United Arab Emirates University
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PROBLEMS IN EXTREMAL GRAPH THEORY AND SPECTRAL GRAPH THEORY
No full textSpectral graph theory is a subfield of algebraic graph theory that studies the matrices associated with graphs. It lives at the nexus of Linear Algebra and Combinatorics. Many intriguing results in the domains of Matrix Theory and Combinatorics have come from studying the eigenvalues of graph matrices; in fact, several open problems in both areas have been resolved. Beyond its theoretical appeal, spectral graph theory has found meaningful applications in theoretical chemistry, particularly in the mathematical classification of chemical graphs. These classifications underpin quantitative structure–property relationships (QSPRs), facilitating the prediction of physicochemical properties such as enthalpy of vaporization, molar refractivity, and boiling point.
This dissertation explores the spectral properties of graph-associated matrices, particularly the inverse sum indeg (ISI), Sombor, and distance matrices. It identifies extremal graphs with respect to their spectral radius and energy, and characterizes graphs having a small number of distinct eigenvalues. New bounds for the ISI and Sombor indices are established for standard graph operations, including the corona, Cartesian, strong, composition, and join of graphs. The study further investigates distance spectra and energies of zero-divisor graphs derived from selected commutative rings, revealing precise structural relationships. Finally, the dissertation connects theoretical findings to chemical graph theory through a QSPR analysis, demonstrating meaningful correlations between ISI-based indices and physicochemical properties of chemical compounds
Proceedings of Emirati Conference on Medical Education 2025
No full textThe Emirati Conference on Medical Education, organized by the National Institute for Health Specialties (NIHS)—the accreditation body for postgraduate medical education in the UAE— is one of the country’s most prestigious scientific events. Held on February 15–16, 2025, at the Fairmont Bab Al Bahr Hotel in Abu Dhabi, the conference aimed to enhance the medical education experience and facilitate the exchange of expertise among professionals in the field. This dynamic platform brought together experts to discuss the latest advancements in medical education, with a focus on improving training and assessment in healthcare.https://scholarworks.uaeu.ac.ae/ecme_ab2025/1000/thumbnail.jp
DEVELOPMENT AND CHARACTERIZATION OF SUSTAINABLE ALUMINUM METAL MATRIX COMPOSITES WITH DATE PALM AGRO-RESIDUES AS REINFORCEMENT
No full textAluminum matrix composites (AMCs) are extensively used in various industrial applications owing to their exceptional mechanical, material, and tribological properties. This led to the development of AMCs with every possible aluminum alloy as matrix, incorporated with various reinforcement materials to achieve desired material properties. There has been an increasing trend in the utilization of agricultural and industrial waste products as reinforcement material in AMCs. Date palm trees produce huge quantity of agricultural waste in different forms. Usually, these wastes are burned or disposed of in landfills which cause environmental pollution. Date palm agro-wastes can be incinerated to produce date palm ash, which can be reinforced with various aluminum alloys to develop sustainable AMCs with superior strength, hardness, wear properties and corrosion resistance. Fabrication of the composite can be done through stir casting method which is the most economical and effective AMC production technique. This doctoral research aims to develop a sustainable approach to improve the material properties of aerospace grade AA7075 aluminum alloy by reinforcing with heat treated date palm ash (DPA) powder. Date palm waste derived ash was used as a low-cost reinforcement after heat treating at 700 °C for 6 hours to eliminate volatile matter and enhance its thermal stability. Thermogravimetric analysis (TGA), scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), X-ray diffraction analysis, and Fourier transform infrared (FTIR) spectroscopy confirmed that the heat-treated DPA can be effectively incorporated as a reinforcement in AA7075 alloy matrix. Aluminum matrix composites were fabricated through stir casting process with varying weight fractions of DPA (1 wt.%, 2 wt.%, and 3 wt.%). Two stage stirring process under inert gas atmosphere was adopted and vacuum assisted die casting was performed to fabricate defect free composites. Microstructural analysis revealed homogenous dispersion of reinforcement particles, grain refinement and good interfacial bonding between matrix and reinforcement up to 2 wt.% DPA addition. Consequently, these composites exhibited significant improvement in ultimate tensile strength (UTS), compression strength and hardness values compared to base alloy. However, further increase in reinforcement content resulted in non-uniform distribution of particulates, particle clustering, and poor wetting, which degraded the mechanical properties of AA7075- 3 wt.% DPA composite. Among the composites fabricated, AA7075- 2 wt.% DPA samples exhibited the highest improvement in mean Vickers hardness (27.74%), UTS (66.91%) and compression strength (34.17%) compared to the corresponding values of as-cast alloy. Incorporating DPA as reinforcement significantly enhanced the corrosion resistance of the material. Utilizing DPA reinforcement in AMCs enhances the material properties, valorizes agricultural waste, and offers a sustainable alternative to high-cost synthetic ceramic reinforcements
