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At least 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

SU-D-204-01: A Methodology Based On Machine Learning and Quantum Clustering to Predict Lung SBRT Dosimetric Endpoints From Patient Specific Anatomic Features

Purpose: To develop a data-mining methodology based on quantum clustering and machine learning to predict expected dosimetric endpoints for lung SBRT applications based on patient-specific anatomic features. Methods: Ninety-three patients who received lung SBRT at our clinic from 2011–2013 were retrospectively identified. Planning information was acquired for each patient, from which various features were extracted using in-house semi-automatic software. Anatomic features included tumor-to-OAR distances, tumor location, total-lung-volume, GTV and ITV. Dosimetric endpoints were adopted from RTOG-0195 recommendations, and consisted of various OAR-specific partial-volume doses and maximum point-doses. First, PCA analysis and unsupervised quantum-clustering was used to explore the feature-space to identify potentially strong classifiers. Secondly, a multi-class logistic regression algorithm was developed and trained to predict dose-volume endpoints based on patient-specific anatomic features. Classes were defined by discretizing the dose-volume data, and the feature-space was zero-mean normalized. Fitting parameters were determined by minimizing a regularized cost function, and optimization was performed via gradient descent. As a pilot study, the model was tested on two esophageal dosimetric planning endpoints (maximum point-dose, dose-to-5cc), and its generalizability was evaluated with leave-one-out cross-validation. Results: Quantum-Clustering demonstrated a strong separation of feature-space at 15Gy across the first-and-second Principle Components of the data when the dosimetric endpoints were retrospectively identified. Maximum point dose prediction to the esophagus demonstrated a cross-validation accuracy of 87%, and the maximum dose to 5cc demonstrated a respective value of 79%. The largest optimized weighting factor was placed on GTV-to-esophagus distance (a factor of 10 greater than the second largest weighting factor), indicating an intuitively strong correlation between this feature and both endpoints. Conclusion: This pilot study shows that it is feasible to predict dose-volume endpoints based on patient-specific anatomic features. The developed methodology can potentially help to identify patients at risk for higher OAR doses, thus improving the efficiency of treatment planning. R01-184173.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

A DNA-Encapsulated and Fluorescent Ag 10 6+ Cluster with a Distinct Metal-Like Core

Silver cluster–DNA complexes are optical chromophores, and pairs of these conjugates can be toggled from fluorescently dim to bright states using DNA hybridization. This paper highlights spectral and structural differences for a specific cluster pair. We have previously characterized a cluster with low emission and violet absorption that forms a compact structure with single-stranded oligonucleotides. We now consider its counterpart with blue absorption and strong green emission. This cluster develops with a single-stranded/duplex DNA construct and is favored by low silver concentrations with ≲8 Ag + :DNA, an oxygen atmosphere, and neutral pH. The resulting cluster displays key signatures of a molecular metal with well-defined absorption/emission bands at 490/550 nm, and with a fluorescence quantum yield of 15% and lifetime of 2.4 ns. The molecular cluster conjugates with the larger DNA host because it chromatographically elutes with the DNA and it exhibits circular dichroism. The silver cluster is identified as Ag 10 6+ using two modes of mass spectrometry and elemental analysis. Our key finding is that it adopts a low-dimensional shape, as determined from a Ag K-edge extended X-ray absorption fine structure analysis. The Ag 0 in this oxidized cluster segregates from the Ag + via a sparse number of metal-like bonds and a denser network of silver–DNA bonds. This structure contrasts with the compact, octahedral-like shape of the violet counterpart to the blue cluster, which is also a Ag 10 6+ species. Here, we consider that the blue- and violet-absorbing clusters may be isomers with shapes that are controlled by the secondary structures of their DNA templates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Quantifying and Zoning Urban Heat Island Effects Using Unsupervised Machine Learning

This work explores the Urban Heat Island (UHI) effects in Maricopa County, Arizona, employing a simulation-based approach that combines large-scale building energy modeling with advanced spatial analysis. Utilizing the Automatic Building Energy Modeling (AutoBEM) software suite, we simulated the energy consumption for approximately 1.35 million buildings based on the Model America version 1.0 (MAv1) dataset. Our methodology incorporated spatial analysis at multiple scales, including individual buildings, clusters of zones determined by K-means clustering, and geographical level evaluation based on Zip codes. The results revealed significant variations in energy consumption and heat emissions across different building types and urban zones. High-emission hotspots identified through clustering pointed to areas most contributing to the UHI effects. Zip code-based area analysis further contextualized these findings, offering an urban context-based perspective on emission distribution and informing potential urban energy policies for mitigating UHI effects.

