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This study aims to quantify and analyse the dynamics of land use and land cover (LULC) changes over three decades in the rapidly urbanizing city of Abha, Saudi Arabia, and to assess urban growth using Morphological Spatial Pattern Analysis (MSPA).

Using the Support Vector Machine (SVM) classification in Google Earth Engine, changes in land use in Abha between 1990 and 2020 are accurately assessed. This method leverages cloud computing to enhance the efficiency and accuracy of big data analysis. Additionally, MSPA was employed in Google Colab to analyse urban growth patterns.

The study demonstrates significant expansion of urban areas in Abha, growing from 62.46 km² in 1990 to 271.45 km² in 2020, while aquatic habitats decreased from 1.36 km² to 0.52 km². MSPA revealed a notable increase in urban core areas from 41.66 km² in 2001 to 194.97 km² in 2021, showcasing the nuanced dynamics of urban sprawl and densification.

The novelty of this study lies in its integrated approach, combining LULC and MSPA analyses within a cloud computing framework to capture the dynamics of city and environment. The insights from this study are poised to influence policy and planning decisions, particularly in fostering sustainable urban environments that accommodate growth while preserving natural habitats. This approach is crucial for devising strategies that can adapt to and mitigate the environmental impacts of urban expansion.

The different performance tests were conducted on diesel engine compression ignition (CI) mode and CRDi engine.

The CI engine was suitably modified to CRDi engine with Toroidal re-entrant combustion chamber (TRCC) and was run in dual-fuel (DF) mode. Hydrogen (H₂) was supplied at different flow rates during the suction stroke, and 0.22 Kg/h of hydrogen fuel flow rate (HFFR) was found to be optimum. Diesel and biodiesel were used as pilot fuels. The CRDi engine with DF mode was run at various injection pressures, and 900 bar was found to be optimum injection pressure (IP) with 10o before top dead center (bTDC) as fuel injection timing (IT).

These operating engine conditions increased formation of oxides of nitrogen (NOx), which were reduced by exhaust gas recycle (EGR). With EGR of 15%, CRDi engine resulted in 12.6% lower brake thermal efficiency (BTE), 5.5% lower hydrocarbon (HC), 7.7% lower carbon monoxide (CO), 26% lower NOx at 80% load as compared to the unmodified diesel engine (CI mode).

The current research is an effort to study and evaluate the performance of CRDi engine in DF mode with diesel-H₂ and BCPO-H₂ fuel combinations with TRCC.

Adopting new technologies to improve supply chain activities and processes is essential due to increasingly complex and dynamic business environments. Particularly in the pharmaceutical industry, high-quality standards must be met, requiring transparency and visibility in the supply chain. This research aims at investigating the implementation of blockchain technology in the supply chain of an Egyptian pharmaceutical company.

The research applies a single case-study approach building on the theoretical underpinnings of transaction cost economics. Twenty-five semistructured interviews were conducted with pharmacies and employees of the case company to identify the blockchain technologies' potential for pharmaceutical supply in Egypt. Further analyzing the frequencies of the codes, the authors elaborate on specific relationships between the observed practices.

The research revealed the potential benefits of adopting blockchain technology. Transaction costs are indeed positively impacted by reduced contracting costs, processing costs and lead times, also ensuring the safe delivery of medications. However, the findings also highlight obstacles related to running costs, awareness and company culture. Regarding supply chain governance, blockchain technology can enhance collaboration within the supply chain as well as with important stakeholders.

Insufficient management of pharmaceutical supply chains (PSC) may affect a company's reputation but also disrupt the patient's healing process due to temperature damage and counterfeit medicines. Blockchain governance, in this vein, can ensure a safer and more reliable supply of pharmaceutical products. For intraorganizational purposes, however, cloud solutions, barcoding and generally digital platforms are rated more frequently than blockchain solutions.

The present study contributes to an advanced understanding how blockchain technology supports PSC, particularly in an emerging country context like Egypt. It thereby confirms and extends previous research as well as adds to the theoretical underpinnings of digitalized supply chains.

Joule heating effect is a pervasive phenomenon in electro-osmotic flow because of the applied electric field and fluid electrical resistivity across the microchannels. Its effect in electro-osmotic flow field is an important mechanism to control the flow inside the microchannels and it includes numerous applications.

This research article details the numerical investigation on alterations in the profile of stream wise velocity of simple Couette-electroosmotic flow and pressure driven electro-osmotic Couette flow by the dynamic viscosity variations happened due to the Joule heating effect throughout the dielectric fluid usually observed in various microfluidic devices.

