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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 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Learning random networks for compression of still and moving images

Image compression for both still and moving images is an extremely important area of investigation, with numerous applications to videoconferencing, interactive education, home entertainment, and potential applications to earth observations, medical imaging, digital libraries, and many other areas. We describe work on a neural network methodology to compress/decompress still and moving images. We use the 'point-process' type neural network model which is closer to biophysical reality than standard models, and yet is mathematically much more tractable. We currently achieve compression ratios of the order of 120:1 for moving grey-level images, based on a combination of motion detection and compression. The observed signal-to-noise ratio varies from values above 25 to more than 35. The method is computationally fast so that compression and decompression can be carried out in real-time. It uses the adaptive capabilities of a set of neural networks so as to select varying compression ratios in real-time as a function of quality achieved. It also uses a motion detector which will avoid retransmitting portions of the image which have varied little from the previous frame. Further improvements can be achieved by using on-line learning during compression, and by appropriate compensation of nonlinearities in the compression/decompression scheme. We expect to go well beyond the 250:1 compression level for color images with good quality levels.

Gelenbe, Erol↗
DOE OSTI2025

Symplectic neural network and its application to charged particle dynamics in electromagnetic fields

Recently, machine learning models have shown many successes in various applications in science and technology. In this work, we focus on the charged particle dynamics, with the development of a class of symplectic neural networks, including a linear version, SympMat, and a nonlinear version, HénonNet. Both are designed to preserve the structure of Hamiltonian systems. We show that they can be used to model relevant Hamiltonian systems of interest in plasma physics and astrophysics, for linear and nonlinear charged particle dynamics, with the potential to bridge multi-scale simulations. These symplectic neural networks are adapted to the applications in plasma simulations and particle-wave interaction with parametric dependence and periodicity, where we have investigated their performance and accuracy. In particular, SympMat is shown to outperform the traditional Boris particle pusher down to the sub-gyroperiod scale in the case of charged particles in uniform magnetic fields. HénonNet successfully predicts the hot electron distribution, which is validated against theoretical results. These results highlight the potential of symplectic neural networks as a trajectory integrator for particle-in-cell simulations or a fast surrogate to replace conventional numerical schemes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2005

It's a Sooty Problem: Black Carbon and Aerosols from Space

Our knowledge of atmospheric aerosols (smoke, pollution, dust or sea salt particles, small enough to be suspended in the air), their evolution, composition, variability in space and time and interaction with solar radiation, clouds and precipitation is lacking despite decades of research. Just recently we recognized that understanding the global aerosol system is fundamental for progress in climate change and hydrological cycle research. While a single instrument was used to demonstrate 50 yrs ago that the global CO2 levels are rising, posing thread to our climate, we need an may of satellites, surface networks of radiometers, elaborated laboratory and field experiments coupled with chemical transport models to understand the global aerosol system. This complexity of the aerosol problem results from their short lifetime (1 week), variability of the chemical composition and complex chemical and physical processes in the atmosphere. The result is a heterogeneous distribution of aerosol and their properties. The new generation of satellites and surface networks of radiometers provides exciting opportunities to measure the aerosol properties and their interaction with clouds and climate. However farther development in the satellite capability, aerosol chemical models and climate models is needed to fully decipher the aerosol secrets with accuracy required to predict future climates.

Kaufman, Yoram J.↗
DOE OSTI2025

Using Neural Networks for Low Energy Reconstruction and Neutron Identification in the MicroBooNE LArTPC

Identifying and reconstructing final-state neutrons from neutrino interactions in Liquid Argon Time Projection Chambers (LArTPCs) will enhance future oscillation measurements by recovering missing energy and improving neutrino interaction channel identification. However, neutrons are challenging to reconstruct as the majority leave only small, isolated charge signatures known as blips. Here we present initial efforts to identify neutrons in the MicroBooNE LArTPC with low energy protons from neutron-argon inelastic interactions that present as blips below the traditional tracking threshold in the TPC. Unlike for tracks, there is no algorithmic method to determine direction for blips since they span only a few wires. Therefore, we developed and trained a Recurrent Neural Network (RNN) to reconstruct the directionality of proton-induced blips, allowing us to separate signal from background by selecting blips that point back to the neutrino vertex. The model achieves a preliminary average angular resolution of 17 degrees when tested on a simulated sample of protons over 6 MeV in kinetic energy. This novel tool will enhance neutron detection in LArTPCs and expand a broad range of other low-energy physics searches such as for solar and supernova neutrinos.

