Correlate Toxicity Order Numbers with Metal Ion Characteristics through the Ridge Regression (RR) Method

2012 ◽  
Vol 599 ◽  
pp. 155-158
Author(s):  
Xiao Li Li ◽  
Lan Wang ◽  
Yu Li
Keyword(s):  
Heavy Metals ◽  
Correlation Analysis ◽  
Regression Model ◽  
Ridge Regression ◽  
Metal Ion ◽  
Regression Models ◽  
Quantitative Relationship ◽  
Simulation Efficiency ◽  
Positive Coefficients

In this paper, we use 9 ion characteristics (choose from 14 ion characteristics by correlation analysis) in 19 metals to construct the simulate model through ridge regression (RR) which can remove the high multicollinearity among the ion characteristics. Two multi-parameter regression models were established: the first multi-parameter QICAR model was used to distinguish the quantitative relationship between the ion characteristics and toxicity order numbers (TON). We can found that the parameters AN, Xm, AN/ΔIP, AW and Xm2r have positive coefficients, and AN and AW have the more contribution to the toxicity of heavy metals. The parameters ΔE0, |logKOH|, AR/AW and δP have negative coefficients, and δP does the most negatively influence to the toxicity. The second model we constructed to simulate the toxicity order numbers (TON) of metals that hard to test by experiment. The regression model provided the high simulate ability, with Nash-Suttcliffe simulation efficiency coefficients (NSC) of 0.94 for the modeling phase.

scholarly journals General Regression Model for Predicting Surface Topography after Abrasive Blasting

Metals ◽  
2018 ◽  
Vol 8 (11) ◽  
pp. 938 ◽  
Author(s):  
Dagmar Draganovská ◽  
Gabriela Ižaríková ◽  
Anna Guzanová ◽  
Janette Brezinová
Keyword(s):  
Correlation Analysis ◽  
Regression Model ◽  
Surface State ◽  
Correlation Matrix ◽  
Regression Models ◽  
Roughness Parameters ◽  
Anova Analysis ◽  
Surface Irregularities ◽  
General Regression ◽  
Abrasive Blasting

Abrasive blasting modifies the surface state of pre-treated materials in terms of surface irregularities. Bearing in mind that the roughness characteristics affect the components functionality, it is essential to study and evaluation the surface state of pre-treated materials. The paper deals with evaluation of relation between individual parameters of roughness of the blasted surfaces by the correlation analysis. Based on the measured values on the surfaces which were blasted by various types of blasting devices, the correlation matrix was set and the standard of statistic importance of correlation between the monitored parameters was determined from it. The correlation coefficient was also set. There were found regression models using ANOVA (ANalysis Of Variance). Based on the analysis of the results were also proposed sets of roughness parameters, which can be used in the assessment of the blasted surfaces.

An QSAR Model for Predicting PBDEs Toxicity Established Based on Ridge Regression

2013 ◽  
Vol 663 ◽  
pp. 922-925 ◽  
Author(s):  
Yu Li ◽  
Long Jiang ◽  
Xiao Li Li ◽  
Jing Ya Wen
Keyword(s):  
Correlation Analysis ◽  
Ridge Regression ◽  
Preliminary Analysis ◽  
Qsar Model ◽  
Efficiency Coefficient ◽  
Quantitative Structure ◽  
Simulation Efficiency ◽  
Structure Activity ◽  
Quantitative Structure Activity Relationships ◽  
Polybrominated Biphenyl

In this paper, the ridge regression (RR) method was employed to establish the quantitative structure-activity relationships (QSAR) model for predicting toxicity with 15 polybrominated biphenyl ethers (PBDEs) and their 27 kinds of quantum descriptors. Quantum descriptors used to establish the QSAR model were filtrated out based on correlation analysis and variables importance of project (VIP) supported by partial least squares (PLS). The multicollinearity among the descriptors was removed during the calculation of RR method in order to ensure the validation of the final regression equation. The research showed that descriptors of Δα, αxx, αxy, αxz, αyz, βxxy and βyyy had significant effect on toxicity. The model with the simulation efficiency coefficient of 0.916 could be used to predict the toxicity of the unchecked PBDEs and as a preliminary analysis for environmental risk of organic compounds.

scholarly journals Use of a Big Data Analysis in Regression of Solar Power Generation on Meteorological Variables for a Korean Solar Power Plant

