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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
NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields, in part due to a culture of open data sharing and reuse. AI/ML methodology is well-suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Inexperienced researchers can produce models that perform poorly outside of the training dataset. Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Casaletto↗
NASA NTRS1981

Teaching/learning principles

The potential remote sensing user community is enormous, and the teaching and training tasks are even larger; however, some underlying principles may be synthesized and applied at all levels from elementary school children to sophisticated and knowledgeable adults. The basic rules applying to each of the six major elements of any training course and the underlying principle involved in each rule are summarized. The six identified major elements are: (1) field sites for problems and practice; (2) lectures and inside study; (3) learning materials and resources (the kit); (4) the field experience; (5) laboratory sessions; and (6) testing and evaluation.

Hankins, D. B.↗
DOE OSTI2025

Data-driven emulation of modal aerosol microphysics via neural operator-based modeling

The complexity and the small characteristic scales of aerosol microphysical processes pose a big challenge for accurate and efficient Earth system simulations at regional and global scales. In this work, we construct and evaluate a surrogate model: the aerosol deep operator network (ADON), a physics-inspired dual-net architecture for emulating the aerosol microphysics parameterization suite in the version 2 of the Energy Earth System Model (E3SMv2). The current version of the surrogate model is trained on a dataset comprising 9.8 million samples obtained from a global E3SMv2 simulation with the horizontal resolution of about one degree under cloud-free conditions. Incorporating domain spatial and temporal coordinates, as well as principle components extracted from training data, the dual-net surrogate model effectively captures the intricate representations of aerosol and the relationship with atmospheric state variables, achieving an R-squared score over $$95.7\%$$ for all the lognormal aerosol modes in the extrapolated regime. The validated model provides feature importance of input variables and their impact on the predictive capacity of the surrogate model in relation to the E3SM. The computational cost of online inference time deployed on CPUs and GPUs with lower precisions highlights ADON’s efficiency and potential in robust predictive modeling for large-scale Earth system computations.

Bai, Zhe↗
NASA NTRS1975

Methods of body orientation in space in the absence of support under weightless conditions

The experience accumulated in training subjects in methods of body orientation in space indicates the necessity of clear planning of the training process. After theoretical familiarization with the principles of body orientation in space and reviewing training films, practical mastery of the body orientation methods begins with working out of the individual elements on the Zhukovskiy stool. Then, the correctness and sequence of movements are carefully mastered in water, and the motor skills are then reinforced under time deficit conditions, on the vaulting bars, trampolines, and, in the concluding stage of training, the methods of orienting the body in space in weightlessness are worked out in laboratory-aircraft, with and without the spacesuit and with and without a load.

Yeremin, A. V.↗
NASA NTRS2024

Transfer of NASA Technology to the DoD: Mitigating Motion Sickness with Autogenic Feedback Training Exercise

Motion sickness poses a significant safety risk, particularly in the context of aviation. Given its prevalence among aviators and its detrimental impact on performance, researchers have attempted to identify effective mitigation strategies for motion sickness. Currently, many of the existing interventions are pharmacological, and while effective, they present a problem due to their associated adverse side effects. The primary goals of the current research were 1) demonstrate the value of the application of Autogenic-Feedback Training Exercise (AFTE), a physiological training program developed by NASA to mitigate the impact of operational stressors such as motion sickness and spatial disorientation on human physiology and performance and 2) evaluate a new enhanced version of AFTE training software and demonstrate the ability to apply it remotely and train other personnel to administer it. AFTE combines principles of autogenic therapy, and biofeedback in training individuals to control their physiological reactions through a series of relaxation and arousal exercises. This study included twenty-six participants, 17 men and 9 women. On day 1 participants were tested in a rotating chair (pre-test) to determine their motion sickness tolerance (measured as minutes of rotation); days 2-5 consisted of four AFTE training sessions, each 30-min. in duration for a total of 2 hours; and on day 6 the participants were re-tested in the rotating chair. Results revealed a significant increase in motion sickness tolerance with AFTE on the post-test when compared to pre-test, participants had significantly lower symptom diagnostic scores post-test, and there was no significant gender effect. In addressing the first goal it was concluded that the modified 2-hour version of AFTE significantly improved motion sickness tolerance with participants experiencing fewer symptoms. A second goal of this research was to refine and transfer a NASA technology, software and methods for applications within DOD by training other personnel to administer AFTE. A comparison of NASA and NAMRU-D trainers on AFTE outcome for improving participants’ motion sickness tolerance revealed no significant difference indicating the successful transfer of methods to DOD. In addition, remote AFTE training of military aviators at distant sites was feasible. A third goal was to identify individual patterns of interoceptive abilities and related autonomic metrics able to predict stress response and training outcome. These data included measures of personality traits obtained from questionnaires and specific autonomic measures (e.g., heart rate variability) collected by NAMRU-D investigators. These results will be reported in a separate paper.

