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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.
S. R. Bierman
Nuclear Technology | Volume 26 | Number 3 | July 1975 | Pages 352-381
Technical Note | Chemical Processing | doi.org/10.13182/NT75-A24435
Articles are hosted by Taylor and Francis Online.
Fourteen different critical systems of plutonium-uranium homogeneous mixtures are arranged in a format amenable for use in validating calculational techniques and checking nuclear data. Each experimental system is completely defined under a single benchmark number. For the data that permit it, multiple critical dimensions are given for each fuel composition and geometry. Wherever possible, experimentally determined correction factors are provided for reducing the complexity of the critical assemblies.