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Results for “regression analysis”

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 sources NASA NTRS DOE OSTI

DOE OSTI2011

Evaluation of syngas production unit cost of bio-gasification facility using regression analysis techniques

Evaluation of economic feasibility of a bio-gasification facility needs understanding of its unit cost under different production capacities. The objective of this study was to evaluate the unit cost of syngas production at capacities from 60 through 1800Nm 3/h using an economic model with three regression analysis techniques (simple regression, reciprocal regression, and log-log regression). The preliminary result of this study showed that reciprocal regression analysis technique had the best fit curve between per unit cost and production capacity, with sum of error squares (SES) lower than 0.001 and coefficient of determination of (R 2) 0.996. The regression analysis techniques determined the minimum unit cost of syngas production for micro-scale bio-gasification facilities of $0.052/Nm 3, under the capacity of 2,880 Nm 3/h. The results of this study suggest that to reduce cost, facilities should run at a high production capacity. In addition, the contribution of this technique could be the new categorical criterion to evaluate micro-scale bio-gasification facility from the perspective of economic analysis.

Bio-gasification↗

DOE OSTI2019

Using Regression Analysis Method to Develop a Material Outgassing Model

Regression analysis is a powerful statistical method that allows us to examine the relationship among multiple variables of interest. There are many types of regression analysis, practically speaking, they all identify the influence of one or more independent variables on a dependent variable. Dependent variable is a main factor of interest that we are trying to assess, and independent variables are input factors which we believe to have an impact on dependable variable.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Discriminating between adaptive and carcinogenic liver hypertrophy in rat studies using logistic ridge regression analysis of toxicogenomic data: The mode of action and predictive models

Chemical exposure often results in liver hypertrophy in animal tests, characterized by increased liver weight, hepatocellular hypertrophy, and/or cell proliferation. While most of these changes are considered adaptive responses, there is concern that they may be associated with carcinogenesis. In this study, we have employed a toxicogenomic approach using a logistic ridge regression model to identify genes responsible for liver hypertrophy and hypertrophic hepatocarcinogenesis and to develop a predictive model for assessing hypertrophy-inducing compounds. Logistic regression models have previously been used in the quantification of epidemiological risk factors. DNA microarray data from the Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System were used to identify hypertrophy-related genes that are expressed differently in hypertrophy induced by carcinogens and non-carcinogens. Data were collected for 134 chemicals (72 non-hypertrophy-inducing chemicals, 27 hypertrophy-inducing non-carcinogenic chemicals, and 15 hypertrophy-inducing carcinogenic compounds). After applying logistic ridge regression analysis, 35 genes for liver hypertrophy (e.g., Acot1 and Abcc3) and 13 genes for hypertrophic hepatocarcinogenesis (e.g., Asns and Gpx2) were selected. The predictive models built using these genes were 94.8% and 82.7% accurate, respectively. Pathway analysis of the genes indicates that, aside from a xenobiotic metabolism-related pathway as an adaptive response for liver hypertrophy, amino acid biosynthesis and oxidative responses appear to be involved in hypertrophic hepatocarcinogenesis. Early detection and toxicogenomic characterization of liver hypertrophy using our models may be useful for predicting carcinogenesis. In addition, the identified genes provide novel insight into discrimination between adverse hypertrophy associated with carcinogenesis and adaptive hypertrophy in risk assessment. - Highlights: • Hypertrophy (H) and hypertrophic carcinogenesis (C) were studied by toxicogenomics. • Important genes for H and C were selected by logistic ridge regression analysis. • Amino acid biosynthesis and oxidative responses may be involved in C. • Predictive models for H and C provided 94.8% and 82.7% accuracy, respectively. • The identified genes could be useful for assessment of liver hypertrophy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Full-Field Strain Measurement Integrated with Two Dimension Regression Analysis to Evaluate the Bi-Modulus Elastic Properties of Isotropic and Transversely Isotropic Materials

