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Mathematical Modeling of COVID-19 Transmission and Vaccination: The Case of UAE
No full textA Master of Science thesis in Mathematics by Manal W. Almuzini entitled, “Mathematical Modeling of COVID-19 Transmission and Vaccination: The Case of UAE”, submitted in June 2022. Thesis advisors are Dr. Abdul Salam Jarrah and Dr. Hana Sulieman. Soft copy is available (Thesis, Approval Signatures, Completion Certificate, and AUS Archives Consent Form).Mathematical models are widely used in simulating infectious diseases. They are employed to investigate the disease transmission dynamics, forecast its spreading pattern, check the effectiveness of the interventions, or any other point of interest regarding the disease progression. In general, models are expressed in terms of Differential Equations. In this thesis, we propose a mathematical model called the SEIR-VD model (S: Susceptible, E: Exposed, I: Infected, R: Recovered, V: Vaccinated, and D: Deaths) to study the COVID-19 progression and forecast its spreading. We examine the model characteristics and complete its mathematical analysis (stability points, basic reproduction number, and sensitivity analysis). We use the data of the UAE during the vaccination intervention as a case study. Our numerical analysis includes parameter estimation, curve fitting, prediction, and model validation. For the numerical analysis of our proposed SEIR-VD model, we employed a switched hybrid forced model developed in [1] for which the main time interval is divided into sub-intervals, and over these subintervals, the model parameters are forced to be a time-dependent function with the time considered continuous for some selected parameters and discrete for others. Different scenarios for vaccine intervention are considered in order to determine certain rates of fully immunized population. The proposed model can be used to investigate COVID-19 dynamics in other countries when relevant data are available to feed the model.College of Arts and SciencesDepartment of Mathematics and StatisticsMaster of Science in Mathematics (MSMTH
In-Between Projection Interpolation in Cone-Beam CT Imaging using Convolutional Neural Networks
No full textRespiratory-Correlated cone beam computed tomography (4D-CBCT) is an emerging image-guided radiation therapy (IGRT) technique that is used to account for the uncertainties caused by respiratory-induced motion in the radiotherapy treatment of tumors in thoracic and upper-abdomen regions. In 4D-CBCT, projections are sorted into bins based on their respiratory phase and a 3D image is reconstructed from each bin. However, the quality of the resulting 4D-CBCT images is limited by the streaking artifacts that result from having an insufficient number of projections in each bin. In this work, an interpolation method based on Convolutional Neural Networks (CNN) is proposed to generate new in-between projections to increase the overall number of projections used in 4D-CBCT reconstruction. Projections simulated using XCAT phantom were used to assess the proposed method. The interpolated projections using the proposed method were compared to the corresponding original projections by calculating the peak-signal-to-noise ratio (PSNR), root mean square error (RMSE), and structural similarity index measurement (SSIM). Moreover, the results of the proposed method were compared to the results of existing standard interpolation methods, namely, linear, spline, and registration-based methods. The interpolated projections using the proposed method had an average PSNR, RMSE, and SSIM of 35.939, 4.115, and 0.968, respectively. Moreover, the results achieved by the proposed method surpassed the results achieved by the existing interpolation methods tested on the same dataset. In summary, this work demonstrates the feasibility of using CNN-based methods in generating in-between projections and shows a potential advantage to 4D-CBCT reconstruction
Exploring Semi-Supervised Learning Algorithms for Camera Trap Images
No full textA Master of Science thesis in Computer Engineering by Ali Reza Sajun entitled, “Exploring Semi-Supervised Learning Algorithms for Camera Trap Images”, submitted in August 2022. Thesis advisor is Dr. Imran Zualkernan. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Animal Extinction and biodiversity loss is a major challenge faced by ecologists in today’s world due to increasing populations and industrialization. In their fight to protect endangered species, ecologists use remote camera traps that allow them to monitor animals in remote locations. A drawback of current camera traps is that captured images need to be manually labeled through an error-prone, tedious and expensive process. Images from camera traps can be automatically labelled using deep learning techniques. However, these images are expensive to label, and the data is highly unbalanced. Semi-supervised learning can be utilized to address the lowlabelling issue, but the problem of handling unbalanced data remains unexplored. This thesis explored how state-of-the-art semi-supervised learning algorithm like FixMatch based on consistency regularization and pseudo-labeling and its variants