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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.”
Oleg Roderick, Mihai Anitescu, Paul Fischer
Nuclear Science and Engineering | Volume 164 | Number 2 | February 2010 | Pages 122-139
Technical Paper | doi.org/10.13182/NSE08-79
Articles are hosted by Taylor and Francis Online.
In this work we describe a polynomial regression approach that uses derivative information for analyzing the performance of a complex system that is described by a mathematical model depending on several stochastic parameters.We construct a surrogate model as a goal-oriented projection onto an incomplete space of polynomials; find coordinates of the projection by regression; and use derivative information to significantly reduce the number of the sample points required to obtain a good model. The simplified model can be used as a control variate to significantly reduce the sample variance of the estimate of the goal.For our test model, we take a steady-state description of heat distribution in the core of the nuclear reactor core, and as our goal we take the maximum centerline temperature in a fuel pin. For this case, the resulting surrogate model is substantially more computationally efficient than random sampling or approaches that do not use derivative information, and it has greater precision than linear models.