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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Amyloplast sedimentation dynamics in maize columella cells support a new model for the gravity-sensing apparatus of roots

Quantitative analysis of statolith sedimentation behavior was accomplished using videomicroscopy of living columella cells of corn (Zea mays) roots, which displayed no systematic cytoplasmic streaming. Following 90 degrees rotation of the root, the statoliths moved downward along the distal wall and then spread out along the bottom with an average velocity of 1.7 microm min(-1). When statolith trajectories traversed the complete width or length of the cell, they initially moved horizontally toward channel-initiation sites and then moved vertically through the channels to the lower side of the reoriented cell where they again dispersed. These statoliths exhibited a significantly lower average velocity than those sedimenting on distal-to-side trajectories. In addition, although statoliths undergoing distal-to-side sedimentation began at their highest velocity and slowed monotonically as they approached the lower cell membrane, statoliths crossing the cell's central region remained slow initially and accelerated to maximum speed once they reached a channel. The statoliths accelerated sooner, and the channeling effect was less pronounced in roots treated with cytochalasin D. Parallel ultrastructural studies of high-pressure frozen-freeze-substituted columella cells suggest that the low-resistance statolith pathway in the cell periphery corresponds to the sharp interface between the endoplasmic reticulum (ER)-rich cortical and the ER-devoid central region of these cells. The central region is also shown to contain an actin-based cytoskeletal network in which the individual, straight, actin-like filaments are randomly distributed. To explain these findings as well as the results of physical simulation experiments, we have formulated a new, tensegrity-based model of gravity sensing in columella cells. This model envisages the cytoplasm as pervaded by an actin-based cytoskeletal network that is denser in the ER-devoid central region than in the ER-rich cell cortex and is linked to stretch receptors in the plasma membrane. Sedimenting statoliths are postulated to produce a directional signal by locally disrupting the network and thereby altering the balance of forces acting on the receptors in different plasma membrane regions.

NASA Program Fundamental Space Biology↗
NASA NTRS2019

Ensuring Flexibility and Security in SDN-Based Spacecraft Communication Networks Through Risk Assessment

Software-defined networking (SDN) has enabled elastic networking and resource distribution in cloud computing. The centralization and separation of the Control Plane also offers a high degree of network configurability and management, which can be used to mitigate and manage threats to the network. Space communication networks have historically been restricted and circuit switching in these networks has been a manual process. This study evaluates the potential role of SDN in space communication networks from a networking security standpoint. The evaluation covers the networking security needs of spacecraft missions and their associated assets. The results from the evaluation lead to a risk assessment that identifies vulnerabilities in an SDN-based communications architecture. Security challenges introduced into the network from integrating SDN are also considered. A risk register summarizes the severity of the attack outcomes, as well as occurrence likelihood. The study identifies Denial-of-Service (DoS) attacks as a new threat (presently unmitigated by existing security controls) that would be prevalent in an SDN-based space communication environment. A Mininet-based emulation testbed is built to demonstrate the susceptibility of spacecraft flight software to a flooding DoS attack when on an interconnected SDN-managed network. This type of attack would be highly consequential to mission assets, and therefore SDN-based space communications would need to be resilient to such attacks. Future work will need to be performed to fully characterize DoS attack methods that can apply to the space communication scenario, as well as to devise a comprehensive DoS-resilient solution.

Baker, Dylan Z.↗
DOE OSTI2022

The Edge of Exploration: An Edge Storage and Computing Framework for Ambient Noise Seismic Interferometry Using Internet of Things Based Sensor Networks

Recent technological advances have reduced the complexity and cost of developing sensor networks for remote environmental monitoring. However, the challenges of acquiring, transmitting, storing, and processing remote environmental data remain significant. The transmission of large volumes of sensor data to a centralized location (i.e., the cloud) burdens network resources, introduces latency and jitter, and can ultimately impact user experience. Edge computing has emerged as a paradigm in which substantial storage and computing resources are located at the “edge” of the network. In this paper, we present an edge storage and computing framework leveraging commercially available components organized in a tiered architecture and arranged in a hub-and-spoke topology. The framework includes a popular distributed database to support the acquisition, transmission, storage, and processing of Internet-of-Things-based sensor network data in a field setting. We present details regarding the architecture, distributed database, embedded systems, and topology used to implement an edge-based solution. Lastly, a real-world case study (i.e., seismic) is presented that leverages the edge storage and computing framework to acquire, transmit, store, and process millions of samples of data per hour.

