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At least 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Reverberation measurements of the inner radius of the dust torus in 17 Seyfert galaxies

We present the results of a dust reverberation survey for 17 nearby Seyfert 1 galaxies, which provides the largest homogeneous data collection for the radius of the innermost dust torus. A delayed response of the K-band light curve after the V-band light curve was found for all targets, and 49 measurements of lag times between the flux variation of the dust emission in the K band and that of the optical continuum emission in the V band were obtained by the cross-correlation function analysis and also by an alternative method for estimating the maximum likelihood lag. The lag times strongly correlated with the optical luminosity in the luminosity range of M{sub V} = –16 to –22 mag, and the regression analysis was performed to obtain the correlation log Δt (days) = –2.11 – 0.2 M{sub V} assuming Δt∝L {sup 0.5}, which was theoretically expected. We discuss the possible origins of the intrinsic scatter of the dust lag-luminosity correlation, which was estimated to be approximately 0.13 dex, and we find that the difference of internal extinction and delayed response of changes in lag times to the flux variations could have partly contributed to intrinsic scatter. However, we could not detect any systematic change of the correlation with the subclass of the Seyfert type or the Eddington ratio. Finally, we compare the dust reverberation radius with the near-infrared interferometric radius of the dust torus and the reverberation radius of broad Balmer emission lines. The interferometric radius in the K band was found to be systematically larger than the dust reverberation radius in the same band by the about a factor of two, which could be interpreted by the difference between the flux-weighted radius and response-weighted radius of the innermost dust torus. The reverberation radius of the broad Balmer emission lines was found to be systematically smaller than the dust reverberation radius by about a factor of four to five, which strongly supports the unified scheme of the Seyfert type of active galactic nuclei (AGNs). Moreover, we examined the radius-luminosity correlations for the hard X-ray (14-195 keV) and the [O IV] λ25.89 μm emission-line luminosities, which would be applicable for obscured AGNs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Triangle Method for Dense ReLU Layers [SWR-25-72]

This software is an implementation of the methods for initializing and training neural networks to be more efficient per parameter, described more fully below and in the related publication: In theory, depth should make a ReLU network EXPONENTIALLY more efficient by enabling it to produce an exponential number of piecewise linear sections in its output. This reasoning is largely based on the work of mathematicians that have hand-constructed networks that make good use of depth. In practice however, even very deep ReLU networks that have been randomly initialized will behave identically to their shallow counterparts - missing an entire exponential dimension of efficiency. The triangle method is a first attempt at realizing the exponential potential of deep networks. Instead of randomly setting weights, we force pairs of neurons in each layer learn to build triangles (i.e. functions from [0,1] -> [0,1] that look like triangles). This is a very efficient pattern for generating lots of linear pieces because composing two triangular functions doubles the number of pieces with each composition. The triangle method is more than just a different initialization, it is a new paradigm of training. Instead of making direct updates to the matrix weights, we do an extra step of backpropagation to collect the derivatives of the loss function with respect to the shapes of the triangles, training them to tilt left or right. This process essentially holds the networks hand throughout the loss landscape and forces it to always use depth effectively by producing triangular shapes internally. This can produce several orders of magnitude of improvement on convex one-dimensional regression problems. Much more theoretical work is needed to realize its full potential beyond this context, but the implementation in this repository will still work in arbitrary numbers of dimensions. The file Triangle_Method.py is a generalized form of the method that will build each neuron its own custom 1-d convex activation function (with exponential efficiency). Example usage on one dimensional problems can be found in Example_Usage.ipynb and an example of using this in a real neural network can be found in Example_VGG16_CIFAR10.ipynb.

