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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
Senate EPW Committee to hold Nieh nomination hearing
Nieh
The Senate Environment and Public Works Committee will hold a nomination hearing Wednesday for Ho Nieh, President Donald Trump’s nominee to serve as commission at the Nuclear Regulatory Commission.
Trump nominated Nieh on July 30 to serve as NRC commissioner the remainder of a term that will expire June 30, 2029, as Nuclear NewsWire previously reported.
Nieh has been vice president of regulatory affairs at Southern Nuclear since 2021, though since June 2024 he has been at the Institute of Nuclear Power Operations as a loaned executive.
A return to the NRC: If confirmed by the Senate, Nieh would be returning to the NRC after three previous stints totaling nearly 20 years.
Christopher Wallace, Curtis McEwan, Graeme West, William Aylward, Stephen McArthur
Nuclear Technology | Volume 206 | Number 5 | May 2020 | Pages 697-705
Technical Paper | doi.org/10.1080/00295450.2019.1697174
Articles are hosted by Taylor and Francis Online.
This paper summarizes a novel approach to improved localization of fuel defects by fusing existing data sources and methods within a neural network model to make accurate and quantifiable identification earlier than existing processes. The approach is demonstrated through application to a CANDU reactor and utilizes a small, manually labeled set of delayed neutron data augmented with neutronic power data to train a neural network to estimate the probability of a fuel channel containing a defect. Results demonstrate that the model is often capable of identifying likely defects earlier than existing methods and could support earlier decision making to enable a reduction in cost and time required to localize defects. The approach described has broader application to other reactor types given the general difficulty of detecting fuel defects via fission product measurement and the large quantities of ancillary parameters normally already recorded that can be leveraged using machine learning techniques.