58 GEOSCIENCES↗
DOE OSTI2025

Designing reinforcement learning algorithms for building HVAC control: From experimental observation to simulation comparisons

Advanced supervisory-level control with reinforcement learning (RL) is regarded as a promising solution for HVAC systems to minimize energy consumption while maintaining thermal comfort and indoor air quality. However, most RL applications were conducted in the simulation environment rather than real-world HVAC systems. This paper developed a value-based RL controller termed Deep Q-Network (DQN) for a typical central HVAC system and evaluated its performance in a building test facility. By comparing DQN with a rule-based controller, the study not only demonstrated the cases where DQN could properly maintain indoor comfort but also discussed possible reasons why DQN failed in some other situations. Recognizing the limitations of value-based RL algorithms from the experimental tests, a simulation study was conducted to compare DQN with an alternative RL approach, an actor–critic algorithm termed Deep Deterministic Policy Gradient (DDPG). In scenarios with a relatively large action space, DDPG outperformed DQN by requiring fewer computational resources and achieving better thermal comfort, lower energy consumption, and more stable control actions. The findings suggest that the ability of DDPG to handle continuous control variables more effectively allows for faster convergence in training and more precise control in practice, which enhances the overall efficiency and reliability of the HVAC system.

Guo, Fangzhou↗
DOE OSTI2023

Viral but not bacterial community successional patterns reflect extreme turnover shortly after rewetting dry soils

As central members of soil trophic networks, viruses have the potential to drive substantial microbial mortality and nutrient turnover. Pinpointing viral contributions to terrestrial ecosystem processes remains a challenge, as temporal dynamics are difficult to unravel in the spatially and physicochemically heterogeneous soil environment. In Mediterranean grasslands, the first rainfall after seasonal drought provides an ecosystem reset, triggering microbial activity during a tractable window for capturing short-term dynamics. In this report we simulated precipitation in microcosms from four distinct dry grassland soils and generated 144 viromes, 84 metagenomes and 84 16S ribosomal RNA gene amplicon datasets to characterize viral, prokaryotic and relic DNA dynamics over 10 days. Vastly different viral communities in each soil followed remarkably similar successional trajectories. Wet-up triggered a significant increase in viral richness, followed by extensive compositional turnover. Temporal succession in prokaryotic communities was much less pronounced, perhaps suggesting differences in the scales of activity captured by viromes (representing recently produced, ephemeral viral particles) and total DNA. Still, differences in the relative abundances of Actinobacteria (enriched in dry soils) and Proteobacteria (enriched in wetted soils) matched those of their predicted phages, indicating viral predation of dominant bacterial taxa. Rewetting also rapidly depleted relic DNA, which subsequently reaccumulated, indicating substantial new microbial mortality in the days after wet-up, particularly of the taxa putatively under phage predation. Production of abundant, diverse viral particles via microbial host cell lysis appears to be a conserved feature of the early response to soil rewetting, and results suggest the potential for ‘Cull-the-Winner’ dynamics, whereby viruses infect and cull but do not decimate dominant host populations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Universal Monte Carlo Event Generator

