United Arab Emirates University
United Arab Emirates University: Scholarworks@UAEU / جامعة الاماراتNot a member yet
5830 research outputs found
Sort by
ANTIMICROBIAL RESISTANCE (AMR) EMERGENCE IN SEWAGE WASTEWATER: AN INVESTIGATION BASED ON ADVANCED GENOMICS METHODS
Get PDFAntimicrobial resistance (AMR) is a global health worry, which makes the treatment of infectious diseases difficult by compromising the effectiveness of antimicrobial medications. This issue got worsen by the use of antibiotics without proper prescription and care during the time of COVID-19. At that time more than 78% of patients were prescribed with the use of medications like azithromycin and cephalosporins. The urban wastewater systems like wastewater treatment plants (WWTPs), hospital effluents, and residential wastewater were found to be bursting with antibiotic-resistant bacteria (ARB) and resistance genes (ARGs). This research used advanced metagenomics and LC-MS/MS techniques for the examination of ARBs, irregularities of antibiotics and unique dynamics of ARGs throughout different wastewater sources in the United Arab Emirates (UAE). The samples were collected from three different sources including WWTPs, hospital wastewater and residential communities for the estimation of resistance due to the interactions among microbial taxa and ARGs. The techniques of solid-phase extraction (SPE) and LC-MS/MS were implied to investigate the variability of antibiotics, whereas metagenomics identified the profiles of ARGs by means of databases like CARD and ResFinder. The bioinformatics tools like Kraken and Bracken were used for the composition of the microbial communities determining impact of taxonomic groupings for spreading ARGs. Moreover, links between ARGs, microbial taxa, and environmental variables were evaluated by using statistical and network analyses. The major outcomes of the study show that the most common antibiotics are cefuroxime, vancomycin, and ciprofloxacin, with the highest amounts of ciprofloxacin found in hospital wastewater. Significant taxonomic groups like as Flavobacteriales, Sphingobacteriales, and Planctomycetales were discovered through metagenomics and bioinformatics tools helping in breaking the organic materials and spreading ARGs. The dominant ARGs like blaTEM, blaCTX-M, and tetA were identified in hospital wastewater and WWTPs. In WWTPs the stage of primary effluent (PE) showed the maximum bacterial richness, while returned activated sludge (RAS) displayed different kinds of resistant bacteria like Aeromonas sobria and Escherichia coli. The presence of multidrug-resistant microorganisms (MDR) in the samples of AS and RAS underscores their critical part as major ARGs reservoirs. Overall, the analyses show the role of wastewater systems in spreading ARGs and ARBs. The ineffectiveness of WWTPs for removing ARGs from wastewater emphasize the need for better monitoring of wastewater with refined treatment tools and techniques to limit the prevalence of AMR. This study dictates a comprehensive understanding of AMR emergence and its environmental effects in the area by the combination LC-MS/MS and metagenomic techniques. The results feature the correlation between the resistance and microbial networks, delivering important evidence for devising the strategies for fighting AMR and protecting public health
Smart Traffic Intersections: Leveraging ISAC and Millimeter-Waves for Advanced Vehicle Platooning
Get PDFThe rapid advancement of self-driving cars is reshaping the transportation industry and accelerating the development of smart cities. Vehicle platooning, a key capability of autonomous vehicles, has the potential to enhance traffic efficiency, reduce congestion, and improve safety at intersections. However, maintaining platoon cohesion, minimizing latency, and optimizing traffic signal interactions remain significant challenges. This study addresses these issues by leveraging Integrated Sensing and Communication (ISAC) technology with millimeter-Waves (mmWaves) signals to optimize platooning performance at traffic signal intersections. The research identifies gaps in existing Vehicle-to-Everything (V2X) communication frameworks, particularly in managing platoon movements in urban traffic intersections. To bridge these gaps, a novel algorithm is proposed to enhance the coordination of vehicle platoons through an intelligent traffic signal system. The capabilities of ISAC-based mmWaves communication are evaluated through key metrics, including latency, throughput, and bit error rate (BER), under varying modulation schemes (M-PSK, M-QAM). Results demonstrate that ISAC technology significantly improves intersection management by reducing clearance times, strengthening platoon stability, and mitigating accident risks. These findings highlight the transformative impact of ISAC-driven communication on urban mobility, offering a scalable and efficient solution for future smart city applications
SUSTAINABLE DESALINATION: CARBON CAPTURE FROM REJECT BRINE USING UAE DATE PALM LEAF
