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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Aerospace video imaging systems for rangeland management

This paper presents an overview on the application of airborne video imagery (VI) for assessment of rangeland resources. Multispectral black-and-white video with visible/NIR sensitivity; color-IR, normal color, and black-and-white MIR; and thermal IR video have been used to detect or distinguish among many rangeland and other natural resource variables such as heavy grazing, drought-stressed grass, phytomass levels, burned areas, soil salinity, plant communities and species, and gopher and ant mounds. The digitization and computer processing of VI have also been demonstrated. VI does not have the detailed resolution of film, but these results have shown that it has considerable potential as an applied remote sensing tool for rangeland management. In the future, spaceborne VI may provide additional data for monitoring and management of rangelands.

Everitt, J. H.↗
NASA NTRS1995

Making intelligent systems team players. A guide to developing intelligent monitoring systems

This reference guide for developers of intelligent monitoring systems is based on lessons learned by developers of the DEcision Support SYstem (DESSY), an expert system that monitors Space Shuttle telemetry data in real time. DESSY makes inferences about commands, state transitions, and simple failures. It performs failure detection rather than in-depth failure diagnostics. A listing of rules from DESSY and cue cards from DESSY subsystems are included to give the development community a better understanding of the selected model system. The G-2 programming tool used in developing DESSY provides an object-oriented, rule-based environment, but many of the principles in use here can be applied to any type of monitoring intelligent system. The step-by-step instructions and examples given for each stage of development are in G-2, but can be used with other development tools. This guide first defines the authors' concept of real-time monitoring systems, then tells prospective developers how to determine system requirements, how to build the system through a combined design/development process, and how to solve problems involved in working with real-time data. It explains the relationships among operational prototyping, software evolution, and the user interface. It also explains methods of testing, verification, and validation. It includes suggestions for preparing reference documentation and training users.

Land, Sherry A.↗
NASA NTRS2004

K-12 Students as Ground Observers of Contrails in Support of Scientific Research

Scientists are very interested in the formation of contrails, both the type and the coverage. To be detected by a satellite-born instrument, the contrail must be of a certain size, which means that some contrails go undetected. The K-12 education community is assisting with the study of contrails by participating in a network of student observers. To provide a venue for student contrail observations, the GLOBE Contrails protocol was developed as part of the GLOBE Atmospheric Science protocols. The first year of observations has provided a rich resource for researcher.

Chambers, Lin H.↗
NASA NTRS1999

Nondestructive Detection of Cracks in Ceramics Using Vicinal Illumination

Cracks and other defects in ceramic materials can be difficult or impossible to examine and photograph due to the extreme lack of contrast. A method for inspecting translucent ceramics using scattered light, also known as vicinal illumination, will be described. This method has been known in the ceramics industry for quite some time, but is not well known in the testing and failure analysis community. Electronics applications include substrates, packages, multilayer capacitors, and thin film resistors. Ceramic materials are used in electronic applications as microcircuit packages and substrates which carry signals and power between microcircuits. Fine cracks in ceramic materials can result in mechanical failures, electrical failures, and loss of hermeticity. Often, fine cracks are difficult or impossible to detect using standard nondestructive inspection techniques such as visual inspection, ultrasonic inspection, or vapor crack detection. Dye penetrant inspection is usually effective, but contaminates the part, which is unacceptable for space flight hardware. One effective nondestructive inspection method of detecting cracks involves examining the way in which light scatters through the ceramic material when viewed with a standard bright field reflected light microscope. This method, termed vicinal illumination, has been used for detecting cracks during failure analyses of several part types, and screening of space flight hardware. The technique has proven effective on several different types of ceramic materials as well. A related method for use with dark field equipment has also been used to successfully locate otherwise invisible cracks.

Hull, Scott M.↗
NASA NTRS2015

Application of Data Cubes for Improving Detection of Water Cycle Extreme Events

As part of an ongoing NASA-funded project to remove a longstanding barrier to accessing NASA data (i.e., accessing archived time-step array data as point-time series), for the hydrology and other point-time series-oriented communities, "data cubes" are created from which time series files (aka "data rods") are generated on-the-fly and made available as Web services from the Goddard Earth Sciences Data and Information Services Center (GES DISC). Data cubes are data as archived rearranged into spatio-temporal matrices, which allow for easy access to the data, both spatially and temporally. A data cube is a specific case of the general optimal strategy of reorganizing data to match the desired means of access. The gain from such reorganization is greater the larger the data set. As a use case of our project, we are leveraging existing software to explore the application of the data cubes concept to machine learning, for the purpose of detecting water cycle extreme events, a specific case of anomaly detection, requiring time series data. We investigate the use of support vector machines (SVM) for anomaly classification. We show an example of detection of water cycle extreme events, using data from the Tropical Rainfall Measuring Mission (TRMM).