autonomic nervous system↗
DOE OSTI2024

An Ethics-Based Review of Generative Artificial Intelligence: Assuring Responsible Use (Version 1.0)

The rapid expansion of generative artificial intelligence (GenAI) has generated excitement regarding its potential benefits and concern over its ethical implications. Governments, corporations, and standards organizations have described ethical principles to direct GenAI's development and use; however, practical guidance for implementing these principles is limited. Addressing this gap is critical, especially considering the array of risks associated with GenAI, such as legal liabilities, privacy concerns, security threats, and potential misuse. Robust policies and procedures are critical to support responsible deployment of GenAI. This report examines Pacific Northwest National Laboratory (PNNL)’s approach to promoting responsible GenAI use. Proposed initiatives include developing policies based on ethical principles, creating a governance process to review projects relative to those principles, and implementing onboarding processes for training staff. The governance framework described in this report adapts the structure and principles of Institutional Review Boards (IRBs), traditionally used in human subjects research, for GenAI ethical review, providing oversight. Ethical principles guiding responsible GenAI usage include transparency and accountability, privacy, fairness, safety, security, and validity and reliability. To operationalize these principles, we propose forming a GenAI Assurance Council (GAC) that mirrors the IRB's structure. The GAC will evaluate GenAI projects across privacy, accountability, transparency, safety, security, fairness, and validity dimensions. Complementing policy and governance is AI literacy training to support staff understanding of GenAI's ethical implications. An initial training effort for AI Incubator Chat—a GenAI tool deployed at PNNL—showed promising results, underscoring the importance of clear guidelines and user accountability. Collaborative efforts and the dissemination of best practices are also discussed. The proposed GAC model and AI literacy training provide a blueprint for establishing ethical GenAI use and governance, offering practical tools to bridge the gap between ethical principles and real-world applications. The responsible integration of GenAI at PNNL entails a multifaceted approach involving policy development, ethical governance, and AI literacy training. The positive initial feedback and collaborative opportunities position PNNL to lead by example in GenAI's responsible use, reflecting a proactive stance in addressing the ethical, legal, and societal challenges associated with this emerging technology. PNNL's systematic and ethical approach to GenAI offers a model for other institutions to emulate, promoting safe and responsible technological advancements in the AI domain.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Facet-dependent structure and dissociation of water at pristine IrO 2 /water interfaces

Understanding the microscopic structure of water at metal oxide interfaces is crucial for advancing electrocatalysis. IrO 2 , specifically, has shown exceptional activity for electrochemical water oxidation, but we currently lack a fundamental understanding of how the surface structure of IrO 2 impacts water reactivity. In this work, we developed a machine learning potential trained to first-principles accuracy for modeling IrO 2 /water interfaces across different facets: (110), (100), (101), and (001). Using extensive machine learning molecular dynamics simulations, we investigated the spontaneous dissociation of water molecules at these interfaces. Our results reveal a distinct dissociation probability trend: (110) > (100) ≈ (101) > (001), which we attribute primarily to the reaction thermodynamics of surface water dissociation. A strong correlation is observed between the surface Ir–O bond distances and the dissociation probabilities, highlighting the role of surface geometry in modulating reactivity. As a consequence, the interfacial solvation structures and hydrogen bonding environments are dynamically tuned by the varying water dissociation capabilities across facets. This work elucidates how water dissociation energetics depend on surface orientation and interfacial structure, offering atomistic insights into manipulating reaction chemistry at electrocatalytic interfaces.

