Arabian Gulf University is a university in the city of Manama, in the Kingdom of Bahrain. It is accredited by the Ministry of Education, Bahrain, and governed by Gulf Cooperative Countries, and is a member of Federation of the Universities of the Islamic World. Entry into the university is restricted to GCC nationals, with other Arab nationals considered only if vacancies are available.
Hexavalent chromium [Cr(VI)] removal was evaluated using two facultative anaerobic bacteria isolated from a microbial mat near a chromite mining site: Bacillus sp. S9 (Gram-positive) and Enterobacter sp. Z11 (Gram-negative). Both strains effectively removed Cr(VI) under anaerobic conditions, with total removal efficiencies of 98.3 ± 0.6
Burnout is an escalating global occupational health challenge, requiring valid and reliable assessment tools. This study validates the Copenhagen Burnout Inventory (CBI) for assessing burnout among Sudanese workers in the education, healthcare, and banking sectors, where burnout prevalence is high. Utilizing the 19-item CBI, translated into Arabic, the study measured burnout across three dimensions: Personal Burnout (PB), Work-related Burnout (WB), and Client-related Burnout (CB). A total of 1068 participants were surveyed, including 438 teachers (41%), 326 healthcare workers (30.5%), and 304 bank employees (28.5%). Exploratory and Confirmatory Factor Analyses confirmed the construct validity of the CBI, while concurrent validity was supported through moderate to high correlations with the Maslach Burnout Inventory (MBI) domains, except for a weak correlation between Depersonalization and PB/WB. Reliability was established through discriminant validity, all of which were satisfactory across the three groups. The study resulted in two final versions of the CBI: a 17-item version for healthcare workers and a 19-item version for teachers and bank employees. Both versions are available in Arabic, and stakeholders are recommended to use the CBI tailored to each sector's specific psychometric properties. This tailored approach ensures accurate measurement of burnout, aiding psychologists, therapists, and policymakers in addressing and mitigating burnout effectively within each professional group.
In recent years, One-class deep learning approaches, especially, autoencoder-based, have emerged as leading methods in the field of anomaly detection. However, the high dimensional, disparate, and non-homogenous datasets affect the performance of these models in many applications. To address these concerns, this paper presents an improved Interpolated Implicit Rack-Minimized Deep Support Vector Data Description (IIRMDSVDD) autoencoder. The proposed model focused on optimizing the projected latent space to produce more effective separating hyperspheres, which will significantly improve the precision and robustness of anomaly detection. The IIRMDSVDD combines two main mechanisms into the training process for adopting an improved network architecture. The first mechanism implemented an interpolation regularizer to improve the latent space. The second component extends the concept of minimizing the covariance matrix in the latent space by introducing implicit linear layers that connect the encoder and decoder within the model. To show the effectiveness of the proposed model, extensive experiments have been carried out. The experiments were conducted on both benchmark datasets and Internet of Things (IoT)-specific datasets. On the MNIST dataset, the proposed IIRMDSVDD model achieved an average accuracy of 98.8
BACKGROUND:AI tools are increasingly visible in nursing education and practice, yet student exposure and acceptance vary across settings. Limited digital literacy and technology anxiety may contribute to impostor syndrome (IS) in academic and clinical environments. OBJECTIVE:To assess and compare attitudes toward AI and the prevalence of IS among nursing students in five countries (Iraq, Egypt, Saudi Arabia, Jordan, and the UAE) and to examine their association. METHODS:Cross-sectional, descriptive-correlational survey of 1772 undergraduate nursing students from Iraq, Egypt, Saudi Arabia, Jordan, and the UAE (convenience sampling). Instruments were the General Attitudes toward AI Scale (20 items, Positive/Negative subscales) and the Clance Impostor Phenomenon Scale (CIPS; 20 items), plus demographics and technology-related variables (AI information sources, application type, prior training, and AI confidence). Group differences used t-tests/ANOVA with post-hoc tests; associations used Pearson correlations and multivariable linear regression with country fixed effects and robust SEs. RESULTS:Attitudes toward AI and IS differed by country, academic level, work status, information source, application used, and AI confidence (all p < .001). Iraqi students reported the most favorable AI attitudes; Egyptian and Iraqi students showed higher CIPS scores in bivariate analyses. First-year and younger students had higher IS. AI attitudes correlated negatively with IS (r = -0.206, p < .001). In regression, greater AI confidence and academic level predicted higher AI attitudes, whereas higher AI attitudes, upper academic levels, and AI training predicted lower IS. CONCLUSIONS:More favorable attitudes toward AI were associated with lower impostor feelings. Context-appropriate AI education (paired with ethics/academic-integrity guidance) and targeted psychological support may foster technological readiness and mitigate impostor experiences. IMPLICATIONS:Integrate structured AI literacy (where permitted), ensure equitable digital access, and provide mentorship and mental-health support-especially for early-year students.
Introduction:Pain and anxiety are common in pediatric orthopedic procedures such as cast or pin removal, often leading to distress and physiological stress responses. Virtual reality (VR) offers immersive distraction and has shown promise in pediatric procedural care; however, evidence in orthopedic procedures remains limited. This meta-analysis aimed to evaluate the effectiveness of VR compared with standard care in reducing pain, anxiety, and heart rate in children undergoing orthopedic procedures. Methods:A systematic search was conducted in PubMed, Scopus, and Cochrane Library from inception to 20 October 2025 for randomized controlled trials (RCTs) comparing VR distraction and standard care in pediatric patients undergoing orthopedic procedures. Outcomes of interest included pain, anxiety, and heart rate. Statistical analysis was performed with R 4.3.1. Standardized mean differences (SMD) using the Inverse-Variance method and the random-effects method. Results:A total of four RCTs were included in the final meta-analysis, comprising 624 patients, of whom 315 (50%) were distracted with VR during clinical orthopedic procedures (mean age 9.84 years, mean 40% females). In the pooled analysis, VR distraction significantly reduced anxiety (SMD = -0.55, 95% confidence intervals (CI) [-0.76, -0.34]; p < 0.01; I 2 = 0%), pain (SMD = -0.43; 95% CI [-0.68, -0.19]; p < 0.01; I2 = 44%), and heart rate (SMD = -0.34; 95% CI [-0.60, -0.07]; p = 0.01; I2 = 53%). Conclusion:In this meta-analysis of four RCTs including 624 pediatric patients, VR distraction reduced procedural anxiety and, in pooled analyses, was associated with modest but statistically significant reductions in pain and heart rate compared with standard care during predominantly cast-related orthopedic clinic procedures. The integration of this child-friendly, nonpharmacological approach provides enhanced procedural comfort and effective anxiety management.
