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With the continuous development of the wind power industry, wind power plant (WPP) has become the focus of resource development within the industry. Site selection, as the initial stage of WPP development, is directly related to the feasibility of construction and the future revenue of WPP. Therefore, the purpose of this paper is to study the siting of WPP and establish a framework for siting decision-making.

Firstly, a site selection evaluation index system is constructed from four aspects of economy, geography, environment and society using the literature review method and the Delphi method, and the weights of each index are comprehensively determined by combining the Decision-making Trial and Evaluation Laboratory (DEMATEL) and the entropy weight method (EW). Then, prospect theory and the multi-criteria compromise solution ranking method (VIKOR) are introduced to rank the potential options and determine the best site.

China is used as a case study, and the robustness and reliability of the methodology are demonstrated through sensitivity analysis, comparative analysis and ablation experiment analysis. This paper aims to provide a useful reference for WPP siting research.

In this paper, DEMATEL and EW are used to determine the weights of indicators, which overcome the disadvantage of single assignment. Prospect theory and VIKOR are combined to construct a decision model, which also considers the attitude of the decision-maker and the compromise solution of the decision result. For the first time, this framework is applied to WPP siting research.

This paper aims to introduce a novel structure for the physical internet (PI)–enabled sustainable supplier selection and inventory management problem under uncertain environments.

To address hybrid uncertainty both in the objective function and constraints, a novel interactive hybrid multi-objective optimization solution approach combining Me-based fuzzy possibilistic programming and interval programming approaches is tailored.

Various numerical experiments are introduced to validate the feasibility of the established model and the proposed solution method.

Due to its interconnectedness, the PI has the opportunity to support firms in addressing sustainability challenges and reducing initial impact. The sustainable supplier selection and inventory management have become critical operational challenges in PI-enabled supply chain problems. This is the first attempt on this issue, which uses the presented novel interactive possibilistic programming method.

This paper investigates the interactional relationships between sustainable human resource management (SHRM) and organizational performance (OP). Sustainable HRM is an approach that links HRM and sustainability. These studies focused on integrating HR with sustainable developments, such as economic and social aspects, in favour of focusing on the environmental aspect. Organizational change is an ongoing process that has to be managed effectively to keep the change in place for a long time.

A framework was offered to estimate the cause-and-effect relation of the SHRM and OP factors. Data is gathered from professionals from various pharmaceutical industries. This study applied two methods, Fuzzy AHP and DEMATEL Type II. These techniques are used to understand the cause-and-effect factors and their interactions.

It was observed from the findings that the factor of SHRM, such as Social Justice (F2), Green Job Design (F5), Green Training (F6) and Implementation of Green Policy (F8), was the most critical for the pharmaceutical sector that effects Financial performance (F13), Customer Satisfaction (F15) and Market performance (F14). Pharmaceutical firms ought to coordinate public health advocacy efforts, engage in healthcare initiatives and provide financial support for environmentally friendly efforts that improve social and economic conditions.

For this sustainability, managers concentrate on creating an environment that is healthy and acceptable, and they work hard to mitigate the impact of natural factors and repair damage done to the environment; it is essential to move towards sustainable development to resolve environmental problems. Improving HR efficiency is among essential HRM responsibilities, as they expand the knowledge base of the workforce, enhance human capital, and eventually create valuable intangible assets and promote and encourage sustainable pharmaceutical products for some years.

This research paper has presented exclusive worth to the SHRM and organizational performance literature as it employs fuzzy FAHP and DEMATEL type 2. There is less research on SHRM in the pharmaceutical sector with these factors. In addition, FAHP and TYPE 2 DEMATEL are used in very few researches on SHRM approaches.

Developing a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.

Due to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021–2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).

This research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes ($100,000-$300,000, $300,000-$500,000, $500,000-$1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.

The current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.

This research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.

Social engineering is crucial in today’s digital landscape. As technology advances, malicious individuals exploit human judgment and trust. This study explores how age, education and occupation affect individuals’ awareness, skills and perceptions of social engineering.

A quantitative research approach was used to survey a diverse demographic of Egyptian society. The survey was conducted in February 2023, and the participants were sourced from various Egyptian social media pages covering different topics. The collected data was analyzed using descriptive and inferential statistics, including independent samples t-test and ANOVA, to compare awareness and skills across different groups.

The study revealed that younger individuals and those with higher education tend to research social engineering more frequently. Males display a higher level of awareness but score lower in terms of social and psychological consequences as well as types of attacks when compared to females. The type of attack cannot be predicted based on age. Higher education is linked to greater awareness and ability to defend against attacks. Different occupations have varying levels of awareness, skills, and psychosocial consequences. The study emphasizes the importance of increasing awareness, education and implementing cybersecurity measures.

This study’s originality lies in its focus on diverse Egyptian demographics, innovative recruitment via social media, comprehensive exploration of variables, statistical rigor, practical insights for cybersecurity education and diversity in educational and occupational backgrounds.

The gender pay gap is a well-documented phenomenon in labor economics. Based on the 2018 Structure of Earnings Survey (SES), the authors estimate the impact of observable characteristics on the gender pay gap in Visegrad Group countries and provide policy recommendations on reducing the gender pay gap.

