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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
NASA NTRS2014

Data Analysis & Statistical Methods for Command File Errors

This paper explains current work on modeling for managing the risk of command file errors. It is focused on analyzing actual data from a JPL spaceflight mission to build models for evaluating and predicting error rates as a function of several key variables. We constructed a rich dataset by considering the number of errors, the number of files radiated, including the number commands and blocks in each file, as well as subjective estimates of workload and operational novelty. We have assessed these data using different curve fitting and distribution fitting techniques, such as multiple regression analysis, and maximum likelihood estimation to see how much of the variability in the error rates can be explained with these. We have also used goodness of fit testing strategies and principal component analysis to further assess our data. Finally, we constructed a model of expected error rates based on the what these statistics bore out as critical drivers to the error rate. This model allows project management to evaluate the error rate against a theoretically expected rate as well as anticipate future error rates.

Correlation Analysis↗
DOE OSTI2019

XPCA - R Implementation

Principal component analysis (PCA) is arguably the most popular tool in multivariate exploratory data analysis. We consider the question of how to handle heterogeneous variables that include continuous, binary, and ordinal. In the probabilistic interpretation of low-rank PCA, the data has a normal multivariate distribution and, therefore, normal marginal distributions for each column. If some marginals are continuous but not normal, the semiparametric copula-based principal component analysis (COCA) method is an alternative to PCA that combines a Gaussian copula with nonparametric marginals. If some marginals are discrete or semi-continuous, we present a new extended PCA (XPCA) method that also uses a Gaussian copula and nonparametric marginals and accounts for discrete variables in the likelihood calculation by integrating over appropriate intervals. Like PCA, the factors produced by XPCA can be used to find latent structure in data, build predictive models, and perform dimensionality reduction. We implement the new model, XPCA, and its ancestors PCA and COCA in both R and Python. This software provides implementations of XPCA, COCA, and probabilistic PCA in both R and Python. XPCA is written in arXiv report https://arxiv.org/abs/1808.07510. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. SAND2019-10591 M

Kincher-Winoto, Kina↗
DOE OSTI2019

XPCA -Python Implementation

Principal component analysis (PCA) is arguably the most popular tool in multivariate exploratory data analysis. We consider the question of how to handle heterogeneous variables that include continuous, binary, and ordinal. In the probabilistic interpretation of low-rank PCA, the data has a normal multivariate distribution and, therefore, normal marginal distributions for each column. If some marginals are continuous but not normal, the semiparametric copula-based principal component analysis (COCA) method is an alternative to PCA that combines a Gaussian copula with nonparametric marginals. If some marginals are discrete or semi-continuous, we present a new extended PCA (XPCA) method that also uses a Gaussian copula and nonparametric marginals and accounts for discrete variables in the likelihood calculation by integrating over appropriate intervals. Like PCA, the factors produced by XPCA can be used to find latent structure in data, build predictive models, and perform dimensionality reduction. We implement the new model, XPCA, and its ancestors PCA and COCA in both R and Python. This software provides implementations of XPCA, COCA, and probabilistic PCA in both R and Python. XPCA is written in arXiv report https://arxiv.org/abs/1808.07510. SAND2019-10591 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Kincher-Winoto, Kina↗
DOE OSTI2017

General constraints on dark matter decay from the cosmic microwave background

Precise measurements of the temperature and polarization anisotropies of the cosmic microwave background can be used to constrain the annihilation and decay of dark matter. We demonstrate via principal component analysis that the imprint of dark matter decay on the cosmic microwave background can be approximately parametrized by a single number for any given dark matter model. We develop a simple prescription for computing this model-dependent detectability factor, and demonstrate how this approach can be used to set model-independent bounds on a large class of decaying dark matter scenarios. We repeat our analysis for decay lifetimes shorter than the age of the Universe, allowing us to set constraints on metastable species other than the dark matter decaying at early times, and decays that only liberate a tiny fraction of the dark matter mass energy. We set precise bounds and validate our principal component analysis using a Markov chain Monte Carlo approach and Planck 2015 data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Monitoring Aqueous Reprocessing Systems for Detection of Facility Misuse

