Picosecond laser generated plasma as a source of singly charged ions for external injection into an EBIS
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S. Kondrashev↗
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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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Portfolio Management
Enterprise Business Information Services Division (EBIS) supports the Laboratory and its functions through the implementation and support of business information systems on behalf of its business community. EBIS Five Strategic Focus Areas: (1) Improve project estimating, planning and delivery capability (2) Improve maintainability and sustainability of EBIS Application Portfolio (3) Leap forward in IT Leadership (4) Comprehensive Talent Management (5) Continuous IT Security Program. Portfolio Management is a strategy in which software applications are managed as assets
Information Technology Applications↗
Renewable Energy at NASA's Johnson Space Center
NASA's Johnson Space Center has implemented a great number of renewable energy systems. Renewable energy systems are necessary to research and implement if we humans are expected to continue to grow and thrive on this planet. These systems generate energy using renewable sources - water, wind, sun - things that we will not run out of. Johnson Space Center is helping to pave the way by installing and studying various renewable energy systems. The objective of this report will be to examine the completed renewable energy projects at NASA's Johnson Space Center for a time span of ten years, beginning in 2003 and ending in early 2014. This report will analyze the success of each project based on actual vs. projected savings and actual vs. projected efficiency. Additionally, both positive and negative experiences are documented so that lessons may be learned from past experiences. NASA is incorporating renewable energy wherever it can, including into buildings. According to the 2012 JSC Annual Sustainability Report, there are 321,660 square feet of green building space on JSC's campus. The two projects discussed here are major contributors to that statistic. These buildings were designed to meet various Leadership in Energy and Environmental Design (LEED) Certification criteria. LEED Certified buildings use 30 to 50 percent less energy and water compared to non-LEED buildings. The objectives of this project were to examine data from the renewable energy systems in two of the green buildings onsite - Building 12 and Building 20. In Building 12, data was examined from the solar photovoltaic arrays. In Building 20, data was examined from the solar water heater system. By examining the data from the two buildings, it could be determined if the renewable energy systems are operating efficiently. Objectives In Building 12, the data from the solar photovoltaic arrays shows that the system is continuously collecting energy from the sun, as shown by the graph below. Building 12 has two solar inverters, located on the second floor, that collected the data from the solar photovoltaic arrays. The data displayed here is the total energy produced by the system. These are cumulative amounts, so the last point on the graph shows all of the energy collected from the system since the start of its operation. The data shown here was manually collected from the solar inverters. However, the data is also automatically recorded through EBI. Through analysis of both sets of data it was determined that the EBI data was faulty. For example, from the manually collected data it can be determined that a total of 73 kWh of energy was collected between the dates of 1/16/2014 – 1/22/2014. The EBI data reports that approximately 17800 kWh of energy was collected during the same time frame. Not only does this exceed the time frame examined, but it also exceeds the total energy collected from the start of collection as recorded from the inverters. This leads to the belief that there is a malfunction with the automatic recording of the energy. In Building 20, data was examined from the solar water heater dating back many months and found that the pump for the solar water heater system was not operating properly, as exhibited in the graph shown below. The pump operates on a solar energy system, meaning that it collects energy throughout the day from the sun. Because of this, the system would stop operating shortly after the sun set because of a lack of sunlight. At that point, the graph should show a zero flow rate, but as exhibited in the graph below, that is not the case. It is clearly shown that the pump is continuously operating, even during the night. It was also observed that the majority of the time the pump would not turn on at all, despite good weather conditions. This led to the conclusion that the pump is malfunctioning, and needs to be examined and fixed.
McDowall, Lindsay↗
Evaluating and optimizing horticultural regimes in space plant growth facilities
In designing innovative space plant growth facilities (SPGF) for long duration space flight, various limitations must be addressed including onboard resources: volume, energy consumption, heat transfer and crew labor expenditure. The required accuracy in evaluating on board resources by using the equivalent mass methodology and applying it to the design of such facilities is not precise. This is due to the uncertainty of the structure and not completely understanding the properties of all associated hardware, including the technology in these systems. We present a simple criteria of optimization for horticultural regimes in SPGF: Qmax = max [M x (EBI)2/(V x E x T], where M is the crop harvest in terms of total dry biomass in the plant growth system; EBI is the edible biomass index (harvest index), V is volume occupied by the crop; E is the crop light energy supply during growth; T is the crop growth duration. The criterion reflects directly on the consumption of onboard resources for crop production. c2004 COSPAR. Published by Elsevier Ltd. All rights reserved.