water cycle extreme events↗
DOE OSTI2022

Geobacter sp. Strain IAE Dihaloeliminates 1,1,2-Trichloroethane and 1,2-Dichloroethane

We report that chlorinated ethanes, including 1,2-dichloroethane (1,2-DCA) and 1,1,2-trichloroethane (1,1,2-TCA), are widespread groundwater contaminants. Enrichment cultures XR DCA and XR TCA derived from river sediment dihaloeliminated 1,2-DCA to ethene and 1,1,2-TCA to vinyl chloride (VC), respectively. The XR TCA culture subsequently converted VC to ethene via hydrogenolysis. Microbial community profiling demonstrated the enrichment of Geobacter 16S rRNA gene sequences in both the XR DCA and XR TCA cultures, and Dehalococcoides mccartyi (Dhc) sequences were only detected in the ethene-producing XR TCA culture. The presence of a novel Geobacter population, designated as Geobacter sp. strain IAE, was identified by the 16S rRNA gene-targeted polymerase chain reaction and Sanger sequencing. Time-resolved population dynamics attributed the dihaloelimination activity to strain IAE, which attained the growth yields of 0.93 ± 0.06 x 10 7 and 1.18 ± 0.14 x 10 7 cells per μmol Cl -1 released with 1,2-DCA and 1,1,2-TCA as electron acceptors, respectively. In contrast, Dhc growth only occurred during VC-to-ethene hydrogenolysis. Our findings discover a Geobacter sp. strain capable of respiring multiple chlorinated ethanes and demonstrate the involvement of a broader diversity of organohalide-respiring bacteria in the detoxification of 1,2-DCA and 1,1,2-TCA.

1,1,2-trichloroethane↗
DOE OSTI2023

Seroprevalence of Brucella spp. and Rift Valley fever virus among slaughterhouse workers in Isiolo County, northern Kenya

Brucella spp. and Rift Valley fever virus (RVFV) are classified as priority zoonotic agents in Kenya, based on their public health and socioeconomic impact on the country. Data on the pathogen-specific and co-exposure levels is scarce due to limited active surveillance. This study investigated seroprevalence and co-exposure of Brucella spp. and RVFV and associated risk factors among slaughterhouse workers in Isiolo County, northern Kenya. A cross-sectional serosurvey was done in all 19 slaughterhouses in Isiolo County, enrolling 378 participants into the study. The overall seroprevalences for Brucella spp. and RVFV were 40.2% (95% CI: 35.2–45.4) and 18.3% (95% CI: 14.5–22.5), respectively while 10.3% (95% CI 7.4%-13.8%) of individuals were positive for antibodies against both Brucella spp. and RVFV. Virus neutralisation tests (VNT) confirmed anti-RVFV antibodies in 85% of ELISA-positive samples. Our seroprevalence results were comparable to community-level seroprevalences previously reported in the area. Since most of the study participants were not from livestock-keeping households, our findings attribute most of the detected infections to occupational exposure. The high exposure levels indicate slaughterhouse workers are the most at-risk population and there is need for infection, prevention, and control programs among this high-risk group. This is the first VNT confirmation of virus-neutralising antibodies among slaughterhouse workers in Isiolo County and corroborates reports of the area being a high-risk RVFV area as occasioned by previously reported outbreaks. This necessitates sensitization campaigns to enhance awareness of the risks involved and appropriate mitigation measures.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Quantum Devices for Neutrino and Rare Particle Detection (Final Report)

This report summarizes the technical work completed under the DOE-QuanISED program. This grant allowed the MIT group --and associated groups on the project, including UMass Amherst and Argonne National Laboratory -- to explore the use of superconducting bolometers for the purpose of neutrino and low mass dark matter detection. These low threshold bolometric detection techniques have wide applicability in nuclear and particle physics, including searching for low mass weakly-interacting dark matter; detecting neutrinos via coherent neutrino scattering; searching for axions and nuclear non-proliferation. In addition, we explored how using techniques readily used by the quantum computing community–such as multiplexed microwave resonators and quantum amplifiers—could be used as a low noise readout scheme for an array of superconducting detectors. We briefly summarized the accomplishments under the project, and what future research directions have spawned from this research.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Laser-induced graphene gas sensors for environmental monitoring

