Abstract. Climate change and sea-level rise (SLR) pose escalating threats to coastal cities, intensifying the need for efficient and accurate methods to predict potential flood hazards. Traditional physics-based hydrodynamic simulators, although precise, are computationally expensive and impractical for city-scale coastal planning applications. Deep Learning (DL) techniques offer promising alternatives, however, they are often constrained by challenges such as data scarcity and high-dimensional output requirements. Leveraging a recently proposed vision-based, low-resource DL framework, we develop a novel, lightweight Convolutional Neural Network (CNN)-based model designed to predict coastal flooding under variable SLR projections and shoreline adaptation scenarios. Furthermore, we demonstrate the ability of the model to generalize across diverse geographical contexts by utilizing datasets from two distinct regions: Abu Dhabi and San Francisco. Our findings demonstrate that the proposed model significantly outperforms state-of-the-art methods, reducing the mean absolute error (MAE) in predicted flood depth maps on average by nearly 20 %. These results highlight the potential of our approach to serve as a scalable and practical tool for coastal flood management, empowering decision-makers to develop effective mitigation strategies in response to the growing impacts of climate change. Project Page: https://caspiannet.github.io/ (last access: 22 January 2026).

Purpose The purpose of this paper is to examine the association between the level of risk disclosure (RD) and ESG disclosure of non-financial firms listed in Abu Dhabi Stock Exchange (ADX) and Dubai Financial Market (DFM), using Refinitiv Financial Solutions Database that concurs with increased legislation and increased public awareness of ESG issues. Design/methodology/approach For this research, data were collected from 44 non-financial listed firms using content analysis to estimate the level of RD. ESG data were extracted from the Refinitiv Financial Solutions Database. This study observed these firms over the period from 2018 to 2023. An ordered logit model was employed to estimate the model. Findings The research findings show: First, RD increased considerably from 2018 to 2021 as a result of the socio-economic circumstances caused by the COVID-19 pandemic. However, it decreased in 2022 and 2023. Second, the highest average ESG performance rating is observed in the logistics sector, while the lowest ESG performance ratings are observed in the transportation and construction sectors; the average industry ESG rating scale is “C”. Third, firms with higher ESG rating scales report more risk-related information in their annual reports. Fourth, financially performant firms and younger firms demonstrate better corporate governance ratings. Practical implications The findings have important policy implications, particularly for UAE regulators to strengthen ESG disclosure regulations and develop mandatory sustainability reporting frameworks aligned with international standards to enhance corporate transparency and accountability. Originality/value This paper offers significant practical and theoretical contributions. Practically, it provides UAE policymakers and regulators with evidence-based insights into current risk and ESG disclosure practices, enabling them to develop targeted frameworks that enhance corporate transparency and align with international standards. The findings also guide practitioners in improving their disclosure strategies to meet stakeholder and regulatory expectations. Theoretically, this is the first study to examine the association between RD and ESG disclosure using an ESG rating scale in the UAE market. It advances legitimacy and stakeholder theories by demonstrating how firms use integrated disclosure to manage stakeholder pressures in emerging markets. The research provides a foundation for future scholars investigating sustainability reporting in developing economies, particularly in the understudied GCC region.

ABSTRACT The Eastern Mediterranean and Middle East (EMME) region is a climate change hotspot experiencing increasing heat extremes. Understanding outdoor thermal discomfort in this region is critical for climate‐resilient urban planning and public health. This study examines the spatial and temporal patterns of thermal discomfort across the EMME over the 1980–2023 period, using ERA5 reanalysis data for dry‐bulb temperature, relative humidity and wind speed. Thermal discomfort is quantified using five indices: effective temperature, heat index, humidex, wet bulb globe temperature and discomfort index. All indices indicate increasing summer thermal discomfort across the region, with a pronounced shift around 1998 that coincides with a ‘mega El Niño’ event and a positive phase of the Indian Ocean Dipole. Cities in the central EMME exhibit the strongest warming, with an annual‐mean increase of 0.54°C per decade—nearly three times the global mean. Warming is strongest in summer, with northern cities warming by up to 0.73°C per decade in summer, compared with 0.52°C per decade for the all‐months (annual‐mean) trend; southeastern cities show a weaker contrast, increasing by up to 0.42°C per decade in summer versus 0.40°C per decade in the annual‐mean. Relative humidity generally declines in northern and central cities, including Tehran and Jerusalem (−1.41% and −1.07% per decade), while southeastern cities such as Riyadh show modest summer increases of 0.37% per decade. Diurnal variability analysis indicates that Abu Dhabi is the only city with a strong linear trend in the summer diurnal temperature range (), whereas the summer diurnal relative humidity range exhibits more pronounced trends, notably in Jerusalem (−0.123% per year, ). Post‐1998, summer warming intensified across the EMME (0.40°C–0.56°C per decade), with northern cities experiencing relative humidity declines of up to 0.66% per decade, highlighting escalating thermal discomfort.