NASA Discipline Life Support Systems↗
Comparison of exact and approximate formulas for the Mott correction to energy loss of relativistic heavy ions
A comparison is conducted between an exact numerical calculation and two approximate analytic expressions for the Mott correction to energy loss of heavy ions. A complete tabulation of the Mott correction is presented together with a comparison of some of the approximate expressions for the Mott correction due to Ahlen (1978) and Morgan and Eby (1973). Comparison is made with results of an experimental calibration of the HEAO-3 Heavy Cosmic Ray experimental chambers at the Bevalac.
Eby, P. B.↗
Experimental boundary integral equation applications in speckle interferometry
A complete data analysis system utilizing laser speckle interferometry and Experimental Integral Equation Method (EBIE) is described. A host computer provides the optical data analysis and serves as an input device to a PDP11/VAX computer for numerical analysis. The basic theory of pointwise filtering and digital correlation is described as experimental data input to the BIE method.
Fraley, J. E.↗
Oxygenated Organic Chemicals in the Pacific Troposphere: Distribution, Sources and Chemistry
Airborne measurements of a large number of oxygenated organic chemicals (Oxorgs) were carried out in the Pacific troposphere (0.1-12 km) in the Spring of 2001 (Feb. 24-April 10). Specifically these measuremen ts included acetone, methylethyl ketone (MEK), methanol, ethanol, ace taldehyde, propionaldehyde, PANS, and organic nitrates. Complementary measurements of formaldehyde, organic peroxides, and tracers were al so available. Ox-orgs were abundant in the clean troposphere and were greatly enhanced in the outflow regions from Asia. Their mixing ratios were typically highest in the lower troposphere and declined toward s the upper troposphere and the lowermost stratosphere. Their total a bundance (Ox-orgs) significantly exceeded that of NMHC (C2-C8 NMHC). A comparison of these data with observations collected some seven yea rs earlier (Feb.-March, 1994), did not reveal any significant changes . Throughout the troposphere mixing ratios of Ox-orgs were strongly c orrelated with each other as well as with tracers of fossil and bioma sshiof'uel combustion. Analysis of the relative enhancement of selected Oxorgs with respect to CH3Cl and CO in twelve sampled plumes, origi nating from fires, is used to assess their primary and secondary sour ces from biomass combustion. The composition of these plumes also ind icates a large shift of reactive nitrogen into the PAN reservoir ther eby limiting ozone formation. The Harvard 3-D photochemical model, th at uses state of the art chemistry and source information, is used to compare simulated and observed mixing ratios of selected species. A 1 -D model is used to explore the chemistry of aldehydes. These results will be presented.
Singh, Hanwant B.↗
Critical Team Composition Issues for Long-Distance and Long-Duration Space Exploration: A Literature Review, an Operational Assessment, and Recommendations for Practice and Research
Prevailing team effectiveness models suggest that teams are best positioned for success when certain enabling conditions are in place (Hackman, 1987; Hackman, 2012; Mathieu, Maynard, Rapp, & Gilson, 2008; Wageman, Hackman, & Lehman, 2005). Team composition, or the configuration of member attributes, is an enabling structure key to fostering competent teamwork (Hackman, 2002; Wageman et al., 2005). A vast body of research supports the importance of team composition in team design (Bell, 2007). For example, team composition is empirically linked to outcomes such as cooperation (Eby & Dobbins, 1997), social integration (Harrison, Price, Gavin, & Florey, 2002), shared cognition (Fisher, Bell, Dierdorff, & Belohlav, 2012), information sharing (Randall, Resick, & DeChurch, 2011), adaptability (LePine, 2005), and team performance (e.g., Bell, 2007). As such, NASA has identified team composition as a potentially powerful means for mitigating the risk of performance decrements due to inadequate crew cooperation, coordination, communication, and psychosocial adaptation in future space exploration missions. Much of what is known about effective team composition is drawn from research conducted in conventional workplaces (e.g., corporate offices, production plants). Quantitative reviews of the team composition literature (e.g., Bell, 2007; Bell, Villado, Lukasik, Belau, & Briggs, 2011) are based primarily on traditional teams. Less is known about how composition affects teams operating in extreme environments such as those that will be experienced by crews of future space exploration missions. For example, long-distance and long-duration space exploration (LDSE) crews are expected to live and work in isolated and confined environments (ICEs) for up to 30 months. Crews will also experience communication time delays from mission control, which will require crews to work more autonomously (see Appendix A for more detailed information regarding the LDSE context). Given the unique context within which LDSE crews will operate, NASA identified both a gap in knowledge related to the effective composition of autonomous, LDSE crews, and the need to identify psychological and psychosocial factors, measures, and combinations thereof that can be used to compose highly effective crews (Team Gap 8). As an initial step to address Team Gap 8, we conducted a focused literature review and operational assessment related to team composition issues for LDSE. The objectives of our research were to: (1) identify critical team composition issues and their effects on team functioning in LDSE-analogous environments with a focus on key composition factors that will most likely have the strongest influence on team performance and well-being, and 1 Astronaut diary entry in regards to group interaction aboard the ISS (p.22; Stuster, 2010) 2 (2) identify and evaluate methods used to compose teams with a focus on methods used in analogous environments. The remainder of the report includes the following components: (a) literature review methodology, (b) review of team composition theory and research, (c) methods for composing teams, (d) operational assessment results, and (e) recommendations.
