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This paper reviews and analyses renewable energy options, namely underground thermal, solar, wind and marine wave energy, in seaport cargo terminal operations.

Four renewable energy options that are deployed or tested in different ports around the world are qualitatively examined for their overall implementation potential and characteristics, and their cost and benefits. An application to the port of Singapore is discussed.

Geophysical conditions are key criteria in assessing renewable energy options. In the case of Singapore, solar power is the only suitable renewable energy option.

Being a capital-intensive establishment with high intensities of cargo operations, seaports usually involve a high level of energy consumption. The study of renewable energy options contributes to seaport sustainability.

A key recommendation is to implement a smart energy management system that enables the mixed use of renewable energy to match energy demand and supply optimally and achieve higher energy efficiency.

The use of renewable energy as an eco-friendlier energy source is underway in various ports. However, there is almost no literature that analyses and compares various renewable energy options potentially suitable for cargo terminal operations in ports. This paper narrows the knowledge gaps.

We identify and further aggregate the most commonly engaged risk factors in dry port projects into dimensions. Noting the importance of developing a multi-perspective view of risk, we further assess the priority, interdependency and heterogeneity of the identified risk dimensions.

We identified 44 risk factors from the literature, which were aggregated via exploratory factor analysis (EFA) into 8 major risk dimensions. We employ a fuzzy-based decision-making trial and evaluation laboratory (DEMATEL) relationship map to articulate various relationships among the risk dimensions.

“Cost” emerged as the most important risk influencing the success of the dry port project, followed by “location,” “accessibility,” “infrastructural” and “operational,” which were also ranked prominently.

This study offers significant insight into the management of risk in dry port projects. By aggregating key risk factors into distinct dimensions, we develop a structured framework for effective risk assessment and management. The insights gleaned from the study extend globally, as it serves as a concrete knowledge base to understand potential barriers to successful dry port projects.

The purpose of this paper is to investigate the influence of environmental and cultural factors on the benchmarking implementation process and management control within organizations in the United Arab Emirates (UAE). By exploring the complex interplay of these factors, the study aims to uncover how environmental considerations and cultural dynamics shape the effectiveness and outcomes of benchmarking initiatives in the UAE's unique business environment. The research seeks to provide valuable insights for organizations in the UAE to optimize their benchmarking practices and enhance their overall performance and competitiveness.

The study adopts a mixed-methods approach, combining qualitative and quantitative methods to comprehensively explore the influence of environmental and cultural factors on benchmarking implementation and management control in the UAE. This study draws on the integration of two main theoretical perspectives: institutional theory and contingency theory. This is the first attempt to integrate these different frameworks in a single study. The study presents a case study of Emirates Industrial City (EIC), which has been recognized by global industries for boosting efficiency, cost control, quality and overall operations. The quality method known as benchmarking maximizes the potential for organizations to achieve optimal levels of production efficiency.

This paper provides compelling evidence that the benchmarking implementation process and management control in the UAE are significantly influenced by the complex interplay of environmental and cultural factors. By recognizing the importance of environmental sustainability and cultural values in guiding benchmarking practices, UAE organizations can optimize their performance and competitiveness. The findings contribute valuable insights to the existing literature, offering practical implications for UAE organizations seeking to leverage benchmarking as a strategic tool for growth and continuous improvement. The findings reveal that UAE organizations incorporating environmental considerations into benchmarking practices demonstrate a proactive approach to sustainability, aligning their goals with eco-friendly practices. Cultural influences, including a culture of collaboration and openness to external learning, contribute to successful benchmarking adoption and knowledge sharing. Moreover, the study highlights that the integration of benchmarking outcomes into the management control process positively correlates with organizational performance. UAE organizations that leverage benchmarking data for decision-making and performance evaluation exhibit higher levels of competitiveness and efficiency.

This paper has important implications for organizations in the UAE seeking to optimize their benchmarking practices and management control. The study's findings can guide organizations in aligning their benchmarking efforts with environmental sustainability goals and cultural values to enhance performance and competitiveness. Understanding the influence of environmental and cultural factors on benchmarking adoption and implementation allows organizations to foster a benchmarking culture that embraces knowledge sharing and learning. Managers can tailor their approaches to accommodate cultural nuances and enhance the effectiveness of benchmarking initiatives.

This paper contributes to the existing body of knowledge in several ways. Integrated approach: By examining the complex interplay of environmental and cultural factors, this study takes an integrated approach of institutional and contingency theories to understanding their influence on benchmarking implementation and management control. It offers a comprehensive view of how these factors interact to shape organizational practices and outcomes. UAE context: The study focuses specifically on the UAE, providing insights into benchmarking practices within the unique environmental and cultural context of the nation. This research addresses a gap in the literature by examining the influence of these factors in a distinct business environment.

