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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Exploratory analysis and performance prediction of big data transfer in High-performance Networks

Big data transfer in large-scale scientific and business applications is increasingly carried out over connections with guaranteed bandwidth provisioned in High-performance Networks (HPNs) via advance bandwidth reservation. Provisioning agents need to carefully schedule data transfer requests, compute network paths, and allocate appropriate bandwidths. Such reserved bandwidths, if not fully utilized, could be simply wasted due to the exclusive access during the approved time window, and cause extra overhead and complexity for resource management. This calls for accurate performance prediction to reserve bandwidths that match actual needs and avoid over-provisioning. We employ machine learning algorithms to predict big data transfer performance based on extensive performance measurements collected in the past several years from data transfer tests using different protocols and toolkits between various end sites on several real-life physical or emulated testbeds. We first analyze the performance patterns in response to a comprehensive list of parameters in end-host systems, network connections, and data transfer applications, which motivate the use of machine learning and also help us identify the effects of latent factors. We then propose threshold- and clustering-based methods to eliminate negative effects of latent factors in data preprocessing and build a robust performance predictor based on customized domain-oriented loss functions. The performance of the proposed methods is verified by extensive experiments using SVR and RFR as well as theoretical analysis of the general performance bound.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Predictability of Extreme Precipitation in Western U.S. Watersheds Based on Atmospheric River Occurrence, Intensity, and Duration

In this study, we quantified the relationship between atmospheric rivers (ARs) and occurrence and magnitude of extreme precipitation in western U.S. watersheds, using AR tracking results of Atmospheric River Tracking Method Intercomparison Project and precipitation from a high-resolution regional climate simulation. Our analysis indicates that ARs have the potential of predicting extreme precipitation events at daily scale, with Gilbert Skill Score of ~0.2, and monthly extreme precipitation amount in the west coast watershed is closely related to AR intensity, with correlation coefficients of up to 0.6. The relationship between ARs and precipitation is most significant in the Pacific Northwest and California. Lastly, using K-means clustering algorithm, AR events can be classified into three categories with distinct features: weak ARs, flash ARs, and prolonged ARs. Flash ARs and prolonged ARs, though accounting for less than 50% of total AR events, are more important in controlling regional extreme precipitation patterns, and should be prioritized for future studies of hydrological extreme events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Analysis of intraspecies diversity reveals a subset of highly variable plant immune receptors and predicts their binding sites

The evolution of recognition specificities by the immune system depends on the generation of receptor diversity and on connecting the binding of new antigens with the initiation of downstream signaling. In plant immunity, the innate Nucleotide-Binding Leucine-Rich Repeat (NLR) receptor family enables antigen binding and immune signaling. In this study, we surveyed the NLR complements of 62 ecotypes of Arabidopsis thaliana and 54 lines of Brachypodium distachyon and identified a limited number of NLR subfamilies that show high allelic diversity. We show that the predicted specificity-determining residues cluster on the surfaces of Leucine-Rich Repeat domains, but the locations of the clusters vary among NLR subfamilies. By comparing NLR phylogeny, allelic diversity, and known functions of the Arabidopsis NLRs, we formulate a hypothesis for the emergence of direct and indirect pathogen-sensing receptors and of the autoimmune NLRs. These findings reveal the recurring patterns of evolution of innate immunity and can inform NLR engineering efforts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

On the effect of Ga and In substitutions in the Ca 11 Bi 10 and Yb 11 Bi 10 bismuthides crystallizing in the tetragonal Ho 11 Ge 10 structure type

The Ga- and In-substituted bismuthides Ca 11 Ga x Bi 10– x , Ca 11 In x Bi 10– x , Yb 11 Ga x Bi 10– x , and Yb 11 In x Bi 10– x ( x < 2) can be readily synthesized employing molten Ga or In metals as fluxes. They crystallize in the tetragonal space group I 4/ mmm and adopt the Ho 11 Ge 10 structure type (Pearson code tI 84; Wyckoff sequence n 2 m j h 2 e 2 d ). The structural response to the substitution of Bi with smaller and electron-poorer In or Ga has been studied by single-crystal X-ray diffraction methods for the case of Ca 11 In x Bi 10– x [ x = 1.73 (2); octabismuth undecacalcium diindium]. The refinements show that the In atoms substitute Bi only at the 8 h site. The refined interatomic distances show an unconventional – for this structure type – bond-length distribution within the anionic sublattice. The latter can be viewed as consisting of isolated Bi 3- anions and [In 4 Bi 8 20- ] clusters for the idealized Ca 11 In 2 Bi 8 model. Formal electron counting and first-principle calculations show that the peculiar bonding in this compound drives the system toward an electron-precise state, thereby stabilizing the observed bond-length pattern.