The Oaxaca-Blinder decomposition is applied to estimate the values of explained and unexplained parts of the gender pay gap. Gender pay gap in unadjusted as well as adjusted form is estimated using data on the individual level.

The results show that unadjusted gender pay gap proved to be stable at more than 20%. The authors found evidence that education widens gender pay gap implying that men have higher returns on education than women. Tertiary education proved to be the highest contributor to widening of gender pay gap. Results also show that there is strong sectoral and occupational segregation. Decomposition proved that only 21% of gender pay gap could be explained by observed characteristics. The unexplained part showed negative values, meaning women would have higher wages, if they had characteristics like men.

Structure of Earnings Survey data are published every four years; therefore the authors’ dataset from year 2018 might not completely reflect today's reality. Unfortunately, newer data are note available yet. Second, Structure of Earning Survey data do not contain variables representing social factors of respondents like marital status, number of children or labour market absence due to birth or childcare. Third, data used for this study do not contain firms that have less than 10 employees; therefore, considerable portion of the labour market is omitted.

Results of this study will help policymakers understand the roots and causes of the gender pay gap in Visegrad Group countries but addressing this issue requires further research.

This study aims to explore whether globalization and technology are harmful to health using a global panel data set of 52 countries over the period 1990–2019.

The study focused on four continents: Africa, the Americas, Asia/Oceania and Europe. The authors used four advanced econometric methodologies, which include the standard panel fixed effect (FE), Arellano–Bover/Blundell–Bond dynamic panel, Hausman–Taylor specification and two-stage least squares (FE-2SLS)/Lewbel-2SLS approaches.

The empirical evidence highlights the significance of globalization and technology in promoting global health. The findings suggest that globalization has various impacts on global health indicators and that technology is useful in tracking, monitoring and promoting global health. In addition, the empirical evidence indicates that a truly health-centred process of globalization and technological innovation can only be realized by ensuring that the interests of countries and vulnerable populations to health risks are adequately considered in international decision-making regarding global economic integration.

The authors suggest that achieving the aspiration of global health will entail the use of globalization and information technology to extend human activities and provide equal access to global health.

The purpose of this study is to assess the performance of fuel blends containing ethanol and gasoline in spark ignition engines. The aim is to explore alternative fuels that can enhance performance while minimizing or eliminating adverse environmental impacts, particularly in the context of limited fossil fuel availability and the need for sustainable alternatives.

The authors used the Ricardo Wave software to evaluate the performance of fuel blends with varying ethanol content (represented as E0, E10, E25, E40, E55, E70, E85 and E100) in comparison to gasoline. The assessment involved different composition percentages and was conducted at various engine speeds (1,500, 3,000, 4,500 and 6,000 rpm). This methodology aims to provide a comprehensive understanding of how different ethanol-gasoline blends perform under different conditions.

The study found that, across all fuel blends, the highest brake power (BP) and the highest brake-specific fuel consumption (BSFC) were observed at 6,000 rpm. Additionally, it was noted that the presence of ethanol in gasoline fuel blends has the potential to increase both the BP and BSFC. These findings suggest that ethanol can positively impact the performance of spark-ignition engines, highlighting its potential as an alternative fuel.

This research contributes to the ongoing efforts in the automotive industry to find sustainable alternative fuels. The use of Ricardo Wave software for performance assessment and the comprehensive exploration of various ethanol-gasoline blends at different engine speeds add to the originality of the study. The emphasis on the potential of ethanol to enhance engine performance provides valuable insights for motor vehicle manufacturers and researchers working on alternative fuel solutions.

This study investigates the factors that influence citizens trust in public leaders [i.e. presidents, members of parliament (MPs) and local government leaders (LGs)] in 34 countries in Africa between 2019 and 2021.

Individual-level data with a sample size of 48,084 was obtained from the Afro-Barometer round 8 survey only and analyzed using multivariate binary logistic regression.

Several important and intriguing observations were made from this analysis: (1) the performance of public leaders influences citizens trust in their leaders; (2) the perceived corruption of public leaders and civil servants and the level of corruption influence citizens trust in public leaders; (3) perceived neighborhood problems (i.e. fear of violence, fear of terrorism and service delivery) influence citizens trust in their public leaders and (4) the socio-demographic characteristics of citizens (i.e. age, religion, education, location, employment and political party affiliation) influence citizens trust in their public leaders.

This study is exceptional in two ways: (1) it examines and compares citizens trust in public leaders across different levels, i.e. presidents, MPs and LGs in Africa and (2) it examines and compares the factors influencing citizens trust in public leaders in Africa comparatively.

This paper examines the effects and root causes of client influence within the valuation profession in Kenya.

This study adopted a mixed research design incorporating a survey and experiment of registered and practising valuers in Kenya and interviews of key informants from registered and practising valuers, valuers' clients (commercial banks) and professional bodies.

The study found that client influence negatively impacts the valuation profession, contributing to inaccurate valuation outcomes, and it exists because of the valuation environment, represented by limited and unreliable information in Kenya and many other developing countries.

This study makes a critical contribution to the empirical literature as it introduces new insights into the impacts and causes of client influence by demonstrating how the valuation environment, characterised by poor information, contributes to client influence in Kenya, which is typical of many other developing countries.