Silva, Liani Isabel [Unlisted, US]↗
NASA NTRS2004

Tower Mesonetwork Climatology and Interactive Display Tool

Forecasters at the 45th Weather Squadron and Spaceflight Meteorology Group use data from the tower network over the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) to evaluate Launch Commit Criteria, and issue and verify forecasts for ground operations. Systematic biases in these parameters could adversely affect an analysis, forecast, or verification. Also, substantial geographical variations in temperature and wind speed can occur under specific wind directions. To address these concerns, the Applied Meteorology Unit (AMU) developed a climatology of temperatures and winds from the tower network, and identified the geographical variation and significant tower biases. The mesoclimate is largely driven by the complex land-water interfaces across KSC/CCAFS. Towers with close proximity to water typically had much warmer nocturnal temperatures and higher wind speeds throughout the year. The strongest nocturnal wind speeds occurred from October to March whereas the strongest mean daytime wind speeds occurred from February to May. These results of this project can be viewed by forecasters through an interactive graphical user interface developed by the AMU. The web-based interface includes graphical and map displays of mean, standard deviation, bias, and data availability for any combination of towers, variables, months, hours, and wind directions.

Case, Jonathan L.↗
DOE OSTI2018

Acoustic Event Location and Background Noise Characterization on a Free Flying Infrasound Sensor Network in the Stratosphere

We present that a variety of Earth surface and atmospheric sources generate low frequency sound waves that can travel great distances. Despite a rich history of ground-based sensor studies, very few experiments have investigated the prospects of free floating microphone arrays at high altitudes. However, recent initiatives have shown that such networks have very low background noise and may sample an acoustic wave field that is fundamentally different than that at Earth’s surface. The experiments have been limited to at most two stations at altitude, making acoustic event detection and localization difficult. We describe the deployment of four drifting microphone stations at altitudes between 21 and 24 km above sea level. The stations detected one of two regional ground-based chemical explosions as well as the ocean microbarom while traveling almost 500 km across the American Southwest. The explosion signal consisted of multiple arrivals; signal amplitudes did not correlate with sensor elevation or source range. The waveforms and propagation patterns suggest interactions with gravity waves in the 35-45 km altitude. A sparse network method that employed curved wave front corrections was able to determine the backazimuth from the free flying network to the acoustic source. Episodic signals similar to those seen on previous flights in the same region were noted, but their source remains unclear. Lastly, background noise levels were commensurate with those on infrasound stations in the International Monitoring System below 2 seconds.

58 GEOSCIENCES↗
DOE OSTI2025

Intermolecular Interactions in Direct Air Capture Materials: Insights from Charge Density Analysis

Direct air capture (DAC) materials enable the removal of CO 2 from the atmosphere, but improving their efficiency requires a detailed understanding of the intermolecular interactions that govern CO 2 sorption and release. Here, we present an experimental electron density study of methylglyoxal-bis(iminoguanidine) (MGBIG), a promising DAC material, using high-resolution X-ray and neutron diffraction data combined with quantum crystallographic analysis. This approach bridges theoretical and experimental data by quantifying electron density distributions and revealing how hydrogen bonds stabilize CO 2 -derived carbonate phases and may influence the desorption behavior. We identify distinct hydrogen-bonding environments in two crystalline carbonate phases: P1, a transient kinetic product, and P3, a thermodynamically stable phase. Multipolar refinement and electrostatic potential and multipole moment calculations precisely map electron density distributions, revealing key hydrogen bonds involved in CO 2 capture. Topological analysis of electron density highlights a cooperative hydrogen-bonding network in the thermodynamically favored P3 phase, where enhanced electron density delocalization and water-mediated interactions contribute to a more stable lattice. Energetic analyses confirm that stronger hydrogen bonding networks enhance the stability of P3 with a binding energy of −607.0 kJ/mol and greater lattice stability (−847.3 kJ/mol) compared to P1 (−302.5 and −571.0 kJ/mol, respectively). Electrostatic potential maps further illustrate polarization patterns that may influence the stability of the binding of CO 2 and release conditions. These findings establish a direct experimental framework for linking electron density distributions to intermolecular interactions in DAC materials, providing a rational design strategy for optimizing sorbents with improved CO 2 capture efficiency and reduced energy demands.

