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A reduced model for phase-change problems with radiation using simplified PN approximations
No full textRadiative heat transfer in phase-change media is of great interest in many thermal applications in sciences and engineering involving internal melting or solidification. In these problems at high temperature, a mathematical model used to describe the heat transfer and phase change should also include equations accounting for thermal radiation. Using the integro-differential equation for the radiative intensity in these models results in a system of coupled equations for which its numerical solution is computationally very demanding. In the present study, we develop a class of efficient reduced models for phase-change problems accounting for grey thermal radiation. The novelty in these models lies in the fact that effects of thermal radiation are well captured in phasechange materials without solving the computationally demanding radiative transfer equation. The model is derived from the enthalpy formulation and the simplified Pɴ approximations of spherical harmonics. The integro-differential equation for the full radiative transfer is replaced by a set of differential equations which are independent of the angle variable and easy to solve using conventional computational methods. To solve the coupled equations, we implement a second-order implicit scheme for the time integration and a mixed finite element method for the space discretization. A Newton-based algorithm is also adopted for solving the nonlinear systems resulting from the considered monolithic approach. The performance of the proposed reduced models is analyzed on several test examples for coupled radiative heat transfer and phase-change problems in two and three space dimensions. The results presented in this study demonstrate that the proposed models can accurately predict the temperature distributions and capture the phase-change interfaces in melting and solidification examples, all while maintaining a very low computational cost.Royal SocietyAmerican University of SHarja
The Impact of Written Praise on L2 Writing Performance, Motivation, and Grit
No full textA Master of Arts thesis in Teaching English to Speakers of Other Languages (TESOL) by Vighnesh Prasad Kizhepat entitled, “The Impact of Written Praise on L2 Writing Performance, Motivation, and Grit”, submitted in April 2025. Thesis advisor is Dr. Ozgur Parlak. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).College of Arts and SciencesDepartment of EnglishMaster of Arts in Teaching English to Speakers of Other Languages (MA TESOL
The Impact Of Digitalization On Marketing Strategies Used By Tech Companies
No full textA Master of Business Administration (MBA) thesis by Sara Alhammadi entitled, “The Impact of Digitalization Oo Marketing Strategies Used By Tech Companies”, submitted in January 2025. Thesis advisor is Dr. Norita Ahmad. Soft copy is available (Thesis, Approval Signatures, Completion Certificate, and AUS Archives Consent Form).Digital transformation has profoundly altered marketing strategies within the technology sector of the United Arab Emirates. Understanding the influence of digitalization on marketing is essential before exploring the diverse digital marketing tactics utilized by companies. The concepts of digital marketing campaigns and digital marketing strategies are often conflated, though they differ significantly. Digital marketing campaigns consist of targeted actions executed as part of a broader digital marketing strategy, aimed at progressing toward defined goals. These strategies are crucial for enhancing customer engagement, with personalization emerging as a fundamental aspect of contemporary marketing. By customizing interactions to align with individual customer preferences, businesses can foster a greater sense of value and satisfaction among their clientele. Feedback from dissatisfied customers serves as a critical resource for organizational growth. Digital marketing enables companies to interact with clients and gather essential insights to improve their products or services. Technology firms capitalize on these opportunities to engage directly with their target audience, effectively presenting their offerings to key decision-makers. Therefore, the thesis proposal focuses on the implementation of digital marketing strategies in the technology sector of the United Arab Emirates, employing a literature review to draw meaningful conclusions on the evolving marketing demographics and digital environment in the region.School of Business AdministrationDepartment of Management, Strategy and EntrepreneurshipMaster of Business Administration (MBA
Exploring the Transformative Potential of Generative AI in Mechanical Engineering Education
No full textA Master of Science thesis in Engineering Systems Management by Mohannad Alghazo entitled, “Exploring the Transformative Potential of Generative AI in Mechanical Engineering Education”, submitted in April 2025. Thesis advisor is Dr. Vian Ahmed and thesis co-advisor is Dr. Zied Bahroun. