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At least 631 records · Page 35

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Development and validation of a mathematical model of heart rate response to fluid perturbation

Physiological closed-loop controlled (PCLC) medical devices monitor and automatically adjust the patient’s condition by using physiological variables as feedback, ideally with minimal human intervention to achieve the target levels set by a clinician. PCLC devices present a challenge when it comes to evaluating their performance, where conducting large clinical trials can be expensive. Virtual physiological patients simulated by validated mathematical models can be utilized to obtain preclinical evidence of safety and assess the performance of the PCLC medical device during normal and worst-case conditions that are unlikely to happen in a limited clinical trial. A physiological variable that plays a major role during fluid resuscitation is heart rate (HR). For in silico assessment of PCLC medical devices regarding fluid perturbation, there is currently no mathematical model of HR validated in terms of its predictive capability performance. This paper develops and validates a mathematical model of HR response using data collected from sheep subjects undergoing hemorrhage and fluid infusion. The model proved to be accurate in estimating the HR response to fluid perturbation, where averaged between 21 calibration datasets, the fitting performance showed a normalized root mean square error (NRMSE) of 7.41 ± 2.8%. The model was also evaluated in terms of model predictive capability performance via a leave-one-out procedure (21 subjects) and an independent validation dataset (6 subjects). Two different virtual cohort generation tools were used in each validation analysis. The generated envelope of virtual subjects robustly met the defined acceptance criteria, in which 95% of the testing datasets presented simulated HR patterns that were within a deviation of 50% from the observed data. In addition, out of 16000 and 18522 simulated subjects for the leave one-out and independent datasets, the model was able to generate at least one virtual subject that was close to the real subject within an error margin of 9.56 ± 3.15% and 11.1 ± 1.22% NRMSE, respectively. In conclusion, the model can generate valid virtual HR physiological responses to fluid perturbation and be incorporated into future non-clinical simulated testing setups for assessing PCLC devices intended for fluid resuscitation.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Anthropogenic Contributions to the 2021 Pacific Northwest Heatwave

Abstract Daily maximum temperatures during the 2021 heatwave in the Pacific Northwest United States and Canada shattered century old records. Multiple causal factors, including anthropogenic climate change, contributed to these high temperatures, challenging traditional methods of attributing human influence. We demonstrate that the observed 2021 daily maximum temperatures are far above the bounds of Generalized Extreme Value distributions fitted from historical data. Hence, confidence in Granger causal inference statements about the human influence on this heatwave is low. Alternatively, we present a more conditional hindcast attribution study using two regional models. We performed ensembles of simulations of the heatwave to investigate how the event would have changed if it had occurred without anthropogenic climate change and with future warming. We found that global warming caused a ∼0.8°C–1°C increase in heatwave temperatures. Future warming would lead to a ∼5°C increase in heatwave temperature by the end of the 21st century.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

A machine-learning-driven data labeling pipeline for scientific analysis in MLExchange

This study introduces a novel labeling pipeline to accelerate the labeling process of scientific data sets by using artificial intelligence (AI)-guided tagging techniques. This pipeline includes a set of interconnected web-based graphical user interfaces (GUIs), where Data Clinic and MLCoach enable the preparation of machine learning (ML) models for data reduction and classification, respectively, while Label Maker is used for label assignment. Throughout this pipeline, data can be accessed through a direct connection to a file system or through Tiled for access through Hypertext Transfer Protocol (HTTP). Our experimental results present three use cases where this labeling pipeline has been instrumental for the study of large X-ray scattering data sets in the area of pattern recognition, the remote analysis of resonant soft X-ray scattering data and the fine-tuning process of foundation models. These use cases highlight the labeling capabilities of this pipeline, including the ability to label large data sets in a short period of time, to perform remote data analysis while minimizing data movement and to enhance the fine-tuning process of complex ML models with human involvement.

