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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

State-Specific Prevalence of Inactivity, Self-Rated Health Status, and Severe Joint Pain Among Adults With Arthritis — United States, 2019

Arthritis is associated with joint pain, disability, and physical inactivity, potentially resulting in poor quality of life. The Centers for Disease Control and Prevention analyzed 2019 Behavioral Risk Factor Surveillance System data to estimate state-specific arthritis prevalence and, among adults with arthritis, the prevalence of physical inactivity, fair/poor self-rated health status, and severe joint pain. Among adults with arthritis, age-standardized prevalences of physical inactivity, fair/poor health status, and severe joint pain were high in all states and highest in southeastern states. Increased promotion and use of evidence-based public health interventions for arthritis may improve health-promoting behaviors and health outcomes among adults with arthritis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Diagnostic-free onboard battery health assessment

Diverse usage patterns induce complex and variable aging behaviors in lithiumion batteries, complicating accurate health diagnosis and prognosis. Separate diagnostic cycles are often used to untangle the battery’s current state of health from prior complex aging patterns. However, these same diagnostic cycles alter the battery’s degradation trajectory, are time-intensive, and cannot be practically performed in onboard applications. Here, in this work, we leverage portions of operational measurements in combination with an interpretable machine learning model to enable rapid, onboard battery health diagnostics and prognostics without offline diagnostic testing and the requirement of historical data. We integrate mechanistic constraints within an encoder-decoder architecture to extract electrode states in a physically interpretable latent space and enable improved reconstruction of the degradation path. The health diagnosis model framework can be flexibly applied across diverse application interests with slight fine-tuning.

battery aging reconstruction↗
DOE OSTI2021

National variability in Americans’ COVID-19 protective behaviors: Implications for vaccine roll-out

Protective behaviors such as mask wearing and physical distancing are critical to slow the spread of COVID-19, even in the context of vaccine scale-up. Understanding the variation in self-reported COVID-19 protective behaviors is critical to developing public health messaging. The purpose of the study is to provide nationally representative estimates of five self-reported COVID-19 protective behaviors and correlates of such behaviors. In this cross-sectional survey study of US adults, surveys were administered via internet and telephone. Adults were surveyed from April 30-May 4, 2020, a time of peaking COVID-19 incidence within the US. Participants were recruited from the probability-based AmeriSpeak® national panel. Brief surveys were completed by 994 adults, with 73.0% of respondents reported mask wearing, 82.7% reported physical distancing, 75.1% reported crowd avoidance, 89.8% reported increased hand-washing, and 7.7% reported having prior COVID-19 testing. Multivariate analysis (p critical value .05) indicates that women were more likely to report protective behaviors than men, as were those over age 60. Respondents who self-identified as having low incomes, histories of criminal justice involvement, and Republican Party affiliation, were less likely to report four protective behaviors, though Republicans and individuals with criminal justice histories were more likely to report having received COVID-19 testing. The majority of Americans engaged in COVID-19 protective behaviors, with low-income Americans, those with histories of criminal justice involvement, and self-identified Republicans less likely to engage in these preventive behaviors. Culturally competent public health messaging and interventions might focus on these latter groups to prevent future infections. These findings will remain highly relevant even with vaccines widely available, given the complementarities between vaccines and protective behaviors, as well as the many challenges in delivering vaccines.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Embedded sensors integrated into critical components for in situ health monitoring of steam turbines