Artemesia tridentatais a foundational plant taxon in western North America and an important medicinal plant threatened by climate change. Low-cost fabrication of sensors is critical for developing large-area sensor networks for understanding and monitoring a range of environmental conditions. However, the availability of materials and manufacturing processes is still in the early stages, limiting the capacity to develop cost-effective sensors at a large scale. In this study, we demonstrate the fabrication of low-cost flexible sensors using laser-induced graphene (LIG); a graphitic material synthesized using a 450-nm wavelength bench top laser patterned onto polyimide substrates. We demonstrate the effect of the intensity and focus of the incident beam on the morphology and electrical properties of the synthesized material. Raman analyses of the synthesized LIG show a defect-rich graphene with a crystallite size in the tens of nanometers. This shows that the high level of disorder within the LIG structure, along with the porous nature of the material provide a good surface for gas adsorption. The initial characterization of the material has shown an analyte response represented by a change in resistance of up to 5% in the presence of volatile organic compounds (VOCs) that are emitted and detected byArtemisiaspecies. Bend testing up to 100 cycles provides evidence that these sensors will remain resilient when deployed across the landscapes to assess VOC signaling in plant communities. The versatile low-cost laser writing technique highlights the promise of low-cost and scalable fabrication of LIG sensors for gas sensor monitoring.

Chemistry↗
NASA NTRS1999

Mars Sample Handling and Requirements Panel (MSHARP)

In anticipation of the return of samples from Mars toward the end of the first decade of the next century, NASA's Office of Space Sciences chartered a panel to examine how Mars samples should be handled. The panel was to make recommendations in three areas: (1) sample collection and transport back to Earth; (2) certification of the samples as nonhazardous; and (3) sample receiving, curation, and distribution. This report summarizes the findings of that panel. The samples should be treated as hazardous until proven otherwise. They are to be sealed within a canister on Mars, and the canister is not to be opened until within a Biosafety Hazard Level 4 (BSL-4) containment facility here on Earth. This facility must also meet or exceed the cleanliness requirements of the Johnson Space Center (JSC) facility for curation of extraterrestrial materials. A containment facility meeting both these requirements does not yet exist. Hazard assessment and life detection experiments are to be done at the containment facility, while geochemical characterization is being performed on a sterilized subset of the samples released to the science community. When and if the samples are proven harmless, they are to be transferred to a curation facility, such as that at JSC.

Carr, Michael H.↗
DOE OSTI2021

Dissecting the dominant hot spring microbial populations based on community-wide sampling at single-cell genomic resolution

With advances in DNA sequencing and miniaturized molecular biology workflows, rapid and affordable sequencing of single-cell genomes has become a reality. Compared to 16S rRNA gene surveys and shotgun metagenomics, large-scale application of single-cell genomics to whole microbial communities provides an integrated snapshot of community composition and function, directly links mobile elements to their hosts, and enables analysis of population heterogeneity of the dominant community members. To that end, we sequenced nearly 500 single-cell genomes from a low diversity hot spring sediment sample from Dewar Creek, British Columbia, and compared this approach to 16S rRNA gene amplicon and shotgun metagenomics applied to the same sample. We found that the broad taxonomic profiles were similar across the three sequencing approaches, though several lineages were missing from the 16S rRNA gene amplicon dataset, likely the result of primer mismatches. At the functional level, we detected a large array of mobile genetic elements present in the single-cell genomes but absent from the corresponding same species metagenome-assembled genomes. Moreover, we performed a single-cell population genomic analysis of the three most abundant community members, revealing differences in population structure based on mutation and recombination profiles. While the average pairwise nucleotide identities were similar across the dominant species-level lineages, we observed differences in the extent of recombination between these dominant populations. Most intriguingly, the creek's Hydrogenobacter sp. population appeared to be so recombinogenic that it more closely resembled a sexual species than a clonally evolving microbe. Together, this work demonstrates that a randomized single-cell approach can be useful for the exploration of previously uncultivated microbes from community composition to population structure.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection

