fourier Transform, Quantum Signal Processing, and the Quantum Eigenvalue Transformation. Furthermore, we give clean denotational semantics grounded in categorical quantum mechanics. Finally, we implement a prototype compiler that efficiently translates terms of our language to quantum circuits, and prove that it is sound with respect to these semantics. Collectively, these contributions show that this construct offers a principled and practical step toward more abstract and structured quantum programming.

A Digital Engineering Framework for Piston Pin Bearings via Multi-Physics Thermo-Elasto-Hydrodynamic Modeling

2026-02-03T05:04:01Z

A Digital Engineering Framework for Piston Pin Bearings via Multi-Physics Thermo-Elasto-Hydrodynamic Modeling Shu, Zhiyuan; Tian, Tian The piston pin operates under severe mechanical and thermal conditions, making accurate lubrication prediction essential for engine durability. This study presents a comprehensive digital engineering framework for piston pin bearings, built upon a fully coupled thermo-elasto-hydrodynamic (TEHD) formulation. The framework integrates: (1) a Reynolds-equation hydrodynamic solver with temperature-/pressure-dependent viscosity and cavitation; (2) elastic deformation obtained from FEA (finite element analysis)-based compliance matrices; (3) a break-in module that iteratively adjusts surface profiles before steady-state simulation; (4) a three-body heat transfer model resolving heat conduction, convection, and solid–liquid interfacial heat exchange. Applied to a heavy-duty diesel engine, the framework reproduces experimentally observed behaviors, including bottom-edge rounding at the small end and the slow unidirectional drift of the floating pin. By integrating multi-physics modeling with design-level flexibility, this work aims to provide a robust digital twin for the piston-pin system, enabling virtual diagnostics, early-stage failure prediction, and data-driven design optimization for engine development.

The Wafold: Curvature-Driven Termination and Dimensional Compression in Black Holes

2026-02-03T05:04:13Z

The Wafold: Curvature-Driven Termination and Dimensional Compression in Black Holes Viaña, Javier This work explores a geometric description of black holes in which spacetime terminates on a curvature-triggered hypersurface rather than extending to an interior singularity. We study the implications of a scenario in which, upon reaching a critical curvature threshold, the three-dimensional spatial geometry compresses into a thin, closed boundary identified here as the wafold. Beyond this, the manifold would no longer continue, and all mass–energy and information would be confined to the hypersurface itself. This framework combines two well-explored paths: (1) curvature-driven geometric compression, in which extreme curvature forces the bulk degrees of freedom to become supported on a thin hypersurface (without altering the underlying dimensionality of spacetime), and (2) the motivation underlying the holographic principle, namely that black-hole entropy scales with surface area rather than volume, suggesting that information is governed by a boundary geometry rather than a bulk volume. We elaborate a dimensional conversion law that would be required to describe the collapse of spatial volume into surface area as a conserved flux of geometric capacity across the wafold, and we analyze the resulting consequences of treating this hypersurface as the terminal boundary of the manifold.

Design and Implementation of an Automated Thermal Imaging Device for Lower Limb Prosthetic Applications

2026-02-03T05:04:10Z

Design and Implementation of an Automated Thermal Imaging Device for Lower Limb Prosthetic Applications Pizarro, Daniel; Huegel, Joel C.; Diaz, Elias; Alemon, Beatriz; Herr, Hugh; Felix-Herran, Luis C. Since elevated temperature and humidity may occur at the prosthetic socket–skin interface, it is essential to collect thermal data from the residual limb, as this information serves as an indicator of adverse effects such as irritation, postural problems, and significant damage to health. These data are obtained non-invasively through the execution of a thermal imaging (TI) procedure. However, the precision and repeatability of a TI procedure rely significantly on its execution technique. This work presents the design and implementation of a mechatronic device that automates a thermal imaging technique. The application of the device is in lower-limb prosthetics evaluation. The proposed system improves data acquisition consistency by reducing execution time and minimizing human error, thereby enhancing the reproducibility and reliability of thermal measurements. The introduced device, Thermal Imaging Booth, proposes an automated solution for TI standardization in clinical and research settings. By minimizing inconsistencies, this system improves the diagnostic potential of thermography, facilitating its adoption in biomedical applications.

