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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 OSTI2023

Conserved unique peptide patterns (CUPP) online platform 2.0: implementation of +1000 JGI fungal genomes

Carbohydrate-processing enzymes, CAZymes, are classified into families based on sequence and three-dimensional fold. Because many CAZyme families contain members of diverse molecular function (different EC-numbers), sophisticated tools are required to further delineate these enzymes. Such delineation is provided by the peptide-based clustering method CUPP, Conserved Unique Peptide Patterns. CUPP operates synergistically with the CAZy family/subfamily categorizations to allow systematic exploration of CAZymes by defining small protein groups with shared sequence motifs. The updated CUPP library contains 21,930 of such motif groups including 3,842,628 proteins. The new implementation of the CUPP-webserver, https://cupp.info/, now includes all published fungal and algal genomes from the Joint Genome Institute (JGI), genome resources MycoCosm and PhycoCosm, dynamically subdivided into motif groups of CAZymes. This allows users to browse the JGI portals for specific predicted functions or specific protein families from genome sequences. Thus, a genome can be searched for proteins having specific characteristics. All JGI proteins have a hyperlink to a summary page which links to the predicted gene splicing including which regions have RNA support. The new CUPP implementation also includes an update of the annotation algorithm that uses only a fourth of the RAM while enabling multi-threading, providing an annotation speed below 1 ms/protein.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Genetic Analysis of the Full-Length gag Gene from the Earliest Korean Subclade B of HIV-1: An Outbreak among Korean Hemophiliacs

We determined the earliest full-length HIV-1 gag gene sequences in 110 patients with HIV-1, including 20 hemophiliacs (HPs) and 90 local controls (LCs). The gag gene from stored sera was amplified using RT-PCR, and was subjected to direct sequencing. Phylogenetic analysis indicated that 94 and 16 sequences belonged to the Korean subclade of HIV-1 subtype B (KSB) and subtype B, respectively. A total of 12 signature pattern amino acids were found within the KSB, distinct from the worldwide consensus of subtype B. Within the KSB, the gag gene sequences from donors O and P and those from the 20 HPs comprised two subclusters. In particular, sequences from donor O strongly clustered with those of eight HPs. Moreover, signature pattern analysis indicated that 14 signature nucleotides were shared between the HPs and LCs within KSB (p < 0.01). Among the 14 nucleotides, positions 9 and 5 belonged to clusters O and P, respectively. In conclusion, signature pattern analysis for the gag gene revealed 12 signature pattern residues within the KSB and also confirmed the previous conclusion that the 20 HPs were infected with viruses due to incompletely inactivated clotting factor IX. This study is the first genetic analysis of the HIV-1 gag gene in Korea.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Comparative hazard analysis and toxicological modeling of diverse nanomaterials using the embryonic zebrafish (EZ) metric of toxicity

The integration of rapid assays, large data sets, informatics and modeling can overcome current barriers in understanding nanomaterial structure-toxicity relationships by providing a weight-of-the-evidence mechanism to generate hazard rankings for nanomaterials. Here we present the use of a rapid, low-cost assay to perform screening-level toxicity evaluations of nanomaterials in vivo. Calculated EZ Metric scores, a combined measure of morbidity and mortality, were established at realistic exposure levels and used to develop a predictive model of nanomaterial toxicity. Hazard ranking and clustering analysis of 68 diverse nanomaterials revealed distinct patterns of toxicity related to both core composition and outermost surface chemistry of nanomaterials. The resulting clusters guided the development of a predictive model of gold nanoparticle toxicity to embryonic zebrafish. In addition, our findings suggest that risk assessments based on the size and core composition of nanomaterials alone may be wholly inappropriate, especially when considering complex engineered nanomaterials. These findings reveal the need to expeditiously increase the availability of quantitative measures of nanomaterial hazard and broaden the sharing of that data and knowledge to support predictive modeling. In addition, research should continue to focus on methodologies for developing predictive models of nanomaterial hazard based on sub-lethal responses to low dose exposures.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