Milkert, Max [National Renewable Energy Laboratory↗
DOE OSTI2017

E-waste collection in Italy: Results from an exploratory analysis

Highlights: • The performance of the Italian household collection system for E-waste is analysed in a panel data regression analysis. • N. of Collection points, % of female, % of waste separately collected are positively correlated with kg of E-waste collected. • Population density is negatively correlated with kg of E-waste collected. • The Stochastic Frontier Model identifies the optimal production function. • Tuscany, Sardinia and Sicily are the best performing regions. - Abstract: This study looks at the performance of household electrical and electronic waste (WEEE) collection in 20 Italian regions from 2008 to 2015. The impact of several explicative variables on the results of e-waste collection is evaluated. The independent variables are socio-economic and demographic ones (age, gender, household size, education level, migration and income) along with technical-organisational variables (population density, presence of metropoles, macro regions, characteristics of the territory, percentage of household waste collected separately and number of e-waste collection points). The results show that the presence of collection points, the percentage of household waste collected separately and the percentage of females are positively correlated with the kg collected per inhabitant per year. For example, a variation of 1% of input (presence of collection points) corresponds to a 0.25% variation in the output (collection results) while 1% difference in the percentage of females in the population corresponds to a 7.549% difference in the collection rate. Population density, instead, is negatively correlated. It is interesting to note that there is a discrepancy between the Southern regions and the Centre regions (the former have an outcome 0.66 times lower than the latter) while the Northern regions perform similarly to the Centre ones. Moreover, the first year (2008) had a very low performance compared to the following years when the scheme constantly improved, mainly due to the additional collection points available. The Stochastic Frontier Model allows for the identification of the optimal production function among the 20 Italian regions. The best performing region is Tuscany (in the Centre), followed by Sardinia and Sicily (in the South).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

FY16 Status Report on NEAMS Neutronics Activities

The goal of the NEAMS neutronics effort is to develop a neutronics toolkit for use on sodium-cooled fast reactors (SFRs) which can be extended to other reactor types. The neutronics toolkit includes the high-fidelity deterministic neutron transport code PROTEUS and many supporting tools such as a cross section generation code MC 2 -3, a cross section library generation code, alternative cross section generation tools, mesh generation and conversion utilities, and an automated regression test tool. The FY16 effort for NEAMS neutronics focused on supporting the release of the SHARP toolkit and existing and new users, continuing to develop PROTEUS functions necessary for performance improvement as well as the SHARP release, verifying PROTEUS against available existing benchmark problems, and developing new benchmark problems as needed. The FY16 research effort was focused on further updates of PROTEUS-SN and PROTEUS-MOCEX and cross section generation capabilities as needed.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

Comparing theory based and higher-order reduced models for fusion simulation data

We consider using regression to fit a theory-based log-linear ansatz, as well as higher order approximations, for the thermal energy confinement of a Tokamak as a function of device features. We use general linear models based on total order polynomials, as well as deep neural networks. The results indicate that the theory-based model fits the data almost as well as the more sophisticated machines, within the support of the data set. The conclusion we arrive at is that only negligible improvements can be made to the theoretical model, for input data of this type.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1974

Implementation of an experimental program to investigate the performance characteristics of OMEGA navigation

A theoretical formulation of differential and composite OMEGA error is presented to establish hypotheses about the functional relationships between various parameters and OMEGA navigational errors. Computer software developed to provide for extensive statistical analysis of the phase data is described. Results from the regression analysis used to conduct parameter sensitivity studies on differential OMEGA error tend to validate the theoretically based hypothesis concerning the relationship between uncorrected differential OMEGA error and receiver separation range and azimuth. Limited results of measurement of receiver repeatability error and line of position measurement error are also presented.

Baxa, E. G., Jr.↗
NASA NTRS1992

Advanced statistical methods for improved data analysis of NASA astrophysics missions

The investigators under this grant studied ways to improve the statistical analysis of astronomical data. They looked at existing techniques, the development of new techniques, and the production and distribution of specialized software to the astronomical community. Abstracts of nine papers that were produced are included, as well as brief descriptions of four software packages. The articles that are abstracted discuss analytical and Monte Carlo comparisons of six different linear least squares fits, a (second) paper on linear regression in astronomy, two reviews of public domain software for the astronomer, subsample and half-sample methods for estimating sampling distributions, a nonparametric estimation of survival functions under dependent competing risks, censoring in astronomical data due to nondetections, an astronomy survival analysis computer package called ASURV, and improving the statistical methodology of astronomical data analysis.

Feigelson, Eric D.↗
NASA NTRS2001

A New Ensemble Canonical Correlation Prediction Scheme for Seasonal Precipitation

Department of Mathematical Sciences, University of Alberta, Edmonton, Canada This paper describes the fundamental theory of the ensemble canonical correlation (ECC) algorithm for the seasonal climate forecasting. The algorithm is a statistical regression sch eme based on maximal correlation between the predictor and predictand. The prediction error is estimated by a spectral method using the basis of empirical orthogonal functions. The ECC algorithm treats the predictors and predictands as continuous fields and is an improvement from the traditional canonical correlation prediction. The improvements include the use of area-factor, estimation of prediction error, and the optimal ensemble of multiple forecasts. The ECC is applied to the seasonal forecasting over various parts of the world. The example presented here is for the North America precipitation. The predictor is the sea surface temperature (SST) from different ocean basins. The Climate Prediction Center's reconstructed SST (1951-1999) is used as the predictor's historical data. The optimally interpolated global monthly precipitation is used as the predictand?s historical data. Our forecast experiments show that the ECC algorithm renders very high skill and the optimal ensemble is very important to the high value.