Get PDFThe United Arab Emirates faces significant challenges in managing reject brine from desalination processes. Additionally, the energy-intensive nature of desalination contributes to greenhouse gas emissions, particularly carbon dioxide (CO2). This study explores the potential of date palm leaf ash, an underutilized agricultural waste, as a sustainable material for brine desalination and CO2 capture. Date palm leaves were converted into ash through incineration at 550°C and 750°C, followed by chemical activation using potassium hydroxide. The study evaluated the ash\u27s efficiency in removing key ions (Na+, Mg2+, Ca2+, K+) from reject brine and capturing CO2, while optimizing process conditions using response surface methodology. The results demonstrated that activated date palm leaf ash, under optimized conditions, effectively removed up to 37% of sodium, 25% of magnesium, 42% of calcium, and 35% of potassium. The ash also exhibited a CO2 capture capacity of 2.94 mmol CO2/g ash, comparable to traditional materials like activated carbon and biochar. Advanced characterization techniques, including SEM, EDS, FTIR, and XRD, revealed that the ash\u27s porous structure and chemical composition contributed to its adsorption and carbon capture capabilities. The study also highlighted the importance of activation temperature and CO2 interaction in enhancing the ash\u27s performance. These findings suggest that date palm leaf ash is a promising, cost-effective, and sustainable solution for brine desalination and CO2 capture, aligning with the principles of a circular economy. The use of agricultural waste not only addresses environmental challenges but also provides a dual-purpose material for water treatment and carbon sequestration. Future research should focus on optimizing production processes, assessing long-term performance, and exploring broader applications in wastewater treatment and environmental remediation
MEASUREMENT OF GROUNDWATER AND CONTAMINANT FLUXES IN FRACTURES USING A COMBINED SYSTEM OF PASSIVE FLUX METER AND MULTIPORT SAMPLER
Get PDFGroundwater and contaminant movement in fractured rock aquifers is highly variable. Its dependence on fracture apertures and orientation as well as fracture network interconnectivity is not well understood. This poses a challenge to the measurement of groundwater and contaminant fluxes, especially when using open-hole techniques, which significantly alter natural flow conditions by connecting different fractures along an open borehole or a well. In this work, the use of Fractured Rock Passive Flux Meter (FRPFM) with invisible tracer and visible dye component to measure groundwater fluxes and identify geometric fracture parameters is explored through laboratory experiments. The invisible tracer component results showed that water and contaminant fluxes were measured with relative errors of ±25% and ±14%, respectively. The results also showed that water flux was measured correctly by up to 50% of tracer loss, but beyond this point, the measurements became less accurate as tracer displacement rate declined. For the visible dye component, we used the deep learning model YOLOv8 to accurately identify the dye marks and measure their areas and widths from images of the dyed fabric. Results showed that groundwater fluxes were measured with relative errors of ±23% and ±16% based on and , respectively, with an overall relative error of ±20%. The YOLOv8 model showed very good accuracy by achieving high precision = 0.99 and recall =0.75 for both object detection and mask predictions
INVESTIGATING TEACHERS’ PERSPECTIVES ON THE PRACTICES OF PUBLIC-SCHOOL INSPECTIONS IN THE UNITED ARAB EMIRATES
Get PDFThis research paper investigates public school teachers’ perceptions of the effectiveness of school inspections in the United Arab Emirates (UAE). Additionally, it examines the impact of teachers’ demographic factors on these perceptions. Data were collected using an online, structured, five-point Likert-scale survey distributed via email to teachers in UAE public schools by the Emirates Schools Establishment (ESE). A total of 218 teachers across the UAE participated in this study. The research focused on four key domains within the School Inspection Perception Framework (SIPF): Perceived Effectiveness of School Inspections (PESI), Perceived Effectiveness of School Inspection Training (PESIT), Perceived Credibility of School Inspections (PCSI), and Perceived Usefulness of School Inspection Feedback (PUSIF). The collected data were analyzed using various statistical methods, including descriptive statistics, reliability analysis, normality tests, independent-samples t-tests, analysis of variance (ANOVA), and post hoc tests. Findings indicate that teachers perceive the school inspection process as effective, credible, and beneficial for enhancing educational quality and professional development. However, the study found that gender and location significantly influence teachers’ perceptions of the school inspection framework. Specifically, female teachers and teachers in the emirate of Abu Dhabi reported more positive perceptions, particularly regarding the usefulness of inspection feedback and the credibility of inspectors. Other demographic factors, such as teachers’ qualifications, teaching positions, and years of teaching experience, did not significantly affect perceptions in the examined domains. Finally, the study identifies areas for improvement, including enhancing the clarity of school inspection criteria and increasing accessibility to school inspection training for teachers
Graduate Students’ Use of Artificial Intelligence Applications in Scientific Research: Prevalence and Association with Perceived Impostor Syndrome