organic↗
DOE OSTI2024

Automating the Analysis of Large Language Models Responses through Zero-Shot Question Answering

Recent advancements in Large Language Models (LLMs) have shown significant potential in various applications, yet their evaluation, particularly in zero-shot question answering scenarios, remains a challenging task. In this study, our objective was to explore precision metrics for Large Language Models (LLM) and design and implement a software pipeline to automatically evaluate LLMs' outputs under zero-shot question answering. Zero-shot question answering involves a model providing answers to questions about topics it hasn't seen during training. It leverages the principles of zero-shot learning by relying on semantic understanding and generalization from related knowledge. The data used was metadata from medical databases on congenital heart disease. We explored eleven LLM metrics and selected three for our evaluation: BLEU, BERTScore, and MoverScore. BLEU calculates a score based on the overlap of n-grams (contiguous sequences of n items, typically words) between the machine-generated translation and the reference translations. Higher BLEU scores indicate better correspondence between the machine-generated and human-generated translations. BERTScore is a metric used to evaluate the quality of machine-generated text by measuring the similarity of token embeddings produced by BERT (Bidirectional Encoder Representations from Transformers) between the generated text and reference text. MoverScore is a metric that quantifies the dissimilarity between the distributions of word embeddings from machine-generated text and reference text, emphasizing semantic similarity over exact token overlap. We also introduced HBKI, a composite metric summarizing these approaches. We tested five models —GPT-3, Llama-2, Gemini 1.5 Pro, Solar 10.7B, and Mixtral-8x7b. Our software pipeline, designed and implemented using Object-Oriented Programming principles, allows users to customize the selection and extraction of features for topics of interest in their own research. Our results show that MoverScore delivered the most precise evaluation of the LLM's outputs, while Mixtral-8x7b achieved the best overall performance in extracting metadata from the databases.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1998

Collaboration in Complex Medical Systems

Improving our understanding of collaborative work in complex environments has the potential for developing effective supporting technologies, personnel training paradigms, and design principles for multi-crew workplaces. USing a sophisticated audio-video-data acquisition system and a corresponding analysis system, the researchers at University of Maryland have been able to study in detail team performance during real trauma patient resuscitation. The first study reported here was on coordination mechanisms and on characteristics of coordination breakdowns. One of the key findings was that implicit communications were an important coordination mechanism (e.g. through the use of shared workspace and event space). The second study was on the sources of uncertainty during resuscitation. Although incoming trauma patients' status is inherently uncertain, the findings suggest that much of the uncertainty felt by care providers was related to communication and coordination. These two studies demonstrate the value of and need for creating a real-life laboratory for studying team performance with the use of comprehensive and integrated data acquisition and analysis tools.

Xiao, Yan↗
NASA NTRS2018

Enriching the Twitter Stream Increasing Data Mining Yield and Quality Using Machine Learning