With the Jefferson Lab 12 GeV physics program underway and plans for the future Electron-Ion Collider (EIC), the nuclear physics community is entering a new era of exploration of QCD phenomena involving extensive data taking and event-level processing. This brings with it the potential for unprecedented access to multidimensional particle momentum distributions (PMDs) that can be connected to various theoretical frameworks by unfolding the emergent quantum mechanical properties of QCD using the PMDs. In practice, the PMDs are rendered as discretized histograms (typically one- or two-dimensional projections), and detector effects must be taken into account to unfold the pure detector effect-free PMDs that can be connected with theory. One of the challenges in this new era is obtaining faithful reconstructions of the multidimensional PMDs that preserve all of the inherent particle correlations. In this LDRD project we developed a novel approach using machine learning (ML) that solves this challenge, by avoiding entirely the need to use histograms as the main numerical technique to obtain the detector effect-free PMDs. The new approach is, moreover, scalable to higher dimensional PMDs. The central idea involves training neural networks (NNs) to generate synthetic event-level data (momenta 4-vectors of final state particles) to preserve all correlations among the particles. This is achieved by converting the trial synthetic vertex-level events to detector-level events using detector simulators. The NNs are then tuned using a specialized distance metric between the synthetic detector events and the real detector events.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1986

Support for global science - Remote sensing's challenge

Advances in remote sensing techniques are discussed. The benefits possible to remote sensing with the new Earth Observing System, which is composed of the Space Station and coorbiting and polar satellite platforms, are examined. Current changes in the remote sensing field, which involve a change from an industrial society to an informational society, force technology to high technology with high touch, short term to long term, centralized to decentralized, hierarchies to networks, and either/or to multiple option systems are studied. The explanatory and objective types of analyses for investigating biophysical, geochemical, and socioeconomic processes are described; the procedures include: morphometric, cause and effect, temporal and functional and ecological system analyses, inventory, mapping, monitoring, and modeling.

Estes, J. E.↗
NASA NTRS1986

Programming support environment issues in the Byron programming environment

Issues are discussed which programming support environments need to address in order to successfully support software engineering. These concerns are divided into two categories. The first category, issues of how software development is supported by an environment, includes support of the full life cycle, methodology flexibility, and support of software reusability. The second category contains issues of how environments should operate, such as tool reusability and integration, user friendliness, networking, and use of a central data base. This discussion is followed by an examination of Byron, an Ada based programming support environment developed at Intermetrics, focusing on the solutions Byron offers to these problems, including the support provided for software reusability and the test and maintenance phases of the life cycle. The use of Byron in project development is described briefly, and some suggestions for future Byron tools and user written tools are presented.

Larsen, Matthew J.↗
NASA NTRS1993

CONVEX mini manual

The use of the CONVEX computers that are an integral part of the Supercomputing Network Subsystems (SNS) of the Central Scientific Computing Complex of LaRC is briefly described. Features of the CONVEX computers that are significantly different than the CRAY supercomputers are covered, including: FORTRAN, C, architecture of the CONVEX computers, the CONVEX environment, batch job submittal, debugging, performance analysis, utilities unique to CONVEX, and documentation. This revision reflects the addition of the Applications Compiler and X-based debugger, CXdb. The document id intended for all CONVEX users as a ready reference to frequently asked questions and to more detailed information contained with the vendor manuals. It is appropriate for both the novice and the experienced user.

Tennille, Geoffrey M.↗
DOE OSTI2023

Videoshare

SAND2023-05132O Videoshare is an application that allows users to communicate in a variety of ways, including voice, video, and text, over a local network without having to use a central server or an internet connection. The software features: • Screen-sharing and connecting to external IP cameras. • Ability to hold group calls on computers running the same software and network. • Ability to connect to other computers running the same software. • Curating potential connections and manually adding IP addresses to available connections. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

McIlraith, Aiden↗
NASA NTRS1986

Wind profiler demonstration system

The National Oceanic and Atmospheric Administration (NOAA) has started procurement of a 30-station demonstration network of Doppler radar wind profilers to be deployed in the central United States by 1989. The purposes of this demonstration network are: (1) to assess the impact of a large wind profiler network on meteorological forecasting; (2) to test wind profiler hardware designed specifically for commercial production and widespread deployment; and (3) to provide wind data for research programs involving weather phenomena in the central United States.