Traditional approaches to material control and accounting at used nuclear fuel reprocessing facilities relies in large part on laboratory-based destructive assay of periodic samples taken from key stages. These techniques are highly accurate, but limited inspection frequency may limit the size and operation of a reprocessing facility. Online monitoring techniques using inline detectors can be applied to fuel reprocessing systems to improve the situational awareness as the facility is operating to provide additional indicators of the evolving state of the facility between normal inspection periods, potentially alerting regulators to the need for additional inspection. The Multi-Isotope Process (MIP) Monitor combines medium-resolution gamma detectors with statistical data analysis techniques to monitor reprocessing systems for anomalous conditions resulting from process control issues or malfeasance. Gamma spectra collected from the H-Canyon reprocessing facility were modeled using principal component analysis to demonstrate the efficacy of the MIP Monitor for detecting notional diversion scenarios.

safeguards, nuclear fuel reprocessing, gamma spect↗
NASA NTRS1983

Shuttle Imaging Radar-A (SIR-A) data as a complement to Landsat Multispectral Scanner (MSS) data

Principal components analysis and supervised classifications were performed on two dates of Landsat multispectral scanner (MSS) data registered to one date of Shuttle Imaging Radar-A (SIR-A) data in a wheat-growing area of New South Wales, Australia. The purpose was to evaluate SIR-A data as a complement to Landsat MSS data in an agricultural environment. The SIR-A data was filtered using a 7 x 7 pixel moving window median filter. Principal components analysis indicated the SIR-A data were discriminating between trees and agricultural fields. Supervised classifications using wheat, pasture, trees, and idle classes resulted in increased accuracies for wheat and pasture and slightly decreased accuracies for trees and idle for the Landsat MSS/SIR-A registered data sets over the Landsat MSS alone. Overall classification accuracies were unchanged for one date and substantially increased for the other when the SIR-A data were added to the Landsat MSS data.

Henninger, D. L.↗
DOE OSTI2015

Big Data in Reciprocal Space: Sliding Fast Fourier Transforms for Determining Periodicity

Significant advances in atomically resolved imaging of crystals and surfaces have occurred in the last decade allowing unprecedented insight into local crystal structures and periodicity. Yet, the analysis of the long-range periodicity from the local imaging data, critical to correlation of functional properties and chemistry to the local crystallography, remains a challenge. Here, we introduce a Sliding Fast Fourier Transform (FFT) filter to analyze atomically resolved images of in-situ grown La5/8Ca3/8MnO3 films. We demonstrate the ability of sliding FFT algorithm to differentiate two sub-lattices, resulting from a mixed-terminated surface. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) of the Sliding FFT dataset reveal the distinct changes in crystallography, step edges and boundaries between the multiple sub-lattices. The method is universal for images with any periodicity, and is especially amenable to atomically resolved probe and electron-microscopy data for rapid identification of the sub-lattices present.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Using the optimal combined index weight ratio to improve the probability of anomaly detection in big area additive manufacturing

Big Area Additive Manufacturing (BAAM) of composites requires significant time, energy, and material, so it is critical to reduce production inefficiencies to make functional parts without multiple iterations. Statistical process control coupled with Principal Component Analysis (PCA) is a powerful technique that provides a quick, computationally inexpensive, and intuitive way for operators to detect defects that form in a manufacturing process without massive datasets. Recently, a combined index that is a weighted sum of the Hotelling's T 2 and squared residual error statistics has been proposed that can be monitored in one chart, improving interpretation accuracy and simplicity. However, the literature does not offer a formal method to optimise the weights. Here, we introduce two new approaches to the traditional weight selection approach using simulated and BAAM image data. Approach 1 uses a theoretically motivated optimum inspired by probabilistic principal component analysis. Approach 2 systematically varies the ratio of the weights to find the optimum. We show that approach 1 delivers optimal anomaly detection performance in select cases while approach 2 fares better in practice. Surprisingly, we also show that choosing a more complex PCA model has a minimal negative impact on anomaly detection performance compared to a more simplistic model.