Chemistry↗
DOE OSTI2024

Investigating the relationship between microbial network features of giant kelp “seedbank” cultures and subsequent farm performance

Microbial inoculants can increase the yield of cultivated crops and are successful in independent trials; however, efficacy drops in large-scale applications due to insufficient consideration of microbial community dynamics. The structure of microbiomes, in addition to the impact of individual taxa, is an important factor to consider when designing growth-promoting inoculants. Here, we investigate the microbial network and community assembly patterns of Macrocystis pyrifera gametophyte germplasm cultures (collectively referred to as a “seedbank”) used to cultivate an offshore farm in Santa Barbara, California, and identify network features associated with increased biomass of mature sporophytes. We found that [1] several network features, such as clustering coefficient and edge ratios, significantly vary with biomass outcomes; [2] gametophytes that become low- or high-biomass sporophytes have different hub taxa; and [3] microbial community assembly of gametophyte germplasm cultures is niche-driven. Overall, this study describes microbial community dynamics in M . pyrifera germplasm cultures and ultimately supports the development of early life stage inoculants that can be used on seaweed cultivars to increase biomass yield.

Osborne, Melisa G. (ORCID:0000000154980057)↗
NASA NTRS2003

Intermediate Element Abundances In Galaxy Clusters

We present the average abundances of the intermediate elements obtained by performing a stacked analysis of all the galaxy clusters in the archive of the X-ray telescope AKA. We determine the abundances of Fe, Si, S, and Ni as a function of cluster temperature (mass) from 1 - 10 keV, and place strong upper limits on the abundances of Ca and Ar. In general, Si and Ni are overabundant with respect to Fe, while Ar and Ca are very underabundant. The discrepancy between the abundances of Si, S , Ar, and Ca indicate that the alpha-elements do not behave homogeneously as a single group. We show that the abundances of the most well-determined elements Fe, Si, and S in conjunction with recent theoretical supernovae yields do not give a consistent solution for the fraction of material produced by Type Ia and Type II supernovae at any temperature or mass. The general trend is for higher temperature clusters to have more of their metals produced in Type II supernovae than in Type Ias. The inconsistency of our results with abundances in the Milky Way indicate that spiral galaxies are not the dominant metal contributors to the intracluster medium (ICM). The pattern of elemental abundances requires an additional source of metals beyond standard SNIa and SNII enrichment. The properties of this new source are well matched to those of Type II supernovae with very massive, metal-poor progenitor stars. These results are consistent with a significant fraction of the ICM metals produced by an early generation of population III stars.

White, Nicholas E.↗
DOE OSTI2015

Generation of brain pseudo-CTs using an undersampled, single-acquisition UTE-mDixon pulse sequence and unsupervised clustering

Purpose: MR-based pseudo-CT has an important role in MR-based radiation therapy planning and PET attenuation correction. The purpose of this study is to establish a clinically feasible approach, including image acquisition, correction, and CT formation, for pseudo-CT generation of the brain using a single-acquisition, undersampled ultrashort echo time (UTE)-mDixon pulse sequence. Methods: Nine patients were recruited for this study. For each patient, a 190-s, undersampled, single acquisition UTE-mDixon sequence of the brain was acquired (TE = 0.1, 1.5, and 2.8 ms). A novel method of retrospective trajectory correction of the free induction decay (FID) signal was performed based on point-spread functions of three external MR markers. Two-point Dixon images were reconstructed using the first and second echo data (TE = 1.5 and 2.8 ms). R2{sup ∗} images (1/T2{sup ∗}) were then estimated and were used to provide bone information. Three image features, i.e., Dixon-fat, Dixon-water, and R2{sup ∗}, were used for unsupervised clustering. Five tissue clusters, i.e., air, brain, fat, fluid, and bone, were estimated using the fuzzy c-means (FCM) algorithm. A two-step, automatic tissue-assignment approach was proposed and designed according to the prior information of the given feature space. Pseudo-CTs were generated by a voxelwise linear combination of the membership functions of the FCM. A low-dose CT was acquired for each patient and was used as the gold standard for comparison. Results: The contrast and sharpness of the FID images were improved after trajectory correction was applied. The mean of the estimated trajectory delay was 0.774 μs (max: 1.350 μs; min: 0.180 μs). The FCM-estimated centroids of different tissue types showed a distinguishable pattern for different tissues, and significant differences were found between the centroid locations of different tissue types. Pseudo-CT can provide additional skull detail and has low bias and absolute error of estimated CT numbers of voxels (−22 ± 29 HU and 130 ± 16 HU) when compared to low-dose CT. Conclusions: The MR features generated by the proposed acquisition, correction, and processing methods may provide representative clustering information and could thus be used for clinical pseudo-CT generation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Exploring urban typologies using comprehensive analysis of transportation dynamics

