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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Florida Atlantic Coast Telemetry (FACT) Array: A Working Partnership

The Florida Atlantic Coast Telemetry (FACT) Array is a collaborative partnership of researchers from 24 different organizations using passive acoustic telemetry to document site fidelity, habitat preferences, seasonal migration patterns, and reproductive strategies of valuable sportfish, sharks, and marine turtles. FACT partners have found that by bundling resources, they can leverage a smaller investment to track highly mobile animals beyond a study area typically restrained in scale by funds and manpower. FACT is guided by several simple rules: use of the same type of equipment, locate receivers in areas that are beneficial to all researchers when feasible, maintain strong scientific ethics by recognizing that detection data on any receiver belongs to the tag owner, do not use other members detection data without permission and acknowledge FACT in publications. Partners have access to a network of 480 receivers deployed along a continuum of habitats from freshwater rivers to offshore reefs and covers 1100 km of coastline from the Dry Tortugas, Florida to South Carolina and extends to the Bahamas. Presently, 49 species, (25 covered by Fisheries Management Plans and five covered by the Endangered Species Act) have been tagged with 2736 tags in which 1767 tags are still active.

Scheidt, Douglas↗
NASA NTRS2017

Evaluating Process Effectiveness to Reduce Risk

It is well documented that government agencies do not have the same incentive as the private sector to focus on process effectiveness and continual improvement of those processes. It is also well documented whenever government agencies fail to deliver efficient, effective, consistent, and fair services to the citizens. In spite of the various "reinventing government" and "effectiveness initiatives" of the past decades, and in spite of the efforts on the part of many agencies to improve, government in general still lags behind industry in creating a culture of effective processes and systems. While the tragic events that unfolded recently in Flint, Michigan, teach us that running government "like a business" does not always take the needs of the citizenry into account, there are many lessons and techniques from the private sector that government agencies can use to improve. The incentive to improve, while mandated by various administrations1, needs to come from within the workforce, in order to effectively take root. The best, most effective incentive is to reduce, control or eliminate risk. Government agencies face some of the same risks as the private sector, while some are unique. While ISO 310002 has been around since 2009, risk has taken on increased visibility within the private sector with the advent of the emphasis on risk-based thinking in ISO 9001:20153. The relationship between risk-based thinking and effective processes is simple and direct. Those processes that are well thought out and standardized (i.e. Plan-Do-Check-Act), will have taken into account the applicable policy, statutory, regulatory, safety, quality and technical parameters, which may not occur to someone performing the process with minimal experience or training; and thus protect the employees, the public and the agency from statutory and regulatory violations; delay in providing services; non-delivery of services; harm to public or employee safety and health; cost overruns; breaches in security; loss of confidence in government; failure of publicly funded projects; damage to the environment; ethics violations, and the list goes on; with local, national and even international consequences. The Plan-Do-Check-Act process, also known as the "process approach" can be used at any time to establish and standardize a process, and it can also be used to check periodically for "process creep" (i.e., informal, unauthorized changes that have occurred over time), any necessary updates and improvements. While ISO 9001 compliance is not mandated for all government agencies, if interpreted correctly, it can be useful in establishing a framework and implementing effective management systems and processes.4 Another method that can be used to evaluate effectiveness is the scorecard definitions in Mallory's Process Management Standard5 as a basis for evaluating work on the process level on effective, and continuously improved and improving processes. With processes on the lower end of the scale, agencies are vulnerable to a great many risks, with employees and managers making up many of the rules as they go, leading to the above listed negative results. Without clear guidance for nominal operations, off-nominal situations can, and do, increase the likelihood of chaos. In an increasingly technical environment, with inter-agency communication and collaboration becoming the norm, agencies need to come to grips with the fact that processes can become rapidly outdated, and that the technical community should take on an increased role in the maturation of the agency's processes. Industry has long known that effective processes are also efficient, and process improvement methods such as Kaizen, Lean, Six Sigma, 5S, and mistake proofing lead to increased productivity, improved quality, and decreased cost. Again, government agencies have different concerns, but inefficiencies and mistakes can have dire and wide reaching consequences for the public that they serve. While no one goes to work planning to cause harm, it is up to agencies to establish upper level systems, which make establishment and compliance with processes possible. Again, Mallory provides us with a Systems Management Standard6, similar to the Process Management Standard, with a scale of 0-5 for systems effectiveness and maturity. Deming determined that "eighty-five percent of the reasons for failure are deficiencies in the systems and process rather than the employee. The role of management is to change the process rather than badgering individual employees to do better." 7 It is not just the working level employees who need effective processes, but the mid-and upper level managers as well. A disciplined management culture sets the tone for the employees, aids both routine and off-nominal decision-making, and incorporates risk -based thinking into the systems and processes as a matter of normal activity. Figure 1, illustrates the relationship between ineffective and effective processes and risk, through the use of the "stoplight" colors that are commonly used to show serious situations (red), situations which may be improving or deteriorating depending on trends (yellow), and situations that are under control and continuously improved (green).