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).The advent of Generative Artificial Intelligence (GAI) presents new opportunities and challenges in Mechanical Engineering Education (MEE). However, literature lacks the exploration of GAI’S application in this field. Therefore, this research highlights this gap by evaluating various free versions of GAI tools, including Code Copilot, ChatGPT/ScholarGPT, Gemini, Claude, and ChatPDF, across various aspects of the MEE curriculum. The study classifies and analyzes these tools according to their effectiveness in computational/conceptualization problems, theoretical problem-solving, image analysis & schematics, research, CAD drawing, simulation, and coding. Subsequently, a mixed exploratory research approach was deployed, incorporating qualitative and quantitative methodologies. Variables were identified through a systematic literature review and expert interviews and were then validated through surveys data analysis. Statistical techniques, including Relative Importance Index (RII), Cronbach’s α, Confirmatory Factor Analysis (CFA), and Partial Least Squares Structural Equation modeling (PLS-SEM), were conducted to identify the most significant factors and validate them, as well as to assess the relationships between enablers, challenges, strategies, psychological factors, and faculty and student perceptions of GAI. Findings suggest that Code Copilot is the most effective for computational tasks and coding related applications, while ChatGPT excels in theoretical problems, CAD drawing, and simulation, ChatPDF is particularly valuable for research, whereas Gemini and Claude demonstrate moderate effectiveness across multiple domains. PLS-SEM results confirm that enablers, challenges, and strategies influence faculty and student perceptions of GAI integration. Moreover, survey data underscores a preference for gradual GAI implementation, focusing on design, simulation, coding, and academic writing in prior to full-scale integration. Future research should expand the participant pool to include more ME faculty and students, explore advanced GAI versions, and examine direct integration of GAI tools within engineering software to enhance learning experiences.College of EngineeringDepartment of Industrial EngineeringMaster of Science in Engineering Systems Management (MSESM
Machine Learning Based Maintenance Strategies for Enhancing PV System Performance in UAE
No full textA Master of Science thesis in Electrical Engineering by Omar Abdelaziz entitled, “Machine Learning Based Maintenance Strategies for Enhancing PV System Performance in UAE”, submitted in May 2025. Thesis advisor is Dr. Mostafa Shaaban and thesis co-advisor is Dr. Ahmed Osman. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE
Experimental Investigation Into the Fire Performance of GFRP-Reinforced Concrete Beams
No full textA Master of Science thesis in Civil Engineering by Mohammed Al Dawood entitled, “Experimental Investigation Into the Fire Performance of GFRP-Reinforced Concrete Beams”, submitted in June 2025. Thesis advisor is Dr. Farid Abed. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).This thesis presents an experimental investigation into the fire performance of GFRP-reinforced concrete beams, focusing on flexural capacity, bond behavior, and the effectiveness of fire-protective strategies. Full-scale beams were subjected to standard fire exposure per ASTM E119, with key variables including fire protection type (intumescent paint, cement-based mortar), anchorage zone length, and lap splice detailing. Results show that unprotected GFRP-reinforced beams exhibited rapid degradation in flexural capacity, with ultimate moments dropping by over 40% after 180 minutes of fire exposure, and bottom bar temperatures exceeding 550°C—well above the glass transition point of 110°C. Beams protected with Sikacrete 213F mortar showed a maximum internal temperature of only 150°C, displaying minimal spalling or cracking. Intumescent-painted beams offered partial protection, with bottom bar temperatures around 450°C and moderate structural deterioration. Beams with reduced anchorage zones and shorter lap splices displayed an increase in deflection. Comparative analysis confirms that only cementitious coatings consistently preserve structural integrity. Additionally, results show that an increase in lap splice length retains approximately 84% of the moment capacity of a continuously reinforced beam, whereas a shorter lap splice length does not. Additionally, during fire exposure, longer lap splice exhibits similar behavior to continuously reinforced beam. These findings highlight the vulnerability of GFRP systems to fire and support the development of targeted fire-resistance guidelines for the safe design and application of GFRP-reinforced concrete elements.College of EngineeringDepartment of Civil EngineeringMaster of Science in Civil Engineering (MSCE
Exploring waste sorting behavior and its antecedents among university students in the UAE using an extended theory of planned behavior
No full textThis study quantitatively examines waste sorting behavior (WSB) and its antecedents among university students in the United Arab Emirates (UAE), a Middle Eastern Gulf country. Using an extended version of the Theory of Planned Behavior, this research investigates the impacts of attitudes, subjective norms and perceived behavioral control on waste sorting intention (WSI), and the impacts of WSI, personal norms, perceived knowledge, trust in the waste management process and convenient infrastructure on WSB. We collected data using a self-administered research questionnaire on a sample of 353 students and used partial least squares structural equation modelling (PLS-SEM) to analyse the data. The results revealed that attitudes (β = 0.433, p < 0.001), subjective norms (β = 0.163, p = 0.002) and perceived behavioral control (β = 0.137, p = 0.017) significantly positively impacted students’ WSI. Moreover, WSI (β = 0.444, p < 0.001), perceived behavioral control (β = 0.153, p = 0.001), perceived knowledge (β = 0.144, p = 0.003) and personal norms (β = 0.182, p < 0.001) significantly positively impacted students’ WSB. However, trust in the local waste management system and convenient infrastructure were not significant variables for predicting students’ WSB. The R-squared values for WSI and WSB are 0.376 and 0.527, respectively. Practical implications of this study include actionable recommendations for policymakers and educational institutions aimed at enhancing sustainable waste sorting practices
Unsupervised Urban Tree Biodiversity Mapping from Street Imagery