This study investigates the prevalence and sources of microplastics (MPs, 300–5,000 µm) in the marine environment of Abu Dhabi Emirate, UAE—an underexplored region with significant anthropogenic influence. Samples were collected from ten ecologically distinct site categories, including areas near oilfields, near desalination plants, port and marinas, aquaculture activities, public beaches, confined areas, newly developed areas, point sources, near offshore islands and natural habitats. “Natural habitats” showed the lowest MP levels (3.33 particles/100 g sediment; 4.5 P/L water), while sites near oilfields, ports, and offshore islands had the highest (8.2–9.3 P/L water; 5.0–6.6 P/100 g sediment). A total of 1,493 MPs were characterized by size, shape, and color. Polymer analysis of 240 MPs identified acrylonitrile–butadiene–styrene (31%), cellulose acetate (27%), nylon-66 (20%), and PET (10%) as dominant types. Smaller MPs (100–300 µm) were also quantified at selected categories. Pollution Load Index (PLI) analysis, using natural habitats as a baseline, indicated the greatest anthropogenic impact near offshore oilfields and islands, highlighting spatial variations in MP contamination.

AI decision-making is transforming the healthcare industry by increasing efficiency and improving patient outcomes. Despite concerns about job displacement and data privacy, the use of AI is critical for improving healthcare systems and developing leadership in medical innovation. Thus, the goal of this study was to create a framework that identifies elements that improve AI decision-making and its impact on organisational performance, with organisational learning acting as a mediator, incorporating both direct and indirect relationships between these aspects. This paradigm analyses how AI adoption elements might improve decision-making processes and, as a result, boost overall performance, assisted by the organization’s constant learning and adaptability. To validate the framework, a questionnaire survey was administered to 1,033 employees at three Abu Dhabi hospitals which are the Tawam Hospital, Shaikh Khalifa Medical City (SKMC), and Ambulatory Healthcare Services (AHS). The validation was carried by utilising SmartPLS software. All five direct and indirect relationships were found to be statistically significant, demonstrating that organisational learning had partial mediation effects. This suggests that all of the identified characteristics have a major impact on AI decision-making tool adoption, with organisational learning serving as a key mediator. The framework highlights the relationships between the factors to boost AI adoption in the UAE healthcare business, solving concerns such as long wait times and administrative challenges through better technological integration.

The lack of data on the relationship between dysphagia and major esophageal motility disorders (MEMDs) in the United Arab Emirates (UAE) has presented challenges for clinical management of dysphagia. This study aims to describe the characteristics of patients with dysphagia and MEMDs. We created a retrospective cohort using data from all patients who underwent High Resolution Esophageal Manometry (HREM) at Sheikh Shakhbout Medical Center (SSMC) for different indications between July 2020 and February 2023. The patients were categorized into two groups based on whether findings included dysphagia or not. Data on age, gender, body mass index (BMI), nationality, opioid use, comorbidities and their endoscopic and or imaging findings were collected from electronic health records retrospectively. HREM was performed based on Chicago Classification version 3.0 protocol. There were 125 patients who underwent HREM; 104 patients met the inclusion criteria (48 men and 56 women) of which 64 (61.5%, 64/104) had dysphagia and 37 (35.6%, 37/104) had superimposed major motility disorder. Ineffective esophageal motility (IEM) was most common (37.8%, 14/37), followed by achalasia (27.0%, 10/37), esophagogastric junction outflow obstruction (EGJOO) (24%, 9/37), Aperistalsis (8.1%, 3/37), and 2.7% (1/37) had Distal Esophageal Spasm (DES). There was a statistically significant relationship with gender and nationality among those with and without a MEMDs who had dysphagia (p < 0.05). This is the first study in the UAE to investigate the association between non-obstructive dysphagia and MEMDs. It demonstrated that dysphagia as a presenting symptom is more likely to be associated with an MEMD on HREM.