Chavez, Tanny (ORCID:0000000193172896)↗

DOE OSTI2020

Biofidelic Digital Head Model Software

Researchers at Los Alamos have extensive capabilities in biofidelic modeling, simulation and software development for predicting traumatic brain injury (TBI). Since injury-causing experiments cannot be performed on live humans, and head dummies are incapable of predicting brain deformations/strain, our approach uses magnetic resonance imaging (MRI) data for the brain combined with advanced computational methods and substructure constitutive properties to identify harmful accelerations that may develop diffused axonal injury in the brain. The goal is to develop this capability to analyze the effectiveness of personal protective equipment (PPE) such as, helmets and the advanced energy-absorbing materials used in PPE to mitigate brain injury. Los Alamos is seeking a collaborative partner (through a cooperative research and development agreement) to develop this capability toward a potential technology that will have commercial viability.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

Human Factors and Technologies Design to Improve User Acceptance of Pooled Rideshare for Increasing Transportation System Energy Efficiency

This multi-year project delivered a comprehensive, human-factors-driven framework to understand, model, and improve pooled rideshare (PR) adoption in the United States. Through three large-scale national survey studies involving more than 16,000 participants across multiple cities and demographic groups, the research established one of the most extensive datasets to date on user perceptions, behavioral barriers, and service expectations related to pooled rideshare. These data revealed key human factors barriers of user acceptance of PR and suggested potential actionable experience optimizations that could lead to increased PR usage. This foundational knowledge guided the development of novel human-factors models and behavioral choice models that quantify how psychological, demographic, and trip-level factors influence willingness to pool. Building on these empirical insights, the project developed advanced behavioral modeling tools, including mixed logit and integrated choice and latent variable models, to capture both observable and latent influences on PR adoption. These models significantly improved the ability to predict riders’ acceptance of pooled trips, explaining choice heterogeneity through latent constructs such as safety, service experience, privacy concerns, time sensitivity, and environmental attitudes. Together, these models provide a robust analytical foundation for designing PR systems that more effectively meet user needs. The project translated human-factors insights and behavioral models into actionable technology innovations by extending POLARIS—an agent-based, activity-based travel simulation platform—into a fully functional pooled rideshare simulation environment. New PR modules, acceptance models, and regional scenarios were implemented for Greenville, SC and Austin, TX, enabling high-fidelity validation of algorithmic strategies under realistic demand and traffic conditions. The simulation platform supported the development and evaluation of adaptive discount-based assignment algorithms, enhanced willingness-to-pay formulations, demographic-aware incentive mechanisms, and a proactive joint assignment and repositioning strategy. Simulation results demonstrated substantial gains in pooling uptake, average vehicle occupancy, energy efficiency, and fleet profitability. In Greenville, pooling adoption more than doubled, while reductions in vehicle-miles traveled and energy consumption were significant. In Austin, pooling improvements were achieved with minimal service-quality trade-offs, and profitability increased across all fleet sizes. Through this research, we developed a comprehensive understanding of the human factors barriers that limit user acceptance of pooled rideshare services. These insights enabled the design of human-factors-aware pooled rideshare technologies that more effectively address user concerns and improve adoption rates. By integrating these models into an advanced agent-based simulation framework, we demonstrated that higher adoption of pooled rideshare can lead to measurable improvements in energy efficiency and system performance. Together, these contributions establish a validated pathway from human-centered analysis to technology development and energy-saving outcomes, supporting national goals for more sustainable and efficient mobility systems.

