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2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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Empowering the next generation: ANS’s newest book focuses on careers in nuclear energy
A new career guide for the nuclear energy industry is now available: The Nuclear Empowered Workforce by Earnestine Johnson. Drawing on more than 30 years of experience across 16 nuclear facilities, Johnson offers a practical, insightful look into some of the many career paths available in commercial nuclear power. To mark the release, Johnson sat down with Nuclear News for a wide-ranging conversation about her career, her motivation for writing the book, and her advice for the next generation of nuclear professionals.
When Johnson began her career at engineering services company Stone & Webster, she entered a field still reeling from the effects of the Three Mile Island incident in 1979, nearly 15 years earlier. Her hiring cohort was the first group of new engineering graduates the company had brought on since TMI, a reflection of the industry-wide pause in nuclear construction. Her first long-term assignment—at the Millstone site in Waterford, Conn., helping resolve design issues stemming from TMI—marked the beginning of a long and varied career that spanned positions across the country.
Keisuke Fujii, Motoshi Goto, Shigeru Morita, Masahiro Hasuo
Fusion Science and Technology | Volume 69 | Number 2 | April 2016 | Pages 514-525
Technical Paper | doi.org/10.13182/FST15-168
Articles are hosted by Taylor and Francis Online.
The Balmer-α line profile observed from high-temperature magnetized plasmas can be interpreted as the sum of narrow and broad components corresponding to the emission from atoms generated in edge and core regions, respectively. The inversion of this line profile reveals the atom density distribution in the plasma. The inversion method we reported in previous studies [Nucl. Fusion, 55, 063029 (2015) and Rev. Sci. Instrum., 85, 023502 (2014)] requires a regularization parameter that must be manually tuned to avoid overfitting. Therefore, it has been difficult to evaluate the uncertainty of the results. Here, we report an improved method based on Bayesian statistics in which the regularization parameter is interpreted as an adjustable parameter, which is then marginalized for the uncertainty evaluation. Two types of prior distributions were examined. The first is an empirical prior that assumes the smoothness of a solution, and the second is based on a diffusion model of hydrogen atoms. We found the use of the diffusion model as prior information to have an advantage with respect to the accuracy of the core region atom density.