including Auxiliary Balanced Classifier (ABC), Distribution Aligning Refinery of Pseudo-label (DARP) and Bi-sampling Strategy (BiS) performed on such highly unbalanced data. Imbalance ratios of 1, 50, 100 and 150 across labeled proportions of 10%, 40%, 60%, and 80% were considered. Additional experiments were also conducted on the benchmarking datasets of CIFAR10, CIFAR100 and SVHN. The primary results are that the resampling strategy worked best when applied to FixMatch for low levels of imbalance across the data sets. For example, an F1-score of 68% for CIFAR10 at 40% labeled data was achieved as opposed to an F1-score of 60% with the plain FixMatch. However, addition of auxiliary loss or pseudo-label balancing showed negligible improvements. The results also suggest that a major factor mediating the performance was complexity of the features. In general, the semi-supervised techniques performed well for the camera trap data with F1-scores of up to 58%. However, the overall performance on the camera trap data was affected by classes belonging to very small animals which occupied a small subset of the image frame and, therefore tended to be misclassified.College of EngineeringDepartment of Computer Science and EngineeringMaster of Science in Computer Engineering (MSCoE
Immunomodulatory and Anti-Inflammatory Effects of Berberine in Lung Tissue and its Potential Application in Prophylaxis and Treatment of COVID-19
No full textNatural products with known safety profiles are a promising source for the discovery of new drug leads. Berberine presents an example of one such phytochemical that has been extensively studied for its anti-inflammatory and immunomodulatory properties against myriads of diseases, ranging from respiratory disorders to viral infections. A growing body of research supports the pluripotent therapeutic role berberine may play against the dreaded disease COVID-19. The exact pathophysiological features of COVID-19 are yet to be elucidated. However, compelling evidence suggests inflammation and immune dysregulations as major features of this disease. Being a potent immunomodulatory and anti-inflammatory agent, berberine may prove to be useful for the prevention and treatment of COVID-19. This review aims to revisit the pharmacological anti-inflammatory and immunomodulatory benefits of berberine on a multitude of respiratory infections, which like COVID-19, are known to adversely affect the airways and lungs. We speculate that berberine may help alleviate COVID-19 via preventing cytokine storm, restoring Th1/Th2 balance, and enhancing cell-mediated immunity. Furthermore, the role this promising phytochemical plays on other important inflammatory mediators involved in respiratory disorders will be underscored. We further highlight the role of berberine against COVID-19 by underscoring direct evidence from in silico, in vitro, and in vivo studies suggesting the inhibitory potential berberine may play against three critical SARS-CoV-2 targets, namely main protease, spike protein, and angiotensin-converting enzyme 2 receptor. Further preclinical and clinical trials are certainly required to further substantiate the efficacy and potency of berberine against COVID-19 in humans
A Static and Snapthrough Analysis of an Innovative Bistable Composite Wing
No full textA Master of Science thesis in Mechanical Engineering by Sara Hijazi entitled, “A Static and Snapthrough Analysis of an Innovative Bistable Composite Wing”, submitted in December 2022. Thesis advisor is Dr. Samir Emam. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).This thesis presents a numerical analysis of the room-temperature shapes and snapthrough response of thermally induced hybrid bistable symmetric laminates. The bistable laminate is clamped at one end and free at the other end to extend the applications of bistable laminates to non-free-free boundary conditions. The hybrid layup resolves the issue of losing the bistability of the laminate when attached to a larger structure or clamped. Bidirectional (BD) glass-epoxy layers are symmetrically embedded in the laminate’s layup to trigger the bistability. An approximate analytical model that is based on the Rayleigh-Ritz method along with the ABAQUS Finite Element (FE) package are used in the analysis. The model is validated against the results available in the literature and a good agreement is obtained. The significance of the BD layers on the thermally induced room-temperature shapes and the snapthrough response is examined. Three parameters are considered: the BD layers’ width, thickness, and location from the laminate’s center. It is found out that the three parameters greatly affect the static equilibrium shapes and the snapthrough/snapback response. This analysis complements the ongoing research on the bistable laminates for morphing applications. The second part of this study is to propose an innovative design of a bistable wing which has a symmetric flat platform followed by a winglet that utilizes the modified hybrid symmetric bistable laminate. The snap-through and snap-back responses under concentrated load of the proposed design is investigated. The proposed design is referred to as an innovative hybrid bistable laminate i-HBSL throughout this thesis.College of EngineeringDepartment of Mechanical EngineeringMaster of Science in Mechanical Engineering (MSME