Bell, Suzanne T.↗
GeneLab Phase 2: Integrated Search Data Federation of Space Biology Experimental Data
The GeneLab project is a science initiative to maximize the scientific return of omics data collected from spaceflight and from ground simulations of microgravity and radiation experiments, supported by a data system for a public bioinformatics repository and collaborative analysis tools for these data. The mission of GeneLab is to maximize the utilization of the valuable biological research resources aboard the ISS by collecting genomic, transcriptomic, proteomic and metabolomic (so-called omics) data to enable the exploration of the molecular network responses of terrestrial biology to space environments using a systems biology approach. All GeneLab data are made available to a worldwide network of researchers through its open-access data system. GeneLab is currently being developed by NASA to support Open Science biomedical research in order to enable the human exploration of space and improve life on earth. Open access to Phase 1 of the GeneLab Data Systems (GLDS) was implemented in April 2015. Download volumes have grown steadily, mirroring the growth in curated space biology research data sets (61 as of June 2016), now exceeding 10 TB/month, with over 10,000 file downloads since the start of Phase 1. For the period April 2015 to May 2016, most frequently downloaded were data from studies of Mus musculus (39) followed closely by Arabidopsis thaliana (30), with the remaining downloads roughly equally split across 12 other organisms (each 10 of total downloads). GLDS Phase 2 is focusing on interoperability, supporting data federation, including integrated search capabilities, of GLDS-housed data sets with external data sources, such as gene expression data from NIHNCBIs Gene Expression Omnibus (GEO), proteomic data from EBIs PRIDE system, and metagenomic data from Argonne National Laboratory's MG-RAST. GEO and MG-RAST employ specifications for investigation metadata that are different from those used by the GLDS and PRIDE (e.g., ISA-Tab). The GLDS Phase 2 system will implement a Google-like, full-text search engine using a Service-Oriented Architecture by utilizing publicly available RESTful web services Application Programming Interfaces (e.g., GEO Entrez Programming Utilities) and a Common Metadata Model (CMM) in order to accommodate the different metadata formats between the heterogeneous bioinformatics databases. GLDS Phase 2 completion with fully implemented capabilities will be made available to the general public in September 2017.
Space Biology↗
GeneLab Analysis Working Group Kick-Off Meeting
Goals to achieve for GeneLab AWG - GL vision - Review of GeneLab AWG charter Timeline and milestones for 2018 Logistics - Monthly Meeting - Workshop - Internship - ASGSR Introduction of team leads and goals of each group Introduction of all members Q/A Three-tier Client Strategy to Democratize Data Physiological changes, pathway enrichment, differential expression, normalization, processing metadata, reproducibility, Data federation/integration with heterogeneous bioinformatics external databases The GLDS currently serves over 100 omics investigations to the biomedical community via open access. In order to expand the scope of metadata record searches via the GLDS, we designed a metadata warehouse that collects and updates metadata records from external systems housing similar data. To demonstrate the capabilities of federated search and retrieval of these data, we imported metadata records from three open-access data systems into the GLDS metadata warehouse: NCBI's Gene Expression Omnibus (GEO), EBI's PRoteomics IDEntifications (PRIDE) repository, and the Metagenomics Analysis server (MG-RAST). Each of these systems defines metadata for omics data sets differently. One solution to bridge such differences is to employ a common object model (COM) to which each systems' representation of metadata can be mapped. Warehoused metadata records are then transformed at ETL to this single, common representation. Queries generated via the GLDS are then executed against the warehouse, and matching records are shown in the COM representation (Fig. 1). While this approach is relatively straightforward to implement, the volume of the data in the omics domain presents challenges in dealing with latency and currency of records. Furthermore, the lack of a coordinated has been federated data search for and retrieval of these kinds of data across other open-access systems, so that users are able to conduct biological meta-investigations using data from a variety of sources. Such meta-investigations are key to corroborating findings from many kinds of assays and translating them into systems biology knowledge and, eventually, therapeutics.