Chowdhury, Shovan [ORNL]↗
DOE OSTI2017

Air Flow Modeling in the Wind Tunnel of the FHWA Aerodynamics Laboratory at Turner-Fairbank Highway Research Center

Computational fluid dynamics (CFD) modeling is widely used in industry for design and in the research community to support, compliment, and extend the scope of experimental studies. Analysis of transportation infrastructure using high performance cluster computing with CFD and structural mechanics software is done at the Transportation Research and Analysis Computing Center (TRACC) at Argonne National Laboratory. These resources, available at TRACC, were used to perform advanced three-dimensional computational simulations of the wind tunnel laboratory at the Turner-Fairbank Highway Research Center (TFHRC). The goals were to verify the CFD model of the laboratory wind tunnel and then to use versions of the model to provide the capability to (1) perform larger parametric series of tests than can be easily done in the laboratory with available budget and time, (2) to extend testing to wind speeds that cannot be achieved in the laboratory, and (3) to run types of tests that are very difficult or impossible to run in the laboratory. Modern CFD software has many physics models and domain meshing options. Models, including the choice of turbulence and other physics models and settings, the computational mesh, and the solver settings, need to be validated against measurements to verify that the results are sufficiently accurate for use in engineering applications. The wind tunnel model was built and tested, by comparing to experimental measurements, to provide a valuable tool to perform these types of studies in the future as a complement and extension to TFHRC’s experimental capabilities. Wind tunnel testing at TFHRC is conducted in a subsonic open-jet wind tunnel with a 1.83 m (6 foot) by 1.83 m (6 foot) cross section. A three component dual force-balance system is used to measure forces acting on tested models, and a three degree of freedom suspension system is used for dynamic response tests. Pictures of the room are shown in Figure 1-1 to Figure 1-4. A detailed CAD geometry and CFD model of the wind tunnel laboratory at TFHRC was built and tested. Results were compared against experimental wind velocity measurements at a large number of locations around the room. This testing included an assessment of the air flow uniformity provided by the tunnel to the test zone and assessment of room geometry effects, such as influence of the proximity the room walls, the non-symmetrical position of the tunnel in the room, and the influence of the room setup on the air flow in the room. This information is useful both for simplifying the computational model and in deciding whether or not moving, or removing, some of the furniture or other movable objects in the room will change the flow in the test zone.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Reproduction of the Yucca Mountain Project TSPA-LA Uncertainty and Sensitivity Analyses and Preliminary Upgrade of Models

Sandia National Laboratories (SNL) continued evaluation of total system performance assessment (TSPA) computing systems for the previously considered Yucca Mountain Project (YMP). This was done to maintain the operational readiness of the computing infrastructure (computer hardware and software) and knowledge capability for total system performance assessment (TSPA) type analysis, as directed by the National Nuclear Security Administration (NNSA), DOE 2010. This work is a continuation of the ongoing readiness evaluation reported in Lee and Hadgu (2014) and Hadgu et al. (2015). The TSPA computing hardware (CL2014) and storage system described in Hadgu et al. (2015) were used for the current analysis. One floating license of GoldSim with Versions 9.60.300, 10.5 and 11.1.6 was installed on the cluster head node, and its distributed processing capability was mapped on the cluster processors. Other supporting software were tested and installed to support the TSPA-type analysis on the server cluster. The current tasks included verification of the TSPA-LA uncertainty and sensitivity analyses, and preliminary upgrade of the TSPA-LA from Version 9.60.300 to the latest version 11.1. All the TSPA-LA uncertainty and sensitivity analyses modeling cases were successfully tested and verified for the model reproducibility on the upgraded 2014 server cluster (CL2014). The uncertainty and sensitivity analyses used TSPA-LA modeling cases output generated in FY15 based on GoldSim Version 9.60.300 documented in Hadgu et al. (2015). The model upgrade task successfully converted the Nominal Modeling case to GoldSim Version 11.1. Upgrade of the remaining of the modeling cases and distributed processing tasks will continue. The 2014 server cluster and supporting software systems are fully operational to support TSPA-LA type analysis.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2000

