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At least 613 records · Page 34

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DOE OSTI2014

Periodic ordering of clusters and stripes in a two-dimensional lattice model. I. Ground state, mean-field phase diagram and structure of the disordered phases

The short-range attraction and long-range repulsion between nanoparticles or macromolecules can lead to spontaneous pattern formation on solid surfaces, fluid interfaces, or membranes. In order to study the self-assembly in such systems we consider a triangular lattice model with nearest-neighbor attraction and third-neighbor repulsion. At the ground state of the model (T = 0) the lattice is empty for small values of the chemical potential μ, and fully occupied for large μ. For intermediate values of μ periodically distributed clusters, bubbles, or stripes appear if the repulsion is sufficiently strong. At the phase coexistences between the vacuum and the ordered cluster phases and between the cluster and the lamellar (stripe) phases the entropy per site does not vanish. As a consequence of this ground state degeneracy, disordered fluid phases consisting of clusters or stripes are stable, and the surface tension vanishes. For T > 0 we construct the phase diagram in the mean-field approximation and calculate the correlation function in the self-consistent Brazovskii-type field theory.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

Coupled macrospins: Mode dynamics in symmetric and asymmetric vertices

We report the microwave response of symmetric and asymmetric threefold clusters with nearly contacting segments that can serve as the node in a Kagome artificial spin ice lattice. The structures are patterned on a coplanar waveguide and consist of elongated and nearly-contacting ellipses with uniform thickness. Branches of the ferromagnetic resonance spectra display mode softening that correlates well with the calculations, whereas agreement between the measured and simulated static magnetization is more qualitative.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Development of a three-dimensional APOLLO3 neutrons deterministic scheme for the CABRI reactor

CABRI is an experimental reactor to study the fuel behavior during reactivity injection transients. These transients being highly multiphysics, the development of suitable modeling and simulation tools to simulate them is important for the optimization of the tests and the control of the experimental conditions. This paper focuses on the development of an APOLLO3 deterministic core calculation tool dedicated to the CABRI transient analysis. It represents the first stage of the incremental process for the implementation of a multiphysics time-dependent modeling of the CABRI transient. The neutron calculation scheme is based on a classical two-step approach. The first step consists of a 281-energy group calculation flux with the TDT-MOC (Method Of Characteristics) solver for cross-section space and energy (23 groups) collapsing for the CABRI different assembly clusters. The bias on a 2D core neutron calculation due to the self-shielding calculation and collapsing on a restricted pattern are investigated thanks to a comparison with a direct full 2D calculation on a quarter of core. The second step relies on a pin-resolved transport 3D transport core calculation with the SN solver MINARET. A progressive numerical validation process is followed to quantify the calculation biases on reactivity and reaction rates at each step using reference calculations with the stochastic code TRIPOLI4. The next development stage toward a multiphysics scheme will be the implementation of the 3D-kinetics equation resolution and the coupling with a core thermal-hydraulics model. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2019

Earthquake Arrival Association with Backprojection and Graph Theory

The association of seismic-wave arrivals with causative earthquakes becomes progressively more challenging as arrival detection methods become more sensitive, and particularly when earthquake rates are high. For instance, seismic waves arriving across a monitoring network from several sources may overlap in time, false arrivals may be detected, and some arrivals may be of unknown phase (e.g., P or S waves). We propose an automated method to associate arrivals with earthquake sources and obtain source locations applicable to such situations. To do so, we use a pattern detection metric based on the principle of back-projection to reveal candidate sources followed by graph-theory-based clustering and an integer linear optimization routine to associate arrivals with the minimum number of sources necessary to explain the data. We find that this method solves for all sources and phase assignments simultaneously, rather than in a sequential greedy procedure as is common in other association routines. We demonstrate our method on both synthetic and real data from the Integrated Plate Boundary Observatory Chile seismic network of northern Chile. For the synthetic tests, we report results for cases with varying complexity, including rates of 500 earthquakes/day and 500 false arrivals/station/day, for which we measure true positive detection accuracy of >95%. For the real data, we develop a new catalog between 1 January 2010 and 31 December 2017 containing 817,548 earthquakes, with detection rates on average 279 earthquakes/day and a magnitude-of-completion of M~1.8. A subset of detections are identified as sources related to quarry and industrial site activity, and we also detect thousands of foreshocks and aftershocks of the 1 April 2014 Mw 8.2 Iquique earthquake. During the highest rate of aftershock activity, >600 earthquakes/day are detected in the vicinity of the Iquique earthquake rupture zone.

