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2026 Nuclear Energy Conference & Expo (NECX)
August 24–27, 2026
Dallas, TX|Hilton Anatole
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Texas A&M welcomes uranium conversion research
The Texas A&M Engineering Experiment Station (TEES) has signed a research agreement with Quantum Leap Energy (QLE) “to advance and de-risk the commercial production of high-purity uranium hexafluoride (UF6).”
QLE is an Austin, Texas–based subsidiary of ASP Isotopes (ASPI), which is developing an isotope enrichment platform for applications in nuclear energy, nuclear medicine, and semiconductors. QLE specializes in the uranium conversion step of the nuclear fuel cycle—the conversion of yellowcake uranium concentrate (U3O8) into UF6 prior to enrichment.
Zigu Guo, Linhui Sun, Jing Yan, Yandan Lin
Nuclear Science and Engineering | Volume 200 | Number 8 | August 2026 | Pages 1951-1974
Research Article | doi.org/10.1080/00295639.2025.2546753
Articles are hosted by Taylor and Francis Online.
Cognitive style, as a stable individual characteristic, provides critical insights into safety training and personnel selection in nuclear power plants; however, the underlying mechanism by which cognitive style influences situation awareness (SA) remains unclear.This study combines subjective scales, behavioral tasks, eye movements, and electroencephalography data to explore the influence of cognitive styles on the three stages of operators’ SA: perception, understanding, and prediction. It also constructs a SA assessment model based on cognitive styles.
The results revealed that field-dependent operators were more susceptible to information interference during the comprehension and projection stages, leading to increased cognitive and mental workloads, accompanied by significant activation in the parietal and temporal regions. These effects subsequently impaired attentional resource allocation and situational comprehension abilities, resulting in lower overall SA compared to field-independent operators. The grey wolf optimizer-support vector machine model constructed using subjective, physiological, and behavioral features achieved an identification accuracy of 83.3%. This research provides theoretical and methodological support for enhancing safety training and personnel selection in nuclear power plant control rooms.