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Meeting Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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
The U.S. Million Person Study of Low-Dose-Rate Health Effects
There is a critical knowledge gap regarding the health consequences of exposure to radiation received gradually over time. While there is a plethora of studies on the risks of adverse outcomes from both acute and high-dose exposures, including the landmark study of atomic bomb survivors, these are not characteristic of the chronic exposure to low-dose radiation encountered in occupational and public settings. In addition, smaller cohorts have limited numbers leading to reduced statistical power.
Christopher Wallace, Curtis McEwan, Graeme West, William Aylward, Stephen McArthur
Nuclear Technology | Volume 206 | Number 5 | May 2020 | Pages 697-705
Technical Paper | doi.org/10.1080/00295450.2019.1697174
Articles are hosted by Taylor and Francis Online.
This paper summarizes a novel approach to improved localization of fuel defects by fusing existing data sources and methods within a neural network model to make accurate and quantifiable identification earlier than existing processes. The approach is demonstrated through application to a CANDU reactor and utilizes a small, manually labeled set of delayed neutron data augmented with neutronic power data to train a neural network to estimate the probability of a fuel channel containing a defect. Results demonstrate that the model is often capable of identifying likely defects earlier than existing methods and could support earlier decision making to enable a reduction in cost and time required to localize defects. The approach described has broader application to other reactor types given the general difficulty of detecting fuel defects via fission product measurement and the large quantities of ancillary parameters normally already recorded that can be leveraged using machine learning techniques.