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The Education, Training & Workforce Development Division provides communication among the academic, industrial, and governmental communities through the exchange of views and information on matters related to education, training and workforce development in nuclear and radiological science, engineering, and technology. Industry leaders, education and training professionals, and interested students work together through Society-sponsored meetings and publications, to enrich their professional development, to educate the general public, and to advance nuclear and radiological science and engineering.
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April 8–10, 2021
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NC State celebrates 70 years of nuclear engineering education
An early picture of the research reactor building on the North Carolina State University campus. The Department of Nuclear Engineering is celebrating the 70th anniversary of its nuclear engineering curriculum in 2020–2021. Photo: North Carolina State University
The Department of Nuclear Engineering at North Carolina State University has spent the 2020–2021 academic year celebrating the 70th anniversary of its becoming the first U.S. university to establish a nuclear engineering curriculum. It started in 1950, when Clifford Beck, then of Oak Ridge, Tenn., obtained support from NC State’s dean of engineering, Harold Lampe, to build the nation’s first university nuclear reactor and, in conjunction, establish an educational curriculum dedicated to nuclear engineering.
The department, host to the 2021 ANS Virtual Student Conference, scheduled for April 8–10, now features 23 tenure/tenure-track faculty and three research faculty members. “What a journey for the first nuclear engineering curriculum in the nation,” said Kostadin Ivanov, professor and department head.
Dan G. Cacuci, Mihaela Ionescu-Bujor
Nuclear Science and Engineering | Volume 165 | Number 1 | May 2010 | Pages 1-17
Technical Paper | dx.doi.org/10.13182/NSE09-37A
Articles are hosted by Taylor and Francis Online.
When n measurements and/or computations of the same (unknown) quantity yield data points xj with corresponding standard deviations (uncertainties) j such that the distances [vertical bar]xj - xk[vertical bar] between any two data points are smaller than or comparable to the sum (j + k) of their respective uncertainties, the respective data points are considered to be consistent or to agree within error bars. However, when the distances [vertical bar]xj - xk[vertical bar] are larger than (j + k), the respective data are considered to be inconsistent or discrepant. Inconsistencies can be caused by unrecognized or ill-corrected experimental effects (e.g., background corrections, dead time of the counting electronics, instrumental resolution, sample impurities, calibration errors). Although there is a nonzero probability that genuinely discrepant data could occur (for example, for a Gaussian sampling distribution with standard deviation , the probability that two equally precise measurements would be separated by more than 2 is erfc(1) [approximately equal] 0.157), it is much more likely that apparently discrepant data actually indicate the presence of unrecognized errors.This work addresses the treatment of unrecognized errors by applying the maximum entropy principle under quadratic loss, to the discrepant data. Novel results are obtained for the posterior distribution determining the unknown mean value (i.e., unknown location parameter) of the data and also for the marginal posterior distribution of the unrecognized errors. These novel results are considerably more rigorous, are more accurate, and have a wider range of applicability than extant recipes for handling discrepant data.