GeneLab↗
Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery
The next era in human space exploration is rapidly approaching and will require the use of countermeasures to deep space health hazards. The development of countermeasures (or, the re-purposing of existing agents) will be highly dependent on our understanding of basic biological responses to space stressors (e.g. ionizing radiation, altered gravitational fields, altered day-night cycles, confinement, isolation, hostile-closed environments, distance-duration from Earth, exposure to celestial regolith, etc.). The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, imaging, whole organism and behavior). We will discuss here several strategies that NASA’s Biological and Physical Science Division has put in place to maximize the return on investment for spaceflight bioscience data. Open Science, as a scientific philosophy, is the concept that the more people who have access to the data, the more knowledge will be gained from it. This guiding principle led NASA to develop GeneLab in 2015. GeneLab houses spaceflight and relevant ground-based multi-omics data, and has grown to ~400 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, rodent, small animal, and microbial space experiments. GeneLab provides users with various tools for data analysis and a visualization portal that allows users to interact with gene expression data from space-related ‘omics experiments. Open Science is also about building scientific communities, and with this spirit in mind, GeneLab has spawned several Analysis Working Groups (AWGs), comprised of more than 200 volunteer scientists. The AWGs initially provided feedback on the processing pipeline and metadata ‘omics standards for GeneLab. Over the last few years, they have become a community-driven science enterprise, engaging in large meta-analysis of GeneLab datasets, resulting in 10 publications (beyond the originally submitted research). Overall, the Open Science nature of GeneLab has resulted in a high degree of data re-use, resulting in 38 additional publications derived from the original 67 publication over the past four years. The enormous success and knowledge gained from GeneLab has led to a collection of sister NASA “Open Science Data Repositories (OSDR)” and research support groups. These include the NASA Ames Life Sciences Data Archive (ALSDA), the NASA Biological Institutional Scientific Collection (NBISC), and the Biospecimen Sharing Program (BSP). All are adopting the GeneLab data architecture system to maximize open-access, find-ability, accessibility, interoperability, and reusability (FAIR). ALSDA collects and curates phenotypic-physiological bioimaging-behavioral data from space and space-relevant non-human experiments, oftentimes coming from the same omics-associated experimental datasets found in GeneLab. Since 2021, a community of ~100 researchers have rallied around ALSDA, to provide feedback in a new ALSDA AWG focused on phenotypic-physiological investigation-sample-assay metadata standards (e.g., Micro-Computed Tomography, Light/Fluorescence Microscopy, Western Blot, Flow Cytometry, Novel Object Recognition, Elevated Plus Maze, etc. of ~50 assays collected). These standards are part of a new single point-of-entry data submission portal for all non-human Space Biology and Human Research Program principal investigators, to submit, curate, and share their research data. With open-access space biological data now collected and curated together with rich metadata, and with the potential for linkage to “big data” from the international biological and medical communities (NIH, EBI, etc.), the artificial intelligence and machine learning (AI/ML) era has started for Space Biology. Several other talks will cover these topics in this conference.