Chadwick, R. B.↗
NASA NTRS1994

Alarm- And Power-Monitoring System

Electronic central monitoring system, called Remote Monitor Alarm System, RMAS, used to monitor malfunction alarms and power supplies of remotely located equipment modules of transmitting and receiving equipment in fiber-optic communication network at Kennedy Space Center. Includes central monitoring unit at location convenient for technicians, plus remote terminal unit at each remote site containing equipment to be monitored.

Stute, Rob↗
DOE OSTI2022

Hierarchical distributed voltage regulation

Embodiments of the disclosure provide for hierarchically distributed voltage regulation and power flow optimization in a power distribution network. According to one embodiment, a regional coordinator system may determine power flow for an autonomous grid (AG) representing a portion of the distribution network based on a set of voltage parameters from nodes in the AG. The set of voltage parameters my include a current voltage magnitude. The regional coordinator system may provide, to a central controller system of the distribution network, one or more power flow parameters based on the determined power flow for the AG.

Zhou, Xinyang↗
NASA NTRS1986

The C23A system, an exmaple of quantitative control of plant growth associated with a data base

The architecture of the C23A (Chambers de Culture Automatique en Atmosphere Artificielles) system for the controlled study of plant physiology is described. A modular plant growth chambers and associated instruments (I.R. CO2 analyser, Mass spectrometer and Chemical analyser); network of frontal processors controlling this apparatus; a central computer for the periodic control and the multiplex work of processors; and a network of terminal computers able to ask the data base for data processing and modeling are discussed. Examples of present results are given. A growth curve analysis study of CO2 and O2 gas exchanges of shoots and roots, and daily evolution of algal photosynthesis and of the pools of dissolved CO2 in sea water are discussed.

Andre, M.↗
DOE OSTI2022

Hypothesis-Agnostic Network-Based Analysis of Real-World Data Suggests Ondansetron is Associated with Lower COVID-19 Any Cause Mortality

Background: The COVID-19 pandemic generated a massive amount of clinical data, which potentially hold yet undiscovered answers related to COVID-19 morbidity, mortality, long-term effects, and therapeutic solutions.Objectives: The objectives of this study were (1) to identify novel predictors of COVID-19 any cause mortality by employing artificial intelligence analytics on real-world data through a hypothesis-agnostic approach and (2) to determine if these effects are maintained after adjusting for potential confounders and to what degree they are moderated by other variables.Methods: A Bayesian statistics-based artificial intelligence data analytics tool (bAIcis®) within the Interrogative Biology® platform was used for Bayesian network learning and hypothesis generation to analyze 16,277 PCR+ patients from a database of 279,281 inpatients and outpatients tested for SARS-CoV-2 infection by antigen, antibody, or PCR methods during the first pandemic year in Central Florida. This approach generated Bayesian networks that enabled unbiased identification of significant predictors of any cause mortality for specific COVID-19 patient populations. These findings were further analyzed by logistic regression, regression by least absolute shrinkage and selection operator, and bootstrapping.Results: We found that in the COVID-19 PCR+ patient cohort, early use of the antiemetic agent ondansetron was associated with decreased any cause mortality 30 days post-PCR+ testing in mechanically ventilated patients.Conclusions: The results demonstrate how a real-world COVID-19-focused data analysis using artificial intelligence can generate unexpected yet valid insights that could possibly support clinical decision making and minimize the future loss of lives and resources.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Insights from application of a hierarchical spatio-temporal model to an intensive urban black carbon monitoring dataset