Background: Measuring the physical properties of shale is critical for optimizing engineering activities such as geothermal energy generation and hydraulic fracturing. Shale is a transversely isotropic material. Furthermore, this material can also include micro and macro cracks at different locations and orientations that cause it to behave differently under tensile or compressive loading. Objective: In this work, a combined experimental–numerical approach is proposed to evaluate the bi-modulus elastic properties of isotropic and transversely isotropic materials. Methods: Full-field strain measurements for a circular disk under diametral compression are integrated with a regression analysis technique to evaluate the elastic properties of bi-modulus materials subjected to tensile and compressive loads using two loading configurations on the same specimen. Digital Image Correlation (DIC) is used to measure the full-field strains. Subsequently, in the case of an isotropic material, a linear least-squares approach is utilized to process the experimentally determined strains in conjunction with analytical expressions of the stress fields (in terms of far-field loading) to determine the elastic modulus E, the shear modulus G, and the Poisson’s ratio $v$. In the case of a transversely isotopic material, such as shale, a finite element model is implemented to determine the stress fields (again in terms of far-field loading), which is followed by repeating the previous regression analysis in an iterative process to estimate the elastic parameters. Results: The results show that the proposed technique successfully provides a complete set of elastic properties as a function of both the loading condition and the principal material directions. The technique is validated by measurements on a known isotropic material and then applied to determine the properties of shale. Conclusion: In this work, the proposed approach is successfully used to calculate the bi-modulus elastic response of poly(methyl meth- acrylate) (PMMA) and shale. As expected, PMMA exhibits an isotropic response with no bi-modulus effect, however, shale exhibits both transverse isotropy and a bi-modulus effect. Therefore, this approach holds promise for investigating the elastic properties of materials like rocks and fiber-reinforced composite laminates as functions of the principal material directions and the loading conditions.

42 ENGINEERING↗

DOE OSTI2023

Notes on Regression Analysis for Radar Parameter Estimation

A fundamental task of radar, beyond merely detecting a target, is to estimate some parameters associated with it. For example, this might include range, direction, velocity, etc. In any case, multiple measurements, often noisy, need to be processed to yield a ‘best estimate’ of the parameter. A common mathematical method for doing so is called “Regression” analysis. The goal is to minimize the expected squared error in the estimate. Even when alternate algorithms are considered, the least squared-error regression analysis is the benchmark against which alternatives are compared.

47 OTHER INSTRUMENTATION↗

DOE OSTI2012

A Visual Analytics Approach for Correlation, Classification, and Regression Analysis

New approaches that combine the strengths of humans and machines are necessary to equip analysts with the proper tools for exploring today's increasing complex, multivariate data sets. In this paper, a novel visual data mining framework, called the Multidimensional Data eXplorer (MDX), is described that addresses the challenges of today's data by combining automated statistical analytics with a highly interactive parallel coordinates based canvas. In addition to several intuitive interaction capabilities, this framework offers a rich set of graphical statistical indicators, interactive regression analysis, visual correlation mining, automated axis arrangements and filtering, and data classification techniques. The current work provides a detailed description of the system as well as a discussion of key design aspects and critical feedback from domain experts.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2012

Air Leakage of US Homes: Regression Analysis and Improvements from Retrofit

LBNL Residential Diagnostics Database (ResDB) contains blower door measurements and other diagnostic test results of homes in United States. Of these, approximately 134,000 single-family detached homes have sufficient information for the analysis of air leakage in relation to a number of housing characteristics. We performed regression analysis to consider the correlation between normalized leakage and a number of explanatory variables: IECC climate zone, floor area, height, year built, foundation type, duct location, and other characteristics. The regression model explains 68% of the observed variability in normalized leakage. ResDB also contains the before and after retrofit air leakage measurements of approximately 23,000 homes that participated in weatherization assistant programs (WAPs) or residential energy efficiency programs. The two types of programs achieve rather similar reductions in normalized leakage: 30% for WAPs and 20% for other energy programs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Fast and Non-Destructive Determination of Water Content in Ionic Liquids at Varying Temperatures by Raman Spectroscopy and Multivariate Regression Analysis

Imidazolium acetate ionic liquids (ILs) have been utilized as promising solvents in many applications that involve varying water content and temperature. These experimental variables affect the anion-cation intermolecular interactions, which in turn influence the performance of the ILs in these applications. Here, this paper shows Raman spectroscopy can be used as an operando method to measure water content in IL solvents when simultaneous temperature changes may occur. The Raman spectra of 1-alkyl-3-methylimidazolium acetate ILs (alkyl chain length n = 2, 4, 6, 8) with varying water content (from 0.028 to 0.899 water mole fraction) and temperature (from 78.1 K to 423.1 K) were measured. Increasing the water content or decreasing the temperature of the tested ILs weakens the anion-cation intermolecular interactions. The water content of these ILs can be quantified even in conditions when the temperature is changing using Raman spectroscopy combined with multivariate regression analysis, including principal component regression (PCR), partial-least-squares regression (PLSR), and artificial neural networks (ANNs). The ANN model combined with partial-least-squares (PLS) achieves the highest prediction accuracy of water content in ILs at varying temperatures (RMSECV = 0.017, R 2 CV = 99.1%, RMSEP = 0.019, R 2 P = 98.8%, RPD = 8.93). Raman spectroscopy provides a potential fast non-destructive operando method to monitor the water content of ILs even in applications when the temperature may be simultaneously altered; this information can lead to the optimized use of these ILs in many applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2012

