ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
Explore the many uses for nuclear science and its impact on energy, the environment, healthcare, food, and more.
Explore membership for yourself or for your organization.
Conference Spotlight
2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
Latest Magazine Issues
Sep 2025
Jan 2025
Latest Journal Issues
Nuclear Science and Engineering
October 2025
Nuclear Technology
September 2025
Fusion Science and Technology
Latest News
NNSA awards BWXT $1.5B defense fuels contract
The Department of Energy’s National Nuclear Security Administration has awarded BWX Technologies a contract valued at $1.5 billion to build a Domestic Uranium Enrichment Centrifuge Experiment (DUECE) pilot plant in Tennessee in support of the administration’s efforts to build out a domestic supply of unobligated enriched uranium for defense-related nuclear fuel.
Shengyuan Yan, Kai Yao, Fengjiao Li, Yingying Wei, Cong Chi Tran
Nuclear Technology | Volume 208 | Number 10 | October 2022 | Pages 1540-1552
Technical Paper | doi.org/10.1080/00295450.2022.2049965
Articles are hosted by Taylor and Francis Online.
The accurate assessment of human error probability (HEP) has an important impact on the safety of nuclear power plants. Therefore, it is necessary to develop a HEP model. This study analyzes the validity, sensitivity, and relationship between HEP and the indices of eye response and the subjective rating method. The analysis result showed that there is a correlation between HEP and the indices of eye response, subjective workload, and situation awareness level. Therefore, a back propagation neural network model was developed based on these indices. The correlation coefficient is more than 0.95 between the predicted data of the developed model and the target data. Also, the root mean square error was 0.0073, 0.0083, and 0.0077, and the determination coefficient was 0.965, 0.933, and 0.931 for the training, validation, and testing data sets, respectively. Therefore, the developed back propagation neural network model has reliable prediction accuracy for HEP.