Shepherd, Christena C.↗
NASA NTRS2016

The Astrobiology Primer v2.0

Astrobiology is the science that seeks to understand the story of life in our universe. Astrobiology includes investigation of the conditions that are necessary for life to emerge and flourish, the origin of life, the ways that life has evolved and adapted to the wide range of environmental conditions here on Earth, the search for life beyond Earth, the habitability of extraterrestrial environments, and consideration of the future of life here on Earth and elsewhere. It therefore requires knowledge of physics, chemistry, biology, and many more specialized scientific areas including astronomy, geology, planetary science, microbiology, atmospheric science, and oceanography. However, astrobiology is more than just a collection of different disciplines. In seeking to understand the full story of life in the Universe in a holistic way, astrobiology asks questions that transcend all these individual scientific subjects. Astrobiological research potentially has much broader consequences than simply scientific discovery, as it includes questions that have been of great interest to human beings for millennia (e.g., are we alone?) and raises issues that could affect the way the human race views and conducts itself as a species (e.g., what are our ethical responsibilities to any life discovered beyond Earth?).

Domagal-Goldman, Shawn D.↗
NASA NTRS2019

Space Research Project Management Can Benefit from Engineering Technology Selection Methods

Many engineering methods have been developed to help management select technology for a system design or further research. The simplest way to compare technologies is to use a checklist containing all the more or less important selection criteria, so that nothing is overlooked. The criteria usually include cost, safety, reliability and maintainability, and potential problems such as noise generation and microgravity sensitivity. The next step typically is to weight and score all the criteria. The process of weighting and scoring is helpful in bringing out different priorities and reaching a shared point of view. Group technology selection methods are designed to highlight initial disagreements and produce a shared consensus. Often a frank discussion led by management rather than decision analysts can be more effective. The final selection depends on management and engineering judgment and may include programmatic and organizational factors that are beyond the engineering checklist. The objective of engineering technology selection methods is to provide engineering information to assist management in making sound decisions. Project management and technology selection are assumed to use rational engineering analytic methods, but they often do not. The reason is that human insight, intuition, and “gut feel,” rather than logic, more frequently determine our decisions. Project selection and management are strongly influenced by nonrational psychological influences, which can produce unjustified confidence and determination. Nevertheless, there is a strong need for space projects to do rational project analysis and selection. Demonstrating a rational spirit is necessary for a scientific and technical organization. Professional ethics at its best requires an open, honest, and fair process, without damaging politics. Rational analysis can help improve good projects and avoid selecting bad ones. A sanity check using rational analysis guided by a checklist can help avoid egregious and damaging errors.

Jones, Harry W.↗
NASA NTRSDate not supplied

TRUST, Trustworthiness and EOSDIS

In recent years there has been considerable attention by the international scientific research and applications community to ensure high quality of data and information management. The terms FAIR (Findable, Accessible, Interoperable, Reusable) data, TRUST (Transparency, Responsibility, User Community, Sustainability, and Technology) principles, and CARE (Collective Benefit, Authority to Control, Responsibility, and Ethics) principles have come into vogue during the last decade. NASA has been managing data and information for over 60 years. NASA’s Earth Observing System Data and Information System (EOSDIS) has been in operation for over 25 years, managing most of NASA’s Earth science data. Trustworthiness is a goal that NASA has always strived to achieve or exceed, because it: enables the success of any NASA science mission; inspires general science research and applications; justifies the cost of operations; contributes to the value of NASA’s Open Data Policy; and influences the long term, historical view for the data collection. Given the recent growth of interest in TRUST principles, it is useful to assess and show how NASA’s attention to trustworthiness maps into those principles. This presentation addresses shows how the various steps that have been taken by the Earth Science Data and Information System (ESDIS) Project in the implementation and evolution of EOSDIS map into the TRUST principles.