Social media data streams are important sources of real-time and historical global information for science applications. At the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), we are exploring the Twitter data stream for its potential in augmenting the validation program of NASA Earth science missions, specifically the Global Precipitation Measurement (GPM) mission. We have implemented a tweet processing infrastructure that outputs classified precipitation tweets. Inputs are "passive" tweets, along with a smaller number of tweets from "active" participants, i.e., those knowingly contributing to our effort. The "active" tweets, presumably of higher quality, enrich the Twitter stream. "Active" sources include data scraped from other social media (e.g., public Facebook posts) and data from existing crowdsourcing programs (e.g., mPING reports). In addition, there is likely relevant precipitation information in images and documents that are the end points of links often included in tweets. Information derived from these "active" sources could then be tweeted into the Twitter stream, thus enriching its quality. The objective of our current work is to mine these tweet­ linked images and documents, using neural networks, to increase the information content and quality related to precipitation. For images, we classified them as either precipitation-related or not. For training and validation, we used images obtained via the Google custom search API. We created two models: (1) by training a simple Convolutional Neural Network and (2) by using transfer learning principles to adapt a pre-trained object recognition model. For documents, both those linked to tweets and the tweet contents, we trained Hierarchical Attention Networks to determine precipitation occurrence, type, and intensity. For training and validation, we used a keyword-filtered tweet data set labelled with ground truth data from Dark Sky (an API to retrieve weather-related labels) and the National Severe Storms Laboratory's Multi­ Radar/Multi-Sensor (MRMS) system. Our results demonstrated the efficacy of our machine learning approaches for enriching the Twitter stream, to derive information potentially useful for validation of earth science satellite data.

Albayrak, Arif↗
NASA NTRS1988

Proceedings of the First NASA Ada Users' Symposium

Ada has the potential to be a part of the most significant change in software engineering technology within NASA in the last twenty years. Thus, it is particularly important that all NASA centers be aware of Ada experience and plans at other centers. Ada activity across NASA are covered, with presenters representing five of the nine major NASA centers and the Space Station Freedom Program Office. Projects discussed included - Space Station Freedom Program Office: the implications of Ada on training, reuse, management and the software support environment; Johnson Space Center (JSC): early experience with the use of Ada, software engineering and Ada training and the evaluation of Ada compilers; Marshall Space Flight Center (MSFC): university research with Ada and the application of Ada to Space Station Freedom, the Orbital Maneuvering Vehicle, the Aero-Assist Flight Experiment and the Secure Shuttle Data System; Lewis Research Center (LeRC): the evolution of Ada software to support the Space Station Power Management and Distribution System; Jet Propulsion Laboratory (JPL): the creation of a centralized Ada development laboratory and current applications of Ada including the Real-time Weather Processor for the FAA; and Goddard Space Flight Center (GSFC): experiences with Ada in the Flight Dynamics Division and the Extreme Ultraviolet Explorer (EUVE) project and the implications of GSFC experience for Ada use in NASA. Despite the diversity of the presentations, several common themes emerged from the program: Methodology - NASA experience in general indicates that the effective use of Ada requires modern software engineering methodologies; Training - It is the software engineering principles and methods that surround Ada, rather than Ada itself, which requires the major training effort; Reuse - Due to training and transition costs, the use of Ada may initially actually decrease productivity, as was clearly found at GSFC; and real-time work at LeRC, JPL and GSFC shows that it is possible to use Ada for real-time applications.

Source record↗
NASA NTRS2003

Some approaches to medical support for Martian expedition

Medical support in a Martian expedition will be within the scope of crew responsibilities and maximally autonomous. Requirements to the system of diagnostics in this mission include considerable use of means and methods of visualization of the main physiological parameters, telemedicine, broad usage of biochemical analyses (including "dry" chemistry), computerized collection, measurement, analysis and storage of medical information. The countermeasure system will be based on objective methods of crew fitness and working ability evaluation, individual selection of training regimens, and intensive use of computer controlled training. Implementation of the above principles implies modernization and refinement of the countermeasures currently used by space crews of long-term missions (LTM), and increases of the assortment of active and passive training devices, among them a short-arm centrifuge. The system of medical care with the functions of prevention, clinical diagnostics and timely treatment will be autonomous, too. The general requirements to medical care during the future mission are the following: availability of conditions and means for autonomous urgent and special medical aid and treatment of the most possible states and diseases, "a hospital", and assignment to the crew of one or two doctors. To ensure independence of medical support and medical care in an expedition to Mars an automated expert system needs to be designed and constructed to control the medical situation as a whole. c2003 Published by Elsevier Science Ltd.

