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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 1,513 records · Page 84
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Effective Team Support: From Task and Cognitive Modeling to Software Agents for Time-Critical Complex Work Environments

The purpose of this research contract was to perform multidisciplinary research between CMU psychologists, computer scientists and NASA researchers to design a next generation collaborative system to support a team of human experts and intelligent agents. To achieve robust performance enhancement of such a system, we had proposed to perform task and cognitive modeling to thoroughly understand the impact technology makes on the organization and on key individual personnel. Guided by cognitively-inspired requirements, we would then develop software agents that support the human team in decision making, information filtering, information distribution and integration to enhance team situational awareness. During the period covered by this final report, we made substantial progress in completing a system for empirical data collection, cognitive modeling, and the building of software agents to support a team's tasks, and in running experiments for the collection of baseline data.

Remington, Roger W.↗
NASA NTRS2008

Proving Correctness for Pointer Programs in a Verifying Compiler

This research describes a component-based approach to proving the correctness of programs involving pointer behavior. The approach supports modular reasoning and is designed to be used within the larger context of a verifying compiler. The approach consists of two parts. When a system component requires the direct manipulation of pointer operations in its implementation, we implement it using a built-in component specifically designed to capture the functional and performance behavior of pointers. When a system component requires pointer behavior via a linked data structure, we ensure that the complexities of the pointer operations are encapsulated within the data structure and are hidden to the client component. In this way, programs that rely on pointers can be verified modularly, without requiring special rules for pointers. The ultimate objective of a verifying compiler is to prove-with as little human intervention as possible-that proposed program code is correct with respect to a full behavioral specification. Full verification for software is especially important for an agency like NASA that is routinely involved in the development of mission critical systems.

Kulczycki, Gregory↗
NASA NTRS2008

HDF-EOS 5 Validator

A computer program partly automates the task of determining whether an HDF-EOS 5 file is valid in that it conforms to specifications for such characteristics as attribute names, dimensionality of data products, and ranges of legal data values. ["HDF-EOS" and variants thereof are defined in "Converting EOS Data From HDF-EOS to netCDF" (GSC-15007-1), which is the first of several preceding articles in this issue of NASA Tech Briefs.] Previously, validity of a file was determined in a tedious and error-prone process in which a person examined human-readable dumps of data-file-format information. The present software helps a user to encode the specifications for an HDFEOS 5 file, and then inspects the file for conformity with the specifications: First, the user writes the specifications in Extensible Markup Language (XML) by use of a document type definition (DTD) that is part of the program. Next, the portion of the program (denoted the validator) that performs the inspection is executed, using, as inputs, the specifications in XML and the HDF-EOS 5 file to be validated. Finally, the user examines the output of the validator.

Ullman, Richard↗
NASA NTRS2019

Exploring Beyond Earth's Atmosphere with Human-Machine Teams

NASA's highly successful Kepler Mission has revolutionized our understanding of the Galaxy. We now know that planets, even Earth-size planets in the habitable zone, are common. With the end of the Kepler Mission we now look to the future with the Transiting Exoplanet Survey Satellite (TESS) which will discover thousands of exoplanets in orbit around the brightest stars in the sky. In a two-year survey, TESS will perform an all-sky search of more than 200,000 stars for temporary drops in brightness caused by planetary transits. With Kepler and TESS, humanity is finally at the verge of studying the masses, sizes, densities, orbits, and atmospheres of a large cohort of small planets, including a sample of rocky worlds in the habitable zones of their host stars which may prove to host life. The massive data sets generated by Kepler and TESS must be meticulously combed for the weakest planetary signals every month. While a daunting and error-prone task for humans, this is an exciting opportunity for the breakthroughs recently seen in machine learning. Specifically, traditional methods for identifying planet transits require extensive data processing pipelines followed by extensive human vetting. This manual process risks loss of information due to the data processing and to inconsistency and biases due to individual human vetters. The latest advancements in machine learning will allow an objective classifier to minimize the losses of information and greatly lessen the burden on the human vetters, in addition to providing assessment of quality and score to each planet candidate, freeing the humans to concentrate on border cases and other more interesting investigations.

