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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 OSTI2023

A machine learning approach for identifying variables associated with risk of developing neutralizing antidrug antibodies to factor VIII

A key unmet need in the management of hemophilia A (HA) is the lack of clinically validated markers that are associated with the development of neutralizing antibodies to Factor VIII (FVIII) (commonly referred to as inhibitors). This study aimed to identify relevant biomarkers for FVIII inhibition using Machine Learning (ML) and Explainable AI (XAI) using the My Life Our Future (MLOF) research repository. The dataset includes biologically relevant variables such as age, race, sex, ethnicity, and the variants in the F8 gene. In addition, we previously carried out Human Leukocyte Antigen Class II (HLA-II) typing on samples obtained from the MLOF repository. Using this information, we derived other patient-specific biologically and genetically important variables. These included identifying the number of foreign FVIII derived peptides, based on the alignment of the endogenous FVIII and infused drug sequences, and the foreign-peptide HLA-II molecule binding affinity calculated using NetMHCIIpan. The data were processed and trained with multiple ML classification models to identify the top performing models. The top performing model was then chosen to apply XAI via SHAP, (SHapley Additive exPlanations) to identify the variables critical for the prediction of FVIII inhibitor development in a hemophilia A patient. Using XAI we provide a robust and ranked identification of variables that could be predictive for developing inhibitors to FVIII drugs in hemophilia A patients. These variables could be validated as biomarkers and used in making clinical decisions and during drug development. The top five variables for predicting inhibitor development based on SHAP values are: (i) the baseline activity of the FVIII protein, (ii) mean affinity of all foreign peptides for HLA DRB 3, 4, & 5 alleles, (iii) mean affinity of all foreign peptides for HLA DRB1 alleles), (iv) the minimum affinity among all foreign peptides for HLA DRB1 alleles, and (v) F8 mutation type.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1997

Of red planets and indigo computers: Mars database visualization as an example of platform downsizing

The last decade has witnessed tremendous advancements in the computer hardware and software used to perform scientific visualization. In this paper, we consider how the visualization of a particular data set, the digital terrain model derived from the Viking orbiter imagery, has been realized in four distinct projects over this period. These examples serve to demonstrate how the vast improvements in computational performance both decrease the cost of such visualization efforts and permit an increasing level of interactivity. We then consider how even today's graphical systems require the visualization designer to make intelligent choices and tradeoffs in database rendering. Finally, we discuss how insights gleaned from an understanding of human visual perception can guide these design decisions, and suggest new options for visualization hardware and software.

NASA Center ARC↗
NASA NTRS2016

A Year in Space: Early Results and Lessons Learned from the First Year-Long Expedition Aboard the International Space Station

Two ISS crewmembers recently completed the first year-long orbital stay in two decades. International cooperation was central to the success of Mikhail Kornienko from Russia and Scott Kelly from the United States. Their expedition leveraged current mission experience and capitalized on recent advances in health monitoring technology. This unique effort began in 2012 when the program managers of the ISS partner nations adopted two separate goals: greater multilateral cooperation to increase efficiency of inflight research and a year-long expedition to gain familiarity with in-flight durations approaching that required for a Mars mission. These goals were unified when a set of bilateral Russian and American human research investigations was assigned to the year-long mission, augmented by additional investigations from Europe and Japan. For example, Kelly was assigned 18 investigations (twice the complement on standard six-month missions) including two joint U.S.-Russian studies, and two Russian and two Japanese studies. The core set of American investigations was a repetition of six studies Kelly had done on his previous six-month ISS mission, to allow a direct comparison of physiological and behavioral responses of the longer and shorter durations in this single individual. The remainder of his assignments plus those of Kornienko were drawn from currently active national investigations documenting human adaptation to long-duration spaceflight factors or effectiveness of countermeasures against known deleterious adaptations. The two joint U.S.-Russian investigations were the flagship biomedical studies of the year-long expedition. The "Fluid Shifts" study collocated American research equipment alongside a Russian operational stressor device to document the pattern and impacts of the headward fluid shift long known to occur in weightlessness, including its role in ocular changes recently observed in some astronauts. The "Field Test" study investigated the ability of the astronaut and cosmonaut to perform rudimentary tasks requiring sensorimotor coordination immediately after landing to define human capabilities soon after landing on Mars after an extended transit. Both investigations were highly successful in large part to the thorough integration of the implementation processes of the two partners. Kelly's assignment as the one-year crewmember also provided a serendipitous opportunity for the "Twins Study" comparing changes in his body at the genetic level with those occurring on Earth in his identical twin brother Mark Kelly. Data analysis from this expedition will commence in earnest after frozen in-flight samples are delivered to Earth in May aboard a commercial cargo spacecraft. Detailed results are expected in early 2017. Preliminary results and implementation improvements will be reviewed in this presentation.

