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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A NWB-based dataset and processing pipeline of human single-neuron activity during a declarative memory task

A challenge for data sharing in systems neuroscience is the multitude of different data formats used. Neurodata Without Borders: Neurophysiology 2.0 (NWB:N) has emerged as a standardized data format for the storage of cellular-level data together with meta-data, stimulus information, and behavior. A key next step to facilitate NWB:N adoption is to provide easy to use processing pipelines to import/export data from/to NWB:N. Here, we present a NWB-formatted dataset of 1863 single neurons recorded from the medial temporal lobes of 59 human subjects undergoing intracranial monitoring while they performed a recognition memory task. We provide code to analyze and export/import stimuli, behavior, and electrophysiological recordings to/from NWB in both MATLAB and Python. The data files are NWB:N compliant, which affords interoperability between programming languages and operating systems. This combined data and code release is a case study for how to utilize NWB:N for human single-neuron recordings and enables easy re-use of this hard-to-obtain data for both teaching and research on the mechanisms of human memory.

Science & Technology - Other Topics↗
DOE OSTI2023

An Overview of the Risk Assessment Information System

This technical memorandum (TM) presents an overview of the Risk Assessment Information System (RAIS), a collection of web-based tools designed to assist with the environmental risk assessment process. The objective of the RAIS is to be a single resource for the risk assessment process, providing guidance when planning and performing the steps: data assessment, exposure assessment, toxicity assessment, and risk characterization. The RAIS evolved as a result of the initial remediation efforts at various United States (U.S.) Department of Energy (DOE) facilities. The goal was to increase the efficiency and transparency of the human health and ecological assessments being performed by DOE’s Office of Environmental Management, Oak Ridge Operations (ORO) office by providing a repository for toxicity information, physicochemical data, risk assessment procedures, standardized risk calculation methods, and web-based tools. Since the initial launch in 1996, the RAIS has expanded its user base outside of the federal government and now has users from over 100 countries, universities, states, and local governments. What sets the RAIS apart from other risk assessment sites are the publicly available, searchable toxicity and physicochemical databases and the wide range of chemical and radionuclide risk calculation tools. The purpose of this TM is to present the RAIS tools in order of the website menus and explain how they fit in the risk assessment process. In addition, this TM describes differences between the chemical and radionuclide tools of the RAIS. Screening level equations, chronic daily intake equations, and default exposure factors used in the chemical and radionuclide calculators are included in the appendices of this TM. This TM is not intended to be a detailed guide to risk assessment or the RAIS tools. The tools on the RAIS can be used to comply with procedures from multiple agencies, including but not limited to DOE, U.S. Environmental Protection Agency (EPA), U.S. Department of Defense (DoD), and many state governments. Further information on the RAIS tools can be found in the user guides and tutorials available on the webpage.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
NASA NTRS1993

Intelligent data presentation for real-time spacecraft monitoring

This paper describes an intelligent user interface that is currently under development. The interface serves as a front end for real-time spacecraft monitoring software. The software operates under circumstances in which neither an intelligent human without automated assistance nor an automated system without intelligence are sufficiently effective. The user interface is supported by dynamic trade-off evaluation (DTE), a new technique that has been developed to automate general types of performance trade-offs in real-time problem solving systems. In this application, DTE is used to perform dynamic input data management for the purpose of determining which input data should be monitored in time constrained situations and how to present the monitoring information to a human analyst who has the ultimate responsibility for the spacecraft. This application has demonstrated that DTE can be used to dynamically vary the data that is monitored, making it possible to detect and correctly analyze all anomalous data by examining only a subset of the total input data. In carefully structured experimental evaluations that use real spacecraft data and real decision making, DTE provides the ability to handle a three-fold increase in input data (in real-time) without loss of performance and to intelligently present the information to a mission analyst.

