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Division Spotlight
Radiation Protection & Shielding
The Radiation Protection and Shielding Division is developing and promoting radiation protection and shielding aspects of nuclear science and technology — including interaction of nuclear radiation with materials and biological systems, instruments and techniques for the measurement of nuclear radiation fields, and radiation shield design and evaluation.
Meeting Spotlight
Conference on Nuclear Training and Education: A Biennial International Forum (CONTE 2023)
February 6–9, 2023
Amelia Island, FL|Omni Amelia Island Resort
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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February 2023
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Latest News
Show support for a Lego nuclear power plant
A creative fan of Lego—and nuclear power—has designed a nuclear power plant out of the famous building blocks and has submitted the idea to the Lego Group for possible production—but first, the idea needs the support of the public.
Zixu Xu, Guofeng Qu, Min Yan, Su Shen, Yu Huang, Xin Zhang, Lei Chen, Xingquan Liu, Jifeng Han
Nuclear Technology | Volume 208 | Number 12 | December 2022 | Pages 1847-1857
Technical Paper | doi.org/10.1080/00295450.2022.2076489
Articles are hosted by Taylor and Francis Online.
The performance of a prompt gamma neutron activation analysis (PGNAA) system for lower-weight landmine detection is investigated in this study. A total of 2880 characteristic gamma-ray spectra of 10 buried samples (five explosives and five nonexplosives), within a weight range of 0.01 to 10 kg and a hidden depth of 2.5 to 15 cm, under 0%, 10%, and 20% soil moisture conditions, were generated using Monte Carlo N-Particle Code 5 (MCNP5). The conventional characteristic peak analysis method was not applicable to lower-weight sample detection. The discrimination accuracy was acceptable only under 0% soil moisture when explosives exceeded 2 kg with the discrimination accuracy exceeding 80%. Four machine learning models, including radial basis function (RBF) neural network, fully connected neural network, XGBoost, and LightGBM, were used to perform whole-spectrum analysis, and better performance was demonstrated. The discrimination accuracy exceeded 90% in most cases, and the RBF neural network was demonstrated to be the best performance (96.6% for explosives and 95.1% for nonexplosives). All four of these models were insensitive to soil moisture. The minimum detectable weight of 0.02 kg for the simulation data provided valuable reference for experimental applications. These results indicate that machine learning was an effective method for lower-weight landmine detection using PGNAA under complicated conditions.