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AI-Aided Robotic Wide-Range Water Quality Monitoring System
No full textWaterborne illnesses lead to millions of fatalities worldwide each year, particularly in developing nations. In this paper, we introduce a comprehensive system designed for the autonomous early detection of viral outbreaks transmitted through water to ensure sustainable access to healthy water resources, especially in remote areas. The system utilizes an autonomous water quality monitoring setup consisting of an airborne water sample collector, an autonomous sample processor, and an artificial intelligence-aided microscopic detector for risk assessment. The proposed system replaces the time-consuming conventional monitoring protocol by automating sample collection, sample processing, and pathogen detection. Furthermore, it provides a safer processing method against the spillage of contaminated liquids and potential resultant aerosols during the heat fixation of specimens. A morphological image processing technique of light microscopic images is used to segment images, assisting in selecting a unified appropriate input segment size based on individual blob areas of different bacterial cultures. The dataset included harmful pathogenic bacteria (A. baumanii, E. coli, and P. aeruginosa) and harmless ones found in drinking water and wastewater (E. faecium, L. paracasei, and Micrococcus spp.). The segmented labeled dataset was used to train deep convolutional neural networks to automatically detect pathogens in microscopic images. To minimize prediction error, Bayesian optimization was applied to tune the hyperparameters of the networks’ architecture and training settings. Different convolutional networks were tested in accordance with different required output labels. The neural network used to classify bacterial cultures as harmful or harmless achieved an accuracy of 99.7%. The neural network used to identify the specific types of bacteria achieved a cumulative accuracy of 93.65%.American University of Sharja
Immunoliposomes with High-Frequency Ultrasound and Microbubble-Mediated Triggering for Herceptin-Positive Targeted Breast Cancer Therapy
No full textA Doctor of Philosophy Dissertation in Materials Science and Engineering by Waad Hussein Abuwatfa entitled, “Immunoliposomes with High-Frequency Ultrasound and Microbubble-Mediated Triggering for Herceptin-Positive Targeted Breast Cancer Therapy”, submitted in September 2024. Dissertation advisor is Dr. Ghaleb Husseini and dissertation co-advisor is Dr. William Pitt. Soft copy is available (Dissertation, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).College of Arts and SciencesMultidisciplinary ProgramsPhD in Materials Science and Engineering (PhD-MSE
Finite Element Evaluation of Full 3D Effective Properties Of d₃₁ Piezoelectric Macro-Fibre Composites
No full textPiezoelectric Macro-Fibre Composites (MFC) transducers have become very popular since they combine the conformability of epoxy-matrix composites and the electromechanical energy density of piezoceramic materials. Since they are heterogeneous and made of several different materials (piezoceramic fibres, epoxy matrix, electrode and protective layers), a methodology is required to relate their effective properties with the individual properties of its components. This work presents the evaluation of all necessary effective properties for a full 3D finite element analysis. For the evaluation of the remaining effective piezoelectric coefficients, new local problems were added to those presented in the l iterature. The results are compared to those available in the literature and provided by the manufacturer. A discussion on the full 3D effective properties and their application for full 3D finite element analysis of smart structures with bonded MFCs is also presented
GFRP-Reinforced Concrete Columns: State-of-the-Art, Behavior, and Research Needs
No full textThis comprehensive review paper delves into the utilization of Glass Fiber-Reinforced Polymer (GFRP) composites within the realm of concrete column reinforcement, spotlighting the surge in structural engineering applications that leverage GFRP instead of traditional steel to circumvent the latter’s corrosion issues. Despite a significant corpus of research on GFRP-reinforced structural members, questions about their compression behavior persist, making it a focal area of this review. This study evaluates the properties of GFRP bars and their impact on the structural behavior of concrete columns, addressing variables such as concrete type and strength, cross-sectional geometry, slenderness ratio, and reinforcement specifics under varied loading protocols. With a dataset spanning over 250 publications from 1988 to 2024, our findings reveal a marked increase in research interest, particularly in regions like China, Canada, and the United States, highlighting GFRP’s potential as a cost-effective and durable alternative to steel. However, gaps in current knowledge, especially concerning Ultra-High-Performance Concrete (UHPC) reinforced with GFRP, underscore the necessity for targeted research. Additionally, the contribution of GFRP rebars to compressive column capacity ranges from 5% to 40%, but current design codes and standards underestimate this, necessitating