Schwuttke, U. M.↗
NASA NTRS2003

Monitoring Outpatient Care

Each year, health care costs for managing chronically ill patients increase as the life expectancy of Americans continues to grow. To handle this situation, many hospitals, doctors practices, and home care providers are turning to disease management, a system of coordinated health care interventions and communications, to improve outpatient care. By participating in daily monitoring programs, patients with congestive heart failure, chronic obstructive pulmonary disease, diabetes, and other chronic conditions requiring significant self-care are facing fewer emergency situations and hospitalizations. Cybernet Medical, a division of Ann Arbor, Michigan-based Cybernet Systems Corporation, is using the latest communications technology to augment the ways health care professionals monitor and assess patients with chronic diseases, while at the same time simplifying the patients interaction with technology. Cybernet s newest commercial product for this purpose evolved from research funded by NASA, the National Institute of Mental Health, and the Advanced Research Projects Agency. The research focused on the physiological assessment of astronauts and soldiers, human performance evaluation, and human-computer interaction. Cybernet Medical's MedStar Disease Management Data Collection System is an affordable, widely deployable solution for improving in-home-patient chronic disease management. The system's battery-powered and portable interface device collects physiological data from off-the-shelf instruments.

Source record↗
NASA NTRS2022

Risk of Performance and Behavioral Health Decrements Due to Inadequate Cooperation, Coordination, and Psychosocial Adaptions within a Team

The Risk of Performance and Behavioral Health Decrements Due to Inadequate Cooperation, Coordination, Communication, and Psychosocial Adaptation within a Team (the Team Risk) is primarily performance-focused, with a secondary emphasis on behavioral health outcomes resulting from team performance and interpersonal interactions. Monitoring tools, measures, and countermeasures are aimed at enhancing team processes and team composition configurations to optimize team performance and functioning. Long-duration exploration missions (LDEMs) will include major challenges that could affect team performance, including social isolation, physical confinement, a small and diverse crew, communication delays between crew and ground, limited or no crew rotation or evacuation options, limited or no resupply, and a high-consequence environment. Each of these conditions will affect the crew’s coordination, cooperation, psychological well-being, and performance. Although the International Space Station (ISS) remains important for studies that require spaceflight testing and validation, the current conditions on the ISS do not adequately mimic the exploration environment that is required for National Aeronautics and Space Administration (NASA) teams research, and thus access to terrestrial or ground-based analogs of LDEM conditions is paramount. The emphasis on analogs for research is reflected in this updated evidence review of the Team Risk, and includes data from studies conducted at isolated, confined, extreme (ICE) environments (e.g., Antarctic stations), and from several mission simulation analogs such as the Human Exploration Research Analog (HERA) (HERA Experiment Information Package, 2014), also known as isolated, confined, controlled (ICC) environments. These studies have characterized many team factors regarding LDEMs, and the Team Risk has now matured from risk characterization to focusing more on countermeasure development. Because spaceflight evidence for team-level research is lacking, no reliable data is available to quantify the impact of team-level variables on individual and team-level outcomes during spaceflight missions. Until recently, no systematic attempt had been undertaken to measure the performance effects of team cohesion, team composition, team training, or team-related psychosocial adaptation during spaceflight. The Team Risk is a relatively young research area for NASA, with substantial growth only since the 2000s, and with limited access to spaceflight performance data. As a result, spaceflight evidence is lacking to identify specifically what team composition, level of training, amount of cohesion, or quality of psychosocial adaptation is necessary to reduce the risk of performance errors in space. However, astronaut journals and interviews and reports from spaceflight subject matter experts (SMEs) provide testimonies that team performance during spaceflight is important for mission success and to maintain crew health. Team spaceflight data is now being collected as part of the Spaceflight Standard Measures task (Clement, 2021)—a set of core measurements related to many human spaceflight risks that are collected from astronauts before, during, and after long-duration missions. The team-related standard measures focus on team cohesion, team performance, group living, team climate, and team processes. Collection of standard measures data is ongoing and published data is not yet available. Finally, although spaceflight evidence is lacking, evidence gleaned from ground studies and spaceflight analog studies will help close the gaps outlined in the Team Risk. Ground-based studies provide quantitative evidence for team functioning in ICE environments. Academic research on teams has produced dozens of meta-analyses that can be used to understand the general relationships among team inputs (e.g., team member characteristics and skills, job context), team processes, and emergent states (e.g., coordination, communication, cooperation, cohesion, trust, shared cognition), and team outcomes (e.g., effectiveness, errors, adaptation). Teams are complex, incorporating individual characteristics of team members, but also existing at a level that is greater than the sum of its parts. Therefore, the Team Risk must be integrated with other individual-focused NASA Human Research Program (HRP) risks, including Behavioral Medicine (BMed), Sleep, and Human-Systems Integration Architecture (HSIA), and emerging research indicates more integration may needed between the Team Risk and the physiologically oriented risks. Much of this integration occurs through the Human Systems Risk Board (HSRB). A lack of team functioning may be a stressor in some circumstances, but the team often acts as a countermeasure. For example, support for team leaders and teammates can facilitate individual functioning and encourage psychological and physically healthy behaviors and attitudes. However, more research is needed regarding teams during LDEMs and the remaining gaps in the research are described in the current report.