Protein-ligand docking is a computational method for identifying drug leads. The method is capable of narrowing a vast library of compounds down to a tractable size for downstream simulation or experimental testing and is widely used in drug discovery. While there has been progress in accelerating scoring of compounds with artificial intelligence, few works have bridged these successes back to the virtual screening community in terms of utility and forward-looking development. We demonstrate the power of high-speed ML models by scoring 1 billion molecules in under a day (50 k predictions per GPU seconds). We showcase a workflow for docking utilizing surrogate AI-based models as a pre-filter to a standard docking workflow. Our workflow is ten times faster at screening a library of compounds than the standard technique, with an error rate less than 0.01% of detecting the underlying best scoring 0.1% of compounds. Our analysis of the speedup explains that another order of magnitude speedup must come from model accuracy rather than computing speed. In order to drive another order of magnitude of acceleration, we share a benchmark dataset consisting of 200 million 3D complex structures and 2D structure scores across a consistent set of 13 million “in-stock” molecules over 15 receptors, or binding sites, across the SARS-CoV-2 proteome. We believe this is strong evidence for the community to begin focusing on improving the accuracy of surrogate models to improve the ability to screen massive compound libraries 100 × or even 1000 × faster than current techniques and reduce missing top hits. The technique outlined aims to be a fast drop-in replacement for docking for screening billion-scale molecular libraries.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2005

Automated Wildfire Detection Through Artificial Neural Networks

Wildfires have a profound impact upon the biosphere and our society in general. They cause loss of life, destruction of personal property and natural resources and alter the chemistry of the atmosphere. In response to the concern over the consequences of wildland fire and to support the fire management community, the National Oceanic and Atmospheric Administration (NOAA), National Environmental Satellite, Data and Information Service (NESDIS) located in Camp Springs, Maryland gradually developed an operational system to routinely monitor wildland fire by satellite observations. The Hazard Mapping System, as it is known today, allows a team of trained fire analysts to examine and integrate, on a daily basis, remote sensing data from Geostationary Operational Environmental Satellite (GOES), Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensors and generate a 24 hour fire product for the conterminous United States. Although assisted by automated fire detection algorithms, N O M has not been able to eliminate the human element from their fire detection procedures. As a consequence, the manually intensive effort has prevented NOAA from transitioning to a global fire product as urged particularly by climate modelers. NASA at Goddard Space Flight Center in Greenbelt, Maryland is helping N O M more fully automate the Hazard Mapping System by training neural networks to mimic the decision-making process of the frre analyst team as well as the automated algorithms.

Miller, Jerry↗
DOE OSTI2022

Capturing the microbial volatilome: an oft overlooked 'ome'

Among the diverse metabolites produced by microbial communities, some are volatile. Volatile organic compounds (VOCs) are vigorously cycled by microbes as metabolic substrates and products and as signaling molecules. Yet, current microbial metabolomic studies predominantly focus on nonvolatile metabolites and overlook VOCs, which therefore represent a missing component of the metabolome. Advances in VOC detection now allow simultaneous observation of the numerous VOCs constituting the ‘volatilome’ of microbial systems. Here, we present a roadmap for integrating and advancing VOC and other ‘omics approaches and highlight the potential for realtime VOC measurements to help overcome limitations in discrete ‘omics sampling. Including volatile metabolites in metabolomics, both conceptually and in practice, will build a more comprehensive understanding of microbial processes across ecological communities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Assessing the Impact of Lightning NOx Emissions in CMAQ Using Lightning Flash Data from WWLLN over the Contiguous United States

Comparison of lightning flash data from the National Lightning Detection Network (NLDN) and from the World Wide Lightning Location Network (WWLLN) over the contiguous United States (CONUS) for the 2016–2018 period reveals temporally and spatially varying flash rates that would influence lightning NO x (LNO x ) production due to known detection efficiency differences especially during summer months over land (versus over ocean). However, the lightning flash density differences between the two networks show persistent seasonal patterns over geographical regions (e.g., land versus ocean). Since the NLDN data are considered to have higher accuracy (lightning detection with >95% efficiency), we developed scaling factors for the WWLLN flash data based on the ratios of WWLLN to NLDN flash data over time (months of year) and space. In this study, sensitivity simulations using the Community Multiscale Air Quality (CMAQ) model are performed utilizing the original data sets (both NLDN and WWLLN) and the scaled WWLLN flash data for LNO x production over the CONUS. The model performance of using the different lightning flash datasets for ambient O 3 and NO x mixing ratios that are directly impacted by LNO x emissions and the wet and dry deposition of oxidized nitrogen species that are indirectly impacted by LNO x emissions is assessed based on comparisons with ground-based observations, vertical profile measurements, and satellite products. During summer months, the original WWLLN data produced less LNO x emissions (due to its lower lightning detection efficiency) compared to the NLDN data, which resulted in less improvement in model performance than the simulation using NLDN data as compared to the simulation without any LNO x emissions. However, the scaled WWLLN data produced LNO x estimates and model performance comparable with the NLDN data, suggesting that scaled WWLLN may be used as a substitute for the NLDN data to provide LNO x estimates in air quality models when the NLDN data are not available (e.g., due to prohibitive cost or lack of spatial coverage).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A red algal polysaccharide influences the multicellular development of the choanoflagellate Salpingoeca rosetta

