Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “functional regression”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

A non-parametric consistency test of the ΛCDM model with Planck CMB data

Non-parametric reconstruction methods, such as Gaussian process (GP) regression, provide a model-independent way of estimating an underlying function and its uncertainty from noisy data. We demonstrate how GP-reconstruction can be used as a consistency test between a given data set and a specific model by looking for structures in the residuals of the data with respect to the model's best-fit. Applying this formalism to the Planck temperature and polarisation power spectrum measurements, we test their global consistency with the predictions of the base ΛCDM model. Our results do not show any serious inconsistencies, lending further support to the interpretation of the base ΛCDM model as cosmology's gold standard.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Biochar application as a tool to decrease soil nitrogen losses (NH3 volatilization, N2O emissions, and N leaching) from croplands: Options and mitigation strength in a global perspective

Biochar application to croplands has been proposed as a potential strategy to decrease losses of soil-reactive nitrogen (N) to the air and water. However, the extent and spatial variability of biochar function at the global level are still unclear. Using Random Forest regression modelling of machine learning based on data compiled from the literature, we mapped the impacts of different biochar types (derived from wood, straw, or manure), and their interactions with biochar application rates, soil properties, and environmental factors, on soil N losses (NH3 volatilization, N2O emissions, and N leaching) and crop productivity. The results show that a suitable distribution of biochar across global croplands (i.e., one application of <40 t ha-1 wood biochar for poorly buffered soils, such as those characterized by soil pH<5, organic carbon<1%, or clay>30%; and one application of <80 t ha-1 wood biochar, <40 t ha-1 straw biochar, or <10 t ha-1 manure biochar for other soils) could achieve an increase in global crop yields by 222–766 Tg yr-1 (4%–16% increase), a mitigation of cropland N2O emissions by 0.19–0.88 Tg N yr-1 (6%–30% decrease), a decline of cropland N leaching by 3.9–9.2 Tg N yr-1 (12%–29% decrease), but also a fluctuation of cropland NH3 volatilization by -1.9–4.7 Tg N yr-1 (-12%–31% change). The decreased sum of the three major reactive N losses amount to 1.7–9.4 Tg N yr-1, which corresponds to 3%–14% of the global cropland total N loss. Biochar generally has a larger potential for decreasing soil N losses but with less benefits to crop production in temperate regions than in tropical regions.

biochar, soil amendment, nitrogen cycle, climate c↗
DOE OSTI2018

Rock-Forming Minerals Radiation-Induced Volumetric Expansion – Revisiting Literature Data

Neutron radiation-induced volumetric expansion (RIVE) of concrete aggregate is recognized as a major degradation mechanism causing extensive damage to concrete constituents (Hilsdorf et al. 1978; Seeberger and Hilsdorf 1982; Field et al. 2015). Nearly 400 RIVE data obtained in test-reactors on varied rock-forming minerals were collected by Denisov et al. (2012). These data were input into the Oak Ridge National Laboratory (ORNL) irradiated minerals, aggregates and concrete (IMAC) database and were reanalyzed in order to develop a general empirical model for minerals RIVE and interpret the susceptibility of silicates toward expansion. Furthermore, the empirical models best regression coefficient (r2 ≈ 0.95) is obtained by combining two different modeling techniques: (1) an interpolation-like model based on the relative distance to existing data points, and, (2) a nonlinear regression model assuming varied mathematical forms to describe RIVE as a function of the neutron fluence3 and the average irradiation temperature. The susceptibility to develop irradiation-induced expansion greatly varies with the nature of minerals. Silicates, i.e., [SiO4]4– bearing minerals show a wide range of maximum RIVEs, from a few percents to what appears as a bounding value of 17.8% for quartz. The maximum RIVE of varied silicates appears to be governed, macroscopically, by three parameters: (1) Primarily, the dimensionality of silicate polymerization (DOSP), (2) the relative number of Si–O bond per unit cell, and, (3) the relative bonding energy (RBE) of the unit cell.

