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Young Members Group
The Young Members Group works to encourage and enable all young professional members to be actively involved in the efforts and endeavors of the Society at all levels (Professional Divisions, ANS Governance, Local Sections, etc.) as they transition from the role of a student to the role of a professional. It sponsors non-technical workshops and meetings that provide professional development and networking opportunities for young professionals, collaborates with other Divisions and Groups in developing technical and non-technical content for topical and national meetings, encourages its members to participate in the activities of the Groups and Divisions that are closely related to their professional interests as well as in their local sections, introduces young members to the rules and governance structure of the Society, and nominates young professionals for awards and leadership opportunities available to members.
Meeting Spotlight
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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Latest News
Framatome, KHNP to investigate producing Lu-177 in South Korea
Framatome and Korea Hydro & Nuclear Power (KHNP) announced the signing of a memorandum of understanding to explore the possibility of producing the medical isotope Lutetium-177 at KHNP’s Wolsong nuclear power plant in South Korea. The companies also will investigate the feasibility of using the plant to support Korean production of medical radioisotopes in the future.
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.