Operational flexibility is desired in today’s coal-fired power plants to balance power grids by compensating for the variable electricity supply from renewable energy sources and distributed gensets. This demanding requirement accelerates materials degradation and makes in-situ health monitoring essential. Life monitoring of components and subsystems is thus seen as essential in assessing material and mechanical behavior to estimate system reliability, move to a conditionbased maintenance strategy and determine time to failure of the units in their actual operating conditions. Vibration monitoring can be exploited for blade tip timing to measure blade vibration amplitude and tip clearance to detect any deterioration taking place in the condition of steam turbine blades. A failure of a rotating blade can lead to severe turbine damage followed by extensive repairs and loss of power production. A blade vibration monitoring system can help early detection of abnormal blade vibration behavior. In conjunction with a health monitoring system, the vibration characteristics can be analyzed to support a pro-active maintenance and inspection schedule. While the feasibility of this inspection technique has been amply demonstrated, there is a need to install induction probes to magnetize the blade for signal output. Siemens, in partnership with Raytheon Technologies Research Corporation (RTRC), proposes a holistic approach to develop embedded sensors to utilize radio frequency for not only coupling to sensors, but as the sensing modality. The goal of this project is to “embed” the novel sensing approach by using either additively manufactured or extruded waveguides on rotating blades for recording, evaluation and monitoring of blade vibrations in Low Pressure turbines, with applications extending to aero engines

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Question-answering system extracts information on injection drug use from clinical notes

Background. Injection drug use (IDU) can increase mortality and morbidity. Therefore, identifying IDU early and initiating harm reduction interventions can benefit individuals at risk. However, extracting IDU behaviors from patients’ electronic health records (EHR) is difficult because there is no other structured data available, such as International Classification of Disease (ICD) codes, and IDU is most often documented in unstructured free-text clinical notes. Although natural language processing can efficiently extract this information from unstructured data, there are no validated tools. Methods. Here, to address this gap in clinical information, we design a question-answering (QA) framework to extract information on IDU from clinical notes for use in clinical operations. Our framework involves two main steps: (1) generating a gold-standard QA dataset and (2) developing and testing the QA model. We use 2323 clinical notes of 1145 patients curated from the US Department of Veterans Affairs (VA) Corporate Data Warehouse to construct the gold-standard dataset for developing and evaluating the QA model. We also demonstrate the QA model’s ability to extract IDU-related information from temporally out-of-distribution data. Results. Here, we show that for a strict match between gold-standard and predicted answers, the QA model achieves a 51.65% F1 score. For a relaxed match between the gold-standard and predicted answers, the QA model obtains a 78.03% F1 score, along with 85.38% Precision and 79.02% Recall scores. Moreover, the QA model demonstrates consistent performance when subjected to temporally out-of-distribution data. Conclusions. Our study introduces a QA framework designed to extract IDU information from clinical notes, aiming to enhance the accurate and efficient detection of people who inject drugs, extract relevant information, and ultimately facilitate informed patient care.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Mining and Validating Social Media Data for COVID-19–Related Human Behaviors Between January and July 2020: Infodemiology Study

Background: Health authorities can minimize the impact of an emergent infectious disease outbreak through effective and timely risk communication, which can build trust and adherence to subsequent behavioral messaging. Monitoring the psychological impacts of an outbreak, as well as public adherence to such messaging, is also important for minimizing long-term effects of an outbreak. Objective: We used social media data from Twitter to identify human behaviors relevant to COVID-19 transmission, as well as the perceived impacts of COVID-19 on individuals, as a first step toward real-time monitoring of public perceptions to inform public health communications. Methods: We developed a coding schema for 6 categories and 11 subcategories, which included both a wide number of behaviors as well codes focused on the impacts of the pandemic (eg, economic and mental health impacts). We used this to develop training data and develop supervised learning classifiers for classes with sufficient labels. Classifiers that performed adequately were applied to our remaining corpus, and temporal and geospatial trends were assessed. We compared the classified patterns to ground truth mobility data and actual COVID-19 confirmed cases to assess the signal achieved here. Results: We applied our labeling schema to approximately 7200 tweets. The worst-performing classifiers had F1 scores of only 0.18 to 0.28 when trying to identify tweets about monitoring symptoms and testing. Classifiers about social distancing, however, were much stronger, with F1 scores of 0.64 to 0.66. We applied the social distancing classifiers to over 228 million tweets. We showed temporal patterns consistent with real-world events, and we showed correlations of up to –0.5 between social distancing signals on Twitter and ground truth mobility throughout the United States. Conclusions: Behaviors discussed on Twitter are exceptionally varied. Twitter can provide useful information for parameterizing models that incorporate human behavior, as well as for informing public health communication strategies by describing awareness of and compliance with suggested behaviors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Comparison of Social Media, Syndromic Surveillance, and Microbiologic Acute Respiratory Infection Data: Observational Study