Abstract As urban areas continue to expand and develop, categorizing cities into typologies offers a valuable framework for understanding metropolitan dynamics and fostering inter-city collaboration. However, existing typologies related to urban mobility have limitations, failing to consider cities within a single large urban region and often overlooking crucial dimensions such as trip demand and traffic flow. In this paper, we introduce a transportation-focused characterization for cities within a large urban region, specifically the San Francisco Bay Area, California. We incorporate over 40 metrics across five transportation dimensions: trip demand, road network, multi-modal network, traffic flow, and land use. Specifically, for the trip demand dimension, we include metrics capturing residents’ trip characteristics, such as mode share, intra-city trips, and inter-city trips. Additionally, we analyze the purpose of trips entering the city to gain a deeper understanding of incoming trip patterns. In the traffic flow dimension, we examine metrics like vehicle miles traveled, delay, and congestion to assess the traffic conditions on the street network. These, combined with other dimensions, provide a comprehensive view of a city’s transportation dynamics. Using unsupervised machine learning clustering methods, we identified eight distinct typologies for the Bay Area: Live Work Cities; Job and Activity Magnet Cities; Anchor Cities; Multi-modal Cities; Hyper-connected Cities; Low-density Residential Cities; Medium-density Residential Cities; and Mixed-use Residential Cities. Our findings show that many clusters are strongly influenced by trip demand and traffic flow metrics. Finally, we examine the practicality of this typology and its potential to guide collaborative transportation management strategies. The typologies provide a foundation for dialogue among Bay Area cities, focusing on evaluating shared characteristics and leveraging successes or challenges to develop unified strategies for transportation management.

Kuncheria, Anu↗
DOE OSTI2018

Kekulé valence bond order in an extended Hubbard model on the honeycomb lattice with possible applications to twisted bilayer graphene

Using large-scale quantum Monte Carlo simulations, we exactly solve a model of fermions hopping on the honeycomb lattice with cluster charge interactions, which has been proposed as an effective model with possible application to twisted bilayer graphene near half-filling. Here, we find an interaction driven semimetal to insulator transition to an insulating phase consisting of a valence bond solid with Kekulé pattern. Finite size scaling reveals that the phase transition of the semimetal to Kekulé valence bond solid phase is continuous and belongs to chiral XY universality class.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2019

Use of Very High-Resolution Optical Data for Landslide Mapping and Susceptibility Analysis Along the Karnali Highway, Nepal

The Karnali highway is a vital transport link and the only primary roadway that connects the remote Karnali region to the lowlands in Mid-Western Nepal. Every year there are reports of landslides blocking the road, making this area largely inaccessible. However, little effort has focused on systematically identifying landslides and landslide-prone areas along this highway. In this study, landslides were mapped with an object-based approach from very high-resolution optical satellite imagery obtained by the DigitalGlobe constellation in 2012 and PlanetScope in 2018. Landslides ranging from 10 to 30,496 sq.m were detected within a 3 km buffer along the highway. Most of the landslides were located at lower elevations (between 500–1500 m) and on steep south-facing slopes. Landslides tended to cluster closer to the highway, near drainage channels and away from faults. Landslides were also most prevalent within the Kuncha Formation geologic class, and the forested and agricultural land cover classes. A susceptibility map was then created using a logistic regression methodology to highlight patterns in landslide activity. The landslide susceptibility map showed a good prediction rate with an area under the curve (AUC) of 0.90. A total of 33% of the study arealies in high/very high susceptibility zones. The map highlighted the lower elevated areas between Bangesimal and Manma towns with the Kuncha Formation geologic class as being the most hazardous. The banks of the Karnali River, its tributaries and areas near the highway were also highly susceptible to landslides. The results highlight the potential of very high-resolution optical imagery for documenting detailed spatial information on landslide occurrence, which enables susceptibility assessment in remote and data scarce regions such as the Karnali highway.

