Search NASA⌕ Search

SEARCH · Search NASA

Results for “correlation mining”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Unraveling Microbial Communities Associated with Methylmercury Production in Paddy Soils

Here, we report rice consumption is now recognized as an important pathway of human exposure to the neurotoxin methylmercury (MeHg), particularly in countries where rice is a staple food. Although the discovery of a two-gene cluster hgcAB has linked Hg methylation to several phylogenetically diverse groups of anaerobic microorganisms converting inorganic mercury (Hg) to MeHg, the prevalence and diversity of Hg methylators in microbial communities of rice paddy soils remain unclear. We characterized the abundance and distribution of hgcAB genes using third-generation PacBio long-read sequencing and Illumina short-read metagenomic sequencing, in combination with quantitative PCR analyses in several mine-impacted paddy soils from southwest China. Both Illumina and PacBio sequencing analyses revealed that Hg methylating communities were dominated by iron-reducing bacteria (i.e., Geobacter ) and methanogens, with a relatively low abundance of hgcA + sulfate-reducing bacteria in the soil. A positive correlation was observed between the MeHg content in soil and the relative abundance of Geobacter carrying the hgcA gene. Phylogenetic analysis also uncovered some hgcAB sequences closely related to three novel Hg methylators, Geobacter anodireducens , Desulfuromonas sp. DDH964, and Desulfovibrio sp. J2, among which G. anodireducens was validated for its ability to methylate Hg. Lastly, these findings shed new light on microbial community composition and major clades likely driving Hg methylation in rice paddy soils.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Tethys Water Demand Data

U.S. water demand varies sharply by sector and region as land use, population, weather patterns, and economic activity co-evolve. High-resolution water demand data is required to capture these dynamics, support integrated energy-water-land modeling, and local-to-regional water scarcity assessments. This dataset contains gridded (1/8 degree), monthly, multi-sector water demand dataset for the contiguous United States (CONUS) covering 1980-2100 across eight future scenarios of human-Earth system change. The dataset covers irrigation, thermoelectric, municipal (public-supply and domestic), livestock, manufacturing, and mining demands, separately for withdrawals and consumption, and includes per-cell renewable vs. non-renewable water source attributions. The dataset is validated against the latest USGS 2010-2020 water-use data for the three largest water demand sectors (Domestic, Electricity, and Irrigation), with correlations ranging from 0.73-0.95 at the HUC6 scale. The two datasets largely agree on an aggregate basis with per-sector bias falling within +/-7%, but they disagree on the spatial allocation of water with individual HUC6 basins having normalized RMSE from 68-171% and median absolute percent difference from 37-86%. This dataset advances prior global products by combining state-resolved sectoral demands from GCAM-USA, future power-plant siting from the CERF model, and scenario-consistent high-resolution climate and population forcing data across the eight scenarios.

GCAM-USA↗
DOE OSTI2020

Phonon spectrum of underdoped HgBa 2 CuO 4+δ investigated by neutron scattering

The cuprates exhibit a prominent charge-density-wave (CDW) instability with a wave vector along [100], i.e., the Cu-O bond direction. Whereas CDW order is most prominent at moderate doping and low temperature, there exists increasing evidence for dynamic charge correlations throughout a large portion of the temperature-doping phase diagram. In particular, the signatures of incipient charge order have been observed as phonon softening and/or broadening near the CDW wave vector approximately halfway through the Brillouin zone. Most of this work is focused on moderately doped cuprates, for which the CDW order is robust, or on optimally doped samples, for which the superconducting transition temperature (T c ) attains its maximum. Here we present a time-of-flight neutron scattering study of phonons in simple-tetragonal HgBa 2 CuO 4+δ (T c = 55 K) at a low doping level where prior work showed the CDW order to be weak. We employ and showcase a new software-based technique that mines a large number of measured Brillouin zones for useful data in order to improve accuracy and counting statistics. Density-functional theory has not provided an accurate description of phonons in HgBa2CuO4+δ, yet we find the right set of parameters to qualitatively reproduce the data. The notable exception is a dispersion minimum in the longitudinal Cu-O bond-stretching branch along [100]. Here, this discrepancy suggests that, while CDW order is weak, there exist significant dynamic charge correlations in the optic phonon range at low doping, near the edge of the superconducting dome.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Predicting taxonomic and functional structure of microbial communities in acid mine drainage