Poisson Regression Analysis of Illness and Injury Surveillance Data

The Department of Energy (DOE) uses illness and injury surveillance to monitor morbidity and assess the overall health of the work force. Data collected from each participating site include health events and a roster file with demographic information. The source data files are maintained in a relational data base, and are used to obtain stratified tables of health event counts and person time at risk that serve as the starting point for Poisson regression analysis. The explanatory variables that define these tables are age, gender, occupational group, and time.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2013

A Visual Analytics Approach for Correlation, Classification, and Regression Analysis

New approaches that combine the strengths of humans and machines are necessary to equip analysts with the proper tools for exploring today s increasing complex, multivariate data sets. In this paper, a visual data mining framework, called the Multidimensional Data eXplorer (MDX), is described that addresses the challenges of today s data by combining automated statistical analytics with a highly interactive parallel coordinates based canvas. In addition to several intuitive interaction capabilities, this framework offers a rich set of graphical statistical indicators, interactive regression analysis, visual correlation mining, automated axis arrangements and filtering, and data classification techniques. This chapter provides a detailed description of the system as well as a discussion of key design aspects and critical feedback from domain experts.

Steed, Chad A↗

DOE OSTI2019

Evaluating Environmental Remediation Performance at Radwaste Sites Using Multiple, Censored Regression Analysis - 19348

Located on the U.S. Department of Energy's Hanford Site in Richland,Washington, the 300 Area's remediation performance monitoring requires assessment of concentration trends and calculation of means and lower and upper confidence limits on the mean. The performance monitoring wells for uranium are located within 400 m of the Columbia River and groundwater elevations and uranium concentrations are influenced by groundwater-surface water interactions. A multiple regression approach was used to assess trends because multiple covariates can be used to account for the observed fluctuations in groundwater elevation and concentration. Incorporation of covariates accounting for these groundwater-surface water interactions (i.e., river stage) reduces the random error in the trend estimation and improves the estimates of remediation performance metrics (i.e., mean and confidence limits on the mean) over traditional, non-parametric methods. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

Responsiveness of miscanthus and switchgrass yields to stand age and nitrogen fertilization: A meta‐regression analysis

Abstract Optimal management of the perennial bioenergy crops, miscanthus and switchgrass, requires an understanding of their responsiveness to nitrogen (N) fertilizer at different maturity stages across locations and growing conditions. Earlier studies that have examined the yield response of these crops to N and stand age using field experiments or meta‐analysis techniques provide mixed evidence. We extend earlier studies by applying a multi‐level mixed‐effects (MLME) meta‐regression model to conduct a more extensive multivariate regression of yield response of these crops to N and stand age, while controlling for climate and location conditions and unobserved factors related to study design. Our findings are based on 1403 and 2811 yield observations for miscanthus and switchgrass, respectively, from experiments conducted between 2002 and 2019 across the rainfed region in the United States. We find statistically significant evidence that an additional year of maturity increases miscanthus and switchgrass yields but at a decreasing rate; yields peak at the 7th and 6th year respectively, for the observed range of applied N rates and stands. We also find that an increase in N application increases yield by a statistically significant level, but at a declining rate; the magnitude of the yield response to N is, however, small and varies with the age of the crop. The impact of N is larger on older compared to younger and middle‐aged stands of miscanthus. In contrast, the impact of N on switchgrass is larger on middle‐aged compared to younger and older stands of switchgrass. We do not find a statistically significant effect of soil productivity on yield for either crop. This analysis provides a basis for developing N application recommendations and optimal rotation age for miscanthus and switchgrass and shows that these energy crops can grow just as productively on low productivity land as on high productivity land.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2012

Regression analysis of technical parameters affecting nuclear power plant performances

