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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.
Jacob A. Farber, Daniel G. Cole, Ahmad Y. Al Rashdan, Vaibhav Yadav
Nuclear Technology | Volume 205 | Number 8 | August 2019 | Pages 1043-1052
Technical Paper – Special section on Big Data for Nuclear Power Plants | doi.org/10.1080/00295450.2018.1534484
Articles are hosted by Taylor and Francis Online.
This paper presents data-driven methods to detect loss-of-coolant accidents (LOCAs) in the primary side of a pressurized water reactor. Process data for a variety of accident scenarios have been generated and collected using a generic pressurized water reactor simulator. The data have been used to train kernel density functions, which estimate nonparametric probability density functions based on training data. These density functions have then been used with Bayesian hypothesis testing and maximum likelihood estimation to detect the onset of the LOCAs and to identify where in the primary side the leaks have occurred. The methods have been able to detect the LOCAs for all scenarios tested with an average detection delay of one-seventh the time for the reactor to trip. Furthermore, the methods have been able to correctly identify the leak locations for 92.3% of the scenarios tested, with higher success rates for larger leaks.