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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.
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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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Argonne opens registration for D&D training course
Registration is open for Argonne National Laboratory’s Facility Decommissioning Training Course, a four-day instruction designed for those responsible for the decontamination and decommissioning of nuclear facilities and who are looking to understand the full breadth and depth of the D&D processes.
The next session will be held July 16–19 in Santa Fe, N.M. Information on the course and how to register can be found here.
Thomas E. Booth
Nuclear Science and Engineering | Volume 166 | Number 2 | October 2010 | Pages 175-178
Technical Note | doi.org/10.13182/NSE09-101
Articles are hosted by Taylor and Francis Online.
This technical note shows that it is possible and effective to use Monte Carlo variance-reduction methods for the probability of initiation problem. The benefits are threefold. First, the proper use of variance reduction obviates using an arbitrary definition of a “divergent chain.” Second, because chains of all lengths are allowed, there is no bias introduced by ignoring some long chains because they meet the divergent chain definition. Third, variance-reduction methods might drastically increase the efficiency of some of these calculations.