Peepco: Batch-Based Consistency Optimization

2026-02-01T07:32:05Z

Peepco: Batch-Based Consistency Optimization Kuraj, Ivan; Feser, John; Polikarpova, Nadia; Solar-Lezama, Armando We present batch-based consistency, a new approach for consistency optimization that allows programmers to specialize consistency with application-level integrity properties. We implement the approach with a two-step process: we statically infer optimal consistency requirements for executions of bounded sets of operations, and then, use the inferred requirements to parameterize a new distributed protocol to relax operation reordering at run time when it is safe to do so. Our approach supports standard notions of consistency. We implement batch-based consistency in Peepco, demonstrate its expressiveness for partial data replication, and examine Peepco’s run-time performance impact in different settings.

Finch: Sparse and Structured Tensor Programming with Control Flow

2026-02-01T07:31:58Z

Finch: Sparse and Structured Tensor Programming with Control Flow Ahrens, Willow; Collin, Teodoro; Patel, Radha; Deeds, Kyle; Hong, Changwan; Amarasinghe, Saman From FORTRAN to NumPy, tensors have revolutionized how we express computation. However, tensors in these, and almost all prominent systems, can only handle dense rectilinear integer grids. Real world tensors often contain underlying structure, such as sparsity, runs of repeated values, or symmetry. Support for structured data is fragmented and incomplete. Existing frameworks limit the tensor structures and program control flow they support to better simplify the problem. In this work, we propose a new programming language, Finch, which supports both flexible control flow and diverse data structures. Finch facilitates a programming model which resolves the challenges of computing over structured tensors by combining control flow and data structures into a common representation where they can be co-optimized. Finch automatically specializes control flow to data so that performance engineers can focus on experimenting with many algorithms. Finch supports a familiar programming language of loops, statements, ifs, breaks, etc., over a wide variety of tensor structures, such as sparsity, run-length-encoding, symmetry, triangles, padding, or blocks. Finch reliably utilizes the key properties of structure, such as structural zeros, repeated values, or clustered non-zeros. We show that this leads to dramatic speedups in operations such as SpMV and SpGEMM, image processing, and graph analytics.

Smooth, Integrated Proofs of Cryptographic Constant Time for Nondeterministic Programs and Compilers

2026-02-01T07:32:16Z

Smooth, Integrated Proofs of Cryptographic Constant Time for Nondeterministic Programs and Compilers Conoly, Owen; Erbsen, Andres; Chlipala, Adam Formal verification of software and compilers has been used to rule out large classes of security-critical issues, but risk of unintentional information leakage has received much less consideration. It is a key requirement for formal specifications to leave some details of a system's behavior unspecified so that future implementation changes can be accommodated, and yet it is nonetheless expected that these choices would not be made based on confidential information the system handles. This paper formalizes that notion using omnisemantics and plain single-copy assertions, giving for the first time a specification of what it means for a nondeterministic program to be constant-time or more generally to avoid leaking (a part of) its inputs. We use this theory to prove data-leak-free execution of core cryptographic routines compiled from Bedrock2 C to RISC-V machine code, showing that the smooth specification and proof experience omnisemantics provides for nondeterminism extends to constant-time properties in the same setting. We also study variants of the key program-compiler contract, highlighting pitfalls of tempting simplifications and subtle consequences of how inputs to nondeterministic choices are constrained. Our results are backed by modular program-logic and compiler-correctness theorems, and they integrate into a neat end-to-end theorem in the Coq proof assistant.