Internet data can be used to improve infectious disease models. However, the representativeness and individual-level validity of internet-derived measures are largely unexplored as this requires ground truth data for study. This study sought to identify relationships between Web-based behaviors and/or conversation topics and health status using a ground truth, survey-based dataset. This study leveraged a unique dataset of self-reported surveys, microbiological laboratory tests, and social media data from the same individuals toward understanding the validity of individual-level constructs pertaining to influenza-like illness in social media data. Logistic regression models were used to identify illness in Twitter posts using user posting behaviors and topic model features extracted from users’ tweets. Of 396 original study participants, only 81 met the inclusion criteria for this study. Of these participants’ tweets, we identified only two instances that were related to health and occurred within 2 weeks (before or after) of a survey indicating symptoms. It was not possible to predict when participants reported symptoms using features derived from topic models (area under the curve [AUC]=0.51; P=.38), though it was possible using behavior features, albeit with a very small effect size (AUC=0.53; P≤.001). Individual symptoms were also generally not predictable either. The study sample and a random sample from Twitter are predictably different on held-out data (AUC=0.67; P≤.001), meaning that the content posted by people who participated in this study was predictably different from that posted by random Twitter users. Individuals in the random sample and the GoViral sample used Twitter with similar frequencies (similar @ mentions, number of tweets, and number of retweets; AUC=0.50; P=.19). To our knowledge, this is the first instance of an attempt to use a ground truth dataset to validate infectious disease observations in social media data. The lack of signal, the lack of predictability among behaviors or topics, and the demonstrated volunteer bias in the study population are important findings for the large and growing body of disease surveillance using internet-sourced data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Individual low-carbon behaviors and influencing factors: Insights from a behavior survey study in China

Climate change is a major global concern. Greenhouse gas emissions that cause global climate change are directly or indirectly affected by human activities. Individual low-carbon behaviors are crucial in reducing CO 2 emissions and improving environmental and ecological health. To effectively promote individual low-carbon behavior, this study designed a questionnaire on the factors influencing individual low-carbon intentions and behavior based on theoretical models of environmental behavior. A total of 2430 valid questionnaires were collected in China. This study focuses on analyzing the impact of demographic characteristics, internal and external factors on individual low-carbon behaviors and their interrelationships. The research shows correlations between internal and external factors in determining low-carbon intention or behaviors. Internal factors-related low-carbon behavior is not closely linked with demographic variables, whereas the external factors-related low-carbon behavior vary significantly by age, residence, education, marital status, occupation, and income. The findings have important implications for designing effective policies to promote low-carbon behaviors, such as creating a more favorable external environment and increasing the use of policy tools for reducing CO 2 emission.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Poster Abstract: Leveraging Large Language Models to Reveal Interpretable Cooling Behaviors from Smart Thermostat Data