Electron density↗
DOE OSTI2013

Quantitative assessment of biological impact using transcriptomic data and mechanistic network models

Exposure to biologically active substances such as therapeutic drugs or environmental toxicants can impact biological systems at various levels, affecting individual molecules, signaling pathways, and overall cellular processes. The ability to derive mechanistic insights from the resulting system responses requires the integration of experimental measures with a priori knowledge about the system and the interacting molecules therein. We developed a novel systems biology-based methodology that leverages mechanistic network models and transcriptomic data to quantitatively assess the biological impact of exposures to active substances. Hierarchically organized network models were first constructed to provide a coherent framework for investigating the impact of exposures at the molecular, pathway and process levels. We then validated our methodology using novel and previously published experiments. For both in vitro systems with simple exposure and in vivo systems with complex exposures, our methodology was able to recapitulate known biological responses matching expected or measured phenotypes. In addition, the quantitative results were in agreement with experimental endpoint data for many of the mechanistic effects that were assessed, providing further objective confirmation of the approach. We conclude that our methodology evaluates the biological impact of exposures in an objective, systematic, and quantifiable manner, enabling the computation of a systems-wide and pan-mechanistic biological impact measure for a given active substance or mixture. Our results suggest that various fields of human disease research, from drug development to consumer product testing and environmental impact analysis, could benefit from using this methodology. - Highlights: • The impact of biologically active substances is quantified at multiple levels. • The systems-level impact integrates the perturbations of individual networks. • The networks capture the relationships between the biological mechanisms. • Four exposure experiments have been assessed to validate the methodology. • The impact results were consistent with the corresponding phenotypic measures.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Tunable Stochasticity in an Artificial Spin Network

Metamaterials present the possibility of artificially generating advanced functionalities through engineering of their internal structure. Artificial spin networks, in which a large number of nanoscale magnetic elements are coupled together, are promising metamaterial candidates that enable the control of collective magnetic behavior through tuning of the local interaction between elements. In this work, the motion of magnetic domain-walls in an artificial spin network leads to a tunable stochastic response of the metamaterial, which can be tailored through an external magnetic field and local lattice modifications. This type of tunable stochastic network produces a controllable random response exploiting intrinsic stochasticity within magnetic domain-wall motion at the nanoscale. An iconic demonstration used to illustrate the control of randomness is the Galton board. In this system, multiple balls fall into an array of pegs to generate a bell-shaped curve that can be modified via the array spacing or the tilt of the board. A nanoscale recreation of this experiment using an artificial spin network is employed to demonstrate tunable stochasticity. Furthermore, this type of tunable stochastic network opens new paths toward post-Von Neumann computing architectures such as Bayesian sensing or random neural networks, in which stochasticity is harnessed to efficiently perform complex computational tasks.

Artificial spin network↗
DOE OSTI2017

Warming alters energetic structure and function but not resilience of soil food webs

Climate warming is predicted to alter the structure, stability, and functioning of food webs. Yet, despite the importance of soil food webs for energy and nutrient turnover in terrestrial ecosystems, the effects of warming on these food webs—particularly in combination with other global change drivers—are largely unknown. Here, we present results from two complementary field experiments that test the interactive effects of warming with forest canopy disturbance and drought on energy flux in boreal–temperate ecotonal forest soil food webs. The first experiment applied a simultaneous above- and belowground warming treatment (ambient, +1.7 °C, +3.4 °C) to closed-canopy and recently clear-cut forest, simulating common forest disturbance6. The second experiment crossed warming with a summer drought treatment (-40% rainfall) in the clear-cut habitats. We show that warming reduces energy flux to microbes, while forest canopy disturbance and drought facilitates warming-induced increases in energy flux to higher trophic levels and exacerbates the reduction in energy flux to microbes, respectively. Contrary to expectations, we find no change in whole-network resilience to perturbations, but significant losses in ecosystem functioning. Warming thus interacts with forest disturbance and drought, shaping the energetic structure of soil food webs and threatening the provisioning of multiple ecosystem functions in boreal–temperate ecotonal forests.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Constructing Hubbard models for the hydrogen chain using sliced-basis density matrix renormalization group

