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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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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The Second Skin: A Wearable Sensor Suite that Enables Real-Time Human Biomechanics Tracking Through Deep Learning

Objective: Real-time determination of human kinematics and kinetics could advance biomechanics research and enable valuable applications of biofeedback and generalizable exoskeleton control. Here, this work aims to investigate a taskindependent, user-independent method for obtaining precise realtime joint state estimation across lower-body joints during a wide variety of tasks. Methods: We developed a generalizable sensing approach using a suit comprised of inertial measurement units (IMUs) and pressure insoles. With the suit, we collected a dataset of 33 tasks commonly performed during construction and hazardous waste cleanup (N = 10). We then trained deep learning user-independent, task-agnostic models to estimate joint lowerbody kinematics and dynamics using only worn sensor data. We likewise computed joint kinematics and dynamics analytically from sensor data to serve as a comparison tool for model results. Results: Our models achieved overall angle estimation root-meansquared-errors (RMSE) of 6.56±.92°, 8.60±1.01°, 7.58±.89°, and 6.00±.73° compared to 13.9±.1.3°, 15.31±1.0°, 10.76±.70°, and 7.56±.48° via analytical methods at the lower back, hip, knee, and ankle, respectively. Likewise, our models achieved overall normalized moment estimation RMSEs of .207±.069 Nm/kg, .242±.044 Nm/kg, .202±.038 Nm/kg, and .193±.034 Nm/kg compared to .306±.036 Nm/kg, .407±.021 Nm/kg, 1.18 ±.022 Nm/kg, and 1.73±.071 Nm/kg via analytical methods at the lower back, hip, knee, and ankle, respectively. Conclusion: These results are comparable to other state-of-the-art wearable sensing systems, establishing deep learning as a viable sensing approach that generalizes to new users and tasks. Significance: This work shows promise for enabling accurate real-world biomechanical data collection and enhancement of biofeedback systems and wearable robot control.

Casey, Ryan T. F. [Georgia Institute of Technology↗
DOE OSTI2025

Proactive Assignment Strategy With Human Choice Models for Boosting Pooled Rideshare Service

This study analyzes various human factors considerations in estimating discounts for pooled rideshare trips. The discounts are utilized in an optimization-based rideshare assignment strategy (proactive strategy) and compared against each other, as well as a heuristic strategy attempting to replicate current real-world pooling rates. Simulations within Austin, Texas and Greenville, South Carolina, reveal the proactive strategy’s ability to increase average vehicle occupancy by 0.23 persons/mile in Austin and 0.52 persons/mile in Greenville. A significant ability to decrease trip rejections and increase profitability is also observed. Finally, the strengths of particular combinations of factors are discussed relative to their effectiveness in each region.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Human activities shape global patterns of decomposition rates in rivers

Rivers and streams contribute to global carbon cycling by decomposing immense quantities of terrestrial plant matter. However, decomposition rates are highly variable and large-scale patterns and drivers of this process remain poorly understood. Using a cellulose-based assay to reflect the primary constituent of plant detritus, we generated a predictive model (81% variance explained) for cellulose decomposition rates across 514 globally distributed streams. A large number of variables were important for predicting decomposition, highlighting the complexity of this process at the global scale. Predicted cellulose decomposition rates, when combined with genus-level litter quality attributes, explain published leaf litter decomposition rates with high accuracy (70% variance explained). Finally, our global map provides estimates of rates across vast understudied areas of Earth and reveals rapid decomposition across continental-scale areas dominated by human activities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Conformational flexibility is a critical factor in designing broad-spectrum human norovirus protease inhibitors

Human norovirus (HuNoV) is a leading cause of gastroenteritis worldwide and is associated with significant morbidity, mortality, and economic impact. There are currently no licensed antiviral drugs for the treatment of HuNoV-associated gastroenteritis. The HuNoV protease plays a critical role in the initiation of virus replication by cleaving the polyprotein. Thus, it is an ideal target for developing antiviral small-molecule inhibitors. While rupintrivir, a potent small-molecule inhibitor of several picornavirus proteases, effectively inhibits GI.1 protease, it is an order of magnitude less effective against GII protease. Other GI.1 protease inhibitors also tend to be less effective against GII proteases. To understand the structural basis for the potency difference, we determined the crystal structures of proteases of GI.1, pandemic GII.4 (Houston and Sydney), and GII.3 in complex with rupintrivir. These structures show that the open substrate pocket in GI protease binds rupintrivir without requiring significant conformational changes, whereas, in GII proteases, the closed pocket flexibly extends, reorienting arginine-112 in the BII-CII loop to accommodate rupintrivir. Structures of R112A protease mutants with rupintrivir, coupled with enzymatic and inhibition studies, suggest R112 is involved in displacing both substrate and ligands from the active site, implying a role in the release of cleaved products during polyprotein processing. Thus, the primary determinant for differential inhibitor potency between the GI and GII proteases is the increased flexibility in the BII-CII loop of the GII proteases caused by the H-G mutation in this loop. Therefore, the inherent flexibility of the BII-CII loop in GII proteases is a critical factor to consider when developing broad-spectrum inhibitors for HuNoV proteases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