Lauren Blackwell Landon↗
NASA NTRS1990

A system performance throughput model applicable to advanced manned telescience systems

As automated space systems become more complex, autonomous, and opaque to the flight crew, it becomes increasingly difficult to determine whether the total system is performing as it should. Some of the complex and interrelated human performance measurement issues are addressed that are related to total system validation. An evaluative throughput model is presented which can be used to generate a human operator-related benchmark or figure of merit for a given system which involves humans at the input and output ends as well as other automated intelligent agents. The concept of sustained and accurate command/control data information transfer is introduced. The first two input parameters of the model involve nominal and off-nominal predicted events. The first of these calls for a detailed task analysis while the second is for a contingency event assessment. The last two required input parameters involving actual (measured) events, namely human performance and continuous semi-automated system performance. An expression combining these four parameters was found using digital simulations and identical, representative, random data to yield the smallest variance.

Haines, Richard F.↗
DOE OSTI2024

ChatGPT and Other Large Language Models for Cybersecurity of Smart Grid Applications

Cybersecurity breaches targeting electrical substations constitute a significant threat to the integrity of the power grid, necessitating comprehensive defense and mitigation strategies. Any anomaly in information and communication technology (ICT) should be detected for secure communications between devices in digital substations. This paper proposes large language models (LLMs), e.g., ChatGPT, for the cybersecurity of IEC 61850-based communications. Multi-cast messages such as generic object oriented system events (GOOSE) and sampled values (SV) are used for case studies. The proposed LLM-based cybersecurity framework includes, for the first time, data pre-processing of communication systems and human-in-the-loop (HITL) training (considering the cybersecurity guidelines recommended by humans). The results show a comparative analysis of detected anomaly data carried out based on the performance evaluation metrics for different LLMs. A hardware-in-the-loop (HIL) testbed is used to generate and extract a dataset of IEC 61850 communications.

ChatGPT↗
NASA NTRS1992

Development of a test protocol for evaluating EVA glove performance

Testing gloved hand performance involves work from several disciplines. Evaluations performed in the course of reenabling a disabled hand, designing a robotic end effector or master controller, or hard-suit design have all yielded relevant information, and, in most cases, produced performance test methods. Most times, these test methods have been primarily oriented toward their parent discipline. For space operations, a comparative test which would provide a way to quantify pressure glove and end effector performance would be useful in dividing tasks between humans and robots. Such a test would have to rely heavily on sensored measurement, as opposed to questionnaires, to produce relevant data. However, at some point human preference would have to be taken into account. This paper presents a methodology for evaluating gloved hand performance which attempts to respond to these issues. Glove testing of a prototype glove design using this method is described.

Hinman, Elaine M.↗
NASA NTRS1999

Measuring Astronaut Performance in Microgravity: Loads and Modeling

Quantitative analysis of human performance in microgravity is important for both scientific investigations and spacecraft engineering design. By collecting and evaluating the kinematics and kinetics data of astronauts in space, it becomes possible to characterize human motor strategies, postural behavior in weightlessness, improve the design of orbital modules, help maintain a quiescent microgravity for acceleration-sensitive material science and life science experiments (NASA JSC, 1996), and optimize the human operative capabilities during long-duration space missions. Hence, there is a need for a precise measurement of the forces and moments exerted by the astronauts on the space station and their postures and movements.