58 GEOSCIENCES↗

DOE OSTI2024

The Near-Gapless Penicillium fuscoglaucum Genome Enables the Discovery of Lifestyle Features as an Emerging Post-Harvest Phytopathogen

Penicillium spp. occupy many diverse biological niches that include plant pathogens, opportunistic human pathogens, saprophytes, indoor air contaminants, and those selected specifically for industrial applications to produce secondary metabolites and lifesaving antibiotics. Recent phylogenetic studies have established Penicillium fuscoglaucum as a synonym for Penicillium commune, which is an indoor air contaminant and toxin producer and can infect apple fruit during storage. During routine culturing on selective media in the lab, we obtained an isolate of P. fuscoglaucum Pf_T2 and sequenced its genome. The Pf_T2 genome is far superior to available genomic resources for the species. Our assembly exhibits a length of 35.1 Mb, a BUSCO score of 97.9% complete, and consists of five scaffolds/contigs representing the four expected chromosomes. It was determined that the Pf_T2 genome was colinear with a type specimen P. fuscoglaucum and contained a lineage-specific, intact cyclopiazonic acid (CPA) gene cluster. For comparison, a highly virulent postharvest apple pathogen, P. expansum strain TDL 12.1, was included and showed a similar growth pattern in culture to our Pf_T2 isolate but was far more aggressive in apple fruit than P. fuscoglaucum. The genome of Pf_T2 serves as a major improvement over existing resources, has superior annotation, and can inform forthcoming omics-based work and functional genetic studies to probe secondary metabolite production and disparities in aggressiveness during apple fruit decay.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

The Apache Point Observatory Galactic Evolution Experiment (APOGEE)

The Apache Point Observatory Galactic Evolution Experiment (APOGEE), one of the programs in the Sloan Digital Sky Survey III (SDSS-III), has now completed its systematic, homogeneous spectroscopic survey sampling all major populations of the Milky Way. After a three-year observing campaign on the Sloan 2.5 m Telescope, APOGEE has collected a half million high-resolution ( R ∼ 22,500), high signal-to-noise ratio (>100), infrared (1.51–1.70 μ m) spectra for 146,000 stars, with time series information via repeat visits to most of these stars. This paper describes the motivations for the survey and its overall design—hardware, field placement, target selection, operations—and gives an overview of these aspects as well as the data reduction, analysis, and products. An index is also given to the complement of technical papers that describe various critical survey components in detail. Finally, we discuss the achieved survey performance and illustrate the variety of potential uses of the data products by way of a number of science demonstrations, which span from time series analysis of stellar spectral variations and radial velocity variations from stellar companions, to spatial maps of kinematics, metallicity, and abundance patterns across the Galaxy and as a function of age, to new views of the interstellar medium, the chemistry of star clusters, and the discovery of rare stellar species. As part of SDSS-III Data Release 12 and later releases, all of the APOGEE data products are publicly available.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1988

Cluster analysis of multiple planetary flow regimes

A modified cluster analysis method developed for the classification of quasi-stationary events into a few planetary flow regimes and for the examination of transitions between these regimes is described. The method was applied first to a simple deterministic model and then to a 500-mbar data set for Northern Hemisphere (NH), for which cluster analysis was carried out in the subspace of the first seven empirical orthogonal functions (EOFs). Stationary clusters were found in the low-frequency band of more than 10 days, while transient clusters were found in the band-pass frequency window between 2.5 and 6 days. In the low-frequency band, three pairs of clusters determined EOFs 1, 2, and 3, respectively; they exhibited well-known regional features, such as blocking, the Pacific/North American pattern, and wave trains. Both model and low-pass data exhibited strong bimodality.

