Uncertainty quantification in the machine-learning inference from neutron star probability distribution to the equation of state
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The project investigated biogeochemical processes at terrestrial-aquatic interfaces (TAIs), focusing on soil microsite heterogeneity and its impact on greenhouse gas (GHG) fluxes. Using laboratory experiments, modeling, and data integration, researchers explored redox-driven microbial processes under fluctuating hydrological conditions. Key advancements included modifying the DAMM-GHG model to incorporateelectron acceptor availability and enhancing the AquaMEND model for improved microbial metabolism representation. Results highlighted microsite redox variability as a key driver of GHG fluxes, informing Earth system models. The project fostered interdisciplinary collaborations, student training, and the development of novel modeling frameworks to improve Earth'senergy budget.
Presentation for Expert Workshop on Challenges and Solutions to implementation and reliable operation of Large-Scale Gaseous Hydrogen Infrastructure
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With the rapid development of advanced reactors and numerous companies requesting pre-application engagements with the Nuclear Regulatory Commission (NRC), there is need for the NRC to best prepare themselves for new challenges presented by advanced technologies. One specific reactor type of interest is the molten salt reactor (MSR) that uses liquid salt as the fuel for the reactor. Liquid salt fuel reactors have specific challenges with regards to performing material control and accounting (MC&A). The challenges result from the fuel form being a continuous fissile material form rather than discrete units like is found with fuel assemblies in traditional light water reactors (LWRs). The goal of this document is to provide the NRC with guidance on the different approaches and technologies that may be proposed by reactor designers to address these problems to prepare staff for regulatory reviews.
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Poster for presentation at 2025 Photovoltaics Reliability Workshop, hosted by NREL. Poster serves as a review requirement for project.
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Despite state-of-the-art technologies like artificial intelligence, human judgment is critically essential in cooperative systems, such as the multi-agent system (MAS), which collect information among agents based on multiple-cue judgment. Human agents can prevent impaired situational awareness of automated agents by confirming situations under environmental uncertainty. System error caused by uncertainty can result in an unreliable system environment, and this environment affects the human agent, resulting in non-optimal decision-making in MAS. Thus, it is necessary to know how human behavior is changed to capture system reliability under uncertainty. Another issue affecting MAS is time delay, which can delay agent information transfer, resulting in low performance and instability. However, it is difficult to find studies on the influence of time delay on human agents. This study is about understanding the human decision-making process under a specific system reliability environment by uncertainty with time delay. We used concepts of expected and unexpected uncertainty to implement reliability of the system usage environment with three types of time delay conditions: no time delay, regular time delay, and irregular time delay conditions. We used electroencephalogram (EEG) for human cognitive neural mechanisms in multiple-cue judgment systems to understand human decision-making. In the reliability of system usage environment, the unreliable system environment significantly creates less memory load by less utilization of system rules for decision-making. In terms of time delay, delayed information delivery does not significantly affect memory load for decision-making.
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Collision cross sections of ions and electrons in cesium vapor