Comparing Synoptic Pattern Evolution for Flash‐Flood‐Producing and Non‐Flash‐Flood‐Producing Mesoscale Convective Systems in the United States

Understanding how the short-term evolution of synoptic weather patterns influence Mesoscale Convective Systems (MCSs) is essential, as these systems are responsible for over half of central U.S. flash floods, leading to substantial socioeconomic and water resource management impacts. This study analyzes long-term MCS data, flash flood reports, and atmospheric reanalyses from 2007 to 2017 using a machine learning clustering algorithm to examine how the synoptic weather patterns evolve prior to MCS initiation. While the clusters reflect seasonal and regional differences in MCS occurrence, they do not consistently distinguish between MCSs that do and do not produce flash floods. Systems in the southern Great Plains are more flood-prone when a synoptic-scale forcing, located near the system, drives strong water vapor transport from the nearby moisture source. More generally under different synoptic weather patterns, a broader precipitating area is the most dominant factor governing MCS flash flood potential.

atmospheric dynamics↗
DOE OSTI2015

Influence of molecular clustering on the interpretation of diffractograms of hydrocarbon films from tokamak T-10

The influence of molecular clustering on the formerly suggested interpretation of diffraction patterns of hydrocarbon films formed in the vacuum vessel of the tokamak T-10 is analyzed numerically. The simulation of clustering of simple hydrocarbon molecules C(D, H){sub 4}, C{sub 2}(D, H){sub 4}, and C{sub 6}(D, H){sub 6} and molecules composed of curved graphene (fullerenes and toroidal nanotubes) is carried out with the rigid body molecular dynamics method. It is shown that formerly neglected atomic correlations C–C and C–D(H) in the amorphous hydrocarbon component decrease the calculated values of the scattered intensity in the range of scattering vector modulus 5 < q < 20 nm{sup –1} because of homogenization of scatters on the spatial scale of ∼1 nm. The allowance for these correlations does not change the diffraction patterns in the range q > 20 nm{sup –1}. The results suggest the necessity to introduce to the procedure of determining the structural content of the films, similar to those from the tokamak T-10, the clusters formed by the van der Waals adhesion of hydrocarbon molecules to “graphene” nanoparticles. This simplifies the mathematical optimization to the former level of complexity—but for an extended ensemble of objects—and makes it possible to calculate the diffraction patterns of these objects using the distributed computing resources. A modified algorithm of structural content identification on the basis of joint X-ray and neutron diffractometry is suggested.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Observations of the Bright Star in the Globular Cluster 47 Tucanae (NGC 104)

The Bright Star in the globular cluster 47 Tucanae (NGC 104) is a post-asymptotic giant branch (post-AGB) star of spectral type B8 III. The ultraviolet spectra of late-B stars exhibit myriad absorption features, many due to species unobservable from the ground. The Bright Star thus represents a unique window into the chemistry of 47 Tuc. We have analyzed observations obtained with the Far Ultraviolet Spectroscopic Explorer, the Cosmic Origins Spectrograph aboard the Hubble Space Telescope, and the Magellan Inamori Kyocera Echelle Spectrograph on the Magellan Telescope. By fitting these data with synthetic spectra, we determine various stellar parameters (T {sub eff} = 10,850 ± 250 K, logg=2.20±0.13) and the photospheric abundances of 26 elements, including Ne, P, Cl, Ga, Pd, In, Sn, Hg, and Pb, which have not previously been published for this cluster. Abundances of intermediate-mass elements (Mg through Ga) generally scale with Fe, while the heaviest elements (Pd through Pb) have roughly solar abundances. Its low C/O ratio indicates that the star did not undergo third dredge-up and suggests that its heavy elements were made by a previous generation of stars. If so, this pattern should be present throughout the cluster, not just in this star. Stellar-evolution models suggest that the Bright Star is powered by a He-burning shell, having left the AGB during or immediately after a thermal pulse. Its mass (0.54 ± 0.16M {sub ⊙}) implies that single stars in 47 Tuc lose 0.1–0.2 M {sub ⊙} on the AGB, only slightly less than they lose on the red giant branch.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Self-Taught Anomaly Detection With Hybrid Unsupervised/Supervised Machine Learning in Optical Networks