Sliced-basis DMRG(sb-DMRG) is used to simulate a chain of hydrogen atoms and to construct low-energy effective Hubbard-like models. The downfolding procedure first involves a change of basis to a set of atom-centered Wannier functions constructed from the natural orbitals of the exact DMRG one-particle density matrix. The Wannier function model is then reduced to a fewer-parameter Hubbard-like model, whose parameters are determined by minimizing the expectation value of the Wannier Hamiltonian in the ground state of the Hubbard Hamiltonian. This indirect variational procedure not only yields compact and simple models for the hydrogen chain, but also allows us to explore the importance of constraints in the effective Hamiltonian, such as the restricting the range of the single-particle hopping and two-particle interactions, and to assess the reliability of more conventional downfolding. The entanglement entropy for a model's ground state, cut in the middle, is an important property determining the ability of DMRG and tensor networks to simulate the model, and here we study its variation with the range of the interactions. Counterintuitively, we find that shorter ranged interactions often have larger entanglement.

36 MATERIALS SCIENCE↗
NASA NTRS1993

Internetworking satellite and local exchange networks for personal communications applications

The demand for personal communications services has shown unprecedented growth, and the next decade and beyond promise an era in which the needs for ubiquitous, transparent and personalized access to information will continue to expand in both scale and scope. The exchange of personalized information is growing from two-way voice to include data communications, electronic messaging and information services, image transfer, video, and interactive multimedia. The emergence of new land-based and satellite-based wireless networks illustrates the expanding scale and trend toward globalization and the need to establish new local exchange and exchange access services to meet the communications needs of people on the move. An important issue is to identify the roles that satellite networking can play in meeting these new communications needs. The unique capabilities of satellites, in providing coverage to large geographic areas, reaching widely dispersed users, for position location determination, and in offering broadcast and multicast services, can complement and extend the capabilities of terrestrial networks. As an initial step in exploring the opportunities afforded by the merger of satellite-based and land-based networks, several experiments utilizing the NASA ACTS satellite and the public switched local exchange network were undertaken to demonstrate the use of satellites in the delivery of personal communications services.

Wolff, Richard S.↗
DOE OSTI2021

A role for Rab30 in retrograde trafficking and maintenance of endosome-TGN organization

Highlights: • Rab30 localised primarily to TGN and recycling endosomes. • Rab30 plays a major role in the spatial organization of the TGN and recycling endosomes. • Deficiency of Rab30 results in a perturbation in endosome-TGN retrograde transport. • Interactive partners of Rab30 include Arf1 and Arf4. • Findings highlight the interconnected network between the TGN and endosomal system. Rab30 is a poorly characterized small GTPase. Here we show that Rab30 is localised primarily to the TGN and recycling endosomes in a range of cell types, including primary neurons; minor levels of Rab30 were also detected throughout the Golgi stack and early endosomes. Silencing of Rab30 resulted in the dispersal of both early and recycling endosomes and TGN compartments in HeLa cells. By analyzing cargo trafficking in Rab30-silenced and Rab30-overexpressing HeLa cells, we demonstrate that Rab30 plays a role in retrograde trafficking of TGN38 from endosomes to the Golgi, but has no apparent role in the endocytic recycling of the transferrin receptor to the plasma membrane. Five interactive partners with Rab30 were identified by pull-down and MS analysis using GFP-tagged Rab30 mutant, Rab30(Q68L). Two of the interactive partners identified were Arf1 and Arf4, known regulators of endosome to TGN retrograde transport. Knockdown of Arf1 and Arf4 results in GFP-Rab30 decorated tubules arising from the recycling endosomes, suggesting association of Rab30 with tubular carriers. Overall our data demonstrates a role for Rab30 in regulating retrograde transport to the TGN and maintenance of endosomal-TGN organization.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Agricultural management and plant selection interactively affect rhizosphere microbial community structure and nitrogen cycling