Frequent heatwaves and hot summers increasingly challenge occupant comfort, health, and energy grid stability. Addressing these challenges requires a detailed understanding of household cooling behaviors, such as thermostat adjustments and adaptive responses to extreme conditions. Traditional analyses often rely on aggregated numerical metrics that overlook subtle but important household-specific variations. In this study, we introduce a generalizable methodology that integrates large language models (LLMs) with vision capabilities to enable scalable and detailed analysis of residential thermostat data. Using Ecobee's Donate Your Data (DYD) dataset—which provides five-minute records of indoor temperatures, thermostat setpoints, and HVAC runtimes—we focus on two U.S. cities with contrasting summer climates : Austin (TX) and Phoenix (AZ). Because raw time-series data are not well suited for direct LLM analysis, we transform them into visual representations, such as daily indoor temperature trajectories and weekly runtime histograms, to better capture behavioral variations. Leveraging LLMs' visual interpretation, we extract descriptive behavioral features, including temperature preferences, time-of-day cooling orientation, anticipatory versus reactive heatwave responses, and behavioral consistency. These semantic features support unsupervised clustering to identify distinct occupant archetypes at scale, revealing differences—such as morning-centric anticipatory coolers versus households that shift toward warmer setpoints during heatwaves—that can inform demand response, resilience planning, and health-aware interventions. By converting raw numerical data into interpretable behavioral patterns, this methodology enables scalable and practical analysis of occupant behavior, supporting actionable insights for comfort, resilience, and energy management.

Nihar, Kopal↗
DOE OSTI2022

Health Management Clinic Report FY18

The Health Management Clinic (HMC) is a worksite specialty clinic designed to provide an exceptional level of health care for Sandia employees with diabetes, cholesterol and blood pressure disorders, and for those employees that need help with smoking cessation, depression, anxiety, sleep disorders, or weight management. With a unified commitment to the best care practices available, the HMC is Sandia’s interface to workplace healthcare and health plan services. The HMC provides Sandia employees access to onsite screenings, health care exams, preventative health education, disease management education, care management, periodic laboratory testing, immunizations, podiatry services, and behavioral, fitness, and nutrition counseling/education. Our multidisciplinary team of health professionals consists of physicians, nurses, medical assistants, certified diabetes educators, dietitians, health educators, and exercise specialists. Services offered by the Health Management clinic have been designed to reduce further complications from disease states and promote healthy behavior changes for Sandia employees.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Willingness to Receive mHealth Services Among Patients with Diabetes on Chronic Follow-up in Public Hospitals in Eastern Ethiopia: Multicenter Mixed-Method Study

Background: Management of diabetes requires a long-term care strategy, including support for adherence to a healthy lifestyle and treatment. Exploring the willingness of patients with diabetes to receive mHealth services is essential for designing efficient and effective services. This study aimedto determine willingness to receive mHealth services and associated factors, as well as explore the barriers to receive mHealth services among patients with diabetes. Methods: A multicenter mixed-method study was employed from September 1 to November 30, 2022. For the quantitative part, a total of 365 patients with diabetes receiving chronic follow-up at three public hospitals were enrolled. Data were gathered using structured questionnaires administered by interviewers, entered into Epi-data version 4.6, and analyzed using Stata version 17. A binary and multivariable logistic regression model was computed to identify the associated factors. For qualitative, eight key informants and seven in-depth interviews were conducted. After verbatim transcription and translation, the data were thematically analyzed using ATLAS.ti V. 7.5. Results: Overall, 77.3% had access to a mobile phone, and 74.5% of them were willing to receive mHealth services. Higher odds of willingness to receive mHealth services were reported among patients with an age below 35 years [AOR = 4.11 (1.15– 14.71)], attended formal education [AOR = 2.63 (1.19– 5.77)], without comorbidity [AOR = 3.6 (1.54– 8.41)], < 1-hour travel to reach a health facility [AOR = 3.57 (1.03– 12.36)], answered unknown calls [AOR = 2.3 (1.04– 5.13)], and were satisfied with health-care provider service [AOR = 2.44 (1.04– 5.72)]. In the qualitative part, infrastructure, health facilities, socioeconomic factors, and patients’ behavioral factors were major identified barriers to receiving mHealth services. Conclusion: In this study, the willingness to receive mHealth services for those who have access to mobile phones increased. Additionally, the study highlighted common barriers to receiving mHealth services.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Computational epidemiological tools for pandemic analysis, understanding, and response

This suite of software tools is being developed to enhance and analyze computational epidemiological models that incorporate realistic disease dynamics and human behavior, with the goal of supporting epidemic and pandemic response. Specifically, the tools enable data analysis, feature extraction, data synthesis, machine learning model development, and prediction of key public health outcomes, such as cases, hospitalizations, deaths, and behavioral responses, for airborne infectious diseases like COVID-19 and influenza.