EXPLORING THE RECURSIVE MEANING-MAKING AND MULTILITERACY ACTIVITIES OF ARAB COLLEGE STUDENTS THROUGH ENGAGEMENT WITH VIDEO GAMES: A PHENOMENOLOGICAL STUDY
No full textThis study aims to explore the engagement of Arab college students as an assemblage of actors and actants within video games, focusing on their dynamic interactions within social networks. It seeks to examine the fluid nature of social structures by questioning conventional distinctions between societal and multimodal literacy elements, such as video games, and their contributions to multiliteracies through the study of unique digital communities and affinity spaces within video gaming communities. The study highlights Arab college students\u27 perceptions of meaning-making processes and negotiation strategies in video games, including social interactions within these virtual communities, challenges encountered during gameplay, and the integration of video games into broader literacy events and practices. The research employed a phenomenological and narrative approach to analyze the lived experiences of Arab college students (n=14) and their engagement in social and physical networking. Reflective narratives enabled participants to express their individual perspectives, while semi-structured interviews provided a more in-depth exploration of their experiences and the meanings they attached to them. This dual method provided space for both personal reflection and dialogic exchange, resulting in a more nuanced understanding of how students positioned themselves in gaming contexts. The findings revealed a holistic perspective on how Arab college students fortify their engagement with video games, creating a system that emerges, evolves, and sometimes wavers, emphasizing the interconnectedness of multiliteracies and recursive meaning making. Students reported not only entertainment but also opportunities for language practice, collaboration, and exploration of their identity. Challenges such as balancing study commitments, cultural expectations, and online stereotypes were also acknowledged; yet, participants demonstrated resilience by adapting their own practices. The results suggest that video games operate as dynamic literacy spaces that extend beyond recreation, enabling Arab students to negotiate meaning and foster learning across digital and physical domains
دراسة تحليلية لواقع الفراغات العمرانية المفتوحة في وسط مدينة الرياض An analytical study of the reality of open urban spaces in the Center of Riyadh
No full textAbstract:
Open urban spaces, public squares and squares are considered among the main elements in cities because of their great cultural and vital importance, and one of the most important factors in achieving sustainability and raising the quality of life. Many cities, especially capitals, have many open urban spaces, squares and squares, which are considered a major attraction for residents, whether in the centers of residential neighborhoods or in the centers of old or new cities. Some capitals have paid attention to their urban spaces in terms of rehabilitating historical public squares or creating public spaces and squares in the center of cities to enhance the concept of identity and culture and support the economic, tourism and entertainment components. The most important examples of this are Place de la Concorde in Paris, Times Square in New York City, and Red Square in Moscow. These squares and squares are considered the beating heart of these capitals, which attract people of all kinds to them and witness everyone mixing in one crucible, out of a desire to satisfy the spirit of participation and fusion in common societal values.
Vision 2030 and the national transformation initiatives in the Kingdom of Saudi Arabia in the field of urban development, especially in the city of Riyadh, have enhanced the number, type and percentage of spaces in terms of creating many open spaces and proposing strategic projects that enhance the proportion of open urban spaces and public squares and supporting tourism and recreational activities. These efforts have been concentrated in many areas of the city, but the city center still needs to make more efforts to address the lack of public spaces and squares, as most of these spaces do not reflect the city’s urban, historical, political, economic, tourism and entertainment importance.