BACKGROUND: Rhizosphere microbial communities are key regulators of plant performance, yet few studies have assessed the impact of different management approaches on the rhizosphere microbiomes of major crops. Rhizosphere microbial communities are shaped by interactions between agricultural management and host selection processes, but studies often consider these factors individually rather than in combination. We tested the impacts of management (M) and rhizosphere effects (R) on microbial community structure and co-occurrence networks of maize roots collected from long-term conventionally and organically managed maize-tomato agroecosystems. We also explored the interaction between these factors (M × R) and how it impacts rhizosphere microbial diversity and composition, differential abundance, indicator taxa, co-occurrence network structure, and microbial nitrogen-cycling processes. RESULTS: Host selection processes moderate the influence of agricultural management on rhizosphere microbial communities, although bacteria and fungi respond differently to plant selection and agricultural management. We found that plants recruit management-system-specific taxa and shift N-cycling pathways in the rhizosphere, distinguishing this soil compartment from bulk soil. Rhizosphere microbiomes from conventional and organic systems were more similar in diversity and network structure than communities from their respective bulk soils, and community composition was affected by both M and R effects. In contrast, fungal community composition was affected only by management, and network structure only by plant selection. Quantification of six nitrogen-cycling genes (nifH, amoA [bacterial and archaeal], nirK, nrfA, and nosZ) revealed that only nosZ abundance was affected by management and was higher in the organic system. CONCLUSIONS: Plant selection interacts with conventional and organic management practices to shape rhizosphere microbial community composition, co-occurrence patterns, and at least one nitrogen-cycling process. Reframing research priorities to better understand adaptive plant-microbe feedbacks and include roots as a significant moderating influence of management outcomes could help guide plant-oriented strategies to improve productivity and agroecosystem sustainability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2011

b' search at the LHC

We consider the production and detection of a sequential, down type quark via the mode pp{yields}b'b-bar'{yields}W{sup +}W{sup -}tt-bar{yields}l{nu}{sub l}8j at the LHC, with the collision energy {radical}(s)=10 TeV and the total integrated luminosity around 1 fb{sup -1}. We assume m{sub b'}=m{sub t'}=600 GeV. A full reconstruction is employed and the signal and background discrimination is studied within a neural network approach. Our results show that this mode can make a useful contribution to the b' search.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2012

Structures, Energetics and Spectroscopic Fingerprints of Water Clusters n = 2-24

Water's function as a universal solvent and its role in mediating several biological functions that are responsible for sustaining life has created tremendous interest in the understanding of its structure at the molecular level.1 Due to the size of the simulation cells and the sampling time needed to compute many macroscopic properties, most of the initial simulations are performed using a classical force field whereas several processes that involve chemistry are subsequently probed with electronic structure based methods. A significant effort has therefore been devoted towards the development of classical force fields for water.2 Clusters of water molecules are useful in probing the intermolecular interactions at the microscopic level as well as providing information about the subtle energy differences that are associated with different bonding arrangements within a hydrogen bonded network. They moreover render a quantitative picture of the nature and magnitude of the various components of the intermolecular interactions such as exchange, dispersion, induction etc. They can finally serve as a vehicle for the study of the convergence of properties with increasing size.

08 HYDROGEN↗
DOE OSTI2025

Ca X ML: Chemistry‐informed machine learning explains mutual changes between protein conformations and calcium ions in calcium‐binding proteins using structural and topological features

Proteins' flexibility is a feature in communicating changes in cell signaling instigated by binding with secondary messengers, such as calcium ions, associated with the coordination of muscle contraction, neurotransmitter release, and gene expression. When binding with the disordered parts of a protein, calcium ions must balance their charge states with the shape of calcium-binding proteins and their versatile pool of partners depending on the circumstances they transmit. Accurately determining the ionic charges of those ions is essential for understanding their role in such processes. However, it is unclear whether the limited experimental data available can be effectively used to train models to accurately predict the charges of calcium-binding protein variants. Here, we developed a chemistry-informed, machine-learning algorithm that implements a game theoretic approach to explain the output of a machine-learning model without the prerequisite of an excessively large database for high-performance prediction of atomic charges. We used the ab initio electronic structure data representing calcium ions and the structures of the disordered segments of calcium-binding peptides with surrounding water molecules to train several explainable models. Network theory was used to extract the topological features of atomic interactions in the structurally complex data dictated by the coordination chemistry of a calcium ion, a potent indicator of its charge state in protein. Our design created a computational tool of Ca X ML, which provided a framework of explainable machine learning model to annotate ionic charges of calcium ions in calcium-binding proteins in response to the chemical changes in an environment. Our framework will provide new insights into protein design for engineering functionality based on the limited size of scientific data in a genome space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