3-dimensional printing↗
DOE OSTI2020

Whose Gas is it anyway? Differentiating the Source of a Large Soil Vapor Plume beneath Two Adjacent Waste Sites - 20487

DOE contractor CH2M Hill Plateau Remediation Company is currently responsible for conducting groundwater contamination monitoring at several RCRA treatment, storage, and disposal units located on the Hanford Site in Richland, Washington State. The Nonradioactive Dangerous Waste Landfill treatment, storage, and disposal unit presents a distinct groundwater monitoring problem because of a large multi-contaminant soil vapor plume beneath it that is a likely source of low-level volatile organic compound groundwater contamination. Adjacent to Nonradioactive Dangerous Waste Landfill is the Solid Waste Landfill. Volatile organic compounds are inventory components of both the Nonradioactive Dangerous Waste Landfill and the Solid Waste Landfill. Therefore, it is possible that both sites could be contributing to the soil vapor plume. For regulatory purposes, it is important to differentiate which site is the primary contributor of volatile organic compounds to the plume. An approach was developed to identify the primary volatile organic compound source of the soil vapor plume beneath Nonradioactive Dangerous Waste Landfill and Solid Waste Landfill. The site conceptual model hypothesis of vapor-phase volatile organic compound transport to the dissolved phase in groundwater was tested by a simple mathematical model of vapor/liquid equilibrium concentrations at the groundwater/air interface. Once it was shown that vapor-phase volatile organic compound transport to groundwater was a valid conceptual model for Nonradioactive Dangerous Waste Landfill and Solid Waste Landfill, spatial and statistical methods were used to determine the primary site contributing to the majority of volatile organic compounds to the soil vapor plume. Average groundwater chloroform, tetrachloroethene, and trichloroethene concentrations from Nonradioactive Dangerous Waste Landfill and Solid Waste Landfill monitoring network wells were plotted on maps of the facilities and immediate vicinities and compared to soil vapor sampling probe locations. Principal component analysis and mixing ratios were used to identify source contributions of each treatment, storage, and disposal unit to the plume. Results of the vapor/liquid equilibrium concentrations mathematical model showed that transport phenomena outweigh steady-state equilibria. Estimated vapor/liquid equilibrium concentrations were considerably lower than soil vapor measurements. The results indicate that dynamic vadose zone and groundwater factors such as decreased vapor concentrations with depth, vapor dilution from dispersion in the vadose zone, and advective and diffusional volatile organic compound dilution in groundwater result in groundwater volatile organic compound concentrations much less than would be measured under steady-state equilibrium conditions. Site source contribution differentiation by principal component analysis and mixing ratios was inconclusive using actual soil gas data because of the similarity in concentration values in both datasets for Nonradioactive Dangerous Waste Landfill and Solid Waste Landfill. Similar data populations suggest mixing of the vapor contributions from both sites by dispersion through the soil matrix pore spaces. However, when groundwater volatile organic compound data were compared between the Nonradioactive Dangerous Waste Landfill and Solid Waste Landfill monitoring networks, Solid Waste Landfill mean concentrations were higher, suggesting more vapor-phase volatile organic compound transport to groundwater at those locations. Simulated volatile organic compound soil vapor and groundwater datasets created to test the methods developed for this study show that the method can be successful in source differentiation when significantly different datasets are compared. This paper will describe a method of testing a conceptual model for vapor-phase contaminant transport to groundwater and for differentiating site sources of contaminants comprising a mixed-constituent soil vapor plume. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Bounding uncertainty in functional data: A case study

Functional data are fast becoming a preeminent source of information across a wide range of industries. A particularly challenging aspect of functional data is bounding uncertainty. In this unique case study, we present our attempts at creating bounding functions for selected applications at Sandia National Laboratories (SNL). The first attempt involved a simple extension of functional principal component analysis (fPCA) to incorporate covariates. Though this method was straightforward, the extension was plagued by poor coverage accuracy for the bounding curve. This led to a second attempt utilizing elastic methodology which yielded more accurate coverage at the cost of more complexity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Discovering the desirable alleles contributing to the lignocellulosic biomass traits in saccharum germplasm collections for energy cane improvement

