Search Results (258)

Crisis management has evolved from a reactive organizational function into a strategic capability grounded in organizational learning, knowledge-based processes, and behavioral alignment, thereby enhancing institutional resilience in volatile environments. This study examines how organizational and financial determinants contribute to crisis management success in the United Arab Emirates (UAE). It integrates crisis management culture as a learning-oriented mediating capability. It incorporates a Theory of Planned Behavior (TPB)-based behavioral extension to explain how attitude, subjective norms, and perceived behavioral control shape intention toward crisis-related compliance. Using SPSS regression analysis and Partial Least Squares Structural Equation Modeling (PLS-SEM), the findings indicate that policies, procedures, and financial stability exert significant positive effects on crisis management success, whereas trained human resources show no direct significant impact. Crisis management culture emerges as a key mediating mechanism that enables knowledge integration, supports organizational learning processes, and translates structural preparedness into coordinated action. The TPB-based extension further shows that attitude, subjective norms, and perceived behavioral control significantly predict intention, and that intention is positively associated with crisis management success. The results suggest that effective crisis governance depends not only on formal structures and financial resources but also on learning-oriented cultures and behavioral mechanisms that transform institutional knowledge into coordinated crisis responses. Full article

Significant progress in legal natural language processing (NLP) has enabled advancements in tasks such as legal judgment prediction, case retrieval, and question answering. However, the development of analogous technologies for Arabic legal texts remains severely constrained by the scarcity of large-scale, publicly available benchmarks for summarisation and classification. This paper addresses this gap by introducing a novel, comprehensive dataset of 9699 Arabic legal cases sourced from the Saudi Board of Grievances. This corpus is unique in pairing full-length court decisions with expertly human-crafted abstractive summaries and multi-class category labels (Administrative, Commercial, and Criminal), establishing a dedicated benchmark for Arabic legal NLP. The dataset was constructed via a robust, reproducible pipeline that ensures high textual fidelity, incorporating specialised optical character recognition (OCR) via Google Document AI and precise structural segmentation into facts, reasons, and summaries. To establish robust baselines, we conduct an extensive empirical evaluation of seven summarisation models—encompassing four extractive algorithms (TextRank, LexRank, Latent Semantic Analysis, and Luhn) and three transformer-based abstractive architectures (AraT5v2, AraBART, and mBART)—each evaluated in both base and fine-tuned configurations. Results across ROUGE, BERTScore, BLEU metrics and human evaluation demonstrate substantial performance gains achieved through domain-specific fine-tuning, with the fine-tuned AraBART model achieving the strongest performance among all evaluated models. Furthermore, we present a novel analysis of the downstream utility of generated summaries by evaluating their performance on legal category classification using five machine learning models. This investigation reveals a strong positive correlation between summarisation quality and classification accuracy, empirically demonstrating that domain-adapted abstractive summarisation not only enhances intrinsic evaluation scores but also significantly boosts extrinsic task performance. By providing this essential dataset and comprehensive benchmarking, our work contributes a much-needed resource to the field, facilitating future research and innovations in Arabic legal text analysis. Full article

