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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Lawrence Livermore National Laboratory Environmental Report 2021

The purposes of the Lawrence Livermore National Laboratory Environmental Report 2021 are to record Lawrence Livermore National Laboratory’s (LLNL’s) compliance with environmental standards and requirements, describe LLNL’s environmental protection and remediation programs, and present the results of environmental monitoring at the two LLNL sites—the Livermore Site and Site 300. The report is prepared for the U.S. Department of Energy (DOE) by LLNL’s Environmental Functional Area. Submittal of the report satisfies requirements under DOE Order 231.1B, “Environment, Safety and Health Reporting,” and DOE Order 458.1, “Radiation Protection of the Public and Environment.” The report is distributed electronically and is available at https://saer.llnl.gov/, the website for the LLNL annual environmental report. Previous LLNL annual environmental reports beginning with 1994 are also on the website. Some references in the electronic report text are underlined, which indicates that they are clickable links. Clicking on one of these links will open the related document, data workbook, or website. Sampling location maps throughout this report were created using ArcGIS® software by Esri. The report begins with an executive summary, which provides the purpose of the report and an overview of LLNL’s compliance and monitoring results. The first three chapters provide background information: Chapter 1 is an overview of the location, meteorology, and hydrogeology of the two LLNL sites; Chapter 2 is a summary of LLNL’s compliance with environmental regulations; and Chapter 3 is a description of LLNL’s environmental programs with an emphasis on the Environmental Management System including pollution prevention. The majority of the report covers LLNL’s environmental monitoring programs and monitoring data for 2021: effluent and ambient air monitoring and dose assessment (Chapter 4); waters, including wastewater, storm water runoff, surface water, rain, and groundwater (Chapter 5); and terrestrial, including soil, sediment, vegetation, foodstuff, ambient radiation, and special status wildlife and plants (Chapter 6). The remaining two chapters discuss LLNL’s groundwater remediation program (Chapter 7), and quality assurance for the environmental monitoring programs (Chapter 8). Complete monitoring data, which are summarized in the body of the report, are provided in Appendix A. The report uses Système International units, consistent with the federal Metric Conversion Act of 1975 and Executive Order 12770, “Metric Usage in Federal Government Programs” (1991). For ease of comparison to environmental reports issued prior to 1991, dose values and many radiological measurements are given in both metric and U.S. customary units. A conversion table is provided in the glossary. The report is the responsibility of LLNL’s Environmental Functional Area. Monitoring data were obtained through the combined efforts of the Environmental Functional Area; Environmental Restoration Department; Physical and Life Sciences Environmental Monitoring Radiological Laboratory; and the Radiation Protection Functional Area.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

On-Line Waste Library V5.0 Supporting Information

The On-Line Waste Library is a website that contains information regarding United States Department of Energy-managed high-level waste, spent nuclear fuel, and other wastes that are likely candidates for deep geologic disposal, with links to supporting documents for the data. This report provides supporting information for the data for which an already published source was not available.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Developing ML/AI Methods for High-Throughput Characterization of Multiple-Sensor Streams of Tokamak Dynamics for High-Speed Control (Final Report)

This project evaluated and developed new mathematical and algorithmic techniques capable of handling (in real-time) the growing amounts of data generated by modern fusion research. While existing numerical linear algebra (NLA) methods provide the backbone to classical data analysis and algorithms, these methods fundamentally do not port to distributed architectures nor do they allow low-latency data reduction for control. Motivated by the needs for modern fusion reactors, this project explored and implemented new numerical methods to characterize plasma dynamics, respond in real-time to discharge evolution, and to process massive-scale data accurately and rapidly more fully. This project links expertise in multiple-sensor diagnostics of tokamak plasma dynamics from Columbia University’s Plasma Physics Laboratory with expertise in massive-scale data reduction and extreme data control algorithms at Columbia University’s Data Science Institute. This interdisciplinary project (i) applied machine learning methods, (ii) implemented a properly-trained neural-network for very fast processing of high-speed plasma videography, and (ii) developed the applied mathematical methods, based on randomized-NLA (rNLA) routines, for data analysis, reduction, and real-time control. The Columbia University High Beta Tokamak-Extended Pulse (HBT-EP) facility provided data to test new algorithms and partnership with Columbia University's Data Sciences Institute evaluated the broader use of new algorithms for many challenging control applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Genomes_SET2_178 - Shared

