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ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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General Kenneth Nichols and the Manhattan Project
Nichols
The Oak Ridger has published the latest in a series of articles about General Kenneth D. Nichols, the Manhattan Project, and the 1954 Atomic Energy Act. The series has been produced by Nichols’ grandniece Barbara Rogers Scollin and Oak Ridge (Tenn.) city historian David Ray Smith. Gen. Nichols (1907–2000) was the district engineer for the Manhattan Engineer District during the Manhattan Project.
As Smith and Scollin explain, Nichols “had supervision of the research and development connected with, and the design, construction, and operation of, all plants required to produce plutonium-239 and uranium-235, including the construction of the towns of Oak Ridge, Tennessee, and Richland, Washington. The responsibility of his position was massive as he oversaw a workforce of both military and civilian personnel of approximately 125,000; his Oak Ridge office became the center of the wartime atomic energy’s activities.”
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.