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We evaluated the best-available evidence for the effects of receiving business-as-usual or naturally delivered special education services in K-12 US schools. Our best-evidence synthesis of 44 empirical studies evaluated which outcome domains and disability types have been investigated and whether findings varied by the rigor of the study design and methods. Regression-based studies comparing students with educational disabilities (SWED) to students without disabilities (SWOD) yielded mostly negative associations of receiving special education with academic achievement, behavior, and long-term or other outcomes. In contrast, regression-based studies that contrasted SWED receiving special education to other SWED not receiving special education produced a pattern of estimates similar to quasi-experimental designs that contrast SWED to SWOD. The most rigorous designs utilized quasi-experimental methods that compared SWED receiving special education services with SWED not receiving special education services, and generally reported more positive than negative evidence of receiving special education services across most outcome domains. Future research that utilizes rigorous quasi-experimental methodology and appropriate comparison groups to investigate the effectiveness of special education is needed, particularly for nonachievement outcome domains.

Sustainable manufacturing is one of the most important and most challenging issues in present industrial scenario. With the intention of diminish negative effects associated with cutting fluids, the machining industries are continuously developing technologies and systems for cooling/lubricating of the cutting zone while maintaining machining efficiency. In the present study, three regression based machine learning techniques, namely, polynomial regression (PR), support vector regression (SVR) and Gaussian process regression (GPR) were developed to predict machining force, cutting power and cutting pressure in the turning of AISI 1045. In the development of predictive models, machining parameters of cutting speed, depth of cut and feed rate were considered as control factors. Since cooling/lubricating techniques significantly affects the machining performance, prediction model development of quality characteristics was performed under minimum quantity lubrication (MQL) and high-pressure coolant (HPC) cutting conditions. The prediction accuracy of developed models was evaluated by statistical error analyzing methods. Results of regressions based machine learning techniques were also compared with probably one of the most frequently used machine learning method, namely artificial neural networks (ANN). Finally, a metaheuristic approach based on a neural network algorithm was utilized to perform an efficient multi-objective optimization of process parameters for both cutting environment.

The purpose of this paper is to propose a simple regression‐based method of forecasting daily electricity demand, which may serve as a more accurate benchmark for short‐term forecasts.

In order to make more efficient use of the calendar effects in electricity demand, including weekend, and seasonal effects, while maintaining the parsimony of the forecasting model, the authors match the demand on each day of an entire year with the average of the corresponding days in recent years. This matching‐day approach substantially simplifies the modeling procedure of complex periodicity in electricity demand without loss of information.

With daily data on electric power system load in New England, the authors' method provides quite accurate forecasts. The mean absolute percentage error (MAPE) (2.1 percent) is significantly lower than those of the seasonal ARIMA and exponential smoothing method, and also comparable to the performance of more sophisticated methods in the literature.

The authors' method needs to be modified or augmented by other techniques when the periodicity is not stable due to time trends, economic crises, and other factors.

The management of electric utility providers as well as professional forecasters may use this method as a handy benchmark.

While previous studies focus mainly on accuracy of forecasts, the method presented in the paper is developed with the balance between accuracy and ease of use in mind.

Cost estimation is an important decision‐making process where many factors are interrelated in a complex manner, thus making it difficult to analyse and model using conventional mathematical methods. Artificial neural networks (ANNs) offer an alternative approach to modelling cost estimation. ANNs are simple mathematical models that self‐organize information from training data. This paper explores the use of ANNs in cost estimation. Research issues investigated are twofold. First, this paper compares the performance of ANNs to a regression‐based method which leads to a better understanding of the applicability of ANNs. Second, this paper identifies the effect of different configurations of neural networks on estimating accuracy. Experimental results demonstrate the many advantages and disadvantages of using neural networks in modelling cost estimation.

In most developing countries like Pakistan, the gap between rich and poor has widened over time. This polarization in the society hinders economic growth and acts as a barrier for development and well-being. The proportion of income distribution varies across the population sub-groups in Pakistan. Therefore, it is important to study the income distribution effects across the four provinces of Pakistan.

This study attempts to explore the root causes of income inequality and its changes in a dynamic context across the four provinces of Pakistan over a decade (2005–2006 to 2015–2016) by using a regression-based inequality decomposition method.

Age, gender and higher education are the most prominent factors explaining the level of inequality across the four provinces of Pakistan. Higher education enhances the level of inequality in all provinces but contributes negatively to its changes except for Balochistan. Skilled agricultural and fishery workers in Balochistan have contributed significantly to reducing the level of inequality over the decade but not to its changes. Healthy contribution of the unpaid family workers in economic activities has reduced the level of inequality in Punjab and Balochistan and contributed positively to the change in income inequality. Employer or self-employed workers enhance the level of income inequality but contribute negatively to its changes for Punjab and Balochistan.

