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May 31–June 3, 2026
Denver, CO|Sheraton Denver
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AI at work: Southern Nuclear’s adoption of Copilot agents drives fleet forward
Southern Nuclear is leading the charge in artificial intelligence integration, with employee-developed applications driving efficiencies in maintenance, operations, safety, and performance.
The tools span all roles within the company, with thousands of documented uses throughout the fleet, including improved maintenance efficiency, risk awareness in maintenance activities, and better-informed decision-making. The data-intensive process of preparing for and executing maintenance operations is streamlined by leveraging AI to put the right information at the fingertips for maintenance leaders, planners, schedulers, engineers, and technicians.
Jagjit Singh Matharu, Vidya Devi
Nuclear Science and Engineering | Volume 193 | Number 3 | March 2019 | Pages 314-324
Technical Paper | doi.org/10.1080/00295639.2018.1538280
Articles are hosted by Taylor and Francis Online.
This paper presents a novel approach for uncertainty propagation of neutron-induced activation cross-section measurement using unscented transformation (UT). Generally, the first-order sensitivity analysis (sandwich formula) method is used for uncertainty propagation in cross-section measurement. It is based on a linear approximation of Taylor series expansion of the function of input parameters and gives satisfactory results for smooth nonlinear functions having relatively small uncertainties. On the contrary, the UT technique is completely defined by the moments of random process and hence produces better results for error propagation in the nonlinear case with large uncertainties. The UT method is easier to implement and gives results as accurate as the sandwich formula and Monte Carlo techniques. This work examines the application of the UT method in nuclear science as an alternate to the sandwich formula and Monte Carlo methods.