Response times serve as reliable measures of engagement that are less prone to self-report biases. By utilizing response times, we examined the indicators of Successful Time Management (STM) and Unsuccessful Time Management (UTM) across the 36 countries and benchmarking participants in eTIMSS 2019 and investigated their effects on mathematics achievement through a multilevel analysis. The results showed that students use their time differentially based on item correctness, reflecting the need to include item correctness in the analyses when timing data are examined. Overall, higher-performing students exhibited higher levels of STM and lower levels of UTM, indicating they efficiently managed their time on items they answered correctly and invested relatively more time on challenging items. Conversely, lower-performing students tended to have lower STM and higher UTM, suggesting difficulties or disengagement with challenging items. However, significant differences across countries and benchmarking participants were identified, with the United States and Abu Dhabi being among the outliers. These findings provide insights into how students allocate their time during assessments, and important implications for interpreting student performance are discussed.

This study investigates police officers’ perceptions of advanced technological tools in crime investigations within Abu Dhabi, United Arab Emirates. Drawing on a sample of 375 officers selected through simple random sampling, the research examines five core technologies: crime mapping, facial recognition, car-mounted cameras, body-worn cameras, and license plate readers. Descriptive statistical analysis was employed to evaluate perceptions across dimensions of effectiveness, challenges, and impact on investigative outcomes. The findings reveal generally positive attitudes toward technology adoption, with facial recognition, body-worn cameras, and car-mounted cameras perceived most favourably for their contributions to accountability, transparency, and investigative accuracy. Crime mapping and license plate readers were also valued, particularly for hotspot identification and traffic enforcement, though respondents highlighted challenges related to data accuracy, training adequacy, false positives, and privacy concerns. Overall, the results underscore the importance of organizational capacity, technical support, and ethical safeguards in ensuring that technological innovation enhances policing effectiveness. The study contributes to the discourse on law enforcement modernization in the UAE by providing empirical evidence that can inform policy, guide resource allocation, and strengthen the integration of technology into investigative practice.

Late Quaternary climate change in the northeastern Arabian Peninsula recorded in the oxygen isotope composition of carbonate cements of aeolianites

The Abu Dhabi Police Traffic Department has increasingly adopted innovative Information and Communication Technology (ICT) to enhance traffic management, operational efficiency, and public safety. However, empirical evidence on how officers’ perceptions of specific ICT innovation characteristics influence individual job performance remains limited. This study investigates the perceived impact of ICT innovation on job performance using a hierarchical ranking approach grounded in the Diffusion of Innovation theory. Data were collected through structured questionnaires distributed to operational and executive staff, yielding 338 valid responses. Descriptive statistics and mean index-based hierarchical ranking were employed to evaluate the relative importance of five ICT innovation attributes: relative advantage, compatibility, trialability, observability, and complexity. The findings reveal consistently high perceptions across all attributes, indicating a strong positive relationship between ICT innovation and officers’ job performance. Relative advantage emerged as the most influential factor, followed by compatibility and trialability, while observability and low complexity provided additional supportive effects. The narrow range of mean scores reflects strong consensus among respondents regarding the effectiveness of ICT systems in improving work efficiency, decision-making accuracy, and routine task performance. This study contributes empirical evidence to the limited literature on ICT innovation in policing and highlights the critical role of officers’ perceptions in shaping technology adoption. The findings provide practical guidance for the Abu Dhabi Police and other public sector law enforcement organizations seeking to optimize ICT investments to enhance employee performance, operational efficiency, and service delivery outcomes.

Type of the article: Research ArticleAbstractGrowing regulatory and investor scrutiny of sustainability has intensified the need to understand whether green finance and environmental, social, and governance (ESG) practices translate into measurable corporate performance gains in emerging markets. This study examines how green finance and ESG practices influence multidimensional corporate performance among firms listed on the UAE stock market. Data were collected via a structured survey of finance and sustainability managers in companies listed on the Abu Dhabi Securities Exchange and the Dubai Financial Market; 167 valid responses (87.4% response rate) were analyzed using structural equation modeling. The survey was administered in the UAE in May 2025. Results show positive effects of green finance on efficiency (β = 0.341, p &amp;amp;lt; 0.01), effectiveness (β = 0.206, p &amp;amp;lt; 0.01), sustainability (β = 0.157, p = 0.041), and customer satisfaction (β = 0.248, p &amp;amp;lt; 0.01). ESG practices also improve efficiency (β = 0.288, p &amp;amp;lt; 0.01), effectiveness (β = 0.179, p = 0.032), sustainability (β = 0.194, p &amp;amp;lt; 0.01), and customer satisfaction (β = 0.142, p = 0.028). Joint modeling indicates complementary effects, with the strongest combined impacts on sustainability (β = 0.376, p &amp;amp;lt; 0.01) and customer satisfaction (β = 0.408, p &amp;amp;lt; 0.01); model fit is strong (GFI = 0.91; CFI = 0.96; RMSEA = 0.014). Overall, integrating green financing with robust ESG practices enhances corporate outcomes in the UAE, providing practical guidance for managers, investors, and policymakers.AcknowledgmentThe authors are thankful to the Deanship of Graduate Studies and Scientific Research at University of Bisha for supporting this work through the Fast-Track Research Support Program.

