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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
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
G7 pledges support for nuclear at Italy meeting
The Group of Seven (G7) recommitted its support for nuclear energy in the countries that opt to use it at a Ministerial Meeting on Climate in Italy last month.
In a statement following the April meeting, the group committed to support multilateral efforts to strengthen the resilience of nuclear supply chains, referencing the goal set by 25 countries during last year’s COP28 climate conference in Dubai to triple global nuclear generating capacity by 2050.
Chenhao Zeng, Wanchang Lai, Jinge Zhou, Hongjian Lin, Xiaojie Feng, Yongping Yu, Runqiu Gu, Shangqing Sun, Jinfei Wu
Nuclear Technology | Volume 209 | Number 4 | April 2023 | Pages 549-559
Technical Paper | doi.org/10.1080/00295450.2022.2133515
Articles are hosted by Taylor and Francis Online.
We address the performance of airborne gamma detection systems equipped with a NaI(Tl) detector to monitor radionuclides in specific areas. In particular, we analyze the use of the fast singular value decomposition (FSVD) algorithm to improve the nuclide recognition ability of the system and effectively trace radioactivity in a complex background environment. We first present a theoretical analysis of the FSVD algorithm and illustrate the nuclide recognition algorithm step by step. The core of the algorithm is singular value decomposition and parameter estimation based on a Gaussian Markov linear regression model. From the estimated values of the parameters, information about radionuclides can be effectively extracted. We assume the presence of a strong background due to a high concentration of 222Rn and its progeny, which is simulated using GEANT4. By adding trace elements of 131I and 137Cs and changing the relative emissivity, the ratio of the total energy peak count of 131I and 137Cs to the background environment interval count of the corresponding 222Rn and its progeny are controlled. Assuming a counting ratio equal to 0.005, the FSVD algorithm is still able to effectively discriminate the presence of a small number of nuclides, reflecting very excellent recognition ability. Finally, based on data from an airborne gamma detection system in a self-control radon chamber, the FSVD algorithm is employed to recognize the trace of 137Cs nuclides in a strong radon background. A DURRIDGE RAD7 radon measuring instrument is used to monitor the radon concentration in the radon chamber. The actual measurement results show that the FSVD algorithm can effectively detect 137Cs nuclides.