life sciences↗
Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery
The next era in human space exploration is rapidly approaching and will require the use of countermeasures to deep space health hazards. The development of countermeasures (or, there-purposing of existing agents) will be highly dependent on our understanding of basic biological responses to space stressors (e.g. ionizing radiation, altered gravitational fields, altered day-night cycles, confinement, isolation, hostile-closed environments, distance-duration from Earth, exposure to celestial regolith, etc.). The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, imaging, whole organism and behavior). We will discuss here several strategies that NASA's Biological and Physical Science Division has put in place to maximize the return on investment for spaceflight bioscience data. Open Science, as a scientific philosophy, is the concept that the more people who have access to the data, the more knowledge will be gained from it. This guiding principle led NASA to develop GeneLab in 2015. GeneLab houses spaceflight and relevant ground-based multi-omics data, and has grown to ~400 transcriptomatic, proteomic, metabolomic and epigenomic datasets from plant, rodent, small animal, and microbial space experiments. GeneLab provides users with various tools for data analysis and a visualization portal that allows users to interact with gene expression data from space-related 'omics experiments. Open Science is also about building scientific communities, and with this spirit in mind, GeneLab has spawned several Analysis Working Groups (AWGs), comprised of more than 200 volunteer scientists. The AWGs initially provided feedback on the processing pipeline and metadata 'omics standards for GeneLab. Over the last few years, they have become a community-driven science enterprise, engaging in large meta-analysis of GeneLab datasets, resulting in 10 publications (beyond the originally submitted research). Overall, the Open Science nature of GeneLab has resulted in a high degree of data-use, resulting in 40 enabled publications by open data. The enormous success and knowledge gained from GeneLab has led to a collection of sister NASA "Open Science Data Repositories (OSDR)" and research support groups. These include the NASA Ames Life Sciences Data Archive (ALSDA), the NASA Biological Institutional Scientific Collection (NBISC), and the Biospecimen Sharing Program (BSP). All are adopting the GeneLab data architecture system to maximize open-access, find-ability, accessibility, interoperability, and reusability (FAIR). ALSDA collects and curates phenotypic-physiological bioimaging-behavioral data from space and space-relevant non-human experiments, oftentimes coming from the same omics-associated experimental datasets found in GeneLab. Since 2021, a community of ~100 researchers have rallied around ALSDA, to provide feedback in a new ALSDA AWG focused on phenotypic-physiological investigation-sample-assay metadata standards (e.g., Micro-Computed Tomography, Light/Flourescence Microscopy, Western Blot, Flow Cytometry, Novel Object Recognition, Elevated Plus Maze, etc. of ~50 assays collected). These standards are part of a new single point-of-entry data submission portal for all non-human Space Biology and Human Research Program principal investigators, to submit, curate, and share their research data. With open-access space biological data now collected and curated together with rich metadata, and with the potential for linkage to "big data" from the international biological and medical communities (NIH, EBI, etc.), the artificial intelligence and machine learning (AI/ML) era has started for Space Biology.
omics↗
Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery
The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, whole organism, behavior; tabular, imagery). Open Science is the concept that the more people have access to scientifically curated data, the more knowledge will be gained. This led NASA to start the development of GeneLab in 2015. GeneLab houses spaceflight and space-analog multi-omics datasets from plant, rodent, small animal, and microbial experiments. The success and knowledge gained from GeneLab led to a new alliance of NASA “Open Science Data Repositories” (OSDR), which include the Ames Life Sciences Data Archive (ALSDA) and the NASA Biological Institutional Scientific Collection (NBISC). Both are adopting the GeneLab data system, so data are more findable, accessible, interoperable, and reusable (FAIR). OSDR systems provide users the ability to upload, download, search, share, analyze, and visualize. Open Science also needs strong confidence in the data, which is gained through building science communities. With ~400 current members, GeneLab and ALSDA formed Analysis Working Groups (AWGs) to provide feedback on processing pipelines, metadata curation standards (for ‘omics and phenotypic-physiological-behavioral assays), and to collaborate in effectively reusing data. The AWG also led to the development of the Radiation Biology Ontology (RBO), ensuring radiation metadata are efficiently captured, connected, and interoperable. Feedback from the AWG provided design input toward the new single point-of-entry data submission portal for all investigators to submit, curate, and share their research data. Space biological data is now maximally open access, collected-curated with rich metadata, and formatted for interoperability to enable systems biology, meta-analysis, knowledge graphs, machine learning, modeling, and other reuse approaches. With potential for further federation of OSDR for data mining with traditional biological and medical databases (NIH, NCI, EBI, etc.), a new era for space biology has begun to support the knowledge discovery necessary for Lunar and Martian missions.
Ryan T Scott↗
Pegasus thermal design
Electronic and micrometeoroid detector panels for Pegasus thermal design
TEMPERATURE CONTROL↗
Contribution of the spatial part of the metric tensor to the observed frequency shift in the maser clock experiment
Formula for frequency shift of maser clock in orbit to identical maser on earth
Eby, P.↗
The gravitational red shift
Relativistic red shift effect and its measurement by hydrogen maser clock
Eby, P.↗
A millimeter wave parabolic antenna for communications with a synchronous satellite
Satellite mounted mm wave parabolic dish antenna for tracking communication satellite in synchronous orbit
Eby, R. J.↗
General relativistic precession of a gyroscope in an inclined orbit
General relativistic precession of gyroscope in inclined orbit
Eby, P.↗