Phenotyping Methods: The accessions (which includes 21 taxa and 1,177 accessions) in the World Collection of Sugarcane and Related Grasses (WCSRG) was evaluated for the following traits: arenchyma, internode length and diameter, pubescence, pith, Brix, stalk number and fiber. A core of 300 accessions that included each species in the World Collection was selected by using the Maximization Strategy in MStrat software. Results: The core had a higher diversity rating than random selections of 300 accessions. The Shannon–Weaver Diversity Index scores of the core and whole collection were similar indicating that the majority of the diversity was captured by the core collection. The ranges and medians between the core and WCSRG were similar; only two of the trait medians were not significant at P = 0.05 using the non-parametric Wilcoxon method and the coincidence rate (CR % = 96.2) was high (>80) indicating that extreme values were retained. Thus, the phenotypic diversity of these traits in the WCSRG was well represented by the core collection. Associations Methods: Genotypic and phenotypic data were collected for 1002 accessions of the WCSRG including 209 SSR markers. Association analysis was performed using both General Linear (GLM) and Maximum Likelihood (MLM) models. Different core collections with 300 accessions each were selected based on genotypic, phenotypic and combined data based on the Maximization Strategy in MStrat software. Results: A major portion of the genotyping involving SNPs is being conducted by Dr. Jianping Wang of the University of Florida under the DOE award DE-FG 02-11ER 65214 and the genotypic and phenotypic associations will be reported separately next year. In the current, study forty one and seventeen markers were found to be associated with traits using the GLM and MLM methods respectively including associations with arenchyma, internode length and diameter, pubescence, pith, and Sugar Cane Yellow Leaf Virus. The data indicates that each of the cores and the World Collection are similar to each other genotypically and phenotypically, but the core that was selected using only genotypic data was significantly different phenotypically. This indicates that there is not enough association between the genotypic and phenotypic diversity as to select using only genotypic diversity and get the full phenotypic diversity. Core Collection: Creation and Phenotyping Methods: To evaluate this germplasm for breeding purposes, a representative diversity panel selected from the WCSRG of approximately 300 accessions was planted at Canal Point, FL in three replications. These accessions were measured for stalk height and stalk number multiple times throughout the growing season and Brix and fresh biomass during harvest in 2013 and, stalk height, stalk number, stalk diameter, internode length, Brix and fresh and dry biomass was determined in the ratoon crop harvest in 2014. Results: In correlations of multiple measurements, there were higher correlations for early measurements of stalk number and stalk height with harvest traits like Brix and fresh weight. Hybrids had higher fresh mass and Brix while Saccharum spontaneum had higher stalk number and dry mass. The heritability of hybrid mass traits was lower in the ratoon crop. According to the principal component analysis, the diversity panel was divided into two groups. One group had accessions with high stalk number and high dry biomass like S. spontaneum and the other groups contained accessions with higher Brix and fresh biomass like S. officinarum. Mass traits correlated with each other as expected but hybrids had lower correlations between fresh and dry mass. Stalk number and the mass traits correlated with each other except in S. spontaneum and hybrids in the first ratoon. There were 110 accessions not significantly different in Brix from the commercial sugarcane checks including 10 S. spontaneum accessions. There were 27 dry and 6 fresh mass accessions significantly higher than the commercial sugarcane checks. Core Collection: Fiber analysis Methods: A biomass sample was taken from each accession then shredded and dried. Fiber analysis was then performed on each sample. The acetyl groups, acid insoluble lignin, acid soluble lignin, arabinan, glucan, holocellulose, total lignin, structural ash, and xylan were quantified on a % fiber basis and nonstructural ash on a % total basis. Results: There were significant, but not large differences between species for holocellulose, lignin, acetyl, acid soluble lignin, nonstructural ash, and glucan. For each trait, S. spontaneum had significantly more holocellulose, glucan, lignin, and nonstructural ash and less acetyl and acid soluble lignin than the other species. In all populations, glucan and was positively correlated with holocellulose were positively correlated and glucan and and holocellulose were negatively correlated with lignin. In hybrids, internode length correlated positively with holocellulose and nonstructural ash and negatively with lignin. The heritability estimates for each of the fiber component traits is low indicating that environment is an important factor in fiber composition. Principal component analysis indicated that a large amount of diversity exists within each of the species.