Smith, Jeffrey C.↗
DOE OSTI2020

A Visual Analytics Framework for Reviewing Streaming Performance Data

Understanding and tuning the performance of extreme-scale parallel computing systems demands a streaming approach due to the computational cost of applying offline algorithms to vast amounts of performance log data. Analyzing large streaming data is challenging because the rate of receiving data and limited time to comprehend data make it difficult for the analysts to sufficiently examine the data without missing important changes or patterns. To support streaming data analysis, we introduce a visual analytic framework comprising of three modules: data management, analysis, and interactive visualization. The data management module collects various computing and communication performance metrics from the monitored system using streaming data processing techniques and feeds the data to the other two modules. The analysis module automatically identifies important changes and patterns at the required latency. In particular, we introduce a set of online and progressive analysis methods for not only controlling the computational costs but also helping analysts better follow the critical aspects of the analysis results. Finally, the interactive visualization module provides the analysts with a coherent view of the changes and patterns in the continuously captured performance data. Through a multi-faceted case study on performance analysis of parallel discrete-event simulation, we demonstrate the effectiveness of our framework for identifying bottlenecks and locating outliers.

Human-centered computing↗
DOE OSTI2018

Department of Energy Office of Legacy Management Progress on Defense-Related Uranium Mines Program - 18119

The Defense-Related Uranium Mines (DRUM) Program provides the structure and basis for the US Department of Energy Office of Legacy Management (LM) to manage the verification and validation (V and V) of defense-related uranium mines (mines) that provided ore to the Atomic Energy Commission from 1947 to 1970. V and V activities are conducted to fully understand the scope of potential problems posed by these mines by determining their location, reclamation or remediation status, and potential impacts to public safety, human health, and the environment. Land management agencies have limited resources and have generally not conducted comprehensive inventories of abandoned uranium mines, thus adding to the need to undertake this program. LM has entered into memoranda of understanding (MOUs) with the US Bureau of Land Management (BLM) and US Forest Service (USFS) to facilitate a better understanding of the scope of the problem posed by these mines. Currently, LM has executed MOUs with the BLM state offices in Colorado, New Mexico, Utah, and Wyoming and the USFS Rocky Mountain Region, which includes the national forests in Colorado, Wyoming, and South Dakota. This collaborative effort with partner agencies has led LM to develop a V and V Work Plan that describes the numerous activities and types of data to be collected at the mines. V and V activities verify actual mine location, collect coordinates of mining-related features, collect radiological data and soil samples, identify potential physical safety hazards, prioritize mines using a risk-based screening approach, and report the information to partner agencies. The partner agencies may use the reports to assess priorities and determine if any further action is warranted. The DRUM Program goals are to: - Improve data quality and content in the LM national inventory of abandoned uranium mines; - Conduct site-specific reconnaissance at mines for data verification and validation; - Exchange mine information with other federal agencies and state governments to help address mines presenting the greatest risks; - Develop reports for each mine that will contain a risk scoring assessment to help each agency develop priorities. The preliminary risk screening methodology was based on a joint effort between LM and BLM to develop factors that would be based on field observations, field data, and laboratory data using a multiple-lines-of-evidence approach. The multiple-lines-of-evidence assessment allows land managers the opportunity and flexibility to query specific criteria and make rankings based on specific priorities. The risk screening for each mine is based on evaluating the primary hazards of physical safety and risks to human health and several modifying factors that include accessibility and ecological/environmental risks. Gamma radiation scans are performed across the disturbed area of the mine, and soil samples are taken from waste rock piles and drainages, when applicable. This paper presents the results of performing inventory, environmental sampling, and risk ranking along with other accomplishments and lessons learned during the initial year of V and V activities and reporting. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2016

