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Assessing the Impact of Sustainability Risks on Disaster and Pandemic Vulnerabilities: A Global Perspective
No full textThis study examines the impact of failing to achieve Sustainable Development Goals (SDGs) on disaster and pandemic vulnerabilities, providing a country-level perspective to inform resilience planning. The study introduces the concept of SDG-related risk, defined as the probability of not achieving the desired SDG, and classifies these risks into three categories: high, medium, and low. Using a Bayesian Belief Network (BBN) framework, two probabilistic models were developed to evaluate the influence of SDG performance on disaster risk and COVID-19 vulnerability across 165 countries. The results highlight that shortcomings in SDGs such as ‘quality education’, ‘sustainable cities and communities’, ‘no poverty’, and ‘affordable and clean energy’ significantly increase disaster and pandemic risks. Conversely, strong performance in ‘peace, justice, and strong institutions’ and ‘life on land’ enhances systemic resilience, reducing vulnerability. Countries with very high disaster risk are particularly exposed to deficiencies in SDGs related to ‘peace, justice, and strong institutions’, ‘sustainable cities and communities’, and ‘good health and well-being’. For COVID-19 risk, ‘affordable and clean energy’ emerges as the most critical SDG influencing high-risk exposure, whereas ‘climate action’ is pivotal in predicting low-risk states. These findings demonstrate the cascading risks posed by failing to achieve critical SDGs and emphasize the need for targeted interventions to mitigate vulnerabilities to disasters and pandemics, providing actionable insights for sustainable resilience strategies
H-Adaptive and Gaussian Processes Techniques for High-Accuracy State Estimation of Ground Vehicles
No full textA Master of Science thesis in Mechatronics Engineering by Karim Diab entitled, “H-Adaptive and Gaussian Processes Techniques for High-Accuracy State Estimation of Ground Vehicles”, submitted in April 2025. Thesis advisor is Dr. Mamoun Abdel-Hafez. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).College of EngineeringMultidisciplinary ProgramsMaster of Science in Mechatronics Engineering (MSMTR
Impact of Electrical Stimulation on Mental Stress, Depression, and Anxiety: A Systematic Review
No full textIndividuals experiencing high levels of stress face significant impacts on their overall well-being and quality of life. Electrical stimulation techniques have emerged as promising interventions to address mental stress, depression, and anxiety. This systematic review investigates the impact of different electrical stimulation approaches on these types of disorders. The review synthesizes data from 30 studies, revealing promising findings and identifying several research gaps and challenges. The results indicate that electrical stimulation has the potential to alleviate symptoms of anxiety, depression, and tension, although the degree of efficacy varies among different patient populations and modalities. Nevertheless, the findings also underscore the necessity of standardized protocols and additional research to ascertain the most effective treatment parameters. There is also a need for integrated methodologies that combine hybrid EEG-fNIRS techniques with stress induction paradigms, the exploration of alternative stimulation modalities beyond tDCS, and the investigation of the combined effects of stimulation on stress. Despite these challenges, the growing body of evidence underscores the potential of electrical stimulation as a valuable tool to manage mental stress, depression, and anxiety, paving the way for future advancements in this field.American University of SharjahCollege of EngineeringDepartment of Electrical Engineerin
Content-Symmetrical Multidimensional Transpose of Image Sequences for the High Efficiency Video Coding (HEVC) All-Intra Configuration
No full textEnhancing the quality of video coding whilst maintaining compliance with the syntax of video coding standards is challenging. In the literature, many solutions have been proposed that apply mainly to two-pass encoding, bitrate control algorithms, and enhancements of locally decoded images in the motion-compensation loop. This work proposes a pre- and post-coding solution using the content-symmetrical multidimensional transpose of raw video sequences. The content-symmetrical multidimensional transpose results in images composed of slices of the temporal domain whilst preserving the video content. Such slices have higher spatial homogeneity at the expense of reducing the temporal resemblance. As such, an all-intra configuration is an excellent choice for compressing such images. Prior to displaying the decoded images, a content-symmetrical multidimensional transpose is applied again to restore the original form of the input images. Moreover, we propose a lightweight two-pass encoding solution in which we apply systematic temporal subsampling on the multidimensional transposed image sequences prior to the first-pass encoding. This noticeably reduces the complexity of the encoding process of the first pass and gives an indication as to whether or not the proposed solution is suitable