Predicting the dynamics of community composition and functional attributes responding to environmental changes is an essential goal in community ecology but remains a major challenge, particularly in microbial ecology. Here, by targeting a model system with low species richness, we explore the spatial distribution of taxonomic and functional structure of 40 acid mine drainage (AMD) microbial communities across Southeast China profiled by 16S ribosomal RNA pyrosequencing and a comprehensive microarray (GeoChip). Similar environmentally dependent patterns of dominant microbial lineages and key functional genes were observed regardless of the large-scale geographical isolation. Functional and phylogenetic β-diversities were significantly correlated, whereas functional metabolic potentials were strongly influenced by environmental conditions and community taxonomic structure. Using advanced modeling approaches based on artificial neural networks, we successfully predicted the taxonomic and functional dynamics with significantly higher prediction accuracies of metabolic potentials (average Bray-Curtis similarity 87.8) as compared with relative microbial abundances (similarity 66.8), implying that natural AMD microbial assemblages may be better predicted at the functional genes level rather than at taxonomic level. Furthermore, relative metabolic potentials of genes involved in many key ecological functions (for example, nitrogen and phosphate utilization, metals resistance and stress response) were extrapolated to increase under more acidic and metal-rich conditions, indicating a critical strategy of stress adaptation in these extraordinary communities. Collectively, our findings indicate that natural selection rather than geographic distance has a more crucial role in shaping the taxonomic and functional patterns of AMD microbial community that readily predicted by modeling methods and suggest that the model-based approach is essential to better understand natural acidophilic microbial communities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Data from: An updated gene atlas for maize reveals organ-specific and stress-induced genes

Maize(Zea mays L.), a model species for genetic studies, is one of the two most important crop species worldwide.The genome sequence of the reference genotype, B73, representative of the stiff stalk heterotic group was recently updated (AGPv4) using long‐read sequencing and optical mapping technology. To facilitate the use of AGPv4 and to enable functional genomic studies and association of genotype with phenotype, we determined expression abundances for replicated mRNA‐sequencing datasets from 79 tissues and five abiotic/biotic stress treatments revealing 36,207 expressed genes. Characterization of the B73 transcriptome across six organs revealed 4,154 organ‐specific and 7,704 differentially expressed genes following stress treatment. Gene co‐expression network analyses revealed 12 modules associated with distinct biological processes containing 13,590 genes providing a resource for further association of gene function based on co‐expression patterns. Presence/absence variants (PAVs) previously identified using whole genome resequencing data from 61 additional inbred lines were enriched in organ‐specific and stress‐induced differentially expressed genes, suggesting PAVs may function in phenological variation and adaptation to environment. Relative to core genes conserved across the 62 profiled inbreds, PAVs have lower expression abundances which are correlated with their frequency of dispersion across inbreds and on average, have significantly fewer co‐expression network connections suggesting that a subset of PAVs may be on an evolutionary path to pseudogenization. To facilitate use by the community, we developed the Maize Genomics Resource website(maize.plantbiology.msu.edu) for viewing and data‐mining these resources and deployed two new views on the Maize electronic Fluorescent Pictograph Browser (bar.utoronto. ca/efp_maize)

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Rheological Properties of Enzymatically Hydrolyzed Corn Stover Pretreated via Deacetylation and Mechanical Refining

Lignocellulosic biomass is a feedstock for fuels and chemicals that does not compete with food resources and has less contaminants than refuse-derived biomass feedstocks. To convert lignocellulosics to biofuels or value-added products, multiple processing steps are typically necessary. One method of producing biofuels from lignocellulosic biomass utilizes a deacetylation and mechanical refining pretreatment and an enzymatic hydrolysis reaction to produce fermentable sugars from cellulose and hemicellulose. The rheological properties of biomass, such as yield stress and plastic viscosity, change during enzymatic hydrolysis and alter the energy requirements for pumping and mixing, an important consideration for the design of processing equipment. The dynamic changes in rheological properties that occur in a corn stover feedstock undergoing enzymatic hydrolysis are characterized in this work, and the influence on pressure losses in piping systems is estimated. Two rheometer geometries were fabricated with stereolithography 3D printing to reduce wall slip and sample ejection. The slurries have complex rheological behaviors that include shear-thinning behavior. Shear stress ramps were performed on samples at 20 and 50 degrees C using the custom geometries, and the Herschel-Bulkley model was fit to the data. The dynamic nature of the rheological properties is correlated with changes in the average fiber length at various extents of reaction, and the influence of solids concentration on the observed rheology and piping pressure losses is discussed.