The advantages of the Joule heating effect are not only to control the velocity in microchannels but also to act as an active method to enhance the mixing efficiency. The results of numerical investigations reveal that the thermal field due to Joule heating effect causes considerable variation of dynamic viscosity across the microchannel to initiate a shear flow when EDL (Electrical Double Layer) thickness is increased and is being varied across the channel.

This research work suggest how joule heating can be used as en effective mechanism for flow control in microfluidic devices.

This paper examines the financial ratios that may have a significant effect on the efficiency in Malaysian listed companies. Nine financial ratios measure seven variables which are firm visibility, tangibility, working capital, leverage, liquidity, productivity and profitability.

Data are collected from 108 public listed companies in Malaysia. The data extracted from companies' annual reports for three years 2012–2014. STATA software analysis is used to examine these relationships.

The results show each of tangibility and liquidity have negative relationships with efficiency ratio. In against of that, profitability, working capital and productively positively link to efficiency. Leverage which is measured by two ratios – Debt ratio and Debt equity ratio – shows mix results. Debt ratio shows a positive but not significant relationship with efficiency ratio and Debt equity ratio shows a negative significant relationship with efficiency ratio.

The results benefit companies, investors, economists and governments regulators in Malaysia-to understand the efficiency determinants, so help to make the right decision to enhance the efficiency level in companies which leads to enhance the amount of investments which in turn, enhance the country's economy in general.

This study differs than previous studies number of aspects: first the study covers a three years' period between 2012 and 2014, this period presents the movement of Malaysian current into depreciation with more than 45 percent of its value. Second, in the Malaysia context, this study examines new variables such as firm visibility, tangibility, and productivity. Third, the results of this study will help managers, shareholders, investors, regulators and other parties to make right decisions that will enhance the level of firm efficiency which enhances the investments and the economy of Malaysia.

Nurses' turnover intention has become a major issue in developing countries with high power distance cultures. Therefore, the authors attempt to investigate how turnover intention among nurses' can be reduced through paternalistic leadership (PL). The authors further investigate the mediating role of job satisfaction between the associations of benevolent, moral and authoritarian dimensions of PL with turnover intention. Finally, the authors examined perceived organizational support (POS) as a conditional variable between job satisfaction and turnover intention.

The authors collected data from 374 nurses working in public and private hospitals of high power distance culture using a questionnaire-based survey on convenience basis.

Structural equation modeling confirms that benevolent and moral dimensions of PL positively affect nurses' job satisfaction which helps them reduce their turnover intention. While the authoritarian dimension of PL negatively affects job satisfaction to further enhance their turnover intention. In addition, the authors noted POS as a conditional variable to trigger the negative effect of job satisfaction on turnover intention.

The authors used a cross-sectional design to collect responses and ensured the absence of common method variance through Harman's Single factor test.

This study identified the mechanism (job satisfaction and POS) through which benevolent, moral and authoritative dimensions of PL predict turnover intention among nurses working in high power distance culture.

護士有離職意向，在擁有高權力距離文化的發展中國家，已成為一個重大的問題。因此，我們擬探討如何可以透過採用家長式領導、把護士離職的意欲減低，繼而研究工作滿足感，在離職意向與家長式領導中仁慈、道德和獨裁這三個層面的關係中所起的中介作用。最後，我們就組織支持感，作為是工作滿足感與離職意向之間的一個條件變數，進行了研究。

本研究透過採用在便利的基礎上進行的問卷調查，從374名在高權力距離文化的公營和私營醫院內工作的護士取得數據，進行分析。

結構方程模型證實了家長式領導中的仁慈和道德這兩個層面，會對可減低護士離職意欲的工作滿足感，產生積極的影響。家長式領導中的獨裁層面、則會對護士的工作滿足程度產生負面的影響，繼而增強其離職意欲。而且，我們確認了組織支持感是一個會增強工作滿足感與離職意向之間負相聯的條件變數。

我們以橫斷面的設計法來收集回應，並透過採用哈曼 (Harman) 的單因素檢定法，來確保共同方法變異不會存在。

本研究確定了一個 (工作滿足感與組織支持感) 機制，透過這機制，家長式領導中的仁慈、道德和獨裁這三個層面可預測於高權力距離文化工作的護士的離職意向。

As publishers and academia swiftly head towards e-textbooks, it is important to understand how students feel about using e-textbooks as a primary learning tool. This paper discusses results of a small-scale study looking into how a group of language learners view and use e-textbooks as learning tools in ESL classrooms. The paper concludes by offering teaching implications that could ease integrating e-textbooks in language classrooms in a more effective and efficient manner.