09 BIOMASS FUELS↗
DOE OSTI2018

ToF-SIMS characterization of glyoxal surface oxidation products by hydrogen peroxide: A comparison between dry and liquid samples

As one of the simplest volatile organic compounds (VOCs), glyoxal and its oxidation products were considered to be important precursors to aqueous secondary organic aerosol (aqSOA) formation. Herein, we analyzed products from glyoxal oxidation by hydrogen peroxide in dry and liquid samples using time-of-flight secondary ion mass spectrometry (ToF-SIMS). ToF-SIMS spectra and spectral principal component analysis (PCA) were used to investigate surface oxidation products. Dry samples were prepared on clean silicon wafers. Liquid samples consisting of glyoxal and hydrogen peroxide (H2O2) were introduced to a vacuum compatible microfluidic reactor prior to UV illumination or dark aging followed by in situ liquid SIMS analysis. A number of reaction products were observed in both dry and liquid samples; different oligomers and carboxylic acids could be formed depending on reaction conditions. In addition, hydrolyzed products were observed in the liquid samples, but not in the dry samples. Although dry samples reveal some products of the aqueous process, they are not fully representative as results from those of the aqueous samples. Our findings suggest that the ability to characterize the liquid surface reaction products provides more realistic information of the reaction products associated with aqSOA formation in the atmosphere. Meanwhile, the high mass resolution spectra from the dry sample SIMS measurement are helpful to identify oxidation products in the liquid samples.

, Aqueous Secondary Organic Aerosol, Sample Prepar↗
DOE OSTI2016

Planck 2015 results: XIV. Dark energy and modified gravity

For this research, we study the implications of Planck data for models of dark energy (DE) and modified gravity (MG) beyond the standard cosmological constant scenario. We start with cases where the DE only directly affects the background evolution, considering Taylor expansions of the equation of state w(a), as well as principal component analysis and parameterizations related to the potential of a minimally coupled DE scalar field. When estimating the density of DE at early times, we significantly improve present constraints and find that it has to be below ~2% (at 95% confidence) of the critical density, even when forced to play a role for z < 50 only. We then move to general parameterizations of the DE or MG perturbations that encompass both effective field theories and the phenomenology of gravitational potentials in MG models. Lastly, we test a range of specific models, such as k-essence, f(R) theories, and coupled DE. In addition to the latest Planck data, for our main analyses, we use background constraints from baryonic acoustic oscillations, type-Ia supernovae, and local measurements of the Hubble constant. We further show the impact of measurements of the cosmological perturbations, such as redshift-space distortions and weak gravitational lensing. These additional probes are important tools for testing MG models and for breaking degeneracies that are still present in the combination of Planck and background data sets. All results that include only background parameterizations (expansion of the equation of state, early DE, general potentials in minimally-coupled scalar fields or principal component analysis) are in agreement with ΛCDM. Finally, when testing models that also change perturbations (even when the background is fixed to ΛCDM), some tensions appear in a few scenarios: the maximum one found is ~2σ for Planck TT+lowP when parameterizing observables related to the gravitational potentials with a chosen time dependence; the tension increases to, at most, 3σ when external data sets are included. It however disappears when including CMB lensing.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