Fire safety in high-rise buildings represents a critical challenge in the United Arab Emirates (UAE), where intensive urbanization, extreme climatic conditions, and multilayered regulatory frameworks impose unique competency demands on architects and Fire Safety Engineering (FSE) consultants. Despite this, no empirically validated competency framework exists that simultaneously addresses both professional groups and is tailored to the specificities of the UAE context. This study aimed to construct and empirically validate such a framework. A three-phase sequential exploratory mixed-method design was employed. In the first phase, a systematic literature review yielded a preliminary set of 69 competency indicators organized within a Knowledge, Skills and Attitudes (KSA) structure. In the second phase, a three-round Delphi technique with an expert panel of 18 specialists validated the set to 62 final indicators. In the third phase, importance–performance analysis (IPA) was conducted on a sample of 250 professionals actively engaged in fire safety projects across four UAE. IPA identified 16 priority competency gaps, most pronounced in digital transformation (BIM, CFD, AI; gap = 1.23), proactive client advisory competencies (gap = 1.21), and regulatory navigation and Civil Defence coordination (gap = 1.00). A counterintuitive finding emerged whereby architects systematically rated competencies higher than FSE consultants across all dimensions (all p < 0.05). Psychometric validation confirmed excellent instrument reliability (Cronbach’s Alpha > 0.95) and a theoretically consistent three-factor KSA structure explaining 70.06% of variance. The developed framework of 62 empirically validated indicators represents the first competency model of its kind for architects and FSE consultants in the Gulf Cooperation Council (GCC) region. Its findings provide a direct empirical basis for curriculum reform, Continuing Professional Development (CPD) programmes, and professional licencing standards in the UAE and across the GCC region. The study makes three original contributions: the first empirically validated UAE-specific competency framework for these professional groups; a methodological combination of Delphi, IPA, EFA, Mann–Whitney, and Kruskal–Wallis not previously applied in fire safety competency research; and empirical confirmation that 74% of indicators required original development or adaptation, demonstrating the limitations of generic international competency models in the UAE context. Full article

This paper explores how higher education institutions (HEIs) in the Gulf Cooperation Council (GCC) are advancing the Sustainable Development Goals (SDGs) amid rapidly evolving national development agendas. This study reviews publicly available institutional documents and global SDG ranking data to identify patterns of SDG integration: through academic programs, research, and community engagement. The data shows active engagement of the universities in the region linked with varying SDGs. The analysis also reveals that sustainability initiatives in Gulf universities are not purely educational or environmental undertakings; rather, they function as strategic instruments aligned with national visions, international positioning and soft power objectives. Accordingly, this study assesses institutional commitment to the SDGs as expressed through, and made visible by, publicly available reporting, rather than the effectiveness or real-world impact of that engagement, which the available data cannot establish. Guided by theoretical perspectives, the paper argues that SDG engagement remains largely shaped by global ranking frameworks and policy imperatives. While the GCC higher education sector is increasingly embedded in the global sustainability discourse, meaningful localization of SDG practices and data transparency remain limited. By drawing attention to these dynamics, the study contributes to the literature on higher education and sustainable development in the Arab Gulf, emphasizing the need for context-sensitive frameworks and stronger regional collaboration to advance the 2030 Agenda. It calls for strengthened collaboration, capacity development, and tailored policy approaches to fully harness the transformative potential of the SDGs. Future research should explore the sociopolitical drivers of SDG adoption to deepen understanding of HEIs’ contributions to sustainable development in the region. Full article

This study examined Kefiran, an exopolysaccharide derived from milk kefir grains, as a novel biopolymer for encapsulating phenolic extracts from sunflower cake and its antimicrobial properties in the development of natural and functional food ingredients. Kefiran was obtained from kefir grains using three extraction protocols: hot water (M1), hot water with 30% trichloroacetic acid (M2), and mild heat combined with ultrasound at 60 °C (M3). The ultrasound-assisted method produced the highest carbohydrate concentration. Spectrophotometric assays (phenol–sulfuric and Bradford), Fourier transform infrared spectroscopy, scanning electron microscopy, thermogravimetric analysis, and water-holding capacity were employed to characterize the composition, structure, and morphology of the extracts, revealing well-preserved polysaccharide fingerprints and a highly porous microstructure, consistent with their potential application in food systems. Kefiran was then evaluated as an encapsulating agent in complex coacervation at pH 3.75, using three Kefiran-based wall formulations (M1, M2, and M3) with gum arabic and whey protein isolate (WPI) as co-wall materials, and their performance was compared with gum arabic and WPI controls. Across formulations, coacervate microcapsules achieved high encapsulation efficiencies (83–93%), tunable particle sizes, and predominantly negative zeta potentials, indicative of good colloidal stability. The Kefiran extract and coacervate microcapsules demonstrated significant antioxidant and antimicrobial activity against Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Candida albicans, with minimum inhibitory concentrations ranging from 250 to 1000 µg/mL. The findings support ultrasound-extracted Kefiran as a multifunctional biopolymer suitable for bioactive delivery and as a natural antimicrobial component in advanced functional food formulations. Full article

