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The purpose of this paper is to identify significant factors of environmental variables and pollutants that have an effect on PM_2.5 through wavelet and regression analysis.

In order to provide stable data set for regression analysis, multiresolution analysis using wavelets is conducted. For the sampled data, multicollinearity among the independent variables is removed by using principal component analysis and multiple linear regression analysis is conducted using PM_2.5 as a dependent variable.

It is found that few pollutants such as NO₂, NO_x, SO₂, benzene and environmental factors such as ambient temperature, solar radiation and wind direction affect PM_2.5. The regression model developed has high R² value of 91.9 percent, and the residues are stationary and not correlated indicating a sound model.

The research provides a framework for extracting stationary data and other important features such as change points in mean and variance, using the sample data for regression analysis. The work needs to be extended across all areas in India and for various other stationary data sets there can be different factors affecting PM_2.5.

Control measures such as control charts can be implemented for significant factors.

Rules and regulations can be made more stringent on the factors.

The originality of this paper lies in the integration of wavelets with regression analysis for air pollution data.

Coal is the basic energy and essential resource in China, which is crucial to the economic lifeline and energy security of the country. Coal mining has been ever exposed to potential safety risks owing to the complex geologic environment. Effective safety supervision is a vital guarantee for safe production in coal mines. This paper aims to explore the impacts of the internet+ coal mine safety supervision (CMSS) mode that is being emerged in China.

In this study, the key factors influencing CMSS are identified by social network analysis. They are used to develop a multiple linear regression model of law enforcement frequency for conventional CMSS mode, which is then modified by an analytical hierarchy process to predict the law enforcement frequency of internet+ CMSS mode.

The regression model demonstrated high accuracy and reliability in predicting law enforcement frequency. Comparative analysis revealed that the law enforcement frequency in the internet+ mode was approximately 40% lower than the conventional mode. This reduction suggests a potential improvement in cost-efficiency, and the difference is expected to become even more significant with an increase in law enforcement frequency.

To the best of the authors’ knowledge, this is one of the few available pieces of research which explore the cost-efficiency of CMSS by forecasting law enforcement frequency. The study results provide a theoretical basis for promoting the internet+ CMSS mode to realize the healthy and sustainable development of the coal mining industry.

The purpose of this paper is to address and overcome the problem that a single prediction model cannot accurately fit a data sequence with large fluctuations.

Initially, the grey linear regression combination model was put forward. The Discrete Grey Model (DGM)(1,1) model and the multiple linear regression model were then combined using the entropy weight method. The grain yield from 2010 to 2015 was forecasted using DGM(1,1), a multiple linear regression model, the combined model and a GM(1,N) model. The predicted values were then compared against the actual values.

The results reveal that the combination model used in this paper offers greater simulation precision. The combination model can be applied to the series with fluctuations and the weights of influencing factors in the model can be objectively evaluated. The simulation accuracy of GM(1,N) model fluctuates greatly in this prediction.

The combined model adopted in this paper can be applied to grain forecasting to improve the accuracy of grain prediction. This is important as data on grain yield are typically characterised by large fluctuation and some information is often missed.

This paper puts the grey linear regression combination model which combines the DGM(1,1) model and the multiple linear regression model using the entropy weight method to determine the results weighting of the two models. It is intended that prediction accuracy can be improved through the combination of models used within this paper.

The purpose of this paper is to investigate the quality determinants influencing the adoption of e-government services in Oman and compare the performance of multiple regression and neural network models in identifying the significant factors influencing adoption in Oman.

Primary data concerning service quality determinants and demographic variables were collected using a structured questionnaire survey. The variables selected in the design of the questionnaire were based on an extensive literature review. Factor analysis, multiple linear regression and neural network models were employed to analyze data.

The study found that quality determinants: responsiveness, security, efficiency and reliability are statistically significant predictors of adoption. The neural network model performed better than the regression model in the prediction of e-government services’ adoption and was able to characterize the non-linear relationship of the aforementioned predictors with the adoption of e-government services. Further, the neural network model was able to identify demographic variables as significant predictors.

