ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
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Division Spotlight
Isotopes & Radiation
Members are devoted to applying nuclear science and engineering technologies involving isotopes, radiation applications, and associated equipment in scientific research, development, and industrial processes. Their interests lie primarily in education, industrial uses, biology, medicine, and health physics. Division committees include Analytical Applications of Isotopes and Radiation, Biology and Medicine, Radiation Applications, Radiation Sources and Detection, and Thermal Power Sources.
Meeting Spotlight
2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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
College students help develop waste-measuring device at Hanford
A partnership between Washington River Protection Solutions (WRPS) and Washington State University has resulted in the development of a device to measure radioactive and chemical tank waste at the Hanford Site. WRPS is the contractor at Hanford for the Department of Energy’s Office of Environmental Management.
Tom Burr, Brian Williams, Stephen Croft, Morgan White, Ken Hanson
Nuclear Science and Engineering | Volume 173 | Number 1 | January 2013 | Pages 15-27
Technical Paper | doi.org/10.13182/NSE11-112
Articles are hosted by Taylor and Francis Online.
Meta-analysis aims to combine results from multiple experiments. For example, a neutron reaction rate or cross section is typically measured in multiple experiments, and a single estimate and its uncertainty are provided for users of the estimated reaction rate. It is often difficult to combine estimates from multiple laboratories because there can be important differences in experimental protocols among laboratories and because laboratories do not always provide all the information needed to assess the estimate's uncertainty, particularly if total uncertainty (random and systematic) is required. The paper illustrates that explicit measurement error models are essential for understanding measurement processes and for guiding how to combine multiple measurements, whether the measurements are consistent or not. We emphasize that both the consensus estimate and its estimated uncertainty depend on the assumed measurement error model, and we investigate measurement error model selection options for two examples.