Mo, Kingtse↗

NASA NTRS1998

Classification and evolution of EF-hand proteins

Forty-five distinct subfamilies of EF-hand proteins have been identified. They contain from two to eight EF-hands that are recognizable by amino acid sequence as being statistically similar to other EF-hand domains. All proteins within one subfamily are congruent to one another, i.e. the dendrogram computed from one of the EF-hand domains is similar, within statistical error, to the dendrogram computed from another(s) domain. Thirteen subfamilies--including Calmodulin, Troponin C, Essential light chain, Regulatory light chain--referred to collectively as CTER, are congruent with one another. They appear to have evolved from a single ur-domain by two cycles of gene duplication and fusion. The subfamilies of CTER subsequently evolved by gene duplications and speciations. The remaining 32 subfamilies do not show such general patterns of congruence; however, some--such as S100, intestinal calcium binding protein (calbindin 9 kd), and trichohylin--do not form congruent clusters of subfamilies. Nearly all of the domains 1, 3, 5, and 7 are most similar to other ODD domains. Correspondingly the EVEN numbered domains of all 45 subfamilies most closely resemble EVEN domains of other subfamilies. Many sequence and chemical characteristics do not show systemic trends by subfamily or species of host organisms; such homoplasy is widespread. Eighteen of the subfamilies are heterochimeric; in addition to multiple EF-hands they contain domains of other evolutionary origins.

Non-NASA Center↗

NASA NTRS2006

Interconnect Performance Evaluation of SGI Altix 3700 BX2, Cray X1, Cray Opteron Cluster, and Dell PowerEdge

We study the performance of inter-process communication on four high-speed multiprocessor systems using a set of communication benchmarks. The goal is to identify certain limiting factors and bottlenecks with the interconnect of these systems as well as to compare these interconnects. We measured network bandwidth using different number of communicating processors and communication patterns, such as point-to-point communication, collective communication, and dense communication patterns. The four platforms are: a 512-processor SGI Altix 3700 BX2 shared-memory machine with 3.2 GB/s links; a 64-processor (single-streaming) Cray XI shared-memory machine with 32 1.6 GB/s links; a 128-processor Cray Opteron cluster using a Myrinet network; and a 1280-node Dell PowerEdge cluster with an InfiniBand network. Our, results show the impact of the network bandwidth and topology on the overall performance of each interconnect.

Fatoohi, Rod↗

DOE OSTI2019

Dissimilar responses of fungal and bacterial communities to soil transplantation simulating abrupt climate changes

Both fungi and bacteria play necessary roles in regulating soil carbon cycling. To implement future carbon stability, it is imperative to understand their responses to environmental changes, which is subject to large uncertainty. As current global warming is causing range shifts toward higher latitudes, we conducted three reciprocal soil transplantation experiments over large transects in 2005 to simulate abrupt climate changes. Six years after soil transplantation, fungal biomass of transplanted soils showed a general pattern of changes from donor sites to destination, which were more obvious in bare fallow soils than in maize cropped soils. Strikingly, fungal community compositions were clustered by sites, demonstrating that fungi of transplanted soils acclimatized to the destination environment. Several fungal taxa displayed sharp changes in relative abundance, including Podospora, Chaetomium, Mortierella and Phialemonium. In contrast, bacterial communities remained largely unchanged. Consistent with the important role of fungi in affecting soil carbon cycling, 8.1%-10.0% of fungal genes encoding carbon-decomposing enzymes were significantly (p < 0.01) increased as compared with those from bacteria (5.7%-8.4%). To explain these observations, we found that fungal occupancy across samples was mainly determined by annual average air temperature and rainfall, whereas bacterial occupancy was more closely related to soil conditions, which remained stable 6 years after soil transplantation. Together, these results demonstrate dissimilar response patterns and resource partitioning between fungi and bacteria, which may have considerable consequences for ecosystem-scale carbon cycling.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Molecular orbital symmetry-driven trimer formation in Kagome correlated electron materials

