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Richard C. Lanza. Chow, Jijun Massachusetts Institute of Technology. Dept. of Nuclear Science and Engineering. Massachusetts Institute of Technology. Department of Nuclear Science and Engineering 2010-05-25T21:11:51Z 2010-05-25T21:11:51Z 2009 2009 http://hdl.handle.net/1721.1/55262 613192765 Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Nuclear Science and Engineering, 2009. Cataloged from PDF version of thesis. Includes bibliographical references (p. 41). Nuclear and radiological events are large-impact, hard-to-predict rare events, whose associated probability is exceedingly low. They can exert monumental impacts and lead to grave environmental and economic consequences. Identifying common trends of these events can help to assess the threat, and to combat it with better detection capabilities and practices. One way to achieve this is to model the events with established statistical and mathematical distributions. Power-law distribution is a good candidate because it is a probability distribution with asymptotic tails, and thus can be applied to study patterns of rare events of large deviations, such as those involving nuclear and radiological materials. This thesis, based on the hypothesis that nuclear and radiological events follow the power-law growth model, assembles published data of four categories of events - incidents of nuclear and radiological materials, incidents of radioactive attacks, unauthorized activities of illicit trafficking, and incidents of nuclear terrorism, and investigates whether specific distributions such as the power-law can be applied to analyze the data. Data are gathered from a number of sources. Even though data points are collected, the databases are far from complete, mainly due to the limited amount of public information that is available to the outside party, rendering the modeling task difficult and challenging. Furthermore, there may exist many undocumented instances, underscoring the fact that the reporting is an ongoing effort. (cont.) To compile a comprehensive dataset for analytical purposes, a more efficient method of collecting data should be employed. This requires gathering information through various means, including different departmental or governmental domains that are available to the public as well as professional insight and support. In addition, to facilitate better management of nuclear and radiological events, technological capacities to track them need to be strengthened, and information sharing and coordination need to be enhanced not only on regional but also on national and international levels. by Jijun Chow. S.B. 42 p. eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Nuclear Science and Engineering. Power-law distributions in events involving nuclear and radiological materials Thesis application/pdf Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="908e9e4d-d0c2-46a0-9f4d-f1ad97e44a45"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Power-law distributions in events involving nuclear and radiological materials</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2009</PublicationDate> <Authors> <Author> <DisplayName>Chow, Jijun</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Nuclear Science and Engineering.</Keyword> <Abstract>Nuclear and radiological events are large-impact, hard-to-predict rare events, whose associated probability is exceedingly low. They can exert monumental impacts and lead to grave environmental and economic consequences. Identifying common trends of these events can help to assess the threat, and to combat it with better detection capabilities and practices. One way to achieve this is to model the events with established statistical and mathematical distributions. Power-law distribution is a good candidate because it is a probability distribution with asymptotic tails, and thus can be applied to study patterns of rare events of large deviations, such as those involving nuclear and radiological materials. This thesis, based on the hypothesis that nuclear and radiological events follow the power-law growth model, assembles published data of four categories of events - incidents of nuclear and radiological materials, incidents of radioactive attacks, unauthorized activities of illicit trafficking, and incidents of nuclear terrorism, and investigates whether specific distributions such as the power-law can be applied to analyze the data. Data are gathered from a number of sources. Even though data points are collected, the databases are far from complete, mainly due to the limited amount of public information that is available to the outside party, rendering the modeling task difficult and challenging. Furthermore, there may exist many undocumented instances, underscoring the fact that the reporting is an ongoing effort.</Abstract> <Abstract>(cont.) To compile a comprehensive dataset for analytical purposes, a more efficient method of collecting data should be employed. This requires gathering information through various means, including different departmental or governmental domains that are available to the public as well as professional insight and support. In addition, to facilitate better management of nuclear and radiological events, technological capacities to track them need to be strengthened, and information sharing and coordination need to be enhanced not only on regional but also on national and international levels.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>