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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2018

Toxic metal(loid)-based pollutants and their possible role in autism spectrum disorder

Highlights: • There is a significant relationship between ASD and various environmental factors. • A possible association exists between ASD and exposure to toxic metals. • Lead, mercury, aluminum, and arsenic may be involved in the etiology of ASD. • Toxic metals may induce neuroinflammation and oxidative stress. • Modulation of toxic metal body burden may improve ASD. Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social interaction, verbal and non-verbal communication, and stereotypic behaviors. Many studies support a significant relationship between many different environmental factors in ASD etiology. These factors include increased daily exposure to various toxic metal-based environmental pollutants, which represent a cause for concern in public health. This article reviews the most relevant toxic metals, commonly found, environmental pollutants, i.e., lead (Pb), mercury (Hg), aluminum (Al), and the metalloid arsenic (As). Additionally, it discusses how pollutants can be a possible pathogenetic cause of ASD through various mechanisms including neuroinflammation in different regions of the brain, fundamentally occurring through elevation of the proinflammatory profile of cytokines and aberrant expression of nuclear factor kappa B (NF-κB). Due to the worldwide increase in toxic environmental pollution, studies on the role of pollutants in neurodevelopmental disorders, including direct effects on the developing brain and the subjects’ genetic susceptibility and polymorphism, are of utmost importance to achieve the best therapeutic approach and preventive strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Proposed Critical Thinking Metrics for Power Grid Trainees

The TRS Intro to Critical Thinking for Operational Decision-Making course for power grid operators seeks to help trainees improve their use of critical thinking skills in operation environments. To determine the effectiveness of this training, we have identified 3 categories of evaluation measures: critical thinking metrics, situation awareness (SA) evaluations, and behavioral observations. Critical thinking metrics seek to directly measure trainee’s ability to use critical thinking skills through their verbalized responses to different questions or scenarios. SA will be evaluated during various points throughout simulated exercises completed during the training. SA evaluations aim to determine the level of a trainee’s ability to perceive, comprehend and anticipate events in the operational environment. This construct will be assessed through verbalized responses to questions informed by the Situation Awareness Global Assessment Technique (SAGAT). Behavioral observations capture how trainees use the system, including which simulator displays or windows they use or actions they perform and in what order. These observations help us understand how the critical thinking training may inform operator action. We hypothesize that while these measures are correlated with each other, they provide different perspectives on each trainee’s ability to gain SA and use critical thinking skills in operation environments. By comparing these measures in a pre-assessment and a post-assessment, we can determine how much improvement a trainee gains through the training. These measures may also be used during the training as appropriate. This document specifies these 3 categories of measures, which components of these measures currently seem to be the best candidates to use in the training course, and which parts of the training course is related to each component.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Modeling and evaluating user behavior in exploratory visual analysis

Empirical evaluation methods for visualizations have traditionally focused on assessing the outcome of the visual analytic process as opposed to characterizing how that process unfolds. There are only a handful of methods that can be used to systematically study how people use visualizations, making it difficult for researchers to capture and characterize the subtlety of cognitive and interaction behaviors users exhibit during visual analysis. To validate and improve visualization design, however, it is important for researchers to be able to assess and understand how users interact with visualization systems under realistic scenarios. This paper presents a methodology for modeling and evaluating the behavior of users in exploratory visual analysis. We model visual exploration using a Markov chain process comprising transitions between mental, interaction, and computational states. These states and the transitions between them can be deduced from a variety of sources, including verbal transcripts, videos and audio recordings, and log files. This model enables the evaluator to characterize the cognitive and computational processes that are essential to insight acquisition in exploratory visual analysis, and reconstruct the dynamics of interaction between the user and the visualization system. We illustrate this model with two exemplar user studies, and demonstrate the qualitative and quantitative analytical tools it affords.

97 MATHEMATICS AND COMPUTING↗