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Division Spotlight
Robotics & Remote Systems
The Mission of the Robotics and Remote Systems Division is to promote the development and application of immersive simulation, robotics, and remote systems for hazardous environments for the purpose of reducing hazardous exposure to individuals, reducing environmental hazards and reducing the cost of performing work.
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
G7 pledges support for nuclear at Italy meeting
The Group of Seven (G7) recommitted its support for nuclear energy in the countries that opt to use it at a Ministerial Meeting on Climate in Italy last month.
In a statement following the April meeting, the group committed to support multilateral efforts to strengthen the resilience of nuclear supply chains, referencing the goal set by 25 countries during last year’s COP28 climate conference in Dubai to triple global nuclear generating capacity by 2050.
Patrick J. O’Neal, Sunil S. Chirayath, Qi Cheng
Nuclear Science and Engineering | Volume 196 | Number 7 | July 2022 | Pages 811-823
Technical Paper | doi.org/10.1080/00295639.2021.2024037
Articles are hosted by Taylor and Francis Online.
A nuclear forensics technique, based on the maximum likelihood method, for the attribution of reactor type, fuel burnup, and time since irradiation (TSI) of separated pure plutonium (Pu) samples was previously developed at Texas A&M University. The method utilized measured values of ten intra-elemental isotope ratios in the Pu sample and a large database consisting of the values for these ratios as a function of the three attributes: reactor type, fuel burnup, and TSI. However, this method failed for Pu samples with mixed attributes. Hence, a new technique based on machine learning methods was developed that matched the capabilities of the previous maximum likelihood method for pure Pu samples. This new methodology used support vector machines for reactor-type discrimination and Gaussian process regression for fuel burnup quantification. The TSI was calculated analytically using the predicted reactor type and fuel burnup. This new method holds great potential for the attribution of mixed Pu samples.