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Modeling and robust prediction of high heating values of municipal solid waste based on ultimate analysis

ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2025年第1期47卷 2239-2256页

作者： Ibikunle, Rotimi A. Lukman, Adewale F. Titiladunayo, Isaac F. Akeju, Emmanuel A. Dahunsi, Samuel O. Landmark Univ Dept Mech Engn Omu Aran Nigeria Landmark Univ Dept Phys Sci Km 4 IpetuOmu Aran RdPMB 1001 Omu Aran Kwara State Nigeria Fed Univ Technol Akure Dept Mech Engn Akure Nigeria Landmark Univ Dept Biol Sci Omu Aran Nigeria

The heating/calorific value of municipal solid waste (MSW) is essential in selecting or designing the appropriate waste to energy (WTE) systems. Experimental evaluation of the heating value of solid fuels is labor intensive, costly, and subject to experimental errors. Different models have been established to predict the high heating values of MSW and other solid fuels, from the ultimate analysis. However, the reliability of OLS estimator used in the linear regression model depends on the non-violation of assumptions that include independency of the predictor variables and normality of the error term. In this study, a new technique of robust estimators is employed to solve the problem of non-normality and dependency of the predictor variables in the linear regression model. The Robust ridge, robust Liu and robust K-L estimators were applied to mitigate the problems of multicollinearity and non-normality in the linear regression model. Eight (8) models were developed, and the adequacies were evaluated using the coefficient of determination (R2), adjusted R2, Akaike criterion (AIC), the mean squared error and the Schwarz criterion (SBIC). The eighth model is considered as the best because it has the highest adjusted R2 (0.9710), the least mean squared error (1.9564), minimum AIC (133.2755) and SBIC (145.9437). The selected model with the robust K-L estimator is finally used to predict the high heating/calorific value of the ultimate analysis.

关键词： Municipal solid waste high heating value ultimate analysis linear regression models robust estimators

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Effects of Treatments With Biochar From Crop Residues on Soil Detachment Capacity in Rills After Deforestation

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SOIL USE AND MANAGEMENT 2025年第2期41卷

作者： Parhizkar, Misagh Lucas-Borja, Manuel Esteban Filianoti, Pasquale Giuseppe Fabio Tziolas, Nikolaos Zupanc, Vesna Zema, Demetrio Antonio Agr Res Educ & Extens Org AREEO Rice Res Inst Iran Rasht Iran Univ Guilan Fac Agr Sci Rasht Iran Mediterranean Univ Reggio Calabria Agr Dept Reggio Di Calabria Italy Castilla La Mancha Univ Sch Adv Agr & Forestry Engn Dept Agroforestry Technol & Sci & Genet Albacete Spain Univ Mediterranea Reggio Calabria Dept Civil Energy Environm & Mat Engn DICEAM Via Graziella Reggio Di Calabria Italy Univ Florida Inst Food & Agr Sci Southwest Florida Res & Educ Ctr Dept Soil Water & Ecosyst Sci Immokalee FL USA Univ Ljubljana Biotech Fac Ljubljana Slovenia

Rill erosion is the most common form in steep and long hillslopes, but studies about the biochar effects on soil detachment capacity in rills are scarce. This study has simulated soil detachment in rills (D-c) by a laboratory flume on samples collected from forest soils in Northern Iran. The samples were treated with biochars (from rotten fruits of peach and almond, sugarcane bagasse, and straw of wheat, corn, and cotton) or untreated. D-c has been measured under three soil slopes and four water flow rates. The erodibility factor (K-r) and critical shear stress (tau(c)), important parameters for erosion models, have been estimated from interpolations between D-c and the shear stress. regression equations have also been proposed to predict D-c from stream power or its unit value. The experiments have shown that wheat biochar is the most effective in reducing D-c (-35% compared to the untreated soils). This reduction has been associated with higher organic matter (+65%) and stability of aggregates (> 80%) as well as with lower bulk density (-10%) of soils treated with this biochar. These effects may be due to the highest content of organic carbon, specific surface area, and cation exchange capacity of the substrate. No significant differences in D-c have been measured for the soils treated with the other biochars. K-r was 15%-35% lower compared to the untreated soils, while tau(c) noticeably increased after the treatment with all biochars. The stream power was a more accurate D-c estimator than its unit value (Nash and Sutcliffe's coefficient > 0.90). This study helps land managers and hydrologists select the most effective substrate for soil conservation and estimate rill erosion in forests under similar environmental conditions.