58 GEOSCIENCES↗
DOE OSTI2017

A Method to Measure the Transverse Magnetic Field and Orient the Rotational Axis of Stars

Direct measurements of stellar magnetic fields are based on the splitting of spectral lines into polarized Zeeman components. With a few exceptions, Zeeman signatures are hidden in data noise, and a number of methods have been developed to measure the average, over the visible stellar disk, of longitudinal components of the magnetic field. At present, faint stars are only observable via low-resolution spectropolarimetry, which is a method based on the regression of the Stokes V signal against the first derivative of Stokes I . Here, we present an extension of this method to obtain a direct measurement of the transverse component of stellar magnetic fields by the regression of high-resolution Stokes Q and U as a function of the second derivative of Stokes I . We also show that it is possible to determine the orientation in the sky of the rotation axis of a star on the basis of the periodic variability of the transverse component due to its rotation. The method is applied to data, obtained with the Catania Astrophysical Observatory Spectropolarimeter along the rotational period of the well known magnetic star β CrB.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1984

Characterizing the scientific potential of satellite sensors

Eleven thematic mapper (TM) radiometric calibration programs were tested and evaluated in support of the task to characterize the potential of LANDSAT TM digital imagery for scientific investigations in the Earth sciences and terrestrial physics. Three software errors related to integer overflow, divide by zero, and nonexist file group were found and solved. Raw, calibrated, and corrected image groups that were created and stored on the Barker2 disk are enumerated. Black and white pixel print files were created for various subscenes of a San Francisco scene (ID 40392-18152). The development of linear regression software is discussed. The output of the software and its function are described. Future work in TM radiometric calibration, image processing, and software development is outlined.

Source record↗
NASA NTRS1989

Development of a funding, cost, and spending model for satellite projects

The need for a predictive budget/funging model is obvious. The current models used by the Resource Analysis Office (RAO) are used to predict the total costs of satellite projects. An effort to extend the modeling capabilities from total budget analysis to total budget and budget outlays over time analysis was conducted. A statistical based and data driven methodology was used to derive and develop the model. Th budget data for the last 18 GSFC-sponsored satellite projects were analyzed and used to build a funding model which would describe the historical spending patterns. This raw data consisted of dollars spent in that specific year and their 1989 dollar equivalent. This data was converted to the standard format used by the RAO group and placed in a database. A simple statistical analysis was performed to calculate the gross statistics associated with project length and project cost ant the conditional statistics on project length and project cost. The modeling approach used is derived form the theory of embedded statistics which states that properly analyzed data will produce the underlying generating function. The process of funding large scale projects over extended periods of time is described by Life Cycle Cost Models (LCCM). The data was analyzed to find a model in the generic form of a LCCM. The model developed is based on a Weibull function whose parameters are found by both nonlinear optimization and nonlinear regression. In order to use this model it is necessary to transform the problem from a dollar/time space to a percentage of total budget/time space. This transformation is equivalent to moving to a probability space. By using the basic rules of probability, the validity of both the optimization and the regression steps are insured. This statistically significant model is then integrated and inverted. The resulting output represents a project schedule which relates the amount of money spent to the percentage of project completion.

Johnson, Jesse P.↗
DOE OSTI2023

Refining fast simulation using machine learning

At the CMS experiment, a growing reliance on the fast Monte Carlo application (FastSim) will accompany the high luminosity and detector granularity expected in Phase 2. The FastSim chain is roughly 10 times faster than the application based on the GEANT4 detector simulation and full reconstruction referred to as FullSim. However, this advantage comes at the price of decreased accuracy in some of the final analysis observables. In this contribution, a machine learning-based technique to refine those observables is presented. We employ a regression neural network trained with a sophisticated combination of multiple loss functions to provide post-hoc corrections to samples produced by the FastSim chain. The results show considerably improved agreement with the FullSim output and an improvement in correlations among output observables and external parameters. This technique is a promising replacement for existing correction factors, providing higher accuracy and thus contributing to the wider usage of FastSim.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Interpretable, extensible linear and symbolic regression models for charge density prediction using a hierarchy of many-body correlation descriptors