Arabic natural language processing (NLP) faces major difficulties due to the language’s rich morphological structure and the scarcity of high-quality datasets, especially for educational material distributed across diverse online platforms. Many existing large-scale corpus construction methods depend on extensive web crawling followed by substantial post-processing. This process may introduce irrelevant or low-quality data and often fails to represent the target domain adequately. As a result, a robust approach to developing corpora tailored for domain-sensitive educational NLP systems and linguistic depth is critical, as most current resources are inadequate. This paper presents ArabicEduCrawler, an AI-assisted focused crawling framework designed to improve the acquisition, discovery, and organization of Arabic educational web content. The framework integrates domain-aware source selection, in-crawl Arabic language detection using FastText, large language model (LLM)-assisted XPath extraction, and metadata retrieval to support corpus quality and traceability. Its two-layer architecture combines dynamic web crawling using Scrapy-Playwright with advanced NLP processing, including automatic linguistic annotation with GateNLP and Stanza and a sentence-aware chunking strategy designed for transformer-compatible token limits. Experiments across four major Arabic educational domains resulted in the creation of the Arabic Educational Web Corpus (AraEdu-WC), which consists of 101,770 documents segmented into approximately 286 k text chunks, with more than 50 million tokens, 289,778 sentences, and nearly 3.5 million named entities. The system achieved a harvest ratio of 95.25%, indicating its effectiveness in filtering and retaining relevant content. The sentence-aware chunking evaluation showed consistent improvements in top-ranked retrieval, achieving the highest Hit Rate@10 and MRR@10 across all four embedding models. In particular, the multilingual-E5-large model achieved a Hit Rate@10 of 70%, Precision@10 of 18%, and MRR@10 of 57%. These findings demonstrate that the proposed approach provides an effective balance between crawl efficiency, language purity, and content richness, offering a high-quality Arabic educational corpus for downstream NLP and retrieval research. Full article

Mental health discourse may reflect social relationships, cultural norms, and religious factors that shape how individuals express and interpret distress. Existing NLP research on mental health has advanced the detection of depression, anxiety, suicide risk, and related clinical signals using text mining, neural classification, transformer-based models, and, more recently, large language models. However, most systems treat text primarily as a clinical signal rather than examining the social and cultural contexts in which distress is expressed. Arabic NLP research remains even more limited, largely focusing on detecting clinical conditions while overlooking contextual factors that shape mental health questions. This work introduces ContextMental, a multi-label annotation schema and benchmark dataset for modeling sociocultural context in Arabic mental health questions. The dataset contains 2677 questions, including 552 instances with contextual labels, enabling fine-grained analysis of social, cultural, and religious dimensions. An AraBERT-based classification framework is further developed using imbalance-aware optimization, semi-supervised pseudo-labeling, and adaptive threshold calibration. Experimental results indicate that pseudo-label augmentation improves overall classification performance, suggesting that semi-supervised learning can support context-aware Arabic mental health classification. This study provides a context-aware annotation framework, a benchmark dataset, and an AraBERT-based baseline modeling pipeline for Arabic mental health NLP, thereby supporting future research on socially, culturally, and religiously grounded language technologies. Full article