DebriSat Fragment Characterization System and Processing Status

The DebriSat project is a continuing effort sponsored by NASA and DoD to update existing break-up models using data obtained from hypervelocity impact tests performed to simulate on-orbit collisions. After the impact tests, a team at the University of Florida has been working to characterize the fragments in terms of their mass, size, shape, color and material content. The focus of the post-impact effort has been the collection of 2 mm and larger fragments resulting from the hypervelocity impact test. To date, in excess of 125K fragments have been recovered which is approximately 40K more than the 85K fragments predicted by the existing models. While the fragment collection activities continue, there has been a transition to the characterization of the recovered fragments. Since the start of the characterization effort, the focus has been on the use of automation to (i) expedite the fragment characterization process and (ii) minimize the effects of human subjectivity on the results; e.g., automated data entry processes were developed and implemented to minimize errors during transcription of the measurement data. At all steps of the process, however, there is human oversight to ensure the integrity of the data. Additionally, repeatability and reproducibility tests have been developed and implemented to ensure that the instrumentations used in the characterization process are accurate and properly calibrated.

Rivero, M.↗
DOE OSTI2019

Identification of candidate diagnostic and prognostic biomarkers for human prostate cancer: RPL22L1 and RPS21

Prostate cancer (PCa) is one of the most common malignancies in men worldwide. This study was designed to investigate the potential of Ribosomal Protein L22-like1 (RPL22L1) and Ribosomal Protein S21 (RPS21) as diagnostic and prognostic biomarkers for PCa. First, RPL22L1 and RPS21 were screened as the key molecular of PCa by bioinformatics analysis. Subsequently, the prostate tissue samples were stained for antibodies against RPL22L1 and RPS21. The unbiased signal quantification was performed by ImageJ software, and the results showed that the expression of RPL22L1 and RPS21 exhibited significant differences between the PCa tissues and the normal prostate tissues. Receiver-operating characteristics (ROC) curves were prepared, and then the area under the curve (AUC) values of RPL22L1 and RPS21 were calculated as 0.798 and 0.768, and the likelihood ratio (LR) values of RPL22L1 and RPS21 were calculated as 2.86 and 2.53. These data implied that the over-expression of RPL22L1 and RPS21 is associated with the presence of PCa. The further analysis suggested that the expression of RPL22L1 and RPS21 were significantly higher in high Gleason grade than they were in low Gleason grade. In addition, in vitro studies were undertaken to evaluate the roles of RPL22L1 and RPS21 in PCa. The results revealed that these genes promote PCa cell proliferation, migration and invasion, and inhibit PCa cell apoptosis. Taken together, these data showed that RPL22L1 and RPS21 exhibited higher expression in human prostate cancer tissue, and involved in PCa cell proliferation and invasion. This research provided a novel insight into diagnostic and prognostic biomarkers for PCa.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Interpreting machine learning prediction of fire emissions and comparison with FireMIP process-based models

Annual burned areas in the United States have increased 2-fold during the past decades. With more large fires resulting in more emissions of fine particulate matter, an accurate prediction of fire emissions is critical for quantifying the impacts of fires on air quality, human health, and climate. This study aims to construct a machine learning (ML) model with game-theory interpretation to predict monthly fire emissions over the contiguous US (CONUS) and to understand the controlling factors of fire emissions. The optimized ML model is used to diagnose the process-based models in the Fire Modeling Intercomparison Project (FireMIP) to inform future development. Results show promising performance for the ML model, Community Land Model (CLM), and Joint UK Land Environment Simulator-Interactive Fire And Emission Algorithm For Natural Environments (JULES-INFERNO) in reproducing the spatial distributions, seasonality, and interannual variability of fire emissions over the CONUS. Regional analysis shows that only the ML model and CLM simulate the realistic interannual variability of fire emissions for most of the subregions (r >0.95 for ML and r =0.14~0.70 for CLM), except for Mediterranean California, where all the models perform poorly (r =0.74 for ML and r <0.30 for the FireMIP models). Regarding seasonality, most models capture the peak emission in July over the western US. However, all models except for the ML model fail to reproduce the bimodal peaks in July and October over Mediterranean California, which may be explained by the smaller wind speeds of the atmospheric forcing data during Santa Ana wind events and limitations in model parameterizations for capturing the effects of Santa Ana winds on fire activity. Furthermore, most models struggle to capture the spring peak in emissions in the southeastern US, probably due to underrepresentation of human effects and the influences of winter dryness on fires in the models. As for extreme events, both the ML model and CLM successfully reproduce the frequency map of extreme emission occurrence but overestimate the number of months with extremely large fire emissions. Comparing the fire PM 2.5 emissions from the ML model with process-based fire models highlights their strengths and uncertainties for regional analysis and prediction and provides useful insights into future directions for model improvements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