NeuroChat: A Neuroadaptive AI Chatbot for Customizing Learning Experiences

2026-02-01T07:32:11Z

NeuroChat: A Neuroadaptive AI Chatbot for Customizing Learning Experiences Baradari, D?nya; Kosmyna, Nataliya; Petrov, Oscar; Kaplun, Rebecah; Maes, Pattie Generative AI is reshaping education by enabling personalized, on-demand learning experiences. However, current AI systems lack awareness of the learner’s cognitive state, limiting their adaptability. In parallel, electroencephalography (EEG)-based neuroadaptive systems have shown promise in enhancing engagement through real-time physiological feedback. This paper introduces NeuroChat, a neuroadaptive AI tutor that integrates real-time EEG-based engagement tracking with a large language model to adapt its conversational responses. By continuously monitoring learners’ cognitive engagement, NeuroChat dynamically adjusts content complexity, tone, and response style in a closed-loop interaction. In a within-subjects study (n = 24), NeuroChat significantly increased both EEG-measured and self-reported engagement compared to a non-adaptive chatbot. However, no significant differences in short-term learning outcomes were observed. These findings demonstrate the feasibility of real-time brain–AI interaction for education and highlight opportunities for deeper personalization, longer-term adaptation, and richer learning assessment in future neuroadaptive systems. CUI ’25, Waterloo, ON, Canada

From Synthetic to Human: The Gap Between AI-Predicted and Actual Pro-Environmental Behavior Change After Chatbot Persuasion

2026-02-01T07:32:08Z

From Synthetic to Human: The Gap Between AI-Predicted and Actual Pro-Environmental Behavior Change After Chatbot Persuasion Doudkin, Alexander; Pataranutaporn, Pat; Maes, Pattie Pro-environmental behavior (PEB) is vital to combat climate change, yet turning awareness into intention and action remains elusive. We explore large language models (LLMs) as tools to promote PEB, comparing their impact across 3,600 participants: real humans (n=1,200), simulated humans based on actual participant data (n=1,200), and fully synthetic personas (n=1,200). All three participant groups faced either personalized chatbots, standard chatbots, or static statements, employing four persuasion strategies (moral foundations, future self-continuity, action orientation, or ”freestyle” chosen by the LLM). Results reveal a ”synthetic persuasion paradox”: synthetic and simulated participants significantly change their post-intervention PEB stance, while human attitudes barely shift. Simulated participants better approximate human behavior but still overestimate effects. This disconnect underscores LLM’s potential for pre-evaluating PEB interventions but warns of its limits in predicting human responses. We call for refined synthetic modeling and sustained and extended human trials to align conversational AI’s promise with tangible sustainability outcomes. CUI ’25, Waterloo, ON, Canada

Adaptive Approximation Schemes for Matching Queues

2026-02-01T07:32:06Z

Adaptive Approximation Schemes for Matching Queues AmaniHamedani, Alireza; Aouad, Ali; Saberi, Amin We study a continuous-time, infinite-horizon dynamic bipartite matching problem. Suppliers arrive according to a Poisson process; while waiting, they may abandon the queue at a uniform rate. Customers on the other hand must be matched upon arrival. The objective is to minimize the expected long-term average cost subject to a throughput constraint on the total match rate. Previous literature on dynamic matching focuses on ”static” policies, where the matching decisions do not depend explicitly on the state of the supplier queues, achieving constant-factor approximations. By contrast, we design ”adaptive” policies, which leverage queue length information, and obtain near-optimal polynomial-time algorithms for several classes of instances. First, we develop a bi-criteria fully polynomial-time approximation scheme for dynamic matching on networks with a constant number of queues—that computes a (1−є)-approximation of the optimal policy in time polynomial in both the input size and 1/є. A key new technique is a hybrid LP relaxation, which combines static and state-dependent LP approximations of the queue dynamics, after a decomposition of the network. Networks with a constant number of queues are motivated by deceased organ donation schemes, where the supply types can be divided according to blood and tissue types. The above algorithm, combined with a careful cell decomposition gives a polynomial-time approximation scheme for dynamic matching on Euclidean networks of fixed dimension. The Euclidean case is of interest in ride-hailing and spatial service platforms, where the goal is to fulfill as many trips as possible while minimizing driving distances. STOC ’25, Prague, Czechia

Output-Sensitive Approximate Counting via a Measure-Bounded Hyperedge Oracle, or: How Asymmetry Helps Estimate ��-Clique Counts Faster