PROPERTIES OF CARBON–OXYGEN WHITE DWARFS FROM MONTE CARLO STELLAR MODELS

We investigate properties of carbon–oxygen white dwarfs with respect to the composite uncertainties in the reaction rates using the stellar evolution toolkit, Modules for Experiments in Stellar Astrophysics (MESA) and the probability density functions in the reaction rate library STARLIB. These are the first Monte Carlo stellar evolution studies that use complete stellar models. Focusing on 3 M{sub ⊙} models evolved from the pre main-sequence to the first thermal pulse, we survey the remnant core mass, composition, and structure properties as a function of 26 STARLIB reaction rates covering hydrogen and helium burning using a Principal Component Analysis and Spearman Rank-Order Correlation. Relative to the arithmetic mean value, we find the width of the 95% confidence interval to be ΔM{sub 1TP} ≈ 0.019 M{sub ⊙} for the core mass at the first thermal pulse, Δt{sub 1TP} ≈ 12.50 Myr for the age, Δlog(T{sub c}/K) ≈ 0.013 for the central temperature, Δlog(ρ{sub c}/g cm{sup −3}) ≈ 0.060 for the central density, ΔY{sub e,c} ≈ 2.6 × 10{sup −5} for the central electron fraction, ΔX{sub c}({sup 22}Ne) ≈ 5.8 × 10{sup −4}, ΔX{sub c}({sup 12}C) ≈ 0.392, and ΔX{sub c}({sup 16}O) ≈ 0.392. Uncertainties in the experimental {sup 12}C(α,γ){sup 16}O, triple-α, and {sup 14}N(p,γ){sup 15}O reaction rates dominate these variations. We also consider a grid of 1–6 M{sub ⊙} models evolved from the pre main-sequence to the final white dwarf to probe the sensitivity of the initial–final mass relation to experimental uncertainties in the hydrogen and helium reaction rates.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

First study of reionization in tilted flat and untilted non-flat dynamical dark energy inflation models

We examine the effects of dark energy dynamics and spatial curvature on cosmic reionization by studying reionization in tilted spatially flat and untilted non-flat XCDM and $\phi$CDM dynamical dark energy inflation models that best fit the Planck 2015 cosmic microwave background (CMB) anisotropy and a large compilation of non-CMB data. We carry out a detailed statistical study, based on a principal component analysis and a Markov chain Monte Carlo analysis of a compilation of lower-redshift reionization data to estimate the uncertainties in the cosmological model reionization histories. We find that, irrespective of the nature of dark energy, there are significant differences between the reionization histories of the spatially flat and non-flat models. Although both the flat and non-flat models can accurately match the low-redshift ( z ≲ 6) reionization observations, there is a clear discrepancy between high-redshift ( z > 7) Lyman-α emitter data and the predictions from non-flat models. This is solely due to the fact that the non-flat models have a significantly larger electron scattering optical depth, τ el , compared to the flat models, which requires an extended and much earlier reionization scenario supported by more high-redshift ionizing sources in the non-flat models. Non-flat models also require strong redshift evolution in the photon escape fraction, which can become unrealistically high (≳1) at some redshifts. However, τ el is about 0.9-σ lower in the tilted flat ΛCDM model when the new Planck 2018 data are used and this reduction will partially alleviate the tension between the non-flat model predictions and the data.

Astronomy & Astrophysics↗
DOE OSTI2015

Background model systematics for the Fermi GeV excess

The possible gamma-ray excess in the inner Galaxy and the Galactic center (GC) suggested by Fermi-LAT observations has triggered a large number of studies. It has been interpreted as a variety of different phenomena such as a signal from WIMP dark matter annihilation, gamma-ray emission from a population of millisecond pulsars, or emission from cosmic rays injected in a sequence of burst-like events or continuously at the GC. We present the first comprehensive study of model systematics coming from the Galactic diffuse emission in the inner part of our Galaxy and their impact on the inferred properties of the excess emission at Galactic latitudes 2° < |b| < 20° and 300 MeV to 500 GeV. We study both theoretical and empirical model systematics, which we deduce from a large range of Galactic diffuse emission models and a principal component analysis of residuals in numerous test regions along the Galactic plane. We show that the hypothesis of an extended spherical excess emission with a uniform energy spectrum is compatible with the Fermi-LAT data in our region of interest at 95% CL. Assuming that this excess is the extended counterpart of the one seen in the inner few degrees of the Galaxy, we derive a lower limit of 10.0° (95% CL) on its extension away from the GC. We show that, in light of the large correlated uncertainties that affect the subtraction of the Galactic diffuse emission in the relevant regions, the energy spectrum of the excess is equally compatible with both a simple broken power-law of break energy E{sub break} = 2.1 ± 0.2 GeV, and with spectra predicted by the self-annihilation of dark matter, implying in the case of b-bar b final states a dark matter mass of m{sub χ}=49{sup +6.4}{sub −5.4} GeV.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Machine Learning-Based Investigation of the Associations between Residential Power Consumption and Weather Conditions