Wearable Real-time Mental Stress Detector
No full textA Master of Science thesis in Biomedical Engineering by Lamis Abdul Kader entitled, “Wearable Real-time Mental Stress Detector”, submitted in November 2022. Thesis advisor is Dr. Hasan Al-Nashash and thesis co-advisors are Dr. Usman Tariq and Dr. Fares Al-Shargie. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Mental stress causes physical diseases in addition to behavioral, emotional, and monetary complications. Thus, research is set on detecting mental stress to reduce the risk of damage to an individual’s well-being. There is plenty of evidence and solutions in the literature that help assess and detect stress, highlighting its importance. Assessment and detection of stress can be performed using many physiological signals, including the Electroencephalogram (EEG) and the Galvanic Skin Response (GSR). Moreover, many commercialized systems used to detect stress with EEG require a controlled environment with many channels, which prohibits its daily use. Those systems are also complex and expensive. Fortunately, there is a rise to using wearable devices for monitoring stress, which offers more flexibility to monitor stress through physiological signals. In this thesis, a wearable-monitoring system that integrates both EEG and GSR physiological signals was developed. The novelty of the proposed device is that it requires only one channel for acquiring both EEG and GSR signals. By sensor fusion, we were able to achieve improved accuracy, lower cost, and easier to use device. Power spectrum analysis and machine learning were applied on the acquired signals to detect the elevation of and classify mental stress. Furthermore, the optimum electrode location on the scalp was investigated for stress detection using one channel. This was achieved by utilizing a specially designed mechanical framework with a rail-like structure to allow the flexibility of electrode positioning. The proposed system was tested on 20 human subjects. Results demonstrate the capability of the system in classifying 2 levels of mental stress with a maximum accuracy of 70.3% when using EEG, 93% when using GSR, and 84.6% when using both EEG and GSR.College of EngineeringMultidisciplinary ProgramsMaster of Science in Biomedical Engineering (MSBME
Bridge Structural Health Monitoring Using Mobile Sensor Networks
No full textA Master of Science thesis in Civil Engineering by Fouad Mostafa Fouad Amin entitled, “Bridge Structural Health Monitoring Using Mobile Sensor Networks”, submitted in December 2022. Thesis advisor is Dr. Mohammad AlHamaydeh. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Vehicles moving along a bridge will dynamically interact with the bridge's vibrations. An acceleration sensor on the vehicle can record these vibrations’ signals resulting from different sources of interaction, namely, the bridge vibrations, road roughness, vehicle properties, and speed. Specific algorithms must be implemented to extract the bridge properties from such a contaminated signal. This thesis develops and integrates a framework that extracts the bridge's vibrational properties via multiple vehicle sensor readings. In addition, two new techniques for source separation are introduced. The developed framework is divided into two stages. The first part deals with source separation by removing the vehicle dynamics and road roughness effects from the original signal recorded by the sensor. Two approaches, from the literature, can be used to remove the vehicle dynamics, namely, Frequency Response Function (FRF) and Ensemble Empirical Modal Decomposition (EEMD). Moreover, a new hybrid algorithm (FRF+EEMD) is introduced and tested. Next, two methods are tested to remove the road roughness profile; Second-Order Blind Identification (SOBI) algorithm, from the literature, and Signal Subtraction Algorithm (SSA). SSA is a newly proposed technique where the difference between two vehicles’ responses is used to get the pure bridge frequencies. Then these frequencies are allowed to pass by filtering out the remaining frequencies from the deconvoluted vehicle response to get the pure bridge response. Source separation is carried out for multiple vehicles, then a sparse observation matrix is generated that has bridge vibration readings in both space and time coordinates. In the second stage of the framework, the sparse matrix is completed using the ALS algorithm. New signal processing techniques are introduced to compute the initial guess of the frequencies and prepare the data for structured optimization analysis. The new hybrid technique (FRF+EEMD) and SSA have proven to be comparable or superior to other algorithms found in the literature. Moreover, the introduced signal processing techniques were able to distinguish between vertical and torsional modes of vibration and automatically detect initial guess values close to the actual values of fundamental frequencies.College of EngineeringDepartment of Civil EngineeringMaster of Science in Civil Engineering (MSCE
Studying Beating Characteristics of Bovine Sperm cells using Microfluidics