Remote Sensing↗
NASA NTRSDate not supplied

Transforming Science Prioritization Processes Using Artificial Intelligence

Artificial Intelligence (AI) and Machine Learning (ML) have potential to augment significantly the current labor-intensive processes of science prioritization, specifically by the National Academies’ Decadal Survey on behalf of NASA and NSF. Here we summarize what we believe to be the first exploratory demonstration-of-concept results from an application of AI/ML to Survey science prioritization. Specifically, we applied Latent Dirichlet Allocation (LDA) and Natural Language Processing (NLP) to reveal trends in published astrophysics research that may indicate science priorities and which could be applied to strategic planning. For the purpose of the work that we summarize here, AI/ML is able to analyze – that is, to “understand,” in a manner of speaking – a vast amount of text to reveal complex relationships among research topics, including the growth or decline of science community activities in those topics over time. We trained ourselves and AI/ML algorithms by using ~400,000 abstracts in the period 1998 to 2010 to “forecast” the Academies’ Astro2010 recommendations and compare with the solicited white papers. Comparing our results with actual Astro2010 recommendations allowed us to identify candidate metrics that better predicted the actual results of the Survey. We found, for example, that Compound Annual Growth Rate (CAGR) of papers published in a topic area is a good proxy measure for importance of this topic area of research. With this training complete, we identified candidate astrophysics astrophysics science priorities for the 2021+ period using the research during 2007 - 2019 . We conclude that appropriate application of AI can potentially significantly reduce the current workload of the Decadal Survey processes and reveal otherwise unrecognized characteristics in the body of astronomical research. We emphasize throughout the exploratory nature of our work, encouraging colleagues to pursue promising results further. Our most critical governing assumption was that increased (or decreased) research activity can be used to identify scientific or technology topic areas worthy of increased (or decreased) future emphasis. We discuss advantages, limitations, and recognize the “black box” nature of our technique. We note ethics issues associated, for example, with using AI/ML to reveal “hidden” meanings and biases in published work. Furthermore, inevitable improvements in AI may soon enable widespread and welcome identification of and advocacy for science and technology priorities by disparate and diverse groups and organizations. Consequently, we continue to urge a near-term, in-depth evaluation of appropriate applications of AI, including implications and consequences, as well as support for multiple follow-on assessments, of which ours is only a beginning.

Artificial Intelligence↗
NASA NTRSDate not supplied

Field Assessment of Sensorimotor Function Following Long-Duration Spaceflight

INTRODUCTION: Field assessments of functional task performance following long duration spaceflight are critical to characterize the risk associated with sensorimotor adaptation. A portable test battery involving sit-to-stand, prone-to-stand, walk and turn with obstacle, and tandem walking has been implemented during pre- and postflight testing to provide Sensorimotor Standard Measures that could be implemented in remote test locations. METHODS: To date, 19 astronauts (12 males, 7 females) participated in this study before and after 6–8-month expeditions to the International Space Station (198 ± 70 days, mean ±std). Ethics approvals were obtained, and all subjects provided informed consent. Tests were conducted preflight, within a few hours after landing, and then 1 day and 6–11 days later. Time to stability was the outcome measure for both standing tasks, time to completion and turn rate for the walk and turn task, and percent complete steps for the tandem walking (eyes open and closed). Statistical analyses included mixed effects (multi-level) generalized linear models. RESULTS: Consistent with previous Field Tests, significant effects of spaceflight were observed during the initial testing including longer times to stabilize posture when standing, longer times to complete the short obstacle walk, and fewer correct steps during tandem walking. The recovery timeline varied with task complexity, generally taking longer when either the basis of support was limited (e.g., tandem walk) and/or visual cues were deprived (eyes closed). DISCUSSION: These data suggest that additional sensorimotor-based countermeasures may be necessary to maintain functional performance during long-duration spaceflight. Maintaining core measures as new countermeasures are implemented during future missions will be instrumental in assessing their efficacy. This test battery will also serve as the basis for developing sensorimotor assessments during future space exploration.

