<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-09-20T03:48:22Z</responseDate><request verb="GetRecord" identifier="oai:dspace.mit.edu:1721.1/76951" metadataPrefix="dim">https://dspace.mit.edu/server/oai/request</request><GetRecord><record><header><identifier>oai:dspace.mit.edu:1721.1/76951</identifier><datestamp>2026-06-06T01:06:46Z</datestamp><setSpec>com_1721.1_7582</setSpec><setSpec>com_1721.1_7581</setSpec><setSpec>col_1721.1_131023</setSpec></header><metadata><dim:dim xmlns:dim="http://www.dspace.org/xmlns/dspace/dim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.dspace.org/xmlns/dspace/dim http://www.dspace.org/schema/dim.xsd">
   <dim:field mdschema="dc" element="contributor" qualifier="advisor" lang="en_US">Michael W. Golay.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="author" lang="en_US">Rodewald, Oliver Russell</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="other" lang="en_US">Massachusetts Institute of Technology. Dept. of Nuclear Science and Engineering.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="department">Massachusetts Institute of Technology. Department of Nuclear Science and Engineering</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="accessioned">2013-02-14T15:31:26Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="available">2013-02-14T15:31:26Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="copyright" lang="en_US">2011</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="issued" lang="en_US">2011</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="uri">http://hdl.handle.net/1721.1/76951</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="oclc" lang="en_US">824617723</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Thesis (S.M. and S.B.)--Massachusetts Institute of Technology, Dept. of Nuclear Science and Engineering, 2011.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Cataloged from PDF version of thesis.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Includes bibliographical references (p. 66-67).</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="abstract" lang="en_US">Nuclear Fuel reprocessing is done today with the PUREX process, which has been demonstrated to work at industrial scales at several facilities around the world. Use of the PUREX process results in the creation of a stream of pure plutonium, which allows the process to be potentially used by a proliferator. Safeguards have been put in place by the IAEA and other agencies to guard against the possibility of diversion and misuse, but the cost of these safeguards and the intrusion into a facility they represent could cause a fuel reprocessing facility operator to consider foregoing standard safeguards in favor of diversion detection that is less intrusive. Use of subjective expertise in a Bayesian network offers a unique opportunity to monitor a fuel reprocessing facility while collecting limited information compared to traditional safeguards. This work focuses on the preliminary creation of a proof of concept Bayesian network and its application to a model nuclear fuel reprocessing facility.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="statementofresponsibility" lang="en_US">by Oliver Russell Rodewald.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="degree" lang="en_US">S.M.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="degree" lang="en_US">S.B.</dim:field>
   <dim:field mdschema="dc" element="format" qualifier="extent" lang="en_US">117 p.</dim:field>
   <dim:field mdschema="dc" element="language" qualifier="iso" lang="en_US">eng</dim:field>
   <dim:field mdschema="dc" element="publisher" lang="en_US">Massachusetts Institute of Technology</dim:field>
   <dim:field mdschema="dc" element="rights" lang="en_US">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.</dim:field>
   <dim:field mdschema="dc" element="rights" qualifier="uri" lang="en_US">http://dspace.mit.edu/handle/1721.1/7582</dim:field>
   <dim:field mdschema="dc" element="subject" lang="en_US">Nuclear Science and Engineering.</dim:field>
   <dim:field mdschema="dc" element="title" lang="en_US">Use of Bayesian inference to estimate diversion likelihood in a PUREX facility</dim:field>
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   	&lt;Title>Use of Bayesian inference to estimate diversion likelihood in a PUREX facility&lt;/Title>
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   	&lt;PublicationDate>2011&lt;/PublicationDate>
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        	&lt;DisplayName>Rodewald, Oliver Russell&lt;/DisplayName>
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    &lt;License>http://dspace.mit.edu/handle/1721.1/7582&lt;/License>
    &lt;Keyword>Nuclear Science and Engineering.&lt;/Keyword>
   	&lt;Abstract>Nuclear Fuel reprocessing is done today with the PUREX process, which has been demonstrated to work at industrial scales at several facilities around the world. Use of the PUREX process results in the creation of a stream of pure plutonium, which allows the process to be potentially used by a proliferator. Safeguards have been put in place by the IAEA and other agencies to guard against the possibility of diversion and misuse, but the cost of these safeguards and the intrusion into a facility they represent could cause a fuel reprocessing facility operator to consider foregoing standard safeguards in favor of diversion detection that is less intrusive. Use of subjective expertise in a Bayesian network offers a unique opportunity to monitor a fuel reprocessing facility while collecting limited information compared to traditional safeguards. This work focuses on the preliminary creation of a proof of concept Bayesian network and its application to a model nuclear fuel reprocessing facility.&lt;/Abstract>
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