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The paper aims to study the influence of Industry 4.0 technologies on the business sustainability of private hospitals by focusing on the mediating role of total quality management practices in private hospitals in the West Bank.

Data from the top and middle managers of private hospitals in the West Bank regions of Palestine were collected through a questionnaire assessed for validity and reliability. Furthermore, we used the structural equation modelling technique of partial least squares for the data analysis.

The findings confirm that Industry 4.0 technologies do not affect business sustainability. The findings also emphasise the association between Industry 4.0 technologies and total quality management philosophy, and total quality management completely mediates the relationship between Industry 4.0 and business sustainability.

This study provides practical implications for achieving the hospital sector's sustainability by merging Industry 4.0 technologies with total quality management practices, which provides valuable insights into the hospital's policies and practices and individuals vital to data exchange and policy enforcement within the sustainability of organisations.

This study is one of the first to investigate the combined effects of Industry 4.0 technologies, total quality management and business sustainability in the healthcare industry following the COVID-19 outbreak. This research is one of few empirical works exploring the interface between Industry 4.0 technologies and total quality management in developing countries, specifically Palestine.

The study evaluates whether an application of Ind-AS that converged with IFRS in India has enhanced financial reporting quality (FRQ) and how that is reflected in financial performance (FP).

Design/methodology/approach: The study uses discretionary accruals (DACC) to measure FRQ. In addition, it uses ordinary least square (OLS) regression to examine the association between Corporate Governance attributes, FRQ, and financial performance for a sample of 24 textile companies from 2010 to 2021.

The results indicate that adopting IFRS has a role in monitoring CG attributes to enhance FRQ; this means the financial reporting qualit improves somewhat with some CG attributes under Ind-AS. In addition, the results demonstrate that financial reporting quality positively influences FP.

There are significant effects on authorities and decision-makers. The findings from this research can benefit lawmakers by providing Ind-AS policy enforcement with more consideration. The results are also helpful for policymakers who want to improve CG and need proof of the significance of high FRQ in this respect.

Given the dearth of research on FRQ in India, the study extends prior literature on FRQ by examining the quality of financial reporting according to the transformation to IFRS in Indian textile firms. The theoretical contribution of the current study is the testing of agency theory towards practices of corporate governance mechanisms on FRQ and FP in the context of the textile sector.

This study aims to focus on sustainable supplier selection in a construction company considering a new multi-criteria decision-making (MCDM) method based on dominance-based rough set analysis. The inclusion of sustainability concept in industrial supply chains has started gaining momentum due to increased environmental protection awareness and social obligations. The selection of sustainable suppliers marks the first step toward accomplishing this objective. The problem of selecting the right suppliers fulfilling the sustainable requirements is a major MCDM problem since various conflicting factors are underplay in the selection process. The decision-makers are often confronted with inconsistent situations forcing them to make imprecise and vague decisions.

This paper presents a new method based on dominance-based rough sets for the selection of right suppliers based on sustainable performance criteria relying on the triple bottom line approach. The method applied has its distinct advantages by providing more transparency in dealing with the preference information provided by the decision-makers and is thus found to be more intuitive and appealing as a performance measurement tool.

The technique is easy to apply using “jrank” software package and devises results in the form of decision rules and ranking that further assist the decision-makers in making an informed decision that increases credibility in the decision-making process.

The novelty of this study of its kind is that uses the dominance-based rough set approach for a sustainable supplier selection process.

Today’s one of the most important difficulties is tackling climate change and its effects on the environment. The Paris Agreement states that nations must balance the amount of greenhouse gases they emit and absorb until 2050 to contribute to the mitigation of greenhouse gases and to support sustainable development. According to the agreement, each country must determine, plan and regularly report on its contributions. Thus, it is important for the countries to predict and analyze their net zero performances in 2050. Therefore, the aim of this study is to evaluate European Continent Countries' net zero performances at the targeted year.

The European Continent Countries that ratified the Paris Agreement are specified as decision making units (DMUs). Input and output indicators are specified as primary energy consumption, freshwater withdrawals, gross domestic product (GDP), carbon-dioxide (CO₂) and nitrous-oxide (N₂O) emissions. Data from 1980 to 2019 are obtained and forecasted using autoregressive integrated moving average (ARIMA) until 2050. Then, the countries are clustered based on the forecasts of primary energy consumption and freshwater withdrawals using k-means algorithm. As desirable and undesirable outputs arise simultaneously, the performances are computed using Pure Environmental Index (PEI) and Mixed Environmental Index (MEI) data envelopment analysis (DEA) models.

It is expected that by 2050, CO₂ emissions of seven countries remain constant, N₂O emissions of seven countries remain stable and five countries’ both CO₂ and N₂O emissions remain constant. While it can be seen as success that many countries are expected to at least stabilize one emission, the likelihood of achieving net zero targets diminishes unless countries undertake significant reductions in emissions. According to the results, in Cluster 1, Turkey ranks last, while France, Germany, Italy and Spain are efficient countries. In Cluster 2, the United Kingdom ranks at last, while Greece, Luxembourg, Malta and Sweden are efficient countries.