The Evolution of Stellar Dynamos; Survey for Low Mass Members of NGC2232; An X-Ray Survey of the Open Cluster CR140; Towards a Better Understanding of the Rotation-Activity Relation for Solar-Type Members of the Pleiades

This grant was originally awarded to Dr. Charles Prosser, who died tragically in a car accident in Tucson in 1998. We had hoped to finish the work Charles had started, which involved analysis of ROSAT data for three programs (observations of the clusters NGC2232, Crl4O and the Pleiades) and also analysis of optical data for each cluster in order to allow interpretation of the ROSAT observations. The Pleiades portion of the program was completed during the past year, and a paper published. We have obtained optical imaging of the other two clusters, and those data are being analyzed. Dr. Brian Patten intends to complete analysis of the ROSAT observations and to combine those data with the optical photometry, but progress on those efforts has been slow due to the press of other work (Dr. Patten is responsible for the pipeline processing of data from SWAS). We intend to publish those results as soon as we can, but it will now be completed without further support from this grant.

Stauffer, John R.↗
DOE OSTI2017

Distinction of saffron cod ( Eleginus gracilis ) from several other gadid species by using microsatellite markers

Nine microsatellite loci isolated in saffron cod ( Eleginus gracilis ) have potential applications for population genetics. Polymerase chain reaction products of samples of E. gracilis from northwestern Alaska amplified reliably, produced only one or two microsatellite bands, and had no apparent homozygote excess. A collection of E. gracilis sampled in the Gulf of Alaska (GOA) near Kodiak Island did not amplify reliably at one locus, and allele frequency profiles clustered distinctly (with principal component analysis [PCA]) from the northwestern Alaska collection. Northwestern Alaska and GOA E. gracilis collections were genetically different (on the basis of a standardized genetic differentiation measure [ G ' ST ]=0.313, chord distance [ D chord ]=0.078, P<0.0001) and differed in expected aver-age heterozygosities at shared loci (0.859 and 0.689, respectively). We tested the microsatellite primers on other gadid species endemic to the northern Pacific Ocean, Bering Sea, and Arctic Ocean for cross-species amplification. Not all loci amplified reliably in navaga ( E. nawaga ), Pacific tomcod ( Microgadus proximus ), Arctic cod ( Boreogadus saida ), Pacific cod ( Gadus macrocephalus ), or walleye pollock ( G. chalcogrammus ). Reliable loci varied in microsatellite size profiles and produced distinct PCA clusters and accurate genotype assignments that allowed accurate species identification. Furthermore the identifications support previous morphological and genetically determined systematic classifications and distinguished the geographically separated collections of E. gracilis .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