2026-02-01T07:32:12Z

Output-Sensitive Approximate Counting via a Measure-Bounded Hyperedge Oracle, or: How Asymmetry Helps Estimate ��-Clique Counts Faster Censor-Hillel, Keren; Even, Tomer; Vassilevska Williams, Virginia Dell, Lapinskas and Meeks [DLM SICOMP 2022] presented a general reduction from approximate counting to decision for a class of fine-grained problems that can be viewed as hyperedge counting or detection problems in an implicit hypergraph, thus obtaining tight equivalences between approximate counting and decision for many key problems such as k-clique, k-sum and more. Their result is a reduction from approximately counting the number of hyperedges in an implicit k-partite hypergraph to a polylogarithmic number of calls to a hyperedge oracle that returns whether a given subhypergraph contains an edge. The main result of this paper is a generalization of the DLM result for output-sensitive approximate counting, where the running time of the desired counting algorithm is inversely proportional to the number of witnesses. Our theorem is a reduction from approximately counting the (unknown) number of hyperedges in an implicit k-partite hypergraph to a polylogarithmic number of calls to a hyperedge oracle called only on subhypergraphs with a small “measure”. If a subhypergraph has ui nodes in the ith node partition of the k-partite hypergraph, then its measure is ∏i ui. Using the new general reduction and by efficiently implementing measure-bounded colorful independence oracles, we obtain new improved output-sensitive approximate counting algorithms for k-clique, k-dominating set and k-sum. In graphs with nt k-cliques, for instance, our algorithm (1± є)-approximates the k-clique count in time Õє(nω(k−t−1/3,k−t/3,k−t+2/3) +n2), where ω(a,b,c) is the exponent of na× nb by nb× nc matrix multiplication. For large k and t>2, this is a substantial improvement over prior work, even if ω=2. STOC ’25, Prague, Czechia

Lightweight and Locality-Aware Composition of Black-Box Subroutines

2026-02-01T07:32:14Z

Lightweight and Locality-Aware Composition of Black-Box Subroutines Bansal, Manya; Sharlet, Dillon; Ragan-Kelley, Jonathan; Amarasinghe, Saman Subroutines are essential building blocks in software design: users encapsulate common functionality in libraries and write applications by composing calls to subroutines. Unfortunately, performance may be lost at subroutine boundaries due to reduced locality and increased memory consumption. Operator fusion helps recover performance lost at composition boundaries. Previous solutions fuse operators by manually rewriting code into monolithic fused subroutines, or by relying on heavy-weight compilers to generate code that performs fusion. Both approaches require a semantic understanding of the entire computation, breaking the decoupling necessary for modularity and reusability of subroutines. In this work, we attempt to identify the minimal ingredients required to fuse computations, enabling composition of subroutines without sacrificing performance or modularity. We find that, unlike previous approaches that require a semantic understanding of the computation, most opportunities for fusion require understanding only data production and consumption patterns. Exploiting this insight, we add fusion on top of black-box subroutines by proposing a lightweight enrichment of subroutine declarations to expose data-dependence patterns. We implement our approach in a system called Fern, and demonstrate Fern's benefits by showing that it is competitive with state-of-the-art, high-performance libraries with manually fused operators, can fuse across library and domain boundaries for unforeseen workloads, and can deliver speedups of up to $5\times$ over unfused code.

Prolonged photostability in hexagonal boron nitride quantum emitters

2026-02-01T07:32:52Z

Prolonged photostability in hexagonal boron nitride quantum emitters Li, Sylvia Xin; Ichihara, Takeo; Park, Hyoju; He, Guangwei; Kozawa, Daichi; Wen, Yi; Koman, Volodymyr B; Zeng, Yuwen; Kuehne, Matthias; Yuan, Zhe; Faucher, Samuel; Warner, Jamie H; Strano, Michael S Single-photon emitters are crucial building blocks for optical quantum technologies. Hexagonal boron nitride (hBN) is a promising two-dimensional material that hosts bright, room-temperature single-photon emitters. However, photo instability is a persistent challenge preventing practical applications of these properties. Here, we reveal the ubiquitous photobleaching of hBN vacancy emitters. Independent of the source or the number of hBN layers, we find that the photobleaching of a common emission at 1.98 ± 0.05 eV can be described by two consistent time constants, namely a first bleaching lifetime of 5 to 10 s, and a second bleaching lifetime in the range of 150 to 220 s. Only the former is environmentally sensitive and can be significantly mitigated by shielding O2, whereas the latter could be the result of carbon-assisted defect migration. Annular dark-field scanning transmission electron microscopy of photobleached hBN allows for visualizing vacancy defects and carbon substitution at single atom resolution, supporting the migration mechanism along with X-ray photoelectron spectroscopy. Thermal annealing at 850 °C of liquid exfoliated hBN eliminates both bleaching processes, leading to persistent photostability. These results represent a significant advance to potentially engineer hBN vacancy emitters with the photostability requisite for quantum applications.

