Bearing Capacity Factors for Strip Footings on Soft Clay Stabilized with a Trapezoidal Granular Trench

A Master of Science thesis in Civil Engineering by Abdel Razzaq Abu Othman entitled, “Bearing Capacity Factors for Strip Footings on Soft Clay Stabilized with a Trapezoidal Granular Trench”, submitted in May 2025. Thesis advisor is Dr. Mousa Attom and thesis co-advisor is Dr. Mohammad Yamin. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).College of EngineeringDepartment of Civil EngineeringMaster of Science in Civil Engineering (MSCE

Queuing-Based Optimization of EV Charging Stations: A Case Study of Manama City, Bahrain

A Master of Science thesis in Engineering Systems Management by Hamad A. Rahman Albinali entitled, “Queuing-Based Optimization of EV Charging Stations: A Case Study of Manama City, Bahrain”, submitted in April 2025. Thesis advisor is Dr. Moncer Hariga and thesis co-advisor is Dr. Rami As'ad. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form). Embargo expires July 08, 2026.The transition to electric mobility necessitates proper strategic planning of charging infrastructure to ensure efficiency, accessibility, and user satisfaction. Today, Electric vehicle (EV) charging stations (CSs) must be strategically located to maximize user satisfaction, enhance accessibility, and balance costs for both charging station owners (CSOs) and electric vehicle users (EVUs). However, most existing mathematical models overlook real-world service constraints, often assuming unlimited waiting capacity at CSs. Another often-overlooked aspect in planning EV charging infrastructure is the blocking cost incurred when EVUs arrive at a station that has neither available charging slots nor waiting space. This situation can lead to inefficient and suboptimal infrastructure planning decisions. Additionally, research on the Charging Station Location Problem (CSLP) remains limited in the Middle East, where countries such as the Kingdom of Bahrain (KoB) face significant barriers to EV adoption, such as inadequate charging infrastructure, range anxiety, and policy uncertainty. To address these gaps, this study develops an optimization model for CS deployment considering CSOs and EVUs costs while explicitly accounting for a queuing behavior having a finite queue length. The problem is formulated as a highly complex Mixed-Integer Nonlinear Programming (MINLP) model. To effectively solve this problem, we propose an iterative solution procedure that initially optimizes CSs without considering queuing aspects, including only CSO related and access costs in the objective function. The resulting relaxed problem is formulated as a Mixed Integer Linear Programming (MILP) model and solved through CPLEX optimizer. Subsequently, the results of this MILP model are iteratively refined to incorporate queuing effects, and the process is terminated once no further cost improvements are attained. A case study of Manama City, the capital of the KoB, was conducted to test the model’s ability in identifying the optimal locations for establishing CSs. The results offer practical insights for policy makers and industry stakeholders to develop sustainable and effective EV charging networks in the KoB.College of EngineeringDepartment of Industrial EngineeringMaster of Science in Engineering Systems Management (MSESM

A natural MSSM from a novel SO(10), Yukawa unification, light sparticles, and SUSY implications at LHC

The SO(10) model with a heavy Higgs spectrum consisting of 560 + 560 and a light Higgs spectrum consisting of 2×10 + 320 plet representations of SO(10) is unique among SO(10) models. It has the remarkable property that VEVs of 560 and 560 can simultaneously reduce the rank of the gauge group and further reduce the remaining symmetry down to the Standard Model gauge group. Additionally, on mixing with the light felds all the Higgs felds become heavy except for one pair of light Higgs doublets just as in MSSM. This model has not been fully explored thus far because of the technical difculty of computing the couplings of the heavy and the light Higgs sectors, specifcally the interaction (560 × 560) · 320 involving the coupling of tensor-spinors with a third rank mixed tensor 320. An explicit analysis of such couplings is given in this paper. Spontaneous symmetry breaking of the SO(10) symmetry is carried out by reducing the gauge group to SU(3) c×SU(2)L×U(1)Y with just one pair of light Higgs. Thus a natural deduction of MSSM arises from the SO(10) model with no fne tuning needed. Further, it is shown that the light Higgs doublet of the model is a linear combination of the Higgs doublet felds of the 2×10 and the 320 Higgs felds. It is shown that in this class of SO(10) models b − t − τ unifcation can be achieved with tan β as low as 5–10. An analysis of the sparticle spectrum within g˜SUGRA renormalization group evolution is given which leads to a bi-modal sparticle spectrum consisting of a compressed low mass spectrum for sleptons and weakinos and a high mass spectrum of gluino, squarks, and heavy Higgs. While the LSP is the light neutralino, the NLSP is found to be the light stau lying close to the LSP, while the remaining leptons, and the weakinos are also in close proximity to the LSP with masses in the few hundred GeV range. The cross section for slepton production and weakino production are estimated and appear promising for SUSY at the LHC. However, a more dedicated analysis is needed to predict the size of the supersymmetric signatures at the LHC.National Science FoundationSponsoring Consortium for Open Access Publishing in Particle Physic

EEG-Based Multi-Level Mental State Classification Using Partial Directed Coherence and Graph Convolutional Networks: Impact of Binaural Beats on Stress Mitigation