A Scalable Multi-Modal Framework for High-Fidelity Distributed Human Mobility Simulations

The development of data-driven models for human mobility in urban settings requires access to substantial and diverse real-world data. However, existing historical data often presents challenges such as limited volume, variety, and veracity, as well as missing data and privacy preservation concerns. Also, urban mobility modeling is inherently time-variant, complex, and multi-modal, encompassing everything from individual walking and running to private road travel and large-scale public transportation. These challenges call for innovative solutions to overcome data limitations and compute needs to model mobility behaviors accurately. To address these challenges, we propose a distributed, co-simulation-based architecture DURMOSim that integrates real-world data with scalable, high-fidelity simulations, demonstrating distributed co-simulation feasibility with existing mobility models. DURMOSim underpins a modular integration that would enable using any available mobility simulators for greater extensibility and scalability in performing various urban scenarios. In this paper, we present the design, implementation, and performance evaluation of DURMOSim, highlighting its capability to model population-scale mobility patterns. Our initial results show its ability to dynamically synchronize multiple simulation models at runtime with negligible computational overhead. We believe DURMOSim could be a robust tool for advancing urban mobility research and intelligent transportation systems.

Yoginath, Srikanth [ORNL] (ORCID:0000000184236050)↗
DOE OSTI2026

Decision Making Under Uncertainty Human Subjects Data - Fire Evacuation Task

This dataset contains de-identified data from human subjects experiments, along with the images and code that were used to run the experiments (as a crowdsourced online study). In this study, participants were shown the probability of a house being in the burn zone of a wildfire. They were asked if they would stay in the house or evacuate in that scenario. The probability information was presented in different ways, including text and maps. The studies tested the impact of different visual cues on the participants' patterns of decisions.

Matzen, Laura E. [Sandia National Laboratories (SN↗
DOE OSTI2025

Bayesian And Human Reliability Analysis (hra)-aided Method For The Reliability Analysis Of Software (bahamas)

The purpose of the BAHAMAS code is to provide a simplified process for performing quantitative evaluations of software reliability. The Bayesian and Human Reliability Analysis (HRA)-Aided method for the Reliability Analysis of software (BAHAMAS) was developed specifically to perform quantification under limited data conditions, i.e., when limited testing or operational data are available, such as during early development stages. BAHAMAS essentially examines the quality of a software development life cycle to determine the probability of specific types of software failure. BAHAMAS will have modules to support user input for detailed and simplified analyses. The user interface will also support software common cause failure analysis.

Wang, Congjian (0000000207789927)↗
DOE OSTI2025

Vasomotion in Human Fingers

Introduction: We describe methods by which vasomotion can be recorded in awake and anesthetized human subjects without significant interference from other spontaneous vascular oscillations. Methods: In three separate studies, we used photoplethysmography (PPG) to record vasomotion in fingertips. In Study 1, we induced chemical sympathectomy in the studied hand of 11 awake subjects who received intravenous dexmedetomidine infusions. In Study 2, we administered four progressively increasing intravenous dexmedetomidine infusions to 16 awake volunteers. In Study 3, we recorded vasomotion simultaneously from 6 fingers of 7 patients who were under dexmedetomidine-based anesthesia. Five-minute epochs of PPG recordings that displayed slow vascular oscillations were analyzed for frequency and amplitude. Results: In Study 1, vasomotion frequencies were 0.025 ± 0.008 Hz. In Study 2, vasomotion frequencies were 0.033 ± 0.006 Hz, and 0.032 ± 0.008 Hz during the two highest dexmedetomidine infusion steps. In Study 3, vasomotion frequencies ranged from 0.020 to 0.037 Hz and were observed in all 6 fingers, with no synchrony between the six fingers. Conclusion: The vascular oscillations we observed without significant interference from other spontaneous oscillations are independent of neural activity (Study 1), local in nature (Study 3), and associated with alpha-2-adrenoceptor activation, consistent with known properties of vasomotion.