To date, inequality literature on Pakistan focuses on economic growth and poverty. A handful studies focus on the determinants of income inequality in a static context. This study goes beyond the static decomposition tools and attempts to explore the determinants of inequality in a dynamic context.

The peer review history for this article is available at https://publons.com/publon/10.1108/IJSE-09-2021-0573.

The purpose of this study is to estimate aerodynamic parameters using regularized regression-based methods.

Regularized regression methods used are LASSO, ridge and elastic net.

A viable option of aerodynamic parameter estimation from regularized regression-based methods is found.

Efficacy of the methods is examined on flight test data.

This study provides regularized regression-based methods for aerodynamic parameter estimation from the flight test data.

Lenders in online peer-to-peer (P2P) lending platforms are always non-experts and face severe information asymmetry. This paper aims to achieve the goals of gaining high returns with risk limitations or lowering risks with expected returns for P2P lenders.

This paper used data from a leading online P2P lending platform in America. First, the authors constructed a logistic regression-based credit scoring model and a linear regression-based profit scoring model to predict the default probabilities and profitability of loans. Second, based on the predictions of loan risk and loan return, the authors constructed linear programming model to form the optimal loan portfolio for lenders.

The research results show that compared to a logistic regression-based credit scoring method, the proposed new framework could make more returns for lenders with risks unchanged. Furthermore, compared to a linear regression-based profit scoring method, the proposed new framework could lower risks for lenders without lowering returns. In addition, comparisons with advanced machine learning techniques further validate its superiority.

Unlike previous studies that focus solely on predicting the default probability or profitability of loans, this study considers loan allocation in online P2P lending as an optimization research problem using a new framework based upon modern portfolio theory (MPT). This study may contribute theoretically to the extension of MPT in the specific context of online P2P lending and benefit lenders and platforms to develop more efficient investment tools.

The paper introduced the use of a hybrid system of neural networks support vector machines (NNSVMs) consisting of artificial neural networks (ANNs) and support vector machines (SVMs) to price single-family properties.

The mechanism of the hybrid system is such that its output is given by the SVMs which utilise the results of the ANNs as their input. The results are compared to other property pricing modelling techniques including the standalone ANNs, SVMs, geographically weighted regression (GWR), spatial error model (SEM), spatial lag model (SLM) and the ordinary least squares (OLS). The techniques were applied to a dataset of 3,225 properties sold during the period, January 2012 to May 2014 in Cape Town, South Africa.

The results demonstrate that the hybrid system performed better than ANNs, SVMs and the OLS. However, in comparison to the spatial models (GWR, SEM and SLM) the hybrid system performed abysmally under with SEM favoured as the best pricing technique.

The findings extend the debate in the body of knowledge that the results of the OLS can significantly be improved through the use of spatial models that correct bias estimates and vary prices across the different property locations. Additionally, utilising the result of the hybrid system is thus affected by the black-box nature of the ANNs and SVMs limiting its use to purposes of checks on estimates predicted by the regression-based models.

In this chapter we explore different ways to obtain decompositions of rank-dependent indices of socioeconomic inequality of health, such as the Concentration Index. Our focus is on the regression-based type of decomposition. Depending on whether the regression explains the health variable, or the socioeconomic variable, or both, a different decomposition formula is generated. We illustrate the differences using data from the Ethiopia 2011 Demographic and Health Survey (DHS).

The Old Farmer Pension Program (OFPP) represents Taiwan’s long-standing efforts aiming at improving farm household income and well-being; however, how effective the pension program is in terms of achieving the policy agenda has remained unclear. The paper aims to discuss this issue.

Based on data drawn from the Survey of Family Income and Expenditure during 1999–2013, two identification strategies are used to examine the effect of OFPP. First the authors apply the Blinder-Oaxaca decomposition to address the concern if the program reaches the socially/economically disadvantaged farm households. The second identification strategy involves using the static and dynamic decomposition approaches to identify the major factors contributing to farm household income inequality and the redistribution role of the OFPP.

Results from the Blinder-Oaxaca decomposition indicate that about 60 percent of the income gap can be eliminated if the pension recipients’ socio-economic characteristics are the same as the non-recipient group, suggesting it is the disadvantaged group that receives the old farmer pension. Moreover, the results suggest the significant contributions of household investments in health and human capital as well as diversification toward nonfarm activities, to income inequality among Taiwan’s farm households. Results from the dynamic decomposition suggest that the first-wave adjustment of the OFPP enlarges farm household income inequality, the following two waves of adjustment, however, plays an equalizing role.

This study adds to the literature by providing a methodological refinement promoting the view that it calls for the use of the dynamic (change) decomposition framework to investigate the inequality-enlarging or inequality-equalizing role each income determinant plays.