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Fusion Energy
This division promotes the development and timely introduction of fusion energy as a sustainable energy source with favorable economic, environmental, and safety attributes. The division cooperates with other organizations on common issues of multidisciplinary fusion science and technology, conducts professional meetings, and disseminates technical information in support of these goals. Members focus on the assessment and resolution of critical developmental issues for practical fusion energy applications.
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ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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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!
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Latest News
First astatine-labeled compound shipped in the U.S.
The Department of Energy’s National Isotope Development Center (NIDC) on March 31 announced the successful long-distance shipment in the United States of a biologically active compound labeled with the medical radioisotope astatine-211 (At-211). Because previous shipments have included only the “bare” isotope, the NIDC has described the development as “unleashing medical innovation.”
Young A Suh, Man-Sung Yim
Nuclear Technology | Volume 206 | Number 12 | December 2020 | Pages 1840-1860
Technical Paper | doi.org/10.1080/00295450.2020.1731405
Articles are hosted by Taylor and Francis Online.
Human error has been highlighted as main cause of industrial and nuclear accidents. One of the key issues related to human error is a worker’s fitness for duty (FFD). FFD refers to the mental and physical ability of employees to safely perform their job. The objective of this study is to investigate the feasibility of identifying a worker’s FFD status using biosignals. The FFD statuses examined were with respect to alcohol use, depression, stress, anxiety, and sleep deprivation. Biosignals examined in the study include electrical activity in the brain measured by electroencephalogram and referred to as EEG, electrical activity of the heartbeat measured by electrocardiogram and referred to as ECG, galvanic skin response (GSR), blood volume pulse (BVP), dynamic changes in blood pressure and referred to as BPHEG, and respiration. A total of 114 volunteers participated in the study as experimental subjects from whom biodata were collected during their resting states (eyes closed and eyes open). The steps followed in the study include signal preprocessing, power spectrum feature analysis, important feature selection, and support vector machine (SVM) classification using 5-fold cross validation to identify a worker’s FFD status. Among the 70 biosignal indicators, important features were selected by Multivariate Analysis of Variance (MANOVA). The best model developed with the SVM used 64 biosignal indicators and showed a binary (fit or unfit) classification accuracy of 99.4% and a multi-classification accuracy of 97.7%. While limitations of the current work remain, the study indicates the possibility of implementing an effective FFD management program to reduce human error in plant operations.
A thumbnail sketch of the study is as follows:
1. To reduce human error in nuclear operations, use of biosignals was investigated to identify FFD status of workers.
2. EEG, ECG, GSR, BVP, BPHEG, and respiration signals were used to identify a worker’s FFD status.
3. The SVM-based model was successfully implemented for multi-class and binary-class FFD classification.