Jia, Yunyi↗

NASA NTRS2006

Wissler Simulations of a Liquid Cooled and Ventilation Garment (LCVG) for Extravehicular Activity (EVA)

In order to provide effective cooling for astronauts during extravehicular activities (EVAs), a liquid cooling and ventilation garment (LCVG) is used to remove heat by a series off tubes through which cooling water is circulated. To better predict the effectiveness of the LCG and determine possible modifications to improve performance, computer simulations dealing with the interaction of the cooling garment with the human body have been run using the Wissler Human Model. Simulations have been conducted to predict the heat removal rate for various liquid cooled garment configurations. The current LCVG uses 48 cooling tubes woven into a fabric with cooling water flowing through the tubes. The purpose of the current project is to decrease the overall weight of the LCVG system. In order to achieve this weight reduction, advances in the garment heat removal rates need to be obtained. Currently, increasing the fabric s thermal conductivity along with also examining an increase in the cooling tube conductivity to more efficiently remove the excess heat generated during EVA is being simulated. Initial trials varied cooling water temperature, water flow rate, garment conductivity, tube conductivity, and total number of cooling tubes in the LCVG. Results indicate that the total number of cooling tubes could be reduced to 22 and still achieve the desired heat removal rate of 361 W. Further improvements are being made to the garment network used in the model to account for temperature gradients associated with the spacing of the cooling tubes over the surface of the garment

Kesterson, Matthew↗

NASA NTRSDate not supplied

XHAB - Inflatable Airlock

- Develop the requisite technology for airlock design via analysis and scale-model experimental tests - Design, develop, test, and evaluate a two-person inflatable airlock compatible with an international docking interface - Perform human operations evaluation in microgravity via neutral buoyancy simulation - Maximize opportunities for student involvement at all levels consistent with educational objectives for associated courses This document is best when downloaded and opened in Adobe 2020 version.

David Akin↗

NASA NTRS2009

Effect of Changing Weight and Mass on Human Performance in a Lunar Prototype Spacesuit

Physical effort, compensation, and controllability in a spacesuit can be affected by suit mass and gravity level. Because of limitations in certain reduced-gravity simulators and the finite selection of lunar prototype suits, it is difficult to ascertain how a change in suit mass affects suited human performance. One method of simulating a change in mass is to vary the total gravity-adjusted weight (TGAW), which is defined as the sum of the suit mass and subject mass, multiplied by the gravity level. PURPOSE: To determine if two methods of changing TGAW during parabolic flight - changing suit mass or gravity level - affect subjective ratings of suited human performance equally. METHODS: A custom weight support structure was connected to a lunar prototype spacesuit, allowing the addition of mass to the suit while maintaining a near-constant center of mass. In the varied-weight (VW) series, suit mass (120 kg) was constant at 0.1 G, 0.17 G, and 0.3 G, yielding TGAWs of 196, 333, and 588 N, assuming an 80-kg subject. In the varied-mass (VM) series, gravity level was constant at 0.17 G and suit mass was 89, 120, and 181 kg, yielding TGAWs of 282, 333, and 435 N. The 333 N condition was common to both series. Direct comparison was not possible due to limited adjustability of suit mass and limited options for parabolic profiles. Five astronaut subjects (80.3 11.8 kg) completed 4 different tasks (walk, bag pickup, lunge, and shoveling) in all conditions and provided ratings of perceived exertion (RPE) and the gravity compensation and performance scale (GCPS) upon completion of each task. RESULTS: Where VM and VW series overlapped, RPE and GCPS trendlines were similar. Mean RPE and GCPS at 333 N was 8.4 and 3.7. Mean RPE and GCPS for VM was 7.8 and 3.8 for 282 N and 9.8 and 4.1 for 435 N. Extrapolation of the VM trend to match VW TGAWs 196 and 588 N predicts an RPE of 6.5 and 12.3 and GCPS of 4.4 and 5.9, whereas the measured VW values for RPE were 8.1 and 9.8 and GCPS were 4.4 and 3.7. CONCLUSION: Modeling a change in suit mass by altering weight alone may be an adequate simulation through a limited range when looking at gross metrics of subjective suited human performance. Whether altering weight alone will be sufficient for more precise metrics of human performance, and across a wider range of activities, still needs further study.