The dataset consists of 178 bacterial genomes in total. Of these, 145 genomes were drawn from published literature linked to Biolog Gen III growth data, while 33 genomes came from soil isolates collected at the Cedar Creek Ecosystem Science Research Reserve (CCESR) in Minnesota. The isolates were sequenced on an Illumina HiSeq platform (~50× coverage), assembled with Megahit, and annotated using RAST at PATRIC, with strict quality filtering (<90% completeness or >5% contamination).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Solar-to-Grid Public Data File for Utility-scale (UPV) and Distributed Photovoltaics (DPV) Generation, Capacity Credit, and Value for 2012-2020

Lawrence Berkeley National Laboratory (Berkeley Lab) estimates hourly project-level generation data for utility-scale solar projects and hourly county-level generation data for residential and non-residential distributed photovoltaic (PV) systems in the seven organized wholesale markets and 10 additional Balancing Areas. To encourage its broader use, Berkeley Lab has made this data file public here at OEDI, covering the years 2012-2020. The public project-level dataset is updated annually with data from the previous calendar year. For more information about the research project, including a technical report, briefing material, visualizations, and additional data, please visit the project homepage linked in this submission.

annual solar value↗
DOE OSTI2021

The CopA2-Type P 1B -Type ATPase CcoI Serves as Central Hub for cbb 3 -Type Cytochrome Oxidase Biogenesis

Copper (Cu)-transporting P 1B -type ATPases are ubiquitous metal transporters and crucial for maintaining Cu homeostasis in all domains of life. In bacteria, the P 1B -type ATPase CopA is required for Cu-detoxification and exports excess Cu(I) in an ATP-dependent reaction from the cytosol into the periplasm. CopA is a member of the CopA1-type ATPase family and has been biochemically and structurally characterized in detail. In contrast, less is known about members of the CopA2-type ATPase family, which are predicted to transport Cu(I) into the periplasm for cuproprotein maturation. One example is CcoI, which is required for the maturation of cbb 3 -type cytochrome oxidase (cbb 3 -Cox) in different species. Here, we reconstituted purified CcoI of Rhodobacter capsulatus into liposomes and determined Cu transport using solid-supported membrane electrophysiology. The data demonstrate ATP-dependent Cu(I) translocation by CcoI, while no transport is observed in the presence of a non-hydrolysable ATP analog. CcoI contains two cytosolically exposed N-terminal metal binding sites (N-MBSs), which are both important, but not essential for Cu delivery to cbb 3 -Cox. CcoI and cbb 3 -Cox activity assays in the presence of different Cu concentrations suggest that the glutaredoxin-like N-MBS1 is primarily involved in regulating the ATPase activity of CcoI, while the CopZ-like N-MBS2 is involved in Cu(I) acquisition. The interaction of CcoI with periplasmic Cu chaperones was analyzed by genetically fusing CcoI to the chaperone SenC. The CcoI-SenC fusion protein was fully functional in vivo and sufficient to provide Cu for cbb 3 -Cox maturation. In summary, our data demonstrate that CcoI provides the link between the cytosolic and periplasmic Cu chaperone networks during cbb 3 -Cox assembly.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Aerosol Microphysics and Chemical Measurements at Mt. Soledad and Scripps Pier during the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) from February 2023 to February 2024 UCSD Library Collection