Kim, Kyu-Myong↗
NASA NTRS2005

Optimal Estimation of Clock Values and Trends from Finite Data

We show how to solve two problems of optimal linear estimation from a finite set of phase data. Clock noise is modeled as a stochastic process with stationary dth increments. The covariance properties of such a process are contained in the generalized autocovariance function (GACV). We set up two principles for optimal estimation: with the help of the GACV, these principles lead to a set of linear equations for the regression coefficients and some auxiliary parameters. The mean square errors of the estimators are easily calculated. The method can be used to check the results of other methods and to find good suboptimal estimators based on a small subset of the available data.

stationary increments↗
DOE OSTI2025

Mapping wall-to-wall fractional cover of Arctic tundra plant functional types in Alaska using 20-m spatial resolution satellite imagery and harmonized plot observations

Estimates of fractional cover (fCover) across given land surfaces are used to assess, and often model, vegetation composition and diversity, which are crucial for understanding the health and functioning of terrestrial ecosystems. Remote sensing provides a useful means for scaling local, plot-measured fCover estimates to regional scales. Leveraging a recently synthesized and harmonized plot database, this study generated wall-to-wall maps of fCover for six Alaskan-Arctic plant functional types (PFT), including non-vascular plants, forbs, graminoids, and deciduous and evergreen shrubs, using 20-m satellite data (Sentinel-1, Sentinel-2, ArcticDEM) using a machine learning regression approach, specifically the random forest (RF) algorithm, which is well-suited for handling nonlinear relationships and high-dimensional satellite datasets. This study additionally addressed the spatio-temporal inconsistencies e.g., sampling scale, plot size, and collection year in plot measured fCover by adopting a multivariate outlier detection approach—Cook’s distance—to identify high-quality plots for model training and validation. Our approach achieves high accuracy (R 2 = 0.59–0.93, root mean squared errors = 0.02–0.10 for all PFTs) between plot-observed and satellite-derived fCover when using high-quality plot samples. The mapped fCover characterizes the spatial patterns of different PFTs across the tundra biome at a 20-m resolution, providing key information needed for improved representation of Arctic tundra vegetation in terrestrial biosphere models to better understand climate-vegetation feedback across the Arctic tundra.

Arctic tundra↗
DOE OSTI2023

Effects of Aluminum and Molybdenum on the Phase Stability of Iron-Chromium Alloys: A First-Principles Study

The interaction between solute atoms is critical to the thermodynamic behavior of Fe-Cr alloys, but the effects of non-dilute Al and Mo on the Fe-Cr phase stability and vacancy formation energy are not clearly understood. In this study, density functional theory, cluster expansion, and Monte Carlo simulation are used to predict the effects of ternary solute elements on the thermodynamic properties in multicomponent Fe-Cr alloys. The machine learning regression approach is applied to train and construct energy models that accurately describe the complex chemical interactions. The computational outcomes include the prediction of the partial ternary phase diagram, mixing enthalpy, and vacancy formation energy for different compositions. The phase boundary calculation predicts a pronounced change of Cr solubility in bcc Fe by the addition of Al and the rejection of Al atoms from a’ precipitates. The mixing enthalpy calculation shows that Mo may also reduce the Cr solubility in bcc Fe. Additionally, the simulations show that non-dilute Cr decreases the vacancy formation energy in bcc Fe, while adding Al results in a less significant effect. Finally, the results demonstrate important applications of using machine learning energy models to study model or commercial alloys with multicomponent solute species and point defects.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Estimating PV power from aggregate power measurements within the distribution grid

