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Decommissioning & Environmental Sciences
The mission of the Decommissioning and Environmental Sciences (DES) Division is to promote the development and use of those skills and technologies associated with the use of nuclear energy and the optimal management and stewardship of the environment, sustainable development, decommissioning, remediation, reutilization, and long-term surveillance and maintenance of nuclear-related installations, and sites. The target audience for this effort is the membership of the Division, the Society, and the public at large.
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2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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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
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.
Siyao Gu, Miltiadis Alamaniotis
Nuclear Technology | Volume 210 | Number 1 | January 2024 | Pages 100-111
Research Article | doi.org/10.1080/00295450.2023.2226914
Articles are hosted by Taylor and Francis Online.
Ever since the attack on the World Trade Center on September 11, prevention of nuclear terrorist attacks in urban environments has been a major focus for homeland security. To that end, mobile radiation sensor networks that are deployed within a specific area to acquire consecutive measurements are a first line of defense against the illicit movement of nuclear threats. However, sensor network deployment is a complex process imposed on physical and financial constraints and dynamically varying conditions. In this work, reinforcement learning (RL) is applied to control the sequential deployment of a mobile radiation sensor network within a specific geographic area. RL is utilized for dynamically learning of the environment and subsequent decision making on the optimal position of the network sensors driven by shared mutual information. RL has the benefit of allowing the network to learn and update a deployment strategy online from an initially unknown state.
The performance of the RL method is demonstrated through self-contained exploration and interaction between sensors in a source search scenario for detecting a radioactive source with a set of mobile detectors within the space of the University of Texas at San Antonio campus. Results exhibit the efficiency and efficacy of (a-sequential) RL in comparison to the sequential placement of the mobile sensors, showcasing optimality in accuracy and efficiency in source detection.