2021 ◽  
Vol 11 (4) ◽  
pp. 1776
Author(s):  
Young Seo Kim ◽  
Han Young Joo ◽  
Jae Wook Kim ◽  
So Yun Jeong ◽  
Joo Hyun Moon
Keyword(s):  
Power Generation ◽  
Data Analysis ◽  
Power Plant ◽  
Relative Humidity ◽  
Regression Model ◽  
Regression Models ◽  
Solar Power ◽  
Meteorological Variables ◽  
Solar Power Plant ◽  
Solar Power Generation

This study identified the meteorological variables that significantly impact the power generation of a solar power plant in Samcheonpo, Korea. To this end, multiple regression models were developed to estimate the power generation of the solar power plant with changing weather conditions. The meteorological data for the regression models were the daily data from January 2011 to December 2019. The dependent variable was the daily power generation of the solar power plant in kWh, and the independent variables were the insolation intensity during daylight hours (MJ/m2), daylight time (h), average relative humidity (%), minimum relative humidity (%), and quantity of evaporation (mm). A regression model for the entire data and 12 monthly regression models for the monthly data were constructed using R, a large data analysis software. The 12 monthly regression models estimated the solar power generation better than the entire regression model. The variables with the highest influence on solar power generation were the insolation intensity variables during daylight hours and daylight time.

scholarly journals Estimation of Outdoor PM2.5 Infiltration into Multifamily Homes Depending on Building Characteristics Using Regression Models

2021 ◽  
Vol 13 (10) ◽  
pp. 5708
Author(s):  
Bo-Ram Park ◽  
Ye-Seul Eom ◽  
Dong-Hee Choi ◽  
Dong-Hwa Kang
Keyword(s):  
Regression Analysis ◽  
Correlation Analysis ◽  
Field Test ◽  
Outer Surface ◽  
Regression Models ◽  
Field Tests ◽  
Test Results ◽  
Correlation And Regression Analysis ◽  
Building Characteristics ◽  
Pm2.5 Concentration

The purpose of this study was to evaluate outdoor PM2.5 infiltration into multifamily homes according to the building characteristics using regression models. Field test results from 23 multifamily homes were analyzed to investigate the infiltration factor and building characteristics including floor area, volume, outer surface area, building age, and airtightness. Correlation and regression analysis were then conducted to identify the building factor that is most strongly associated with the infiltration of outdoor PM2.5. The field tests revealed that the average PM2.5 infiltration factor was 0.71 (±0.19). The correlation analysis of the building characteristics and PM2.5 infiltration factor revealed that building airtightness metrics (ACH50, ELA/FA, and NL) had a statistically significant (p < 0.05) positive correlation (r = 0.70, 0.69, and 0.68, respectively) with the infiltration factor. Following the correlation analysis, a regression model for predicting PM2.5 infiltration based on the ACH50 airtightness index was proposed. The study confirmed that the outdoor-origin PM2.5 concentration in highly leaky units could be up to 1.59 times higher than that in airtight units.

scholarly journals Analysis models for variables associated with breastfeeding duration

2013 ◽  
Vol 31 (3) ◽  
pp. 306-314 ◽  
Author(s):  
Edson Theodoro dos S. Neto ◽  
Eliana Zandonade ◽  
Adauto Oliveira Emmerich
Keyword(s):  
Logistic Regression ◽  
Protective Factors ◽  
Regression Model ◽  
Odds Ratio ◽  
Regression Models ◽  
Adjusted Odds Ratio ◽  
Cox Regression ◽  
Bottle Feeding ◽  
Breastfeeding Duration ◽  
Factors Associated

OBJECTIVE To analyze the factors associated with breastfeeding duration by two statistical models. METHODS A population-based cohort study was conducted with 86 mothers and newborns from two areas primary covered by the National Health System, with high rates of infant mortality in Vitória, Espírito Santo, Brazil. During 30 months, 67 (78%) children and mothers were visited seven times at home by trained interviewers, who filled out survey forms. Data on food and sucking habits, socioeconomic and maternal characteristics were collected. Variables were analyzed by Cox regression models, considering duration of breastfeeding as the dependent variable, and logistic regression (dependent variables, was the presence of a breastfeeding child in different post-natal ages). RESULTS In the logistic regression model, the pacifier sucking (adjusted Odds Ratio: 3.4; 95%CI 1.2-9.55) and bottle feeding (adjusted Odds Ratio: 4.4; 95%CI 1.6-12.1) increased the chance of weaning a child before one year of age. Variables associated to breastfeeding duration in the Cox regression model were: pacifier sucking (adjusted Hazard Ratio 2.0; 95%CI 1.2-3.3) and bottle feeding (adjusted Hazard Ratio 2.0; 95%CI 1.2-3.5). However, protective factors (maternal age and family income) differed between both models. CONCLUSIONS Risk and protective factors associated with cessation of breastfeeding may be analyzed by different models of statistical regression. Cox Regression Models are adequate to analyze such factors in longitudinal studies.