The choanoflagellate Salpingoeca rosetta exemplifies the capacity of marine microeukaryotes to integrate environmental cues into their life histories. Some of the cues that S. rosetta detects are lipids from their bacterial prey that induce the development of multicellular colonies called rosettes. In the aquatic environments where S. rosetta would encounter bacterial cues, microbial communities gain refuge and food from algae. For example, microbes in coastal environments benefit from macroalgae that annually release ∼10 14 g of carbon, primarily in the form of polysaccharides that comprise ∼80% of dissolved organic carbon in surface waters. Despite the ecological impact of algae and historical descriptions of choanoflagellates attached to algae, the influence of algae on choanoflagellate life history transitions has remained unexplored. Here, we report that porphyran, a polysaccharide produced by the red macroalga Porphyra umbilicalis, induces multicellular development in S. rosetta. We first noticed this response when S. rosetta formed multicellular rosette colonies while growing in media prepared from P. umbilicalis, even though rosette-inducing bacteria were absent. By biochemically purifying extracts of P. umbilicalis, we identified porphyran as the rosette-inducing factor. This response provides a biochemical insight that indicates algal polysaccharides may serve as cues to mediate associations between choanoflagellates and algae. Moreover, this observation complements evidence from environmental and geochemical studies that show the impact that algae have exerted on the ecology and evolution of eukaryotes, including a rise in algal productivity during the origin of animals, the closest living relatives of choanoflagellates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Amazonian fog harbors viable microbes

Fog formation over tropical forests remains poorly characterized, despite its potential role in bioaerosol dispersion and ecosystem processes. Here, we analyzed fog samples collected at the Amazon Tall Tower Observatory using flow cytometry and culture-based techniques to characterize viable microbial communities. Microbial cell concentrations varied over an order of magnitude across 13 fog events, reaching up to 8 × 104 cells per ml of fog water. Flow cytometry consistently detected metabolically active cells, while culturing and mass spectrometry-based identification yielded eight viable bacterial species and seven fungal taxa. The bacteria Serratia marcescens, Ralstonia pickettii and Sphingomonas paucimobilis exhibited seasonal variations in prevalence. The fungal species identified were primarily mesophilic saprophytes and endophytes, commonly associated with soil and plant surfaces. Our findings indicate that fog harbors viable microbes, including Serratia marcescens and Ralstonia pickettii, which may imply a relevance of fog for microbial dispersal, colonization and nutrient cycling in the Amazon rainforest.

Godoi, Ricardo H. (ORCID:0000000247744870)↗
DOE OSTI2022

Practical galaxy morphology tools from deep supervised representation learning

Astronomers have typically set out to solve supervised machine learning problems by creating their own representations from scratch. We show that deep learning models trained to answer every Galaxy Zoo DECaLS question learn meaningful semantic representations of galaxies that are useful for new tasks on which the models were never trained. We exploit these representations to outperform several recent approaches at practical tasks crucial for investigating large galaxy samples. The first task is identifying galaxies of similar morphology to a query galaxy. Given a single galaxy assigned a free text tag by humans (e.g. ‘#diffuse’), we can find galaxies matching that tag for most tags. The second task is identifying the most interesting anomalies to a particular researcher. Our approach is 100 per cent accurate at identifying the most interesting 100 anomalies (as judged by Galaxy Zoo 2 volunteers). The third task is adapting a model to solve a new task using only a small number of newly labelled galaxies. Models fine-tuned from our representation are better able to identify ring galaxies than models fine-tuned from terrestrial images (ImageNet) or trained from scratch. We solve each task with very few new labels; either one (for the similarity search) or several hundred (for anomaly detection or fine-tuning). This challenges the longstanding view that deep supervised methods require new large labelled data sets for practical use in astronomy. To help the community benefit from our pretrained models, we release our fine-tuning code zoobot. Zoobot is accessible to researchers with no prior experience in deep learning.

79 ASTRONOMY AND ASTROPHYSICS↗