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Division Spotlight
Isotopes & Radiation
Members are devoted to applying nuclear science and engineering technologies involving isotopes, radiation applications, and associated equipment in scientific research, development, and industrial processes. Their interests lie primarily in education, industrial uses, biology, medicine, and health physics. Division committees include Analytical Applications of Isotopes and Radiation, Biology and Medicine, Radiation Applications, Radiation Sources and Detection, and Thermal Power Sources.
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.”
Mattia Zanotelli, J. Wesley Hines, Jamie B. Coble
Nuclear Science and Engineering | Volume 199 | Number 1 | January 2025 | Pages 100-114
Research Article | doi.org/10.1080/00295639.2024.2303165
Articles are hosted by Taylor and Francis Online.
In the nuclear industry, high system reliability requirements are essential since in-service failure can result in undesirable consequences in terms of costs or safety. However, the current approach to maintaining systems and components is costly and known to involve overly conservative periodic maintenance activities. It is, therefore, appropriate to develop monitoring, detection, and predictive tools to enable operators to create optimal maintenance strategies. These strategies can vary from the substitution of an item to its repair, intending to avoid unexpected consequences. The repair can restore the item’s functionality to an as-good-as-new condition (perfect repair) or sometimes can keep some accumulated degradation and change the item’s degradation rate (imperfect or partial repair). Current techniques and models that can perform prognostics with extraordinary accuracy are often designed on the assumption that following maintenance, the item is restored to an as-good-as-new condition. When these models are used to predict items that follow imperfect repairs, the predictions are likely to be inaccurate. Therefore, the present work focuses on the condition-based prognostics of items, considering and handling the criticalities that arise after the items undergo different kinds of repairs. The proposed solution involves a data-driven framework that employs Left-Right Gaussian Hidden Markov Models (LR-GHMMs). These models can intrinsically manage accumulated degradation. The idea is to train different LR-GHMMs, each specific to a degradation path, and then combine them to cover possible intermediate paths. The effectiveness of the approach is tested in two case studies. In the first one, we consider simple artificial sequences that are useful to explain the method’s capabilities. In the second case study, we consider semi-simulated nuclear data describing the degradation transients of a condenser that undergoes fouling. The framework is trained with data collected from items that start without accumulated degradation. The test data represent either new items or items that undergo imperfect repairs. The results demonstrate an attractive elasticity of the framework in adapting to nonstandard degradation behaviors. In addition, the applications provide interpretable and highly accurate outputs.