for the video sequence at hand. Using the HEVC video codec, the experimental results revealed that the proposed solution results in a lower percentage of coding unit splits in comparison to regular HEVC coding without the multidimensional transpose of image sequences. This finding supports the claim of there being increasing spatial coherence as a result of the proposed solution. Additionally, using four quantization parameters, and in comparison to regular HEVC encoding, the resulting BD rate is −15.12%, which indicates a noticeable bitrate reduction. The BD-PSNR, on the other hand, was 1.62 dB, indicating an enhancement in the quality of the decoded images. Despite all of these benefits, the proposed solution has limitations, which are also discussed in the paper.American University of SharjahCollege of EngineeringDepartment of Computer Science and Engineerin
A 3-in-1 multifunctional porous organic polyimide: detection, capture and controlled release of antibacterial drugs
No full textTwo imide-linked porous organic polymers (POPs), PI-POP and NI-POP, were synthesized and evaluated as multifunctional materials for chemical sensing, adsorption, and temperature-responsive release applications. Characterization revealed hierarchical porosity, with surface areas of 723 m2 g−1 for PI-POP and 385 m2 g−1 for NI-POP. Their π-rich frameworks enabled strong fluorescence-based detection of tetracycline (TC) as a probe molecule. PI-POP showed stronger quenching behavior, aligning with the Lehrer model, while NI-POP followed Stern–Volmer dynamics. Beyond sensing, the adsorption behavior of tetracycline was systematically investigated under various conditions, including pH, initial drug concentration, adsorbent dose, and contact time. Optimal removal was observed under neutral pH, with PI-POP achieving ∼90% TC removal at a solid–liquid ratio of 1.5. The higher surface area and pore volume of PI-POP explain its superior uptake capacity and promote it as a more effective adsorbent. Both polymers followed the Elovich model in kinetic studies and aligned with Freundlich (PI-POP) and Temkin (NI-POP) models in isothermal analysis. Comparable removal efficiencies in tap water and buffered systems confirmed their robustness and practical applicability. Furthermore, controlled release tests at physiological temperature showed faster TC release from NI-POP, likely due to weaker host–guest interactions, making it suitable for short-term delivery applications. The released TC exhibited a clear inhibition zone against both E. coli and S. epidermidis. This confirms the retained antibacterial activity and structural integrity of TC post-release. This work highlights the potential of tailored POPs for integrated environmental and biomedical applications.American University of Sharja
Low-Complexity Machine Learning-based Behavioral Modeling of Power Amplifiers
No full textA Master of Science thesis in Electrical Engineering by Mohammad Rabih Aziz entitled, “Low-Complexity Machine Learning-based Behavioral Modeling of Power Amplifiers”, submitted in July 2025. Thesis advisor is Dr. Oualid Hammi. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Linearization is the process of countering the effects of the distortions introduced by power amplifiers when they are driven close to saturation. Digital Predistortion is a popular linearization technique in which a predistorter distorts the input to the power amplifier by applying an inverse function to its behavioral model. The result is a distortion-free output from the transmitter. In this way, behavioral modeling is an important aspect of the linearization process. Among the various behavioral models that have been studied over the years, Neural Networks, or Multiayer Perceptrons, have gained popularity for their ability to capture intricate and dynamic details of the power amplifier’s behavior. Interestingly, it is of great interest to reduce the complexity of these models, as the predistorters are often constrained by computational power and storage limitations. With this motivation, clever preprocessing, optimal model selection, unstructured pruning and quantization are investigated in this work. Specifically, networks with two different input basis functions – RVTDNN and ARVTDNN – are trained on selectively sampled data and optimal models among the pool of implemented models are selected using the Bayesian Information and Akaike Information Criteria. Then, pruning and quantization are applied to the set of optimally selected memory models. Additionally, three more metrics, the NMSE, MSE, and storage size, are used for a comprehensive quantitative analysis of the complexity-performance paradigm. As per the findings, pruning resulted in significant model compression, in terms of the number of parameters, and little impact on the performance for up to 30% sparsity in both models. Further model compression, in terms of storage size, was also observed for both models and their sparse versions after quantization. Moreover, this work introduces Kolmogorov-Arnold Networks for the first time in the discourse on power amplifier behavioral modeling. The results show that two implemented models – RVTDKAN and ARVTDKAN – achieved an NMSE of -37.78 dB and -38.03 dB, respectively, outperforming their Multilayer Perceptron counterparts and demonstrating superior modeling capabilities with a lower parameter count.College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE
Investigating Barriers And Drivers For NZEBS in the UAE: A Mixed-Methods Analysis
No full textA Master of Science thesis in Engineering Systems Management by Fahad Abdalla Mohammed entitled, “Investigating Barriers And Drivers For NZEBS in the UAE: A Mixed-Methods Analysis”, submitted in March 2025. Thesis advisor is Dr. Vian Ahmed. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).College of EngineeringDepartment of Industrial EngineeringMaster of Science in Engineering Systems Management (MSESM
An Assessment Tool for Nature-inspired & Living Laboratory (NILL)™ Buildings
No full textA Doctor of Philosophy Dissertation in Engineering Systems Management by Mariam Ahmed Abdalla Yousif AlAli entitled, “An Assessment Tool for Nature-inspired & Living Laboratory (NILL)™ Buildings”, submitted in April 2025. Dissertation advisor is Dr. Serter Atabay and dissertation co-advisor is Dr. Salwa Beheiry. Soft copy is available (Dissertation, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Laboratory buildings are known for their specialized technical activities that require different building design elements. Moreover, traditional laboratory building design and construction often prioritizes functionality over occupant well-being or sustainability. As such, applying green building rating systems or nature-inspired design strategies in the context of laboratory buildings is yet to be thoroughly investigated. Furthermore, nature-inspired design principles and living laboratory concepts offer promising solutions to enhance the design and construction of nature-inspired living laboratory buildings. As such, this dissertation work filled this gap by providing a comprehensive Nature-inspired & Living Laboratory (NILL) building assessment system to evaluate laboratory buildings based on principles of nature-inspired design and living buildings to promote occupant well-being, sustainability, and work efficiency. The sub-objectives of this research focused on identifying the gap in current literature to introduce the “NILL” novel concept, followed by validating the NILL indicators, assigning weights through Fuzzy AHP analysis, developing the assessment tool and implementing it through a case study of a laboratory buildings. Hence, the research followed a mixed method approach, integrating both qualitative and quantitative data collection and analysis tools, such as literature review, expert interviews through Delphi Rounds, Fuzzy Analytical Hierarchy Process (Fuzzy AHP), Structural Equation Modelling (SEM), and Confirmatory Factor Analysis (CFA) to develop and validate the assessment tool. Finally, a laboratory building was assessed using the NILL Building Tool and highlighted strong security and operational efficiency, but revealed critical gaps in indoor environmental quality, energy and water efficiency, and biophilic integration, emphasizing the need for further design and operational enhancement to comply with higher NILL Building levels. Accordingly, the outcome of this novel research provided an understanding of the current status of laboratories as nature-inspired & living buildings and offered an assessment tool for further application that can be applied within research, academic/teaching, and industry laboratory buildings.College of EngineeringDepartment of Industrial EngineeringPhD in Engineering - Engineering Systems Management (PhD-ESM
Convergence Speed of Bermudan, Randomized Bermudan, and Canadian Options
Full text linkAmerican options have long received considerable attention in the literature, with numerous publications dedicated to their pricing. Bermudan and randomized Bermudan options are broadly used to estimate their prices efficiently. Notably, the penalty method yields option prices that coincide with those of randomized Bermudan options. However, theoretical results regarding the speed of convergence of these approximations to the American option price remain scarce. In this paper, we address this gap by establishing a general result on the convergence speed of Bermudan and randomized Bermudan option prices to their American limits. We prove that for convex payoff functions, the convergence speed is linear; that is, of order 1/n, where n denotes the number of exercisable opportunities in the Bermudan case and serves as the intensity parameter of the underlying Poisson process in the randomized Bermudan case. Our framework is quite general, encompassing Lévy models, stochastic volatility models, and nearly any risk-neutral model that can be incorporated within a strong Markov framework. We extend our analysis to Canadian options, showing under mild conditions a convergence rate of 1/√n to their American limits. To our knowledge, this is the first study addressing the speed of convergence in Canadian option pricing
The Impact of Openness to Experience on L2 Oral Fluency
No full textA Master of Arts thesis in Teaching English to Speakers of Other Languages (TESOL) by Yara Kamal Rabea entitled, “The Impact of Openness to Experience on L2 Oral Fluency”, submitted in November 2024. 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