关键词： aggregate stability bulk density linear regression models organic amendments organic matter stream power

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Effects of multicollinearity and data granularity on regression models of stream temperature

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JOURNAL OF HYDROLOGY 2024年 639卷

作者： Dertli, Halil I. Hayes, Daniel B. Zorn, Troy G. Minist Agr & Forestry Gen Directorate Water Management Bestepe Alparslan Turkes Blvd 71 TR-06560 Ankara Turkiye Michigan State Univ Dept Fisheries & Wildlife 165 Nat Resources Bldg E Lansing MI 48824 USA Michigan Dept Nat Resources Fisheries Div Marquette Fisheries Res Stn 484 Cherry Creek Rd Marquette MI 49855 USA

Water temperature is a key factor influencing biota of stream ecosystems. Hence, it is important to comprehend the environmental drivers of stream temperature for robust prediction of conditions and effective management of stream communities. linear regression models are commonly used for predictive purposes, but their predictive capacity and interpretability can be significantly affected by their complexity and the structure of input data. In some cases, researchers may be obligated to favor prediction power or interpretability while compromising the other. Therefore, insight into relationships between model fit, correlation among predictor variables (i.e., multicollinearity), and level of temporal aggregation of data (i.e., data granularity) may be helpful to reduce such trade-offs. In this paper, we investigated these relationships within a hierarchical set of multiple linear regression (MLR) models examining environmental factors influencing stream temperature dynamics. Our findings showed that as the number of predictor variables (i.e., model complexity) increased, the magnitude of multicollinearity in MLR models increased, but model fit also increased. The results also revealed that using data averaged over longer time frames (i.e., coarser data granularity) yielded high multicollinearity, as indexed by variance inflation factor values (VIF) for all model predictors. This led to higher variance in parameter estimates (i.e., parameter instability) and potential challenges in model interpretation as the sign of parameter estimates changed in many streams examined. Multicollinearity was not the only reason for these changes in the sign of parameter estimates as they were also observed in simple linear regression models across varying levels of data granularity. Based on our findings, we conclude that the selection of data granularity is an important consideration in multiple regression modeling, with profound implications for model interpretability.

关键词： Stream temperature linear regression models Model prediction power Multicollinearity Data granularity Parameter instability

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ALCOHOL-CONSUMPTION regression-models FOR DISTINGUISHING BETWEEN BEVERAGE TYPE EFFECTS AND BEVERAGE PREFERENCE EFFECTS

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AMERICAN JOURNAL OF EPIDEMIOLOGY 1992年第11期135卷 1279-1286页

作者： KIMBALL, AW FRIEDMAN, LA From the Department of Biostatistics School of Hygiene and Public Health The Johns Hopkins University 615 North Wolfe StreetBaltimore MD 21205

In relating health outcomes to alcohol consumption, several investigators have evaluated differences among beverage types, but there is no consistency with respect to models used for this purpose. Furthermore, beverage type effects and beverage preference effects have not been evaluated simultaneously. In this report, the authors propose regression models which permit the simultaneous evaluation of beverage type (congener dose response) effects and beverage preference (sociobehavioral) effects. The presence of sociobehavioral effects can be established even if the variables responsible for them have not been measured or identified. The models are applied to a data set from 589 women who participated in an oral contraceptive study at the Johns Hopkins University (Baltimore, Maryland) in 1988-1989.

关键词： ALCOHOL DRINKING BEVERAGES EPIDEMIOLOGIC METHODS linear regression models

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ON ELIMINATING INFERIOR regression-models

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COMMUNICATIONS IN STATISTICS PART A-THEORY AND METHODS 1982年第7期11卷 751-759页

作者： HUANG, DY PANCHAPAKESAN, S SO ILLINOIS UNIV DEPT MATHCARBONDALEIL 62901

Consider a linear regression model with [p-1] predictor variables which is taken as the "true" *** goal Is to select a subset of all possible reduced models such that all inferior models ‘to be defined’ are excluded with a guaranteed minimum probability.A procedure is proposed for which the exact evaluation of the probability of a correct decision 1s difficult; however, 1t is shown that the probability requirement can be met for sufficiently large sample *** Carlo evaluation of the constant associated with the procedure and some ways to reduce the amount of computations Involved in the Implementation of the procedure are discussed.

