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Conference Spotlight
2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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Latest News
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.
S. González, J. Vega, A. Murari, A. Pereira, M. Beurskens, JET-EFDA Contributors
Fusion Science and Technology | Volume 58 | Number 3 | November 2010 | Pages 755-762
Selected Paper from Sixth Fusion Data Validation Workshop 2010 (Part 2) | doi.org/10.13182/FST10-A10924
Articles are hosted by Taylor and Francis Online.
Massive amounts of data generated by fusion machines (such as JET) require developing automatic methods for data analysis. Edge-localized modes (ELMs) are instabilities occurring in the edge of H-mode plasmas. The aim of this work is to develop an automatic off-line method for identifying and locating ELMs. This method uses Universal Multi-Event Locator (UMEL) as the event locator. The combination of information from D emission and diamagnetic energy allows the recognition of single ELMs. This paper shows the way in which waveforms of a wide range of discharges can be treated and how UMEL is applied in order to identify and locate ELMs independently of the signal amplitudes. A large database of more than 1200 discharges has been used to test the performance of the method obtaining 226 751 ELMs.
