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Conference Spotlight
2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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Fusion Science and Technology
October 2025
Latest News
DOE’s latest fusion energy road map aims to bridge known gaps
The Department of Energy introduced a Fusion Science & Technology (S&T) Roadmap on October 16 as a national “Build–Innovate–Grow” strategy to develop and commercialize fusion energy by the mid-2030s by aligning public investment and private innovation. Hailed by Darío Gil, the DOE’s new undersecretary for science, as bringing “unprecedented coordination across America's fusion enterprise” and advancing President Trump’s January 2025 executive order, on “Unleashing American Energy,” the road map echoes plans issued by the DOE’s Office of Fusion Energy Sciences (FES) in 2023 and 2024, with a new emphasis on the convergence of AI and fusion.
The road map release coincided with other fusion energy events held this week in Washington, D.C., and beyond.
Yiqian Wu, Zhiyao Liu, Ming Jia, Cong Chi Tran, Shengyuan Yan
Nuclear Technology | Volume 206 | Number 1 | January 2020 | Pages 94-106
Technical Paper | doi.org/10.1080/00295450.2019.1620055
Articles are hosted by Taylor and Francis Online.
The development of a model for mental workload (MWL) prediction of an operator in nuclear power plants (NPPs) is necessary but challenging. In this study, the validity, sensitivity, and relationship between the four indices of eye tracking (i.e., pupil dilation, blink rate, fixation rate, and saccadic rate) and subjective rating method (i.e., the National Aeronautics and Space Administration-Task Load Index) of both experts and nonexperts when they are operating the state-oriented procedure system in NPPs are analyzed. An artificial neural network (ANN) is used to develop the MWL prediction model using the data of nonexperts. The correlation analysis results indicate that four eye tracking indices are sensitive to the subjective MWL, but there is no significant difference in the pupil diameter and saccadic rate between the experts and nonexperts. The validity of the proposed ANN-based prediction model is proven by the high correlation coefficient (higher than 0.95) between the original and predicted data. However, when the proposed ANN model was applied to the experts’ data, there was a significant difference between the original and predicted data. Therefore, the proposed prediction model can be applied to the experts’ data but with a certain adjustment to obtain the most possibly reasonable results.