This paper aims to investigate the influence of air and rail transportation on pollution in Turkey from 1970 to 2020.

Fourier Autoregressive Distributive Lags (ADL) and Fourier Fractional ADL cointegration tests (Banerjee et al., 2017; Ilkay et al., 2021) are employed to analyze the relationship be-tween the variables. Cointegration tests that take into account soft transitions under structural changes are implemented. Structural change issues are crucial for this topic since the changes in countries’ environmental policies and transportation habits are shaped by the decisions taken in relation to environmental regulations. Finally, for robustness purposes, we tested the estimated equation with a completely different methodology. Thus, a Machine Learning (ML) analysis is conducted, through a Ridge Regression (RR).

The findings obtained by applying Fourier Autoregressive Distributive Lags (FADL) and Fourier Fractional ADL cointegration tests, which can control for structural changes, reveal the existence of a long-term relationship between the variables. In addition, FMOLS estimates emphasize that economic growth and air transport can lead to increased pollution in the long run, while rail transport reduces it. Moreover, the statistically significant trigonometric terms indicate the existence of a smooth structural change among the variables. Robustness checks are performed through a Machine Learning (ML) analysis, which roughly confirms the previous results.

To our knowledge, existing research in Turkey focuses mainly on road transport, while the impact of rail and air transport on pollution has not yet been investigated. As such, this study will be a significant addition to the academic literature.

Since Industry 4.0 (I4.0) is a new idea in Bangladesh, this study supports I4.0 adoption. Companies struggle to implement I4.0 and fully profit from the fourth industrial revolution’s digital transformation due to its novelty. Although barriers to I4.0 adoption are thoroughly studied, the literature has hardly examined the many aspects that are crucial for I4.0 adoption in Bangladesh’s Ready-Made Garment (RMG) industry. So, the purpose of this study is to investigate the barriers of adopting I4.0 in relation to Bangladesh’s RMG industries to enhance the adoption of I4.0 by developing a framework. Ultimately, the goal of this research is to improve the adoption of I4.0 in Bangladesh.

Through a comprehensive analysis of the existing research, this paper aims to reveal the barriers that must be overcome for I4.0 to be adopted. For evaluating those barriers, a decision analysis framework based on the combination of Delphi technique and Decision-Making Trial and Evaluation Laboratory (DEMATEL) method has been developed. The use of DEMATEL has led to a ranking model of those barriers and a map of how the barriers are connected to each other.

The findings reveal that “I4.0 training”, “Lack of Motivation” and “Resistance to Change” are the most significant barriers for adopting Industry 4.0 in RMG sector of Bangladesh based on their prominence scores.

These findings will help the people who make decisions in the RMG industry of Bangladesh, such as company owners, managers and the executive body, come up with a plan for putting I4.0 practices into place successfully. The decision-making framework developed in this research can be utilized by the RMG industry of Bangladesh and other similar industries in developing countries to figure out how important each barrier is for them and how to get rid of them in order of importance.

As far as the authors are aware, there has not been a comprehensive study of the barriers inhibiting the adoption of I4.0 within the scope of Bangladeshi RMG industry. This work is the first to uncover these barriers and analyze them using the combination of Delphi technique and DEMATEL.

Reducing CO₂ emissions from transportation is crucial for achieving carbon neutrality in the Gulf Cooperation Council (GCC) countries by 2060 or earlier. This study aimed at analyzing transportation-related energy consumption and CO₂ emissions, along with their determinants and mitigation measures planned to achieve carbon neutrality in GCC countries.

To achieve the study objectives, the pressure-state-response (PSR) framework was utilized. Various methods were employed within the PSR framework, including econometric analysis using EViews, energy modeling using the low emissions analysis platform (LEAP) and content analysis of relevant policy and national documents using NVivo.

The results indicated that population and economic growth, along with increased fuel consumption, have led to a growth in transportation-related energy use and CO₂ emissions in the GCC countries. Per capita transportation-related CO₂ emissions in the GCC countries are higher than those of several countries. To achieve carbon-neutral transportation, approximately 1.8 bn metric tons of CO₂ emissions need to be avoided by 2060 or earlier. Strategies related to fuel alternatives, vehicle technologies and mass transit have been planned to reduce transportation-related CO₂ emissions in the GCC countries.

This study employed a holistic approach to analyze transportation-related energy use and CO₂ emissions in the GCC countries. It provides several policy implications and highlights the urgent need for policy innovations to achieve transformative change in the transportation sectors of the GCC countries.