Here this paper proposes a self-taught anomaly detection framework for optical networks. The proposed framework makes use of a hybrid unsupervised and supervised machine learning scheme. First, it employs an unsupervised data clustering module (DCM) to analyze the patterns of monitoring data. The DCM enables a self-learning capability that eliminates the requirement of prior knowledge of abnormal network behaviors and therefore can potentially detect unforeseen anomalies. Second, we introduce a self-taught mechanism that transfers the patterns learned by the DCM to a supervised data regression and classification module (DRCM). The DRCM, whose complexity is mainly related to the scale of the applied supervised learning model, can potentially facilitate more scalable and time-efficient online anomaly detection by avoiding excessively traversing the original dataset. We designed the DCM and DRCM based on the density-based clustering algorithm and the deep neural network structure, respectively. Evaluations with experimental data from two use cases (i.e., single-point detection and end-to-end detection) demonstrate that up to 99% anomaly detection accuracy can be achieved with a false positive rate below 1%.

42 ENGINEERING↗
DOE OSTI2017

Diagnosing Cloud Biases in the GFDL AM3 Model With Atmospheric Classification

In this paper, we define a set of 21 atmospheric states, or recurring weather patterns, for a region surrounding the Atmospheric Radiation Measurement Program's Southern Great Plains site using an iterative clustering technique. The states are defined using dynamic and thermodynamic variables from reanalysis, tested for statistical significance with cloud radar data from the Southern Great Plains site, and are determined every 6 h for 14 years, creating a time series of atmospheric state. The states represent the various stages of the progression of synoptic systems through the region (e.g., warm fronts, warm sectors, cold fronts, cold northerly advection, and high-pressure anticyclones) with a subset of states representing summertime conditions with varying degrees of convective activity. We use the states to classify output from the NOAA/Geophysical Fluid Dynamics Laboratory AM3 model to test the model's simulation of the frequency of occurrence of the states and of the cloud occurrence during each state. The model roughly simulates the frequency of occurrence of the states but exhibits systematic cloud occurrence biases. Comparison of observed and model-simulated International Satellite Cloud Climatology Project histograms of cloud top pressure and optical thickness shows that the model lacks high thin cloud under all conditions, but biases in thick cloud are state-dependent. Frontal conditions in the model do not produce enough thick cloud, while fair-weather conditions produce too much. Finally, we find that increasing the horizontal resolution of the model improves the representation of thick clouds under all conditions but has little effect on high thin clouds. However, increasing resolution also changes the distribution of states, causing an increase in total cloud occurrence bias.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Large-scale protein level comparison of Deltaproteobacteria reveals cohesive metabolic groups

Abstract Deltaproteobacteria, now proposed to be the phyla Desulfobacterota, Myxococcota, and SAR324, are ubiquitous in marine environments and play essential roles in global carbon, sulfur, and nutrient cycling. Despite their importance, our understanding of these bacteria is biased towards cultured organisms. Here we address this gap by compiling a genomic catalog of 1 792 genomes, including 402 newly reconstructed and characterized metagenome-assembled genomes (MAGs) from coastal and deep-sea sediments. Phylogenomic analyses reveal that many of these novel MAGs are uncultured representatives of Myxococcota and Desulfobacterota that are understudied. To better characterize Deltaproteobacteria diversity, metabolism, and ecology, we clustered ~1 500 genomes based on the presence/absence patterns of their protein families. Protein content analysis coupled with large-scale metabolic reconstructions separates eight genomic clusters of Deltaproteobacteria with unique metabolic profiles. While these eight clusters largely correspond to phylogeny, there are exceptions where more distantly related organisms appear to have similar ecological roles and closely related organisms have distinct protein content. Our analyses have identified previously unrecognized roles in the cycling of methylamines and denitrification among uncultured Deltaproteobacteria. This new view of Deltaproteobacteria diversity expands our understanding of these dominant bacteria and highlights metabolic abilities across diverse taxa.

