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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Crystal structure of the human glial fibrillary acidic protein 1B domain

Highlights: • The first crystal structure of the human GFAP 1B domain was determined. • Two parallel coiled-coils pack together lengthwise in an antiparallel configuration. • H-bonds, salt bridges, and hydrophobic interactions stabilize the homotetramer. • Alexander disease mutations disrupt the interactions and the GFAP filament network. Glial fibrillary acidic protein (GFAP) is a homopolymeric type III intermediate filament (IF) that plays essential roles in cell migration, mitosis, development, and signaling in astrocytes and a specific type of glial cells. Its overexpression and genetic mutations lead to abnormal IF networks and accumulation of Rosenthal fibers, which results in the fatal neurodegenerative disorder Alexander disease. Herein, we present the first crystal structure of human GFAP spanning the central coiled-coil 1B domain at 2.5 Å resolution. The domain forms a tetramer comprising two equivalent parallel coiled-coil dimers that pack together in an antiparallel manner. Its assembly is stabilized by extensive networks of intermolecular hydrogen bonds, salt bridges, and hydrophobic interactions. Furthermore, mapping of the GFAP mutations associated with Alexander disease reveals that most involve residues buried in the core of the interface, and are likely to disrupt the intermolecular interactions and/or introduce steric clashes, thereby decreasing GFAP solubility and promoting aggregation. Based on our structural analysis and previous biochemical studies, we propose that GFAP assembles in the A11 mode in which coiled-coil 1B dimers lie in close axial proximity in an antiparallel fashion to provide a stable tetrameric platform for the organization of the GFAP filament.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Mixed ligand coordination polymers with flexible bis-imidazole linker and angular sulfonyldibenzoate: Crystal structure, photoluminescence and photocatalytic activity

Four ternary coordination polymers (CPs) namely, ([Ni(SDB)(BITMB)(H{sub 2}O)]·H{sub 2}O){sub n} (CP1), ([Cd(SDB)(BITMB) (H{sub 2}O)]·(THF)(H{sub 2}O)){sub n} (CP2), ([Zn{sub 2}(SDB){sub 2}(BITMB)]·(THF){sub 2}){sub n} (CP3) and ([Co{sub 2}(SDB){sub 2}(BITMB)]·(Dioxane){sub 3}){sub n} (CP4) composed of angular dicarboxylate SDB (4,4'-sulfonyldibenzoate) and N-donor BITMB (1,3-bis(imidazol-1-ylmethyl)-2,4,6-trimethyl benzene) have been synthesized by solvothermal reactions and characterized by single crystal X-ray diffraction and other physico-chemical techniques. CP1 possesses one-dimensional ribbon type metal–organic motifs glued together by H-bonds and π⋯π interactions, whereas CP2–CP4, exhibit non-interpenetrated sql networks supported by weak supramolecular interactions. Structural diversity of these CPs can be attributed to the coordination geometry adopted by the metal nodes, versatile coordination modes of SDB and conformational flexibility of BITMB. Solid state luminescence properties of CP1–CP4 were explored. Photocatalytic performance of all CPs for the decomposition of metanil yellow by dilute hydrogen peroxide in the presence of visible light was also investigated. 25–83% dye removal from aqueous solutions in the presence of CP1–CP4 was observed. - Graphical abstract: Four new ternary transition metal CPs have been hydrothermally prepared and their structural aspects as well as photocatalytic activity for decolourization of metanil yellow (MY) dye have been investigated. - Highlights: • Four ternary coordination polymers containing Ni, Cd, Zn and Co center are prepared. • Crystal structure and thermal stability of all four CPs has been described. • PL and diffuse reflectance spectra of synthesized CPs have also been examined. • Band gap values suggest semiconducting behavior of prepared CPs. • Photocatalytic activity of CPs for oxidative degradation of metanil yellow is studied.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Learning Sequential Distribution System Restoration via Graph-Reinforcement Learning

We report a distribution service restoration algorithm as a fundamental resilient paradigm for system operators provides an optimally coordinated, resilient solution to enhance the restoration performance. The restoration problem is formulated to coordinate distribution generators and controllable switches optimally. A model-based control scheme is usually designed to solve this problem, relying on a precise model and resulting in low scalability. To tackle these limitations, this work proposes a graph-reinforcement learning framework for the restoration problem. We link the power system topology with a graph convolutional network, which captures the complex mechanism of network restoration in power networks and understands the mutual interactions among controllable devices. Latent features over graphical power networks produced by graph convolutional layers are exploited to learn the control policy for network restoration using deep reinforcement learning. The solution scalability is guaranteed by modeling distributed generators as agents in a multi-agent environment and a proper pre-training paradigm. Comparative studies on IEEE 123-node and 8500-node test systems demonstrate the performance of the proposed solution.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1993