Charles, J. B.↗
NASA NTRS2017

HOTEX: An Approach for Global Mapping of Human Built-Up and Settlement Extent

Understanding the impacts of urbanization requires accurate and updatable urban extent maps. Here we present an algorithm for mapping urban extent at global scale using Landsat data. An innovative hierarchical object-based texture (HOTex) classification approach was designed to overcome spectral confusion between urban and nonurban land cover types. VIIRS nightlights data and MODIS vegetation index datasets are integrated as high-level features under an object-based framework. We applied the HOTex method to the GLS-2010 Landsat images to produce a global map of human built-up and settlement extent. As shown by visual assessments, our method could effectively map urban extent and generate consistent results using images with inconsistent acquisition time and vegetation phenology. Using scene-level cross validation for results in Europe, we assessed the performance of HOTex and achieved a kappa coefficient of 0.91, compared to 0.74 from a baseline method using per-pixel classification using spectral information.

Wang, Panshi↗
NASA NTRS2026

Performance Evaluation of Intravehicular Activity Spacesuit Without the Use of a Liquid Cooling Garment

The Orion Crew Survival Systems (OCSS) suit is equipped with safety technology that protects the crew during launch and re-entry and Intravehicular Activities (IVA). It was designed to be used with a Liquid Cooling Garment (LCG), an undergarment with tubes that circulate water to remove excess heat from a crew member. The objective of this study is to determine if the Modified Advanced Crew Escape Suit (MACES), which has very similar material, assembly, and pressure characteristics as the OCSS suit, can be used without the LCG to maintain the crew within their heat storage requirements where exceedances can lead to cognitive and physiological impairment. Testing was completed for both hot and cold environments where test subjects wore varying configurations of the MACES suit ensemble without the LCG. The tests were conducted in a temperature and humidity-controlled chamber with four test subjects ranging from extra small to large. Target metabolic rates were reached using an arm ergometer. To represent the different suit configurations seen in the concept of operations, the suit was tested with visor-down, visor-up, and without helmet and gloves. The test data was analyzed to determine how the environment, test subject size, metabolic rate, and suit configuration affected heat storage. Furthermore, the test data was used to correlate the METMAN model, a transient metabolic man program that simulates the heat transfer within a crew member’s body and from a crew member to the surrounding environment. The METMAN correlation showed good agreement between predicted heat storage and test data at the end of the test duration, with a coefficient of determination (R 2 ) value of 0.8999 for visor-down cases and R 2 of 0.8902 for visor-up and helmet off cases. A correlated METMAN model allows for the analysis and prediction of IVA suit performance without an LCG in additional scenarios.