Butts, David↗
DOE OSTI2020

Diagnosing and Predicting Mixed-Culture Fermentations with Unicellular and Guild-Based Metabolic Models

Microbiomes are vital to human health, agriculture, and protecting the environment. Predicting behavior of self-assembled or synthetic microbiomes, however, remains a challenge. In this work, we used unicellular and guild-based metabolic models to investigate production of medium-chain fatty acids by a mixed microbial community that is fed multiple organic substrates. Modeling results provided insights into metabolic pathways of three medium-chain fatty acid-producing guilds and identified potential strategies to increase production of medium-chain fatty acids. This work demonstrates the role of metabolic models in augmenting multi-omic studies to gain greater insights into microbiome behavior.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Human Mobility to Parks Under the COVID-19 Pandemic and Wildfire Seasons in the Western and Central United States

In 2020, people's health suffered a great crisis under the dual effects of the COVID-19 pandemic and the extensive, severe wildfires in the western and central United States. Parks, including city, national, and cultural parks, offer a unique opportunity for people to maintain their recreation behaviors following the social distancing protocols during the pandemic. However, massive forest wildfires in western and central US, producing harmful toxic gases and smoke, pose significant threats to human health and affect their recreation behaviors and mobility to parks. In this study, we employed the geographically and temporally weighted regression (GTWR) Models to investigate how COVID-19 and wildfires jointly shaped human mobility to parks, regarding the number of visits per capita, dwell time, and travel distance to parks, during June - September 2020. We detected strong correlations between visitations and COVID-19 incidence in southern Montana, western Wyoming, Colorado, and Utah before August. However, the pattern was weakened over time, indicating the decreasing trend of the degree of concern regarding the pandemic. Moreover, more park visits and lower dwell time were found in parks further away from wildfires and less air pollution in Washington, Oregon, California, Colorado, and New Mexico, during the wildfire season, suggesting the potential avoidance of wildfires when visiting parks. This study provides important insights on people's responses in recreation and social behaviors when facing multiple severe crises that impact their health and wellbeing, which could support the preparation and mitigation of the health impacts from future pandemics and natural hazards.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Battery State of Health Estimator: Cooperative Research and Development Final Report

NREL has developed a software tool to enable Renewance to estimate the degradation of batteries from basic information such as the type of battery and the application of that battery during its first life, so that used batteries may be evaluated for potential repurposing at low cost. This software tool utilizes NREL's BLAST-Lite battery degradation modeling code, which was updated with additional models for commercially produced lithium-ion batteries as a part of this CRADA. The software tool enables users to input details such as battery type and application so that lifetime estimates can be made without any programming or expert battery knowledge. The application input loads in saved values for parameters such as cycles per year, depth-of-discharge, and other battery operating parameters from a file defined by Renewance. These parameters may be modified to refine simulations for specific batteries. The software tool also incorporates a degradation model optimization tool, whereby existing battery degradation models may be tuned according to measured battery health. This ensures that new models still predict degradation behaviors expected from a certain battery chemistry, but with the overall degradation rate tuned to a specific battery make and model. The new model can then be saved for estimating the degradation of other similar batteries. An additional task was planned to utilize machine-learning to enable battery health diagnosis from rapid EIS measurements to accelerate the screening of used batteries. This task was not completed due to lack of available data for training a machine-learning model. CRADA benefit to DOE, Participant, and US Taxpayer: Further development of open-source software tool BLAST-Lite for predicting the lifetime of commercially produced Lithium-ion batteries (NREL SWR-22-69).

