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Division Spotlight
Radiation Protection & Shielding
The Radiation Protection and Shielding Division is developing and promoting radiation protection and shielding aspects of nuclear science and technology — including interaction of nuclear radiation with materials and biological systems, instruments and techniques for the measurement of nuclear radiation fields, and radiation shield design and evaluation.
Meeting Spotlight
2025 ANS Annual Conference
June 15–18, 2025
Chicago, IL|Chicago Marriott Downtown
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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Jun 2025
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Nuclear Science and Engineering
July 2025
Nuclear Technology
Fusion Science and Technology
Latest News
Smarter waste strategies: Helping deliver on the promise of advanced nuclear
At COP28, held in Dubai in 2023, a clear consensus emerged: Nuclear energy must be a cornerstone of the global clean energy transition. With electricity demand projected to soar as we decarbonize not just power but also industry, transport, and heat, the case for new nuclear is compelling. More than 20 countries committed to tripling global nuclear capacity by 2050. In the United States alone, the Department of Energy forecasts that the country’s current nuclear capacity could more than triple, adding 200 GW of new nuclear to the existing 95 GW by mid-century.
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.