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Materials Science & Technology
The objectives of MSTD are: promote the advancement of materials science in Nuclear Science Technology; support the multidisciplines which constitute it; encourage research by providing a forum for the presentation, exchange, and documentation of relevant information; promote the interaction and communication among its members; and recognize and reward its members for significant contributions to the field of materials science in nuclear technology.
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2024 ANS Annual Conference
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Las Vegas, NV|Mandalay Bay Resort and Casino
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Remembering Charles E. Till
Charles E. Till
Charles E. Till, an ANS member since 1963 and Fellow since 1987, passed away on March 22 at the age of 89. He earned bachelor’s and master’s degrees from the University of Saskatchewan and a Ph.D. in nuclear engineering from Imperial College, University of London. Till initially worked for the Civilian Atomic Power Department of the Canadian General Electric Company, where he was the physicist in charge of the startup of the first prototype CANDU reactor in Canada.
Till joined Argonne National Laboratory in 1963 in the Applied Physics Division, where he worked as an experimentalist in the Fast Critical Experiments program. He then moved to additional positions of increasing responsibility, becoming division director in 1973. Under his leadership, the Applied Physics Division established itself as one of the elite reactor physics organizations in the world. Both the experimental (critical experiments and nuclear data measurements) and nuclear analysis methods work were internationally recognized. Till led Argonne’s participation in the International Nuclear Fuel Cycle Evaluation (INFCE), and he was the lead U.S. delegate to INFCE Working Group 5, Fast Breeders.
Arvind Sundaram, Hany Abdel-Khalik, Ahmad Al Rashdan
Nuclear Science and Engineering | Volume 196 | Number 8 | August 2022 | Pages 911-926
Technical Paper | doi.org/10.1080/00295639.2022.2043542
Articles are hosted by Taylor and Francis Online.
This work addresses how analysts of a high-valued system (e.g., nuclear reactor, aircraft turbine designs) can extract findable, accessible, interoperable, and reusable scientific data for public dissemination to artificial intelligence and machine-learning (AI/ML) researchers in a manner that cannot be reverse-engineered, potentially compromising sensitive or proprietary information. State-of-the-art methods address this problem through data masking techniques, which allow access to a subset of the information while obfuscating private and potentially identifying information (e.g., personally identifying medical data). These methods are unsuitable for industrial engineering processes, where AI/ML tools need explicit access to all the data available to draw the best inference about the system to help optimize its performance and identify its vulnerabilities, etc. Our novel deceptive infusion of data paradigm provides a solution to this conundrum by developing a mathematical approach capable of concealing the identity of the system while providing full access to all the features employed by AI/ML tools to ensure their optimal performance.