Pukar Amatya↗
NASA NTRS2021

Columnar Aerosol Types and Compositions Over Peninsular Southeast Asia Based on Long-Term AERONET Data

Aerosol chemical components such as black carbon (BC) and brown carbon (BrC) regulate aerosol optical properties, which in turn drive the atmospheric radiative forcing estimations due to aerosols. In this study, we used the long-term measurements from AERONET (Aerosol Robotic Network) to better understand the aerosol types and composition with respect to their seasonal and spatial variabilities in peninsular Southeast Asia (PSEA, here defined as Vietnam, Cambodia, Thailand, Laos, and Myanmar). Two methods (i.e., aerosol type cluster and aerosol component retrieval) were applied to determine the aerosol type and chemical composition during the biomass-burning (BB) season. AERONET sites in northern PSEA showed a higher AOD (aerosol optical depth) compared to that of southern PSEA. Differences in land use pattern, geographic location, and weather regime caused much of the aerosol variability over PSEA. Lower single-scattering albedo (SSA) and higher fine-mode fraction (FMF) values were observed in February and March, suggesting the predominance of BB type aerosols with finer and stronger absorbing particles during the dry season. However, we also found that the peak BB month (i.e., March) in northern PSEA may not coincide with the lowest SSA once dust particles have mixed with the other aerosols. Furthermore, we investigated two severe BB events in March of 2014 and 2015, revealing a significant BrC fraction during BB event days. On high AOD days, although the BC fraction was high, the BrC fraction remained low due to lack of aerosol aging. This study highlights the dominance of carbonaceous aerosols in the PSEA atmosphere during the BB season, while also revealing that transported dust particles and BrC aerosol aging may introduce uncertainties into the aerosol radiative forcing calculation.

Sheng-Hsiang Wang↗
DOE OSTI2025

Reuniting crystallography with real space: Ab initio structure elucidation with 4D-STEM

Structure elucidation via single-crystal methods has historically lacked experimental access to real-space information, instead relying exclusively on diffraction-space measurements of Bragg reflections. Here we exploit the dual-space imaging power of 4D scanning transmission electron microscopy to meaningfully integrate real-space information into the crystallographic workflow. We show that virtual apertures assembled by segmentation of high-angle annular dark-field images enable i) pixel-by-pixel separation of coherent Bragg signal from clusters of closely spaced nanocrystals and ii) selective extraction of integrated intensities from thinner subregions of individual specimens, facilitating retroactive tuning of multiple scattering artifacts. This strategy empowers us to simply pick and choose whichever nanoscale regions of interest generate the highest-quality diffraction patterns, allowing us to solve several independent structures of the metal-organic framework UiO-66 from specimens whose agglomerated morphology proved intractable for conventional microcrystal electron diffraction. Our method is compatible with both rotational and serial approaches to data processing, ultimately divulging the first scanning nanobeam electron diffraction structures determined by direct methods at subangstrom resolution.

Saha, Ambarneil↗
NASA NTRS1983

Cloud cover typing from environmental satellite imagery. Discriminating cloud structure with Fast Fourier Transforms (FFT)

The use of two dimensional Fast Fourier Transforms (FFTs) subjected to pattern recognition technology for the identification and classification of low altitude stratus cloud structure from Geostationary Operational Environmental Satellite (GOES) imagery was examined. The development of a scene independent pattern recognition methodology, unconstrained by conventional cloud morphological classifications was emphasized. A technique for extracting cloud shape, direction, and size attributes from GOES visual imagery was developed. These attributes were combined with two statistical attributes (cloud mean brightness, cloud standard deviation), and interrogated using unsupervised clustering amd maximum likelihood classification techniques. Results indicate that: (1) the key cloud discrimination attributes are mean brightness, direction, shape, and minimum size; (2) cloud structure can be differentiated at given pixel scales; (3) cloud type may be identifiable at coarser scales; (4) there are positive indications of scene independence which would permit development of a cloud signature bank; (5) edge enhancement of GOES imagery does not appreciably improve cloud classification over the use of raw data; and (6) the GOES imagery must be apodized before generation of FFTs.