Mars↗
NASA NTRS1990

30 years of adaptive neural networks - Perceptron, Madaline, and backpropagation

Fundamental developments in feedforward artificial neural networks from the past thirty years are reviewed. The history, origination, operating characteristics, and basic theory of several supervised neural-network training algorithms (including the perceptron rule, the least-mean-square algorithm, three Madaline rules, and the backpropagation technique) are described. The concept underlying these iterative adaptation algorithms is the minimal disturbance principle, which suggests that during training it is advisable to inject new information into a network in a manner that disturbs stored information to the smallest extent possible. The two principal kinds of online rules that have developed for altering the weights of a network are examined for both single-threshold elements and multielement networks. They are error-correction rules, which alter the weights of a network to correct error in the output response to the present input pattern, and gradient rules, which alter the weights of a network during each pattern presentation by gradient descent with the objective of reducing mean-square error (averaged over all training patterns).

Widrow, Bernard↗
DOE OSTI2025

Fundamental microscopic properties as predictors of large-scale quantities of interest: Validation through grain boundary energy trends

Correlations between fundamental microscopic properties computable from first principles, which we term canonical properties, and complex large-scale quantities of interest (QoIs) provide an avenue to predictive materials discovery. Here, we propose that such correlations can be efficiently discovered through simulations utilizing approximate interatomic potentials (IPs), which serve as an ensemble of “synthetic materials”. As a proof of principle we build a regression model relating canonical properties to the symmetric tilt grain boundary (GB) energy curves in face-centered cubic crystals, characterized by the scaling factor in the universal lattice matching model of Runnels et al. (2016), which we take to be our QoI. Our analysis recovers known correlations of GB energy to other properties and discovers new ones. We also demonstrate, using available density functional theory (DFT) GB energy data, that the regression model constructed from IP data is consistent with DFT results, confirming the assumption that the IPs and DFT belong to same statistical pool and thereby validating the approach. Regression models constructed in this fashion can be used to predict large-scale QoIs based on first-principles data and provide a general method for training IPs for QoIs beyond the scope of first-principles calculations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Safe Operations at Roadway Junctions: Intelligent Roadway Infrastructure as Functional Interlocking

Automated vehicle (AV) technology is quickly maturing, and the corresponding infrastructure systems that evaluate traffic and communicate to vehicles requires sophisticated sensing and perception technologies, referred to as intelligent roadway infrastructure (IRI), to complement emerging AV capabilities. IRI provides signals to vehicles, indicating right-of-way for vehicles and communicating to approaching AVs that no other vehicle is failing to yield. This capability, denoted as safety-affirmative signaling, provides a green light or a green arrow as appropriate and affirms through communication links to connected vehicles when it is safe to proceed. About 36% of collisions occur at intersections, with most occurring upon left turns (22.2%) or crossing over (12.6%), and only a small percentage (1.2%) while turning right at an intersection. Of all intersection crashes about half (52.5%) of those vehicles were traveling through a signalized intersection 2. Safety-affirmative signaling would guarantee safety of AV fleet vehicles, by providing the interlocking principle, a term from automated train control that only allows progression through a railway intersection after affirming no opportunity for a crash exists. IRI through safety-affirmative signaling would bring performance and safety to complex roadway intersections where AV transit fleet service is most needed, as well as safety benefits to traditional, non-automated vehicles and vulnerable road users. The implementation of IRI has functional, programmatic, and technical challenges. Research work performed at the National Renewable Energy Laboratory (NREL) in an integrative approach encapsulating these themes, and termed infrastructure perception and control (IPC) is motivated by improved performance (travel time), improved safety (reduced collisions), and improved energy efficiency (less fuel burned and minimized production of greenhouse gases). IPC is intended not only for roadway and intersection applications but also in extension to inform complementary buildings and grid systems to enable better co-management, as vehicles and their charging needs become increasingly integrated into the built environment. The NREL IPC project presents an open-source framework, architecture, and supporting technology to implement IRI, addressing critical issues such as fusion of data, reliability, standardization of data interfaces, and confidence of detection. The framework is informed by previous experience in U.S. Department of Defense research technology, specifically in the use of radar to detect, identify, and track aerial threats. These principles combined with multi-sensor fusion provides for a complete digital twin with known and measurable confidence and accuracy from which safety-affirmative signaling can be developed and deployed.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗
NASA NTRSDate not supplied

