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Conference Spotlight
2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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Industry Update—October 2025
Here is a recap of recent industry happenings:
New international partnership to speed Xe-100 SMR deployment
X-energy, Amazon, Korea Hydro & Nuclear Power, and Doosan Enerbility have formed a strategic partnership to accelerate the deployment of X-energy’s Xe-100 small modular reactors and TRISO fuel in the United States to meet the power demands from data centers and AI. The partners will collaborate in reactor engineering design, supply-chain development, construction planning, investment strategies, long-term operations, and global opportunities for joint AI-nuclear deployment. The companies also plan to jointly mobilize as much as $50 billion in public and private investment to support advanced nuclear energy in the U.S.
Miltiadis Alamaniotis, Andreas Ikonomopoulos, Tatjana Jevremovic, Lefteri H. Tsoukalas
Nuclear Technology | Volume 175 | Number 2 | August 2011 | Pages 480-497
Technical Paper | Radiation Measurements and General Instrumentation | doi.org/10.13182/NT11-A12319
Articles are hosted by Taylor and Francis Online.
Nuclear resonance fluorescence (NRF) has been considered as a promising method for cargo inspection. Almost all isotopes existing in nature yield a unique NRF spectral signature. NRF signals obtained during cargo inspection are aggregates of various signatures from materials hidden inside. The challenge is to identify individual signatures embedded in this signature aggregation. Background noise and spectra overlap to further complicate the NRF signal analysis. This paper addresses these concerns through an intelligent methodology recognizing signature spectra and, subsequently, identifying cargo materials. The methodology relies on fuzzy logic for pattern identification and evaluation of the weighted options involved in decision making. The intelligent methodology is presented using different simulated NRF signal scenarios. The results obtained demonstrate that the algorithm is highly accurate in most spectra carrying a signal-to-noise ratio (SNR) >20 db. Misses and false alarms were observed for isotopes with only one NRF peak (lead) with SNR <35 db. Extensive parameter testing under different scenarios indicated the existence of parameter couples that maximize the accuracy even for SNR values <20 db. In all cases the algorithm execution time was <0.1 s and was significantly faster than that of the maximum likelihood algorithm.