Langwig, Marguerite V. (ORCID:0000000202472816)↗
DOE OSTI2023

Exploratory analysis of machine learning techniques in the Nevada geothermal play fairway analysis

Play fairway analysis (PFA) is commonly used to generate geothermal potential maps and guide exploration studies, with a particular focus on locating and characterizing blind geothermal systems. This study evaluates the application of machine learning techniques to PFA in the Great Basin region of Nevada. Following the evaluation of various techniques, we identified two approaches to PFA that produced promising results, 1) supervised Bayesian probabilistic neural networks to generate geothermal potential maps with confidence intervals, and 2) unsupervised principal component analysis paired with k-means clustering to generate both cluster maps to help identify spatial patterns, as well as new combined feature inputs. We applied these techniques to perform a comparative analysis between two principal sets of geological and geophysical features related to permeability and heat and a set of positive (known geothermal resources) and negative training sites (known drill sites with unsuitable geothermal conditions). We found that these methods constrain previously unrecognized feature controls on geothermal favorability, many of which are spatially organized within the extent of cluster groups and the major structural-hydrologic domains of the study area. Furthermore, we utilized exploratory unsupervised modeling to highlight spatial relationships between input data and predictive output results of our supervised modeling. As a result, we demonstrate how our models compare to the previous Nevada PFA and how the rapid insights these machine learning techniques offer may support future assessments of both known and undiscovered blind geothermal systems in the Great Basin region of Nevada and beyond.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Understanding the Influence of Urban Form on the Spatial Pattern of Precipitation

Urban areas are known to modify the spatial pattern of precipitation climatology. Existing observational evidence suggests that precipitation can be enhanced downwind of a city. Among the proposed mechanisms, the thermodynamic and aerodynamic processes in the urban lower atmosphere interact with the meteorological conditions and can play a key role in determining the resulting precipitation patterns. In addition, these processes are influenced by urban form, such as the impervious surface extent. This study aims to unravel how different urban forms impact the spatial patterns of precipitation climatology under different meteorological conditions. We use the Multi-Radar Multi-Sensor quantitative precipitation estimation data products and analyze the hourly precipitation maps for 27 selected cities across the continental United States from the years 2015–2021 summer months. Results show that about 80% of the studied cities exhibit a statistically significant downwind enhancement of precipitation. Additionally, we find that the precipitation pattern tends to be more spatially clustered in intensity under higher wind speed; the location of radial precipitation maxima is located closer to the city center under low background winds but shifts downwind under high wind conditions. The magnitude of downwind precipitation enhancement is highly dependent on wind directions and is positively correlated with the city size for the south, southwest, and west directions. This study presents observational evidence through a cross-city analysis that the urban precipitation pattern can be influenced by the urban modification of atmospheric processes, providing insight into the mechanistic link between future urban land-use change and hydroclimates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Association rule mining based quantitative analysis approach of household characteristics impacts on residential electricity consumption patterns

The comprehensive understanding of the residential electricity consumption patterns (ECPs) and how they relate to household characteristics can contribute to energy efficiency improvement and electricity consumption reduction in the residential sector. After recognizing the limitations of current studies (i.e. unreasonable typical ECP (TECP) extraction method and the problem of multicollinearity and interpretability for regression and machine learning models), this paper proposes an association rule mining based quantitative analysis approach of household characteristics impact on residential ECPs trying to address them together. First, an adaptive density-based spatial clustering of applications with noise (DBSCAN) algorithm is utilized to create seasonal TECP of each individual customer only for weekdays. K-means is then adopted to group all the TECPs into several clusters. An enhanced Apriori algorithm is proposed to reveal the relationships between TECPs and thirty five factors covering four categories of household characteristics including dwelling characteristics, socio-demographic, appliances and heating and attitudes towards energy. Results of the case study using 3326 records containing smart metering data and survey information in Ireland suggest that socio-demographic and cooking related factors such as employment status, occupants and whether cook by electricity have strong significant associations with TECPs, while attitudes related factors almost have no effect on TECPs. The results also indicate that those households with more than one person are more likely to change ECP across seasons. The proposed approach and the findings of this study can help to support decisions about how to reduce electricity consumption and CO2 emissions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Isolating the Contributions of Specific Network Sites to the Diffuse Vibrational Spectrum of Interfacial Water with Isotopomer-Selective Spectroscopy of Cold Clusters