Timing in private digital telecommunication networks

For proper operation of today's large digital private networks, high quality synchronization must be achieved. A general telecommunication performance objective is to maintain long-term frequency accuracy of ten parts per trillion at all synchronous digital equipment in the network. Many times, however, this is not achieved in private networks. Low quality clocks, errored transmission facilities, and incorrectly designed synchronization plans are often cause for poor performance. It is shown that properly-designed private networks can operate with long term frequency averages between ten parts per trillion to ten parts per million. These performance levels can adversely impact customer applications. The most demanding applications are digital and voice band data, encrypted voice, facsimile, and video. In a typical private network operating at 0.01 parts per million, the user would experience reduced data throughput, dropped encrypted calls, unreadable facsimile pages, or interrupted video transmission dozens of times per day. The major contribution to poor private network synchronization performance is the interaction of Customer Premises Equipment (CPE) clocks and the network facilities used to distribute timing. The performance of typical CPE clocks and facilities, and their impact on customer applications, are discussed. CPE clock performance issues, along with private network architectural constraints, make synchronization planning extremely difficult. Planning is usually costly and requires specialized expertise.

Abate, J. E.↗
DOE OSTI2021

Variational Monte Carlo Calculations of A ≤ 4 Nuclei with an Artificial Neural-Network Correlator Ansatz

Here, the complexity of many-body quantum wave functions is a central aspect of several fields of physics and chemistry where nonperturbative interactions are prominent. Artificial neural networks (ANNs) have proven to be a flexible tool to approximate quantum many-body states in condensed matter and chemistry problems. In this work we introduce a neural-network quantum state ansatz to model the ground-state wave function of light nuclei, and approximately solve the nuclear many-body Schrodinger equation. Using efficient stochastic sampling and optimization schemes, our approach extends pioneering applications of ANNs in the field, which present exponentially scaling algorithmic complexity. We compute the binding energies and point-nucleon densities of A ≤ 4 nuclei as emerging from a leading-order pionless effective field theory Hamiltonian. We successfully benchmark the ANN wave function against more conventional parametrizations based on two- and three-body Jastrow functions, and virtually exact Green's function Monte Carlo results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2013

A Structure-Preserving Model and Sufficient Condition for Frequency Synchronization of Lossless Droop Inverter-Based AC Networks

This paper presents a new method for analysis of frequency synchronization of lossless power networks whose sources are frequency-droop controlled inverters. Unlike most existing approaches, our method focuses on the interaction between inverters and the network to provide new physical insight into how those interactions create frequency synchronization. We introduce two structure-preserving models of such a network (one bus-oriented and one line-oriented), and show that frequency synchronization corresponds to convergence to an equilibrium of these models. We derive a necessary condition for existence of such equilibria, and determine a simple test for their local stability. Finally, we introduce a sufficient condition for frequency synchronization of such networks, and show that it consists of a set of local criteria which can each be determined from local measurements.

Distributed control↗
DOE OSTI2021

Heterogeneity and Hysteresis in the Polymer Collapse of Single Core–Shell Stimuli-Responsive Plasmonic Nanohybrids

Broad application of polymeric stimuli-responsive smart nanohybrids requires understanding the mechanisms governing active control. Ensemble techniques have identified inhomogeneous polymer collapse in microgels that potentially arise from heterogeneous interchain interactions and differences in core size. A single-particle examination would establish the influence of core size and internal polymer network heterogeneity on local interactions that contribute to the observed inhomogeneous polymer collapse dynamics of nanohybrids. Using single-particle dark-field spectroscopy, we investigated the complex polymer collapse profiles of core–shell plasmonic nanohybrids comprising thermoresponsive poly(N-isopropylacrylamide) (pNIPAM)-encapsulated gold nanorods (AuNRs). We report that the polymer collapse behavior was independent of the core size. For thinner polymer shells, we observed hysteresis in the collapse of AuNR@pNIPAMs, likely related to local pNIPAM aggregation due to interchain hydrogen bonding. For thicker polymer shells, we observed a broad polymer collapse distribution that we attributed to a two-step phase transition that arises from a polymer network density gradient. Furthermore, our single-particle approach relates the internal heterogeneity of the polymer network of nanohybrids to the mechanisms underlying heterogeneous phase transitions that traditional, ensemble-averaged approaches are unable to discern.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Domain-Adapted Convolutional Networks for Satellite Image Classification: A Large-Scale Interactive Learning Workflow