Here, density functional theory (DFT) is routinely used to make electronic structure predictions for high-throughput screening of materials and molecules for technologically relevant areas, like the identification of better catalysts, electronic materials, and drug discovery. However, the DFT formalism is limited by (a) its poor (quadratic-to-quartic) scaling, and (b) the need to perform repeated eigenvalue computations of the electronic Hamiltonian as part of its self-consistent field (SCF) iteration procedure to obtain the converged ground state electron density, ρ (r). Approaches that directly predict ρ (r) of a structure with high accuracy can accelerate conventional SCF calculations and can also be used in linearly scaling methods such as orbital-free DFT. To this end, we present a procedure to predict the ground state electron density of molecular and periodic three-dimensional systems directly from the atomic structure with a particular emphasis on physical interpretability. In our framework, ρ (r) is modeled using many-body correlation descriptors that accurately capture the effects of local atomic arrangements in the neighborhood of a grid point. Our use of a linear regression scheme to fit to charge density data enables transparent analysis of the relative contributions of various types of local atomic correlations. By systematically including increasingly complex correlations, our model is shown to accurately predict ρ (r) for a variety of chemically and electronically diverse systems — amorphous Ge, Al(001) slab, crystalline Ga 2 O 3 , molecular benzene, and polyethylene. We then demonstrate a symbolic regression-based protocol to construct easily computable, interpretable features from lower-order correlations that significantly improves our electron density predictions with effectively no increase in the computational cost.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2019

Application of a Temperature-Dependent Load Prediction Method to a RUAG Six-Component Block-Type Balance

Temperature-dependent data of a RUAG six-component block-type balance was analyzed to assess the accuracy of two load prediction methods for temperature-dependent balance data. The supplied data was prepared for the analysis by splitting it into calibration and check load data subsets. The first calibration data subset was obtained at a temperature of 294 Kelvin. The second calibration data subset was obtained at a temperature of 315 Kelvin. A subset of 38 points was extracted from the second data set and used as check loads so that the accuracy of the two load prediction methods could be tested. First, the Iterative Method in combination with an extended independent and dependent variable set was used for the balance load prediction. This approach fits electrical outputs as a function of loads and the temperature and, afterwards, constructs a load iteration scheme from the regression coefficients so that loads can be predicted from outputs and the temperature during a wind tunnel test. The Non-Iterative Method was also used for the load prediction. This alternate method can more easily be implemented in a data system as loads are directly fitted as a function of electrical outputs and the temperature. Analysis results for the axial force are only discussed in the paper as similar results were obtained for the other five load components. Results for both methods clearly show that the cross-product term constructed from either a primary gage load or a primary gage output and the temperature explains the majority of the temperature-dependent part of the predicted balance load. This term models the temperature dependent nature of the gage sensitivity. Therefore, it is recommended to apply primary gage loadings at different temperatures during a balance calibration whenever temperature effects need to be described. These loadings will contain information about the temperature-dependent nature of the gage sensitivities that can be quantified by related cross-product terms in regression models of the data.

strain-gage balance↗
DOE OSTI2016

Proton radius from electron scattering data

Background: The proton charge radius extracted from recent muonic hydrogen Lamb shift measurements is significantly smaller than that extracted from atomic hydrogen and electron scattering measurements. The discrepancy has become known as the proton radius puzzle. Purpose: In an attempt to understand the discrepancy, we review high-precision electron scattering results from Mainz, Jefferson Lab, Saskatoon and Stanford. Methods: We make use of stepwise regression techniques using the F-test as well as the Akaike information criterion to systematically determine the predictive variables to use for a given set and range of electron scattering data as well as to provide multivariate error estimates. Results: Starting with the precision, low four-momentum transfer (Q 2 ) data from Mainz (1980) and Saskatoon (1974), we find that a stepwise regression of the Maclaurin series using the F-test as well as the Akaike information criterion justify using a linear extrapolation which yields a value for the proton radius that is consistent with the result obtained from muonic hydrogen measurements. Applying the same Maclaurin series and statistical criteria to the 2014 Rosenbluth results on GE from Mainz, we again find that the stepwise regression tends to favor a radius consistent with the muonic hydrogen radius but produces results that are extremely sensitive to the range of data included in the fit. Making use of the high-Q 2 data on G E to select functions which extrapolate to high Q 2 , we find that a Pad´e (N = M = 1) statistical model works remarkably well, as does a dipole function with a 0.84 fm radius, G E (Q 2 ) = (1 + Q 2 /0.66 GeV 2 ) -2 . Conclusions: Rigorous applications of stepwise regression techniques and multivariate error estimates result in the extraction of a proton charge radius that is consistent with the muonic hydrogen result of 0.84 fm; either from linear extrapolation of the extreme low-Q 2 data or by use of the Pad´e approximant for extrapolation using a larger range of data. Thus, based on a purely statistical analysis of electron scattering data, we conclude that the electron scattering result and the muonic hydrogen result are consistent. Lastly, it is the atomic hydrogen results that are the outliers.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

