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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
NEA panel on AI hosted at World Governments Summit
A panel on the potential of artificial intelligence to accelerate small modular reactors was held at the World Governments Summit (WGS) in February in Dubai, United Arab Emirates. The OECD Nuclear Energy Agency cohosted the event, which attracted leaders from developers, IT companies, regulators, and other experts.
J. M. Kontoleon
Nuclear Science and Engineering | Volume 70 | Number 3 | June 1979 | Pages 315-317
Technical Note | doi.org/10.13182/NSE79-A20155
Articles are hosted by Taylor and Francis Online.
This Note analyzes the availability of supervised protective systems for nuclear reactors. Failure and repair times are assumed to be exponentially distributed. The availability is maximized, subject to a given fixed amount of resources, by determining the optimum distribution of resources between supervision and repair facilities and by selecting the optimum active-inactive times of the supervisor. The mathematical formulation employs a Markov model continuous in time and alternating between two and three discrete states. Maximization of availability is achieved by using a modified pattern search technique. Computer results illustrate the usefulness of the approach.