Discretized hexagonal boron nitride quantum emitters and their chemical interconversion

2026-02-01T07:32:50Z

Discretized hexagonal boron nitride quantum emitters and their chemical interconversion Kozawa, Daichi; Li, Sylvia Xin; Ichihara, Takeo; Rajan, Ananth Govind; Gong, Xun; He, Guangwei; Koman, Volodymyr B; Zeng, Yuwen; Kuehne, Matthias; Silmore, Kevin S; Parviz, Dorsa; Liu, Pingwei; Liu, Albert Tianxiang; Faucher, Samuel; Yuan, Zhe; Warner, Jamie; Blankschtein, Daniel; Strano, Michael S Quantum emitters in two-dimensional hexagonal boron nitride (hBN) are of significant interest because of their unique photophysical properties, such as single-photon emission at room temperature, and promising applications in quantum computing and communications. The photoemission from hBN defects covers a wide range of emission energies but identifying and modulating the properties of specific emitters remain challenging due to uncontrolled formation of hBN defects. In this study, more than 2000 spectra are collected consisting of single, isolated zero-phonon lines (ZPLs) between 1.59 and 2.25 eV from diverse sample types. Most of ZPLs are organized into seven discretized emission energies. All emitters exhibit a range of lifetimes from 1 to 6 ns, and phonon sidebands offset by the dominant lattice phonon in hBN near 1370 cm−1. Two chemical processing schemes are developed based on water and boric acid etching that generate or preferentially interconvert specific emitters, respectively. The identification and chemical interconversion of these discretized emitters should significantly advance the understanding of solid-state chemistry and photophysics of hBN quantum emission.

Rational Design and Efficacy of Glucose‐Responsive Insulin Therapeutics and Insulin Delivery Systems by Computation Using Connected Human and Rodent Models

2026-02-01T07:32:49Z

Rational Design and Efficacy of Glucose‐Responsive Insulin Therapeutics and Insulin Delivery Systems by Computation Using Connected Human and Rodent Models Yang, Sungyun; Yang, Jing Fan; Gong, Xun; Weiss, Michael A; Strano, Michael S Glucose‐responsive insulins (GRIs) use plasma glucose levels in a diabetic patient to activate a specifically designed insulin analogue to a more potent state in real time. Alternatively, some GRI concepts use glucose‐mediated release or injection of insulin into the bloodstream. GRIs hold promise to exhibit much improved pharmacological control of the plasma glucose concentration, particularly for the problem of therapeutically induced hypoglycemia. Several innovative GRI schemes are introduced into the literature, but there remains a dearth of quantitative analysis to aid the development and optimization of these constructs into effective therapeutics. This work evaluates several classes of GRIs that are proposed using a pharmacokinetic model as previously described, PAMERAH, simulating the glucoregulatory system of humans and rodents. GRI concepts are grouped into three mechanistic classes: 1) intrinsic GRIs, 2) glucose‐responsive particles, and 3) glucose‐responsive devices. Each class is analyzed for optimal designs that maintain glucose levels within the euglycemic range. These derived GRI parameter spaces are then compared between rodents and humans, providing the differences in clinical translation success for each candidate. This work demonstrates a computational framework to evaluate the potential clinical translatability of existing glucose‐responsive systems, providing a useful approach for future GRI development.

