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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.
Haining Zhou, Volkan Seker, Thomas Downar
Nuclear Technology | Volume 206 | Number 6 | June 2020 | Pages 839-861
Technical Paper | doi.org/10.1080/00295450.2020.1746620
Articles are hosted by Taylor and Francis Online.
The paper presents a self-adaptive feature selection algorithm we developed for solving high-dimensional uncertainty quantification problems. The development of the algorithm was motivated and supported by the benchmarking of the Transient Reactor Test (TREAT) transient test 2857. The generalized polynomial chaos expansion scheme was adopted to decompose the response functions. Our algorithm was applied to select the dominant basis from the candidate polynomial basis in a self-adaptive manner by assigning weights to the polynomial basis and adjusting the weights using the least absolute shrinkage and selection operator regularization–estimated coefficients through iterations. The developed algorithm can recognize the significant basis terms in the polynomial expansion of the response functions and therefore build a sparse polynomial expansion using a limited number of samples. The algorithm was implemented and verified through three different TREAT modeling cases. The testing results demonstrated the general stability and prediction performance of our algorithm and provided useful information about the uncertainty mechanism of the TREAT transient test 2857.