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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Considerations for AMI-Based Operations for Distribution Feeders

More than $5 billion in investments in advanced metering infrastructure (AMI) technologies, AMI deployments, as pervasive secondary network voltage monitoring systems, provide opportunities for utility operations and controls. This paper focuses on the considerations for AMI-based tools and techniques as the industry moves toward operationalizing such large data sets. Phase identification is a first such tool. Numerous distribution network analysis, monitoring, and control applications - including volt/volt-ampere reactive control, state estimation, and distribution automation - require accurate phase connectivity information in the system models. The phase connectivity database maintained by utilities is inaccurate because of a significant amount of missing data, restoration activities, and network reconfiguration. Existing phase identification techniques that estimate phase connectivity work well in distribution feeders that have low or no photovoltaic (PV) generation; however, they fail to identify the phases accurately when considerable PV generation is present. This work addresses the phase identification problem in the presence of high PV generation using statistical analysis methods. Further, insights into the AMI data requirements for this application in terms of data window length and resolution are provided using sensitivity analysis performed on an actual distribution feeder model of San Diego Gas & Electric Company. The results of this study show that the phase connectivity, even in the presence of high PV generation, can be accurately identified using statistical analysis of AMI data of 1 day.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1993

A review and analysis of neural networks for classification of remotely sensed multispectral imagery

A literature survey and analysis of the use of neural networks for the classification of remotely sensed multispectral imagery is presented. As part of a brief mathematical review, the backpropagation algorithm, which is the most common method of training multi-layer networks, is discussed with an emphasis on its application to pattern recognition. The analysis is divided into five aspects of neural network classification: (1) input data preprocessing, structure, and encoding; (2) output encoding and extraction of classes; (3) network architecture, (4) training algorithms; and (5) comparisons to conventional classifiers. The advantages of the neural network method over traditional classifiers are its non-parametric nature, arbitrary decision boundary capabilities, easy adaptation to different types of data and input structures, fuzzy output values that can enhance classification, and good generalization for use with multiple images. The disadvantages of the method are slow training time, inconsistent results due to random initial weights, and the requirement of obscure initialization values (e.g., learning rate and hidden layer size). Possible techniques for ameliorating these problems are discussed. It is concluded that, although the neural network method has several unique capabilities, it will become a useful tool in remote sensing only if it is made faster, more predictable, and easier to use.

Paola, Justin D.↗
NASA NTRS1986

Networking ground-based images of Comet Halley during the Giotto encounter

During the period immediately before and after the European, Russian, and Japanese spacecraft encounters with Comet Halley in early March 1986, sequences of ground-based electronic images of the comet, obtained at Table Mountain Observatory (TMO), CA, were transmitted via the Space Physics Analysis Network (SPAN) to the European Space Operations Centre (ESOC), and to University College London (UCL). During the 48-h period when the European Space Agency spacecraft Giotto was within the extended coma of Comet Halley, the ground-based images revealed that the comet displayed several spectacular near-nuclear and large-scale features. The TMO images provided a format for the interpretation of the unique in situ results obtained during the closest of the five spacecraft encounters with Comet Halley.

Rees, David↗
DOE OSTI2023

Tripogon loliiformis tolerates rapid desiccation after metabolic and transcriptional priming during initial drying

Abstract Crop plants and undomesticated resilient species employ different strategies to regulate their energy resources and growth. Most crop species are sensitive to stress and prioritise rapid growth to maximise yield or biomass production. In contrast, resilient plants grow slowly, are small, and allocate their resources for survival in challenging environments. One small group of plants, termed resurrection plants, survive desiccation of their vegetative tissue and regain full metabolic activity upon watering. However, the precise molecular mechanisms underlying this extreme tolerance remain unknown. In this study, we employed a transcriptomics and metabolomics approach, to investigate the mechanisms of desiccation tolerance in Tripogon loliiformis , a modified desiccation-tolerant plant, that survives gradual but not rapid drying. We show that T. loliiformis can survive rapid desiccation if it is gradually dried to 60% relative water content (RWC). Furthermore, the gene expression data showed that T. loliiformis is genetically predisposed for desiccation in the hydrated state, as evidenced by the accumulation of MYB, NAC, bZIP, WRKY transcription factors along with the phytohormones, abscisic acid, salicylic acid, amino acids (e.g., proline) and TCA cycle sugars during initial drying. Through network analysis of co-expressed genes, we observed differential responses to desiccation between T. loliiformis shoots and roots. Dehydrating shoots displayed global transcriptional changes across broad functional categories, although no enrichment was observed during drying. In contrast, dehydrating roots showed distinct network changes with the most significant differences occurring at 40% RWC. The cumulative effects of the early stress responses may indicate the minimum requirements of desiccation tolerance and enable T. loliiformis to survive rapid drying. These findings potentially hold promise for identifying biotechnological solutions aimed at developing drought-tolerant crops without growth and yield penalties.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1996