Human Thermal Modeling↗
DOE OSTI2025

Neural network-based classification and regression of magnetohydrodynamic modes in tokamaks

We present a machine learning-based magnetohydrodynamic (MHD) classifier and regressor that utilizes real or complex-valued 3D magnetic sensor array data to determine neoclassical tearing mode (NTM) onset times in tokamaks with millisecond accuracy. The input dataset consists of poloidal profiles of complex Fourier amplitudes with an n = 1 toroidal mode number from 144 human-labeled ITER Baseline Scenario discharges in the DIII-D tokamak, spanning both tearing-dominated and sawtooth-dominated regimes. Since m, n = 2,1 NTMs frequently emerge alongside sawteeth at the same frequency in this scenario, the focus is on isolating the m = 1 and m = 2 components of the n = 1 MHD mode near the tearing onset. To improve model regularization and prediction stability, singular value decomposition was applied to balance the sawtooth and tearing datasets. The enriched datasets facilitated training neural networks that learn the key distinguishing features of sawtooth and tearing modes in the poloidal profiles of their magnetic amplitude and phase. When the modes occur independently, the networks achieve perfect classification due to the modes’ distinct characteristics and low measurement noise. In the more experimentally relevant case where both modes coexist, the networks maintain exceptional performance across key metrics. Tests on synthetic data with known ground truth demonstrate the superior accuracy of the neural network trained on complex-valued input compared to models using real amplitude, phase, or pseudo-complex data, achieving both a mean time delay and standard deviation below 1 ms. Notably, standard linear regression methods fitting the dominant singular modes to the data closely match the neural network’s performance. Applying these methods across a broad range of H-mode scenarios will enable future studies to systematically identify dominant NTM triggers as scenario-specific variables, paving the way for more effective tearing mode avoidance strategies in future fusion reactor designs.

machine learning↗
DOE OSTI2026

Hierarchical semi-Markov models with duration-aware dynamics for activity sequences

Residential electricity demand at granular scales is driven by what people do and for how long. Accurately forecasting this demand for applications like microgrid management and demand response therefore requires generative models for activities that can produce realistic daily activity sequences, capturing both the timing and duration of human behavior. This paper develops a generative model of human activity sequences using nationally representative time-use diaries at a 10-min resolution. We use this model to quantify which demographic factors are most critical for improving predictive performance. We propose a hierarchical semi-Markov framework that addresses two key modeling challenges. First, a time-inhomogeneous Markov router learns the patterns of “which activity comes next.” Second, a semi-Markov hazard component explicitly models activity durations, capturing “how long” activities realistically last. To ensure statistical stability when data are sparse, the model pools information across related demographic groups and time blocks. The entire framework is trained and evaluated using survey design weights to ensure our findings are representative of the U.S. population. On a held-out test set, we demonstrate that explicitly modeling durations with the hazard component provides a substantial and statistically significant improvement over purely Markovian models. Furthermore, our analysis reveals a clear hierarchy of demographic factors: Sex, Day-Type, and Household Size provide the largest predictive gains, while Region and Season, though important for energy calculations, contribute little to predicting the activity sequence itself. The result is an interpretable and robust generator of synthetic activity traces, providing a high-fidelity foundation for downstream energy systems modeling.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Targeting S-adenosylmethionine biosynthesis with a novel allosteric inhibitor of Mat2A

S-Adenosyl-L-methionine (SAM) is an enzyme cofactor used in methyl transfer reactions and polyamine biosynthesis. The biosynthesis of SAM from ATP and L-methionine is performed by the methionine adenosyltransferase enzyme family (Mat; EC 2.5.1.6). Human methionine adenosyltransferase 2A (Mat2A), the extrahepatic isoform, is often deregulated in cancer. We identified a Mat2A inhibitor, PF-9366, that binds an allosteric site on Mat2A that overlaps with the binding site for the Mat2A regulator, Mat2B. Studies exploiting PF-9366 suggested a general mode of Mat2A allosteric regulation. Allosteric binding of PF-9366 or Mat2B altered the Mat2A active site, resulting in increased substrate affinity and decreased enzyme turnover. These data support a model whereby Mat2B functions as an inhibitor of Mat2A activity when methionine or SAM levels are high, yet functions as an activator of Mat2A when methionine or SAM levels are low. The ramification of Mat2A activity modulation in cancer cells is also described.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Spread spectrum time domain reflectometry (SSTDR) and frequency domain reflectometry (FDR) cable inspection using machine learning