The Mira-Titan Universe. III. Emulation of the Halo Mass Function

We construct an emulator for the halo mass function over group and cluster mass scales for a range of cosmologies, including the effects of dynamical dark energy and massive neutrinos. The emulator is based on the recently completed Mira-Titan Universe suite of cosmological N-body simulations. The main set of simulations spans 111 cosmological models with 2.1 Gpc boxes. We extract halo catalogs in the redshift range z = [0.0, 2.0] and for masses M-200c >= 10(13)M(circle dot)/h. The emulator covers an eight-dimensional hypercube spanned by {Omega(m)h(2), Omega(b)h(2), Omega(nu)h(2), sigma(8), h, n(s), w(0), w(a)}; spatial flatness is assumed. We obtain smooth halo mass functions by fitting piecewise second-order polynomials to the halo catalogs and employ Gaussian process regression to construct the emulator while keeping track of the statistical noise in the input halo catalogs and uncertainties in the regression process. For redshifts z less than or similar to 1, the typical emulator precision is better than 2% for 10(13)-10(14)M(circle dot)/h and <10<^> M similar or equal to 101(circle dot)(54)/h. For comparison, fitting functions using the traditional universal form for the halo mass function can be biased at up to 30% at M similar or equal to 10(144)M(circle dot)/h for z = 0. Our emulator is publicly available at https://github.com/SebastianBocquet/MiraTitanHMFemulator.

cosmology: theory↗
DOE OSTI2018

Biochemical methane potential (BMP) tests: Reducing test time by early parameter estimation

Highlights: • Early parameters prediction of BMP model is achieved by using sensitivity functions. • Minimum testing time for early parameter estimation is related to kinetic constant. • Balanced regression improves the accuracy of estimated parameters. - Abstract: Biochemical methane potential (BMP) test is a key analytical technique to assess the implementation and optimisation of anaerobic biotechnologies. However, this technique is characterised by long testing times (from 20 to >100 days), which is not suitable for waste utilities, consulting companies or plants operators whose decision-making processes cannot be held for such a long time. This study develops a statistically robust mathematical strategy using sensitivity functions for early prediction of BMP first-order model parameters, i.e. methane yield (B{sub 0}) and kinetic constant rate (k). The minimum testing time for early parameter estimation showed a potential correlation with the k value, where (i) slowly biodegradable substrates (k ≤ 0.1 d{sup −1}) have a minimum testing times of ≥15 days, (ii) moderately biodegradable substrates (0.1 −1 ) have a minimum testing times between 8 and 15 days, and (iii) rapidly biodegradable substrates (k ≥ 0.2 d{sup −1}) have testing times lower than 7 days.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1996

Power law behavior of RR-interval variability in healthy middle-aged persons, patients with recent acute myocardial infarction, and patients with heart transplants