This study highlights the importance of service quality in the adoption of e-government services and suggests that an enhanced focus and investment on improving quality of the design and delivery of e-government services can have a positive impact on the usage of the services, thereby enabling the Oman Government in achieving the governance objectives for which these technologies were employed.

Studies in the area of e-government typically focus either on technology adoption problems or service quality problems. The role of service quality in adoption is rarely addressed. The research presented in this paper is of great value to the institutions that are involved in the development of technology-based e-government services in Oman.

This research aims to evaluate the impact of barriers experienced by Brazilian companies in adopting Industry 4.0 (I4.0).

As a methodological approach, the survey method was used, adopting the use of the questionnaire for data collection. From the feedback of 99 companies (with an index of 80%), quantitative analyzes of the data were carried out with the aid of factor analysis and linear regression to validate the proposed structural model.

The barriers construct does not impact the I4.0 adoption construct. Directly evaluating the effect of the variables that make up the barriers construct in the I4.0 adoption construct, it was observed that three barriers affect effectively the adoption of I4.0: technological infrastructure; financial constraint and lack of understanding of the benefits of I4.0.

As a limitation, the research was conducted only in the Brazilian context, requiring the development of future studies in other countries that can strengthen the findings of this research.

In addition, the results achieved provide relevant insights into public policymakers and business managers, helping them to deeply understand the barriers that impact the adoption of I4.0. This facilitates the propagation of I4.0 concepts in the context of Brazilian companies and in the formulation of public policies adapted to each sector, allowing a more assertive action in the face of the types of barriers experienced by organizations during the adoption of I4.0.

The findings can help practitioners and policymakers to understand in detail this new industrial model and the difficulties that prevent its implementation.

From an extensive literature review, no studies were identified that statistically validate which barriers effectively affect the adoption of I4.0. This research is a pioneer in proposing a structural model to analyze the barriers experienced by workers during the adoption of I4.0, exploring Brazilian companies, from different economic sectors and sizes. It is noteworthy that the literature still focuses efforts on manufacturing companies.

Cash flow forecasting is an indispensable tool for construction companies, and is essential for the survival of any contractor at all stages of the work. A simple and fast technique of forecasting cash flow accurately is required, considering the short time available and the associated cost. Seeks to examine this issue.

The paper argues that instead of producing an S‐curve that is based on historical projects combined (state‐of‐the‐art is based on classifying projects into groups and producing a standard curve for each group simply by fitting one curve into the historical data), here the attempt is to produce an individual S‐curve for an individual project. A sample of data from 50 projects was collected and 20 criteria were identified to classify these projects. Using the most influential criteria, a multiple linear regression model was created to forecast the programme of works and hence the S‐curves. A further six projects were used to validate and test the model.

The results of the model developed in this paper were compared with previous models and evaluated. It is concluded that the model produced more accurate results than existing value and cost models.

The paper proposes an alternative and novel approach to the development of standard value and cost commitment S‐curves. This approach is based on a multiple linear regression model of the programmes of works.

The purpose of the paper is to investigate voluntary human resources disclosure (hereinafter referred to as “HR disclosure”) by the largest companies domiciled in Czechia. The key research questions are: What is the quantity of disclosure on various topics related to HR? Is there a significant difference in the quantity of HR disclosure between companies? Which factors influence the quantity of HR disclosure?

A quantitative content analysis (CA) of annual reports of the 50 largest companies domiciled in Czechia was used. An established coding scheme is used to code annual reports, and subsequently, various statistical methods (descriptive statistics, correlation analysis, multiple linear regression) are used to answer the key research questions.

Primarily, social information is reported (what a company does for its employees) as information on the contribution of employees to the company’s value is rudimentary. Secondly, there is a significant difference in the quantity of HR disclosure between companies. Finally, the findings of the regression analysis confirm the impact of presence on the stock exchange and size and on the quantity of HR disclosure.

The annual reports of 50 companies from one country are analysed. The study provides a basis for further research.

The findings of this study may inspire companies to improve their HR disclosure, while policymakers should consider imposing more concrete demands on HR disclosure.

Quantitative CA research into the HR disclosure of companies domiciled in Czechia is nearly non-existent. This study fills this gap.