This research deals with a general analytical overview of the reality of open urban spaces and public squares in the center of the city of Riyadh, especially in the old and new commercial and lively areas, in addition to an analysis of those spaces and the extent of their efficiency, suitability, and compatibility with the importance and role of the city center. The research found that there is a shortage in the type, number and percentage of open urban spaces in the center of Riyadh, and a large discrepancy in the fulfillment of these spaces to the requirements of open urban spaces in the city centres. The research proposed a number of recommendations centered around the necessity of conducting a comprehensive urban study of downtown Riyadh to identify defects and weak points in the open urban spaces, in addition to amending some urban controls in the city center to address the problem and make those spaces meet the needs and requirements of users.
ملخص
تعتبر الفراغات العمرانية المفتوحة والساحات العامة والميادين من العناصر الرئيسية في المدن لما لها من أهمية ثقافية وحيوية كبيرة، وأحد أهم عوامل تحقيق الاستدامة ورفع مستوى جودة الحياة. وتتمتع العديد من المدن خصوصاً العواصم بالعديد من الفراغات العمرانية المفتوحة والساحات والميادين التي تعتبر عنصر جذب كبير للسكان سواء في مراكز الأحياء السكنية أو في مراكز المدن القديمة أو الجديدة. ودأبت بعض العواصم على الاهتمام بفراغاتها العمرانية من حيث إعادة تأهيل الساحات العامة التاريخية أوباستحداث فراغات وساحات عامة في وسط المدن لتعزيز مفهوم الهوية والثقافة ودعم المقومات الاقتصادية والسياحية والترفيهية. ومن أهم الأمثلة على ذلك ساحة الكونكورد في باريس وميدان التايمز سكوير في مدينة نيويورك والساحة الحمراء في موسكو. وتعتبر هذه الساحات والميادين القلب النابض لتلك العواصم التي تجذب الناس بمختلف أنواعهم إليها وتشهد اختلاط الجميع في بوتقة واحدة رغبة في إشباع روح المشاركة والانصهار في قيم مجتمعية مشتركة.
وقد عززت رؤية 2030 ومبادرات التحول الوطني في المملكة العربية السعودية في مجال التنمية العمرانية خصوصاً في مدينة الرياض عدد ونوع ونسبة الفراغات من حيث استحداث فراغات مفتوحة عديدة واقتراح مشروعات استراتيجية تعزز من رفع نسبة الفراغات العمرانية المفتوحة والساحات العامة ودعم الأانشطة السياحية والترفيهية. وقد تركزت تلك الجهود في مناطق عديدة من المدينة، إلا أن وسط المدينة مازال يحتاج لبذل مزيد من الجهود لمعالجة نقص تلك الفراغات والساحات العامة، حيث أن أغلب تلك الفراغات لا يعكس أهمية المدينة العمرانية والتاريخية والسياسية والاقتصادية والسياحية والترفيهية.
يتناول هذا البحث نظرة تحليلية عامة على واقع الفراغات العمرانية المفتوحة والساحات العامة في وسط مدينة الرياض، خصوصاً في المنطقة التجارية والحيوية القديمة والجديدة، إضافةً إلى تحليل تلك الفراغات ومدى كفاءتها وملائمتها وتوافقها مع أهمية ودور وسط المدينة. وقد توصل البحث إلى وجود نقص في نوع وعدد ونسبة الفراغات العمرانية المفتوحة في وسط مدينة الرياض، وإلى تفاوت كبير في تحقيق تلك الفراغات لمتطلبات الفراغات العمرانية المفتوحة في أواسط المدن. وقد اقترح البحث عدد من التوصيات تركزت حول ضرورة عمل دراسة عمرانية شاملة لوسط مدينة الرياض لتحديد أماكن الخلل ونقاط الضعف في الفراغات العمرانية المفتوحة، إضافةً إلى تعديل بعض الضوابط العمرانية في وسط المدينة لمعالجة المشكلة وتلبية تلك الفراغات احتياجات ومتطلبات المستخدمين.