DEEP NEAR-IR OBSERVATIONS OF THE GLOBULAR CLUSTER M4: HUNTING FOR BROWN DWARFS

We present an analysis of deep Hubble Space Telescope (HST)/Wide Field Camera 3 near-IR (NIR) imaging data of the globular cluster (GC) M4. The best-photometry NIR color–magnitude diagram (CMD) clearly shows the main sequence extending toward the expected end of the hydrogen-burning limit and going beyond this point toward fainter sources. The white dwarf (WD) sequence can be identified. As such, this is the deepest NIR CMD of a GC to date. Archival HST optical data were used for proper-motion cleaning of the CMD and for distinguishing the WDs from brown dwarf (BD) candidates. Detection limits in the NIR are around F110W ≈ 26.5 mag and F160W ≈ 27 mag, and in the optical around F775W ≈ 28 mag. Comparing our observed CMDs with theoretical models, we conclude that we have reached beyond the H-burning limit in our NIR CMD and are probably just above or around this limit in our optical–NIR CMDs. Thus, any faint NIR sources that have no optical counterpart are potential BD candidates, since the optical data are not deep enough to detect them. We visually inspected the positions of NIR sources that are fainter than the H-burning limit in F110W and for which the optical photometry did not return a counterpart. We found in total five sources for which we did not get an optical measurement. For four of these five sources, a faint optical counterpart could be visually identified, and an upper optical magnitude was estimated. Based on these upper optical magnitude limits, we conclude that one source is likely a WD, one source could be either a WD or BD candidate, and the remaining two sources agree with being BD candidates. No optical counterpart could be detected for just one source, which makes this source a good BD candidate. We conclude that we found in total four good BD candidates.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

THE MULTI-EPOCH NEARBY CLUSTER SURVEY: TYPE Ia SUPERNOVA RATE MEASUREMENT IN z {approx} 0.1 CLUSTERS AND THE LATE-TIME DELAY TIME DISTRIBUTION

We describe the Multi-Epoch Nearby Cluster Survey, designed to measure the cluster Type Ia supernova (SN Ia) rate in a sample of 57 X-ray selected galaxy clusters, with redshifts of 0.05 < z < 0.15. Utilizing our real-time analysis pipeline, we spectroscopically confirmed twenty-three cluster SNe Ia, four of which were intracluster events. Using our deep Canada-France-Hawaii Telescope/MegaCam imaging, we measured total stellar luminosities in each of our galaxy clusters, and we performed detailed supernova (SN) detection efficiency simulations. Bringing these ingredients together, we measure an overall cluster SN Ia rate within R{sub 200} (1 Mpc) of 0.042{sup +0.012}{sub -0.010}{sup +0.010}{sub -0.008} SNuM (0.049{sup +0.016}{sub -0.014}{sup +0.005}{sub -0.004} SNuM) and an SN Ia rate within red-sequence galaxies of 0.041{sup +0.015}{sub -0.015}{sup +0.005}{sub -0.010} SNuM (0.041{sup +0.019}{sub -0.015}{sup +0.005}{sub -0.004} SNuM). The red-sequence SN Ia rate is consistent with published rates in early-type/elliptical galaxies in the 'field'. Using our red-sequence SN Ia rate, and other cluster SN measurements in early-type galaxies up to z {approx} 1, we derive the late-time (>2 Gyr) delay time distribution (DTD) of SN Ia assuming a cluster early-type galaxy star formation epoch of z{sub f} = 3. Assuming a power-law form for the DTD, {Psi}(t){proportional_to}t{sup s} , we find s = -1.62 {+-} 0.54. This result is consistent with predictions for the double degenerate SN Ia progenitor scenario (s {approx} -1) and is also in line with recent calculations for the double detonation explosion mechanism (s {approx} -2). The most recent calculations of the single degenerate scenario DTD predicts an order-of-magnitude drop-off in SN Ia rate {approx}6-7 Gyr after stellar formation, and the observed cluster rates cannot rule this out.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