In the literature, generally, CO₂ emission is considered as greenhouse gas. Moreover, none of the studies measured the net-zero performance of the countries in 2050 employing analytical techniques. This study objects to investigate how well European Continent Countries can comply with the necessities of the Agreement. Besides CO₂ emission, N₂O emission is also considered and the data of European Continent Countries in 2050 are estimated using ARIMA. Then, countries are clustered using k-means algorithm. DEA models are employed to measure the performances of the countries. Finally, forecasts and models validations are performed and comprehensive analysis of the results is conducted.

An efficient e-waste management system is developed, aided by deep learning techniques. Here, a smart bin system using Internet of things (IoT) sensors is generated. The sensors detect the level of waste in the dustbin. The data collected by the IoT sensor is stored in the blockchain. Here, an adaptive deep Markov random field (ADMRF) method is implemented to determine the weight of the wastes. The performance of the ADMRF is boosted by optimizing its parameters with the help of the improved corona virus herd immunity optimization algorithm (ICVHIOA). Here, the main objective of the developed ADMRF-based waste weight prediction is to minimize the root mean square error (RMSE) and mean absolute error (MAE) rate at the time of testing. If the weight of the bins is more than 80%, then an alert message will be sent to the waste collector directly. Optimal route selection is carried out using the developed ICVHIOA for efficient collection of wastes from the smart bin. Here, the main objectives of the optimal route selection are to reduce the distance and time to minimize the operational cost and the environmental impacts. The collected waste is then considered for recycling. The performance of the implemented IoT and blockchain-based smart dustbin is evaluated by comparing it with other existing smart dustbins for e-waste management.

The developed e-waste management system is used to collect the waste and to avoid certain diseases caused by the dumped waste. Disposal and recycling of the e-waste is necessary to decrease pollution and to manufacture new products from the waste.

The RMSE of the implemented framework was 33.65% better than convolutional neural network (CNN), 27.12% increased than recurrent neural network (RNN), 22.27% advanced than Resnet and 9.99% superior to long short-term memory (LSTM).

The proposed E-waste management system has given an enhanced performance rate in weight prediction and also in optimal route selection when compared with other conventional methods.

This study aims to address the inherent uncertainties within closed-loop supply chain (CLSC) networks through the application of a multi-objective approach, specifically focusing on the optimization of integrated production and transportation processes. The primary purpose is to enhance decision-making in supply chain management by formulating a robust multi-objective model.

In dealing with uncertainty, this study uses Pythagorean fuzzy numbers (PFNs) to effectively represent and quantify uncertainties associated with various parameters within the CLSC network. The proposed model is solved using Pythagorean hesitant fuzzy programming, presenting a comprehensive and innovative methodology designed explicitly for handling uncertainties inherent in CLSC contexts.

The research findings highlight the effectiveness and reliability of the proposed framework for addressing uncertainties within CLSC networks. Through a comparative analysis with other established approaches, the model demonstrates its robustness, showcasing its potential to make informed and resilient decisions in supply chain management.

This study successfully addressed uncertainty in CLSC networks, providing logistics managers with a robust decision-making framework. Emphasizing the importance of PFNs and Pythagorean hesitant fuzzy programming, the research offered practical insights for optimizing transportation routes and resource allocation. Future research could explore dynamic factors in CLSCs, integrate real-time data and leverage emerging technologies for more agile and sustainable supply chain management.

This research contributes significantly to the field by introducing a novel and comprehensive methodology for managing uncertainty in CLSC networks. The adoption of PFNs and Pythagorean hesitant fuzzy programming offers an original and valuable approach to addressing uncertainties, providing practitioners and decision-makers with insights to make informed and resilient decisions in supply chain management.

This study explores the implementation of personalised information access, driven by machine learning, in Nigerian public libraries. The purpose of this paper is to address existing challenges, enhance the user experience and bridge the digital divide by leveraging advanced technologies.

This study assesses the current state of Nigerian public libraries, emphasising challenges such as underfunding and lack of technology adoption. It proposes the integration of machine learning to provide personalised recommendations, predictive analytics for collection development and improved information retrieval processes.

The findings underscore the transformative potential of machine learning in Nigerian public libraries, offering tailored services, optimising resource allocation and fostering inclusivity. Challenges, including financial constraints and ethical considerations, are acknowledged.

This study contributes to the literature by outlining strategies for responsible implementation and emphasising transparency, user consent and diversity. The research highlights future directions, anticipating advancements in recommendation systems and collaborative efforts for impactful solutions.

The purpose of this paper is to introduce an aid for teaching transverse flux permanent magnet machines (TFPMs) with emphasis on their torque production.

The Lorentz force law is applied to fictitious current loops emulating the permanent magnets (PMs) mounted on the rotor according to different arrangements; the air gap flux density is created by the armature current.