This dataset includes guest instrument measurements and other PI products for aerosol microphysics and chemical measurements collected at Mt. Soledad and Scripps Pier during the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) from February 2023 to February 2024. The measurements include the following instruments at Mt. Soledad: High-Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-ToF-AMS, Aerodyne), Scanning Electrical Mobility Spectrometer (SEMS, Brechtel Manufacturing Inc.), Aerodynamic Particle Sizer (APS, Droplet Measurements Technologies), Single Particle Soot Photometer (SP2, Drople Measurements Technologies), Meteorological Station (WXT520, Vaisala), Ozone (Teco), and trace gas proxies (Teledyne). In addition, the analyses of particle filters collected at Mt. Soledad for three dry-diameter size cuts (<1 micron, <0.5 micron, <0.18 micron) and at Scripps Pier for one dry-diameter size cut (<1 micron) by Fourier Transform Infrared (FTIR) and X-ray Fluorescence (XRF) are reported. A differential mobility analyzer operated as a scanning mobility particle sizer (SMPS, TSI Inc.), a printed particle optical spectrometer (POPS, Grimm), and a continuous flow diffusion cloud condensation nuclei (CCN, DMT) counter provide the mobility aerosol size distribution (30-360 nm), optical size distribution (150 - 6000 nm), size-resolved CCN distribution (30-360 nm) at 0.2, 0.4, 0.6, 0.8, and 1.0% supersaturation. Measurements are reported for both sampling from an isokinetic aerosol inlet and from a Counterflow Virtual Impactor (CVI, Brechtel Manufacturing Inc.). The data are available at the following link: https://library.ucsd.edu/dc/collection/bb0898306q

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Forensic Signature Detection of Yersinia Pestis Culturing Practices Across Institutions Using a Bayesian Network

The field of bioforensics is focused on the analysis of evidence from a biocrime. Existing laboratory analyses can identify the specific strain of an organism in the evidence, as well signatures of the specific culture batch of organisms, such as low-frequency contaminants or indicators of growth and processing methods. To link these disparate types of physical data to potential suspects, investigators may need to identify institutions or individuals whose access to strains and culturing practices match those identified from the evidence. In this work we present a Bayesian statistical network to fuse different types of analytical measurements that predict the production environment of a Yersinia pestis sample under investigation with automated test processing of scientific publications to identify institutions with a history of growing Y. pestis under similar conditions. Furthermore, the textual and experimental signatures were evaluated recursively to determine the overall sensitivity of the network across all levels of false positives. We illustrate that institutions associated with several specific culturing practices can be accurately selected based on the experimental signature from only a few analytical measurements. These findings demonstrate that similar Bayesian networks can be generated generically for many organisms of interest and their deployment is not prohibitive due to either computational or experimental factors.

Bayesian networks↗
DOE OSTI2015

Development of a Knowledgebase to Integrate, Analyze, Distribute, and Visualize Microbial Community Systems Biology Data

We have developed a flexible knowledgebase system, ggKbase, (http://gg.berkeley.edu), to enable effective data analysis and knowledge generation from samples from which metagenomic and other ‘omics’ data are obtained. Within ggKbase, data can be interpreted, integrated and linked to other databases and services. Sequence information from complex metagenomic samples can be quickly and effectively resolved into genomes and biologically meaningful investigations of an organism’s metabolic potential can then be conducted. Critical features make analyses efficient, allowing analysis of hundreds of genomes at a time. The system is being used to support research in multiple DOE-relevant systems, including the LBNL SFA subsurface science biogeochemical cycling research at Rifle, Colorado. ggKbase is supporting the research of a rapidly growing group of users. It has enabled studies of carbon cycling in acid mine drainage ecosystems, biologically-mediated transformations in deep subsurface biomes sampled from mines and the north slope of Alaska, to study the human microbiome and for laboratory bioreactor-based remediation investigations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

The DOE Joint Genome Institute Enabling Advances in Energy & Environmental Research (2016 Fact Sheet)

The U.S. Department of Energy (DOE) Joint Genome Institute (JGI) is the only federally-funded high-throughput genome sequencing and analysis facility dedicated to genomes of non-medical microbes, microbial communities, plants, fungi and other targets relevant to DOE missions in energy, climate, and environment. DOE JGI provides collaborators around the world with access to massive-scale DNA sequencing to underpin modern systems biology research and provide fundamental data on key genes that may link to biological functions, including microbial metabolic pathways and enzymes that are used to generate fuel molecules, affect plant biomass formation, degrade contaminants, or capture CO 2 . The information can then be used to optimize organisms for biofuels production and other DOE missions.