This study investigates the role of knowledge management in enhancing project success within the oil and gas sector, with a focus on Abu Dhabi National Oil Company (ADNOC). Project management success is increasingly recognised as a multidimensional concept that extends beyond the traditional constraints of time, cost, and quality to encompass stakeholder satisfaction, organisational learning, and long-term strategic impact. To explore this, the research examined five key dimensions of knowledge management which are Knowledge Leadership, Knowledge Culture, Knowledge Processes, Knowledge Technology, and Knowledge Sharing. A purposive sampling technique was employed to ensure the inclusion of respondents with relevant expertise, resulting in a sample of 179 ADNOC employees directly engaged in project management activities. Data were analysed in two stages: first, normality was assessed using skewness and kurtosis values, confirming the suitability of the dataset for statistical testing; second, descriptive statistics were examined through mean scores derived from Likert-scale responses. The findings revealed that employees generally perceive ADNOC as supportive of knowledge management practices. Knowledge Leadership, Culture, Processes, and Sharing were consistently rated positively, highlighting strengths in organisational support, reward systems, documentation, expertise, and collaborative behaviours. However, Knowledge Technology showed greater variation, with weaker engagement in external sharing and active technology-based participation, indicating areas for improvement. Overall, the study concludes that ADNOC has established a strong foundation for knowledge management, but further reinforcement in technological adoption, collaboration, and collective knowledge confidence is required to fully embed knowledge practices and achieve sustainable project success.

Volunteering is increasingly recognized as a key contributor to personal and societal well-being. This study examines its impact on well-being indicators among working individuals in Abu Dhabi, focusing on job satisfaction, work-life balance, mental health, social relations, and happiness. Using data from a large-scale survey, a path analysis explored the direct and indirect effects of volunteering on well-being. The study analysed volunteering frequency alongside subjective health, job satisfaction, income satisfaction, and mental health. Bio-demographic variations in volunteering and its effects were assessed across gender, marital status, education, living region, nationality, household headship, and age. Volunteering was significantly associated with higher job satisfaction, better subjective health, and greater happiness. Mental health and social relations played crucial roles in shaping volunteering behaviour, reinforcing its psychological and social benefits. Work-life balance influenced both well-being and volunteer engagement. Demographic differences indicated that age, education, region, nationality, and household headship shape volunteering patterns, underscoring the need for targeted policies. Volunteering enhances workforce well-being and supports workplace satisfaction. Policies promoting volunteerism in workplaces can foster a healthier, more engaged workforce, emphasizing social engagement as a vital mechanism for well-being improvement.

Type of the article: Research ArticleHerding behavior often emerges in uncertain market conditions, when investors, confronted with limited or ambiguous information, tend to imitate their peers’ actions instead of relying on their own analytical assessments. This follow-on herd mentality phenomenon engenders analogous trading behavior among market participants, potentially undermining market efficiency. During times of increased volatility, such behavioral patterns become more noticeable, which has a substantial impact on asset values and skews the efficiency of financial markets. This study explores herding in the UAE stock markets during the COVID-19 outbreak, focusing on the Dubai Financial Market (DFM) and the Abu Dhabi Securities Exchange (ADX). Using daily data from January 1, 2019 through December 31, 2021, the Cross-Sectional Absolute Deviation (CSAD) model is implemented in static and dynamic forms to explore nonlinear and evolving aspects of investor behavior. The analysis indicates that during the initial months of the pandemic, clear evidence of herding emerged in the Dubai Financial Market (γ₃ = –3.087; p &amp;amp;lt; 0.05), whereas the Abu Dhabi Exchange did not display statistically meaningful signs of such behavior. This contrast highlights how herding behaviors are not uniform across markets; they are shaped by factors such as institutional structures, liquidity levels, and the overall composition of traders. The results offer valuable implications for regulators, policymakers, and large investors, providing insights into how behavioral patterns can affect market resilience in emerging markets. Moreover, the study’s findings highlight the importance of timely disclosure and targeted investor awareness initiatives in reducing irrational reactions during periods of distress or crisis.Acknowledgment We are grateful to our co-authors for their invaluable contributions and collaboration in this research. A special thanks to the young independent researchers who are also the co-authors for their dedication and efforts in data collection and analysis, which have significantly enriched this study. We also appreciate the support provided by our institutions and the insightful feedback from our peers and reviewers.