Satellite imagery often exhibits large spatial extent areas that encompass object classes with considerable variability. This often limits large-scale model generalization with machine learning algorithms. Notably, acquisition conditions, including dates, sensor position, lighting condition, and sensor types, often translate into class distribution shifts introducing complex nonlinear factors and hamper the potential impact of machine learning classifiers. Here, this article investigates the challenge of exploiting satellite images using convolutional neural networks (CNN) for settlement classification where the class distribution shifts are significant. We present a large-scale human settlement mapping workflow based-off multiple modules to adapt a pretrained CNN to address the negative impact of distribution shift on classification performance. To extend a locally trained classifier onto large spatial extents areas we introduce several submodules: First, a human-in-the-loop element for relabeling of misclassified target domain samples to generate representative examples for model adaptation; second, an efficient hashing module to minimize redundancy and noisy samples from the mass-selected examples; and third, a novel relevance ranking module to minimize the dominance of source example on the target domain. The workflow presents a novel and practical approach to achieve large-scale domain adaptation with binary classifiers that are based-off CNN features. Experimental evaluations are conducted on areas of interest that encompass various image characteristics, including multisensors, multitemporal, and multiangular conditions. Domain adaptation is assessed on source–target pairs through the transfer loss and transfer ratio metrics to illustrate the utility of the workflow.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Integration of Fullerenes as Electron Acceptors in 3D Graphene Networks: Enhanced Charge Transfer and Stability through Molecular Design

Here, we report a concept that allows the integration of the characteristic properties of [60]fullerene in 3D graphene networks. In these systems, graphene provides high electrical conductivity and surface area while fullerenes add high electron affinity. We use molecular design to optimize the interaction between 3D graphene networks and fullerenes, specifically in the context of stability and charge transfer in an electrochemical environment. We demonstrated that the capacity of the 3D graphene network is critcally improved upon the addition of C 60 and C 60 monoadducts by providing additional acceptor states in the form of low-lying lowest unoccupied molecular orbitals of C 60 and its derivative. Guided by experimental results and first-principles calculations, we synthesized and tested a C 60 monoadduct with increased stability by strengthening the 3D graphene–C 60 van-der-Waals interactions. The synthesis approach and stabilization strategy presented here is expected to benefit the integration of graphene–C 60 hybrid materials in solar cell and charge storage applications.

25 ENERGY STORAGE↗
DOE OSTI2021

Heterospecific Neighbor Plants Impact Root Microbiome Diversity and Molecular Function of Root Fungi

Within the forest community, competition and facilitation between adjacent-growing conspecific and heterospecific plants are mediated by interactions involving common mycorrhizal networks. The ability of plants to alter their neighbor’s microbiome is well documented, but the molecular biology of plant-fungal interactions during competition and facilitation has not been previously examined. We used a common soil-plant bioassay experiment to study molecular plant-microbial interactions among rhizosphere communities associated with Pinus taeda (native host) and Populus trichocarpa (non-native host). Gene expression of interacting fungal and bacterial rhizosphere communities was compared among three plant-pairs: Populus growing with Populus , Populus with Pinus , and Pinus with Pinus . Our results demonstrate that heterospecific plant partners affect the assembly of root microbiomes, including the changes in the structure of host specific community. Comparative metatranscriptomics reveals that several species of ectomycorrhizal fungi (EMF) and saprotrophic fungi exhibit different patterns of functional and regulatory gene expression with these two plant hosts. Heterospecific plants affect the transcriptional expression pattern of EMF host-specialists (e.g., Pinus -associated Suillus spp.) on both plant species, mainly including the genes involved in the transportation of amino acids, carbohydrates, and inorganic ions. Alteration of root microbiome by neighboring plants may help regulate basic plant physiological processes via modulation of molecular functions in the root microbiome.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Locating fast-varying line disturbances with the frequency mismatch

In an attempt to provide an efficient method for line disturbance identification in complex networks of diffusively coupled agents, we recently proposed to leverage the frequency mismatch. The frequency mismatch filters out the intricate combination of interactions induced by the network structure and quantifies to what extent the trajectory of each agent is affected by the disturbance. In this previous work, we provided some analytical evidence of its efficiency when the perturbation is assumed to be slow. Here we claim that the frequency mismatch performs actually well for most disturbance regimes. This is shown through a series of simulations and is backed up by an analytical argument. Therefore, we argue that the frequency mismatch is an efficient and elegant tool for line disturbance location in complex networks of diffusively coupled agents.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