09 BIOMASS FUELS↗
DOE OSTI2021

Hospice Landscape Report

In July 2018, CMS requested assistance from Oak Ridge National Laboratory (ORNL) to provide expert data science support aimed at developing algorithms for data mining of medical data for operational and payment purposes. The project is intended to be exploratory: work is aimed at alleviating challenges associated with improper payments, specifically audit methodologies and targeting and changes to risk scores. Project goals include developing new, sophisticated methods for audit targeting and improved profile– payment error correlations, specifically focused on Medicare Part C, the program under which MAOs provide health care services to beneficiaries. ORNL conducted RADV analyses against RAPS and EDS data as well as a hospice landscape analysis per a January 2015 dataset that included Medicare beneficiaries who were in hospice in 2017 and 2018. Ongoing work under this project also involves development of predictive models for RADV investigations and hospice landscape.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Conservation of Species- and Trait-Based Modeling Network Interactions in Extremely Acidic Microbial Community Assembly

Understanding microbial interactions is essential to decipher the mechanisms of community assembly and their effects on ecosystem functioning, however, the conservation of species- and trait-based network interactions along environmental gradient remains largely unknown. Here, by using the network-based analyses with three paralleled data sets derived from 16S rRNA gene pyrosequencing, functional microarray, and predicted metagenome, we test our hypothesis that the network interactions of traits are more conserved than those of taxonomic measures, with significantly lower variation of network characteristics along the environmental gradient in acid mine drainage. The results showed that although the overall network characteristics remained similar, the structural variation was significantly lower at trait levels. The higher conserved individual node topological properties at trait level rather than at species level indicated that the responses of diverse traits remained relatively consistent even though different species played key roles under different environmental conditions. Additionally, the randomization tests revealed that it could not reject the null hypothesis that species-based correlations were random, while the tests suggested that correlation patterns of traits were non-random. Furthermore, relationships between trait-based network characteristics and environmental properties implied that trait-based networks might be more useful in reflecting the variation of ecosystem function. Taken together, our results suggest that deterministic trait-based community assembly results in greater conservation of network interaction, which may ensure ecosystem function across environmental regimes, emphasizing the potential importance of measuring the complexity and conservation of network interaction in evaluating the ecosystem stability and functioning.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

An updated gene atlas for maize reveals organ-specific and stress-induced genes

Maize (Zea mays L.), a model species for genetic studies, is one of the two most important crop species worldwide. The genome sequence of the reference genotype, B73, representative of the stiff stalk heterotic group was recently updated (AGPv4) using long-read sequencing and optical mapping technology. To facilitate the use of AGPv4 and to enable functional genomic studies and association of genotype with phenotype, we determined expression abundances for replicated mRNA-sequencing datasets from 79 tissues and five abiotic/biotic stress treatments revealing 36 207 expressed genes. Characterization of the B73 transcriptome across six organs revealed 4154 organ-specific and 7704 differentially expressed (DE) genes following stress treatment. Gene co-expression network analyses revealed 12 modules associated with distinct biological processes containing 13 590 genes providing a resource for further association of gene function based on co-expression patterns. Presence–absence variants (PAVs) previously identified using whole genome resequencing data from 61 additional inbred lines were enriched in organ-specific and stress-induced DE genes suggesting that PAVs may function in phenological variation and adaptation to environment. Relative to core genes conserved across the 62 profiled inbreds, PAVs have lower expression abundances which are correlated with their frequency of dispersion across inbreds and on average have significantly fewer co-expression network connections suggesting that a subset of PAVs may be on an evolutionary path to pseudogenization. Here, to facilitate use by the community, we developed the Maize Genomics Resource website (maize.plantbiology.msu.edu) for viewing and data-mining these resources and deployed two new views on the maize electronic Fluorescent Pictograph Browser (bar.utoronto.ca/efp_maize).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Localization of deformation in fluid-saturated sandstone

Herein we discuss a laboratory study of inelastic deformation and localization was conducted to evaluate microseismicity and fracture in fluid-saturated rock. Plane strain compression experiments were performed on oil- and water-saturated Berea sandstone under dry, drained, undrained, and quasi-unjacketed conditions. Parameters associated with the elastic and inelastic material response were accurately measured and used in a constitutive model to predict localization of deformation under the drained and undrained conditions. Acoustic emission (AE) activity was recorded and the onset of inelastic response in fluid-saturated rock, similar to dry rock, coincided with an increase in AE rate. Fracture initiation and propagation were successfully monitored by AE locations. Clustering of AE events and the start of inhomogeneous deformation inferred from displacement measurements are shown to be well correlated with the model prediction of the onset of localized deformation under various rock-fluid interaction conditions.

