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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.”
Andrea Murari, Guido Vagliasindi, Eleonora Arena, Paolo Arena, Luigi Fortuna, JET-EFDA Contributors
Fusion Science and Technology | Volume 58 | Number 2 | October 2010 | Pages 685-694
Selected Paper from the Sixth Fusion Data Validation Workshop 2010 (Part 1) | doi.org/10.13182/FST10-A10893
Articles are hosted by Taylor and Francis Online.
In practically all fields of science, measurements are affected by noise, which can sometimes be modeled with an appropriate probability distribution function. The results of measurements are therefore known only with uncertainties that sometimes can be significant. In many cases the noise source is independent of the system to be studied and the quantities to be measured. In this paper, a numerical approach to handle statistical uncertainties, due to an independent noise source, in a fuzzy logic system is developed. Numerical analysis and various tests with a benchmark show how statistical error bars can be interpreted as an independent "axis of complexity" with respect to the fuzzy boundaries of the membership functions. The uncertainties in the inputs can be transferred to the output and handled separately from the system intrinsic fuzzyness. The main advantages of this independent treatment of the measurement errors are shown in the case of a binary classification task: the regime confinement identification in high-temperature tokamak plasmas. Significant improvements in the correct prediction rate have been achieved with respect to the classification performed without considering the error bars in the measurements.