The influence of preexisting host rock damage on fault network localization

The transition from stable to unstable fracture propagation occurs when fractures begin to interact and link. Thus, fracture network coalescence controls how rocks and engineered structures fail. Here, to constrain the factors that influence localization in shear zones under brittle conditions, we build discrete element method models with a rough fault embedded in a shear zone. We add varying numbers of diffuse, randomly-placed weaknesses to examine the influence of diffuse damage on fracture network localization. The number of weaknesses controls the localization behavior of the fault network and the final fault geometry. We quantify localization using the Gini coefficient of the fracture volume, which measures the nonuniformity in a population. Each model generally increases in localization toward failure. However, models with more diffuse damage experience delocalization phases that are superimposed on the overall trend of increasing localization. The observed link between delocalization and host rock damage may help explain the varying localization of low magnitude seismicity in southern California. Models with more diffuse damage produce more complex fault geometries comprised of several parallel strands of wing cracks. The propagation of these wing cracks reduces the shear stress acting on the model boundaries, indicating that this fracture development increases the mechanical efficiency of the system.

58 GEOSCIENCES↗
DOE OSTI2020

Earth microbial co-occurrence network reveals interconnection pattern across microbiomes

Microbial interactions shape the structure and function of microbial communities; microbial association networks in specic environments have been widely developed to explore these complex systems, but their wired pattern across microbiomes in various environments at the global scale remains unexplored. Here we have inferred an Earth microbial association network from a communal catalogue with 23,595 samples and 12,646 exact sequence variants from 14 environments in the Earth Microbiome Project dataset. Results: This non-random scale-free Earth microbial association network consisted of 8 taxonomy distinct modules linked with dierent environments, which featured environment specic microbial associations. Dierent topological features of subnetworks inferred from datasets trimmed into uniform size indicate distinct association patterns in the microbiomes of various environments. The proportions of specialist edges, which ranged from 43.0% to 65.7%, highlight that environmental specic microbial associations are essential features of microbiomes in various environments. Based on edge-overlap similarity, the microbiomes of various environments were clustered into two groups, which were mainly bridged by the microbiomes of plant and animal surface. Acidobacteria Gp2 and Nisaea were identied as hubs in most of subnetworks. Negative edges proportions ranged from 1.9% in the soil subnetwork to 48.9% the non-saline surface subnetwork, suggesting various environments experience distinct intensities of competition or niche dierentiation. Conclusion: This investigation provides a new resource for examining Earth microbial association patterns across environments and emphasizes the network perspective for comprehensively understanding unique microbiome features. Keywords: Association pattern; Earth microbiomes; Genelist edges; Network hubs; Negative associations; Specialist edges; Topological properties

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Reinforcement learning based automated history matching for improved hydrocarbon production forecast

History matching aims to find a numerical reservoir model that can be used to predict the reservoir performance. An engineer and model calibration (data inversion) method are required to adjust various parameters/properties of the numerical model in order to match the reservoir production history. In this study, we develop deep neural networks within the reinforcement learning framework to achieve automated history matching that will reduce engineers’ efforts, human bias, automatically and intelligently explore the parameter space, and remove the need of large set of labeled training data. To that end, a fast-marching-based reservoir simulator is encapsulated as an environment for the proposed reinforcement learning. The deep neural-network-based learning agent interacts with the reservoir simulator within reinforcement learning framework to achieve the automated history matching. Reinforcement learning techniques, such as discrete Deep Q Network and continuous Deep Deterministic Policy Gradients, are used toth, used to train the learning agents. The continuous actions enable the Deep Deterministic Policy Gradients to explore more states at each iteration in a a learning episode; consequently, a better history matching is achieved using this algorithm as compared to Deep Q Network. For simplified dual-target composite reservoir models, the best history-matching performances of the discrete and continuous learning methods in terms of normalized root mean square errors are 0.0447 and 0.0038, respectively. Furthermore, our study shows that continuous action space achieved by the deep deterministic policy gradient drastically outperforms deep Q network.

42 ENGINEERING↗
DOE OSTI2017

DYNAMIC - The Nonlinear Dynamical Network Simulator v. 0.1

The purpose of the DYNAMIC software system is to perform simulations to validate a new research concept called “Chaotic Logic” which we recently published. The problem to be solved is to simulate certain networks of nonlinearly-interacting dynamical variables, and collect statistics on their behavior, to show that they can reliably perform useful computations. The dynamical trajectories for such networks are computed by time integration of their Hamilton’s equations.

