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The Evolution of Shopping Centers and Changing User Preferences in Jeddah, Saudi Arabia: Trends, Drivers, and Future Prospects
No full textThe global retail landscape has experienced profound transformation, driven by shifting user behaviors, technological advancements, and a growing demand for more immersive and experiential environments. In Saudi Arabia, particularly in the city of Jeddah, shopping centers have evolved beyond their traditional role as purely commercial spaces into integrated destinations that offer a diverse range of leisure, entertainment, and lifestyle experiences. This shift reflects broader societal changes, including rising user expectations for environments that combine convenience, social interaction, and emotional engagement.
Despite this transformation, limited academic research has addressed how changing user preferences are actively reshaping the spatial design, tenant mix, and activity programming of shopping centers in the region. This study aims to fill this gap by investigating the evolution of shopping centers and examining user behavior patterns in Jeddah through a mixed-methods approach, combining theoretical insights with empirical data. A structured survey was conducted among 273 respondents, with a 5% margin of error at a 90% confidence level, ensuring a reliable representation of user attitudes.
The collected data were analyzed using descriptive statistics, cross-tabulations, and thematic analysis, supported by visualizations to enhance interpretability. The findings highlight the increasing importance of experiential retailing and suggest strategies for adapting mall development to meet emerging user demands. Moreover, the study offers broader implications for urban planning, commercial design, and retail management in rapidly modernizing cities facing similar shifts in market dynamics and user expectations
Energy Harvesting from Human Footstep in Smart Cities as Alternative Renewable Energy Source
No full textThe rapid urbanization of smart cities urgently needs innovative energy solutions. This
Capstone project introduces an innovative alternative renewable energy source that captures
the energy produced from human footsteps through piezoelectric technology, converting
mechanical pressure into electrical energy. Different piezoelectric tile designs were explored
to ensure optimum energy conversion efficiency: cantilever, curved elements, and
diaphragm-based configurations. A complete system architecture has been developed,
consisting of a full-wave bridge rectifier and a buck-boost converter for energy storage and
regulation. Current issues related to renewable energy resource sustainability and urban
energy demands are discussed, taking into account social, ethical, and professional
implications in the integration of such systems into smart city infrastructures. We carry out
technical and economic feasibility by simulations and modeling, presenting in detail the
performance analysis of the system, cost implications, and potential risks. Our findings
suggest that piezoelectric energy harvesting being one of the most promising and
environment-friendly solutions to energize low-energy urban devices and to contribute
toward the call for sustainable development in smart cities. The project not only emphasizes
technological innovation but also underscores its practical implications for creating resilient,
green urban infrastructures
Filmmaking As Self-Expression Investigating Different Social and Cultural Influences on Saudi Cinematic-Arts Students Productions
No full textResearch on media consumption has clearly demonstrated the significance of movies in people lives. However, there is a dearth of research on youth making movies and how they view the films made by other young people. The main conclusions of a research project on the use of filmmaking for young people's communication and self-expression are compiled in this research paper. The approach used in this study was to let young students express themselves through their own media projects and share their work with other students in order to gain insights into their opinions and viewpoints. The researcher is a professor of filmmaking at Effat University in Saudi Arabia, and the research was carried out at the Cinematic Arts department of the Faculty of Architecture and Design. The researcher taught 21 young Saudi students from various socioeconomic backgrounds how to make short films during a level-3 film production course that took place over one academic semester. The students worked in groups to design, shoot and edit eight short films covering various topics. A sample of 17 teenagers representing the students in other academic levels were then shown the investigated film productions. After watching the films, the audiences' feedback and reactions to each production were observed and analyzed. The young filmmakers were exposed afterwards to the remarks left by the audiences, and their responses were also examined and analyzed. The study managed to show the impact of different social and cultural factors on Saudi cinematic-arts students’ film productions, and eventually a group of valuable recommendations related to the investigated research topic were generated.
Mediating effect of intolerance of uncertainty between fear of war and mental health in adults during the Israel-Palestine war of 2023.
