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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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NECX debut: Shaping the next era of energy
The sold-out inaugural Nuclear Energy Conference & Expo (NECX) got off to a roaring start in Atlanta, Ga., Tuesday morning with an opening plenary that was a live highlight reel discussing the latest industry achievements.
Starting with a lively promo video that left the audience amped up for Entergy’s CEO and NEI chair Drew Marsh, who welcomed everyone to the event, hosted jointly by the American Nuclear Society and the Nuclear Energy Institute. He spoke to a full house of more than 1,300 attendees, promising a blend of science, technology, policy, and advocacy centered around the future of nuclear energy.
Patrick Maedgen, Benjamin Wellons, Shikha Prasad, Jian Tao
Nuclear Technology | Volume 208 | Number 10 | October 2022 | Pages 1522-1539
Technical Paper | doi.org/10.1080/00295450.2022.2045533
Articles are hosted by Taylor and Francis Online.
Various machine learning techniques have been implemented to assist in neutron-gamma discrimination with great success compared to traditional methods. Despite this, the fundamental structure of a pulse shape as it relates to machine learning has not yet been explored in detail, and the optimal number of pulse vector features needed for training is still unknown. In this study, support vector machines (SVMs) using linear, radial basis, and exponential kernel functions are fitted on data of two different forms: waveforms that partially cover the original pulses and principal components extracted from those pulses. The described methods correctly classified 98.02% for neutrons and 97.84% for gamma rays. The efficiency of the SVM was improved by extracting principal components from the waveforms. That is, fewer features were needed to discriminate between neutrons and gamma rays without negatively impacting the classification accuracy. This study also shows that utilizing a nonlinear kernel significantly reduces the number of features required to reach high classification accuracy. SVMs that did this could make accurate classifications 97% of the time with data that had fewer than 50 features.