No full textA Master of Science thesis in Machine Learning by Diaa Addeen Abuhani entitled, “Unsupervised Urban Tree Biodiversity Mapping from Street Imagery”, submitted in August 2025. Thesis advisor is Dr. Imran Zualkernan and thesis co-advisor is Dr. Martina Mazzarella. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Urban tree biodiversity plays a critical role in climate resilience, ecological stability, and livability. However, large-scale biodiversity assessments remain limited due to the need for taxonomic labels and expert-led field surveys. In this work, we introduce an unsupervised clustering framework that combines visual embeddings and spatial priors to assess urban tree biodiversity directly from street-level imagery without reliance on labeled data. Our method accurately captures key ecological indicators, particularly Shannon and Simpson entropies, and provides reasonable estimates of species richness across diverse urban contexts. By leveraging the inherent spatial distribution of trees alongside visual features, our approach remains robust to geographic variability and domain shifts. We validate our framework across multiple cities and demonstrate its capacity to recover genus-level biodiversity patterns that align with known ecological distributions. This work provides a scalable pathway for monitoring urban biodiversity and offers a step toward more generalizable, data-efficient ecological assessment tools in support of nature-based urban planning.College of EngineeringDepartment of Computer Science and EngineeringMaster of Science in Machine Learning (MSMLR
Utilization of AI to Predict Shear Strength Parameters of Soil Based on Their Physical Properties
No full textA Master of Science thesis in Civil Engineering by Husaen AbdulGhafour entitled, “Utilization of AI to Predict Shear Strength Parameters of Soil Based on Their Physical Properties”, submitted in November 2025. Thesis advisor is Dr. Mousa Attom. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Shear strength is a fundamental geotechnical property that governs soil behavior under loading. Laboratory tests such as the direct shear test and the unconfined compression test are used to evaluate the shear strength parameters, namely cohesion, the angle of internal friction, and the Unconfined Compressive Strength (UCS) of soil before the design and construction of geotechnical structures such as foundations, retaining walls, slopes, and embankments. Although laboratory procedures are well-established and provide reliable measurements, they can be costly, time-consuming, and may not always be practical during early stages of geotechnical investigation. To address this issue, the main objective of this study is to develop machine learning–based predictive models to estimate cohesion, internal friction angle, and UCS based on simple index properties such as Atterberg limits, water content, dry density and grain size distribution to complement laboratory testing and assist engineers in early-stage geotechnical site investigations. Datasets were compiled from published studies and used to train eight machine learning algorithms to predict the target outputs, with hyperparameters tuned using either Optuna’s Tree-Structured Parzen Estimator (TPE) or RandomizedSearchCV with 10-fold cross-validation depending on each model. The results showed that the best performance was achieved by XGBoost for cohesion (R² = 0.738 and NRMSE = 11.02%) and UCS (R² = 0.931 and NRMSE = 6.27%), while the internal friction angle was most accurately predicted by the Multilayer Perceptron (MLP) neural network (R² = 0.884 and NRMSE = 5.66%). SHAP analysis and parametric evaluations conducted on the best performing models showed that cohesion is strongly influenced by Atterberg limits (especially the plastic limit), the internal friction angle depended primarily on clay and sand fractions, while water content was the most influential parameter for UCS prediction.College of EngineeringDepartment of Civil EngineeringMaster of Science in Civil Engineering (MSCE
Navigating Nexus: Cross-Mapping Sustainable Development Goals and Competitiveness for a Resilient Future
No full textThis study investigates two established sustainability metrics—the Sustainable Development Goal Index (SDGI) and the Global Sustainable Competitiveness Index (GSCI)—to explore dependencies between their associated drivers using Bayesian Belief Networks (BBNs). The analysis considers sustainability performance data for 163 countries in 2023, using data from SolAbility’s Sustainable Competitiveness Report and the Sustainable Development Report. The predictive accuracy of the models used in this study is 85.1% for the SDGI and 70.6% for the GSCI for the two extreme states. Key findings underscore the strong interdependence between social and economic SDG dimensions and GSCI, whereas the environmental dimension appears relatively isolated in its contribution toward overall competitiveness. The study identifies key sustainable competitiveness pillars such as governance, social capital, and intellectual capital, highlighting their significant impact on SDGI outcomes. The results reveal that high competitiveness does not uniformly translate to high performance across all sustainability dimensions—with notable disparities in terms of environmental performance. Similarly, strong SDGI performance does not guarantee excellence in sustainable competitiveness. Scenario analysis reveals that high performance in ‘governance’, ‘social capital’, and ‘intellectual capital’ yields a 100% probability of achieving high SDGI performance. Moreover, high performance across the economic, social and environmental SDG dimensions ensures a 52% probability of achieving high sustainable competitiveness. BBNs reveal complex interrelationships among SDG dimensions and the various pillars of sustainable competitiveness, offering a more integrated and holistic approach compared to traditional methods. The proposed approach allows for the identification of critical drivers and offers a framework for scenario analysis, which can inform targeted policymaking and business strategies aimed at enhancing sustainability performance