Scott Jonathan Wood↗
NASA NTRS2023

Exploring the Complexities of Drug Formulation Selection, Storage, and Shelf-Life for Exploration Spaceflight

Medications have been a part of space travel dating back as far as the Apollo missions. Currently, medical kits aboard the ISS contain medications and supplies to help crew members cope with a variety of possible medical events. NASA reported that 1,867 medical events occurred from 1981 to 1998 on space shuttle flights, STS-1 to STS-89; 498 out of the 508 crewmembers on those flights reported experiencing a medical event other than space motion sickness. In 2000, the Institute of Medicine (IOM) convened a committee of experts, Committee on Creating a Vision for Space Medicine during Travel beyond Earth Orbit, to examine the issues surrounding astronaut health and safety for long duration space missions. The primary theme of the committee’s final report is that there is not enough known about the risks to human health during long-duration missions beyond Earth’s orbit and ways to effectively mitigate those risks in an environment of deep space. In 2014, the IOM convened the Committee on Ethics Principles and Guidelines for Health Standards for Long Duration and Exploration Spaceflights and released a report emphasizing the importance of prevention, mitigation, and treatment of major risks to human health during exploration spaceflight. NASA’s Human Research Program has organized five distinct categories of spaceflight hazards summarized by the acronym “RIDGE” (Space Radiation, Isolation and Confinement, Distance from Earth, Gravity fields, and Hostile/Closed Environments) that astronauts may encounter during exploration spaceflight. From those hazards, NASA further derived 30 of the most critical human health and performance risks, including limits to medical care resulting from pharmaceutical degradation. As we prepare for more distant exploration missions to Mars and beyond, risk management planning for astronaut healthcare should include the assembly of a medication formulary that is comprehensive enough to prevent or treat anticipated medical events, remains safe and chemically stable, and retains sufficient potency to last for the duration of the mission. The present editorial will summarize the current state of knowledge regarding innovative formulary optimization strategies, pharmaceutical stability assessment techniques, and storage and packaging solutions that could enhance drug safety and efficacy for future exploration spaceflight missions.

drug degradation risk assessment↗
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 the last two decades, in part thanks to an increasing culture of open data sharing and reuse. Due to its capability for identifying complex relationships and patterns, AI/ML methodology is particularly 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 many key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Even with the positive culture of Open Science and data sharing, inexperienced researchers working quickly without proper checks can produce models that perform poorly outside of the immediate training dataset. Lessons learned from biological AI/ML research indicate that 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 Andrew Casaletto↗
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 NTRSDate not supplied

Breaking Barriers: Integrating Geo-Leo Aerosol Data with an Open-Source Approach

The scientific community is still examining the novel data from geostationary satellite observations and evaluating methods for effectively fusing the polar observations with various spatial and temporal resolutions. However, the merged data will present a significant ""Big Data"" challenge, including processing, storage, data discoverability, accessibility, and migration within cloud computing environments. We have developed an open-source package to fuse aerosol optical depths (AOD) products from six satellite sensors in the past four years (2019~2023), and this presentation will update our recent progress. Using this Python-based package, we produced a level 3 global (AOD) product in a quarter-degree spatial resolution every half-hour, fusing the Level 2 AOD data with the Dark Target aerosol retrieval algorithm from six satellites: three geostationary (GOES-16/17 and Himawari-8) with high temporal resolution, and three polar orbiting (TERRA/MODIS, AQUA/MODIS, and SNPP-VIIRS) with global coverage. By integrating these observations, the diurnal cycle of global AOD in this fused product can be characterized at local, regional, and global scales. Furthermore, we are committed to openness and transparency by providing our package and its associated functionalities as open-source. Our dedication to adhering to the FAIR, CARE, and TRUST principles ensures that our users can rely on the integrity and ethical standards of our work. For instance of Interoperability, this package fuses remote sensing products on demand into desired temporal and spatial domains. It can be run in a central processing unit (CPU) or a Graphics processing unit (GPU) mode. This package will empower researchers and practitioners to use satellite and sensor data efficiently in various applications and research.

Xiaohua Pan↗
NASA NTRSDate not supplied

Clinical Trials at NASA: What Makes A Clinical Trial & What Are the Requirements for International Partners?

ClinicalTrials.gov is a public registry designed to fulfill ethical obligations by providing information about clinical research studies to the general public, patients, medical practitioners and the research community. In the United States, recent revisions to human subject’s regulations have prompted new criteria for what constitutes a clinical trial along with requirements not typically requested for other types of human subject’s research. Identification of investigational clinical trials is the shared responsibility of NASA, the IRB, and the scientific investigators designing and conducting the research. This talk aims to educate attendees on the history of the development of Clinicaltrials.gov, discussion of why these requirements are important to both participants and researchers, and information on how to identify clinical trials research. In addition, we will provide information on the different requirements for clinical trials conducted at NASA or aboard the International Space Station.