BACKGROUND. The purposes of the present study were (1) to establish normal values for the regression of log(power) on log(frequency) for, RR-interval fluctuations in healthy middle-aged persons, (2) to determine the effects of myocardial infarction on the regression of log(power) on log(frequency), (3) to determine the effect of cardiac denervation on the regression of log(power) on log(frequency), and (4) to assess the ability of power law regression parameters to predict death after myocardial infarction. METHODS AND RESULTS. We studied three groups: (1) 715 patients with recent myocardial infarction; (2) 274 healthy persons age and sex matched to the infarct sample; and (3) 19 patients with heart transplants. Twenty-four-hour RR-interval power spectra were computed using fast Fourier transforms and log(power) was regressed on log(frequency) between 10(-4) and 10(-2) Hz. There was a power law relation between log(power) and log(frequency). That is, the function described a descending straight line that had a slope of approximately -1 in healthy subjects. For the myocardial infarction group, the regression line for log(power) on log(frequency) was shifted downward and had a steeper negative slope (-1.15). The transplant (denervated) group showed a larger downward shift in the regression line and a much steeper negative slope (-2.08). The correlation between traditional power spectral bands and slope was weak, and that with log(power) at 10(-4) Hz was only moderate. Slope and log(power) at 10(-4) Hz were used to predict mortality and were compared with the predictive value of traditional power spectral bands. Slope and log(power) at 10(-4) Hz were excellent predictors of all-cause mortality or arrhythmic death. To optimize the prediction of death, we calculated a log(power) intercept that was uncorrelated with the slope of the power law regression line. We found that the combination of slope and zero-correlation log(power) was an outstanding predictor, with a relative risk of > 10, and was better than any combination of the traditional power spectral bands. The combination of slope and log(power) at 10(-4) Hz also was an excellent predictor of death after myocardial infarction. CONCLUSIONS. Myocardial infarction or denervation of the heart causes a steeper slope and decreased height of the power law regression relation between log(power) and log(frequency) of RR-interval fluctuations. Individually and, especially, combined, the power law regression parameters are excellent predictors of death of any cause or arrhythmic death and predict these outcomes better than the traditional power spectral bands.

Non-NASA Center↗
NASA NTRS1994

Remote Sensing as a Landscape Epidemiologic Tool to Identify Villages at High Risk for Malaria Transmission

A landscape approach using remote sensing and Geographic Information System (GIS) technologies was developed to discriminate between villages at high and low risk for malaria transmission, as defined by adult Anopheles albimanus abundance. Satellite data for an area in southern Chiapas, Mexico were digitally processed to generate a map of landscape elements. The GIS processes were used to determine the proportion of mapped landscape elements surrounding 40 villages where An. albimanus data had been collected. The relationships between vector abundance and landscape element proportions were investigated using stepwise discriminant analysis and stepwise linear regression. Both analyses indicated that the most important landscape elements in terms of explaining vector abundance were transitional swamp and unmanaged pasture. Discriminant functions generated for these two elements were able to correctly distinguish between villages with high ind low vector abundance, with an overall accuracy of 90%. Regression results found both transitional swamp and unmanaged pasture proportions to be predictive of vector abundance during the mid-to-late wet season. This approach, which integrates remotely sensed data and GIS capabilities to identify villages with high vector-human contact risk, provides a promising tool for malaria surveillance programs that depend on labor-intensive field techniques. This is particularly relevant in areas where the lack of accurate surveillance capabilities may result in no malaria control action when, in fact, directed action is necessary. In general, this landscape approach could be applied to other vector-borne diseases in areas where: 1. the landscape elements critical to vector survival are known and 2. these elements can be detected at remote sensing scales.

Beck, Louisa R.↗
DOE OSTI2023

Shallow-Water Habitat in the Lower Columbia River Estuary: A Highly Altered System