Artificial intelligence (AI) is increasingly being used in oncology to support early diagnosis and develop personalized treatment plans. However, its successful use in cancer care depends not only on the technology itself, but also on whether people trust it, understand it, and are open to accepting it. This study explored how digitally engaged adults living in Abu Dhabi, UAE, perceive AI-assisted cancer treatment, focusing on how factors like demographics, AI familiarity, and personal cancer experience influence acceptance and concern. A cross-sectional survey was completed by 413 adults using a structured, bilingual (Arabic-English) questionnaire. Participants were asked about their awareness of AI in healthcare, their willingness to accept AI-assisted cancer treatment, and their concerns about issues such as safety, accuracy, cost, and data privacy. Chi-square tests were used to analyze the relationships between these factors. Familiarity with AI had a significant impact on acceptance (p = 0.0006), with those who were very familiar with AI more likely to accept its use in cancer treatment. In contrast, education level (p = 0.664) and personal experience with cancer (p = 0.3562) were not significantly associated with acceptance. Participants who were less familiar with AI expressed more concerns about its accuracy, safety, cost, and the privacy of their data (p = 0.0073), pointing to awareness as a key factor in shaping trust. These findings suggest that trust in AI-assisted cancer care is driven more by how familiar people are with the technology than by their educational background or cancer experience. To build public trust and encourage responsible use of AI in oncology, it is essential to keep healthcare professionals actively involved and to communicate clearly and transparently with patients.

Type of the article: Research ArticleAbstractManaging human resources is crucial for ensuring organizational success. This paper aims to identify the impact of job analysis and human resource planning on staffing strategies in the United Arab Emirates. It also aimed to verify the mediating effect of human resource planning. A research model was developed based on the literature review and theoretical framework. A quantitative cross-sectional questionnaire was used to assess employee attitudes in banks in Abu Dhabi. Specifically, the required data were gathered from 309 banking employees in Abu Dhabi via convenience sampling over the period from February to April 2025. This study employed the structural equation modeling (SEM) approach, with AMOS as the chosen method of analysis. The findings indicate that staffing strategies are positively affected by both job analysis (β = 0.702, p &amp;amp;lt; 0.05) and human resource planning (β = 0.143, p &amp;amp;lt; 0.05). Furthermore, the results verified the mediating role of human resource planning between job analysis and staffing strategies (β = 0.143, p &amp;amp;lt; 0.05). These findings demonstrate a direct, beneficial influence of job analysis and human resource planning on staff strategies and offer noteworthy implications for managers in the banking context.

Background and objective Autologous stem-cell transplantation (ASCT) remains the standard of care for multiple myeloma (MM), yet it is not curative. Over the past decade, there has been growing interest in immunotherapy-based strategies, highlighting the need for predictive biomarkers to tailor post-ASCT treatment. Characterization of immune status via peripheral lymphocyte immunophenotyping is a critical step in this process. This study aimed to characterize peripheral blood lymphocyte immunophenotypes in MM patients undergoing ASCT at baseline, during stem cell mobilization, and on day +21 post-transplant, and to establish descriptive longitudinal benchmarks that may inform immune monitoring in this clinical setting. Methods We conducted a prospective, single-center study at the Abu Dhabi Stem Cells Center (Abu Dhabi, United Arab Emirates (UAE), enrolling all MM patients undergoing ASCT between July 2020 and February 2024 (n = 28), with no additional exclusion criteria. Peripheral blood samples were collected at three time points: before ASCT, on day +21 post-ASCT, and from the apheresis product. Canonical lymphocyte markers were analyzed by flow cytometry using a single, polychromatic 10-color antibody panel on a Navios EX cytometer. Results Of the 28 MM patients studied, 40.3% were women. The age range was 23 to 64 years. Markedly low baseline B cell counts were observed (6-175 cells/µL). A significant post-transplant decrease in helper T cells, B cells, and the CD4/CD8 ratio was observed at day +21 compared with baseline. The mean total lymphocyte dose infused (0.20 × 10⁹ cells/kg) was below published thresholds; however, the major lymphocyte subsets exceeded recommended cutoff values, suggesting a potentially favorable prognosis. Dose ranges for non-conventional lymphocyte subsets were also determined Conclusions T helper and B cells declined during the peri-transplant period in patients with MM who underwent ASCT, suggesting a lack of association with the autograft dose of homologous populations. This study provides preliminary, descriptive benchmarks for minority immune cell populations, supporting their potential relevance as prognostic biomarkers.