No full textA Master of Science thesis in Biomedical Engineering by Aisha Hamidu entitled, “Studying Beating Characteristics of Bovine Sperm cells using Microfluidics”, submitted in December 2022. Thesis advisor is Dr. Mohamed Abdelgawad. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Infertility is a serious global health problem affecting 8-12% of couples worldwide. Both men and women are responsible with nearly half of the infertility cases due to the male factor. Although male infertility can be attributed to various causes such as varicocele, genetic abnormalities and immunological factors, there is a high level of unknown factors in male infertility. This serves as a challenge due to the lack of understanding of these underlying mechanisms and not knowing the factors that are causing infertility in men. Analyzing the ability of sperm cells to reach and fertilize an oocyte is important in defining new factors that can explain the unexplained infertility cases and their causes. The spermatozoa journey from the cervix to the oviducts where fertilization takes place is a complex one and studying it is crucial for exploring new causes of male infertility. Any abnormality that befalls the sperm during its transport within the biophysical and biochemical environment of the human female reproductive tract may result in infertility. In this current study, high speed imaging analysis was used to study the beating pattern of immobilized bovine sperm cells flagellum under different chemical stimuli (Caffeine and Heparin) and physical stimuli (flow and no flow conditions). Bovine sperm cells where immobilized on islands of fibronectin deposited on glass slides using microcontact printing before being subjected to chemical and physical stimuli. Following stimulation with Caffeine, the analysed cells exhibited a decreased beat frequency of 25.07 ± 3.02, 22.08 ± 4.03, and 19.47 ± 2.01 Hz at the three tested concentrations compared to control frequency of, 33.15 ± 10.84 Hz. Heparin had an opposite effect of increasing the frequency to 41.26 ± 4.58 Hz. Both stimulants caused an increase in the flagellum beating amplitude. When sperm cells were subjected to a continuous flow velocity of 100 μm/s, it was noticed that the beating frequency decreased significantly from 33.15 Hz to 16.94 Hz. These results show the heterogenous behaviour of Bovine sperm cells under the different conditions that may exist in the female reproductive tract.College of EngineeringMultidisciplinary ProgramsMaster of Science in Biomedical Engineering (MSBME
Behavior of reinforced concrete beams cast with a proposed geopolymer concrete (GPC) mix
No full textThe aim of this paper is to examine the effects of using Ground Granulated Blast Furnace Slag (GGBFS) as a complete replacement to Ordinary Portland Cement (OPC) in Reinforced Concrete (RC) beams. The proposed GGBFS mix had an air content of 1.4%, a unit weight of 2480 kg/m3, a slump of 201 mm, and a compressive strength of 30 MPa after 56 days of curing. In addition, the GGBFS-based sample have shown an increased durability as it passed less chloride ions when compared to conventional concrete. A total of four beams were cast using the proposed mix and then tested under three-point loading and four-point loading. The beams were categorized into group 1, samples designed to fail in flexure, and group 2, samples designed to fail in shear. The performances of the GGBFS-based specimens were evaluated and compared to the control beams. In flexure, the GGBFS-based sample carried 83% of the control sample’s ultimate load which is considerably less than the expected 96%. Whereas the GGBFS-based shear deficient sample carried 79% of the load carried by the control beam. Although GGBFS samples carried less load, it is concluded that use of GGBFS as a full replacement to OPC is practical as the normalized capacity of GGBFS samples is comparable to that of the control samples. Additionally, using GGBFS contributes to the reduction of CO2 emissions and hence promotes the use of sustainable and green concrete
Exogenous Contrast Agents in Photoacoustic Imaging: An In Vivo Review for Tumor Imaging
No full textThe field of cancer theranostics has grown rapidly in the past decade and innovative ‘biosmart’ theranostic materials are being synthesized and studied to combat the fast growth of cancer metastases. While current state-of-the-art oncology imaging techniques have decreased mortality rates, patients still face a diminished quality of life due to treatment. Therefore, improved diagnostics are needed to define in vivo tumor growths on a molecular level to achieve image-guided therapies and tailored dosage needs. This review summarizes in vivo studies that utilize contrast agents within the field of photoacoustic imaging—a relatively new imaging modality—for tumor detection, with a special focus on imaging and transducer parameters. This paper also details the different types of contrast agents used in this novel diagnostic field, i.e., organic-based, metal/inorganic-based, and dye-based contrast agents. We conclude this review by discussing the challenges and future direction of photoacoustic imaging.American University of Sharja