The Effects of Autogenic Feedback Training Exercise on Heart Rate Variability

INTRODUCTION: The development of Motion Sickness (MS) symptoms is correlated with increased sympathetic influence and irregular patterns of vagal activity. Such autonomic actions can be characterized by indices of heart rate variability (HRV), which reflect autonomic balance through neurocardiac function. Nonpharmacological interventions aimed at attenuating MS symptoms may therefore produce an effect on HRV. One such intervention that has been shown to mitigate MS symptoms is Autogenic Feedback Training Exercise (AFTE), which combines principles of autogenic therapy and biofeedback. AFTE teaches individuals to manipulate various physiological parameters in provocative environments and shows promise as a potential MS intervention in military aviators. The effects of AFTE on HRV have not previously been examined. Understanding HRV changes following AFTE may help to elucidate its indirect effects and inform its implementation for MS mitigation. METHODS: Twenty-four subjects received 2 hours of AFTE over 4 days. Pre- and post-AFTE rotating chair tests, which included stationary periods of baseline data, were conducted to evaluate the effects of AFTE. HRV data were recorded by SOMNOtouch™ NIBP. Post hoc analysis of pre- and post-AFTE short-term HRV (RMSSD, LF, HF, LF/HF) was performed. RESULTS: RMSSD, HF, and LF/HF were not significantly changed following AFTE. However, LF showed a statistically significant (p=0.015) decrease following AFTE. DISCUSSION: AFTE prescribes a respiratory rate of 15 breaths per minute (BPM), which is typically faster than participants’ pre-AFTE BPM (M=12.65). Healthy individuals can increase respiratory sinus arrhythmia (RSA) by slow, deep breathing. However, increasing the respiratory rate to 15 BPM may decrease RSA and subsequently HF. Increasing RSA potentially negates any influence of AFTE on HF, resulting in no significant change. RMSSD is correlated with HF power and was likewise not affected by AFTE. LF power, however, decreased significantly following AFTE, potentially indicating a lower sympathetic response in the post-AFTE measurement.

motion sickness↗
NASA NTRS1993

Selection criteria and facilitation training for the study of groupware

Computer support for planning and decision making groups is a growing trend in the 90s. Groupware is a name often applied to group software and has been defined as 'computer-based systems that support groups engaged in a common task (or goal) and that provide an interface to a shared environment'. Unlike most single-user software, groupware assists user groups in their collaboration, coordination, and communication efforts. This paper focuses on groupware to support the meeting process. These systems are often called group decision support systems (GDSS), electronic meeting systems (EMS), or group support systems (GSS). The term 'meeting support groupware' is used here to include any computer-based system to support meetings. In order to understand this technology, one must first understand groups, what they do and the problems they face, and groupware, a wide range of technology to support group work. Guidelines for selecting groups for study as part of an overall research plan are provided in this document. These were taken from the literature and from persons for whom the information in this paper was targeted. Also, guidelines for facilitation training are discussed. Familiarity with known and accepted techniques are the principle duties of the facilitator and any form of training must include practice in using these techniques.

Robichaux, Barry P.↗
NASA NTRS1975

Stereometric body volume measurement

The following studies are reported: (1) effects of extended space flight on body form of Skylab astronauts using biostereometrics; (2) comparison of body volume determinations using hydrostatic weighing and biostereometrics; and (3) training of technicians in biostereometric principles and procedures.

Herron, R. E.↗