Wearable sensors for monitoring marine environments and their inhabitants

2026-02-01T07:32:54Z

Wearable sensors for monitoring marine environments and their inhabitants Kaidarova, Altynay; Geraldi, Nathan R; Wilson, Rory P; Kosel, Jürgen; Meekan, Mark G; Eguíluz, Víctor M; Hussain, Muhammad Mustafa; Shamim, Atif; Liao, Hanguang; Srivastava, Mani; Saha, Swapnil Sayan; Strano, Michael S; Zhang, Xiangliang; Ooi, Boon S; Holton, Mark; Hopkins, Lloyd W; Jin, Xiaojia; Gong, Xun; Quintana, Flavio; Tovasarov, Adylkhan; Tasmagambetova, Assel; Duarte, Carlos M Human societies depend on marine ecosystems, but their degradation continues. Toward mitigating this decline, new and more effective ways to precisely measure the status and condition of marine environments are needed alongside existing rebuilding strategies. Here, we provide an overview of how sensors and wearable technology developed for humans could be adapted to improve marine monitoring. We describe barriers that have slowed the transition of this technology from land to sea, update on the developments in sensors to advance ocean observation and advocate for more widespread use of wearables on marine organisms in the wild and in aquaculture. We propose that large-scale use of wearables could facilitate the concept of an ‘internet of marine life’ that might contribute to a more robust and effective observation system for the oceans and commercial aquaculture operations. These observations may aid in rationalizing strategies toward conservation and restoration of marine communities and habitats.

Operational Fuel Inefficiency in Cruise Flight: A Worldwide Geospatial Analysis

2026-02-01T03:01:19Z

Operational Fuel Inefficiency in Cruise Flight: A Worldwide Geospatial Analysis Trávník, Marek; Hansman, R. John

Low-Cost Deep Learning for Building Detection with Application to Informal Urban Planning

2026-04-14T03:38:34Z

Low-Cost Deep Learning for Building Detection with Application to Informal Urban Planning González, Lucas; Toutouh, Jamal; Nesmachnow, Sergio This article studies the application of deep neural networks for automatic building detection in aerial RGB images. Special focus is put on accuracy robustness in both well-structured and poorly planned urban scenarios, which pose significant challenges due to occlusions, irregular building layouts, and limited contextual cues. The applied methodology considers several CNNs using only RBG images as input, and both validation and transfer capabilities are studied. U-Net-based models achieve the highest single-model accuracy, with an Intersection over Union (𝐼⁢𝑜⁢𝑈) of 0.9101. A soft-voting ensemble of the best U-Net models further increases performance, reaching a best ensemble 𝐼⁢𝑜⁢𝑈 of 0.9665, improving over state-of-the-art building detection methods on standard benchmarks. The approach demonstrates strong generalization using only RGB imagery, supporting scalable, low-cost applications in urban planning and geospatial analysis.

When the Psyche and the 'Net Collide: Sources of and potential methods for preventing Bad Behavior Online

2026-02-11T15:35:02Z

When the Psyche and the 'Net Collide: Sources of and potential methods for preventing Bad Behavior Online Wedeman, Sara; Clark, David D With the emergence of social networking has come a range of harmful and malicious behaviors online, including disinformation, cyberbullying, and sextortion among others. These behaviors arise from a number of causes, including the incentives of the providers of the social networking platforms and the technical affordances of those platforms, which in some cases facilitate these abuses. This report sheds light on the causes and possible mitigations of these behaviors through the lens of behavioral psychology. Results from psychological research suggest that these abuses play on specific human attributes. To design effective mitigations, it is crucial that these human attributes be understood. This report draws on literature from psychology research to outline the important human behavioral attributes, relates these to some of the important affordances found in social networking applications, and suggests possible approaches that can damp the bad behavior we observe online. This is the final report for an NSF-sponsored study of psychology literature related to the online experience, and the drivers and possible mitigations of bad behavior online.

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.