Properly planned road illumination systems are collectively a public wealth and the commissioning of such systems may require extensive planning, simulation and testing. The purpose of this simulative work is to offer a simple approach to facilitate luminance-based road lighting calculations that can be easier to comprehend and apply to practical designing problems when compared to complex multi-objective algorithms and other convoluted simulative techniques.

Road illumination systems were photometrically simulated with a created model in a validated software platform for specified system design configurations involving high-pressure sodium (HPS) and light-emitting diode (LED) luminaires. Multiple regression analyses were conducted with the simulatively obtained data set to propound a linear model of estimating average luminance, overall uniformity of luminance and energy efficiency of lighting installations, and the simulatively obtained data set was used to explore luminaire power–road surface average luminance characteristics for common geometric design configurations involving HPS and LED luminaires, and four categories of road surfaces.

The six linear equations of the propounded linear model were found to be well-fitted with their corresponding observation sets. Moreover, it was found that the luminaire power–road surface average luminance characteristics were well-fitted with linear trendlines and the increment in road surface average luminance level per watt increment of luminaire power was marginally higher for LEDs.

This neoteric approach of estimating road surface luminance parameters and energy efficiency of lighting installations, and the compendia of luminaire power–road surface average luminance characteristics offer new insights that can prove to be very useful for practical purposes.

This integrative review aimed to identify and synthesis literature on analysis techniques and methodological approaches used to analyse consumer measures in mental health research.

The review included papers published up to January 2020 across seven databases: CINAHL, Web of Science, Medline, PsycINFO, EMBASE, Scopus and Google Scholar. Data search and extraction was conducted according to the recommended guidelines for conducting review by Cochrane and Joanna Briggs Institute. Mixed method synthesis was used to integrate both qualitative and quantitative data into a single synthesis.

The initial search yielded a total of 2,282 papers. A total of 32 papers were included in the synthesis. Most of the included papers (25/32; 78.12%) focused on psychometric properties, whereas 14% (5/32) targeted analysis techniques, and 6.3% (2/32) addressed methodological justification. The measurement models (e.g. psychometric properties) were analysed through validity and reliability testing as part of instrument development and adaptation. The structural models were analysed using techniques such as structural equation modelling, multivariable regression models, intraclass correlation coefficient and partial least squares–structural equation modelling.

Although consumer-reported instruments are analysed using techniques involving linear, hierarchical and longitudinal effects, no attempt has been given to procedures that applied complex data mining or machine learning. Consumer researchers, clinicians and quality management are encouraged to apply rigorous analysis techniques to critically evaluate consumer outcome measures.

This review provides evidence on the analysis techniques in mental health research to inform the training of mental health professionals, students and quality assessment practitioners.

This study aims to emphasize utilization of Predictive Six Sigma to achieve process improvements based on machine learning (ML) techniques embedded in define, measure, analyze, improve, control (DMAIC). With this aim, this study presents selection and utilization of ML techniques, including multiple linear regression (MLR), artificial neural network (ANN), random forests (RF), gradient boosting machines (GBM) and k-nearest neighbors (k-NN) in the analyze and improve phases of Six Sigma DMAIC.

A data set containing 320 observations with nine input and one output variables is used. To achieve the objective which was to decrease the number of fabric defects, five ML techniques were compared in terms of prediction performance and best tools were selected. Next, most important causes of defects were determined via these tools. Finally, parameter optimization was conducted for minimum number of defects.

Among five ML tools, ANN, GBM and RF are found to be the best predictors. Out of nine potential causes, “machine speed” and “fabric width” are determined as the most important variables by using these tools. Then, optimum values for “machine speed” and “fabric width” for fabric defect minimization are determined both via regression response optimizer and ANN surface optimization. Ultimately, average defect number was decreased from 13/roll to 3/roll, which is a considerable decrease attained through utilization of ML techniques in Six Sigma.

Addressing an important gap in Six Sigma literature, in this study, certain ML techniques (i.e. MLR, ANN, RF, GBM and k-NN) are compared and the ones possessing best performances are used in the analyze and improve phases of Six Sigma DMAIC.