09 BIOMASS FUELS↗
DOE OSTI2020

Resolution system that provides access to remote data locally

A system and method that provides access to remote data locally by identifying and downloading linked content from a plurality of remote sources through a communication network; decomposing the downloaded content into local content elements by intercepting links between the content elements and mapping the intercepted links to redirected links that locate the same download content in a local cache or a local server proxy; identifying remote devices requesting access to the local cache or the local server proxy and the device's functionality through a tracking code stored on the remote devices and a probabilistic tracking; and providing access to the local cache or the local server proxy in response to authorizations associated with the identified devices and a user.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

VizBin - an application for reference-independent visualization and human-augmented binning of metagenomic data

Background: Metagenomics is limited in its ability to link distinct microbial populations to genetic potential due to a current lack of representative isolate genome sequences. Reference-independent approaches, which exploit for example inherent genomic signatures for the clustering of metagenomic fragments (binning), offer the prospect to resolve and reconstruct population-level genomic complements without the need for prior knowledge. Results: We present VizBin, a Java™-based application which offers efficient and intuitive reference-independent visualization of metagenomic datasets from single samples for subsequent human-in-the-loop inspection and binning. The method is based on nonlinear dimension reduction of genomic signatures and exploits the superior pattern recognition capabilities of the human eye-brain system for cluster identification and delineation. We demonstrate the general applicability of VizBin for the analysis of metagenomic sequence data by presenting results from two cellulolytic microbial communities and one human-borne microbial consortium. The superior performance of our application compared to other analogous metagenomic visualization and binning methods is also presented. Conclusions: VizBin can be applied de novo for the visualization and subsequent binning of metagenomic datasets from single samples, and it can be used for the post hoc inspection and refinement of automatically generated bins. Due to its computational efficiency, it can be run on common desktop machines and enables the analysis of complex metagenomic datasets in a matter of minutes. The software implementation is available at https://claczny.github.io/ VizBin under the BSD License (four-clause) and runs under Microsoft Windows™, Apple Mac OS X™ (10.7 to 10.10), and Linux.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Principles of proteome allocation are revealed using proteomic data and genome-scale models

Integrating omics data to refine or make context-specific models is an active field of constraint-based modeling. Proteomics now cover over 95% of the Escherichia coli proteome by mass. Genome-scale models of Metabolism and macromolecular Expression (ME) compute proteome allocation linked to metabolism and fitness. Using proteomics data, we formulated allocation constraints for key proteome sectors in the ME model. The resulting calibrated model effectively computed the “generalist” (wild-type) E. coli proteome and phenotype across diverse growth environments. Across 15 growth conditions, prediction errors for growth rate and metabolic fluxes were 69% and 14% lower, respectively. The sector-constrained ME model thus represents a generalist ME model reflecting both growth rate maximization and “hedging” against uncertain environments and stresses, as indicated by significant enrichment of these sectors for the general stress response sigma factor σS. Finally, the sector constraints represent a general formalism for integrating omics data from any experimental condition into constraint-based ME models. The constraints can be fine-grained (individual proteins) or coarse-grained (functionally-related protein groups) as demonstrated here. Furthermore, this flexible formalism provides an accessible approach for narrowing the gap between the complexity captured by omics data and governing principles of proteome allocation described by systems-level models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Metabolome of human gut microbiome is predictive of host dysbiosis

Background: Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. However, the community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome’s interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependent on its community metabolome; an emergent property of the microbiome. Results: Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Conclusions: Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome–host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.

human microbiome↗
DOE OSTI2021

COVID-19: Spatiotemporal social data analytics and machine learning for pandemic exploration and forecasting