Weak Identification and Network Measurement Error in Peer Effects Estimation

2026-01-30T03:06:54Z

Weak Identification and Network Measurement Error in Peer Effects Estimation Wang, William Wei The growing availability of social network data has enabled a surge of research on social interactions. In particular, peer effects, once considered unidentifiable, have now been shown to be identified given knowledge of the network structure. Despite this positive result, questions remain about the existence and nature of peer effects, due to concerns about identification strength and the reliability of network data. This work investigates two key threats to the estimation of peer effects: weak identification and network measurement error. We show that weak instrument problems arise in moderately dense networks due to rapid averaging, leading to slow convergence rates even when estimators remain consistent. On the measurement error side, we show that additive edge weight errors can be mitigated in such networks due to the same averaging phenomena, but the error remains a relevant threat to consistency in sparser networks. We further demonstrate that when both issues are present, the resulting estimators exhibit non-vanishing bias, suggesting that the combined effect of weak instruments and measurement error can be more severe than either problem in isolation. Overall, our results aim to clarify how these non-standard estimation challenges impact our ability to study peer effects using network data.

Seeing Beyond Limits with Physics-Informed Priors

2026-01-30T03:06:34Z

Seeing Beyond Limits with Physics-Informed Priors Liu, Yang Conventional imaging systems are limited by dimensionality and visibility: standard sensors capture only two-dimensional data, while light diffuses or scatters across surfaces and through complex media. This dissertation reformulates imaging as an interplay of optical encoding and neural decoding. It models forward physical processes and iteratively refines them using deep denoisers. By embedding physics-informed priors into this optimization, it aims to surpass conventional limits in dimensionality and visibility. First, I develop Privacy Dual Imaging using an ambient light sensor. This approach tackles both dimensionality and visibility challenges when imaging with a single-point, non-imaging component on smart devices. Inspired by 1984’s “Big Brother” telescreen, I demonstrate how subtle light intensity fluctuations can reveal unseen image information; however, the goal is to highlight privacy concerns, not exploit them. It addresses two visibility limits—pixel-less and lens-less imaging—by using the screen as a spatial modulator and exploiting involuntary motion to create a virtual pinhole effect. A quantized, physics-informed prior improves reconstruction from heavily quantized sensor measurements. Second, I propose Snapshot Compressive Imaging (SCI) augmented with deep plug-and-play physics-informed priors to overcome the dimensionality limit of 2D sensors. SCI compressively encodes multiple temporal, spectral, or angular frames into a single measurement. A deep plug-and-play prior algorithm introduces high-dimensional priors learned from images and videos into the iterative reconstruction process, improving fidelity, speed, and flexibility. Experiments show notable gains in reconstruction quality and efficiency across different SCI datasets, including largeformat 4K UHD scenarios. Third, I introduce Rank-Reduced physics-informed priors, showing that large pretrained AI models—especially diffusion models—can act as general visual priors across both dimensionality and visibility challenges. A relax-then-tighten strategy handles ill-conditioning by applying truncated singular value decomposition to reduce rank deficiencies, followed by a Stable Diffusion refiner (SDEdit) plug-and-play prior that constrains reconstructions to valid image spaces. Simulations and passive non-line-of-sight imaging experiments verify the approach’s stability and effectiveness. Physics-informed priors promise to extend the boundaries of imaging, enabling us to see beyond current dimensionality and visibility limits and to unlock new applications from macro-scale to micro-scale observations.

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.

Foundational Abstractions for Quantum Programming

2026-01-30T03:06:30Z

Foundational Abstractions for Quantum Programming Yuan, Charles Bringing the promise of quantum computation into reality requires not only building a quantum computer but also correctly programming it to run a quantum algorithm. To obtain asymptotic advantage over classical algorithms for applications including simulation, search, and optimization, quantum algorithms rely on the ability of data in quantum superposition to exhibit phenomena such as interference and entanglement. In turn, an implementation of the algorithm as a program must correctly orchestrate these phenomena in the states of qubits. Otherwise, it would yield incorrect outputs or lose quantum computational advantage. Given a quantum algorithm, what are the challenges and costs of realizing it as a program that can run on a physical quantum computer? In this thesis, I answer this question by showing how the basic abstractions of programming upon which many quantum algorithms rely – such as data structures and control flow – can fail to work correctly or efficiently on a quantum computer. I then demonstrate how we can leverage insights from research in programming languages to re-invent the software stack – including abstractions, libraries, and compilers – to meet the demands of quantum algorithms. This approach holds out a promise of expressive and efficient tools to program a quantum computer and thereby practically realize its computational advantage.

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.