Cables are initially qualified for nuclear power plant use for 40 years. As plants extend their operating license to 60 and 80 years, justification for continued cable use must shift to a condition-based approach since it is cost prohibitive to completely replace cables that are likely still capable of performing their design function. The Pacific Northwest National Laboratory (PNNL) Accelerated and Real Time Experimental Nodal Analysis (ARENA) cable motor test bed was used to test the response of a commercial spread spectrum time domain reflectometry (SSTDR) system, a laboratory instrument software-controlled SSTDR, and a vector network analyzer-based frequency domain reflectometry (FDR) system to various cable anomalies. The three instrument systems were able to interrogate cables over a range of frequency bandwidths that can be helpful for human data analysis. Data were subjected to supervised and unsupervised machine learning (ML) analyses to distinguish normal undamaged cable responses from anomalous cable responses. Both supervised and unsupervised ML approaches produced encouraging results with an undamaged/anomalous prediction accuracy from 0.69% to 0.87%. Recommendations for further development and field implementation include increased and more balanced sample sets particularly including more training data.

SSTDR, FDR, Reflectometry, Machine Learning, ARENA↗
DOE OSTI2021

Feature selection and causal analysis for microbiome studies in the presence of confounding using standardization

Abstract Background Microbiome studies have uncovered associations between microbes and human, animal, and plant health outcomes. This has led to an interest in developing microbial interventions for treatment of disease and optimization of crop yields which requires identification of microbiome features that impact the outcome in the population of interest. That task is challenging because of the high dimensionality of microbiome data and the confounding that results from the complex and dynamic interactions among host, environment, and microbiome. In the presence of such confounding, variable selection and estimation procedures may have unsatisfactory performance in identifying microbial features with an effect on the outcome. Results In this manuscript, we aim to estimate population-level effects of individual microbiome features while controlling for confounding by a categorical variable. Due to the high dimensionality and confounding-induced correlation between features, we propose feature screening, selection, and estimation conditional on each stratum of the confounder followed by a standardization approach to estimation of population-level effects of individual features. Comprehensive simulation studies demonstrate the advantages of our approach in recovering relevant features. Utilizing a potential-outcomes framework, we outline assumptions required to ascribe causal, rather than associational, interpretations to the identified microbiome effects. We conducted an agricultural study of the rhizosphere microbiome of sorghum in which nitrogen fertilizer application is a confounding variable. In this study, the proposed approach identified microbial taxa that are consistent with biological understanding of potential plant-microbe interactions. Conclusions Standardization enables more accurate identification of individual microbiome features with an effect on the outcome of interest compared to other variable selection and estimation procedures when there is confounding by a categorical variable.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1962

EFFECTS OF HIGH G CONDITIONS ON PILOT PERFORMANCE

The general development trend in space vehicle design suggests the desirability of maximally using the occupant to both control capsule attitude and to monitor vehicle systems during the boost (and reentry) acceleration phases, as well as during orbital flight. Consequently, much more information is needed concerning man's ability to perform certain control functions under conditions in which he is exposed to accelerations which approach not only his physiological tolerance limits, but also his performance tolerance limits. In addition to the need for more data concerning the acceleration stress that man can endure and still retain the ability to perform control functions, there is a need to know specifically the nature of performance errors which can arise, not only as the direct result of acceleration, but also as secondary effects of acceleration inter acting with other conditions such as the type of control task, the type of control device, the damping and stability parameters, and the pilot's physiological endurance8 . Current concern over the performance capabilities of the human pilot immersed in certain acceleration environments is well founded since there are very few experimental reports describing the effects of these conditions on performance. The present paper attempts to summarize some of the results of recent studies conducted at the Aviation Medical Acceleration Laboratory (AMAL) in which specific pilot performance capabilities were studied under several conditions of acceleration stress.