This task focused on developing a preliminary approach to use machine learning (ML) to explore the relationship between county-level societal variables and COVID-19 parameters, including COVID-19 cases rates and counts and COVID-19 death rates and counts. The objective was to develop and test a prototype approach for linking COVID-19 and county-level data. The task focused on enhancing and applying existing LANL ML techniques to COVID-19. Our novel ML methods have been a subject of a recently approved U.S. patent. The codes based on these methods are already open-source released. Our ML tools (NMFk/NTFk) are applied to extract hidden features (signals, waves) in the analyzed datasets and automatically identify their optimal number. The features are extracted by identifying counties that have similarities between the county-level societal variables and the COVID-19 parameters. These demonstration analyses will facilitate the ongoing pandemic simulations and predictions performed by Los Alamos other institutions, as well as lay the groundwork for future work.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Nuclear Data

PowerPoint presentation targeted for educational use. Nuclear data comes from a variety of sources and in many flavors. Understanding where the data you use comes from and what flavor it is can be essential to understand and interpret your results. This talk will discuss the nuclear data pipeline with particular emphasis on providing links to additional resources that can be used to explore the issues you will encounter.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

PoroTomo: BRAD and BRDY GPS Station RINEX Files - March 26, 2015

This dataset provides links to daily RINEX files for two GPS stations at Brady's Hot Springs as of March 26, 2014. The data is formatted as compressed GNSS RINEX observation files, accessible through formatted CSV files, as well as in links to the complete daily time series data. The CSV files serve as a directory for navigating to the correct FTP locations to access the desired data. Users are advised to cross-reference the filenames and station identifiers (BRAD or BRDY) when downloading files. The files were generated by the UNAVCO GSAC Repository, and all data-related timestamps and units are specified within the CSV structure. Additional Resources, including the original database for direct downloads and links to the previous and following set of RINEX data (as of 12-2-14 and 9-17-2015, respectively), are also provided.

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

Performance of a Low Activity Beta-Sensitive SR{sup 90} Water Monitor for Fukushima

There are large volumes of contaminated water from the stabilization efforts at the damaged Fukushima Nuclear Power Plants. This water is being processed to remove radioactivity for eventual release to the environment. An on-line continuously operating system to confirm that the clean-up system is working properly, and to provide prompt feedback of the results is required by the system operator. While gamma emitting nuclides allow for the straight forward approach of gamma spectroscopy to identify and quantify radioactivity in water, pure beta emitting nuclides such as Sr{sup 90} pose a challenging problem. The relatively short range of beta radiation in water requires optimization of the measurement geometry in terms of the source-detector distance and source-detector interface while retaining a background sensitivity low enough to meet the Minimum Detectable Concentration (MDC) of 10 Bq/kg in 180 minutes. This issue is complicated by the continuum nature of the beta spectrum which does not allow for simple nuclide identification. The use of the Monte-Carlo code MCNP to estimate system performance before prototyping vastly increases the success of the end product. Various parameters such as detector size and thickness, water chamber size, water chamber construction materials were evaluated to help choose the optimum geometry. The final design was a system consisting of two large-area (16 x 35 cm) and thin (0.15 mm) plastic scintillators placed very close to a sealed a water chamber. The size of the chamber was optimized to obtain the maximum efficiency for the nuclide being measured (Sr/Y{sup 90}) but to minimize the efficiency for possible interferences (Ru/Rh{sup 106}, Cs{sup 137}). A thin carbon fiber window was selected with adequate material and thickness to contain the water under pressure, but also thin enough (0.5 mm) to allow enough beta radiation to pass through to the active detector volume. The entire measurement geometry is then housed in a thick lead shield to reduce contributions from external sources to an acceptable level. Data acquisition is accomplished through customized application-specific software that allows for long counting times to attain a low MDC, but also simultaneously provides alarms on short averaging times to achieve a fast response to sudden changes in activity concentration. Multiple monitors are then linked to supervisory software where real time data and alarms are available for analysis in remote locations. The system also allows for remote operation of the unit; check sources, background checks, systems settings and more may be accessed remotely. Testing of the production devices has shown that we can achieve the 10 Bk/kg MDC requirement for Sr{sup 90} in equilibrium with Y{sup 90} with a count time of approximately 20 minutes. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