Norcross, Jason R.↗

NASA NTRS2010

Effect of Changing Weight and Mass on Human Performance in a Lunar Prototype Spacesuit

Physical effort, compensation, and controllability in a spacesuit can be affected by suit mass and gravity level. Because of limitations in certain reduced-gravity simulators and the finite selection of lunar prototype suits, it is difficult to ascertain how a change in suit mass affects suited human performance. One method of simulating a change in mass is to vary the total gravity-adjusted weight (TGAW), which is defined as the sum of the suit mass and subject mass, multiplied by the gravity level. PURPOSE: To determine if two methods of changing TGAW during parabolic flight - changing suit mass or gravity level - affect subjective ratings of suited human performance equally.METHODS: A custom weight support structure was connected to a lunar prototype spacesuit, allowing the addition of mass to the suit while maintaining a near-constant center of mass. In the varied-weight (VW) series, suit mass (120 kg) was constant at 0.1-g, 0.17-g, and 0.3-g, yielding TGAWs of 196, 333, and 588 N, assuming an 80-kg subject. In the varied-mass (VM) series, gravity level was constant at 0.17-g and suit mass was 89, 120, and 181 kg, yielding TGAWs of 282, 333, and 435 N. The 333 N condition was common to both series. Direct comparison was not possible due to limited adjustability of suit mass and limited options for parabolic profiles. Five astronaut subjects (80.311.8 kg) completed 4 different tasks (walk, bag pickup, lunge, and shoveling) in all conditions and provided ratings of perceived exertion (RPE) and the gravity compensation and performance scale (GCPS) upon completion of each task. RESULTS: Where VM and VW series overlapped, RPE and GCPS trend lines were similar. Mean RPE and GCPS at 333 N was 8.4 and 3.7. Mean RPE and GCPS for VM was 7.8 and 3.8 for 282 N and 9.8 and 4.1 for 435 N. Extrapolation of the VM trend to match VW TGAWs 196 and 588 N predicts an RPE of 6.5 and 12.3 and GCPS of 4.4 and 5.9, whereas the measured VW values for RPE were 8.1 and 9.8 and GCPS were 4.4 and 3.7. CONCLUSION: Modeling a change in suit mass by altering weight alone may be an adequate simulation through a limited range when looking at gross metrics of subjective suited human performance. Whether altering weight alone will be sufficient for more precise metrics of human performance, and across a wider range of activities, still needs further study

Chappell, Steve↗

NASA NTRS2019

Psychophysiological Monitoring of Aerospace Crew State

As next-generation space exploration missions necessitate increasingly autonomous systems, there is a critical need to better detect and anticipate crewmember interactions with these systems. The success of present and future autonomous technology in exploration spaceflight is ultimately dependent upon safe and efficient interaction with the human operator. Optimal interaction is particularly important for surface missions during highly coordinated extravehicular activity (EVA), which consists of high physical and cognitive demands with limited ground support. Crew functional state may be affected by a number of variables including workload, stress, and motivation. Real-time assessments of crew state that do not require a crewmember’s time and attention to complete will be especially important to assess operational performance and behavioral health during flight. In response to the need for objective, passive assessment of crew state, the aim of this work is to develop an accurate and precise prediction model of human functional state for surface EVA using multi-modal psychophysiological sensing. The psychophysiological monitoring approach relies on extracting a set of features from physiological signals and using these features to classify an operator’s cognitive state. This work aims to compile a non-invasive sensor suite to collect physiological data in real-time. Training data during cognitive and more complex functional tasks will be used to develop a classifier to discriminate high and low cognitive workload crew states. The classifier will then be tested in an operationally relevant EVA simulation to predict cognitive workload over time. Once a crew state is determined, further research into specific countermeasures, such as decision support systems, would be necessary to optimize the automation and improve crew state and operational performance.

