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Human Factors, Instrumentation & Controls
Improving task performance, system reliability, system and personnel safety, efficiency, and effectiveness are the division's main objectives. Its major areas of interest include task design, procedures, training, instrument and control layout and placement, stress control, anthropometrics, psychological input, and motivation.
Conference on Nuclear Training and Education: A Biennial International Forum (CONTE 2023)
February 6–9, 2023
Amelia Island, FL|Omni Amelia Island Resort
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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University of Florida–led consortium to research nuclear forensics
A 16-university team of 31 scientists and engineers, under the title Consortium for Nuclear Forensics and led by the University of Florida, has been selected by the Department of Energy’s National Nuclear Security Administration (NNSA) to develop the next generation of new technologies and insights in nuclear forensics.
Technical Session|Sponsored by NNPD
Wednesday, December 1, 2021|4:20–5:45PM EST |Columbia 9
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Impact of Potential Source Term Updates in DTRA's HPAC
Joshua Molgaard (Defense Threat Reduction Agency), Tyler Dant (Applied Research Assoc.)
Geometry Effects in the Transport Calculations of the Multiplicity Moments
Victor Dykin (Chalmers Univ. of Technology), Imre Pázsit (Chalmers Univ. of Technology)
A Novel Nuclear Material Control Technique for Pebble Fueled Reactors
Sunil S. Chirayath (Texas A&M Univ.), Claudio A. Gariazzo (ANL), Raffaella Righetti (Texas A&M Univ.), Mohammad Naraghi (Texas A&M Univ.)
An Explainable Artificial Intelligence Approach using a Hopfield Network in Nuclear Security Applications
Miltiadis Alamaniotis (Univ. of Texas, San Antonio), Alexander Heifetz (ANL)
Optimal Sensor Placement with Using Model-Based Reinforcement Learning with Mutual Information
Siyao Gu (Univ. of Texas, San Antonio), Miltiadis Alamaniotis (Univ. of Texas, San Antonio)
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