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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.
Karl Hornyik
Nuclear Technology | Volume 28 | Number 2 | February 1976 | Pages 199-207
Technical Paper | Reactor Siting | doi.org/10.13182/NT76-A31560
Articles are hosted by Taylor and Francis Online.
A method for the assessment of risk involved in shipping toxic compounds past nuclear power plants uses a postulated chain of events, starting with a traffic accident causing instantaneous release of the compound as vapor, and leading to incapacitation of control operators in the nuclear plant, described by deterministic and statistical models as appropriate to the respective event. Statistical treatment of relevant atmospheric conditions is a major improvement over more conservative assumptions commonly made in current analyses of this problem. Consequently, one obtains a substantial reduction in the estimated risk expressed in usual terms of the annual probability of an unacceptable event, in spite of the fact that no credit is taken for protective measures other than potential control room isolation.