Wusk, Grace C.↗

NASA NTRSDate not supplied

Strategy for Risk Quantification of Spaceflight Crew Health and Performance Using Dynamic Probabilistic Risk Assessment

At NASA, the Crew Health and Performance (CHP) system represents the span of countermeasures, capabilities, interventions, and tested processes and procedures that in combination work to mitigate the human component of spaceflight mission risk. Across the varying NASA mental models of the CHP system, the different functionalities needed to meet human flight systems standards can be broken down into specific categories (i.e. medical capability, environmental health, behavioral health). These categories can be further broken down into specific subgroups generally associated with the CHP functionalities meant to mitigate or buy down individual human system risks. Taking a similar development approach we seek to leverage dynamic probabilistic risk assessment as a means to quantify and relatively assess the human risk state within the crew health and performance domain. By utilizing existing tools as integrators, we propose a rapid development strategy for incorporating research and operational data that represent the influence of the CHP system functionalities, in order to provide order of magnitudes estimates of the influence on most human system risks outcomes. The model system utilizes a modest cumulative risk approach and that limits the scope to primary paths of influence between the CHP functionalities and human system risks, thus enabling quick prototypes of the integrative effects of CHP functional combinations to solicit valuable feedback from stakeholders and customers on the data, relationship, and structure of the integration.

Drayton Munster↗

DOE OSTI2022

Always look on the bright side of life: Taking a different perspective on cybersecurity performance

This presentation will introduce participants to key mental models on how to think about and understand safety from two of the leading voices in the discipline – Dr. Erik Hollnagel and Dr. Todd Conklin – and from there we ask the simple question of “how can we apply this to cybersecurity?” At the completion of this presentation attendees will be able to describe Dr. Hollnagel’s Safety-II approach and Dr. Conkiln’s definition of safety as “not the absence of accidents but the presence of effective defenses.” They will be able to explain how these ideas translate to the cybersecurity world broadly, and more specifically to operational technology cybersecurity where physical safety consequences are coupled to control system performance. Finally, participants will be able to identify how selected principles from Weick and Sutcliffe’s high reliability organization theory can help operationalize these key mental models around safety, for improved organizational cybersecurity over the long run.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Confidence-weighted integration of human and machine judgments for superior decision-making

Large language models (LLMs) can surpass humans in certain forecasting tasks. What role does this leave for humans in the overall decision process? One possibility is that humans, despite performing worse than LLMs, can still add value when teamed with them. A human and machine team can surpass each individual teammate when team members’ confidence is well calibrated and team members diverge in which tasks they find difficult (i.e., calibration and diversity are needed). We simplified and extended a Bayesian approach to combining judgments using a logistic regression framework that integrates confidence-weighted judgments for any number of team members. Using this straightforward method, we demonstrated its effectiveness in both image classification and neuroscience forecasting tasks. Combining human judgments with one or more machines consistently improved overall team performance. Our hope is that this simple and effective strategy for integrating the judgments of humans and machines will lead to productive collaborations.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2011

Human Rating the Orion Parachute System

Human rating begins with design. Converging on the requirements and identifying the risks as early as possible in the design process is essential. Understanding of the interaction between the recovery system and the spacecraft will in large part dictate the achievable reliability of the final design. Component and complete system full-scale flight testing is critical to assure a realistic evaluation of the performance and reliability of the parachute system. However, because testing is so often difficult and expensive, comprehensive analysis of test results and correlation to accurate modeling completes the human rating process. The National Aeronautics and Space Administration (NASA) Orion program uses parachutes to stabilize and decelerate the Crew Exploration Vehicle (CEV) spacecraft during subsonic flight in order to deliver a safe water landing. This paper describes the approach that CEV Parachute Assembly System (CPAS) will take to human rate the parachute recovery system for the CEV.

