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Exploring distance learning in higher education: satisfaction and insights from Mexico, Saudi Arabia, Romania, Turkey.
No full textEducation, notably higher education, faced a significant challenge during the last period. Our data exploratory study aims to provide insights into the key factors that define students’ Distance Learning (DL) in the current period. Based on the main findings, we justify our bold proposition for the current era of distance and blended learning in Higher Education. Our research study aims to understand cultural and national differences in four countries: Mexico, Saudi Arabia, Turkey and Romania. It contributes to the theory of DL with a model of six hermeneutic factors for the satisfaction of using the DL method. It investigated and confirmed the capacity of the components to explain 60% of the DL satisfaction variance. Our research study also emphasized the interpretation of the essential findings and the drafting of bold propositions for the DL practice, emphasising academic environments. We identified significant areas of improvement, and we suggested the orchestration of combined efforts. Our research promotes the strategic deployment of DL in the current context as a resilient strategy of institutions for high-impact training and targeting of huge audiences, with emphasis on the deployment of new tools and teaching methods customized for a new, unique value proposition of the DL.Education, notably higher education, faced a significant challenge during the last period. Our data exploratory study aims to provide insights into the key factors that define students’ Distance Learning (DL) in the current period. Based on the main findings, we justify our bold proposition for the current era of distance and blended learning in Higher Education. Our research study aims to understand cultural and national differences in four countries: Mexico, Saudi Arabia, Turkey and Romania. It contributes to the theory of DL with a model of six hermeneutic factors for the satisfaction of using the DL method. It investigated and confirmed the capacity of the components to explain 60% of the DL satisfaction variance. Our research study also emphasized the interpretation of the essential findings and the drafting of bold propositions for the DL practice, emphasising academic environments. We identified significant areas of improvement, and we suggested the orchestration of combined efforts. Our research promotes the strategic deployment of DL in the current context as a resilient strategy of institutions for high-impact training and targeting of huge audiences, with emphasis on the deployment of new tools and teaching methods customized for a new, unique value proposition of the DL
Multi-aggregation Strategies in Ensemble-Based Machine Learning and Deep Learning Models for Cough-Based COVID-19 Detection
No full textRespiratory diseases have become a major area of research, especially during the fight against the Coronavirus Disease (COVID-19) pandemic. While various measures have been taken to prevent and control the spread of the virus, the existing diagnostic methods can be time-consuming and invasive. This paper describes a novel framework utilizing Multi-Aggregation Strategies in Ensemble-Based Machine Learning and Deep Learning (MASE-MDL) to detect COVID-19 through cough analysis. Our method employs a comprehensive feature extraction process from cough audio signals. Initially, raw cough audio data undergo preprocessing steps, including noise reduction and signal normalization, to enhance signal quality and consistency. Subsequently, relevant features are extracted from the preprocessed audio signals. These features encompass a range of acoustic characteristics including, but not limited to, frequency spectrum, temporal patterns, and spectral entropy while capturing diverse aspects of cough sounds indicative of respiratory conditions. Moreover, to leverage the collective intelligence of multiple predictive models, such as machine learning algorithms and recurrent neural networks like GRU and LSTM, we employ a multi-aggregation strategy. This approach combines predictions from diverse models, each trained on distinct feature subsets or utilizing different learning algorithms. Tests on the CoughVid dataset have shown high accuracy, sensitivity, and specificity, indicating its potential as a reliable screening tool for healthcare professionals. Furthermore, this approach helps develop new diagnostic techniques that do not require laboratory tests or CT scans. Ultimately, this could lead to better health outcomes for patients and improved respiratory disease management in hospitals
The mediating role of work-life balance on the relationship between emotional intelligence and job satisfaction among Lebanese critical care nurses.