According to extant Supply Chain Risk Management (SCRM) literature, manufacturing firms must align their choice of SC bridging or buffering strategies with their operating environment to achieve high plant performance and minimize SC disruption impacts. However, very few empirical studies have examined the relative performance of these strategies in dynamic industry environments. This study aims to address this research gap. This study also seeks to supplement the limited empirical research that has examined the empirical relationships between a firm’s Supply Base Complexity (SBC), the likelihood of SC disruptions, and plant performance.

This study uses data from a cross-sectional survey of 202 manufacturing firms in India. The data is analyzed, and the study hypotheses are tested using PLS path modeling and SPSS PROCESS Macro.

The study shows that increased SBC leads to an increased frequency of SC disruptions with a negative impact on plant performance. The study also finds that the firm’s implementation of SC bridging or buffering strategies effectively moderates this performance impact. However, the study results do not support the hypothesis that industry dynamism moderates the relative effectiveness of SC bridging or buffering strategies in mitigating the negative impact of SC disruptions.

The study adds to the limited empirical research examining the SC disruption risk associated with SBC and the resulting performance impact. It addresses a gap in extant research by evaluating the efficacy of SC bridging and buffering strategies in mitigating this performance impact in dynamic industry environments.

Modern human society has continuous advancements that have a negative impact on the quality of the air. Daily transportation, industrial and residential operations churn up dangerous contaminants in our surroundings. Addressing air pollution issues is critical for human health and ecosystems, particularly in developing countries such as Egypt. Excessive levels of pollutants have been linked to a variety of circulatory, respiratory and nervous illnesses. To this end, the purpose of this research paper is to forecast air pollution concentrations in Egypt based on time series analysis.

Deep learning models are leveraged to analyze air quality time series in the 6th of October City, Egypt. In this regard, convolutional neural network (CNN), long short-term memory network and multilayer perceptron neural network models are used to forecast the overall concentrations of sulfur dioxide (SO₂) and particulate matter 10 µm in diameter (PM₁₀). The models are trained and validated by using monthly data available from the Egyptian Environmental Affairs Agency between December 2014 and July 2020. The performance measures such as determination coefficient, root mean square error and mean absolute error are used to evaluate the outcomes of models.

The CNN model exhibits the best performance in terms of forecasting pollutant concentrations 3, 6, 9 and 12 months ahead. Finally, using data from December 2014 to July 2021, the CNN model is used to anticipate the pollutant concentrations 12 months ahead. In July 2022, the overall concentrations of SO₂ and PM₁₀ are expected to reach 10 and 127 µg/m³, respectively. The developed model could aid decision-makers, practitioners and local authorities in planning and implementing various interventions to mitigate their negative influences on the population and environment.

This research introduces the development of an efficient time-series model that can project the future concentrations of particulate and gaseous air pollutants in Egypt. This research study offers the first time application of deep learning models to forecast the air quality in Egypt. This research study examines the performance of machine learning approaches and deep learning techniques to forecast sulfur dioxide and particular matter concentrations using standard performance metrics.

Scaling up smart city infrastructure projects will require a large financial investment. Using public–private partnerships is one of the most effective ways to address budget constraints. Numerous factors have varying degrees of influence on the performance of Public private partnerships (PPP) projects; certain PPP factors are more crucial to the success of a smart city infrastructure project than others, and their influence can be greatly increased when they are fulfilled collectively. This study aims to find out what factors are unique to smart city PPP initiatives, as well as how these factors work together, so that successful smart city infrastructure PPP projects can be scaled up.

The methodology included three sequential stages: identifying the critical success factors (CSF) of PPP for smart cities based on an extensive literature review, collecting data from a sample of 90 PPP practitioners using a Likert scale questionnaire and estimating interrelationships among the CSF and their emergent clusters using structural equation modelling.

The best fit model developed in this study demonstrated the significance of each factor and their interrelationships within their categories in enhancing the performance of PPPs in smart city infrastructure projects. Five categories of critical success factors for PPPs in smart city infrastructure projects have been established: partnership and collaboration; financial sustainability; contractual duties and outsourcing; smart integration; and contract governance.

The proposed model represented the causal interrelationships among relevant critical success factors derived from literature, which may help in directing the organization’s attention and resources to more critical areas, leading to the effective fulfilment of the smart city infrastructure project’s objectives. In addition to the theoretical and methodological contributions, this study produced a usable and readily adaptable list and clusters of critical success factors for research in the area of the implementation of PPP in smart city infrastructure projects.