new models and design provisions that accurately reflect GFRP’s compressive behavior. Moreover, this review identifies other critical areas for future exploration, including the influence of cross-sectional geometry on structural behavior, the application of GFRP in seismic resistance, and the evaluation of the size effect on column strength. Furthermore, the paper calls for advanced studies on the long-term durability of GFRP-reinforced structures under various environmental conditions, environmental and economic impacts of GFRP usage, and the potential of Artificial Intelligence (AI) and Machine Learning (ML) in predicting the performance of GFRP-reinforced columns. Addressing these research gaps is crucial for developing more resilient and sustainable concrete structures, particularly in seismic zones and harsh environmental conditions, and fostering advancements in structural engineering through the adoption of innovative, efficient construction practices.American University of Sharja
Enhanced DC Microgrid Protection: a Neural Network and Wavelet Transform Approach
No full textA Master of Science thesis in Electrical Engineering by Youssef Hesham El Gohary entitled, “Enhanced DC Microgrid Protection: a Neural Network and Wavelet Transform Approach”, submitted in May 2024. Thesis advisor is Dr. Ahmed Osman. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).This thesis introduces an advanced protection scheme for DC microgrids, focusing on enhancing fault detection, classification, and localization while ensuring real-time operation. Leveraging the wavelet transform algorithm and neural networks' pattern recognition capabilities, the proposed system integrates modern techniques for achieving its objectives. The protection coordination scheme encompasses two settings: the primary coordination scheme, activated when the ANN accurately identifies the fault's location, and the backup coordination scheme, activated in the event of inaccuracies or errors in the neural-based algorithm. In this scenario, an optimization model is deployed to ensure that protective devices operate with predefined operation times and parameter settings, aiming to minimize the total operation time of all relays, including primary and backup. This ensures fault isolation regardless of the neural-based algorithm's status, with the optimization problem modeled as an NLP programming problem and solved using the optimization software GAMS. The optimization model acts as a duplicate protection, enhancing the protection system's reliability by providing an additional layer of defense. Furthermore, an innovative inductor injection mechanism is introduced to enhance the protection scheme's effectiveness. By injecting an inductor into the system after fault detection, the rate of fault current rise is significantly reduced, allowing for an expanded SFV (spatial feature vector) size without compromising fault detection accuracy. The inductor injection mechanism enables the SFV to encompass additional time slots, facilitating more comprehensive data input to the neural network for improved fault classification and localization. Additionally, the inductor injection mechanism is carefully selected to balance current damping with fault detection requirements, ensuring optimal system performance under various fault conditions. Simulations using MATLAB Simulink validate the proposed protection scheme's effectiveness, demonstrating high accuracy and reliability with real-time operation and robust error handling mechanisms. This research advances protection systems in DC microgrids, offering improved fault detection, classification, coordination, and localization capabilities.College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE
Mitigating Motion Sickness in Autonomous Vehicles for Improved Passenger Comfort
No full textThis work investigates a method to mediate the onset of Motion Sickness (MS) in passenger autonomous vehicles. The inertial forces acting on passengers are estimated and then analyzed in order to reduce the problematic MS-inducing components. An approach is devised to suppress how much lateral acceleration is experienced by passengers and, consequently, alleviate the occurrence of MS. This approach requires equipping the passenger vehicle with an adaptive suspension system with active roll compensation. The optimal roll angle for MS mitigation is computed based on accurate sensor-fused inertial estimates using an off-the-shelf inertial navigation solution. The proposed algorithm is shown to suppress and attenuate the problematic MS-inducing region of the spectrum up to two orders of magnitude for some frequencies. Quantifying the improvement in ride comfort in terms of the Motion Sickness Dose Value (MSDV) metric, as defined in standard ISO-2631, it is reported that the MSDV was reduced by 113% on average using our proposed methodology.American University of Sharjah under the Open Access Progra
Hypothermia effects on neuronal plasticity post spinal cord injury
No full textBackground
SCI is a time-sensitive debilitating neurological condition without treatment options. Although the central nervous system is not programmed for effective endogenous repairs or regeneration, neuroplasticity partially compensates for the dysfunction consequences of SCI.