This study addresses limitations in EEG-based stress detection research by developing a novel approach to differentiate multiple mental states in different stress baseline population samples. Utilizing EEG signals, graph convolutional neural networks (GCNs), and binaural beats stimulation (BBs), the research investigates stress detection and reduction in two population sample groups with distinct baselines (group 1: low daily baseline, and group 2: stressed daily baseline). The experiment comprises four phases: rest state, control alertness, stress induction, and stress mitigation. Mental states were assessed using behavioral data: reaction time to stimuli (RT) and target detection accuracy, subjective reports: Perceived Stress Scale scores (PSS-10), biochemical indicators: salivary cortisol levels, and neurophysiological measure: EEG effective connectivity via Partial Directed Coherence (PDC). BBs significantly improved target detection accuracy by 31.6% and 22.8% for low and high-stress groups, respectively. PDC connectivity showed a shift to the temporal region during mitigation, indicating a return to a more balanced state. GCN classification achieved accuracies of 76.43 ± 9.01% and 76.32 ± 7.79% for each group, and 76.37 ± 8.40% for a common baseline. While 16-Hz BBs enhanced focusing abilities they did not significantly reduce subjective stress scores. This study highlights the complex relationship between cognitive performance, perceived stress, and neurophysiological measures, emphasizing the need for multifaceted stress research and management approaches.American University of SharjahFourth Forum for Women in ResearchCollege of EngineeringDepartment of Electrical Engineerin

Estimation of Metallic Coating Thicknesses Using Eddy Current Spectroscopy and Machine Learning

A Master of Science thesis in Mechanical Engineering by Atheer Ghiath Aldbaisi entitled, “Estimation of Metallic Coating Thicknesses Using Eddy Current Spectroscopy and Machine Learning”, submitted in March 2025. Thesis advisor is Dr. Bassam Abu-Nabah and thesis co-advisor is Dr. Maen Alkhader. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).College of EngineeringDepartment of Mechanical EngineeringMaster of Science in Mechanical Engineering (MSME

Behavioral Modeling of RF Power Amplifiers Using Real-Valued Time Delay Neural Networks

A Master of Science thesis in Electrical Engineering by Sharafa Idowu Bankole entitled, “Behavioral Modeling of RF Power Amplifiers Using Real-Valued Time Delay Neural Networks”, submitted in April 2025. Thesis advisor is Dr. Oualid Hammi. 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

The Impact of TBLT on L2 Vocabulary Development in SCMC and FTF Environments

A Master of Arts thesis in Teaching English to Speakers of Other Languages (TESOL) by Mustafa Raafat Abunar entitled, “The Impact of TBLT on L2 Vocabulary Development in SCMC and FTF Environments”, 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

Edge-Optimized Deep Learning Architectures for Classification of Agricultural Insects with Mobile Deployment

The deployment of machine learning models on mobile platforms has ushered in a new era of innovation across diverse sectors, including agriculture, where such applications hold immense promise for empowering farmers with cutting-edge technologies. In this context, the threat posed by insects to crop yields during harvest has escalated, fueled by factors such as evolution and climate change-induced shifts in insect behavior. To address this challenge, smart insect monitoring systems and detection models have emerged as crucial tools for farmers and IoT-based systems, enabling interventions to safeguard crops. The primary contribution of this study lies in its systematic investigation of model optimization techniques for edge deployment, including Post-Training Quantization, Quantization-Aware Training, and Data Representative Quantization. As such, we address the crucial need for efficient, on-site pest detection tools in agricultural settings. We provide a detailed analysis of the trade-offs between model size, inference speed, and accuracy across different optimization approaches, ensuring practical applicability in resource-constrained farming environments. Our study explores various methodologies for model development, including the utilization of Mobile-ViT and EfficientNet architectures, coupled with transfer learning and fine-tuning techniques. Using the Dangerous Farm Insects Dataset, we achieve an accuracy of 82.6% and 77.8% on validation and test datasets, respectively, showcasing the efficacy of our approach. Furthermore, we investigate quantization techniques to optimize model performance for on-device inference, ensuring seamless deployment on mobile devices and other edge devices without compromising accuracy. The best quantized model, produced through Post-Training Quantization, was able to maintain a classification accuracy of 77.8% while significantly reducing the model size from 33 MB to 9.6 MB. To validate the generalizability of our solution, we extended our experiments to the larger IP102 dataset. The quantized model produced using Post-Training Quantization was able to maintain a classification accuracy of 59.6% while also reducing the model size from 33 MB to 9.6 MB, thus demonstrating that our solution maintains a competitive performance across a broader range of insect classes.College of EngineeringDepartment of Computer Science and Engineerin

Language, Knowledge, and the Sacred: Al-Bukhārī’s Philosophical and Linguistic Journey in Ḥadīth Compilation

The distinct geographical locations of the Companions of the Prophet Muḥammad after his death necessitated travelling for the acquisition of knowledge, in particular ḥadīth, which became a major topos among early Muslim scholars. Muḥammad b. Ismā‘īl al-Bukhārī (256/810) played a key role in recording the ḥadīth of the Prophet Muḥammad, culminating in his magnum opus, al-Jāmi‘ al-Ṣaḥīḥ. This work profoundly impacted the ḥadīth tradition and the genesis of its sciences. Beyond the empirical quest for authentic traditions, Al-Bukhārī's work can be interpreted through the prisms of linguistics and epistemology, which shaped his methodological approach to knowledge transmission. This paper explores Al-Bukhārī's journeys not only as physical travels but as intellectual and philosophical voyages, emphasizing the interplay between linguistic precision and philosophical inquiry in the authentication and preservation of sacred texts

Introductory Techniques in Material Fabrication - ARC 237

Syllabus for the Department of Architecture course "Introductory Techniques in Material Fabrication - ARC 237", by Instructor(s) Kenneth Tracy for the Summer 2025 semester.College of Architecture, Art and DesignDepartment of Architectur