42 ENGINEERING↗
DOE OSTI2025

Influence of stacking fault energy and hydrogen on deformation mechanisms in high Mn austenitic steels during in-situ tensile testing

High Mn austenitic steels are considered an economical alloy system for hydrogen storage and transport applications. This study used stacking fault energy (SFE) as a design parameter to achieve hydrogen embrittlement (HE)-resistant high Mn austenitic alloys. The role of hydrogen on the deformation mechanisms of low (29 mJ/m 2 ) and high SFE (49 mJ/m 2 ) alloys was evaluated through in-situ neutron diffraction during tensile loading. Hydrogen-precharging increased yield strength, partly due to hydrogen-induced lattice distortion (i.e., solute strengthening). Hydrogen accelerated the increase in defect density, including dislocations and stacking faults. The formation of planar deformation structures (twins and stacking faults), relative to dislocations, plays a critical role in promoting hydrogen-assisted fracture. The stacking fault frequency parameter obtained from neutron diffraction quantifies planar deformation tendencies, correlated with HE sensitivity. The higher SFE alloy exhibited greater resistance to HE, associated with the reduced propensity to form stacking faults and twins upon deformation in the hydrogen-precharged condition.

High Mn austenitic steel↗
DOE OSTI2018

Predicting the 3D fatigue crack growth rate of small cracks using multimodal data via Bayesian networks: In-situ experiments and crystal plasticity simulations

Small crack propagation accounts for most of the fatigue life of engineering structures subject to high cycle fatigue loading conditions. Determining the fatigue crack growth rate of small cracks propagating into polycrystalline engineering alloys is critical to improving fatigue life predictions, thus lowering cost and increasing safety. In this work, cycle-by-cycle data of a small crack propagating in a beta metastable titanium alloy is available via phase and diffraction contrast tomography. Crystal plasticity simulations are used to supplement experimental data regarding the micromechanical fields ahead of the crack tip. Experimental and numerical results are combined into a multimodal dataset and sampled utilizing a non-local data mining procedure. Furthermore, to capture the propensity of body-centered cubic metals to deform according to the pencil-glide model, a non-local driving force is postulated. The proposed driving force serves as the basis to construct a data-driven probabilistic crack propagation framework using Bayesian networks as building blocks. The spatial correlation between the postulated driving force and experimental observations is obtained by analyzing the results of the proposed framework. Results show that the above correlation increases proportionally to the distance from the crack front until the edge of the plastic zone. Moreover, the predictions of the propagation framework show good agreement with experimental observations. Finally, we studied the interaction of a small crack with grain boundaries (GBs) utilizing various slip transmission criteria, revealing the tendency of a crack to cross a GB by propagating along the slip directions minimizing the residual Burgers vector within the GB.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Boron-Based Neutron Scintillator Screen Characterization with X-Rays and Neutrons

Recent work on boron-based neutron scintillator screens suggests these screens can offer superior performance when compared to commonly used screens. Borated neutron scintillator screens perform well in terms of light output (5-6 times greater than a standard Gadox screen) and detection effi-ciency (larger than standard LiF+ZnS screens). However, previously manu-factured boron-based screens have exhibited non-uniform surface coating and a poor mixture between phosphor and converter particles. The objective of this work was to evaluate newly fabricated scintillator screens to deter-mine if enhanced fabrication methods produced a more homogeneous distribution between neutron converter and scintillation phosphor particles. Uniformity of scintillator material deposition was also inspected. This new iteration of screens appeared more uniform than previous generations with the new coating method improving surface chemistry and scintillator material homogeneity. Additionally, a new methodology for screen characterization, involving the correlation of a neutron image taken with a borated scintillator screen to X-ray computed tomography of that same screen, was demonstrated to elucidate a relationship between scintillator screen thickness and relative light output of the screen under neutron exposure. This method suggested that the ideal thickness of scintillator material was ~150 µm to maximize light output of the screen.