Frank, MichaelP.↗
DOE OSTI2020

CeMbio - The Caenorhabditis elegans Microbiome Resource

The study of microbiomes by sequencing has revealed a plethora of correlations between microbial community composition and various life-history characteristics of the corresponding host species. However, inferring causation from correlation is often hampered by the sheer compositional complexity of microbiomes, even in simple organisms. Synthetic communities offer an effective approach to infer cause-effect relationships in host-microbiome systems. Yet the available communities suffer from several drawbacks, such as artificial (thus non-natural) choice of microbes, microbe-host mismatch (e.g., human microbes in gnotobiotic mice), or hosts lacking genetic tractability. Here we introduce CeMbio, a simplified natural Caenorhabditis elegans microbiota derived from our previous meta-analysis of the natural microbiome of this nematode. The CeMbio resource is amenable to all strengths of the C. elegans model system, strains included are readily culturable, they all colonize the worm gut individually, and comprise a robust community that distinctly affects nematode life-history. Several tools have additionally been developed for the CeMbio strains, including diagnostic PCR primers, completely sequenced genomes, and metabolic network models. With CeMbio, we provide a versatile resource and toolbox for the in-depth dissection of naturally relevant host-microbiome interactions in C. elegans.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Improved recovery of cell-cycle gene expression in Saccharomyces cerevisiae from regulatory interactions in multiple omics data

Gene expression is regulated by DNA-binding transcription factors (TFs). Together with their target genes, these factors and their interactions collectively form a gene regulatory network (GRN), which is responsible for producing patterns of transcription, including cyclical processes such as genome replication and cell division. However, identifying how this network regulates the timing of these patterns, including important interactions and regulatory motifs, remains a challenging task. Results We employed four in vivo and in vitro regulatory data sets to investigate the regulatory basis of expression timing and phase-specific patterns cell-cycle expression in Saccharomyces cerevisiae . Specifically, we considered interactions based on direct binding between TF and target gene, indirect effects of TF deletion on gene expression, and computational inference. We found that the source of regulatory information significantly impacts the accuracy and completeness of recovering known cell-cycle expressed genes. The best approach involved combining TF-target and TF-TF interactions features from multiple datasets in a single model. In addition, TFs important to multiple phases of cell-cycle expression also have the greatest impact on individual phases. Important TFs regulating a cell-cycle phase also tend to form modules in the GRN, including two sub-modules composed entirely of unannotated cell-cycle regulators ( STE12-TEC1 and RAP1-HAP1-MSN4 ). Conclusion Our findings illustrate the importance of integrating both multiple omics data and regulatory motifs in order to understand the significance regulatory interactions involved in timing gene expression. This integrated approached allowed us to recover both known cell-cycles interactions and the overall pattern of phase-specific expression across the cell-cycle better than any single data set. Likewise, by looking at regulatory motifs in the form of TF-TF interactions, we identified sets of TFs whose co-regulation of target genes was important for cell-cycle expression, even when regulation by individual TFs was not. Overall, this demonstrates the power of integrating multiple data sets and models of interaction in order to understand the regulatory basis of established biological processes and their associated gene regulatory networks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Disentangling direct from indirect relationships in association networks

Networks are vital tools for understanding and modeling interactions in complex systems in science and engineering, and direct and indirect interactions are pervasive in all types of networks. However, quantitatively disentangling direct and indirect relationships in networks remains a formidable task. Here, we present a framework, called iDIRECT (Inference of Direct and Indirect Relationships with Effective Copula-based Transitivity), for quantitatively inferring direct dependencies in association networks. Using copula-based transitivity, iDIRECT eliminates/ameliorates several challenging mathematical problems, including ill-conditioning, self-looping, and interaction strength overflow. With simulation data as benchmark examples, iDIRECT showed high prediction accuracies. Application of iDIRECT to reconstruct gene regulatory networks in Escherichia coli also revealed considerably higher prediction power than the best-performing approaches in the DREAM5 (Dialogue on Reverse Engineering Assessment and Methods project, #5) Network Inference Challenge. In addition, applying iDIRECT to highly diverse grassland soil microbial communities in response to climate warming showed that the iDIRECT-processed networks were significantly different from the original networks, with considerably fewer nodes, links, and connectivity, but higher relative modularity. Further analysis revealed that the iDIRECT-processed network was more complex under warming than the control and more robust to both random and target species removal ( P < 0.001). As a general approach, iDIRECT has great advantages for network inference, and it should be widely applicable to infer direct relationships in association networks across diverse disciplines in science and engineering.

59 BASIC BIOLOGICAL SCIENCES↗