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Division Spotlight
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.
Meeting Spotlight
2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Nuclear Science and Engineering
June 2024
Nuclear Technology
Fusion Science and Technology
Latest News
U.S. nuclear capacity factors: Ideal for data centers?
Baseload nuclear generation doesn’t get the respect it deserves, if you ask nuclear operators. But the hyperscale data centers that process our digital lives—like the one right next to the Susquehanna plant in northeastern Pennsylvania—are pushing electricity demand up. Clean, reliable capacity now looks a lot more valuable.
Philseo Kim, Man-Sung Yim, Justin V. Hastings, Philip Baxter
Nuclear Technology | Volume 210 | Number 1 | January 2024 | Pages 84-99
Research Article | doi.org/10.1080/00295450.2023.2218241
Articles are hosted by Taylor and Francis Online.
Previous studies have explored the determinants of the nuclear proliferation levels (Explore, Pursue, and Acquire). However, these studies have weaknesses, including endogeneity and multicollinearity among the independent variables. This resulted in tentative predictions of a country’s nuclear program capabilities. The objective of this study is to develop a tool to predict future nuclear proliferation in a country, and thus facilitate its prevention. Specifically, we examine how applying deep learning algorithms can enhance nuclear proliferation risk prediction. We collected important determinants from the literature that were found to be significant in explaining nuclear proliferation. These determinants include economics, domestic and international security and threats, nuclear fuel cycle capacity, and tacit knowledge development in a country. We used multilayer perceptrons in the classification model. The results suggest that detecting a country’s proliferation behavior using deep learning algorithms may be less tentative and more viable than other existing methods. This study provides a policy tool to identify a country’s nuclear proliferation risk pattern. This information is important for developing efforts/strategies to hamper a potential proliferating country’s attempt toward developing a nuclear weapons program.