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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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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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Chris Wagner: The role of Eden Radioisotopes in the future of nuclear medicine
Chris Wagner has more than 40 years of experience in nuclear medicine, beginning as a clinical practitioner before moving into leadership roles at companies like Mallinckrodt (now Curium) and Nordion. His knowledge of both the clinical and the manufacturing sides of nuclear medicine laid the groundwork for helping to found Eden Radioisotopes, a start-up venture that intends to make diagnostic and therapeutic raw material medical isotopes like molybdenum-99 and lutetium-177.
Miltiadis Alamaniotis (Univ of Texas at San Antonio), Asok Ray (Penn State)
Proceedings | Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technolgies (NPIC&HMIT 2019) | Orlando, FL, February 9-14, 2019 | Pages 431-439
Monitoring of Boiling Water Rectors (BWRs) is a complex process that requires the use of a numerous sensors and systems. Acquisition of data and the subsequent processing of it accommodate inference making with regard to the state of the reactor system. System identification promotes decision making with regard to operation action taking. In this paper, we present a new method for serially integrating two machine learning tools and more specifically a neural network and a set of algorithms for learning Gaussian processes. Both sets of tools exhibit learning capabilities, and their integration in the current work offers a two-stage learning schema applied to identification of transient states in BWR. In particular, the proposed methodology utilizes the synergism of a set of Gaussian processes with a feedforward neural network for recognizing the type of loss of coolant accident (LOCA) that occurs in the reactor. The methodology is tested on a set of real-world datasets taken from the FIX-II facility. Results demonstrate efficacy of the method to accurately identify the occurring LOCA among three possible states.