An optimized approach and inflation media for obtaining complimentary mass spectrometry-based omics data from human lung tissue

Human disease states are biomolecularly multifaceted and can span across phenotypic states, therefore it is important to understand diseases on all levels, across cell types, and within and across microanatomical tissue compartments. To obtain an accurate and representative view of the molecular landscape within human lungs, this fragile tissue must be inflated and embedded to maintain spatial fidelity of the location of molecules and minimize molecular degradation for molecular imaging experiments. Here, we evaluated agarose inflation and carboxymethyl cellulose embedding media and determined effective tissue preparation protocols for performing bulk and spatial mass spectrometry-based omics measurements. Mass spectrometry imaging methods were optimized to boost the number of annotatable molecules in agarose inflated lung samples. This optimized protocol permitted the observation of unique lipid distributions within several airway regions in the lung tissue block. Laser capture microdissection of these airway regions followed by high-resolution proteomic analysis allowed us to begin linking the lipidome with the proteome in a spatially resolved manner, where we observed proteins with high abundance specifically localized to the airway regions. We also compared our mass spectrometry results to lung tissue samples preserved using two other inflation/embedding media, but we identified several pitfalls with the sample preparation steps using this preservation method. Overall, we demonstrated the versatility of the inflation method, and we can start to reveal how the metabolome, lipidome, and proteome are connected spatially in human lungs and across disease states through a variety of different experiments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Targeting Phosphatidylinositol 4-Kinase IIIα for Radiosensitization: A Potential Model of Drug Repositioning Using an Anti-Hepatitis C Viral Agent

Purpose: To investigate which isotype of phosphatidylinositol 4-kinase (PI4K) may affect radiosensitivity and examine whether anti–hepatitis C viral (HCV) agents, some of which have been shown to inhibit PI4K IIIα activity, could be repositioned as a radiosensitizer in human cancer cells. Methods and Materials: U251, BT474, and HepG2 cell lines and normal human astrocyte were used. Ribonucleic acid interference, clonogenic assays, Western blotting, immunofluorescence, annexin V assay, lysotracker staining, and β-galactosidase assay were performed. Results: Of the 4 PI4K isotypes, specific inhibition of IIIα increased radiosensitivity. For pharmacologic inhibition of PI4K IIIα, we screened 9 anti-HCV agents by half-maximal inhibitory concentration assay. Simeprevir was selected, and its inhibition of PI4K IIIα activity was confirmed. Combination of simeprevir treatment and radiation significantly attenuated expression of phospho-phospho-PKC and phospho-Akt and increased radiation-induced cell death in tested cell lines. Pretreatment with simeprevir prolonged γH2AX foci formation and down-regulation of phospho-DNA-PKcs, indicating impairment of nonhomologous end-joining repair. Cells pretreated with simeprevir exhibited mixed modes of cell death, including apoptosis and autophagy. Conclusion: These data demonstrate that targeting PI4K IIIα using an anti-HCV agent is a viable approach to enhance the therapeutic efficacy of radiation therapy in various human cancers, such as glioma, breast, and hepatocellular carcinoma.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRSDate not supplied