A Unifying Classification for Functional Regression Modeling

2018 ◽  
Author(s):  
Frédéric Ferraty ◽  
Philippe Vieu
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Functional Model ◽  
Additive Functional ◽  
Regression Modeling ◽  
Functional Regression ◽  
Regression Problem ◽  
Nonparametric Models ◽  
Index Model ◽  
Functional Linear Regression

This article presents a unifying classification for functional regression modeling, and more specifically for modeling the link between two variables X and Y, when the explanatory variable (X) is of a functional nature. It first provides a background on the proposed classification of regression models, focusing on the regression problem and defining parametric, semiparametric, and nonparametric models, and explains how semiparametric modeling can be interpreted in terms of dimension reduction. It then gives four examples of functional regression models, namely: functional linear regression model, additive functional regression model, smooth nonparametric functional model, and single functional index model. It also considers a number of new models, directly adapted to functional variables from the existing standard multivariate literature.

scholarly journals Genotypic parameters for egg production in pure breed hens by using random regression model

2017 ◽  
Vol 47 (5) ◽  
Author(s):  
Priscila Becker Ferreira ◽  
Paulo Roberto Nogara Rorato ◽  
Fernanda Cristina Breda ◽  
Vanessa Tomazetti Michelotti ◽  
Alexandre Pires Rosa ◽  
...  
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Egg Production ◽  
Random Regression ◽  
Broad Sense Heritability ◽  
Residual Matrix ◽  
Random Regression Model ◽  
Residual Covariance ◽  
Genotypic Correlations ◽  
Selection Of

ABSTRACT: This study aimed to test different genotypic and residual covariance matrix structures in random regression models to model the egg production of Barred Plymouth Rock and White Plymouth Rock hens aged between 5 and 12 months. In addition, we estimated broad-sense heritability, and environmental and genotypic correlations. Six random regression models were evaluated, and for each model, 12 genotypic and residual matrix structures were tested. The random regression model with linear intercept and unstructured covariance (UN) for a matrix of random effects and unstructured correlation (UNR) for residual matrix adequately model the egg production curve of hens of the two study breeds. Genotypic correlations ranged from 0.15 (between age of 5 and 12 months) to 0.99 (between age of 10 and 11 months) and increased based on the time elapsed. Egg production heritability between 5- and 12-month-old hens increased with age, varying from 0.15 to 0.51. From the age of 9 months onward, heritability was moderate with estimates of genotypic correlations higher than 90% at the age of 10, 11, and 12 months. Results suggested that selection of hens to improve egg production should commence at the ninth month of age.

scholarly journals Batch Sorption Experiments: Langmuir and Freundlich Isotherm Studies for the Adsorption of Textile Metal Ions onto Teff Straw (Eragrostis tef) Agricultural Waste

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Mulu Berhe Desta
Keyword(s):  
Heavy Metals ◽  
Metal Ions ◽  
Contact Time ◽  
Metal Ion ◽  
Low Cost ◽  
Agricultural Waste ◽  
Equilibrium Concentration ◽  
Eragrostis Tef ◽  
Batch Adsorption ◽  
Textile Effluents

Adsorption of heavy metals (Cr, Cd, Pb, Ni, and Cu) onto Activated Teff Straw (ATS) has been studied using batch-adsorption techniques. This study was carried out to examine the adsorption capacity of the low-cost adsorbent ATS for the removal of heavy metals from textile effluents. The influence of contact time, pH, Temperature, and adsorbent dose on the adsorption process was also studied. Results revealed that adsorption rate initially increased rapidly, and the optimal removal efficiency was reached within about 1 hour. Further increase in contact time did not show significant change in equilibrium concentration; that is, the adsorption phase reached equilibrium. The adsorption isotherms could be fitted well by the Langmuir model. The value in the present investigation was less than one, indicating that the adsorption of the metal ion onto ATS is favorable. After treatment with ATS the levels of heavy metals were observed to decrease by 88% (Ni), 82.9% (Cd), 81.5% (Cu), 74.5% (Cr), and 68.9% (Pb). Results indicate that the freely abundant, locally available, low-cost adsorbent, Teff straw can be treated as economically viable for the removal of metal ions from textile effluents.

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