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Division Spotlight
Education, Training & Workforce Development
The Education, Training & Workforce Development Division provides communication among the academic, industrial, and governmental communities through the exchange of views and information on matters related to education, training and workforce development in nuclear and radiological science, engineering, and technology. Industry leaders, education and training professionals, and interested students work together through Society-sponsored meetings and publications, to enrich their professional development, to educate the general public, and to advance nuclear and radiological science and engineering.
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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May 2024
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Nuclear Science and Engineering
June 2024
Nuclear Technology
Fusion Science and Technology
Latest News
G7 pledges support for nuclear at Italy meeting
The Group of Seven (G7) recommitted its support for nuclear energy in the countries that opt to use it at a Ministerial Meeting on Climate in Italy last month.
In a statement following the April meeting, the group committed to support multilateral efforts to strengthen the resilience of nuclear supply chains, referencing the goal set by 25 countries during last year’s COP28 climate conference in Dubai to triple global nuclear generating capacity by 2050.
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.