Since the 80's many studies have been conducted in order to explicate good and bad performances of commercial nuclear power plants (NPPs), but yet no defined correlation has been found out to be totally representative of plant operational experience. In early works, data availability and the number of operating power stations were both limited; therefore, results showed that specific technical characteristics of NPPs were supposed to be the main causal factors for successful plant operation. Although these aspects keep on assuming a significant role, later studies and observations showed that other factors concerning management and organization of the plant could instead be predominant comparing utilities operational and economic results. Utility quality, in a word, can be used to summarize all the managerial and operational aspects that seem to be effective in determining plant performance. In this paper operational data of a consistent sample of commercial nuclear power stations, out of the total 433 operating NPPs, are analyzed, mainly focusing on the last decade operational experience. The sample consists of PWR and BWR technology, operated by utilities located in different countries, including U.S. (Japan)) (France)) (Germany)) and Finland. Multivariate regression is performed using Unit Capability Factor (UCF) as the dependent variable; this factor reflects indeed the effectiveness of plant programs and practices in maximizing the available electrical generation and consequently provides an overall indication of how well plants are operated and maintained. Aspects that may not be real causal factors but which can have a consistent impact on the UCF, as technology design, supplier, size and age, are included in the analysis as independent variables. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

Predicting the evolution of biomass bulk density through feedstock preprocessing: Discrete element modeling, regression analysis, and pilot-scale validation

Bulk density is an important material property of biomass feedstocks, influencing handling, storage, transport costs, and conversion efficiency. In this study, predictive regression models for loose and tapped bulk densities of Alamo and Cave-in-Rock switchgrass are developed using a comprehensive dataset generated via calibrated bonded-sphere discrete element method (DEM) simulations. Here, a key contribution of this study is the use of a DEM-based approach, which correlates density with moisture content and particle size distribution parameters and enables analysis across a continuous particle size range, overcoming limitations of purely experimental data. For comparison, regression models are also developed using only experimental data from pilot-scale runs at the Biomass Feedstock National User Facility at Idaho National Laboratory. Validation against pilot-scale data showed reasonable prediction accuracy for both model types, particularly for smaller particle sizes (post-secondary grinding). While the experimental model showed slightly better performance matching the validation data in some cases, the DEM-based model benefits from a much larger dataset, reduced predictor multicollinearity, and continuous parameter coverage, highlighting the utility of validated simulation models for developing robust predictive tools for biomass preprocessing applications.

09 - BIOMASS FUELS↗

DOE OSTI2016

Empirical predictive models of daily relativistic electron flux at geostationary orbit: Multiple regression analysis

The daily maximum relativistic electron flux at geostationary orbit can be predicted well with a set of daily averaged predictor variables including previous day's flux, seed electron flux, solar wind velocity and number density, AE index, IMF B z , Dst , and ULF and VLF wave power. As predictor variables are intercorrelated, we used multiple regression analyses to determine which are the most predictive of flux when other variables are controlled. Empirical models produced from regressions of flux on measured predictors from 1 day previous were reasonably effective at predicting novel observations. Adding previous flux to the parameter set improves the prediction of the peak of the increases but delays its anticipation of an event. Previous day's solar wind number density and velocity, AE index, and ULF wave activity are the most significant explanatory variables; however, the AE index, measuring substorm processes, shows a negative correlation with flux when other parameters are controlled. This may be due to the triggering of electromagnetic ion cyclotron waves by substorms that cause electron precipitation. VLF waves show lower, but significant, influence. The combined effect of ULF and VLF waves shows a synergistic interaction, where each increases the influence of the other on flux enhancement. Correlations between observations and predictions for this 1 day lag model ranged from 0.71 to 0.89 (average: 0.78). Furthermore, a path analysis of correlations between predictors suggests that solar wind and IMF parameters affect flux through intermediate processes such as ring current ( Dst ), AE , and wave activity.

58 GEOSCIENCES↗

DOE OSTI2023

Estimating Field-Level Perennial Bioenergy Grass Biomass Yields Using the Normalized Difference Red-Edge Index and Linear Regression Analysis for Central Virginia, USA

We investigated the indicative power of the normalized difference red-edge index (NDRE) for estimating field-level perennial bioenergy grass biomass yields utilizing Sentinel-2 imagery and a linear regression model as a rapid, cost-effective method for biomass yield estimations for bioenergy. We used 2019 data from three study sites containing mature perennial bioenergy grass stands in central Virginia, USA. Of the simulated daily NDRE values based on the temporally weighted averaging of two temporal neighbors, we found the strongest index–yield correlation on 11 August (R = 0.85). We estimated the perennial bioenergy grass biomass yields for (1) all sites using the data pooled from the three sites (all-site estimation) and (2) each site using the data pooled from the other two sites (cross-site estimation). The estimated field-level perennial bioenergy grass biomass yields strongly correlated with the recorded yields (average R2 = 0.76), with a root mean square error (RMSE) of 1.5 Mg/ha and a mean absolute error (MAE) of 1.2 Mg/ha for the all-site estimation. For the cross-site estimation, the site with diverse perennial grass types had the weakest correlation (R2 = 0.44) of the sites, indicating a difficulty in accounting for heterogeneous index–yield relationships in a single model. In addition to identifying a strong indicative power of the NDRE for estimating the overall perennial bioenergy grass biomass yields at a field level, the findings from this study call for an analysis across multiple perennial grasses and a comparison using multiple sites to understand (1) if the indicative power of the index shifts from the biomass of the specific perennial bioenergy grass type to the overall biomass during the growing season and (2) the level of perennial bioenergy grass heterogeneity that may hinder the remotely sensed biomass yield estimation using a single model.