Get PDFThe current study aimed to explore the attitudes of graduate students towards the use of artificial intelligence applications in scientific research and the association with the prevalence and intensity of the perceived impostor syndrome among the students. The study sample consisted of 575 graduate students from several universities in Saudi Arabia. The researchers utilized a mixed-method approach. The scale of Attitude towards AI Technology and the Perceived Impostor Syndrome Scale were administered. The results showed a high positive inclination towards the use of artificial intelligence among graduate students, with 74.3% of students using AI applications in scientific research. The most used AI application was ChatGPT at 67%. The results also highlighted challenges that hinder the use of AI applications in scientific research, such as lack of knowledge and skills, limited resources, high costs, and insufficient technical support. Concerns about misinformation, ethical, and legal aspects were also noted. Additionally, 58% of participants indicated that practical workshops were the most effective type of training to support confidence in using AI. Furthermore, the perceived impostor syndrome rate among graduate students using AI applications was 68% compared to 57% who do not use them. The results indicated no statistically significant relationship between the use of AI applications in scientific research and the perceived impostor syndrome among graduate students.
Keywords: Artificial Intelligence, Scientific Research, Perceived Impostor Syndrome, Graduate Students, Mixed methods
UNRAVELLING HOST DEFENSE AND PATHOGEN VIRULENCE IN FUSARIUM PROLIFERATUM-INDUCED SUDDEN DECLINE SYNDROME OF DATE PALM IN THE UAE
Get PDFSudden decline syndrome (SDS) poses a significant threat to the date palm (Phoenix dactylifera), a crop of critical agricultural and cultural importance in arid regions. Despite its growing impact, the molecular basis of the host-pathogen interactions driving this disease remains poorly understood. This dissertation investigates the disease dynamics of SDS, a severe affliction of date palm caused by the fungal pathogen Fusarium proliferatum DSM 106835 (Fp). The objective of this study is to elucidate the molecular mechanisms underlying Fp infection in date palm and characterize the plant defense responses. To achieve this, whole-genome sequencing (WGS) of the novel Fp strain was integrated with time-series transcriptomics to monitor disease progression in both leaf and root tissues of infected date palms. A high-resolution, chromatin-organized reference genome was generated for Fp, comprising 15,580 predicted genes and 16,321 transcripts. Among these, 6,459 genes were identified as potentially involved in host-pathogen interactions, including a substantial number of plant avirulence determinants associated with disease development and suppression of host immunity. Transcriptomic profiling of infected date palm tissues revealed that Fp triggers both pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) and effector-triggered immunity (ETI), possibly interfering with mitogen-activated protein kinase (MAPK) signaling and weakening host resistance mechanisms. Infected seedlings exhibited hallmark defense responses such as cell wall remodeling, reactive oxygen species (ROS) production, disrupted photosynthetic activity, and a coordinated hormone-mediated defense strategy. In addition, Fp infection induced significant alterations in energy metabolism, including carbohydrate, amino acid, and lipid pathways, alongside modulation of primary and secondary metabolite biosynthesis and defense-related enzymatic activities. This integrated omics approach offers novel insights into the molecular strategies employed by Fp during infection and the corresponding defense responses of date palm. These findings highlight key molecular pathways that could be targeted to enhance disease resistance in this economically and culturally important crop
Knowledge and Use of Teachers of Students with Intellectual Disabilities for Universal Design for Transition
No full textThe present study aimed to identify teachers of students with intellectual disabilities knowledge and Use of Universal Design for Transition (UDT) in intellectual education programs. To achieve the objectives of the study, the researcher yielded the descriptive research, and the questionnaire was used as a tool to collect data, as the study sample consisted of (270) male and female teachers of students with intellectual disabilities in the middle and secondary school. The results indicated that the level of knowledge of teachers of students with intellectual disabilities about UDT was at a high level. The results also indicated that the level of use of UDT by teachers of students with intellectual disabilities was at a high level. The results also showed that there were statistically significant differences in the knowledge and Use of UDT by teachers of students with intellectual disabilities in favor of teachers with five to ten years of experience. Finally, according to its results, the study presented a number of recommendations and research proposals that may contribute to increasing knowledge and use of UDT with students with intellectual disabilities in middle and secondary school.
