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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.
Arvind Sundaram, Hany S. Abdel-Khalik, Mohammad G. Abdo
Nuclear Technology | Volume 209 | Number 1 | January 2023 | Pages 37-52
Technical Paper | doi.org/10.1080/00295450.2022.2102848
Articles are hosted by Taylor and Francis Online.
Business analytics augmented by artificial intelligence and machine learning (AI/ML) have revolutionized the role of data in the modern world. In recent years, businesses have incorporated data into their decision-making process for better prediction, risk assessment, content creation, etc. While such businesses often seek to leverage the full use of their data through third-party AI/ML services, they are often hampered by the risks of data leaks, reverse engineering, stolen technology, etc., that often have disastrous consequences for businesses and their stakeholders alike. This is especially relevant to the nuclear industry where proprietors are reluctant to share nuclear data for fear of misuse despite their willingness to integrate the additional insight provided by AI/ML applications and remain competitive. Thus, there arises a need for data masking prior to its transmission that obfuscates proprietary information while preserving the information relevant for AI/ML applications. In order to meet the needs of industrial data that are significantly different from those of data warehouses, previous work proposed an efficient time and space-scalable data masking paradigm known as the deceptive infusion of data (DIOD) methodology. The present work expands upon this work by leveraging existing reverse-engineering capabilities to facilitate the decomposition of industrial data into its proprietary and AI/ML-relevant parts, referred to as fundamental and inference metadata, respectively. Both sets of metadata are further obfuscated in accordance with the DIOD methodology to create the DIOD rendition of the industrial data, which is rendered immune to reverse engineering by discarding proprietary information and preserving only AI/ML–relevant information. Additionally, constraints of the original DIOD paper are relaxed using mutual information by configuring the methodology to the target AI/ML application to unlock the full potential of the DIOD methodology. Since the present work focuses on the nuclear industry, data from a nuclear reactor is transformed into that from a nonlinear spring-mass system with different levels of data masking as required by the generic system and the target AI/ML application.