United Arab Emirates University
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THE UAE\u27S ROLE IN DEVELOPING STRATEGIES TO COMBAT MONEY LAUNDERING AND TERRORISM AT THE LOCAL, REGIONAL, AND INTERNATIONAL LEVELS
No full textThis thesis presents an analysis of the UAE\u27s role in developing anti-money laundering and counter-terrorism financing strategies at the local, regional, and international levels, focusing on a key problem: whether current strategies are sufficient to address the complexities of financial crimes in the digital age and how their effectiveness can be improved. The study relies on a descriptive analytical approach that includes an analysis of official documents, legislation, and reports at the national and international levels, in addition to a chronological tracing of the country\u27s policies from 2002 to 2025, and an institutional comparison across the three levels (local/regional/international). The study also uses two theoretical frameworks: agency theory to understand governance gaps between stakeholders, and game theory to strategically analyze criminals\u27 interactions with regulatory authorities, while including measurable performance indicators. The study reaches key findings. Domestically, the UAE has adopted an increasingly robust legislative and regulatory approach, starting with Law 2002 and continuing through Decree 20/2018 and subsequent amendments. This is supported by a set of executive regulations and sectoral decisions related to high-risk activities, such as real estate, gold, remittances, and virtual asset service providers, in addition to an updated beneficial ownership registry. An integrated governance structure is also in place, encompassing multi-level committees, including a Supreme Committee, a National Committee, an Executive Office, a Financial Intelligence Unit, and subcommittees. The modern National Strategy 2024–2027 focuses on a risk-based approach, enhancing smart oversight, fostering international cooperation, and capacity building, in addition to digital transformation through goAML and advanced analytics. Results indicate a clear improvement in compliance and reputation, which contributed to the country\u27s removal from the grey list. Performance indicators include the quality of suspicious transaction reports, the efficiency of referrals, convictions, and confiscations, as well as the integration of information exchange at the internal and external levels. The letter highlights the UAE\u27s support for cooperation through several active regional agreements and memberships. At the international level, the UAE\u27s strategy aligns with the Financial Action Task Force (FATF) standards, UN frameworks, and information-sharing networks, with the UAE contributing technically and practically by disseminating practices and platforms that enhance enforceability. The study also identifies remaining gaps, including disparities in awareness and compliance among some non-financial businesses and professions, coordination in free zones, the risks of emerging technologies, and deeper skill enhancement. The study recommends several practical recommendations, such as expanding risk-based supervision to include complex value chains, integrating databases and beneficial ownership using artificial intelligence models to identify patterns, and strengthening professional accreditation and training programs for regulatory authorities and the private sector. Overall, the message emphasizes that the UAE has established an integrated and resilient system that has proven effective, but it needs to accelerate the digitalization of supervision, standardize compliance, and enhance cross-border cooperation to ensure sustainable preparedness against technology-driven financial crimes
ENHANCING IT SECURITY MANAGEMENT WITH AN ADVANCED INTRUSION DETECTION SYSTEM BASED ON MACHINE LEARNING AND EXPLAINABLE AI
No full textThe fast-changing landscape of cyber threats continues to challenge the development of strong and reliable security frameworks for IT management systems. Traditional defense tools, such as Intrusion Detection Systems (IDS), often struggle to keep up with today’s advanced and constantly evolving attack methods. This thesis explores these ongoing challenges and looks into how machine learning (ML) and explainable artificial intelligence (XAI) can be used to boost IDS performance.