Decoding the structural information contained in the interfacial vibrational spectrum of water requires understanding how the spectral signatures of individual water molecules respond to their local hydrogen bonding environments. In this study, we isolated the contributions for the five classes of sites that differ according to the number of donor (D) and acceptor (A) hydrogen bonds that characterize each site. These patterns were measured by exploiting the unique properties of the water cluster cage structures formed in the gas phase upon hydration of a series of cations M+·(H2O)n (M = Li, Na, Cs, NH4, CH3NH3, H3O, and n = 5, 20-22). This selection of ions was chosen to systematically express the A, AD, AAD, ADD, and AADD hydrogen bonding motifs. The spectral signatures of each site were measured using two-color, IR-IR isotopomer-selective photofragmentation vibrational spectroscopy of the cryogenically cooled, mass selected cluster ions in which a single intact H2O is introduced without isotopic scrambling, an important advantage afforded by the cluster regime. The resulting patterns provide an unprecedented picture of the intrinsic line shapes and spectral complexities associated with excitation of the individual OH groups, as well as the correlation between the frequencies of the two OH groups on the same water molecule, as a function of network site. The properties of the surrounding water network that govern this frequency map are evaluated by dissecting electronic structure calculations that explore how changes in the nearby network structures, both within and beyond the first hydration shell, affect the local frequency of an OH oscillator. The qualitative trends are recovered with a simple model that correlates the OH frequency with the network-modulated local electron density in the center of the OH bond.

Yang, Nan↗
DOE OSTI2015

Density-Aware Clustering Based on Aggregated Heat Kernel and Its Transformation

Current spectral clustering algorithms suffer from the sensitivity to existing noise, and parameter scaling, and may not be aware of different density distributions across clusters. If these problems are left untreated, the consequent clustering results cannot accurately represent true data patterns, in particular, for complex real world datasets with heterogeneous densities. This paper aims to solve these problems by proposing a diffusion-based Aggregated Heat Kernel (AHK) to improve the clustering stability, and a Local Density Affinity Transformation (LDAT) to correct the bias originating from different cluster densities. AHK statistically\ models the heat diffusion traces along the entire time scale, so it ensures robustness during clustering process, while LDAT probabilistically reveals local density of each instance and suppresses the local density bias in the affinity matrix. Our proposed framework integrates these two techniques systematically. As a result, not only does it provide an advanced noise-resisting and density-aware spectral mapping to the original dataset, but also demonstrates the stability during the processing of tuning the scaling parameter (which usually controls the range of neighborhood). Furthermore, our framework works well with the majority of similarity kernels, which ensures its applicability to many types of data and problem domains. The systematic experiments on different applications show that our proposed algorithms outperform state-of-the-art clustering algorithms for the data with heterogeneous density distributions, and achieve robust clustering performance with respect to tuning the scaling parameter and handling various levels and types of noise.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A Kinematic Perspective on the Formation Process of the Stellar Groups in the Rosette Nebula

