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Conference Spotlight
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.
Eitan Wacholder, Ezra Elias, Yoram Merlis
Nuclear Technology | Volume 110 | Number 2 | May 1995 | Pages 228-237
Technical Paper | Radioactive Waste Management | doi.org/10.13182/NT95-A35120
Articles are hosted by Taylor and Francis Online.
An optimization artificial neural networks model is developed for solving the ill-posed inverse transport problem associated with localizing radioactive sources in a medium with known properties and dimensions. The model is based on the recurrent (or feedback) Hop-field network with fixed weights. The source distribution is determined based on the response of a limited number of external detectors of known spatial deployment in conjunction with a radiation transport model. The algorithm is tested and evaluated for a large number of simulated two-dimensional cases. Computations are carried out at different noise levels to account for statistical errors encountered in engineering applications. The sensitivity to noise is found to depend on the number of detectors and on their spatial deployment. A pretest empirical procedure is, therefore, suggested for determining an effective arrangement of detectors for a given problem.