The research outlines a smart, adaptive system that combines supervised learning for real-time threat detection, unsupervised models for anomaly analysis, and proactive defense strategies. The goal is to improve detection accuracy, cut down on false alarms, and enable systems to respond automatically to emerging threats.Through testing and evaluation, the thesis shows that Machine Learning powered IDS can significantly enhance an organization’s resilience against cyberattacks. Overall, the findings offer a meaningful step forward in modern IT security, pointing toward more adaptive and intelligent approaches to managing threats
BEHAVIOURAL INTENTIONS AND TECHNOLOGICAL PEDAGOGICAL AND CONTENT KNOWLEDGE OF TEACHERS TOWARDS USING ARTIFICIAL INTELLIGENCE TO TEACH STUDENTS WITH LEARNING DIFFICULTY/DISABILITIES IN THE UNITED ARAB EMIRATES
No full textThe integration of Artificial Intelligence (AI) in education offers significant potential for identifying and supporting all students including those with learning difficulties. Although discussions on potential of AI to advance the learning of students is ongoing, AI usage among teachers to support the teaching of students with learning disabilities in non-western context such as the United Arab Emirates, is unresearched. The goal of this study was to explore the synergy between teachers’ behavioural intention and pedagogical content knowledge towards using AI to teach students with learning difficulties. The study was guided by unified theory of acceptance and use of technology and Technological pedagogical and content knowledge model to examine teachers\u27 intentions toward adopting AI tools to enhance educational outcomes for students with learning disabilities in the UAE. Using a quantitative research approach, a structured survey was completed by 244 teachers from both public and private schools. The data was subjected to analysis such as structural equation modelling to test the structural validity of the theory of planned behaviour. More so, multivariate analysis of variance and path analysis were computed to explore the relationship between behavioural intentions towards AI and teachers’ AI pedagogical content knowledge. The findings provided support from instruments used to measure intentions and AI pedagogical content knowledge. Moreover, differences were found between participants on age on AI-Technology Pedagogical Knowledge and AI-Technology Pedagogical Content Knowledge. Specifically, young teachers had higher AI-technological content knowledge than older counterparts. The implications of the study for educational policymakers, school leaders, and AI developers, are discussed in details
ENHANCING LLM CODE GENERATION: A SYSTEMATIC EVALUATION OF MULTI-AGENT COLLABORATION AND RUNTIME DEBUGGING FOR IMPROVED ACCURACY, RELIABILITY, AND LATENCY
No full textThe use of large language models (LLMs) for automated code generation has emerged as a significant focus within AI research. As these pretrained models continue to evolve, their ability to understand and generate complex code structures has opened up new possibilities for automating intricate programming tasks with greater accuracy. Although contemporary foundational models demonstrate promising results, researchers continue to explore optimal post-training strategies to enhance code quality. These include supervised fine-tuning, retrieval-augmented generation (RAG), debugging, and many others. In this thesis, I combine two such widely used post training approaches—namely (1) multi-agent collaboration and (2) runtime execution of information-based debugging—for improving code generation functionality, reliability, and practical applicability. I perform an empirical study to extend the evaluation of both individual strategies and their combined application. My study uses 19 LLMs to examine the performance of each strategy as well as their composition, offering comprehensive insights into how different post training strategies influence code generation effectiveness. In particular, I implement a chained system that integrates both strategies to assess their combined impact on functional accuracy, code reliability, and generation latency using two benchmark datasets commonly used for code generation. My findings provide valuable insights for organizations seeking robust AI-driven coding solutions by guiding them in selecting models that can better adapt to complex post-training strategies—ultimately fostering the adoption of more effective and reliable code generation technologies. This research addresses the lack of extensive evaluation of post-training techniques designed to enhance code generation using large language models. By covering a broad range of LLMs, various approaches and various dimensions of evaluating LLM based approaches, such as code accuracy, generation latency and code rigorousness, I propose a comprenhensive framework to combine LLM code generation techniques and evaluate them thoroughly
The Legal Protection of Virtual Asset Consumers: An Analytical Study of the Legislative Framework in the United Arab Emirates