Machine learning feature selection analyses were conducted to understand the impact of various weather attributes on power electricity demand and its components in the northwest United States. Load profiles data were obtained from the Northwest Energy Efficiency Alliance (NEEA) and processed to yield hourly load profiles with the same temporal resolution as the weather conditions data obtained from the National Oceanic and Atmospheric Administration (NOAA) for representative weather stations. The data were divided into five seasons; for each season, for each load component, the influences of weather factors were evaluated and quantified using cross-correlation, principal component analysis, and mutual information evaluation; then predictive models were developed based on the ranked/screened factors using the regression tree (RT) approach. After multi-fold cross-validation, the optimal complexity/depth of the regress tree models is found to vary for different load components, and the prediction accuracy using RT and weather data can be higher than 80% for heating and refrigeration, or lower than 30% for vent and cooking. The study provides guidance on improvement of load profile generation, or even serves as a standalone predictive tool for approximating load profiles in balance authorities or climate zones where load profile data is absent.

load composite, load profiling, NEEA, mutual infor↗
DOE OSTI2014

A limited-angle intrafraction verification (LIVE) system for radiation therapy

Purpose: Currently, no 3D or 4D volumetric x-ray imaging techniques are available for intrafraction verification of target position during actual treatment delivery or in-between treatment beams, which is critical for stereotactic radiosurgery (SRS) and stereotactic body radiation therapy (SBRT) treatments. This study aims to develop a limited-angle intrafraction verification (LIVE) system to use prior information, deformation models, and limited angle kV-MV projections to verify target position intrafractionally. Methods: The LIVE system acquires limited-angle kV projections simultaneously during arc treatment delivery or in-between static 3D/IMRT treatment beams as the gantry moves from one beam to the next. Orthogonal limited-angle MV projections are acquired from the beam's eye view (BEV) exit fluence of arc treatment beam or in-between static beams to provide additional anatomical information. MV projections are converted to kV projections using a linear conversion function. Patient prior planning CT at one phase is used as the prior information, and the on-board patient volume is considered as a deformation of the prior images. The deformation field is solved using the data fidelity constraint, a breathing motion model extracted from the planning 4D-CT based on principal component analysis (PCA) and a free-form deformation (FD) model. LIVE was evaluated using a 4D digital extended cardiac torso phantom (XCAT) and a CIRS 008A dynamic thoracic phantom. In the XCAT study, patient breathing pattern and tumor size changes were simulated from CT to treatment position. In the CIRS phantom study, the artificial target in the lung region experienced both size change and position shift from CT to treatment position. Varian Truebeam research mode was used to acquire kV and MV projections simultaneously during the delivery of a dynamic conformal arc plan. The reconstruction accuracy was evaluated by calculating the 3D volume percentage difference (VPD) and the center of mass (COM) difference of the tumor in the true on-board images and reconstructed images. Results: In both simulation and phantom studies, LIVE achieved substantially better reconstruction accuracy than reconstruction using PCA or FD deformation model alone. In the XCAT study, the average VPD and COM differences among different patient scenarios for LIVE system using orthogonal 30° scan angles were 4.3% and 0.3 mm when using kV+BEV MV. Reducing scan angle to 15° increased the average VPD and COM differences to 15.1% and 1.7 mm. In the CIRS phantom study, the VPD and COM differences for the LIVE system using orthogonal 30° scan angles were 6.4% and 1.4 mm. Reducing scan angle to 15° increased the VPD and COM differences to 51.9% and 3.8 mm. Conclusions: The LIVE system has the potential to substantially improve intrafraction target localization accuracy by providing volumetric verification of tumor position simultaneously during arc treatment delivery or in-between static treatment beams. With this improvement, LIVE opens up a new avenue for margin reduction and dose escalation in both fractionated treatments and SRS and SBRT treatments.

62 RADIOLOGY AND NUCLEAR MEDICINE↗