关键词： linear regression models eliminating inferior models multiple correlation guaranteed probability of correct decision

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The application of multiple linear regression methods to FTIR spectra of fingernails for predicting gender and age of human subjects

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Heliyon 2025年第4期11卷 e42815页

作者： Mihaly Cozmuta, L. Technical University of Cluj Napoca North University Center of Baia Mare Victoriei Str. 76 Baia Mare 430122 Romania

The paper explores the accuracy of gender and age prediction of human subjects based on the chemometric analysis of FTIR spectra from fingernails. The baseline and scaling over the 0–1 range were applied to FTIR spectra from fingernails of 123 subjects, and wavenumbers for which absorbance values showed a statistically significant correlation with gender and age were identified. The prediction accuracy was analyzed using: Multiple linear regression, Forward stepwise regression, Backward stepwise regression, Principal component regression, and Partial least squares regression. As regard the gender prediction, the principal component regression model proved to be the most accurate, with 8 extracted components allowing a prediction of 93.50 % (86.00 % for women, 98.63 % for men). The predictive power of the model showed that in case of new subjects, gender classification could be done in 91.06 % of cases (84.00 % for women, 95.89 % for men). For age prediction, the optimal backward stepwise regression model with 6 statistically significant predictors showed an average error of 11.39 % (13.70 % for women, 9.81 % for men). The predictive power of the model was 12.13 % (11.73 % for women, 10.35 % for men). The age prediction was less accurate, with a maximum error of 10.00 % achieved only in the case of 65.04 % of subjects. © 2025 The Author

关键词： Age prediction Cross-validation Fingernails FTIR Gender prediction linear regression models

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Computed tomographic evaluation of the acetabulum for age estimation in an Indian population using principal component analysis and regression models

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INTERNATIONAL JOURNAL OF LEGAL MEDICINE 2022年第6期136卷 1637-1653页

作者： Warrier, Varsha Shedge, Rutwik Garg, Pawan Kumar Dixit, Shilpi Gupta Krishan, Kewal Kanchan, Tanuj All India Inst Med Sci Dept Forens Med & Toxicol Jodhpur 342005 Rajasthan India Natl Forens Sci Univ Sch Forens Sci Agartala 799001 India All India Inst Med Sci Dept Diagnost & Intervent Radiol Jodhpur 342005 Rajasthan India All India Inst Med Sci Dept Anat Jodhpur 342005 Rajasthan India Panjab Univ Dept Anthropol UGC Ctr Adv Study Chandigarh 160014 India

The acetabulum presents as a well-preserved evidence, resistant to taphonomic degradation changes and can thus aid in the age estimation process. A CT-based examination of the acetabulum can further help simplify the process of age estimation by overcoming the time-consuming process of maceration and by doing away with the interference resulting from tissue remnants. The aim of the present study was to evaluate the role of the acetabulum for age estimation in an Indian population through a CT-based examination, using principal component analysis and regression models. CT images of 400 individuals aged 10 years and above were evaluated according to the features defined in the San-Millan-Rissech method of age estimation. Five of the seven morphological features defined by San-Millan-Rissech were appreciable on CT scans, and, to enable further statistical analysis, a cumulative score was computed using these five features. A significant correlation of 0.835 and 0.830 for the right and left acetabulum, respectively, was obtained between computed cumulative scores and chronological age of individuals. No significant sex differences were observed in the scoring of different age-related morphological changes. regression models were generated using individual features and cumulative scores. regression models derived using the cumulative score yielded inaccuracy values of 9.67 years for the right acetabulum and 9.15 years for the left acetabulum. Inaccuracy and bias values were computed for each individual feature, as well as for each decade, using mean point ages established within the original study. Amongst the various features, acetabular rim porosity was seen to have the lowest values of inaccuracy (11.50 years) and bias (2.32 years) and activity on outer edge of acetabular fossa the highest (inaccuracy and bias values of 22.36 years and 21.50 years, respectively). Taking into consideration this differential contribution towards age estimation, weighted coefficients and

关键词： Forensic anthropology Human identification Age estimation Acetabulum Computed tomography linear regression models Principal component analysis Summary age models

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LEAST ABSOLUTE VALUE ESTIMATION IN regression-models - AN ANNOTATED-BIBLIOGRAPHY

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 1984年第4期13卷 513-541页

作者： DIELMAN, TE M. J. Neeley School of Business Fort Worth Texas 76129 United States

This paper presents a comprehensive listing of articles on least absolute value (LAV) estimation as applied to linear and non-linear regression models and in systems of equations. References to the LAV method as applied in approximation theory are also included. Annotations describing the content of each article follow each reference.