No full textIn healthcare settings, particularly in intensive care units, nurses face significant stress due to the high demands of their job. This stress can impact their job satisfaction, mental health, and overall quality of life. Emotional intelligence has been identified as a crucial factor that can mitigate workplace stress and enhance job satisfaction. Moreover, work-life balance is increasingly recognized as a critical factor influencing job satisfaction in the nursing profession.Our study aims at understanding the mediating effect of work-life balance between emotional intelligence and job satisfaction in Lebanese nurses working in the Intensive Care Unit.This study has a cross-sectional design.Nurses working in intensive care units of one hospital (n = 100) were asked to fill an online questionnaire which included the Wong and Law Emotional Intelligence Scale, Work-Life Balance Self-Assessment Scale, and Job Satisfaction Scale.Work Interference with Personal Life and Personal Life Interference with Work acted as significant mediators between emotional intelligence and job satisfaction. Specifically, the direct role of emotional intelligence on job satisfaction was found to be significant, with work interference with personal life (β = .02, SE = .01, p = .001) and personal life interference with work (β = .02, SE = .01, p = .002) showing significant indirect roles. Higher emotional intelligence was directly and significantly associated with more job satisfaction (p < .01).The study underscores the potential benefits of emotional intelligence training and work-life balance promotion in enhancing nurses' job satisfaction.Pending future longitudinal studies, findings cautiously imply that targeting work-life balance could help foster the positive connection between emotional intelligence and Job Satisfaction among nurses. Accordingly, healthcare administrators should prioritize policies that promote flexible scheduling, sufficient staffing levels, and mental health resources, which are essential for maintaining a balance between professional obligations Job Satisfaction and personal life
Aqualympia Jeddah Hub
No full textAqualypia Jeddah Hub is an Olympic-standard aquatic and recreational complex strategically located along
the Red Sea coast in Jeddah, Saudi Arabia. Conceived as a multifunctional destination, the project merges
elite sports facilities with public leisure spaces, aiming to position Jeddah as a regional leader in aquatic
sports, tourism, and community engagement. The design accommodates competitive swimming, diving,
and water polo venues, alongside retail, dining, and entertainment amenities, creating a balanced mix of
high-performance athletic spaces and inclusive recreational environments.
The architectural concept is inspired by the dynamic movement of ocean waves, expressed through fluid
massing that unites three primary zones — the Competition Zone, Training and Recreation Zone, and
Entertainment and Retail Zone — around a central lobby that serves as both a circulation hub and a public
gathering space. This form not only reflects the project’s coastal identity but also optimizes natural lighting,
ventilation, and sea views.
This thesis situates the project within the broader ambitions of Saudi Vision 2030, particularly in advancing
healthy lifestyles, fostering sports culture, and developing tourism infrastructure. The hub addresses
pressing climatic challenges in Jeddah’s hot and humid environment through integrated sustainability
measures, including passive cooling, energy-efficient mechanical systems, and water recycling networks
that connect pool operations to a sustainable sea–pool exchange cycle. Accessibility, inclusivity, and safety
have been embedded into the design to ensure that the facility caters to a diverse user base, from
professional athletes to families and tourists.
The research is organized into five main chapters. Chapter one introduces the project theme, vision, and
objectives, defining its role as both a seasonal Olympic competition venue and a year-round recreational
hub. Chapter two examines international case studies of aquatic sports facilities, analyzing their spatial
planning, circulation patterns, environmental strategies, and architectural expressions to inform the
design’s conceptual and technical framework. Chapter three outlines the optimized program derived from
functional requirements, international standards, and user needs, detailing spaces for athletes, spectators,
visitors, and support staff. Chapter four addresses site analysis and selection, including environmental
assessments such as climate data, solar orientation, prevailing winds, and urban integration. Chapter five
presents the design process, from conceptual development to schematic design, zoning, and spatial
articulation, followed by comprehensive technical documentation covering structure, HVAC systems, and
sustainability features. The chapter concludes with architectural drawings, physical and digital models, and
final renders that visualize the project’s intended experience and identity.
The outcome of this thesis is a design that harmonizes competitive sport, public leisure, and sustainable
tourism, delivering a multifunctional hub that serves as a landmark for Jeddah and the Kingdom. By
integrating environmental responsiveness, cultural relevance, and economic viability, Aqualypia Jeddah
Hub not only meets the technical and spatial demands of an Olympic-standard facility but also enriches the
city’s social and cultural landscape, contributing to its global positioning as a coastal destination for sports
excellence and recreation
Unveiling the dynamics of generative AI adoption: A business intelligence analysis through topic modeling-based bibliometric study
No full textGenerative Artificial Intelligence (GenAI) has gained notable attention in educational literature, with supporters and critics expressing varying opinions. Despite its popularity, only a few reviews are available on the subject,
with limitations such as small sample sizes and limited scope. This study aims to clarify the major themes influencing the discussion on GenAI in educational contexts. It employs a strong Business Intelligence paradigm
and uses bibliometric analysis and topic modeling focusing on the R program’s structural topic model (STM) Package, VOSviewer, and bibliometric software. The results highlight the esteem of GenAI in education and
evidence of international collaboration in the research process dedicated to enhancing the rapidly evolving field of GenAI. The scientometric indexes indicate that the diversity of journals has the significant impact on GenAI in
education. While Lotka’s Law suggests that the field is still in its early stages, the collaborative network demonstrates strong connections among researchers, a positive indicator of future progress. Moreover, the STM
method has identified nine pivotal topics grouped into three categories relating to GenAI in education. By shedding light on these emerging themes, this study provides educators and researchers with valuable insights into the future of GenAI in education
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No full textThis research thesis explores the representation of diverse Arab cultures and their impact on the identity development of teenage women in cinema, with a particular focus on the political and social dynamics within traditional Levantine and Gulf households. The study highlights how regional differences in family structures, societal expectations, and gender roles influence the personal and social identities of young women in these regions. The contrast between the politically active and socially fluid environments of the Levant and the more conservative, patriarchal norms prevalent in the Gulf provides a backdrop for understanding how young women navigate their identities.