The increased integration of photovoltaic (PV) systems in distribution grids reduces visibility and situational awareness for utilities because the PV systems' power production is usually not monitored by them. To address this problem, a method called Contextually Supervised Source Separation (CSSS) has been recently adapted for real-time estimation of aggregate PV active power generation from aggregate net active and reactive power measurements at a point in a radially configured distribution grid (e.g., substation). In its original version, PV disaggregation is formulated as an optimization problem that fits linear regression models for the aggregate PV active power generation and true substation active power load. This paper extends the previous work by adding regularization terms in the objective function to capture additional contextual information such as smoothness, by adding new constraints, by introducing new regressors such as ambient temperature, and by investigating the use of time-varying regressors. Furthermore, we perform extensive parametric analysis to study tuning of the objective function weighting factors in a way that maximizes performance and robustness. The proposed PV disaggregation method can be applied to networks with either a single PV system (e.g., megawatt scale) or many distributed ones (e.g., residential scale) connected downstream of the substation. Simulation studies with real field recorded data show that the enhancements of the proposed method reduce disaggregation error by 58% in winter and 35% in summer compared to previous CSSS-based work. When compared against a commonly used transposition model based approach, the reduction in disaggregation error is more pronounced (78% reduction in winter and 45% in summer). Additional simulations indicate that the proposed algorithm is also applicable for PV systems with time-varying power factors. In conclusion, overall, our results show that—with appropriate modeling and tuning—it is possible to accurately estimate the aggregated PV active power generation of a distribution feeder with minimal or no additional sensor deployment.

14 SOLAR ENERGY↗
DOE OSTI2015

TU-CD-207-01: Characterization of Breast Tissue Composition Using Spectral Mammography

Purpose: To investigate the feasibility of characterizing the chemical composition of breast tissue, in terms of water and lipid, by using spectral mammography in simulation and postmortem studies. Methods: Analytical simulations were performed to obtain low- and high-energy signals of breast tissue based on previously reported water, lipid, and protein contents. Dual-energy decomposition was used to characterize the simulated breast tissue into water and lipid basis materials and the measured water density was compared to the known value. In experimental studies, postmortem breasts were imaged with a spectral mammography system based on a scanning multi-slit Si strip photon-counting detector. Low- and high-energy images were acquired simultaneously from a single exposure by sorting the recorded photons into the corresponding energy bins. Dual-energy material decomposition of the low- and high-energy images yielded individual pixel measurements of breast tissue composition in terms of water and lipid thicknesses. After imaging, each postmortem breast was chemically decomposed into water, lipid and protein. The water density calculated from chemical analysis was used as the reference gold standard. Correlation of the water density measurements between spectral mammography and chemical analysis was analyzed using linear regression. Results: Both simulation and postmortem studies showed good linear correlation between the decomposed water thickness using spectral mammography and chemical analysis. The slope of the linear fitting function in the simulation and postmortem studies were 1.15 and 1.21, respectively. Conclusion: The results indicate that breast tissue composition, in terms of water and lipid, can be accurately measured using spectral mammography. Quantitative breast tissue composition can potentially be used to stratify patients according to their breast cancer risk.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Data and code from: Multivariate bayesian regression model for predicting disposed ash composition at U.S. coal fired power stations

This dataset contains the code and data files needed for implementation of a Multivariate Bayesian Regression model, described in Jin et al. (2025), for the historical prediction of the chemical composition of disposed coal ash at U.S. coal fired power plants as a function of annualized coal purchase data. The integrated coal supply data file (CoalSupplyDataset.csv) represents a compilation of monthly fuel purchase records for the period 1973-2022 at major U.S. power stations. These records were obtained from the U.S. Energy Information Administration. The CSV file also contains, for each coal purchase record, the coal region of the mine as defined by the U.S. Geological Survey. Data entry errors and data gaps in the EIA records were corrected as described in Jin et al. This CSV file represents the integrated coal supply data after corrections were made. The model structure and fitting parameters are encoded in pickle file format (Bayesian.pkl). The model was developed with the coal supply data and coal ash composition data, apportioned according to the Stratified Shuffle Split for training and testing subsets. The model was built using Python and the PyMC library. Reference Publication: Jin, Z.; Huang, J.; Hower, J.C.; Hsu-Kim, H.(2025). Predictive Assessment of the Chemical Composition of Coal Ash in Reserve at U.S. Disposal Sites. Environmental Science & Technology.

Coal ash composition↗
NASA NTRS1982

Tropical cyclone track and genesis forecasting using satellite microwave sounder data

Although many dynamical and statistical prediction schemes are available to forecasters, tropical cyclone track errors are still large. One primary difficulty is that tropical cyclones exist over the data-sparse tropical oceans. Satellite sounders, however, routinely provide numerous data over these areas. Mean layer temperatures from the Scanning Microwave Spectrometer on board the Nimbus 6 satellite are decomposed using empirical orthogonal functions, and the expansion coefficients are related to deviations from the persistence forecast location, to speed change, to direction change and to intensity change. The significance of the regression equations is tested by a null hypothesis of zero correlation coefficient. It appears that significant information about tropical cyclone motion exists in the satellite-estimated mean layer temperatures, especially at upper levels. A physical interpretation of the statistical results is offered, and a one-storm-out independent test is used to test the stability of the equations. Finally, some further work is suggested.