09 BIOMASS FUELS↗

DOE OSTI2020

Conscientious vaccination exemptions in kindergarten to eighth-grade children across Texas schools from 2012 to 2018: A regression analysis

Background: As conscientious vaccination exemption (CVE) percentages rise across the United States, so does the risk and occurrence of outbreaks of vaccine-preventable diseases such as measles. In the state of Texas, the median CVE percentage across school systems more than doubled between 2012 and 2018. During this period, the proportion of schools surpassing a CVE percentage of 3% rose from 2% to 6% for public schools, 20% to 26% for private schools, and 17% to 22% for charter schools. The aim of this study was to investigate this phenomenon at a fine scale. Methods and findings: Here, we use beta regression models to study the socioeconomic and geographic drivers of CVE trends in Texas. Using annual counts of CVEs at the school system level from the 2012–2013 to the 2017–2018 school year, we identified county-level predictors of median CVE percentage among public, private, and charter schools, the proportion of schools below a high-risk threshold for vaccination coverage, and five-year trends in CVEs. Since the 2012–2013 school year, CVE percentages have increased in 41 out of 46 counties in the top 10 metropolitan areas of Texas. We find that 77.6% of the variation in CVE percentages across metropolitan counties is explained by median income, the proportion of the population that holds a bachelor's degree, the proportion of the population that self-reports as ethnically white, the proportion of the population that is English speaking, and the proportion of the population that is under the age of five years old. Across the 10 top metropolitan areas in Texas, counties vary considerably in the proportion of school systems reporting CVE percentages above 3%. Sixty-six percent of that variation is explained by the proportion of the population that holds a bachelor’s degree and the proportion of the population affiliated with a religious congregation. Three of the largest metropolitan areas—Austin, Dallas–Fort Worth, and Houston—are potential vaccination exemption "hotspots," with over 13% of local school systems above this risk threshold. The major limitations of this study are inconsistent school-system-level CVE reporting during the study period and a lack of geographic and socioeconomic data for individual private schools. Conclusions: In this study, we have identified high-risk communities that are typically obscured in county-level risk assessments and found that public schools, like private schools, are exhibiting predictable increases in vaccination exemption percentages. As public health agencies confront the reemerging threat of measles and other vaccine-preventable diseases, findings such as ours can guide targeted interventions and surveillance within schools, cities, counties, and sociodemographic subgroups.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Regression Analysis with the Directed Infusion of Data

Integrating artificial intelligence and machine learning tools into industry necessitates large-scale collaborative efforts that ensure the robust and accurate execution of downstream analytics such as time series prediction, uncertainty quantification, grid optimization, and condition monitoring. However, concerns related to data privacy pervade the nuclear industry due to the proprietary nature of its data and the possibility of data leakage. Legacy techniques such as encryption often require the explicit transmission of data to trustworthy parties, thereby inviting data leakage concerns. The ideal collaboration scenario avoids the explicit dissemination of data/code while maintaining experimental fidelity, which is currently accomplished using various techniques such as trusted execution environments, homomorphic encryption, differential privacy, and multimatrix masking. These techniques, however, often necessitate a trade-off between trust, efficiency, and utility. This article extends a previously proposed technique called the directed infusion of data (DIOD) that ensures data privacy, allows for scalable obfuscation, and combats the risk of data leakage without compromising utility. The experiments discussed in this article examine a regression-type scenario using DIOD with the goal of preserving the inferential link between two variables. Using the point-kinetics equations, regression experiments compare the performance of a model trained using the original data to that of a model trained using the obfuscated data, which produced identical results. Our claim is further strengthened by an information theoretic proof and experiment, which showed that the inferential content between variables remains the same after obfuscation, thereby avoiding the required communication of the proprietary data.

47 - OTHER INSTRUMENTATION↗