Space-Based Information Infrastructure Architecture for Broadband Services

This study addressed four tasks: (1) identify satellite-addressable information infrastructure markets; (2) perform network analysis for space-based information infrastructure; (3) develop conceptual architectures; and (4) economic assessment of architectures. The report concludes that satellites will have a major role in the national and global information infrastructure, requiring seamless integration between terrestrial and satellite networks. The proposed LEO, MEO, and GEO satellite systems have satellite characteristics that vary widely. They include delay, delay variations, poorer link quality and beam/satellite handover. The barriers against seamless interoperability between satellite and terrestrial networks are discussed. These barriers are the lack of compatible parameters, standards and protocols, which are presently being evaluated and reduced.

Price, Kent M.↗
NASA NTRS1990

Satellite image analysis using neural networks

The tremendous backlog of unanalyzed satellite data necessitates the development of improved methods for data cataloging and analysis. Ford Aerospace has developed an image analysis system, SIANN (Satellite Image Analysis using Neural Networks) that integrates the technologies necessary to satisfy NASA's science data analysis requirements for the next generation of satellites. SIANN will enable scientists to train a neural network to recognize image data containing scenes of interest and then rapidly search data archives for all such images. The approach combines conventional image processing technology with recent advances in neural networks to provide improved classification capabilities. SIANN allows users to proceed through a four step process of image classification: filtering and enhancement, creation of neural network training data via application of feature extraction algorithms, configuring and training a neural network model, and classification of images by application of the trained neural network. A prototype experimentation testbed was completed and applied to climatological data.

Sheldon, Roger A.↗
NASA NTRS2003

Regulatory gene networks and the properties of the developmental process

Genomic instructions for development are encoded in arrays of regulatory DNA. These specify large networks of interactions among genes producing transcription factors and signaling components. The architecture of such networks both explains and predicts developmental phenomenology. Although network analysis is yet in its early stages, some fundamental commonalities are already emerging. Two such are the use of multigenic feedback loops to ensure the progressivity of developmental regulatory states and the prevalence of repressive regulatory interactions in spatial control processes. Gene regulatory networks make it possible to explain the process of development in causal terms and eventually will enable the redesign of developmental regulatory circuitry to achieve different outcomes.

Non-NASA Center↗
DOE OSTI2023

Chromosome-level genome assembly of Quercus variabilis provides insights into the molecular mechanism of cork thickness

Quercus variabilis is a deciduous woody species with high ecological and economic value and is a major source of cork in East Asia. Cork from thick softwood sheets have higher commercial value than those from thin sheets. It is extremely difficult to genetically improve Q. variabilis to produce high quality softwood due to the lack of genomic information. Here, we present a high-quality chromosomal genome assembly for Q. variabilis with length of 791,89 Mb and 54,606 predicted genes. Comparative analysis of protein sequences of Q. variabilis with 11 other species revealed that specific and expanded gene families were significantly enriched in the "fatty acid biosynthesis" pathway in Q. variabilis, which may contribute to the formation of its unique cork. Additionally, based on weighted correlation network analysis of time-course (i.e., five important developmental ages) gene expression data in thick-cork versus thin-cork genotypes of Q. variabilis, we identified one co-expression gene module associated with the thick-cork trait. Within this co-expression gene module, 10 hub genes were associated with suberin biosynthesis. Furthermore, we identified a total of 198 suberin biosynthesis-related new candidate genes that were up-regulated in trees with a thick cork layer relative to those with a thin cork layer. Also, we found that some genes related to cell expansion and cell division were highly expressed in trees with a thick cork layer. Collectively, our results revealed that two metabolic pathways (i.e., suberin biosynthesis, fatty acid biosynthesis), along with other genes involved in cell expansion, cell division, and transcriptional regulation, were associated with the thick-cork trait in Q. variabilis, providing insights into the molecular basis of cork development and knowledge for informing genetic improvement of cork thickness in Q. variabilis and closely related species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Diel dynamics of multi-omics in elkhorn fern provide new insights into weak CAM photosynthesis

