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Division Spotlight
Fuel Cycle & Waste Management
Devoted to all aspects of the nuclear fuel cycle including waste management, worldwide. Division specific areas of interest and involvement include uranium conversion and enrichment; fuel fabrication, management (in-core and ex-core) and recycle; transportation; safeguards; high-level, low-level and mixed waste management and disposal; public policy and program management; decontamination and decommissioning environmental restoration; and excess weapons materials disposition.
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2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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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
Excelsior University student section awarded community education grant
The American Nuclear Society Student Section at Excelsior University in Albany, N.Y., was awarded a $5,000 grant from the ANS Student Section Strategic Fund initiative for its program, Empowering Tomorrow’s Nuclear Innovators: A Collaborative Approach to Nuclear Technology Education and Awareness.
Yochan Kim, Jinkyun Park, Mary Presley
Nuclear Science and Engineering | Volume 197 | Number 11 | November 2023 | Pages 2787-2799
PSA 2021 Paper | doi.org/10.1080/00295639.2022.2118481
Articles are hosted by Taylor and Francis Online.
With the development of new digital human-machine interfaces, many discussions in the nuclear industry have focused on the human factors issues that arise from the interfaces. To quantitatively characterize the effects of the interfaces on human reliability, we collected empirical data from a full-scope simulator of the APR1400 nuclear power plant using the Human Reliability Extraction (HuREX) framework. From the numerous variables in the collected data describing the contexts of the performance influencing factors (PIFs), including crew experience, task complexity, and procedure quality, the significant variables were identified by three techniques comprising both qualitative and quantitative analyses. Based on the selected variables, the nominal error probabilities and PIF multipliers were then estimated by logistic regression analysis. This paper interprets the meanings of the estimates and discusses the advantages of the employed variable selection techniques.