Existing regulatory pollutant monitoring networks rely on a small number of centrally located measurement sites that are purposefully sited away from major emission sources. While informative of general air quality trends regionally, these networks often do not fully capture the local variability of air pollution exposure within a community. Recent technological advancements have reduced the cost of sensors, allowing air quality monitoring campaigns with high spatial resolution. The 100×100 black carbon (BC) monitoring network deployed 100 low-cost BC sensors across the 15 km 2 West Oakland, CA community for 100 days in the summer of 2017, producing a nearly continuous site-specific time series of BC concentrations which we aggregated to one-hour averages. Leveraging this dataset, we employed a hierarchical spatio-temporal model to accurately predict local spatio-temporal concentration patterns throughout West Oakland, at locations without monitors (average cross-validated hourly temporal R 2 =0.60). Using our model, we identified spatially varying temporal pollution patterns associated with small-scale geographic features and proximity to local sources. In a sub-sampling analysis, here we demonstrated that fine scale predictions of nearly comparable accuracy can be obtained with our modeling approach by using ~30% of the 100×100 BC network supplemented by a shorter-term high-density campaign.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Graph identification of proteins in tomograms ( GRIP‐Tomo )

Abstract In this study, we present a method of pattern mining based on network theory that enables the identification of protein structures or complexes from synthetic volume densities, without the knowledge of predefined templates or human biases for refinement. We hypothesized that the topological connectivity of protein structures is invariant, and they are distinctive for the purpose of protein identification from distorted data presented in volume densities. Three‐dimensional densities of a protein or a complex from simulated tomographic volumes were transformed into mathematical graphs as observables. We systematically introduced data distortion or defects such as missing fullness of data, the tumbling effect, and the missing wedge effect into the simulated volumes, and varied the distance cutoffs in pixels to capture the varying connectivity between the density cluster centroids in the presence of defects. A similarity score between the graphs from the simulated volumes and the graphs transformed from the physical protein structures in point data was calculated by comparing their network theory order parameters including node degrees, betweenness centrality, and graph densities. By capturing the essential topological features defining the heterogeneous morphologies of a network, we were able to accurately identify proteins and homo‐multimeric complexes from 10 topologically distinctive samples without realistic noise added. Our approach empowers future developments of tomogram processing by providing pattern mining with interpretability, to enable the classification of single‐domain protein native topologies as well as distinct single‐domain proteins from multimeric complexes within noisy volumes.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2012

The Use of the North Alabama Lightning Mapping Array (NALMA) in the Real-Time Operational Warning Environment During the March 2nd, 2012 Severe Weather Outbreak in Northern Alabama

The North Alabama Lightning Mapping Array (NALMA) is a three-dimensional very high frequency (VHF) detection network consisting of 11 sensors spread across north central Alabama and two sensors located in the Atlanta, Georgia region. The primary advantage of this network is that it detects total lightning, or the combination of both cloud-to-ground and intra-cloud lightning, instead of cloud-to-ground lightning alone. This helps to build a complete picture of storm evolution and development, and can serve as a proxy for storm updraft strength, particularly since intra-cloud lightning makes up the majority of all lightning in a typical thunderstorm. While the NALMA data do not directly indicate severe weather, they can indirectly indicate when a storm is strengthening (weakening) due to increases (decreases) in updraft strength, as the updraft is responsible for charging mechanisms within the storm. Data output are VHF radiation sources, which are produced during lightning breakdown processes. These sources are made into 2x2 km source density grids and are ported into the Advanced Weather Interactive Processing System (AWIPS) for National Weather Service (NWS) offices in Huntsville, AL, Nashville, TN, Morristown, TN, and Birmingham, AL, in near real-time. An increase in sources, or source densities, correlates to increased lightning activity and trends in updraft magnitude as long as the storm is within about 125 km of the center of the LMA network. Operationally, these data have been used at the Huntsville NWS office since early 2003 through a collaborative effort with NASA s Short-term Prediction Research and Transition (SPoRT) Center. Since then, total lightning observations have become an essential tool for forecasters during real-time warning operations. One of the operational advantages of the NALMA is the two-minute temporal resolution of the data. This provides forecasters with two to three updates during a typical volume scan of the WSR-88D radar.

White, Kristopher↗