Newman, D.↗
DOE OSTI2014

SU-E-I-46: Sample-Size Dependence of Model Observers for Estimating Low-Contrast Detection Performance From CT Images

Purpose: Recently, task-based assessment of diagnostic CT systems has attracted much attention. Detection task performance can be estimated using human observers, or mathematical observer models. While most models are well established, considerable bias can be introduced when performance is estimated from a limited number of image samples. Thus, the purpose of this work was to assess the effect of sample size on bias and uncertainty of two channelized Hotelling observers and a template-matching observer. Methods: The image data used for this study consisted of 100 signal-present and 100 signal-absent regions-of-interest, which were extracted from CT slices. The experimental conditions included two signal sizes and five different x-ray beam current settings (mAs). Human observer performance for these images was determined in 2-alternative forced choice experiments. These data were provided by the Mayo clinic in Rochester, MN. Detection performance was estimated from three observer models, including channelized Hotelling observers (CHO) with Gabor or Laguerre-Gauss (LG) channels, and a template-matching observer (TM). Different sample sizes were generated by randomly selecting a subset of image pairs, (N=20,40,60,80). Observer performance was quantified as proportion of correct responses (PC). Bias was quantified as the relative difference of PC for 20 and 80 image pairs. Results: For n=100, all observer models predicted human performance across mAs and signal sizes. Bias was 23% for CHO (Gabor), 7% for CHO (LG), and 3% for TM. The relative standard deviation, σ(PC)/PC at N=20 was highest for the TM observer (11%) and lowest for the CHO (Gabor) observer (5%). Conclusion: In order to make image quality assessment feasible in the clinical practice, a statistically efficient observer model, that can predict performance from few samples, is needed. Our results identified two observer models that may be suited for this task.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Bioinformatics Resource Manager: A systems biology web tool for microRNA and omics data integration

Background: The Bioinformatics Resource Manager (BRM) is a web-based tool developed to facilitate identifier conversion and data integration for Homo sapiens (human), Mus musculus (mouse), Rattus norvegicus (rat), Danio rerio (zebrafish), and Macaca mulatta (macaque), as well as perform orthologous conversions among the supported species. In addition to providing a robust means of identifier conversion, BRM also incorporates a suite of miRNA-target databases upon which to query target genes or to perform reverse target lookups using gene identifiers. Results: BRM has the capability to perform cross-species identifier lookups across common identifier types, directly integrate datasets across platform or species by performing identifier retrievals in the background, and retrieve miRNA targets from multiple databases simultaneously and integrate the resulting gene targets with experimental mRNA data. Here we use workflows provided in BRM to integrate RNAseq data across species to identify common biomarkers of exposure after treatment of human lung cells and zebrafish to benzo[a]pyrene (BAP). We further use the miRNA Target workflow to experimentally determine the role of miRNAs as regulators of BAP toxicity and identify the predicted functional consequences of miRNA-target regulation in our system. The output from BRM can easily and directly be uploaded to freely available visualization tools for further analysis. From these examples, we were able to identify an important role for several miRNAs as potential regulators of BAP toxicity in human lung cells associated with cell migration, cell communication, cell junction assembly and regulation of cell death. Conclusions: Overall, BRM provides bioinformatics tools to assist biologists having minimal programming skills with analysis and integration of high-content omics’ data from various transcriptomic and proteomic platforms. BRM workflows were developed in Java and other open-source technologies and are served publicly using Apache Tomcat at https://cbb.pnnl.gov/brm/.

