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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.”
Shuangbao Shu, Ziqiao Yu, Jiaxin Zhang, Zhiqiang Chen, Huajun Liang, Jingjing Chen
Nuclear Science and Engineering | Volume 197 | Number 4 | April 2023 | Pages 589-600
Technical Paper | doi.org/10.1080/00295639.2022.2132101
Articles are hosted by Taylor and Francis Online.
Baseline drift and noise can blur or even drown out a signal and affect analysis results, especially in multivariate analysis. To address the problem of spectrum denoising and baseline correction, this paper proposes an improved dual asymmetric penalized least squares (IDAPLS) baseline correction method. The proposed method first changes the single parameter λ used for balancing fidelity and roughness in the traditional penalty least squares (PLS) method into a new diagonal matrix Λ and uses the fast convergent inverse tangent S-type penalty function to iteratively estimate the noise level. Then, the diagonal matrix Ψ is introduced into the fidelity of the updated energy spectrum, and the element ψi is updated iteratively by using the inverse tangent S-type penalty function. Finally, the baseline of the original signal is obtained when a preset number of iterations or termination criteria are reached. Compared with other methods, IDAPLS solves the problem of underfitted curves when dealing with additive noise that the asymmetric least squares method and adaptive iterative reweighted penalized least squares method would get. The proposed method also retains the advantage of fast PLS and realizes the further approximation of the fitting baseline to the real baseline. Especially, in the case of high noise, this method reduces the error of the traditional PLS method from 30% to less than 5%, which gives a useful reference for nuclear data analysis.