Abstract Background: Misinterpretation of the features observed in the cardiotocograph (CTG) may lead to unnecessary intrapartum operative interventions to women due to over-reaction (“pathological” CTG) l or may result in hypoxic-ischaemic encephalopathy (HIE) or perinatal deaths. Physiological interpretation of CTG involves classification of CTG traces based on the type of hypoxic/non-hypoxic stress and determining the fetal responses to stress to individualise care. Aim: To determine the impact of implementing the principles of physiological interpretation of CTG at two premier tertiary referral hospitals specialising in complex obstetric care in the UAE on the rate of intrapartum emergency caesarean sections (EMLSCS) and HIE. Methods: All staff working in the labour and delivery rooms (LDR) at the Burjeel Medical City (BMC) and Burjeel Hospital, Abu Dhabi (BHAD) underwent intense training on the principles of physiological interpretation of CTG in May 2024. This was followed by implementation of International Expert Consensus Guidelines on Physiological Interpretation of CTG produced by &gt; 50 CTG experts from &gt; 20 countries. This was followed by monthly update sessions and “refreshers” on physiological interpretation of CTG to re-enforce knowledge. The rates of EMLSCS and HIE were analysed. Results: The rates of EMLSCS reduced from 37% and 34% from BHAD and BMC, respectively, to 20% at both maternity units within 13 months. There were no cases of HIE due to CTG misinterpretation during the same period. Conclusion: There was a notable reduction (&gt;40%) in the rate of emergency caesarean section at both tertiary referral hospitals providing complex obstetric care within 13 months of implementing the principles of physiological interpretation of CTG. This decrease was accompanied by an absence of HIE due to CTG misinterpretation at both quaternary referral hospitals for over 13 months.

Social media offers numerous advantages, yet the dark side of it haunts youth. One of the several potential detrimental aspects appears as social media addiction (SmA), which poses risks to the psychological well-being (PW) of youth. Among university students, excessive social media use can lead to SmA and a subsequent decrease in PW. This study scrutinizes the mediating effects of techno-invasion (TI) and techno-overload (TO), instigated by SmA, on students' psychological well-being, a relationship that previous research has not conclusively explored. Our research constructs a model to examine the moderating effects of social capital, specifically through the lenses of social bonding and social bridging, on the nexus between social media-induced challenges and psychological well-being. This investigation examines the associations between SmA, TO and TI through self-esteem among students. Data collected through a three-wave time-lagged design from 991 university students in Abu Dhabi, UAE, were analyzed with the PROCESS macro. The findings reveal that SmA is significantly negatively related to students' self-esteem, which in turn is associated with increased levels of TI and TO, ultimately reducing psychological well-being. Moreover, the study verifies the moderating roles of social bonding and social bridging in the TI-PW and TO-PW relationships. The paper concludes by addressing the broader implications of these findings and suggesting avenues for future research in this domain. This study demonstrates how SmA reduces self-esteem, which in turn increases TO and TI, ultimately leading to a decline in PW. The findings suggest that improving self-esteem and enhancing social capital through stronger connections with family and friends can help alleviate the negative consequences of SmA on students' well-being, offering valuable contributions to the literature on digital stress and mental health.

Abstract This study examines how anomalously high sea surface temperatures (SSTs) in the Arabian Sea and surrounding gulfs contributed to the record‐breaking rainfall (250 mm ) over the United Arab Emirates (UAE) on 16 April 2024, with the greatest impacts in Dubai, Al‐Ain, and Abu Dhabi. Numerical modeling and satellite observations were used to examine the atmospheric and oceanic conditions leading to this extreme event. Sensitivity experiments show that warm SST anomalies enhanced moisture supply and intensified convection, producing larger and more intense convective storms. The warmer SSTs increased both moisture availability and storm intensity. The sensitivity model runs show higher precipitable water across much of the domain in the week preceding 16 April. Results indicate that, while atmospheric dynamics played a key role, SST‐driven moisture was the primary driver of the 16 April heavy precipitation. These findings underscore the role of ocean‐atmosphere interactions in driving extreme weather in arid regions.