AN X-RAY SURVEY OF THE YOUNG STELLAR POPULATION OF THE LYNDS 1641 AND IOTA ORIONIS REGIONS

We present an XMM-Newton survey of the part of the Orion A cloud south of the Orion Nebula. This survey includes the Lynds 1641 (L1641) dark cloud, a region of the Orion A cloud with very few massive stars and hence a relatively low ambient UV flux, and the region around the O9 III star {iota} Orionis. In addition to proprietary data, we used archival XMM data of the Orion Nebula Cluster (ONC) to extend our analysis to a major fraction of the Orion A cloud. We have detected 1060 X-ray sources in L1641 and the {iota} Ori region. About 94% of the sources have Two Micron All Sky Survey and Spitzer counterparts, 204 and 23 being Class II and Class I or protostar objects, respectively. In addition, we have identified 489 X-ray sources as counterparts to Class III candidates, given they are bright in X-rays and appear as normal photospheres at mid-IR wavelengths. The remaining 205 X-ray sources are likely distant active galactic nuclei or other galactic sources not related to Orion A. We find that Class III candidates appear more concentrated in two main clusters in L1641. The first cluster of Class III stars is found toward the northern part of L1641, concentrated around {iota} Ori. The stars in this cluster are more evolved than those in the Orion Nebula. We estimate a distance of 300-320 pc for this cluster showing that it is in the foreground of the Orion A cloud. Another cluster rich in Class III stars is located in L1641 South and appears to be a slightly older cluster embedded in the Orion A cloud. Furthermore, other evolved Class III stars are found north of the ONC toward NGC 1977.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

A multi-wavelength mass analysis of RCS2 J232727.6-020437, A ∼3 × 10{sup 15}M{sub ⊙} galaxy cluster at z = 0.7

We present an initial study of the mass and evolutionary state of a massive and distant cluster, RCS2 J232727.6-020437. This cluster, at z = 0.6986, is the richest cluster discovered in the RCS2 project. The mass measurements presented in this paper are derived from all possible mass proxies: X-ray measurements, weak-lensing shear, strong lensing, Sunyaev–Zel’dovich effect decrement, the velocity distribution of cluster member galaxies, and galaxy richness. While each of these observables probe the mass of the cluster at a different radius, they all indicate that RCS2 J232727.6−020437 is among the most massive clusters at this redshift, with an estimated mass of M{sub 200}∼3×10{sup 15}h{sub 70}{sup −1} M{sub ⊙}. In this paper, we demonstrate that the various observables are all reasonably consistent with each other to within their uncertainties. RCS2 J232727.6−020437 appears to be well relaxed—with circular and concentric X-ray isophotes, with a cool core, and no indication of significant substructure in extensive galaxy velocity data.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1993

Numerical analysis of base flowfield at high altitude for a four-engine clustered nozzle configuration

The objective of this study is to benchmark a four-engine clustered nozzle base flowfield with a computational fluid dynamics (CFD) model. The CFD model is a pressure based, viscous flow formulation. An adaptive upwind scheme is employed for the spatial discretization. The upwind scheme is based on second and fourth order central differencing with adaptive artificial dissipation. Qualitative base flow features such as the reverse jet, wall jet, recompression shock, and plume-plume impingement have been captured. The computed quantitative flow properties such as the radial base pressure distribution, model centerline Mach number and static pressure variation, and base pressure characteristic curve agreed reasonably well with those of the measurement. Parametric study on the effect of grid resolution, turbulence model, inlet boundary condition and difference scheme on convective terms has been performed. The results showed that grid resolution and turbulence model are two primary factors that influence the accuracy of the base flowfield prediction.

Wang, Ten-See↗
DOE OSTI2024

A network approach for multiscale catchment classification using traits

Abstract. The classification of river catchments into groups with similar biophysical characteristics is useful to understand and predict their hydrological behavior. The increasing availability of remote sensing and other large-scale geospatial datasets has enabled the use of advanced data-driven approaches to classify catchments using traits such as topography, geology, climate, land cover, land use, and human influence. Unsupervised clustering algorithms based on the Euclidean distance are commonly used for trait-based classification but are not suitable for highly dimensional data. In this study we present a new network-based method for multi-scale catchment classification, which can be applied to large datasets and used to determine the traits associated with different catchment groups. In this framework, two networks are analyzed in parallel: the first being where the nodes are traits and the second being where the nodes are catchments. In both cases, edges represent pairwise similarity, and a network cluster detection algorithm is used for the classification. The trait network is used to investigate redundancy in the trait data and to condense this information into a small number of interpretable categories. The catchments network is used to classify the catchments into clusters and to identify representative catchments for the different groups using the degree centrality metric. We apply this method to classify 9067 river catchments across the contiguous United States at both regional and continental scales using 274 non-categorical traits. At the continental scale, we identify 25 interpretable trait categories and 34 catchment clusters of sizes greater than 50. We find that catchments with similar trait categories are typically located in the same region, with different spatial patterns emerging among clusters dominated by natural and anthropogenic traits. We also find that the catchment clusters exhibit distinct hydrological behavior based on an analysis of streamflow indices. This network approach provides several advantages over traditional means of classification, including better separation of clusters, the use of alternate similarity metrics that are more suitable for highly dimensional data, and reducing redundancy in the trait information. The paired catchment–trait networks enable analysis of hydrological behavior using the dominant trait categories for each catchment cluster. The approach can be used at multiple spatial scales since the network topologies adjust automatically to reflect the trait patterns at the scale of investigation. Finally, the representative catchments identified as hub nodes in the network can be used to guide transferable observational and modeling strategies. The method is broadly applicable beyond hydrology for classification of other complex systems that utilize different types of trait datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Coarse-grained model of water diffusion and proton conductivity in hydrated polyelectrolyte membrane