关键词： linear regression models nonlinear regression models systems of equations approximatiorl theory

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A Comparison of Peer Influence Estimates from SIENA Stochastic Actor-based models and from Conventional regression Approaches

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SOCIOLOGICAL METHODS & RESEARCH 2022年第1期51卷 357-395页

作者： Ragan, Daniel T. Osgood, D. Wayne Ramirez, Nayan G. Moody, James Gest, Scott D. Univ New Mexico Dept Sociol Albuquerque NM 87131 USA Penn State Univ Criminol & Sociol State Coll PA USA Calif State Univ Northridge Dept Criminol & Justice Studies Northridge CA USA Duke Univ Durham NC USA Univ Virginia Dept Human Serv Curry Sch Educ & Human Dev Charlottesville VA USA

The current study compares estimates of peer influence from an analytic approach that explicitly address network processes with those from traditional approaches that do not. Using longitudinal network data from the PROmoting School-community-university Partnerships to Enhance Resilience peers project, we obtain estimates of social influence on multiple outcomes from both conventional linear modeling approaches and the stochastic actor-based modeling approach of the simulation investigation for empirical network analysis (SIENA) software. Our findings indicate that peer influence estimates from SIENA are not more conservative relative to other methods, that each method is subject to omitted variable bias from stable individual differences, and that imprecision among each method could lead to erroneous conclusions in samples that lack sufficient power. Together, these results underscore the difficulty in obtaining estimates of peer influence from observational data, and they give no indication that results from conventional methods tend to be biased toward overestimating peer influence, relative to SIENA.

关键词： peer influence social networks SIENA stochastic actor-based models linear regression models

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Evaluation of mechanical properties of cement mortars containing pond ash as partial replacement of river sand and prediction of properties by regression models

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EUROPEAN JOURNAL OF ENVIRONMENTAL AND CIVIL ENGINEERING 2024年第11期28卷 2679-2710页

作者： Sharma, Sandesh Vyas, Ashok Kumar Malaviya Natl Inst Technol Civil Engn Jaipur Rajasthan India

Despite vast research on the utilisation of coal ashes in cement mortars, many studies lack a comprehensive evaluation of properties related to mortar for masonry and plaster work. This study investigate properties like workability, compressive strength, drying shrinkage, tensile bond strength and adhesive strength through a systematic replacement of river sand by pond ash in cement mortars. Volumetric mix proportions of 1:3, 1:4, 1:5 and 1:6 (cement: sand) were considered, where river sand was replaced in the range of 20-100% by pond ash. At 30% pond ash content, properties like the compressive strength, tensile bond strength and adhesive strength improved in the range of 21-30%, 13-20% and 14-24%, respectively, for all mix proportions. At 60% pond ash content, these strength properties were comparable with those of control mixes and found suitable for mortars for masonry works. However, the drying shrinkage increased with an increase in pond ash content. At 30% pond ash content, drying shrinkage increased in the range of 26-40% for all mix proportions. The thermogravimetric analysis (TGA) showed that mixes with pond ash demonstrated reduced portlandite content compared to the control mixes, resulting in the formation of secondary C-S-H gel. Simple regression models were developed based on experimental results to establish the relationships between different properties. Based on coefficient of determination (R2), variance inflation factor (VIF) and Durbin-Watson statistics, relationships between the strength and shrinkage properties were very well quantified to be used by researchers and practitioners. Utilisation of pond ash as partial replacement of river sand in mortars proved to be a useful step towards achieving circular economy at the same time reducing threat to depletion of limited natural resources.

关键词： Pond ash cement mortars pozzolanic activity mechanical properties linear regression models

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