Cardiovascular System & Cardiology↗
DOE OSTI2024

Dataset: Breaking the barrier of human-annotated training data for machine-learning-aided plant research using aerial imagery

This dataset supports the implementation described in the manuscript "Breaking the Barrier of Human-Annotated Training Data for Machine-Learning-Aided Biological Research Using Aerial Imagery." It comprises UAV aerial imagery used to execute the code available at https://github.com/pixelvar79/GAN-Flowering-Detection-paper. For detailed information on dataset usage and instructions for implementing the code to reproduce the study, please refer to the GitHub repository.

generative and adversarial learning↗
DOE OSTI2025

Pooled PPIseq: Screening the SARS-CoV-2 and human interface with a scalable multiplexed protein-protein interaction assay platform

Protein-Protein Interactions (PPIs) are a key interface between virus and host, and these interactions are important to both viral reprogramming of the host and to host restriction of viral infection. In particular, viral-host PPI networks can be used to further our understanding of the molecular mechanisms of tissue specificity, host range, and virulence. At higher scales, viral-host PPI screening could also be used to screen for small-molecule antivirals that interfere with essential viral-host interactions, or to explore how the PPI networks between interacting viral and host genomes co-evolve. Current high-throughput PPI assays have screened entire viral-host PPI networks. However, these studies are time consuming, often require specialized equipment, and are difficult to further scale. Here, we develop methods that make larger-scale viral-host PPI screening more accessible. This approach combines the mDHFR split-tag reporter with the iSeq2 interaction-barcoding system to permit massively-multiplexed PPI quantification by simple pooled engineering of barcoded constructs, integration of these constructs into budding yeast, and fitness measurements by pooled cell competitions and barcode-sequencing. We applied this method to screen for PPIs between SARS-CoV-2 proteins and human proteins, screening in triplicate >180,000 ORF-ORF combinations represented by >1,000,000 barcoded lineages. Our results complement previous screens by identifying 74 putative PPIs, including interactions between ORF7A with the taste receptors TAS2R41 and TAS2R7, and between NSP4 with the transmembrane KDELR2 and KDELR3. We show that this PPI screening method is highly scalable, enabling larger studies aimed at generating a broad understanding of how viral effector proteins converge on cellular targets to effect replication.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Characterization of two affinity matured Anti-Yersinia pestis F1 human antibodies with medical countermeasure potential

Yersinia pestis , the causative agent of plague and a biological threat agent, presents an urgent need for novel medical countermeasures due to documented cases of naturally acquired antibiotic resistance and potential person-to-person spread during a pneumonic infection. Immunotherapy has been proposed as a way to circumvent current and future antibiotic resistance. Here, we describe the development and characterization of two affinity matured human antibodies (αF1Ig AM2 and αF1Ig AM8) that promote survival of mice after exposure to aerosolized Y . pestis . We share details of the error prone PCR and yeast display technology-based affinity maturation process that we used. The resultant matured antibodies have nanomolar affinity for Y . pestis F1 antigen, are produced in high yield, and are resilient to 37°C stress for up to 6 months. Importantly, in vitro assays using a murine macrophage cell line demonstrated that αF1Ig AM2 and αF1Ig AM8 are opsonic. Even more importantly, in vivo studies using pneumonic plague mouse models showed that 100% of the mice receiving 500 μg of IgGs αF1Ig AM2 and αF1Ig AM8 survived lethal challenge with aerosolized Y . pestis CO92. Combined, these results provide evidence of the quality and robustness of αF1Ig AM2 and αF1Ig AM8 and support their development as potential medical countermeasures against plague.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Dataset 1: A National and City Dataset on Human Factors in Pooled Rideshare, 2021

Dataset 1: A National and City Dataset on Human Factors in Pooled Rideshare, 2021. Dataset Description: Pooled Rideshare Acceptance Survey - Phase 1 (2021, N = 5,385). This dataset captures responses from a nationally representative sample of 5,385 adults across the United States to understand public acceptance, preferences, and behavioral intentions related to pooled rideshare (PR) services. The primary objective of this research is to provide actionable insights to inform the design, deployment, and policy development of sustainable shared mobility systems. Data was collected via an online survey administered through a national panel provider. Participants ranged in age from 18 to 95 years, and representation from all U.S. regions. The survey instrument was designed to explore numerous dimensions related to PR adoption including demographic traits, current travel habits, rideshare familiarity, trust, safety, environmental attitudes, and user experience preferences. Both rideshare users and non-users were included, offering a diverse range of perspectives. - Phase_1_Final - The dataset includes survey items developed from literature reviews, and prior field studies. Each row represents an individual respondent, and each column corresponds to a variable such as willingness to use pooled rideshare, attitudes toward specific service features, and sociodemographic data. The data is available in both .CSV and .SAV formats. - Phase_1_Final_MapFile - The accompanying data dictionary explains all variable labels, response scales, and codes. An .XLSX format of the full survey instrument is also included to support interpretation and reuse of the dataset.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Dataset 2: A National Dataset on Human Choices in Pooled Rideshare, 2022