Analyzing the Impact of Lunar Dust on Astronaut Health – an Update to the Risk Modeling

INTRODUCTION Lunar dust exposure is an unavoidable hazard during lunar surface operations. Given the large number of surface EVAs expected to be performed during Artemis missions, it is critical that the incidence and extent of symptoms resulting from contact with lunar dust be fully understood in order to accurately predict the resources needed to treat Artemis astronauts. The purpose of this study will be to gather the latest data regarding the risk of and severity of symptoms associated with lunar dust exposure and refine the estimates of the impact of lunar dust on astronaut health for PRA models, both those in operational use and those currently being developed. METHODS The latest data on the pathophysiologic effects of lunar dust on humans will be gathered by querying the peer-reviewed literature as well as NASA technical documents. This data will be supplemented with discussions with hardware designers regarding methods used to mitigate exposure to lunar dust during lunar surface operations. Taken together, this data will be used to refine both the estimates for exposure to lunar dust for astronauts inside and outside lunar habitats, as well as provide evidence to guide estimates for the percentage of astronauts who are exposed to lunar dust who will be symptomatic. RESULTS Querying the peer-reviewed literature and NASA technical documents will begin in October of 2024, with the goal of codifying this information by the end of October. In parallel, discussions will begin with hardware engineering teams, also in October. Using the evidence found will help refine the estimate for resources needed to treat astronauts who exhibit symptoms from lunar dust exposure. DISCUSSION Lunar dust exposure is expected to be a major risk for upcoming exploration missions that include a lunar surface interval. Until recently, most of the information to inform the risk associated with lunar dust exposure has come from anecdotal evidence from Apollo astronauts about their symptoms while on the lunar surface; however, this evidence is sparse. Newer studies are now coming out, looking at the impact of lunar dust on human physiology, including studies on effects to the immune system. Additionally, there are new engineering efforts to attempt to mitigate the exposure to lunar dust from the space suits of astronauts returning to the lunar habitat after surface operations. Thus, developing an updated risk assessment, based on our current understanding of human physiology and our current resources for risk mitigation, is prudent to best estimate overall human health and performance risk for future Artemis missions. This project will lead to an updated and more accurate assessment of the risk of lunar dust exposure for exploration missions and will therefore allow mission planners to more accurately predict resources needed for these exploration-class missions.

D M Nusbaum↗
DOE OSTI2022

Identifying Transient Candidates in the Dark Energy Survey Using Convolutional Neural Networks

The ability to discover new transient candidates via image differencing without direct human intervention is an important task in observational astronomy. For these kind of image classification problems, machine learning techniques such as Convolutional Neural Networks (CNNs) have shown remarkable success. In this work, we present the results of an automated transient candidate identification on images with CNNs for an extant data set from the Dark Energy Survey Supernova program, whose main focus was on using Type Ia supernovae for cosmology. By performing an architecture search of CNNs, we identify networks that efficiently select non-artifacts (e.g., supernovae, variable stars, AGN, etc.) from artifacts (image defects, mis-subtractions, etc.), achieving the efficiency of previous work performed with random Forests, without the need to expend any effort in feature identification. The CNNs also help us identify a subset of mislabeled images. Performing a relabeling of the images in this subset, the resulting classification with CNNs is significantly better than previous results, lowering the false positive rate by 27% at a fixed missed detection rate of 0.05.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Measuring semantic similarities by combining gene ontology annotations and gene co-function networks

Background: Gene Ontology (GO) has been used widely to study functional relationships between genes. The current semantic similarity measures rely only on GO annotations and GO structure. This limits the power of GO-based similarity because of the limited proportion of genes that are annotated to GO in most organisms. Results: We introduce a novel approach called NETSIM (network-based similarity measure) that incorporates information from gene co-function networks in addition to using the GO structure and annotations. Using metabolic reaction maps of yeast, Arabidopsis, and human, we demonstrate that NETSIM can improve the accuracy of GO term similarities. We also demonstrate that NETSIM works well even for genomes with sparser gene annotation data. We applied NETSIM on large Arabidopsis gene families such as cytochrome P450 monooxygenases to group the members functionally and show that this grouping could facilitate functional characterization of genes in these families. Conclusions: Using NETSIM as an example, we demonstrated that the performance of a semantic similarity measure could be significantly improved after incorporating genome-specific information. NETSIM incorporates both GO annotations and gene co-function network data as a priori knowledge in the model. Therefore, functional similarities of GO terms that are not explicitly encoded in GO but are relevant in a taxon-specific manner become measurable when GO annotations are limited.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1974