No full textThis study aims to examine the extent to which the legislative provisions issued at both the federal and local levels in the United Arab Emirates regarding the regulation of virtual assets and their service providers are sufficient to provide adequate legal protection for individuals dealing in virtual assets.The study begins by outlining the concept of virtual assets, their types, characteristics, legal nature, regulatory mechanisms, and the legislative frameworks adopted by the State to regulate them. It further evaluates the adequacy of these legislative provisions in offering sufficient legal protection to virtual asset consumers, while identifying the shortcomings contained therein and proposing appropriate remedies. The study also explores the extent to which legislative provisions found in other laws may contribute to offering alternative solutions to enhance the protection of individuals dealing in virtual assets.To achieve its objectives, the study adopts the analytical method by examining the relevant provisions contained in the Personal Status Law and the Federal Civil Transactions Law.The study reached several findings, the most notable of which is that the United Arab Emirates has indeed established advanced legal frameworks for the regulation of virtual assets. However, these frameworks still require updates to certain provisions in order to keep pace with ongoing developments—particularly concerning the protection of individuals engaging with such assets. Accordingly, the study recommends issuing an independent local legislation in the Emirate of Abu Dhabi to regulate virtual assets and their related activities, in addition to unifying the applicable federal and local legislative frameworks to avoid inconsistencies and ensure harmonization between them
EVALUATING LARGE LANGUAGE MODELS FOR AUTOMATED CV RANKING: A HYBRID EMBEDDING APPROACH FOR ENHANCED RECRUITMENT
No full textIncreasing numbers of applications have revealed limitations in legacy keyword-filtering-based Applicant Tracking Systems (ATS), which commonly overlook candidate potential and ignore contextual or transferable skills. Advances in Natural Language Processing (NLP) and Large Language Models (LLMs) offer an exhilarating alternative, supporting context-sensitive and human-crafted reasoning in candidate evaluation. This thesis systematically evaluates four classes of approaches, lexical models, embedding-based methods, Large Language Models (LLMs), and hybrid ensembles, for automation of Curriculum Vitae (CV) to Job Description (JD) matching without exploiting prior annotations or annotations at match time. Using a combination of publicly available datasets and real-world sample data covering three technical roles, human raters established ground-truth rankings as baselines to measure performance against. We discovered that lexical models achieved efficiency at the loss of poor correlation to human judgment, while embedding-based models, including SBERT and MPNet, raised semantic similarity but did not offer evaluative reasoning. Large Language Models, showed superior correlation to human ranking, reaching high accuracy and contextual comprehension, in spite of results being input-sensitive and computationally costly. The thesis offers empirical insights into prompt engineering, hybrid modeling, and awareness of fairness, and identifies a pivotal role for LLMs in revolutionizing recruitment practice. It concludes that, despite being able to simulate recruiter judgments, hybrid systems outperform and provide stability, and lay foundations for scalable, transparent, and ethically responsible recruitment technologies
DESIGN OF A DEPLOYABLE ROLLED ANTENNA SYSTEM FOR SATELLITE APPLICATIONS
No full textThis project presents the development of a deployable Synthetic Aperture Radar (SAR) antenna designed for a 16U CubeSat platform. The primary challenge in SAR satellite design lies in the need for large antennas to achieve high-resolution imaging, which traditionally results in increased satellite size and cost. To address this, the proposed solution employs a scalable 4 × 21 patch antenna array operating at 1.275 GHz, fabricated on a flexible Polyimide-based Printed Circuit Board (PCB). This flexible PCB allows the antenna to safely roll during deployment, avoiding damage and facilitating compact storage. However, Polyimide poses challenges due to higher losses and suboptimal performance as compared to rigid microwave substrates such as Rogers [1]. The design incorporates a quarter-wave impedance transformer for effective matching and carefully calculated element spacing to preserve radiation pattern integrity.
The mechanical design ensures that the rolled antenna fits within half of the 16U CubeSat volume (a 16-unit CubeSat, approximately 40 × 20 × 20 cm³), which was done considering the bending properties of the PCB. Prior research of DERAC-SAR has proven that the bending of the patch array will maintain the integrity of the rolled sheet, and it will not have any impact in terms of the torque produced during the satellite flight [2]. With all these considerations, the final design was completed for the rolled patch antenna array and the deployed antenna array with a final length of 4 m.