الكلمات المفتاحية: الفراغات العمرانية المفتوحة، الساحات العامة، أواسط المدن، مدينة الريا
Building Better Cities with Artificial Intelligence: A Path to Sustainability and Livability
No full textThe rapid acceleration of urbanization has amplified the multifaceted challenges confronting cities in attaining sustainability and ensuring livability. Artificial Intelligence technologies stand out as transformative tools with the potential to revolutionize urban development through finding innovative solutions to urban challenges. By leveraging AI, cities can optimize resource allocation, enhance decision-making processes, and create urban environments that are both livable and environmentally sustainable. Achieving this transformation requires visionary policymakers, meticulous planning, and a commitment to ethical AI deployment. Through AI\u27s data-driven insights, urban planners can make informed decisions that align with the complex and dynamic needs of urban areas. This research investigates the strategic incorporation of AI into urban planning, infrastructure development, and public service provision. The literature review establishes a theoretical foundation by synthesizing key themes and challenges in the application of AI to urban contexts. Case studies provide contextualized insights into how AI technologies have been implemented across domains such as energy management, transportation, waste systems, healthcare, and citizen engagement. The research is conducted in three stages: first, a comprehensive literature review to evaluate current applications of AI in urban contexts; second, an in-depth analysis of relevant international case studies; and third, the extraction of key lessons learned from these cases. Based on these findings, the research proposes a structured AI lifecycle framework designed to enhance the development of sustainable and livable cities. This framework outlines distinct phases, each with specific data requirements and operational considerations, offering a roadmap for the integration of AI into urban planning and management
PREDICTING CRYPTOCURRENCY PRICES USING STOCHASTIC MODELING
No full textCryptocurrencies are digital currencies that operate independently of central banks and governments. They were designed to overcome the limitations of traditional financial systems through a decentralized, peer-to-peer electronic cash mechanism. Trading in cryptocurrencies offers several advantages, including decentralized and efficient transactions, reduced costs through the elimination of intermediaries, investment opportunities across exchanges, and seamless cross-border remittances. Modeling cryptocurrency prices is therefore essential, not only due to these advantages but also because of the substantial market capitalization of cryptocurrencies, estimated to exceed 900 billion dollars according to CoinMarketCap [6]. The main objective of this thesis is to propose a predictive framework for cryptocurrency valuation by developing a stochastic model that describes the value of a cryptocurrency, such as Bitcoin, using a stochastic differential equation (SDE). The methodology involves collecting historical market data for the chosen cryptocurrency, investigating analytical or numerical solutions to the corresponding SDE, selecting the model parameters using historical data, and implementing a Python program to simulate and visualize the proposed model
ENHANCING LLM CODE GENERATION: A SYSTEMATIC EVALUATION OF MULTI-AGENT COLLABORATION AND RUNTIME DEBUGGING FOR IMPROVING 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, o!ering comprehensive insights into how di!erent post training strategies influence code generation electiveness. 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 e!ective 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
DESIGN AND EVALUATION OF GEOPOLYMER COMPOSITE AS A POTENTIAL SORBENT OF HEAVY METALS FROM WATER
No full textReducing the levels of heavy metals in water is crucial, given their severe environmental and health impacts. Various methods exist for heavy metals removal. Yet, they mostly come with multiple drawbacks, such as costly treatments and limited efficiency. Previous studies have found geopolymer to be a promising sorbent because it is synthesized using by-product materials, making it an eco-friendly and economically sustainable alternative. Limited studies explored the sorption potential of fly ash-slag blended geopolymers; none examined the effect of mix design factors synergically on sorption, mechanical properties, and durability, and few considered solution characteristics and operating conditions simultaneously. Therefore, this thesis aims to develop and evaluate a fly ash-slag blended geopolymer sorbent for heavy metals removal from wastewater under varying conditions. The work was implemented through two main phases. In the first phase, an initial study was conducted to design 16 geopolymer mixes and select the optimum mix. In phase two, the impact of various parameters on the uptake capacity for lead (Pb2+) of the selected optimum geopolymer mix was examined.
