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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.
Paul N. Stevens
Fusion Science and Technology | Volume 5 | Number 1 | January 1984 | Pages 109-114
Deep Penetration: Problem and Method of Solution | Special Section Contents / Sheilding | doi.org/10.13182/FST84-A23084
Articles are hosted by Taylor and Francis Online.
The formal basis of the use of calculated importance information for biasing forward and adjoint Monte Carlo deep penetration shielding problems is presented. The distinction between the “point value” and “event value” functions for adjoint problems is discussed. The analysis reveals that the emergent particle density, and not the particle flux density, is the proper choice of biasing function for the selection of the ad junctor's next collision site. This is analogous to the choice of the event value as the value function for the biased selection of the next collision site in the forward analysis. A numerical illustrative problem consisting of a concrete cylinder with an axial duct, a plane source on the bottom surface, and four joint detectors outside the emergent top surface is used to demonstrate this theory.