Dataset 2: A National Dataset on Human Choices in Pooled Rideshare, 2022. Dataset Description: Pooled Rideshare Acceptance Survey - Phase 2 (2022, N = 2,884). This dataset captures responses from a nationally representative sample of 2,884 adults across the United States to understand choice behaviors between personal and pooled rideshare services. The primary objective of this research is to investigate choice behaviors in rideshare services and provide insights that inform service design, policymaking, and transportation planning, with the aim of encouraging pooled rideshare adoption and enhancing transportation network energy efficiency. Data was collected via an online survey administered through a national panel provider. Participants ranged in age from 18 to 94 years, and representation from all U.S. regions. The survey was designed with a focus on investigating the stated-preference between personal and pooled rideshare services. Each participant responded to 20 stated-preference questions, where they were presented with a hypothesized situation to choose between a personal rideshare option and a pooled rideshare option to complete a trip. The sociodemographic information and attitudes towards factors of pooled rideshare acceptance were also collected to support the comprehensive investigation of participants’ rideshare choice behaviors. - Phase_2_Final - Each row represents an individual respondent, and each column corresponds to a variable such as stated-preference scenario attributes, stated-preference scenario responses, attitudes toward specific service features, and sociodemographic data. The data is available in both .CSV and .SAV formats. - Phase_2_Final_MapFile - The accompanying data dictionary explains all variable labels, response scales, and codes. An .XLSX format of the full survey instrument is included to support interpretation and reuse of the dataset.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Human Subjects in Energy Technology and Policy Research (Symposium Report)

The U.S. Department of Energy (DOE) has a unique and wide-ranging research portfolio. One focus of the department’s research mission is to develop and promote the adoption of technologies that reduce greenhouse gas emissions and result in renewable and clean energy for the country. This research encompasses a surprisingly diverse array of studies, many of which involve the inclusion of human participants that go beyond traditional biomedical investigations. For example, individuals play an important role in DOE research on clean energy sources, development and testing of new energy-efficient technologies, and partnerships with local communities for equitable climate solutions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Characterization of Peripheral Neurophotonic Systems for High-Performance Human-Computer Interfaces (CRADA Final Report)

As part of the Cyclotron Road program, Morphosis Inc. sought to investigate a non-invasive neuromuscular sensing approach for use as an intuitive and secure human–computer interface. These highly miniaturized, wearable neural interfaces were completely non-invasive and maintained stable, high-bandwidth, long-term access to a user’s actions, intent, and identity, while offering an exceptionally high signal-to-noise ratio compared to contemporary neural recording technologies. The widespread adoption of neural interfaces had the potential to reshape how people interact with technology, with profound societal impacts. Millions worldwide suffered from movement and/or speech disabilities, and these tools had the potential to democratize access to technology to enhance autonomy and quality of life. More broadly, interfaces capable of accurately conveying intentions and safeguarding identities could serve as a cornerstone for privacy, trust, and personal authenticity in digital environments. The use of thought-driven control of digitally enabled devices and governance of digital identities had the potential to revolutionize relationships with technology, transforming how people learn, communicate, and interact with the world.

42 ENGINEERING↗
DOE OSTI2024

Anti-idiotype isolation of a broad and potent influenza A virus-neutralizing human antibody

The VH6-1 class of antibodies includes some of the broadest and most potent antibodies that neutralize influenza A virus. Here, we elicit and isolate anti-idiotype antibodies against germline versions of VH6-1 antibodies, use these to sort human leukocytes, and isolate a new VH6-1-class member, antibody L5A7, which potently neutralized diverse group 1 and group 2 influenza A strains. While its heavy chain derived from the canonical IGHV6-1 heavy chain gene used by the class, L5A7 utilized a light chain gene, IGKV1-9, which had not been previously observed in other VH6-1-class antibodies. The cryo-EM structure of L5A7 in complex with Indonesia 2005 hemagglutinin revealed a nearly identical binding mode to other VH6-1-class members. The structure of L5A7 bound to the isolating anti-idiotype antibody, 28H6E11, revealed a shared surface for binding anti-idiotype and hemagglutinin that included two critical L5A7 regions: an FG motif in the third heavy chain-complementary determining region (CDR H3) and the CDR L1 loop. Surprisingly, the chemistries of L5A7 interactions with hemagglutinin and with anti-idiotype were substantially different. Overall, we demonstrate anti-idiotype-based isolation of a broad and potent influenza A virus-neutralizing antibody, revealing that anti-idiotypic selection of antibodies can involve features other than chemical mimicry of the target antigen.

59 BASIC BIOLOGICAL SCIENCES↗