Implemented in a master lecture on special AC machines, the proposed approach has revealed a renewed interest in electromagnetic fundamentals for pedagogical purposes. It makes simple the explanation of the principle of operation of a class of AC machines reputed by the complexity of their magnetic circuits. The latter incorporates axially stacked decoupled sub-circuits, one per phase generating alternating magnetic fields. More specifically, there is common air gap, shared by the machine phases, in which a rotating magnetic field is created by the superposition of the PM contribution and the armature one.

Accounting for the complexity of the magnetic circuits and the three-dimensional (3D) flux paths characterizing TFPMs, a 3D finite element analysis (FEA) is required for the validation of the analytical predictions. Nevertheless, such a 3D FEA validation is far from being obvious to be carried on within a master lecture.

While the basis of Lorentz forces resulting from fictitious current loops emulating PMs has been considered in some referenced papers, its simple and pedagogical application to assess the torque production of several TFPM concepts represents the added value of the present paper.

This study aims to examine the effect of Muslim consumers’ religiosity on their brand verdict regarding clothing brands, through the mediating role of decision-making style, brand status and brand attitude.

Structural equation modeling was used to analyze the data collected from 200 clothing buyers in Mashhad, one of Iran’s religious cities.

The results indicate that intrapersonal religiosity, compared to interpersonal religiosity, has a more significant effect on Muslim consumers’ decision-making styles, and different decision-making styles of Muslim consumers affect their brand verdict through brand status and brand attitude.

The research sample consists solely of respondents from the Islamic religion. Therefore, the impact of religiosity might differ among individuals from other religions, such as Christianity and Judaism.

This study’s findings are crucial for clothing brands, both national and international, that cater to the Muslim customers’ market. They need to consider the degree of religiosity when segmenting and targeting their market. This study shows that clothing brand marketers can best influence the brand verdict of Muslim consumers by targeting those with a brand-loyal decision-making style, focusing on their religious beliefs.

To achieve success in Iran’s Muslim market, marketers must consider their consumers’ religious beliefs and tailor their marketing plans accordingly. This study aims to investigate the impact of religiosity on consumer behavior toward brands in Iran’s Muslim market.

The study aims to identify a model of commercializing entrepreneurial ideas in information and communication technology (ICT) knowledge-based companies.

A mixed method has been used in the research. The participants of the qualitative part were 15 key informants selected by sampling method purposefully and theoretically, while a sample of 205 experts was randomly chosen for the quantitative part. Data collection was completed through a semistructured interview in the qualitative part and by a researcher-made questionnaire in the quantitative part. The reliability of the research was confirmed by Cronbach’s alpha. The validity of the qualitative and quantitative parts was approved, respectively, by the criteria of Corbin and Strauss (2008) and by the content validity. Data analysis was done in the qualitative part through open, axial and selective coding, while in the quantitative part through partial least squares structural equation modeling (PLS-SEM) and adaptive neuro-fuzzy inference systems (ANFIS).

The commercialization model of ICT entrepreneurial ideas was depicted by the paradigmatic version of Corbin and Strauss (2008). The model has been consisted of six sectors as follows: causal conditions (including stimuli of science and technology parks, interests and motivation of managers of ICT knowledge-based company and environmental stimuli), contextual conditions (including skills and abilities of managers of ICT knowledge-based company, status of ICT knowledge-based company and enabling and facilitating legal framework), intervening conditions (including the complex nature of the ICT industry, science and technology parks’ support of companies, facilities and equipment for commercialization of ICT entrepreneurial ideas and economic system stability), strategies (including marketing research, planning and feasibility study of ICT entrepreneurial idea, design and production of ICT product and release and supply of ICT product), consequences (successful commercialization of ICT entrepreneurial ideas in the post-COVID-19 era) and the central phenomenon (ICT entrepreneurial ideas: commercialization in the post-COVID-19 era). Then, the main factors were confirmed through PLS-SEM and ANFIS. Among the factors, interests and motivation of managers of ICT knowledge-based companies, status of ICT knowledge-based companies, facilities and equipment for commercialization of ICT entrepreneurial ideas and release and supply of ICT products were identified as the most influential factors.

The model can help solve the challenges of managers and policymakers to commercialize ICT entrepreneurial ideas. Therefore, innovative production will increase, value will be created for the beneficiaries and economic, social and political growth will occur in the post-Corona era.

Commercialization of ICT entrepreneurial ideas has the potential to affect many aspects of economic and societal activities in the society such as GDP growth, employment, productivity, poverty alleviation, quality of life and education.

The research includes innovation in presenting a multidimensional commercialization model based on an entrepreneurial perspective in the special field of ICT with a mixed approach including grounded theory, PLS-SEM and ANFIS in ICT knowledge-based companies. But the most important innovation of the study is related to the findings. The main categories, subcategories and concepts of the research have been presented in the form of a theory entitled “ICT entrepreneurial ideas: commercialization in the post-COVID-19 era.”