Objective and hypothesis
The purpose of our study is to investigate whether early induction of hypothermia impacts neuronal tissue compensatory mechanisms. Our hypothesis is that although neuroplasticity happens within the neuropathways, both above (forelimbs) and below (hindlimbs) the site of spinal cord injury (SCI), hypothermia further influences the upper limbs’ SSEP signals, even when the SCI is mid-thoracic.
Study design
A total of 30 male and female adult rats are randomly assigned to four groups (n = 7): sham group, control group undergoing only laminectomy, injury group with normothermia (37°C), and injury group with hypothermia (32°C +/-0.5°C).
Methods
The NYU-Impactor is used to induce mid-thoracic (T8) moderate (12.5 mm) midline contusive injury in rats. Somatosensory evoked potential (SSEP) is an objective and non-invasive procedure to assess the functionality of selective neuropathways. SSEP monitoring of baseline, and on days 4 and 7 post-SCI are performed.
Results
Statistical analysis shows that there are significant differences between the SSEP signal amplitudes recorded when stimulating either forelimb in the group of rats with normothermia compared to the rats treated with 2h of hypothermia on day 4 (left forelimb, p = 0.0417 and right forelimb, p = 0.0012) and on day 7 (left forelimb, p = 0.0332 and right forelimb, p = 0.0133) post-SCI.
Conclusion
Our results show that the forelimbs SSEP signals from the two groups of injuries with and without hypothermia have statistically significant differences on days 4 and 7. This indicates the neuroprotective effect of early hypothermia and its influences on stimulating further the neuroplasticity within the upper limbs neural network post-SCI. Timely detection of neuroplasticity and identifying the endogenous and exogenous factors have clinical applications in planning a more effective rehabilitation and functional electrical stimulation (FES) interventions in SCI patients.2021-24 Hong Kong Research Grant Council General Research Fun
Review of Gold Nanoparticles: Synthesis, Properties, Shapes, Cellular Uptake, Targeting, Release Mechanisms and Applications in Drug Delivery and Therapy
No full textThe remarkable versatility of gold nanoparticles (AuNPs) makes them innovative agents across various fields, including drug delivery, biosensing, catalysis, bioimaging, and vaccine development. This paper provides a detailed review of the important role of AuNPs in drug delivery and therapeutics. We begin by exploring traditional drug delivery systems (DDS), highlighting the role of nanoparticles in revolutionizing drug delivery techniques. We then describe the unique and intriguing properties of AuNPs that make them exceptional for drug delivery. Their shapes, functionalization, drug-loading bonds, targeting mechanisms, release mechanisms, therapeutic effects, and cellular uptake methods are discussed, along with relevant examples from the literature. Lastly, we present the drug delivery applications of AuNPs across various medical domains, including cancer, cardiovascular diseases, ocular diseases, and diabetes, with a focus on in vitro and in vivo cancer research.American University of SharjahAl-Jalila FoundationAl Qasimi FoundationPatient’s Friends Committee-SharjahBiosciences and Bioengineering Research InstituteGCC Co-Fund ProgramTakamul programTechnology Innovation Pioneer (TIP) Healthcare AwardsSheikh Hamdan Award for Medical SciencesFriends of Cancer Patients (FoCP)Dana Gas Endowed Chair for Chemical Engineerin
Deepfakes Recognition with Physiological Signals
No full textA Master of Science thesis in Electrical Engineering by Muhammad Riyyan Khan entitled, “Deepfakes Recognition with Physiological Signals”, submitted in April 2024. Thesis advisor is Dr. Usman Tariq and thesis co-advisors are Dr. Hasan Al-Nashash and Dr. Abhinav Dhall. 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