bioinformatics, MicroRNA, systems biology, Genomic↗
DOE OSTI2015

Robot for Investigations and Assessments of Nuclear Areas

RIANA is a remote controlled Robot dedicated for Investigations and Assessments of Nuclear Areas. The development of RIANA is motivated by the need to have at disposal a proven robot, tested in hot cells; a robot capable of remotely investigate and characterise the inside of nuclear facilities in order to collect efficiently all the required data in the shortest possible time. It is based on a wireless medium sized remote carrier that may carry a wide variety of interchangeable modules, sensors and tools. It is easily customised to match specific requirements and quickly configured depending on the mission and the operator's preferences. RIANA integrates localisation and navigation systems. The robot will be able to generate / update a 2D map of its surrounding and exploring areas. The position of the robot is given accurately on the map. Furthermore, the robot will be able to autonomously calculate, define and follow a trajectory between 2 points taking into account its environment and obstacles. The robot is configurable to manage obstacles and restrict access to forbidden areas. RIANA allows an advanced control of modules, sensors and tools; all collected data (radiological and measured data) are displayed in real time in different format (chart, on the generated map...) and stored in a single place so that may be exported in a convenient format for data processing. This modular design gives RIANA the flexibility to perform multiple investigation missions where humans cannot work such as: visual inspections, dynamic localization and 2D mapping, characterizations and nuclear measurements of floor and walls, non destructive testing, samples collection: solid and liquid. The benefits of using RIANA are: - reducing the personnel exposures by limiting the manual intervention time, - minimizing the time and reducing the cost of investigation operations, - providing critical inputs to set up and optimize cleanup and dismantling operations. (authors)

42 ENGINEERING↗
NASA NTRS2006

Recent trends in digital human modeling and the concurrent issues that face human modeling approach

Tremendous strides have been made in the recent years to digitally represent human beings in computer simulation models ranging from assembly plant maintenance operations to occupants getting in and out of vehicles to action movie scenarios. While some of these tools are being actively pursued by the engineering communities, there is still a lot of work that remains to be done for the newly planned planetary exploration missions. For example, certain unique and several common challenges are seen in developing computer generated suited human models for designing the next generation space vehicle. The purpose of this presentation is to discuss NASA s potential needs for better human models and to show also many of the inherent yet not too obvious pitfalls that still are left unresolved in this new arena of digital human modeling. As part of NASA s Habitability and Human Factors Branch, the Anthropometry and Biomechanics Facility has been engaged in studying the various facets of computer generated human physical performance models; for instance, it has been engaged in utilizing three-dimensional laser scan data along with three dimensional video based motion and reach data to gather suited anthropometric and shape and size information that are not available yet in the form of computer mannequins. Our goal is to bring in new approaches to deal with heavily clothed humans (such as, suited astronauts) and to overcome the current limitations of wrongly identifying humans (either real or virtual) as univariate percentiles. We are looking at whole-body posture based anthropometric models as a means to identify humans of significantly different shapes and sizes to arrive at mathematically sound computer models for analytical purposes.

Rajulu, Sudhakar↗
NASA NTRS2011

Spacesuit and Space Vehicle Comparative Ergonomic Evaluation

With the advent of the latest manned spaceflight objectives, a series of prototype launch and reentry spacesuit architectures were evaluated for eventual down selection by NASA based on the performance of a set of designated tasks. A consolidated approach was taken to testing, concurrently collecting suit mobility data, seat-suit-vehicle interface clearances and movement strategies within the volume of a Multi-Purpose Crew Vehicle mockup. To achieve the objectives of the test, a requirement was set forth to maintain high mockup fidelity while using advanced motion capture technologies. These seemingly mutually exclusive goals were accommodated with the construction of an optically transparent and fully adjustable frame mockup. The mockup was constructed such that it could be dimensionally validated rapidly with the motion capture system. This paper will describe the method used to create a motion capture compatible space vehicle mockup, the consolidated approach for evaluating spacesuits in action, as well as the various methods for generating hardware requirements for an entire population from the resulting complex data set using a limited number of test subjects. Kinematics, hardware clearance, suited anthropometry, and subjective feedback data were recorded on fifteen unsuited and five suited subjects. Unsuited subjects were selected chiefly by anthropometry, in an attempt to find subjects who fell within predefined criteria for medium male, large male and small female subjects. The suited subjects were selected as a subset of the unsuited subjects and tested in both unpressurized and pressurized conditions. Since the prototype spacesuits were fabricated in a single size to accommodate an approximately average sized male, the findings from the suit testing were systematically extrapolated to the extremes of the population to anticipate likely problem areas. This extrapolation was achieved by first performing population analysis through a comparison of suited subjects performance to their unsuited performance and then applying the results to the entire range of population. The use of a transparent space vehicle mockup enabled the collection of large amounts of data during human-in-the-loop testing. Mobility data revealed that most of the tested spacesuits had sufficient ranges of motion for tasks to be performed successfully. A failed tasked by a suited subject most often stemmed from a combination of poor field of view while seated and poor dexterity of the gloves when pressurized or from suit/vehicle interface issues. Seat ingress/egress testing showed that problems with anthropometric accommodation does not exclusively occur with the largest or smallest subjects, but rather specific combinations of measurements that lead to narrower seat ingress/egress clearance.