Correlated electron materials with molecular orbital states extending over transition metal clusters can host multiferroicity, spin frustration, and unconventional insulating phases. However, the fundamental criteria that govern cluster formation and stability remain unclear. Here, we identify a symmetry, correlation, and electron-filling-driven criteria that stabilize triangular metal trimers in materials displaying transition metal Kagome patterns. Using density functional theory and chemical bonding analysis, we show that trimer formation emerges when 6–8 electrons occupy molecular orbitals derived from transition metal d-states, achieving near-complete filling of bonding states while avoiding antibonding occupation, and correlations are of intermediate strength. This principle explains the stability of Nb 3 X 8 (X = Cl, Br, I), and more broadly, our findings offer a general design rule to obtain quantum materials with quantum states extended across transition metal clusters.

Kumari, Varsha [Indiana Univ., Bloomington, IN (Un↗

DOE OSTI2021

Discovering Hidden Geothermal Signatures using Unsupervised Machine Learning

Discovering hidden geothermal resources is a very challenging task. It requires the mining of large datasets, including various diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used Play Fairway Analysis (PFA) typically relies on subject-matter expertise to analyze site or regional data to estimate geothermal conditions and prospectivity. Here, we demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset of Southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. However, most of these systems are poorly characterized because of insufficient existing data and limited past explorative studies. This study aims to discover hidden patterns and relationships in the SWNM geothermal dataset to better understand regional hydrothermal conditions. This is achieved by applying an unsupervised machine learning algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden (latent) signatures characterizing datasets, (2) the optimal number of these signatures, (3) dominant data attributes associated with each signature, and (4) spatial distribution of the extracted signatures. Here, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, geothermal attributes at 44 locations in SWNM. NMFk successfully finds data patterns and identifies the spatial associations of hydrothermal signatures with the four physiographic provinces in SWNM (Colorado Plateau, Volcanic Field, Basin and Range, and the Rio Grande rift). The algorithm identified up to 5 hydrothermal signatures in the SWNM datasets that differentiate between low- and medium-temperature hydrothermal systems in different provinces. Also, the algorithm identifies two medium-temperature hydrothermal systems in SWNM that require further exploration for geothermal resource development. Based on our analyses, 12 of the attributes are important to identify medium-temperature hydrothermal systems, and the remaining six attributes are critical to characterize low-temperature hydrothermal systems. Based on the obtained results, we identify potential physiographic provinces for further exploration to characterize them as geothermal resources. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored areas.

58 GEOSCIENCES↗

DOE OSTI2020

Distributed non-negative matrix factorization with determination of the number of latent features

The holistic analysis and understanding of the latent (that is, not directly observable) variables and patterns buried in large datasets is crucial for data-driven science, decision making and emergency response. Such exploratory analyses require devising unsupervised learning methods for data mining and extraction of the latent features, and non-negative matrix factorization (NMF) is one of the prominent such methods. NMF is based on compute-intense non-convex constrained minimization, which, for large datasets requires fast and distributed algorithms. However, current parallel implementations of NMF fail to estimate the number of latent features. In practice, identifying these features is both difficult and significant for pattern recognition and latent feature analysis, especially for large dense matrices. Here, we introduce a distributed NMF algorithm coupled with distributed custom clustering followed by a stability analysis on dense data, which we call DnMFk, to determine the number of latent variables. The results on synthetic data and the classical Swimmer data set demonstrate the accuracy of model determination while scaling nearly linearly across multiple processors for large data. Further, we employ DnMFk to determine the number of hidden features from a terabyte matrix.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Precipitation regime classification for the Mojave Desert: Implications for fire occurrence

