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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
First astatine-labeled compound shipped in the U.S.
The Department of Energy’s National Isotope Development Center (NIDC) on March 31 announced the successful long-distance shipment in the United States of a biologically active compound labeled with the medical radioisotope astatine-211 (At-211). Because previous shipments have included only the “bare” isotope, the NIDC has described the development as “unleashing medical innovation.”
Faouzi Hakimi, Claude Brayer, Amandine Marrel, Fabrice Gamboa, Benoît Habert
Nuclear Science and Engineering | Volume 198 | Number 3 | March 2024 | Pages 578-591
Research Article | doi.org/10.1080/00295639.2023.2197838
Articles are hosted by Taylor and Francis Online.
In the framework of risk assessment in nuclear accidents, simulation tools are widely used to understand and model physical phenomena. These simulation tools take into account a large number of uncertain input parameters. We often use Monte Carlo–type methods to explore their range of variation: The input space is randomly sampled, and a code run is performed on each sampled point. However, some of these code runs may fail to converge. Analyzing these code failures to understand which of the inputs have the most influence on them leads to a better understanding of how the code works. It also intends to improve the robustness of the simulation software and code computations. For this purpose, we propose two complementary approaches performing a statistical analysis of the code failures. The first approach is based on goodness-of-fit tests and compares conditional probability distributions according to code failures to a reference one. A second approach, based on a dependence measure named the Hilbert-Schmidt Independence Criterion, provides another way to measure the global dependence between the inputs and the code failures. The development of this methodology is carried out in the context of severe nuclear accidents. More especially, the presented methods are applied for the study of the simulation code MC3D, which simulates the fuel-coolant interaction in a severe nuclear accident context.