Clinicaltrials.gov↗
NASA NTRSDate not supplied

NASA Earth Science Division’s Commitment to Increasing Safety in the Field

NASA's Earth Science Division (ESD) has led and supported field campaign research over many decades focused on advancing fundamental research, testing new instrument technologies, and promoting career development. ESD field campaign research is conducted over a wide range of projects that vary in size, science focus area, location, platform type, and people. ESD leadership has created a task team to address campaign physical and mental safety, with the goal of providing all participants in NASA field campaign research with an environment that promotes research, safety, inclusivity, and a positive experience. Building on resources that have been developed both within and outside NASA, we report on the task team’s accomplishments and near-term plans, including the recent establishment of a best practices document and guidelines for the development of “agreement of behaviors” document for field teams. We also describe progress in the development of an online training module for field campaign participation, the incorporation of campaign safety language in forthcoming NASA ROSES solicitations, and outreach activities. Finally, we report on recent joint interactions with the NASA Planetary Science Division’s Ethics in Fieldwork team.

Ocean-based measurements↗
NASA NTRSDate not supplied

Implementing Artificial Thinking Autonomy with Model-Based System Engineering

Complex autonomous systems capable of successfully operating independently under ‘known unknowns’ and harsh conditions require paradigm innovation in modern development strategies. In the field of autonomy, developing a system-of-systems which can ostensibly think for itself in the face of ‘unknown unknowns’ is still a field of ongoing research. Maturing the systems architecting and modeling methodologies for developing henceforth named Thinking Autonomous Systems, which are verified with digital mission simulation, can potentially usher in the next generation of artificial intelligence for space exploration. The concept presented in this paper incorporates multiple Model-Based Systems Engineering and simulation methodologies combined as a new paradigm to design a novel, biomimetic thinking autonomy strategy. Anachronistic concepts from classical Kantian philosophy will be leveraged to inspire architectural designs that could be used for complex distributed systems in deep space. To accomplish this, digital transformation of a document-based implementation plan for Thinking Autonomous Systems, generated by experienced NASA software engineers, is implemented for NASA’s Platform for Autonomous Systems by creating descriptive and executable software models in SysML to prototype real-time operating capabilities. This conceptual implementation has been developed by incorporating model-based digital simulations to theorize how a cyberphysical thinking system would achieve specific strategies without crew reliance, while simultaneously being resilient to all operating conditions and remaining functional when devoid of ground communication. Additionally, ensuring that an autonomous system framework is an ethical Artificial Intelligence requires careful consideration of system behavior and accountability, human factors for teaming with a thinking autonomous system, and comparison to other modern approaches used for implementing true autonomy. This paper presents the first steps in formalizing the metacognition required for instantiating a truly Thinking Autonomous System; the approach described symphonizes autonomy characteristics from classical philosophical into a unified software architecture describing human thought. In the future, the foundational models described in this paper can be further leveraged to help advance research into thinking autonomy requirements for future deep space missions as well as for current near-term applications, i.e., living aboard crewed spacecraft like a NASA Gateway cislunar habitat.

Artificial Thought↗
DOE OSTI2024

Exploring the Intersection of AI and Visualization in the Nuclear Industry

This presentation explores the impact of AI and visualization in advancing the nuclear industry by improving safety, operational efficiency, and decision-making processes. It highlights key applications such as real-time monitoring, predictive maintenance, and immersive training, while addressing challenges like data quality, regulatory hurdles, and the need for explainable AI. Additionally, the presentation outlines future directions, emphasizing the potential of AI-driven reactor design, advanced simulation tools, and ethical considerations to drive innovation and sustainability in nuclear operations.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Human Subjects in Energy Technology and Policy Research Symposium Report

The inaugural Human Subjects in Energy Technology & Policy Symposium was held virtually on October 17 and 19, 2023. The symposium gathered professionals supporting and conducting research to develop and deploy energy technologies and policies, with the goals of increasing awareness of what constitutes human subjects research in this field of research, promoting best practices from the proposal stage through study completion, and fostering a culture of collaboration. This report is a summary of this Symposium.

99 GENERAL AND MISCELLANEOUS↗