Keywords: Transition, Linking academic and transition education, Universal design for learning, Middle and high school, Kingdom of Saudia Arabia
EXTREMISM GOVERNANCE IN THE UNITED ARAB EMIRATES: ACASE STUDY USING QUALITATIVE POLICY FRAMEWORK
No full textThis thesis investigates how the United Arab Emirates governs extremism using various models of governance and Gareth Morgan’s metaphorical organizational models. This research goes beyond a focus on security, and looks at extremism as a governance and policy problem related to social cohesion, institutional trust, and legitimacy. The research utilizes a qualitiatve case study design through analyzing federal laws and national strategies, as well as institutional frameworks to understand how authority, coordination and meaning are organized in the UAE’s governance of extremism.The results show that the UAE’s governance of extremism is characterized as a hybrid governance model with hierarchical authority, network collaboration and good governance at play. Hierarchical authority is evident through federal laws such as Federal Law No. 7 of 2014 on Combating Terrorism Crimes, while institutions such as Hedayah and Sawab Centre illustrate collaborative and adaptive governance. Using Morgan’s metaphors, we see that the machine and domination metaphors represent legal components of governance, while culture, organism and brain metaphors depict preventive and institutional approaches, balancing coercion and adaptive logics in the governance of extremism.Ultimately, the study finds that the UAE’s governance of extremism is simultaneously centralized and adaptive, a layered system that provides stability through control, while allowing inclusivity via education, tolerance, and rehabilitation. While successful in achieving security and cohesion, the UAE may face challenges of inclusivity and bottom-up engagement. This study adds to the academic and policy debate, while providing a theorized context-specific model of extremism governance rooted in Emirati cultural, religious and institutional realities that could be utilized for comparative studies across the region
FRAMEWORK FOR NEXT GENERATION SECURITY OPERATION CENTER POWERED BY ARTIFICIAL INTELLIGENCE
No full textThis dissertation presents a comprehensive framework for the evolution of Security Operation Centers (SOCs) through the integration of advanced artificial intelligence (AI), blockchain, and optimization techniques. Motivated by the increasing complexity of cyber threats and the limitations of traditional reactive SOC strategies, this work begins with a systematic literature review that identifies critical gaps in current SOC operations. Based on these insights, a reference architecture is proposed to guide the integration of intelligent components into SOC environments. To address the challenge of secure and trustworthy information sharing, a blockchain-based threat intelligence platform is developed, leveraging Byzantine Fault Tolerance and Zero-Knowledge Proofs for integrity and access control. For intelligent threat detection and response, deep learning models—specifically graph convolutional networks and autoencoders—are coupled with reinforcement learning and fuzzy logic to enable adaptive classification, scoring, and continuous model improvement. At the same time, a reinforcement learning–based intrusion detection system (RL-IDS) is put forward for IoT networks that integrates deep neural networks, domain-specific feature extraction, and hybrid metaheuristic optimization for effective and scalable detection. In addition, an ensemble deep learning model is put forward to improve Security Information and Event Management (SIEM) systems with attention mechanisms and priority assignment of alerts using fuzzy inference. This model effectively captures temporal and spatial threat patterns, substantially improving detection accuracy and reducing false alarm rates. Across all components, experimental evaluations demonstrate superior performance, with detection accuracies exceeding 99%, low false positive and negative rates, and high operational efficiency. The proposed architecture and its subsystems collectively offer a modular, intelligent, and secure foundation for next-generation SOCs. This dissertation contributes novel methodologies across detection, response, and intelligence sharing, aligning academic innovation with practical cybersecurity demands and enabling a shift toward autonomous, AI-driven security operations