Kidder, S. Q.↗
NASA NTRS1986

Characterization of vegetation by microwave and optical remote sensing

Two series of carefully controlled experiments were conducted. First, plots of important crops (corn, soybeans, and sorghum), prairie grasses (big bluestem, switchgrass, tal fescue, orchardgrass, bromegrass), and forage legumes (alfalfa, red clover, and crown vetch) were manipulated to produce wide ranges of phytomass, leaf area index, and canopy architecture. Second, coniferous forest canopies were simulated using small balsam fir trees grown in large pots of soil and arranged systematically on a large (5 m) platform. Rotating the platform produced many new canopies for frequency and spatial averaging of the backscatter signal. In both series of experiments, backscatter of 5.0 GHz (C-Band) was measured as a function of view angle and polarization. Biophysical measurements included leaf area index, fresh and dry phytomass, water content of canopy elements, canopy height, and soil roughness and moisture content. For a subset of the above plots, additional measurements were acquired to exercise microwave backscatter models. These measurements included size and shape of leaves, stems, and fruit and the probability density function of leaf and stem angles. The relationships of the backscattering coefficients and the biophysical properties of the canopies were evaluated using statistical correlations, analysis of variance, and regression analysis. Results from the corn density and balsam fir experiments are discussed and analyses of data from the other experiments are summarized.

Daughtry, C. S. T.↗
NASA NTRS2012

Lower Limb Venous Compliance is Different Between Men and Women Following 60 Days of Head-Down Bedrest but Is Not Associated with Venoconstriction Dysfunction

Space flight-induced orthostatic intolerance (OI) is more prevalent in female (F) than male (M) astronauts. The mechanisms explaining the higher incidence of OI in F are unclear. We tested the hypothesis that venous compliance would be higher in F more than M following 6 deg head-down bed rest (BR) and would be associated with constrictor dysfunction. Using 2-D ultrasound, dorsal hand (DHV) and dorsal foot (DFV) vein compliances were determined in 24 subjects (10 F, 14 M; 35 +/- 1 yr) by measuring mean diameter response to increasing congestion pressure (0, 20, 30, and 40 mmHg) before and after 60 d of BR. Constrictor function was assessed by intravenous infusions of Ketorolac (KE; 1.5 ig/min) Phenylephrine (PE; 3160 ng/min), and L-NMMA (50 ig/min). The effects of BR between F vs. M and hand vs. foot were determined using mixed-effects linear regression. DFV but not DHV compliance changed in response to BR (p=0.012). Mean DFV increased significantly (0.903 mm to 1.191mm) in F but decreased in M (1.353 mm to 1.154 mm). DFV constrictor response was not different between sexes in response to BR (KE; p=0.647, PE; p=0.717, and L-NMMA; p=0.825). These BR data suggest that the higher incidence of OI in F astronauts may be related to increased lower limb venous compliance, contributing to blood pooling upon standing. Notably, changes to DFV compliance was not accompanied by impaired constrictor function.

Westby, Christian M.↗
DOE OSTI2020

Foliar functional traits from imaging spectroscopy across biomes in eastern North America

Summary Foliar functional traits are widely used to characterize leaf and canopy properties that drive ecosystem processes and to infer physiological processes in Earth system models. Imaging spectroscopy provides great potential to map foliar traits to characterize continuous functional variation and diversity, but few studies have demonstrated consistent methods for mapping multiple traits across biomes. With airborne imaging spectroscopy data and field data from 19 sites, we developed trait models using partial least squares regression, and mapped 26 foliar traits in seven NEON (National Ecological Observatory Network) ecoregions (domains) including temperate and subtropical forests and grasslands of eastern North America. Model validation accuracy varied among traits (normalized root mean squared error, 9.1–19.4%; coefficient of determination, 0.28–0.82), with phenolic concentration, leaf mass per area and equivalent water thickness performing best across domains. Across all trait maps, 90% of vegetated pixels had reasonable values for one trait, and 28–81% provided high confidence for multiple traits concurrently. Maps of 26 traits and their uncertainties for eastern US NEON sites are available for download, and are being expanded to the western United States and tundra/boreal zone. These data enable better understanding of trait variations and relationships over large areas, calibration of ecosystem models, and assessment of continental‐scale functional diversity.

Wang, Zhihui↗