England, Scott↗
DOE OSTI2025

Improving Cyber Situational Understanding

Effective cybersecurity operations require the ability to analyze large amounts of information to assess security risks and formulate defensive strategies against adversaries. This has become more complex in recent years as the sprawl and interconnectivity of devices grows through implementation of virtualization, cloud computing, and Internet of Things (IoT). The amount of data and analysis required for effective cybersecurity command and control decisions far exceeds humans’ capacity to perform manually. We characterize the analysis problem as cyber situational understanding. The research presented to improve cyber situational understanding focuses on vulnerability analysis and threat intelligence. Regarding vulnerabilities, entities must analyze and plan work for between thousands and tens of thousands of software vulnerabilities annually. Entities heavily use network firewalls to limit vulnerability exposure. As a result, some of these vulnerabilities permit exposure to adversarial exploitation, whereas others are inaccessible and therefore present negligible risk of exploitation. Distinguishing between high and low risk software vulnerabilities requires a deep understanding of the vulnerability, network firewall protection, and characteristics of the targeted device. This problem is solved by extracting network service features from vulnerability data features using both machine-learning and natural language processing. Then, the network firewall topology is parsed to determine which vulnerabilities are reachable by adversaries. Ultimately, a state-based safety analysis ascertains which vulnerabilities are unsafe. A related vulnerability analysis problem occurs in cybersecurity operations when associating an entity’s hardware and software assets to public vulnerability databases. Assets often reveal hardware and software through installation artifacts and network service identification, and entities store these artifacts in inventory databases. However, software and hardware vendors apply a standard Common Platform Enumeration (CPE) naming convention when publicly reporting vulnerabilities. Associating these two datasets often requires many hours to days of manual inspection. The proposed solution automates the mapping approach of human analysts using fuzzy matching techniques, natural language processing, and, ultimately, machine learning to present a small set of recommendations for mapping the two datasets. The result significantly reduces human analysis time and reduces the occurrence of false positives in vulnerability notifications. Finally, cyber threat intelligence (CTI) requires associating cyber observable artifacts, such as IP addresses, URIs, and file hashes, with cyber threat tactics, techniques, and procedures. Unfortunately, most CTI data is compartmentalized across multiple organizations and cannot be shared due to the legal and reputational risk with cyber threat being associated with the entity. The approach to solving this problem inovlves using a distributed ledger with anonymous token spending and authentication. This allows a consortium of semi-trusted entities to share the workload of curating CTI for a threat sharing community’s cooperative benefit.

Huff, Philip↗
NASA NTRS2003

Associations between errors and contributing factors in aircraft maintenance

In recent years cognitive error models have provided insights into the unsafe acts that lead to many accidents in safety-critical environments. Most models of accident causation are based on the notion that human errors occur in the context of contributing factors. However, there is a lack of published information on possible links between specific errors and contributing factors. A total of 619 safety occurrences involving aircraft maintenance were reported using a self-completed questionnaire. Of these occurrences, 96% were related to the actions of maintenance personnel. The types of errors that were involved, and the contributing factors associated with those actions, were determined. Each type of error was associated with a particular set of contributing factors and with specific occurrence outcomes. Among the associations were links between memory lapses and fatigue and between rule violations and time pressure. Potential applications of this research include assisting with the design of accident prevention strategies, the estimation of human error probabilities, and the monitoring of organizational safety performance.

Professional Competence↗