Stellar kinematics is a powerful tool for understanding the formation process of stellar associations. Here, we present a kinematic study of the young stellar population in the Rosette nebula using recent Gaia data and high-resolution spectra. We first isolate member candidates using the published mid-infrared photometric data and the list of X-ray sources. A total of 403 stars with similar parallaxes and proper motions are finally selected as members. The spatial distribution of the members shows that this star-forming region is highly substructured. The young open cluster NGC 2244 in the center of the nebula has a pattern of radial expansion and rotation. We discuss its implication on the cluster formation, e.g., monolithic cold collapse or hierarchical assembly. On the other hand, we also investigate three groups located around the border of the H ii bubble. The western group seems to be spatially correlated with the adjacent gas structure, but their kinematics is not associated with that of the gas. The southern group does not show any systematic motion relative to NGC 2244. These two groups might be spontaneously formed in filaments of a turbulent cloud. The eastern group is spatially and kinematically associated with the gas pillar receding away from NGC 2244. This group might be formed by feedback from massive stars in NGC 2244. Our results suggest that the stellar population in the Rosette Nebula may form through three different processes: the expansion of stellar clusters, hierarchical star formation in turbulent clouds, and feedback-driven star formation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1973

A comparison of land-use determinations using data from ERTS-1 and high altitude aircraft

A manual interpretation of ERTS-1 MSS system corrected imagery has been performed on a study area within the Houston Area Test Site to classify land use using the Level 1 categories proposed by the Department of the Interior. The two types of imagery used included: (1) black and white transparencies of each band enlarged to a scale of approximately 1:250,000 and (2) color transparencies composited from the computer compatible tapes using the film recorder on a multispectral data analysis station. The results of this interpretation have been compared with the 1970 land use inventory of HATS which was compiled using color ektachrome imagery from high altitude aircraft (scale 1:120,000). Urban data from the same scene was also analyzed using a computer-aided (clustering) technique. The resulting clusters, representing areas of similar content, were compared with existing land use patterns in Houston. A technique was developed to correlate the spectral clusters to specific urban features on aircraft imagery by the location of specific, high contrast objects in particular resolution elements. It was concluded that ERTS-1 data could be used to develop Level 1 and many Level 2 land use categories for regional inventories and perhaps to some degree on a local level.

Lundelius, M. A.↗
DOE OSTI2022

Galactic ArchaeoLogIcaL ExcavatiOns (GALILEO): I. An updated census of APOGEE N-rich giants across the Milky Way

We use the 17th data release of the second phase of the Apache Point Observatory Galactic Evolution Experiment (APOGEE-2) to provide a homogenous census of N-rich red giant stars across the Milky Way (MW). We report a total of 149 newly identified N-rich field giants toward the bulge, metal-poor disk, and halo of our Galaxy. They exhibit significant enrichment in their nitrogen abundance ratios ([N/Fe] ≳ +0.5), along with simultaneous depletions in their [C/Fe] abundance ratios ([C/Fe] < +0.15), and they cover a wide range of metallicities (-1.8 < [Fe/H] < -0.7). The final sample of candidate N-rich red giant stars with globular-cluster-like (GC-like) abundance patterns from the APOGEE survey includes a grand total of ~412 unique objects. These strongly N-enhanced stars are speculated to have been stripped from GCs based on their chemical similarities with these systems. Even though we have not found any strong evidence for binary companions or signatures of pulsating variability yet, we cannot rule out the possibility that some of these objects were members of binary systems in the past and/or are currently part of a variable system. In particular, the fact that we identify such stars among the field stars in our Galaxy provides strong evidence that the nucleosynthetic process(es) producing the anomalous [N/Fe] abundance ratios occurs over a wide range of metallicities. This may provide evidence either for or against the uniqueness of the progenitor stars to GCs and/or the existence of chemical anomalies associated with likely tidally shredded clusters in massive dwarf galaxies such as “Kraken/Koala”, Gaia-Enceladus-Sausage, among others, before or during their accretion by the MW. In conclusion, a dynamical analysis reveals that the newly identified N-rich stars exhibit a wide range of dynamical characteristics throughout the MW, indicating that they were produced in a variety of Galactic environments.

59 BASIC BIOLOGICAL SCIENCES↗