J. E. Palo Tejada

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We introduce and study an artificial neural network inspired by the probabilistic receptor affinity distribution model of olfaction. Our system consists of N sensory neurons whose outputs converge on a single processing linear threshold element. The system's aim is to model discrimination of a single target odorant from a large number p of background(More)
The United States Nuclear Regulatory Commission (USNRC) relies on Probabilistic Risk Assessment (PRA) as one of the main pillars of its risk-informed regulatory and oversight functions. In 2011, the South Texas Project Nuclear Operating Company (STPNOC) initiated a risk-informed project to resolve Generic Safety Issue 191 (GSI-191), which is related to the(More)
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