Gravity↗
NASA NTRS1974

MIDAS, prototype Multivariate Interactive Digital Analysis System, Phase 1. Volume 2: Diagnostic system

The MIDAS System is a third-generation, fast, multispectral recognition system able to keep pace with the large quantity and high rates of data acquisition from present and projected sensors. A principal objective of the MIDAS Program is to provide a system well interfaced with the human operator and thus to obtain large overall reductions in turn-around time and significant gains in throughout. The hardware and software generated in Phase I of the over-all program are described. The system contains a mini-computer to control the various high-speed processing elements in the data path and a classifier which implements an all-digital prototype multivariate-Gaussian maximum likelihood decision algorithm operating 2 x 105 pixels/sec. Sufficient hardware was developed to perform signature extraction from computer-compatible tapes, compute classifier coefficients, control the classifier operation, and diagnose operation. Diagnostic programs used to test MIDAS' operations are presented.

Kriegler, F. J.↗
NASA NTRS1974

MIDAS, prototype Multivariate Interactive Digital Analysis System, phase 1. Volume 3: Wiring diagrams

The Midas System is a third-generation, fast, multispectral recognition system able to keep pace with the large quantity and high rates of data acquisition from present and projected sensors. A principal objective of the MIDAS Program is to provide a system well interfaced with the human operator and thus to obtain large overall reductions in turn-around time and significant gains in throughput. The hardware and software generated in Phase I of the overall program are described. The system contains a mini-computer to control the various high-speed processing elements in the data path and a classifier which implements an all-digital prototype multivariate-Gaussian maximum likelihood decision algorithm operating at 2 x 100,000 pixels/sec. Sufficient hardware was developed to perform signature extraction from computer-compatible tapes, compute classifier coefficients, control the classifier operation, and diagnose operation. The MIDAS construction and wiring diagrams are given.

Kriegler, F. J.↗
NASA NTRS1999

Monitoring and Correcting Autonomic Function Aboard Mir: NASA Technology Used in Space and on Earth to Facilitate Adaptation

The broad objective of the research was to study individual characteristics of human adaptation to long duration spaceflight and possibilities of their correction using autonomic conditioning. The changes in autonomic state during adaptation to microgravity can have profound effects on the operational efficiency of crewmembers and may result in debilitating biomedical symptoms. Ground-based and inflight experiment results showed that certain responses of autonomic nervous system were correlated with, or consistently preceded, reports of performance decrements or the symptoms. Autogenic-Feedback-Training Exercise (AFTE) is a physiological conditioning method that has been used to train people to voluntary control several of their own physiological responses. The specific objectives were: 1) To study human autonomic nervous system (ANS) responses to sustained exposure to microgravity; 2) To study human behavior/performance changes related to physiology; 3) To evaluate the effectiveness of preflight autonomic conditioning (AFTE) for facilitating adaptation to space and readaptation to Earth; and 4) To archive these data for the NASA Life Sciences Data Archive and thereby make this information available to the international scientific community.

Cowings, P.↗
NASA NTRS2014

Fatigue‐Related Countermeasures for Long‐Duration Exploration Missions

The NASA Human Research Program's (HRP) Behavioral Health and Performance Element (BHP) supports and conducts research to mitigate deleterious outcomes related to fatigue, sleep loss, circadian desynchronization, and work overload. Objective evidence indicates that within the context of the International Space Station (ISS), sleep is reduced and there is circadian misalignment. Despite chronic sleep loss and high workloads; however, astronauts successfully complete their missions. Contributing to their success is not only the tremendous skills and capabilities of each astronaut, but also the collaborative team efforts amongst the crew, between flight and ground crews, and through real‐time care provided by medical personnel. It is anticipated that risks to human health and performance will increase in the context of exploration missions, where crewmembers will venture to deep space for extended durations and in small vehicles with limited communication with home. Hence, fatigue‐related countermeasures are being developed and/or validated that include unobtrusive monitoring technologies to detect fatigue‐related performance decrements, environmental countermeasures, and sleep education and training for flight and ground crews. Given that fatigue is an issue in current ISS missions, the BHP works collaboratively with Space Medicine operations to collect data in the operational environment, to validate fatigue-related countermeasures, and provide evidence‐based mitigations. Our presentation will summarize fatigue‐related operational research that is underway through NASA's BHP in partnership with its operational counterparts. Efforts include studies evaluating the effects of hypnotics, lighting protocols as countermeasures for circadian entrainment, and investigations involving education and training. This presentation will further identify, based on flight and terrestrial evidence, additional sleep and circadian countermeasures that may still be needed to support exploration missions. Lessons learned from transitioning research deliverables into ISS operations will also be discussed.