Crassulacean acid metabolism (CAM) has high water-use efficiency (WUE) and is widely recognized to have evolved from C 3 photosynthesis. Different plant lineages have convergently evolved CAM, but the molecular mechanism that underlies C 3 -to-CAM evolution remains to be clarified. Platycerium bifurcatum (elkhorn fern) provides an opportunity to study the molecular changes underlying the transition from C 3 to CAM photosynthesis because both modes of photosynthesis occur in this species, with sporotrophophyll leaves (SLs) and cover leaves (CLs) performing C 3 and weak CAM photosynthesis, respectively. Here, we report that the physiological and biochemical attributes of CAM in weak CAM-performing CLs differed from those in strong CAM species. We investigated the diel dynamics of the metabolome, proteome, and transcriptome in these dimorphic leaves within the same genetic background and under identical environmental conditions. We found that multi-omic diel dynamics in P. bifurcatum exhibit both tissue and diel effects. Our analysis revealed temporal rewiring of biochemistry relevant to the energy-producing pathway (TCA cycle), CAM pathway, and stomatal movement in CLs compared with SLs. We also confirmed that PHOSPHOENOLPYRUVATE CARBOXYLASE KINASE (PPCK) exhibits convergence in gene expression among highly divergent CAM lineages. Gene regulatory network analysis identified candidate transcription factors regulating the CAM pathway and stomatal movement. Taken together, our results provide new insights into weak CAM photosynthesis and new avenues for CAM bioengineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Insights of Molecular Mechanism of Xylem Development in Five Black Poplar Cultivars

Black poplar (Populus deltoides, P. nigra, and their hybrids) is the main poplar cultivars in China. It offers interesting options of large-scale biomass production for bioenergy due to its rapid growth and high yield. Poplar wood properties were associated with chemical components and physical structures during wood formation. In this study, five poplar cultivars, P. euramericana ‘Zhonglin46’ (Pe1), P. euramericana ‘Guariento’ (Pe2), P. nigra ‘N179’ (Pn1), P. deltoides ‘Danhong’ (Pd1), and P. deltoides ‘Nanyang’ (Pd2), were used to explore the molecular mechanism of xylem development. We analyzed the structural differences of developing xylem in the five cultivars and profiled the transcriptome-wide gene expression patterns through RNA sequencing. The cross sections of the developing xylem showed that the cell wall thickness of developed fiber in Pd1 was thickest and the number of xylem vessels of Pn1 was the least. A total of 10,331 differentially expressed genes were identified among 10 pairwise comparisons of the five cultivars, most of them were related to programmed cell death and secondary cell wall thickening. K-means cluster analysis and Gene Ontology enrichment analysis showed that the genes highly expressed in Pd1 were related to nucleotide decomposition, metabolic process, transferase, and microtubule cytoskeleton; whereas the genes highly expressed in Pn1 were involved in cell wall macromolecule decomposition and polysaccharide binding processes. Based on a weighted gene co-expression network analysis, a large number of candidate regulators for xylem development were identified. And their potential regulatory roles to cell wall biosynthesis genes were validated by a transient overexpression system. This study provides a set of promising candidate regulators for genetic engineering to improve feedstock and enhance biofuel conversion in the bioenergy crop Populus.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing

The recent development of deep learning has been mostly focusing on Euclidean data, such as images, videos, audios, etc. However, most real-world information and relation are often expressed as graphs. To efficiently learn from graph data, graph convolutional networks (GCNs) emerge as a promising approach, showing advantages in several practical applications such as social network analysis, knowledge discovery, 3D modeling, motion capturing, etc. Real-world graphs are usually extremely large and imbalanced, posting significant performance demand and design challenges on the hardware dedicated for GCN inference. In this paper, we propose an architecture design called UW-GCN to accelerate graph convolutional network inference. To tackle the major performance bottleneck from workload imbalance, we propose dynamic neighborhood stealing and remote chunk shuffling techniques, relying on hardware flexibility to achieve hardware auto-tuning under negligible area or delay overhead. Specifically, UW-GCN is able to smartly profile the sparse graph pattern while continuously adjusting the workload distribution via routing reconfiguration among parallel processing elements (PEs). The ideal configuration is then reused in the remaining iterations. To the best of our knowledge, this is the first accelerator design particularly for GCN and the first work relying on hardware auto-tuning, which is normally based on software, to achieve near-optimal workload balance in processing sparse structures.

