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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
ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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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Latest News
Nuclear News 40 Under 40 discuss the future of nuclear
Seven members of the inaugural Nuclear News 40 Under 40 came together on March 4 to discuss the current state of nuclear energy and what the future might hold for science, industry, and the public in terms of nuclear development.
To hear more insights from this talented group of young professionals, watch the “40 Under 40 Roundtable: Perspectives from Nuclear’s Rising Stars” on the ANS website.
Patrick J. O’Neal, Sunil S. Chirayath, Qi Cheng
Nuclear Science and Engineering | Volume 196 | Number 7 | July 2022 | Pages 811-823
Technical Paper | doi.org/10.1080/00295639.2021.2024037
Articles are hosted by Taylor and Francis Online.
A nuclear forensics technique, based on the maximum likelihood method, for the attribution of reactor type, fuel burnup, and time since irradiation (TSI) of separated pure plutonium (Pu) samples was previously developed at Texas A&M University. The method utilized measured values of ten intra-elemental isotope ratios in the Pu sample and a large database consisting of the values for these ratios as a function of the three attributes: reactor type, fuel burnup, and TSI. However, this method failed for Pu samples with mixed attributes. Hence, a new technique based on machine learning methods was developed that matched the capabilities of the previous maximum likelihood method for pure Pu samples. This new methodology used support vector machines for reactor-type discrimination and Gaussian process regression for fuel burnup quantification. The TSI was calculated analytically using the predicted reactor type and fuel burnup. This new method holds great potential for the attribution of mixed Pu samples.