Whitmire, A.↗
NASA NTRS2013

Robotic Technology Development at Ames: The Intelligent Robotics Group and Surface Telerobotics

Future human missions to the Moon, Mars, and other destinations offer many new opportunities for exploration. But, astronaut time will always be limited and some work will not be feasible for humans to do manually. Robots, however, can complement human explorers, performing work autonomously or under remote supervision from Earth. Since 2004, the Intelligent Robotics Group has been working to make human-robot interaction efficient and effective for space exploration. A central focus of our research has been to develop and field test robots that benefit human exploration. Our approach is inspired by lessons learned from the Mars Exploration Rovers, as well as human spaceflight programs, including Apollo, the Space Shuttle, and the International Space Station. We conduct applied research in computer vision, geospatial data systems, human-robot interaction, planetary mapping and robot software. In planning for future exploration missions, architecture and study teams have made numerous assumptions about how crew can be telepresent on a planetary surface by remotely operating surface robots from space (i.e. from a flight vehicle or deep space habitat). These assumptions include estimates of technology maturity, existing technology gaps, and likely operational and functional risks. These assumptions, however, are not grounded by actual experimental data. Moreover, no crew-controlled surface telerobotic system has yet been fully tested, or rigorously validated, through flight testing. During Summer 2013, we conducted a series of tests to examine how astronauts in the International Space Station (ISS) can remotely operate a planetary rover across short time delays. The tests simulated portions of a proposed human-robotic Lunar Waypoint mission, in which astronauts in lunar orbit remotely operate a planetary rover on the lunar Farside to deploy a radio telescope array. We used these tests to obtain baseline-engineering data.

robotics↗
NASA NTRS2016

Testing of Space Suit Materials for Mars

Human missions to Mars may require radical changes in our approach to EVA suit design. A major challenge is the balance of building a suit robust enough to complete 50 EVAs in the dirt under intense UV exposure without losing mechanical strength or compromising its mobility. We conducted ground testing on both current and new space suit materials to determine performance degradation after exposure to 2500 hours of Mars mission equivalent UV. This testing will help mature the material technologies and provide performance data that can be used by not only the space suit development teams but for all Mars inflatable and soft goods derived structures from airlocks to habitats.

Larson, Kristine↗
NASA NTRSDate not supplied

Mars Water Well Performance: Experimental Heat Transfer Results Supporting Simulations

Favorable indications of massive quantities of water on Mars have initiated studies of potential changes to human Mars missions. Using a technique known as a Rodriguez Well to melt the ice, store the resulting water in a subsurface ice cavity until needed, and then pump water to the surface for use is one potential means to effect these changes. A computer simulation of the Rodriguez Well in a terrestrial environment is one of the engineering tools being used to characterize the performance of this type of well on Mars. An experiment at the NASA Johnson Space Center gathered data for evaporation rates and convective heat transfer at Mars surface conditions so that this computer simulation could be appropriately modified to predict performance on Mars. Tests have indicated that a pool of water can be maintained at 1°C to 2° C while at Mars surface temperatures and pressures. Preliminary results for evaporation rates and convective heat transfer have been determined and are presented.

Stephen J Hoffman↗