Mojave Desert ecosystem processes are dependent upon the amount and seasonality of precipitation. Multi-decadal periods of drought or above-average rainfall affect landscape vegetation condition, biomass and susceptibility to fire. The seasonality of precipitation events can also affect the likelihood of lightning, a key ignition source for fires. To develop an understanding of precipitation regimes and fire patterns we used monthly average precipitation data and GIS data representing burned areas from 1971-2010. We applied a K-means cluster analysis to the monthly precipitation data identifying three distinct precipitation seasons; winter (October – March), spring (April-June) and summer (July-September) and four discrete precipitation regimes within the Mojave ecoregion.

classification↗

DOE OSTI2014

Big-Data RHEED analysis for understanding epitaxial film growth processes

Reflection high energy electron diffraction (RHEED) has by now become a standard tool for in-situ monitoring of film growth by pulsed laser deposition and molecular beam epitaxy. Yet despite the widespread adoption and wealth of information in RHEED image, most applications are limited to observing intensity oscillations of the specular spot, and much additional information on growth is discarded. With ease of data acquisition and increased computation speeds, statistical methods to rapidly mine the dataset are now feasible. Here, we develop such an approach to the analysis of the fundamental growth processes through multivariate statistical analysis of RHEED image sequence. This approach is illustrated for growth of LaxCa1-xMnO3 films grown on etched (001) SrTiO3 substrates, but is universal. The multivariate methods including principal component analysis and k-means clustering provide insight into the relevant behaviors, the timing and nature of a disordered to ordered growth change, and highlight statistically significant patterns. Fourier analysis yields the harmonic components of the signal and allows separation of the relevant components and baselines, isolating the assymetric nature of the step density function and the transmission spots from the imperfect layer-by-layer (LBL) growth. These studies show the promise of big data approaches to obtaining more insight into film properties during and after epitaxial film growth. Furthermore, these studies open the pathway to use forward prediction methods to potentially allow significantly more control over growth process and hence final film quality.

Big-Data↗

DOE OSTI2020

Subtoxic dose of lithium cobalt oxide nanosheets impacts critical molecular pathways in trout gill epithelial cells

Increasing use of nanoscale lithium cobalt oxide (LCO) particles in nanotechnologies, including catalysis and energy storage, raises concerns over their release into the environment and subsequent biological impact. Here we study the impact of LCO nanosheets on trout gill epithelial cells – model cells for environmental exposures – by global transcriptomics using RNA-Seq. We identify molecular processes impacted by subtoxic and toxic nanoparticle (NP) doses as well as dissolved Li+ and Co2+ ions. We found that the ions, at concentrations released from the toxic NP dose, did not impact cell viability and downregulated the expression of few genes following 24 h exposure, which recovered to normal levels at 48 h. In contrast, the toxic NP dose upregulated the expression of over 1000 genes at each time point, indicating the intact NPs are responsible for perturbing gene expression and toxicity. Importantly, the subtoxic NP dose, despite having no impact on cell viability, upregulated the expression of over 500 genes at 24 h, and 150 genes at 48 h. Clustering analysis showed distinct gene expression profiles induced by the toxic and subtoxic NP doses, and functional enrichment identified pathways with distinct patterns of regulations. The impacted pathways fell into four main functional categories: metabolic and energy related processes, oxygen and hypoxia related processes, membrane binding and internalization processes, and developmental processes. Together, our observations indicate that LCO NP toxicity originates from the intact NP, not the dissolved ions, and even subtoxic NP dose impacts multiple pathways critical to the normal function of the cell.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2014

Spatiotemporal organization of energy release events in the quiet solar corona

Using data from the STEREO and SOHO spacecraft, we show that temporal organization of energy release events in the quiet solar corona is close to random, in contrast to the clustered behavior of flaring times in solar active regions. The locations of the quiet-Sun events follow the meso- and supergranulation pattern of the underling photosphere. Together with earlier reports of the scale-free event size statistics, our findings suggest that quiet solar regions responsible for bulk coronal heating operate in a driven self-organized critical state, possibly involving long-range Alfvénic interactions.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2014

Spatiotemporal Organization of Energy Release Events in the Quiet Solar Corona

sun activity↗