MIDAS, prototype Multivariate Interactive Digital Analysis System, phase 1. Volume 1: System description

The MIDAS System is described as a third-generation fast multispectral recognition system able to keep pace with the large quantity and high rates of data acquisition from present and projected sensors. A principal objective of the MIDAS program is to provide a system well interfaced with the human operator and thus to obtain large overall reductions in turnaround time and significant gains in throughput. The hardware and software are described. The system contains a mini-computer to control the various high-speed processing elements in the data path, and a classifier which implements an all-digital prototype multivariate-Gaussian maximum likelihood decision algorithm operating at 200,000 pixels/sec. Sufficient hardware was developed to perform signature extraction from computer-compatible tapes, compute classifier coefficients, control the classifier operation, and diagnose operation.

Kriegler, F. J.↗
NASA NTRS1981

Quiet short-haul research aircraft familiarization document, revision 1

The design features and general characteristics of the Quiet Short Haul Research Aircraft are described. Aerodynamic characteristics and performance are discussed based on predictions and early flight test data. Principle airplane systems, including the airborne data acquisition system, are also described. The aircraft was designed and built to fulfill the need for a national research facility to explore the use of upper surface blowing, propulsive lift technology in providing short takeoff and landing capability, and perform advanced experiments in various technical disciplines such as aerodynamics, propulsion, stability and control, handling qualities, avionics and flight control systems, trailing vortex phenomena, acoustics, structure and loads, operating systems, human factors, and airworthiness/certification criteria. An unusually austere approach using experimental shop practices resulted in a low cost and high research capability.

Eppel, J. C.↗
NASA NTRS2005

Formaldehyde Concentration Dynamics of the International Space Station Cabin Atmosphere

Formaldehyde presents a significant challenge to maintaining cabin air quality on board crewed spacecraft. Generation sources include offgassing from a variety of non-metallic materials as well as human metabolism. Because generation sources are pervasive and human health can be affected by continual exposure to low concentrations, toxicology and air quality control engineering experts jointly identified formaldehyde as a key compound to be monitored as part the International Space Station's (ISS) environmental health monitoring and maintenance program. Data acquired from in-flight air quality monitoring methods are the basis for assessing the cabin environment's suitability for long-term habitation and monitoring the performance of passive and active controls that are in place to minimize crew exposure. Formaldehyde concentration trends and dynamics served in the ISS cabin atmosphere are reviewed implications to present and future flight operations discussed.

Perry, J. L.↗
NASA NTRS2008

Caution and Warning Alarm Design and Evaluation for NASA CEV Auditory Displays: SHFE Information Presentation Directed Research Project (DRPP) report 12.07

The design of caution-warning signals for NASA s Crew Exploration Vehicle (CEV) and other future spacecraft will be based on both best practices based on current research and evaluation of current alarms. A design approach is presented based upon cross-disciplinary examination of psychoacoustic research, human factors experience, aerospace practices, and acoustical engineering requirements. A listening test with thirteen participants was performed involving ranking and grading of current and newly developed caution-warning stimuli under three conditions: (1) alarm levels adjusted for compliance with ISO 7731, "Danger signals for work places - Auditory Danger Signals", (2) alarm levels adjusted to an overall 15 dBA s/n ratio and (3) simulated codec low-pass filtering. Questionnaire data yielded useful insights regarding cognitive associations with the sounds.

Durand R Begault↗