In Arab societies, cultural norms, family expectations, and social visibility constraints shape how young people encounter and respond to gender-related content in online environments, yet these dynamics remain insufficiently understood. Building on prior survey research and co-design workshops that explored participatory approaches to digital intervention design, this study investigates how Arab youth in Qatar perceive and respond to issues of gender equality and social inclusion in social media contexts.The Qatari context is particularly significant due to its rapid digital transformation combined with strong cultural, religious, and regulatory influences shaping youth online expression. Semi-structured interviews were conducted with thirty-two participants aged 18 to 24 residing in Qatar. The interviews explored social media activity, experiences of social inclusion, views on gender equality, and perceived challenges alongside culturally appropriate solutions. Interview transcripts were verified and analyzed using thematic analysis. The analysis revealed three interrelated thematic domains: determinants of attitudes rooted in cultural norms, values, and beliefs; attitudes toward gender equality and social inclusion ranging from supportive to resistant; and behavioral outcomes reflected in passive or active engagement as well as prosocial and antisocial digital behaviors. This study provides the first in-depth qualitative account of Arab youth’s perceptions of gender equality and social inclusion in digital spaces and offers culturally grounded insights to inform the design of inclusive and context-sensitive digital interventions. Full article

Aim: This study aimed to evaluate self-reported digital health literacy levels and work engagement among nurses in the United Arab Emirates (UAE), while also examining associations with demographic factors and the interplay between digital health literacy and work engagement. Background: The integration of digital technologies into healthcare has transformed patient care, clinical practice, and administration. Nurses, as frontline practitioners, play a crucial role in utilizing digital tools to enhance patient interactions and navigate complex healthcare systems. Methods: Between May and August of 2024, 364 nurses in the United Arab Emirates participated in a cross-sectional design study. A standardized 21-item self-reported Digital Health Literacy questionnaire and a 9-item Utrecht Work Engagement Scale were administered. Descriptive statistics were used, with t-tests, ANOVA, correlations, and multiple linear regression applied. Results: The average score for self-reported digital health literacy (3.05 ± 0.57) and work engagement (4.83 ± 1.13) was high. Gender, age, work experience, and education level showed varying patterns of association with self-reported DHL and work engagement across total and subscale scores. Education level was significantly associated with self-reported DHL but not with work engagement. The overall work engagement score and its subscales were positively correlated with self-reported DHL. Conclusions: Our findings provide a robust basis for subsequent research on DHL and work engagement. These findings support the relevance of self-reported DHL as a factor associated with nurses’ work engagement in digitally intensive healthcare settings. The study reveals that nurses reported high levels of digital health literacy and work engagement. Full article

Although the figure of Salome has received much attention from artists and critics alike in the last century and a half, the sources of the legend of her death by ice remain little known. This article traces the origin, development, and transmission of the legend from late antiquity through the early modern period, highlighting its rich presence in medieval Eastern and near total neglect in medieval Western Christian traditions. Drawing on Syriac, Greek, Coptic, Arabic, and Slavic sources, it demonstrates that the legend emerged before the sixth century in the Christian East, where it circulated widely in apocryphal, hagiographical, and historiographical texts. Early attestations, such as the Epistle of Herod to Pilate, already contain core narrative motifs, which later Byzantine authors elaborated into a sophisticated typological narrative of divine retributive justice. In Greek and Slavic traditions especially, the legend achieves rhetorical refinement through the image of decapitation “not by iron but by ice,” while Syriac, Coptic, and Arabic retellings amplify the punitive dimension through the accumulation of often extravagant forms of divine punishment. In contrast, the medieval Latin West preserves only faint and fragmentary echoes of the legend, typically reducing it to brief notices of drowning and largely stripping it of its typological force. Instead, an alternative motif—Salome swallowed by the earth—gains prominence in Western textual and visual culture, possibly facilitated by the conflation of Salome with her mother. The article argues that the fully developed Eastern narrative entered Western Europe only in the sixteenth century through printed Latin translations of Byzantine historiography, particularly the works of Symeon Metaphrastes and Nicephorus Callistus Xanthopoulos. This print-mediated transmission enabled the legend’s broader reception in early modern Europe and its eventual transformation into a folkloric and literary motif, culminating in the nineteenth-century Decadent literature and art. Full article