Geng, Tong↗
DOE OSTI2023

Global droughts connected by linkages between drought hubs

Abstract Quantifying the spatial and interconnected structure of regional to continental scale droughts is one of the unsolved global hydrology problems, which is important for understanding the looming risk of mega-scale droughts and the resulting water and food scarcity and their cascading impact on the worldwide economy. Using a Complex Network analysis, this study explores the topological characteristics of global drought events based on the self-calibrated Palmer Drought Severity Index. Event Synchronization is used to measure the strength of association between the onset of droughts at different spatial locations within the time lag of 1-3 months. The network coefficients derived from the synchronization network indicate a highly heterogeneous connectivity structure underlying global drought events. Drought hotspot regions such as Southern Europe, Northeast Brazil, Australia, and Northwest USA behave as drought hubs that synchronize regionally and with other hubs at inter-continental or even inter-hemispheric scale. This observed affinity among drought hubs is equivalent to the ‘rich-club phenomenon’ in Network Theory, where ‘rich’ nodes (here, drought hubs) are tightly interconnected to form a club, implicating the possibility of simultaneous large-scale droughts over multiple continents.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1988

Hierarchical neural networks for autonomous data analysis and decision making

A neural network based data analysis and decision making system to increase the autonomy of a planetary rover or similar exploratory vehicle is presented. A hierarchical series of neural networks for real time analysis of scientific images is used. The system under development emphasizes analysis of multispectral images by classifier and feature detector neural networks, to provide information on the mineral composition of a scene. A hierarchy of alternating analysis and decision making networks is being developed to allow increasingly fine scale analysis in regions of the image that are potentially important. It is noted that this system will facilitate both the selection of high priorty scientific information for transmission to earth, and the autonomous collection of rocks and soil for sample return.

Eberlein, Susan↗
NASA NTRS1991

High-speed, cross-continental networks - A necessity

The networking requirements that the Earth Observing System (EOS) program has levied on NASA and the national and international operational networking communities is addressed. The objectives of the EOS program are listed and include the development and operation of an observing system to acquire essential, global earth science data on a long-term, sustained basis and in a manner which maximizes the scientific utility of the data and simplifies data analysis. Network design issues are also discussed, including protocol selection, the Transport Control Protocol/Internet Protocol suite, the Open Systems Interconnect network suite, and the internet evolution. Open and closed network issues are also discussed. A number of networks now in place are assessed including NASNet, and ESNet. These offer possible options and/or solutions to be considered by the NASA Headquarters EOS Data and Information Systems Program.

Wong, C. M.↗
NASA NTRS2021

Geological Factors Impacted Cadmium Availability and use as an Alternative Cofactor for Zinc in the Carbon Fixation Pathways of Marine Diatoms

Transition metal cofactors are crucial for many biological processes. Despite being primarily considered to be toxic, the transition metal cadmium (Cd) was discovered to be a substitute cofactor for zinc (Zn) in photosynthetic carbon fixation pathways of marine diatoms. However, it is not known how conditions in the geosphere impacted Cd availability and its incorporation as an alternative metal cofactor for phytoplankton. We employed mineral chemistry network analysis to investigate which geochemical factors may have influenced the availability of Cd and Zn during the putative time period that the alternative Cd-based pathway evolved. Our results show that Zn minerals are more chemically diverse than are Cd minerals, but Zn- and Cd-containing minerals have similar network centrality values when specifically considering sulfur (S)-containing species. Cadmium and Zn sulfides are the most common Cd- and Zn-containing mineral species over the past 500 million years. In particular, the Cd and Zn sulfides, respectively greenockite and sphalerite, were highly abundant during this time period. Furthermore, S-containing Cd and Zn minerals are commonly co-located in geologic time, allowing them to be weathered and transported to the ocean in tandem, rather than from separate sources. We suggest that the simultaneous weathering of Cd and Zn sulfides allowed for Cd to be a bioavailable direct substitute for Zn in protein complexes during periods of Zn depletion. The biogeochemical cycles of Zn and Cd exemplify the importance of the coevolution of the geosphere and biosphere in shaping primary production in the modern ocean.