No full textThe Israel-Palestine war of 2023 has exposed many individuals to prolonged fear and uncertainty, contributing to significant psychological and behavioral consequences. Fear of war has been shown to exacerbate negative mental health outcomes such as anxiety, depression, aggression and suicidal ideation and reduce overall wellbeing. Intolerance of uncertainty is a trait characterized by difficulty coping with ambiguous situations. It was regarded as a potential mediator in the association between fear of war and these outcomes. Therefore, this study's aim is to investigate the mediating effect of intolerance of uncertainty between fear of war and mental health including anxiety, depression, aggression, suicidal ideation and wellbeing in adults during the Israel-Palestine war of 2023.This study employs a cross-sectional design; it included a total of 484 Lebanese participants. A snowball sampling method via Google forms was employed by the research team to collect data. They were assessed with self-reported measures using The War-related Media Exposure Scale (WarMES), the Buss Perry Aggression Questionnaire-Short Form (BPAQ-SF), Columbia-Suicide Severity Rating Scale (C-SSRS), Intolerance of Uncertainty Scale (IUS) and The World Health Organization 5-item Well-Being Index (WHO-5).Higher fear of war was significantly associated with more inhibitory and prospective anxiety. More prospective anxiety and inhibitory anxiety were significantly associated with more aggression, higher suicidal ideation, higher depression, lower wellbeing and higher anxiety. Fear of war was directly associated with depression, anxiety and lower wellbeing. Whereas the results did not show a direct association with aggression and suicidal ideation. The mediation analysis revealed that inhibitory anxiety and prospective anxiety fully mediated the relation between fear of war and aggression and partially mediated the relation between fear of war and depression, anxiety and wellbeing. Whereas the association between fear of war and suicidal ideation was fully mediated by prospective anxiety but not by inhibitory anxiety.Understanding the role of intolerance of uncertainty is crucial to developing interventions aimed to reduce mental health challenges in populations affected by conflict
The Inverse Power Law-Normal Model for Right-Censored Data With Application to Life Prediction of Organic Light-Emitting Diodes
No full textThis paper extends the IPL-normal life model to accommodate right-censored data while maintaining a stress-free, constant coefficient of variation. It derives new maximum likelihood (ML) estimation equations, including a generalized, explicit form for the coefficient of variation, and contrasts ML with the classical least squares (LS) approach. Despite its complexity, ML is favored for its unique ability to estimate this key parameter. The model is applied to censored OLED life data, building on previous studies with complete data.This work generalizes the inverse power law-normal (IPL-normal) model for complete data to right-censored data, assuming that the coefficient of variation remains constant and free of stress. The maximum likelihood (ML) estimating equations of the model’s accelerating parameters and the general coefficient of variation are derived using new trivial but fundamental identities. The ML estimating equation of the general coefficient of variation is explicit and generalizes its counterpart for complete data, which was previously introduced. The ML method is compared with the classical least squares (LS) technique. Although the ML method is laborious and numerically sensitive, this article favors ML over LS for a drastic reason that only ML can estimate the general coefficient of variation, but it still recommends using both the methods for some other reasons. The generalized IPL-normal model is used to precisely specify the life model of organic light-emitting diodes based on a standard real data of complete samples of lives which was discussed in several previous works but censored in this work
Algorithms for Feature Selection (3rd Edition)
No full textAccurate photovoltaic (PV) power forecasting is essential for grid integration, particularly in maritime climates with dynamic weather patterns. This study addresses high-dimensional meteorological data challenges by systematically evaluating 32 variables across four categories (solar irradiance, temperature, atmospheric, hydrometeorological) for day-ahead PV forecasting using long short-term memory (LSTM) networks. Using six years of data from a 350 kWp solar farm in Scotland, we compare satellite-derived data and local weather station measurements. Surprisingly, downward thermal infrared flux—capturing persistent atmospheric moisture and cloud properties in maritime climates—emerged as the most influential predictor despite low correlation (1.93%). When paired with precipitation data, this two-variable combination achieved 99.81% R2, outperforming complex multi-variable models. Satellite data consistently surpassed ground measurements, with 9 of the top 10 predictors being satellite derived. Our approach reduces model complexity while improving forecasting accuracy, providing practical solutions for energy systems.
Keywords: deep learning; forecasting; long short-term memory; mean absolute; meteorological variablesEffat Universit
2025 22nd International Learning and Technology Conference (L&T)
No full textThis study presents a systematic evaluation of deep learning architectures for photovoltaic (PV) power forecasting, comparing nine model configurations across three architectures (MLP, LSTM, CNN) and three optimizers (Adam, RMSprop, Adagrad). Using six years of hourly data from a 350kWp grid-connected PV system in Scotland, we demonstrate that architectural choice and optimizer selection significantly impact forecasting accuracy. The LSTM-RMSprop configuration achieved superior performance with RMSE of 2.651 kWh and MAE of 1.197 kWh, showing a 90.29% coefficient of determination (R2). This outperforms both CNN (RMSE: 2.767-2.902 kWh) and MLP architectures (RMSE: 3.104-3.115 kWh) across all optimizers. Our main contributions include: (1) comprehensive optimization of model architectures through hyperparameter evaluation, revealing optimal configurations for each model type; (2) systematic evaluation of optimizer impact, demonstrating RMSprop's superiority for LSTM with improved accuracy across architectures; (3) detailed error analysis showing model stability across different conditions, with NRMSE ranging from 31.15% to 39.07%; and (4) practical insights into computational requirements, where CNN architectures achieve fastest training times (2.3-3.8 minutes/epoch) compared to LSTM (3.9-5.9 minutes/epoch) and MLP (36.54-46.72 minutes/epoch). Results demonstrate that LSTM architectures with appropriate optimization can outperform simpler models in PV power forecasting, providing valuable guidance for practical implementations.
