American University in the Emirates

The COVID-19 pandemic has reshaped film consumption patterns in China, driving a notable transition from traditional theatres to over-the-top (OTT) platforms. This study investigates the factors influencing post-pandemic cinephile preferences, particularly the interplay between OTT platforms and traditional cinema-going platforms. The surge in online streaming, boosted by pandemic-induced restrictions, has been critical with regional giants such as iQIYI restyling Chinese entertainment traditions. This research aims to uncover the determinants guiding cinephile choices by analysing demographic variables, technological advancements and consumption patterns. Through a survey in Tier II cities, data from 656 participants were analysed to determine the OTT platform and cinema usage. The findings reveal nuanced trends: females slightly favour OTT platforms, while males prefer traditional cinemas. Young adults aged 21 to 25 years are the most active users of both platforms, with education level slightly affecting usage. Smartphone usage is widespread, with 70% accessing OTT platforms via mobile. While 40% view OTT platforms as challenging traditional cinemas, 65% view OTT platforms in the future. Key factors influencing future cinema-going include OTT platform accessibility and time constraints-over 80% expressed satisfaction 2 Muhammad Yaqoub et al. China Report (2024): 1-19 with their cinema experiences, with peak seasons significantly impacting ticket prices. Comedy, drama and action genres were most popular among cinemagoers, each with over 60% preference. These findings imply that the future of film consumption in China will likely be shaped by the balance and integration of OTT platforms and traditional cinema experiences.

Wireless sensor networks (WSNs) are essential for a wide range of applications, including environmental monitoring and smart city developments, thanks to their ability to collect and transmit diverse physical and environmental data. The nature of WSNs, coupled with the variability and noise sensitivity of cost-effective sensors, presents significant challenges in achieving accurate data analysis and anomaly detection. To address these issues, this paper presents a new framework, called Online Adaptive Kalman Filtering (OAKF), specifically designed for real-time anomaly detection within WSNs. This framework stands out by dynamically adjusting the filtering parameters and anomaly detection threshold in response to live data, ensuring accurate and reliable anomaly identification amidst sensor noise and environmental changes. By highlighting computational efficiency and scalability, the OAKF framework is optimized for use in resource-constrained sensor nodes. Validation on different WSN dataset sizes confirmed its effectiveness, showing 95.4% accuracy in reducing false positives and negatives as well as achieving a processing time of 0.008 s per sample.

This chapter explores the topic of employee wellbeing and quality of life among Saudi Arabian workers. It explores the influences on their overall wellness, covering physical, mental, and emotional health, work–life balance, job satisfaction, and life contentment. The unique cultural and societal factors shaping Saudi Arabian workers’ experiences are examined. The chapter emphasises the importance of prioritising both physical and mental health, while considering Saudi Arabia’s cultural context. It underscores the interconnectedness between job satisfaction and overall life quality. This chapter combines research and practical insights, offering a comprehensive understanding of employee wellbeing in the Saudi Arabian work landscape. It serves as a valuable resource for researchers and practitioners aiming to enhance the work experiences and overall quality of life for Saudi Arabian workers.

This paper aimed to determine the impact of faculty communication style, student proactiveness, and academic discipline on student academic performance and student-faculty relationship quality in the United Arab Emirates (UAE) higher education context. This study also aimed to contribute to the literature by verifying the mediating impact of communication effectiveness between the selected factors. Using a cross-sectional survey design, the study sample comprised 193 university students, and it was analyzed using partial least squares structural equation modeling (PLS-SEM). The results revealed that academic discipline and the professor’s communication style enhanced communication effectiveness, whereas student proactiveness had a minimal effect. The results also show that effective communication significantly influences students’ academic success and the quality of faculty-student interactions. The mediating role of communication effectiveness has also been proven. These findings underscore the importance of robust communication in the faculty-student dynamics and its impact on academic performance in higher education. This research provides valuable strategies for higher education institutes to develop a high-interaction learning environment to ensure a high level of performance for both professors and students.

In the rapidly evolving field of sensor technology, efficient and accurate anomaly detection is critical across applications from environmental monitoring to cybersecurity. Traditional approaches often fail in real-time sensor data scenarios due to high computational requirements and lack of labeled datasets. This paper presents a lightweight, unsupervised anomaly detection framework that combines Continuous Wavelet Transform (CWT) with Support Vector Clustering (SVC), aiming to reduce computational complexity and dynamically adapt to the data flow. Extensive validation on the Intel Berkeley Research Laboratory (IBRL) dataset demonstrates that our method not only handles sensor aberrations effectively, but also achieves a significant detection accuracy of 93.2% for drift readings, confirming its robustness and efficiency.