Naman Srivastava↗
DOE OSTI2026

Rhythmic Mechanisms Governing CAM Photosynthesis in Kalanchoe fedtschenkoi : High-Resolution Temporal Transcriptomics

Crassulacean acid metabolism (CAM) is a specialized photosynthetic pathway that enhances water-use efficiency by temporally separating nocturnal CO 2 uptake from daytime decarboxylation and carbon fixation. To uncover the regulatory mechanisms coordinating these temporal dynamics, we generated high-resolution, 48 h time-course transcriptomes for the CAM model Kalanchoe fedtschenkoi under both 12 h/12 h light/dark (LD) cycles and continuous light (LL). A rhythmicity analysis revealed that diel light cues are the dominant driver of transcript oscillations: 16,810 genes (54.3% of annotated genes) exhibited rhythmic expression only under LD, whereas just 399 genes (1.3%) remained rhythmic under LL. A smaller set of 3009 genes (9.7%) oscillated in both conditions, indicating that the intrinsic circadian clock sustains rhythmicity for a limited subset of the transcriptome. A gene co-expression network analysis revealed extensive integration between circadian clock components, core CAM pathway enzymes, and stomatal regulators, defining regulatory modules that coordinate metabolic and physiological timing. Notably, key hub genes associated with post-translational and post-transcriptional regulation, including the E3 ubiquitin ligase HUB2 and several pentatricopeptide repeat (PPR) proteins, act as central nodes in CAM-associated networks. This discovery implicates epigenetic and organellar regulation as previously unrecognized critical tiers of control in CAM. Together, our results support a regulatory model in which CAM rhythmicity is governed by both external light/dark cues and the endogenous circadian clock through multi-level control spanning transcriptional and protein-level regulation. To support community exploration, we also provide an interactive eFP (electronic Fluorescent Pictograph) browser for visualizing time-resolved gene expression profiles.

09 BIOMASS FUELS↗
DOE OSTI2024

Bacterial hemophilin homologs and their specific type eleven secretor proteins have conserved roles in heme capture and are diversifying as a family

Cellular life relies on enzymes that require metals, which must be acquired from extracellular sources. Bacteria utilize surface and secreted proteins to acquire such valuable nutrients from their environment. These include the cargo proteins of the type eleven secretion system (T11SS), which have been connected to host specificity, metal homeostasis, and nutritional immunity evasion. This Sec-dependent, Gram-negative secretion system is encoded by organisms throughout the phylum Proteobacteria, including human pathogens Neisseria meningitidis, Proteus mirabilis, Acinetobacter baumannii, and Haemophilus influenzae. Experimentally verified T11SS-dependent cargo include transferrin-binding protein B (TbpB), the hemophilin homologs heme receptor protein C (HrpC), hemophilin A (HphA), the immune evasion protein factor-H binding protein (fHbp), and the host symbiosis factor nematode intestinal localization protein C (NilC). Here, we examined the specificity of T11SS systems for their cognate cargo proteins using taxonomically distributed homolog pairs of T11SS and hemophilin cargo and explored the ligand binding ability of those hemophilin cargo homologs. In vivo expression in Escherichia coli of hemophilin homologs revealed that each is secreted in a specific manner by its cognate T11SS protein. Sequence analysis and structural modeling suggest that all hemophilin homologs share an N-terminal ligand-binding domain with the same topology as the ligand-binding domains of the Haemophilus haemolyticus heme binding protein (Hpl) and HphA. We term this signature feature of this group of proteins the hemophilin ligand-binding domain. Network analysis of hemophilin homologs revealed five subclusters and representatives from four of these showed variable heme-binding activities, which, combined with sequence-structure variation, suggests that hemophilins are diversifying in function.

59 BASIC BIOLOGICAL SCIENCES↗