To the best of the authors’ knowledge, this is the first study to identify PPP critical success factors and their themed clusters for smart city infrastructure projects.

Based on the strong feature representation ability of the convolutional neural network (CNN), generous object detection methods in remote sensing (RS) have been proposed one after another. However, due to the large variation in scale and the omission of relevant relationships between objects, there are still great challenges for object detection in RS. Most object detection methods fail to take the difficulties of detecting small and medium-sized objects and global context into account. Moreover, inference time and lightness are also major pain points in the field of RS.

To alleviate the aforementioned problems, this study proposes a novel method for object detection in RS, which is called lightweight object detection with a multi-receptive field and long-range dependency in RS images (MFLD). The multi-receptive field extraction (MRFE) and long-range dependency information extraction (LDIE) modules are put forward.

To concentrate on the variability of objects in RS, MRFE effectively expands the receptive field by a combination of atrous separable convolutions with different dilated rates. Considering the shortcomings of CNN in extracting global information, LDIE is designed to capture the relationships between objects. Extensive experiments over public datasets in RS images demonstrate that our MFLD method surpasses the state-of-the-art methods. Most of all, on the NWPU VHR-10 dataset, our MFLD method achieves 94.6% mean average precision with 4.08 M model volume.

This paper proposed a method called lightweight object detection with multi-receptive field and long-range dependency in RS images.

This study was conducted to investigate the impact of indoor environmental quality (IEQ) and internal design on the performance of students in university dormitories in Tehran and North Cyprus.

Using a survey questionnaire, 298 students living in student dormitories in Tehran and North Cyprus were surveyed for data collection.

Research has shown that the academic performance and well-being of students are heavily impacted by factors related to IEQ and internal design. The study conducted in Tehran and North Cyprus has identified the most effective components of IEQ and internal design for student dormitories. The study suggests that proper ventilation, furniture design, temperature control and lighting design are key factors that significantly affect IEQ and internal design. Control and lighting design are key factors that significantly affect IEQ and internal design.

Originality: The study utilizes a comparative study designed to analyze the differences and similarities between the two locations.

This present study uses political, economic, social, technological, legal and environmental (PESTLE) analysis and the strategic management theory to examine how external factors, namely the coronavirus (COVID-19) pandemic, the industrial revolution (IR) 4.0 technologies, the fuel price crisis and Sultanate of Oman Logistics Strategy (SOLS) 2040, affect the performance of container terminals in Oman.

A hybrid decision-making method that combined the analytical hierarchy process technique and Bayesian network model was used to achieve the objective of the present study.

The COVID-19 pandemic (54.60%) most significantly affected the performance of container terminals in Oman, followed by IR 4.0 technologies (19.66%), SOLS (17.00%) and fuel price crisis (8.74%). Container terminals in Oman were also found to perform “moderately,” considering the uncertainty of external factors.

This study enriches existing literature by using PESTLE analysis to assess the impact of the external business environment on firm performance in the context of the maritime industry as well as highlights how challenging external environmental factors affect the performance of container terminals in Oman.

This study contributes to developing novel study models and determining the performance level of container terminals in Oman considering integrated uncertainties and external factors such as the COVID-19 pandemic, IR 4.0 technologies, the SOLS 2040 and the fuel price crisis.

The study aims to explore dynamic capabilities such as innovation, entrepreneurial leadership, absorptive capability, and the dimension of entrepreneurial orientation in the handicraft sector to enhance supply chain resilience and innovation during unprecedented times. This study also used innovation as a mediating construct and supply chain orientation as a moderating construct.

We gathered data from the handicraft sector in the Uttar Pradesh (UP) in India using a pretested questionnaire. We used variance-based partial least squares structural equation modelling (PLS-SEM) to test our research hypotheses.

Our study indicates that to enhance innovation and improve supply chain resilience, firms should focus on developing dynamic capabilities such as entrepreneurial leadership, absorptive capacity, artificial intelligence (AI), innovativeness, risk-taking ability, and protectiveness. The study highlights the significant role of dynamic capabilities in the handicraft sector during times of crisis, enabling innovation and resilience to risk.

The study highlights the significant role of dynamic capabilities in the handicraft sector during times of crisis, enabling innovation and resilience to risk.

This study provides significant insights into the current understanding of dynamic capability theory and supply chain orientation and expands upon the existing literature in this field. It comprehensively analyses the latest research and advances knowledge in this area.