Cybersecurity awareness has become a critical concern in the context of rapid digital transformation and the growing sophistication of cyber threats. While previous research has identified password security, browser security, and social media activities as key factors influencing cybersecurity awareness, the role of digital literacy remains underexplored, particularly in multicultural environments. This study examines the factors affecting cybersecurity awareness among adults in the United Arab Emirates (UAE), extending the existing theoretical framework by introducing digital literacy as a novel variable. Data were collected from 150 respondents through a structured questionnaire and analyzed using Cronbach’s Alpha, exploratory factor analysis, Pearson correlation analysis, simple and multiple linear regression, the Mann–Whitney U test, and the Kruskal–Wallis test. Results indicate that all four predictors—password security (β = 0.317), browser security (β = 0.149), social media activities (β = 0.209), and digital literacy (β = 0.256)—significantly predict cybersecurity awareness, with the combined model explaining 53.6% of variance (R² = 0.536). Digital literacy showed the strongest correlation with cybersecurity awareness (r = 0.614, p = 0.000). Demographic analyses revealed significant differences across age groups and digital literacy levels, with younger respondents and those with higher digital literacy consistently demonstrating higher levels of cybersecurity awareness. These findings highlight the importance of integrating digital literacy into cybersecurity education programs, particularly in multicultural contexts. From a theoretical perspective, this study extends the existing cybersecurity awareness framework by introducing digital literacy as a novel predictor variable and validates its significance in a unique multicultural environment. From a practical perspective, the findings provide empirically grounded guidelines for the development of culturally adapted cybersecurity education programs, with particular emphasis on age-differentiated approaches and the potential role of younger generations as drivers of cybersecurity awareness in the UAE and similar multicultural contexts. Full article

This article examines the ethical and aesthetic stakes of cultural borrowing in fashion through the case of Yves Saint Laurent’s sustained engagement with Moroccan visual and material traditions. Drawing on postcolonial theory, fashion studies, and aesthetic philosophy and supported by visual analysis and qualitative research, this study interrogates the tension between cultural appreciation and appropriation that structures Saint Laurent’s legacy. His designs amplified global visibility for Moroccan craftsmanship, yet this visibility was mediated through Western systems of authorship that privileged the couturier while obscuring the cultural, spiritual, and artisanal labor underpinning the motifs he reinterpreted. Saint Laurent’s own positionality, born within the colonial milieu of French Algeria, further complicates this dynamic, enabling both cultural intimacy and the exercise of hierarchical distance from the traditions he transformed for Parisian haute couture. This discussion also requires acknowledging that Moroccan cultural heritage, shaped by the intertwined influences of Amazigh, Arab, Islamic, and Jewish traditions, embodies religious meanings that extend beyond the purely aesthetic. By considering the religious, symbolic, and communal values embedded within Moroccan aesthetic forms, this article foregrounds the ethical dilemmas that arise when culturally and spiritually situated practices are reframed within Western fashion. This study ultimately contends that acts of borrowing can serve both as homage and erasure, suggesting that the relationship between appropriation and appreciation is better understood as a flexible spectrum rather than a rigid binary. Full article