Author Keywords
photovoltaic power forecasting
deep learning
hyperparameter optimization
time series prediction
optimization algorithmsEffat Universit
Slavery Before Race: The Racialization of Slavery in Morrison’s A Mercy and Albeshr’s Hend and the Soldiers
No full textThe book is in the process of publication.Slavery existed in the Arabian Peninsula long before it emerged in the USA; it is depicted in both African-American and Middle-Eastern novels . The works of the African American novelist Toni Morrison and the Saudi writer Badriah Albeshr depict slavery and racism in both contexts. Morrison’s A Mercy (2008) pictures the origins of the slave trade in America in the seventeenth century, whereas Albeshr’s Hend and the Soldiers (2006), written in Arabic and translated into English by Sanna Dhahir in 2017, portrays the abolition of slavery in Saudi Arabia in 1962. The former shows that anyone can be in bondage irrespective of their race and circumstances, while the latter demonstrates that slavery is linked to race and results in numerous kinds of compulsion. A Mercy and Hend and the Soldiers show how “race and races are products of social thought and relations . . . [and that] races are categories that society invents, manipulates, or retires when convenient” (Delgado et al. 9). I argue that the comparison of these two narratives can reveal that race is a socially constructed category, shaped by economic, social, and ideological factors; it is not a fixed biological reality
Exploring the smart fitness integrated chair : The future of health-focused furniture design
No full textThe smart fitness integrated chair will be a great enhancement and advancement in the furniture industry, blending it with the comfort and utility of traditional seating, this chair will help fix the problems humans face such as sitting duration in a concerning position, back and neck pain, and body movements. This study aims to solve human body health by creating this smart chair that is interactive with the user by its e!ectiveness in improving posture, increasing physical activity, and enhancing overall wellness. This study’s significance will help fill in the gaps that are found in the market by following some methods that will enhance human life, focusing on Sustainable Development Goals (SDGs) and Saudi Arabia’s Vision 2023. This smart fitness integrated chair will be a solution for enhancing physical and well-being in human lifestyles, marking it a significant step forward in the evolution of the furniture industry. The purpose of this research is to investigate how machine learning, virtual reality, and artificial intelligence (AI) are used in healthcare and what e!ects they have. The project specifically aims to investigate how these technologies can improve overall healthcare delivery, optimize resource allocation, tailor treatment methods, and increase diagnostic accuracy. Examining case studies, literature, and technology advancements in AI in the healthcare industry are all part of the data-collecting process. Analyses, both qualitative and quantitative, are carried out to assess how these technologies a!ect di!erent facets of healthcare delivery. This intelligent integration of exercise components into a chair is a significant step in improving physical well-being in human lifestyles. By tackling the ergonomic issues inherent in typical seating arrangements, the smart fitness integrated chair emerges as a solution-oriented approach, representing a significant step forward in the growth of the furniture sector. Through this investigation, the researchers want to provide significant insights that will inform and drive the continued evolution of healthcare procedures. Multiple benefits will be provided if fitness and technology are integrated into furniture, and these benefits will be listed in this research through the coming phases
Assessing Chinese–Egyptian bilateral trade dynamics under the One Belt One Road initiative: augmented gravity model approach
No full textPurpose This research paper investigates the economic impact of China’s One Belt, One Road (OBOR) initiative on its historical trade partner, Egypt, within a landscape shaped by global interconnectedness. Design/methodology/approach The paper employs an augmented gravity model with a random-effects estimator and robust standard errors to analyze the impact of the OBOR initiative on China–Egypt bilateral trade from 1960 to 2022. Findings The findings reveal a significant increase in trade volume following the implementation of OBOR. The model confirms the continued importance of gross domestic product (GDP) and geographic proximity in facilitating trade. Additionally, the research highlights Egypt’s strategic positioning within the OBOR due to its location and existing infrastructure, such as the Suez Canal. Practical implications These results offer valuable insights for policymakers and stakeholders seeking to optimize bilateral trade strategies and strengthen economic cooperation under the OBOR framework. Originality/value This study contributes to the existing literature by providing a fresh perspective on the impact of OBOR, employing a robust econometric approach and focusing on a specific yet crucial regional trade relationship