25 ENERGY STORAGE↗
DOE OSTI2024

Inappropriate antibiotic access practices at the community level in Eastern Ethiopia

Access to antibiotic medications is critical to achieving the Sustainable Development Goal for good health and well-being. However, non-prescribed and informal sources are implicated as the most common causes of inappropriate antibiotic access practices, resulting in untargeted therapy, which leads to antibiotic resistance. Hence, knowing antibiotic access practices at the community level is essential to target misuse sources. In this study, 2256 household representatives were surveyed between July and September 2023 to examine their antibiotic access practices. Of 1245 household members who received antibiotics, 45.6% did so inappropriately. Non-prescribed antibiotic access was more common among urban residents and individuals not enrolled in health insurance schemes. This means of antibiotic access was also more common among individuals concerned about distance, drug availability, and healthcare convenience at public facilities. In addition, women and rural individuals were more likely to get antibiotics from unauthorized sources. Unrestricted antibiotic dispensing practices in urban areas enabled their non-prescribed access, while unlicensed providers prevailed with this access practice in rural areas. In this regard, personal behaviors and healthcare-related gaps such as the lack of health insurance, inconvenience, and drug unavailability have led community members to seek antibiotics from unofficial and non-prescribed sources. Targeting the identified behavioral and institutional factors can enhance antibiotic access through prescriptions, hence reducing antibiotic resistance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Genetic variations and their interaction with thirdhand smoke exposure on anxiety and memory in Collaborative Cross mice

Thirdhand smoke (THS) is linked to adverse health effects, but the effect of genetic variations on behavioral outcomes is poorly understood. To investigate this, we assessed anxiety- and memory-related behaviors in 820 mice from 21 strains of the genetically diverse Collaborative Cross (CC) mouse that were exposed to THS from 4 through 10 weeks of age. Anxiety was evaluated with a light/dark box assay with a previously established risk score system. Females were generally more sensitive: THS reduced anxiety risk in strains CC013, CC019, and CC051, but increased risk in CC036 and CC061, while males showed no significant effects. Memory was tested using passive avoidance: impairments were observed in both sexes in CC016 and CC019, with sex-dependent effects in CC002 and CC051. A genome-wide association study identified 2,347 SNPs associated with anxiety and 1,568 SNPs with memory, with 32 and 85 SNPs, respectively, interacting with THS exposure. Enrichment analyses revealed distinct biological processes underlying susceptibility, including axonogenesis, synapse organization, cognition, and learning and memory. KEGG pathway analysis identified distinct genetic pathways, including GTPase binding and GTPase regulatory activity, that act as critical molecular switches in the brain that regulate synaptic plasticity, dendritic spine structure, and neuronal signaling, directly influencing anxiety-like behaviors and memory formation. These findings show that THS exposure affects neurobehavioral outcomes in a sex- and genotype-dependent manner, highlighting critical gene-environment interactions and providing a foundation for mechanistic insights into THS neurotoxicity

Anxiety↗
DOE OSTI2022

California wildfire spread derived using VIIRS satellite observations and an object-based tracking system

Changing wildfire regimes in the western US and other fire-prone regions pose considerable risks to human health and ecosystem function. However, our understanding of wildfire behavior is still limited by a lack of data products that systematically quantify fire spread, behavior and impacts. Here we develop a novel object-based system for tracking the progression of individual fires using 375 m Visible Infrared Imaging Radiometer Suite active fire detections. At each half-daily time step, fire pixels are clustered according to their spatial proximity, and are either appended to an existing active fire object or are assigned to a new object. This automatic system allows us to update the attributes of each fire event, delineate the fire perimeter, and identify the active fire front shortly after satellite data acquisition. Using this system, we mapped the history of California fires during 2012–2020. Our approach and data stream may be useful for calibration and evaluation of fire spread models, estimation of near-real-time wildfire emissions, and as means for prescribing initial conditions in fire forecast models.

54 ENVIRONMENTAL SCIENCES↗