Initial CST simulations of 4 × 4 antenna configurations demonstrated promising electromagnetic performance, achieving a bandwidth of 18 MHz, a return loss (S11) of -22 dB, and a gain of 13 dBi at 1.275 GHz. A prototype has been successfully fabricated, validating the feasibility of the design. However, the validation results were not as expected, and challenges were faced with the fabricated antenna. Hence, future work will focus on addressing these fabrication issues and on scaling up the antenna array to meet the demanding performance requirements. The flexible and scalable architecture of the antenna system allows for increased array size without compromising the compact form factor of the CubeSat.
This innovative approach has the potential to reduce satellite size and cost while maintaining or improving SAR imaging performance, offering a viable and cost-effective solution for various spaceborne radar applications. The integration of flexible materials and advanced impedance matching techniques underscores the project\u27s contribution to the evolving field of small satellite SAR systems, offering a practical and scalable solution for enhanced Earth Observation capabilities
REHABILITATION OF REINFORCED CONCRETE COLUMNS PRE-DAMAGED BY CORROSION USING ADVANCED COMPOSITE MATERIALS
No full textReinforced concrete (RC) columns exposed to aggressive environments are highly susceptible to corrosion-induced deterioration, resulting in significant reductions in load-carrying capacity. This research investigates the structural performance of RC circular short columns with varying levels of corrosion damage and evaluates the effectiveness of two composite-based repair techniques, namely carbon fabric-reinforced cementitious matrix (C-FRCM) and carbon fiber-reinforced polymer (C-FRP) composites, combined with concrete cover replacement. The study aims to establish these methods as practical solutions for rehabilitating corrosion-damaged columns under concentric and eccentric loading conditions. The experimental program included 30 RC column specimens tested in two phases. Phase I involved thirteen undamaged columns subjected to eccentricity-to-depth ratios (e/h) ranging from 0.0 to 0.3, eight of which were strengthened using one or two C-FRCM layers. Phase II examined seventeen corroded columns with accelerated corrosion in longitudinal bars (up to 27%) and steel ties (up to 45%), tested under the same e/h ratio range. Six corroded columns were tested without repair, while eleven were repaired using two layers of either C-FRCM or C-FRP composite wraps in the hoop direction before testing. Phase I experimental results indicated that columns with two C-FRCM layers achieved up to 39% load capacity gain under eccentric loading compared to 17% under concentric loading, whereas single-layer strengthening provided minimal improvement due to insufficient confinement and premature fabric–mortar debonding. Phase II experimental results showed that corrosion reduced load capacity by up to 41% under concentric loading and by an average of 17% under eccentric loading, with the effect diminishing at higher eccentricities as corroded tensile cover contributed minimally. Both repair systems restored original capacity, with C-FRP providing superior load capacity enhancement (80%–167%) compared to C-FRCM (49%–86%), attributed to better confinement efficiency. While premature debonding limited C-FRCM performance, it contributed to improved ductility through gradual post-peak degradation. A mechanics-based analytical model was developed and validated against experimental results and literature data to predict column capacity before and after repair. The model accounts for corrosion effects, material nonlinearities, and the interaction between internal steel tie confinement and external composite wrapping. The analytical model aligns well with experimental results and supports the use of advanced composites in the practical rehabilitation of corrosion-damaged RC columns, confirming its reliability and applicability as a simple, accurate tool for structural evaluation and retrofit design. The model also generated P–M interaction diagrams that reasonably reflected the experimental trends, validating its applicability as a robust tool for structural assessment, strengthening, and retrofit design
اختصاص المحكمة الاتحادية العليا بتفسير المعاهدات والاتفاقيات الدولية دراسة تحليلية في النظام القانوني الإمارات
No full textThe Federal Supreme Court\u27s Jurisdiction to Interpret International Treaties and Agreements
This thesis examines and analyzes the interpretation of international treaties and agreements in the legal system of the United Arab Emirates, focusing in particular on the jurisdiction granted to the Federal Supreme Court in this area. It aims to clarify the legal rulings issued in this regard, in light of the UAE Constitution and recent legislative developments, particularly Federal Decree-Law No. (33) of 2022.