Through the lens of these and other films, the research delves into the challenges faced by teenage girls as they grapple with issues of autonomy, gender roles, and cross-cultural friendships. In (Wadjda) , for instance, the protagonist challenges traditional gender norms in a conservative Saudi household, reflecting the tension between individual aspirations and societal expectations. In contrast, (Caramel portrays a group of women navigating personal and societal struggles in a more liberal yet politically complex Lebanese setting, highlighting the fluidity of social norms in the Levant. By analyzing visual storytelling, character development, and dialogue, this study emphasizes the competing values of collectivism versus individualism and traditional versus evolving gender expectations. These films serve as cultural texts that reveal how young Arab women, growing up in politically and socially diverse households, are shaped by the intersections of culture, politics, and gender. Ultimately, this thesis contributes to a deeper understanding of how cinema reflects the complex process of identity formation for
teenage girls in the Arab world
The impact of Financial reporting quality and Debt maturity structure on Investment efficiency
No full textWELLNESS RESORT: Beyond The Desert
No full textWELLNESS RESORT: Beyond The Desert is a culminating architectural endeavor that reinterprets the
healing possibilities of the desert terrains in Saudi Arabia. Motivated by the land’s natural rhythms and
grounded in the objectives of Vision 2030 and the King Salman Charter for Architecture and Urbanism,
the initiative provides a sustainable solution to the increasing global focus on wellness tourism.
The resort transcends luxury by incorporating a comprehensive design strategy that fosters the spiritual,
physical, and mental wellness of its guests. The initiative develops immersive spatial experiences via
three interconnected zones: areas for spiritual retreats to rejuvenate the soul, physical, and mental
amenities for bodily healing, and contemplative learning spaces to foster mental clarity and tranquility.
Every area is influenced by the natural landforms and weather conditions of the desert, leading to a
design that respects the terrain instead of altering it.
Situated in the culturally vibrant and geologically unique area of AlUla, the choice of location enhances
the resort’s goal to foster balance among heritage, wellness, and hospitality. Inspired by ancient desert
architecture and employing local materials, the project incorporates passive design techniques to
minimize environmental impact and ensure comfort under harsh conditions.
This retreat is not just an accommodation but a thoughtfully designed experience of tranquility, stillness,
and bonding—providing visitors beyond mere comfort: a renewal of body, mind, and spirit. This
approach frames architecture as a restorative connection between individuals and the natural
environment
Ensemble Learning and Time-Domain Feature Mining based Appliance Power Consumption Pattern Recognition
No full textAs technology has advanced, using smart meters instead of conventional ones has changed. These smart meters are essential parts of intelligent grids, offering significant advantages to many stakeholders regarding social, environmental, and economic constraints. A detailed method of fine-grained collection and analysis of the metering data is necessary to benefit the different smart grid stakeholders. This results in collecting, transmitting, and processing a large amount of data with an exponentially raised dimension. It seriously constrains the system processing resources, latency, data management, transmission effectiveness, and analysis response time. In this regard, a new hybridization of ensemble learning and time-domain feature extraction is suggested for the automatic classification of appliances through the processing of their power consumption data. Time-domain feature mining makes up for the previously noted deficiency and can result in a considerable real-time data dimension reduction without sacrificing important information. Data in compressed form can therefore be processed, examined, stored, and transmitted with efficiency. It offers a significant reduction in the post cloud-based classification latency along with computational and transmission effectiveness. The classification is carried out using the robust ensemble learning (EL) classifiers. The performance of considered EL classifiers is evaluated for the case of a real multi-class and multi-device smart meter dataset. The devised solution achieves a highest average classification accuracy of 88.33% for a 10-class problem while securing a compression gain of 32.73