In phase one, single and binary fly ash and slag blends were utilized as the binding material. The geopolymer composite was activated using either sodium hydroxide solely or in combination with sodium silicate. The Taguchi method was employed to design geopolymer mixes, having four factors, each with four levels of variation. These factors included the fly ash-to-slag ratio (FA), binder content (BC), the molarity of the sodium hydroxide solution (M), and the sodium silicate-to-sodium hydroxide ratio (SS/SH). The performance of the geopolymer sorbent was rigorously assessed against a comprehensive set of responses categorized into synthesis and performance criteria. The TOPSIS methodology was applied to aggregate the response criteria and determine the optimal mix for superior performance. A sensitivity analysis was performed to study the sensitivity of the results to the weights assigned for each criterion. In this phase, the results showed that the optimum mix consisted of an FA of 33%, BC of 1050 kg/m3, M of 10, and SS/SH of 3.
Phase two investigated the impact of various parameters on the uptake capacity for Pb2+ of the selected optimal geopolymer mix. The Pb2+ was targeted in phase II due to its severe health risks to humans, including physiological damage to human kidneys, liver, brain, and nervous system when present at high levels. Additionally, Pb2+ had the highest recorded removal in phase I compared to the other metals. The parameters that have been investigated include changes in solution characteristics and variations in operational conditions. Furthermore, the impact of introducing a foaming agent to the optimum mix was examined. The results demonstrated that the removal efficiency increases by increasing geopolymer dosage, contact time, temperature, and decreasing geopolymer particle size and Pb2+ initial concentration. Moreover, it has been observed that adding a foaming agent to the geopolymer mix enhances the removal efficiency. The optimum removal efficiency was obtained at a final pH of 5. The kinetic data were found to fit the pseudo-second-order kinetic model. Also, the sorption isotherm study indicated that the experimental data showed a high nonlinearity, and the Langmuir model fits the data better than the Freundlich model. This study demonstrated the potential of using geopolymer as a sorbent in removing heavy metals from water, addressing a critical environmental concern with great implications for practical applications. Future studies should focus on investigating the performance of geopolymer composites in large-scale production and industrial real-life wastewater instead of synthetic wastewater. Additionally, further exploration into the valorization and regeneration of geopolymer composites and sustainable final disposal strategies should be performed. Moreover, expanding the environmental and cost-benefit analyses conducted in this study by including a life cycle assessment to evaluate the geopolymer performance as a sorbent compared to traditional sorbents is recommended
ADVANCING ACADEMIC ADVISING WITH KNOWLEDGE GRAPHS: INTEGRATING MACHINE LEARNING AND LLMS FOR PERSONALIZED COURSE PLANNING
No full textAcademic advising plays a critical role in helping students make informed decisions, improve academic performance, and successfully navigate their university journey. However, with increasing university enrollment, traditional advising methods often struggle to scale, leading to student frustration and overburdened advisors. Additionally, designing course offerings that match student demand is a complex and error-prone process involving multiple stakeholders. To address these challenges, this thesis proposes an automated, data-driven system for generating personalized academic plans for students. The primary aim of this thesis is to develop a system that reduces students’ dependency on advisors while simultaneously providing accurate estimates of course demand to assist in academic planning for upcoming semesters. The proposed system operates in two phases. In the first phase, Knowledge Graphs (KGs) are used to model relationships between courses, prerequisites, and student progress. In the second phase, Machine Learning (ML) techniques and Large Language Models (LLMs) are integrated to further personalize course recommendations. The system is designed to ensure logical course progression while adhering to university-specific academic policies, with a case study conducted at the United Arab Emirates University (UAEU). The generated academic plans demonstrate up to 70% similarity when compared to the generic degree plans provided by the university and show an average of 80% similarity when compared to actual plans followed by graduated students. This work introduces a hybrid system combining Knowledge Graph modeling with Machine Learning personalization for academic advising, offering a scalable and interpretable solution that aligns course planning with student needs and institutional constraints. This thesis addresses the scarcity of research applying Knowledge Graphs for personalized academic planning in universities, bridging the gap between traditional advising practices and automated, data-driven recommendation systems tailored to individual student backgrounds