92 research outputs found

    Liquid Methane Conditioning Capabilities Developed at the NASA Glenn Research Center's Small Multi- Purpose Research Facility (SMiRF) for Accelerated Lunar Surface Storage Thermal Testing

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    Glenn Research Center s Creek Road Cryogenic Complex, Small Multi-Purpose Research Facility (SMiRF) recently completed validation / checkout testing of a new liquid methane delivery system and liquid methane (LCH4) conditioning system. Facility checkout validation was conducted in preparation for a series of passive thermal control technology tests planned at SMiRF in FY10 using a flight-like propellant tank at simulated thermal environments from 140 to 350K. These tests will validate models and provide high quality data to support consideration of LCH4/LO2 propellant combination option for a lunar or planetary ascent stage.An infrastructure has been put in place which will support testing of large amounts of liquid methane at SMiRF. Extensive modifications were made to the test facility s existing liquid hydrogen system for compatibility with liquid methane. Also, a new liquid methane fluid conditioning system will enable liquid methane to be quickly densified (sub-cooled below normal boiling point) and to be quickly reheated to saturation conditions between 92 and 140 K. Fluid temperatures can be quickly adjusted to compress the overall test duration. A detailed trade study was conducted to determine an appropriate technique to liquid conditioning with regard to the SMiRF facility s existing infrastructure. In addition, a completely new roadable dewar has been procured for transportation and temporary storage of liquid methane. A new spherical, flight-representative tank has also been fabricated for integration into the vacuum chamber at SMiRF. The addition of this system to SMiRF marks the first time a large-scale liquid methane propellant test capability has been realized at Glenn.This work supports the Cryogenic Fluid Management Project being conducted under the auspices of the Exploration Technology Development Program, providing focused cryogenic fluid management technology efforts to support NASA s future robotic or human exploration missions

    Work-Unit Absenteeism: Effects of Satisfaction, Commitment, Labor Market Conditions, and Time

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    Prior research is limited in explaining absenteeism at the unit level and over time. We developed and tested a model of unit-level absenteeism using five waves of data collected over six years from 115 work units in a large state agency. Unit-level job satisfaction, organizational commitment, and local unemployment were modeled as time-varying predictors of absenteeism. Shared satisfaction and commitment interacted in predicting absenteeism but were not related to the rate of change in absenteeism over time. Unit-level satisfaction and commitment were more strongly related to absenteeism when units were located in areas with plentiful job alternatives

    Social, Structural and Behavioral Determinants of Overall Health Status in a Cohort of Homeless and Unstably Housed HIV-Infected Men

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    Background: Previous studies indicate multiple influences on the overall health of HIV-infected persons; however, few assess and rank longitudinal changes in social and structural barriers that are disproportionately found in impoverished populations. We empirically ranked factors that longitudinally impact the overall health status of HIV-infected homeless and unstably housed men. Methods and Findings: Between 2002 and 2008, a cohort of 288 HIV+ homeless and unstably housed men was recruited and followed over time. The population was 60 % non-Caucasian and the median age was 41 years; 67 % of study participants reported recent drug use and 20 % reported recent homelessness. At baseline, the median CD4 cell count was 349 cells/ml and 18 % of eligible persons (CD4,350) took antiretroviral therapy (ART). Marginal structural models were used to estimate the population-level effects of behavioral, social, and structural factors on overall physical and mental health status (measured by the SF-36), and targeted variable importance (tVIM) was used to empirically rank factors by their influence. After adjusting for confounding, and in order of their influence, the three factors with the strongest negative effects on physical health were unmet subsistence needs, Caucasian race, and no reported source of instrumental support. The three factors with the strongest negative effects on mental health were unmet subsistence needs, not having a close friend/confidant, and drug use. ART adherence.90 % ranked 5th for its positive influence on mental health, and viral loa

    Apophis planetary defense campaign

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    We describe results of a planetary defense exercise conducted during the close approach to Earth by the near-Earth asteroid (99942) Apophis during 2020 December–2021 March. The planetary defense community has been conducting observational campaigns since 2017 to test the operational readiness of the global planetary defense capabilities. These community-led global exercises were carried out with the support of NASA's Planetary Defense Coordination Office and the International Asteroid Warning Network. The Apophis campaign is the third in our series of planetary defense exercises. The goal of this campaign was to recover, track, and characterize Apophis as a potential impactor to exercise the planetary defense system including observations, hypothetical risk assessment and risk prediction, and hazard communication. Based on the campaign results, we present lessons learned about our ability to observe and model a potential impactor. Data products derived from astrometric observations were available for inclusion in our risk assessment model almost immediately, allowing real-time updates to the impact probability calculation and possible impact locations. An early NEOWISE diameter measurement provided a significant improvement in the uncertainty on the range of hypothetical impact outcomes. The availability of different characterization methods such as photometry, spectroscopy, and radar provided robustness to our ability to assess the potential impact risk

    BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

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    Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

    Antitrust and Regulation

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