Machin, Ricardo A.↗

DOE OSTI2024

Evaluating the impact of anatomical and physiological variability on human equivalent doses using PBPK models

Abstract Addressing human anatomical and physiological variability is a crucial component of human health risk assessment of chemicals. Experts have recommended probabilistic chemical risk assessment paradigms in which distributional adjustment factors are used to account for various sources of uncertainty and variability, including variability in the pharmacokinetic behavior of a given substance in different humans. In practice, convenient assumptions about the distribution forms of adjustment factors and human equivalent doses (HEDs) are often used. Parameters such as tissue volumes and blood flows are likewise often assumed to be lognormally or normally distributed without evaluating empirical data for consistency with these forms. In this work, we performed dosimetric extrapolations using physiologically based pharmacokinetic (PBPK) models for dichloromethane (DCM) and chloroform that incorporate uncertainty and variability to determine if the HEDs associated with such extrapolations are approximately lognormal and how they depend on the underlying distribution shapes chosen to represent model parameters. We accounted for uncertainty and variability in PBPK model parameters by randomly drawing their values from a variety of distribution types. We then performed reverse dosimetry to calculate HEDs based on animal points of departure for each set of sampled parameters. Corresponding samples of HEDs were tested to determine the impact of input parameter distributions on their central tendencies, extreme percentiles, and degree of conformance to lognormality. This work demonstrates that the measurable attributes of human variability should be considered more carefully and that generalized assumptions about parameter distribution shapes may lead to inaccurate estimates of extreme percentiles of HEDs.

Toxicology↗

NASA NTRSDate not supplied

Probabilistic Modeling of a Three-Stage Human Landing System Architecture

Unmitigated uncertainties are known to have previously led to failed development programs; in order to combat these uncertainties, risks and their impacts must be understood and handled to ensure program success. In this paper, a probabilistic methodology to handle uncertainties is demonstrated on a three-element Human Landing System (HLS) concept, which allows tracking of current best estimates of the vehicle’s performance and assessment of its robustness against uncertainties. This methodology has two key parts: ﬁrst, the creation of a dynamic architecture model of a three-element HLS concept; and second, its use with surrogate modeling and range estimating techniques to capture and propagate uncertainties. The DYnamic Rocket EQuation Tool (DYREQT), a space systems synthesis and sizing framework used by NASA, was used as to model the HLS architecture. For the probabilistic analysis, uncertainties of interest within the HLS concept were enumerated and represented as parameters within the DYREQT model as inputs for vehicle stages or mission proﬁle events. Range estimating — a probabilistic method that combines Monte Carlo sampling, focus on critical parameters, and heuristics to assess risk and opportunities — is then adapted with operational parameters as well as vehicle parameters in the DYREQT model to capture mission uncertainty alongside vehicle uncertainty. To perform the range estimation portion of this methodology, the DYREQT model was sampled using a Design of Experiments (DoE) to eﬃciently explore the architecture design space with respect to the set of uncertainty parameters. Then, the results were used to create surrogate models, multivariate regressions that can visualize hypercube trends in the design space, of the architecture with respect to the uncertainty parameters. Using a correlation matrix constructed for the uncertainty parameters, previously independent samples were transformed to perform a Correlated Monte Carlo on the surrogate models. This probabilistic methodology was proved to provide insight into the underlying uncertainties of the three-element HLS architecture.

Stephanie Y Zhu↗

NASA NTRSDate not supplied

Probabilistic Modeling of a Three-Stage Human Landing System Architecture

Stephanie Y. Zhu↗

NASA NTRS2007

POST2 End-To-End Descent and Landing Simulation for the Autonomous Landing and Hazard Avoidance Technology Project

The Program to Optimize Simulated Trajectories II (POST2) is used as a basis for an end-to-end descent and landing trajectory simulation that is essential in determining the design and performance capability of lunar descent and landing system models and lunar environment models for the Autonomous Landing and Hazard Avoidance Technology (ALHAT) project. This POST2-based ALHAT simulation provides descent and landing simulation capability by integrating lunar environment and lander system models (including terrain, sensor, guidance, navigation, and control models), along with the data necessary to design and operate a landing system for robotic, human, and cargo lunar-landing success. This paper presents the current and planned development and model validation of the POST2-based end-to-end trajectory simulation used for the testing, performance and evaluation of ALHAT project system and models.

Fisher, Jody l.↗