Using dissipative particle dynamics (DPD), we simulate nanoscale segregation, water diffusion, and proton conductivity in hydrated sulfonated polystyrene (sPS). We employ a novel model [Lee et al. J. Chem. Theory Comput. 11(9), 4395-4403 (2015)] that incorporates protonation/deprotonation equilibria into DPD simulations. The polymer and water are modeled by coarse-grained beads interacting via short-range soft repulsion and smeared charge electrostatic potentials. The proton is introduced as a separate charged bead that forms dissociable Morse bonds with the base beads representing water and sulfonate anions. Morse bond formation and breakup artificially mimics the Grotthuss mechanism of proton hopping between the bases. The DPD model is parameterized by matching the proton mobility in bulk water, dissociation constant of benzenesulfonic acid, and liquid-liquid equilibrium of water-ethylbenzene solutions. The DPD simulations semi-quantitatively predict nanoscale segregation in the hydrated sPS into hydrophobic and hydrophilic subphases, water self-diffusion, and proton mobility. As the hydration level increases, the hydrophilic subphase exhibits a percolation transition from isolated water clusters to a 3D network. The analysis of hydrophilic subphase connectivity and water diffusion demonstrates the importance of the dynamic percolation effect of formation and breakup of temporary junctions between water clusters. The proposed DPD model qualitatively predicts the ratio of proton to water self-diffusion and its dependence on the hydration level that is in reasonable agreement with experiments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

The phase space of z∼1.2 SpARCS clusters: Using Herschel to probe dust temperature as a function of environment and accretion history

We present a five-band Herschel study (100–500 μm) of three galaxy clusters at z∼1.2 from the Spitzer Adaptation of the Red-Sequence Cluster Survey. With a sample of 120 spectroscopically confirmed cluster members, we investigate the role of environment on galaxy properties utilizing the projected cluster phase space (line-of-sight velocity versus clustercentric radius), which probes the time-averaged galaxy density to which a galaxy has been exposed. We divide cluster galaxies into phase-space bins of (r/r{sub 200})×(Δv/σ{sub v}), tracing a sequence of accretion histories in phase space. Stacking optically star-forming cluster members on the Herschel maps, we measure average infrared star formation rates, and, for the first time in high-redshift galaxy clusters, dust temperatures for dynamically distinct galaxy populations—namely, recent infalls and those that were accreted onto the cluster at an earlier epoch. Proceeding from the infalling to virialized (central) regions of phase space, we find a steady decrease in the specific star formation rate and increase in the stellar age of star-forming cluster galaxies. We perform a probability analysis to investigate all acceptable infrared spectral energy distributions within the full parameter space and measure a ∼4σ drop in the average dust temperature of cluster galaxies in an intermediate phase-space bin, compared to an otherwise flat trend with phase space. We suggest one plausible quenching mechanism which may be consistent with these trends, invoking ram-pressure stripping of the warmer dust for galaxies within this intermediate accretion phase.

79 ASTRONOMY AND ASTROPHYSICS↗