36 - MATERIALS SCIENCE↗
DOE OSTI2017

Consumption Behavior Analytics-Aided Energy Forecasting and Dispatch

For decades, electricity customers have been treated as mere recipients of electricity in vertically integrated power systems. However, as customers have widely adopted distributed energy resources and other forms of customer participation in active dispatch (such as demand response) have taken shape, the value of mining knowledge from customer behavior patterns and using it for power system operation is increasing. Further, the variability of renewable energy resources has been considered a liability to the grid. However, electricity consumption has shown the same level of variability and uncertainty, and this is sometimes overlooked. This article investigates data analytics and forecasting methods to identify correlations between electricity consumption behavior and distributed photovoltaic (PV) output. The forecasting results feed into a predictive energy management system that optimizes energy consumption in the near future to balance customer demand and power system needs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Mining experimental magnetized liner inertial fusion data: Trends in stagnation morphology

In magnetized liner inertial fusion (MagLIF), a cylindrical liner filled with fusion fuel is imploded with the goal of producing a one-dimensional plasma column at thermonuclear conditions. However, structures attributed to three-dimensional effects are observed in self-emission x-ray images. Despite this, the impact of many experimental inputs on the column morphology has not been characterized. We demonstrate the use of a linear regression analysis to explore correlations between morphology and a wide variety of experimental inputs across 57 MagLIF experiments. Results indicate the possibility of several unexplored effects. For example, we demonstrate that increasing the initial magnetic field correlates with improved stability. Although intuitively expected, this has never been quantitatively assessed in integrated MagLIF experiments. We also demonstrate that azimuthal drive asymmetries resulting from the geometry of the “current return can” appear to measurably impact the morphology. In conjunction with several counterintuitive null results, we expect the observed correlations will encourage further experimental, theoretical, and simulation-based studies. Finally, we note that the method used in this work is general and may be applied to explore not only correlations between input conditions and morphology but also with other experimentally measured quantities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Integrated Bioinformatic Analyses and Immune Characterization of New Neisseria gonorrhoeae Vaccine Antigens Expressed during Natural Mucosal Infection

There is an increasingly severe trend of antibiotic-resistant Neisseria gonorrhoeae strains worldwide and new therapeutic strategies are needed against this sexually-transmitted pathogen. Despite the urgency, progress towards a gonococcal vaccine has been slowed by a scarcity of suitable antigens, lack of correlates of protection in humans and limited animal models of infection. N. gonorrhoeae gene expression levels in the natural human host does not reflect expression in vitro, further complicating in vitro-basedvaccine analysis platforms. We designed a novel candidate antigen selection strategy (CASS), based on a reverse vaccinology-like approach coupled with bioinformatics. We utilized the CASS to mine gonococcal proteins expressed during human mucosal infection, reported in our previous studies, and focused on a large pool of hypothetical proteins as an untapped source of potential new antigens. Via two discovery and analysis phases (DAP), we identified 36 targets predicted to be immunogenic, membrane-associated proteins conserved in N. gonorrhoeae and suitable for recombinant expression. Six initial candidates were produced and used to immunize mice. Characterization of the immune responses indicated cross-reactive antibodies and serum bactericidal activity against different N. gonorrhoeae strains. These results support the CASS as a tool for the discovery of new vaccine candidates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

ANS MiNES 2023 Poster

The dynamic mechanical properties of four varieties of high purity graphite as well as the depth of penetration (DoP) of a small-scale shaped charge into these grades was experimentally determined. The grades chosen were PCEA, NBG-18, and NBG-25. These grades provide a wide range of physical properties: in density from 1.80 – 1.85 g/cc, in maximum particle size from 10s to 1000s of µm, and in porosity from 18% to 20%. The quasistatic and dynamic compressive strengths of each grade were determined and correlated to their physical properties. The split Hopkinson pressure bar experiments showed both the dynamic strength and dominant shear failure mechanisms. A small scale shaped charge was used to compare the resistance of graphite to hypervelocity jet impacts: the Teledyne RP-4. The RP-4 has a 1.01” outer diameter and 3.44 g of RDX with an RP-80 booster (86 mg PETN + 123 mg RDX). A select number of samples were analyzed using X-Ray Computed Tomography (XCT), allowing for the full characterization of the undisturbed wound channel. A selection of other samples were physically sectioned and wound channels mapped from the sections. In addition to DoP, the wound geometry was characterized in terms of total volume and diameter at different depths. The wound channel characteristics for each grade were correlated to the compressive strengths and physical properties. In several of the test samples, the wound channel diameter was smaller than the diameter of the shaped charge slug just behind where the slug had penetrated the sample. These results indicated that the wound geometry was dependent on the compressive hysteresis behavior of graphite.

36 MATERIALS SCIENCE↗