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Division Spotlight
Human Factors, Instrumentation & Controls
Improving task performance, system reliability, system and personnel safety, efficiency, and effectiveness are the division's main objectives. Its major areas of interest include task design, procedures, training, instrument and control layout and placement, stress control, anthropometrics, psychological input, and motivation.
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ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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!
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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.”
Anthony Michael Scopatz
Nuclear Science and Engineering | Volume 186 | Number 1 | April 2017 | Pages 83-97
Technical Paper | doi.org/10.1080/00295639.2016.1272384
Articles are hosted by Taylor and Francis Online.
A method for quickly determining deployment schedules that meet any given fuel cycle demands is presented here. This algorithm is fast enough to perform in situ within low-fidelity fuel cycle simulators. It uses Gaussian process regression models to predict the production curve as a function of time and the number of deployed facilities. Each of these predictions is measured against the demand curve using the dynamic time warping distance. The minimum-distance deployment schedule is evaluated in a full fuel cycle simulation, and the generated production curve then informs the model on the next optimization iteration. The method converges within five to ten iterations to a distance that is less than 1% of the total deployable production. This speed of convergence makes it suitable for use even when fuel cycle realizations are expensive, as in higher-fidelity or agent-based simulators. A representative once-through fuel cycle is used to demonstrate the methodology for reactor deployment. However, the algorithm itself is multivariate and may be used to determine the deployment schedules of many facility types that meet a number of independent criteria simultaneously. The once-through, electricity production example was chosen for the simplicity of illustrating the method.