Logan, T. L.↗
DOE OSTI2016

SeekAView: An Intelligent Dimensionality Reduction Strategy for Navigating High-Dimensional Data Spaces

Dealing with the curse of dimensionality is a key challenge in high-dimensional data visualization. We present SeekAView to address three main gaps in the existing research literature. First, automated methods like dimensionality reduction or clustering suffer from a lack of transparency in letting analysts interact with their outputs in real-time to suit their exploration strategies. The results often suffer from a lack of interpretability, especially for domain experts not trained in statistics and machine learning. Second, exploratory visualization techniques like scatter plots or parallel coordinates suffer from a lack of visual scalability: it is difficult to present a coherent overview of interesting combinations of dimensions. Third, the existing techniques do not provide a flexible workflow that allows for multiple perspectives into the analysis process by automatically detecting and suggesting potentially interesting subspaces. In SeekAView we address these issues using suggestion based visual exploration of interesting patterns for building and refining multidimensional subspaces. Compared to the state-of-the-art in subspace search and visualization methods, we achieve higher transparency in showing not only the results of the algorithms, but also interesting dimensions calibrated against different metrics. We integrate a visually scalable design space with an iterative workflow guiding the analysts by choosing the starting points and letting them slice and dice through the data to find interesting subspaces and detect correlations, clusters, and outliers. We present two usage scenarios for demonstrating how SeekAView can be applied in real-world data analysis scenarios.

Krause, Josua↗
DOE OSTI2018

Long noncoding RNA HOTTIP cooperates with CCCTC-binding factor to coordinate HOXA gene expression

Highlights: • HOTTIP is required for activation of distal HOXA genes but its role is restricted by CTCF boundary. • HOTTIP and CTCF coordinate to specify the pattern of histone modifications on HOXA genes in foreskin fibroblasts. • HOTTIP RNA acts as a new interaction partner of CTCF. The spatiotemporal control of HOX gene expression is dependent on positional identity and often correlated to their genomic location within each loci. Maintenance of HOX expression patterns is under complex transcriptional and epigenetic regulation, which is not well understood. Here we demonstrate that HOTTIP, a lincRNA transcribed from the 5′ edge of the HOXA locus, physically associates with the CCCTC-binding factor (CTCF) that serves as an insulator by organizing HOXA cluster into disjoint domains, to cooperatively maintain the chromatin modifications of HOXA genes and thus coordinate the transcriptional activation of distal HOXA genes in human foreskin fibroblasts. Our results reveal the functional connection of HOTTIP and CTCF, and shed light on lincRNAs in gene activation and CTCF mediated chromatin organization.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Impact of wettability alteration on 3D nonwetting phase trapping and transport

We investigate capillary trapping and fluid migration via x-ray computed microtomography (x-ray CMT) of nonwetting phase (air) and wetting phase (brine) in Bentheimer sandstone cores which have been treated to exhibit different degrees of uniform wettability. x-Ray CMT scans were acquired at multiple steps during drainage and imbibition processes, as well as at the endpoints; allowing for assessment of the impact of wettability on nonwetting phase saturation and cluster size distribution, connectivity, topology and efficiency of trapping. Compared with untreated (water-wet) Bentheimer sandstone, cores treated with tetramethoxylsilane (TMS) were rendered weakly water-wet, and cores treated with octadecyltrichlorosilane (OTS) demonstrate intermediate-wet characteristics. As apparent contact angle increases, drainage flow patterns deviate from those derived for water-wet systems, total residual trapping and trapping efficiency decrease, and buoyancy plays a larger role during nonwetting phase mobilization; this has significant implications for CO 2 migration and trapping during CO 2 sequestration operations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

The study of basis sets for the calculation of the structure and dynamics of the benzene-Kr complex

An ab initio intermolecular potential energy surface (PES) has been constructed for the benzene-krypton (BKr) van der Waals (vdW) complex. The interaction energy has been calculated at the coupled cluster level of theory with single, double, and perturbatively included triple excitations using different basis sets. As a result, a few analytical PESs of the complex have been determined. They allowed a prediction of the complex structure and its vibrational vdW states. The vibrational energy level pattern exhibits a distinct polyad structure. Comparison of the equilibrium structure, the dipole moment, and vibrational levels of BKr with their experimental counterparts has allowed us to design an optimal basis set composed of a small Dunning’s basis set for the benzene monomer, a larger effective core potential adapted basis set for Kr and additional midbond functions. Such a basis set yields vibrational energy levels that agree very well with the experimental ones as well as with those calculated from the available empirical PES derived from the microwave spectra of the BKr complex. The basis proposed can be applied to larger complexes including Kr because of a reasonable computational cost and accurate results.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