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Argonne: Where AI research meets education and training
Last September, in the Chicago suburb of Lemont, Ill., Argonne National Laboratory hosted its first AI STEM Education Summit. More than 180 educators from high schools, community colleges, and universities; STEM administrators; and experts in various disciplines convened at “One Ecosystem, Many Pathways–Building an AI-Ready STEM Workforce” to discuss how artificial intelligence is reshaping STEM-related industries, including the implications for the nuclear engineering classroom and workforce.
Günter Hartmann
Nuclear Technology | Volume 32 | Number 3 | March 1977 | Pages 290-296
Technical Paper | Material | doi.org/10.13182/NT77-A31752
Articles are hosted by Taylor and Francis Online.
A model for inspection, and its application to a reprocessing plant of the Nuclear Fuel Services type, have been developed. Two possible means of diverting material are (a) diverting material within the uncertainty of the material balance and (b) falsifying data. Statistical tests are performed by the inspecting authority: In the first case, the inspector compares material unaccounted for (MUF) with a significance threshold, while in the second case, a sample of the reported data is compared with his own remeasurements. Both methods are combined, and the optimal control and diversion strategies are computed (for an assumed probability of a false alarm and the amount to be diverted).