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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.
D. Yuan, P. Guss, T. Ashenfelter
Nuclear Technology | Volume 177 | Number 2 | February 2012 | Pages 273-284
Technical Paper | Radiation Measurements and General Instrumentation | dx.doi.org/10.13182/NT12-A13371
Articles are hosted by Taylor and Francis Online.
Past studies of wavelet technologies for gamma spectral analysis essentially focused on direct fitting of raw gamma spectra, but these studies often failed to produce new benefits for operational adaptation of wavelet analysis. This paper presents a modified wavelet approach with the objective being detecting only the nuclides that do not exist in the environmental background. With this operational objective, wavelet analysis is applied to the background-subtracted count-rate spectra. A preliminary comparison study suggests that this background subtraction - wavelet-fitting process - is independent of the detector type and background radiation and is capable of improving the wavelet peak detection probabilities as compared with earlier published results.