In pursuing Sustainable Development Goals (SDGs), understanding the complex interplay between socioeconomic factors and carbon emissions is paramount. Given Pakistan’s current environmental situation, this study scrutinizes the relationship between institutional quality, internal conflict, GDP, industrialization, foreign direct investment, external conflict, and international tourism influence on Pakistan’s consumption-based and territory-based carbon emissions from 1990 to 2021 in the context of SDGs. Employing a robust analytical framework including ARDL, Single-Fourier-frequency, and Cumulative Fourier-frequency Granger Causality tests, findings reveal that improved institutional quality, industrialization, and foreign direct investment reduce consumption-based emissions. Meanwhile, institutional quality and foreign direct investment decrease territory-based emissions. Conversely, internal conflict and GDP growth intensify both consumption and territory-based emissions. The research contributes valuable insights for policymakers aiming to synchronize national growth with global sustainability goals, emphasizing the delicate balance needed to navigate economic and environmental complexities in achieving the SDGs.

This study focuses on the unequal relationship between energy consumption and its determinants. Past studies have not examined how minor and substantial currency value changes affect energy consumption in the organization for economic cooperation and development (OECD) countries. This study compares the effects of modest and significant exchange rate (ER) changes on energy demand (ED) in OECD countries, which include Greece, Belgium, Ireland, Denmark, Portugal, Norway and Italy. Our work adds to the literature by distinguishing the effect of small to significant changes in currency fluctuations. We do this with a sophisticated model, an updated multiple threshold nonlinear autoregressive distributed lag (MTNARDL). Next, we compare the model’s outcomes to conventional nonlinear autoregressive distributed lag (NARDL) and autoregressive distributed lag (ARDL) models. According to NARDL and ARDL estimates, co-integration is present in the context of Belgium. However, the MTNARDL model division of series suggests cointegration in all sample countries. It implies that this model is superior to previous ones. We conclude with policy recommendations based on the results of our inquiry.

The paper intends to understand the research trends in Covid-19 and SMEs through a literature review and explores the most affected areas of SMEs during the Covid-19 pandemic. The study attempts to highlight the struggles of SMEs during Covid-19 crisis in the GCC. This paper gives a clear understanding of the current state of the research regarding the topic, i.e., the impact of COVID-19 on SMEs in the GCC, being investigated in a comprehensive platform. This paper calls for crisis management during turbulent times such as pandemics. More particularly this paper suggests developing strategies for employee engagement and voice that would lead to solidarity behavior and comradery among employees and managers of SMEs under crisis.

This research can be thought of as an investigation and reflections that seeks to describe the current status of opportunities and challenges resulting, so far, by the eLearning utilization by the Gulf Corporation Council (GCC) Countries. The analysis of the relevant literature indicated that there is a demanding need to use effective digital solutions for contributing towards achieving the UN’s sustainable development goals (SDGs), more particularly SDG 4: Quality Education. This paper hence calls for action for gradual change management in education and raising awareness in the classrooms. Students are yet to fully understand the potential benefits of eLearning, and to accept it as a mode of learning. There needs to be a solution of developing a model by combining “quality of education” and “transfer of digital solutions” as a novel and comprehensive approach to the problem of managing the educational and scientific potential of HEIs in the GCC towards contributing to the SDGs.

Remote sensing technology is a vital component of disaster management, poised to revolutionize how we safeguard lives and property through enhanced prediction, mitigation, and recovery efforts. Disaster management hinges on continuous monitoring of various environments, from urban areas to forests and farms. Data from these observations are relayed to servers, where sophisticated processing algorithms forecast impending disasters. Remote sensing technology operates through a layered framework. The sensing layer acquires raw data, the network layer facilitates data transmission, and the data processing layer extracts meaningful insights. The application layer then leverages these insights to make informed decisions. Elevating the intelligence of remote sensing technology necessitates advancements across these layers. This paper delves into disaster management concepts and highlights the pivotal role played by remote sensing technology. It offers a comprehensive exploration of each layer within the remote sensing technology framework, detailing foundational principles, tools, and methodologies for enhancing intelligence. Addressing challenges inherent to this technology, the paper also presents future-oriented solutions. Furthermore, it examines the influence of wireless network infrastructure, alongside emerging technologies like the Internet of Things, cloud computing, virtual machines, and low-power wireless networks, in nurturing the evolution and sustainability of remote sensing technology.