Abstract Decreases in shallow-water habitat area (SWHA) in the Lower Columbia River and Estuary (LCRE) have adversely affected salmonid populations. We investigate the causes by hindcasting SWHA from 1928 to 2004, system-wide, based on daily higher high water (HHW) and system hypsometry. Physics-based regression models are used to represent HHW along the system as a function of river inflow, tides, and coastal processes, and hypsometry is used to estimate the associated SWHA. Scenario modeling is employed to attribute SWHA losses to levees, flow regulation, diversion, navigational development, and climate-induced hydrologic change, for subsidence scenarios of up to 2 m, and for 0.5 m fill. For zero subsidence, the system-wide annual-average loss of SWHA is 55 ± 5%, or 51 × 10 5 ha/year; levees have caused the largest decrease ( $${54}_{-14}^{+5}$$ 54 - 14 + 5 %, or ~ 50 × 10 5 ha/year). The loss in SWHA due to operation of the hydropower system is small, but spatially and seasonally variable. During the spring freshet critical to juvenile salmonids, the total SWHA loss was $${63}_{-3}^{+2}$$ 63 - 3 + 2 %, with the hydropower system causing losses of 5–16% (depending on subsidence). Climate change and navigation have caused SWHA losses of $${5}_{-5}^{+16}$$ 5 - 5 + 16 % and $${4}_{-6}^{+14}$$ 4 - 6 + 14 %, respectively, but with high spatial variability; irrigation impacts have been small. Uncertain subsidence causes most of the uncertainty in estimates; the sum of the individual factors exceeds the total loss, because factors interact. Any factor that reduces mean or peak flows (reservoirs, diversion, and climate change) or alters tides and along-channel slope (navigation) becomes more impactful as assumed historical elevations are increased to account for subsidence, while levees matter less.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Hydrous SiO 2 in subducted oceanic crust and H 2 O transport to the core-mantle boundary

Subduction of oceanic lithosphere transports surface H 2 O into the mantle. Recent studies show that dense SiO 2 in the form of stishovite, an abundant mineral in subducted oceanic crust at depths greater than ~270 km, has the potential to host and transport a considerable amount of H 2 O into the lower mantle, but the H 2 O storage capacity of SiO 2 phases at high pressure and temperature remains uncertain. We investigate the hydration of stishovite and its higher-pressure polymorphs, β-stishovite and seifertite, with in situ X-ray diffraction experiments at high pressures and temperatures. The H 2 O contents in SiO 2 phases are quantified based on observed increases in unit cell volume relative to the anhydrous SiO 2 system. Density functional theory (DFT) computations permit calibration of water content as a function of volume change based on interstitial substitution of H 2 O. Regression of our experimental data indicates an H 2 O storage capacity in stishovite of ~3.5 wt% in the transition zone and shallow lower mantle, decreasing to about 0.8 wt% at the base of the mantle. We find that SiO 2 -bearing subducted oceanic crust can accommodate all the H 2 O in slab lithosphere that survives sub-arc dehydration. Hydration of silica phases in subducted oceanic crust and their unparalleled capacity to host significant amounts of H 2 O even at high mantle temperatures provides a unique mechanism for transport and storage of water in the deepest mantle.

58 GEOSCIENCES↗
DOE OSTI2013

Synthesis and modification of nano-sized TiO{sub 2} for photo-degradation process under visible light irradiation; a Placket–Burman experimental design

Graphical abstract: - Highlights: • Nanosized TiO{sub 2} doped with nitrogen and iron was produced by sol-gel method. • Linear models were derived to relate the phase and particle size to synthesis factors. • High photocatalytic activity was observed for acetaldehyde degradation. - Abstract: In this research, nitrogen and iron (III) co-doped nano TiO{sub 2} (N-Fe-TiO{sub 2}) was prepared by sol-gel method using ammonium chloride and iron chloride solutions as nitrogen and iron sources, respectively. The effect of synthesis parameters (weight ratios of N/TiO{sub 2} and Fe/TiO{sub 2}, synthesis temperature, calcination time and temperature) was simultaneously investigated on the quality of the N-Fe-TiO{sub 2} product by a two-level Placket–Burman experimental design. The synthesized powders were characterized by XRD, XPS and UV–Vis spectroscopy techniques. The mean crystal size and anatase content were determined and evaluated as the linear functions of so-called synthesis parameters by the statistical analysis and regression. The 2%N-1%Fe-TiO{sub 2} product by 11 nm mean crystal size and 78% anatase content, synthesized at 35 °C and calcined at 500 °C during 2 h, exhibited the most significant activity during the photo-degradation of acetaldehyde under visible light irradiation and its efficiency was obtained about four times more than photocatalytic activity of Degussa TiO{sub 2}-P25.

36 MATERIALS SCIENCE↗