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NCSD provides communication among nuclear criticality safety professionals through the development of standards, the evolution of training methods and materials, the presentation of technical data and procedures, and the creation of specialty publications. In these ways, the division furthers the exchange of technical information on nuclear criticality safety with the ultimate goal of promoting the safe handling of fissionable materials outside reactors.
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June 15–18, 2025
Chicago, IL|Chicago Marriott Downtown
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ANS announces 2025 Presidential Citations
One of the privileges of being president of the American Nuclear Society is awarding Presidential Citations to individuals who have demonstrated outstanding effort in some manner for the benefit of ANS or the nuclear community at large. Citations are conferred twice each year, at the Annual and Winter Meetings.
ANS President Lisa Marshall has named this season’s recipients, who will receive recognition at the upcoming Annual Conference in Chicago during the Special Session on Tuesday, June 17.
Miltiadis Alamaniotis, Sangkyu Lee, Tatjana Jevremovic
Nuclear Technology | Volume 191 | Number 1 | July 2015 | Pages 41-57
Technical Paper | Radiation Transport and Protection | doi.org/10.13182/NT14-75
Articles are hosted by Taylor and Francis Online.
Radioisotope identification from low-count-rate spectra or spectra obtained through low-resolution detectors constitutes a challenging environment for accurate spectral analysis. The use of intelligent processing algorithms is a significant step in analyzing spectra, conceivably increasing the accuracy of the nuclide identification in such scenarios. This paper introduces an intelligent methodology for automated processing of low-count gamma-ray spectra acquired with a scintillation detector aimed at identifying radioisotope patterns, and it evaluates the performance of this methodology against a set of experimentally acquired gamma-ray spectra. The novel methodology adopts tools from the “artificial intelligence library” to preprocess the spectrum and subsequently identify radioisotopes. In particular, in the preprocessing step, the measured spectrum is divided into equally long energy intervals, whose values are replaced with those computed by a support vector regressor equipped with a linear kernel function. In the next step, the fuzzy logic–based identifier matches spectral peaks with entries in the spectral library, aiming at identifying isotopic signatures in the spectrum. The proposed intelligent methodology is benchmarked against the multiple-linear-regression (MLR) spectrum-fitting algorithm. Assessment results demonstrate the effectiveness of the proposed methodology in identifying isotopes compared with the MLR algorithm by significantly reducing the number of false detections and improving correct detection performance. Furthermore, the proposed methodology exhibits an overall higher detection sensitivity (by 13.3%) and precision (by 46.8%) than those obtained with MLR.