The thesis concludes that the UAE legislator has granted the Federal Supreme Court exclusive jurisdiction to interpret international treaties, and that this interpretation is achieved through an independent, substantive lawsuit, not based on a traditional dispute between parties, but rather aimed at clarifying ambiguity surrounding international texts. The letter also explained that the lawsuit is subject to special procedures, which may arise at the request of a competent authority or upon referral from the trial courts. The Federal Supreme Court relies on international rules in its interpretation, particularly those stipulated in the 1969 Vienna Convention on the Law of Treaties.
The letter also explained that the Federal Supreme Court\u27s decisions in this regard enjoy absolute authoritativeness and comprehensive binding force for all authorities and individuals in the state, making their interpretation a fundamental pillar for achieving harmony between domestic legislation and international obligations. The study also addressed the relationship between international law and national law in the UAE system, concluding that the UAE adopts the principle of dualism, whereby international treaties do not acquire the force of law until they are ratified and officially published.
In light of these findings, the thesis recommended a number of legislative and procedural proposals, including amending the constitutional texts to explicitly state the jurisdiction of the Federal Supreme Court, regulating the procedures for interpretation lawsuits through executive regulations, and defining the status of treaties within the national legislative system to avoid any potential future contradictions
UTILIZING DATE FRUIT POMACE AS A CORROSION GREEN INHIBITOR FOR NON FERROUS ALLOYS
No full textThis thesis explores a novel green corrosion inhibitor derived from date fruit pomace extract (DFPE) for the protection of aluminum alloy 3003 in highly corrosive environments, specifically hydrochloric acid (HCl) and natural seawater. The central aim is to promote the sustainable reuse of agro-waste as an effective and eco-friendly alternative to conventional toxic inhibitors, aligning with environmental and industrial safety goals.
A comprehensive methodology was employed, including Soxhlet extraction (using both non-polar n-hexane and polar methanol-water solvents), rotary evaporation, and FTIR analysis to identify the active functional groups responsible for inhibition. Corrosion behavior was systematically evaluated using both mechanical (weight loss) and electrochemical techniques such as Tafel polarization and electrochemical impedance spectroscopy (EIS) under varied temperatures (30-60°C) and DFPE concentrations (from 0 up to 16,000 ppm), following ASTM G31 standards.
The results confirmed that inhibition efficiency (IE%) improved with increasing DFPE concentration and temperature. The optimum performance was observed at 14,000 ppm in 0.1 M HCl and 8,000 ppm in seawater, both at 60°C, achieving efficiencies up to 96%. Electrochemical tests validated these findings through significant reductions in corrosion current density (Icorr) and increases in charge transfer resistance (Rct). FTIR spectra indicated the presence of phenolic, hydroxyl, and carbonyl groups, supporting the mechanism of adsorptive film formation.
This study not only presents one of the first reported uses of date fruit pomace for aluminum corrosion protection, but also delivers a comparative analysis between mechanical and electrochemical methods, ensuring high data reliability. Importantly, the inhibition performance of DFPE was comparable to well-known commercial inhibitors such as Zn²⁺ and BTA, especially in chloride-rich media.
These findings validate DFPE as a viable green inhibitor for real-world applications, particularly in oilfield operations, marine structures, and desalination plants. The results support the industrial relevance of agro-waste valorization, offering a cost-effective, sustainable, and environmentally safe corrosion mitigation strategy for aluminum infrastructure