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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.
R. W. Hardie, W. W. Little, R. P. Omberg
Nuclear Technology | Volume 26 | Number 1 | May 1975 | Pages 115-116
Technical Note | Reactor | doi.org/10.13182/NT75-A24409
Articles are hosted by Taylor and Francis Online.
A wide range of expressions is currently used to calculate the doubling time of fast breeder reactors. These formulations often result in significantly different numerical values for the doubling time using the same reactor data. Since the treatment of out-of-reactor inventory and fuel cycle losses is usually responsible for the differences, an expression for the doubling time accounting for these effects in a simple and logical manner is developed.