Blueberry anthocyanins (BAs) exhibit strong antioxidant and lipid-regulating activities; however, their chemical instability and low oral bioavailability limit their practical application. In this study, two plant-based bilayer nanocarriers were developed using soybean lecithin as the lipid core and gum arabic (GA) or carrageenan (CGN) as the shell polysaccharide. The optimized systems achieved encapsulation efficiencies of 79.7% and 81.9%, respectively. Structural analyses showed that anthocyanins were stably incorporated into the carriers through multiple non-covalent interactions and transformed from a crystalline to an amorphous state. The two shell polysaccharides exhibited distinct conformation-dependent protective behaviors: GA provided better thermal protection, whereas CGN showed superior resistance to light, metal ions, ascorbic acid, and simulated intestinal digestion. After INFOGEST digestion, anthocyanin retention in the intestinal phase was 47% and 51% for the GA- and CGN-coated systems, respectively, and antioxidant activity was better preserved than in the free anthocyanin group. In an oleic-acid-induced HepG2 lipid accumulation model, the CGN carrier showed good biocompatibility and significantly enhanced the lipid-lowering effect of anthocyanins, with the most pronounced reduction in intracellular triglycerides. These results indicate that the CGN carrier has considerable potential for maintaining anthocyanin stability, modulating digestive behavior, and enhancing biological efficacy, and provide a reference for the design of plant-based delivery systems for bioactive ingredients. Full article

Drone-based smart sensing increasingly relies on Vision–Language Models (VLMs) for real-time scene interpretation, obstacle detection, and autonomous navigation reasoning. Deploying such systems at scale demands not only high perceptual accuracy but also energy efficiency, a critical constraint on battery-powered Unmanned Aerial Vehicle (UAV) platforms, and linguistic flexibility for multinational operational contexts. We present a systematic benchmarking framework that jointly evaluates perception performance and inference energy for five open-source VLMs across thirteen languages spanning six language families, including three low-resource varieties (Arabic, Basque, and Luxembourgish). Using imagery sampled from the Berkeley DeepDrive 10K (BDD10K), each model is evaluated on four sensing tasks of increasing difficulty scored via a sentence-transformer backbone, with energy measured following the AI Energy Score methodology (Wh per 1000 queries) through continuous NVML-based GPU power sampling. Across 65 language–model observations, LLaVA-1.6 achieves the highest perception score ($\overline{S}=0.160$) while Phi-3-Vision attains the best energy efficiency ($66.3$ Wh/1000 queries); energy consumption and task accuracy are statistically uncorrelated (Spearman $\rho =0.001$; $p=0.995$). A formal UAV inference energy model instantiated for four commercial platforms confirms LLaVA-1.6 as Pareto-optimal on heavy-lift platforms (DJI Matrice 300/350 RTK) and LLaVA-1.5 on the energy-constrained Matrice 30; compact UAVs such as the Mavic 3 Enterprise exceed the budget of all evaluated models at standard query rates. Friedman tests reveal significant cross-language variability in energy demands (${\chi }^2=40.43$; $p=3.5\times {10}^{-8}$) and navigation reasoning performance (${\chi }^2=13.35$; $p=0.010$). Critically, we document a double penalty for low-resource languages, which simultaneously incur higher inference energy costs and lower task accuracy, with direct implications for equitable multilingual UAV deployments. Full article

Background: Supply chain management (SCM) research has expanded across the Middle East and North Africa (MENA), yet the field remains fragmented. Limited synthesis exists on how regional conditions shape research themes, theories, and methods. Methods: This study applies the PRISMA 2020 protocol to review SCM articles indexed in Scopus and Web of Science from January 2000 to March 2025. After screening and eligibility assessment, 512 peer-reviewed studies were retained. Bibliometric mapping and thematic coding were used to identify publication trends, research streams, theoretical lenses, and methodological patterns. Results: SCM research increased sharply after 2015, reflecting national diversification agendas, logistics reform, digitalization, and exposure to global supply chain disruptions. Three dominant streams were identified: resilience, sustainability, and digital transformation. Research output is concentrated in Saudi Arabia and the United Arab Emirates, while cross-country comparative studies remain scarce. Empirical studies rely mainly on cross-sectional surveys and SEM-based analysis, with limited longitudinal, qualitative, mixed-method, and comparative work across the region. Conclusions: The study develops an integrative SCM capability framework linking regional structural conditions, capability development, and supply chain outcomes. The findings support managers and policymakers seeking resilient, sustainable, and digitally enabled supply chains, and define clear future research priorities for the MENA region. Full article