Masters Theses

Permanent URI for this collection

https://hdl.handle.net/11073/8815

Masters theses submitted by AUS graduate students.

Browse

Optimal Assignment of Mobile Charging Stations for On-The-Move Electric Vehicles
(2023-04) Hamza, Zakieh Ghassan; Osman, Ahmed; Hassan, Mohamed
lectric vehicles (EVs) are gaining increasing interest due to their zero emissions and relatively reduced running cost. However, the availability of charging energy is a main concern for many EV users. Therefore, a mobile charging station (MCS) facility is a potential solution that helps overcome many of the EV charging issues. With MCSs, EVs can be charged more easily with less waiting time compared with traditional fixed charging stations (FCSs). This thesis proposes a new approach to mobile charging stations for electric vehicles. From the perspective of the MCS operator, the goal is to maximize the revenues by increasing the number of served EVs with high required energy among several requests raised to MCS while maintaining a minimum operation cost throughout the charging service. A mobile charging station operating agency (MCSOA) is proposed for running an assignment and dispatching mechanism (ADM). Considering the randomness of EV charging requests and MCS locations, the MCSOA runs a dynamic optimization problem that is formulated as a mixed integer non-linear programming (MINLP) model to assign the most profitable EVs and dispatch the MCS to the optimal charging location, aiming to maximize the total profits of MCSs. Furthermore, the performance of the proposed ADM mechanism has been simulated using real-world traffic flow data of Dubai and Sharjah – UAE. The performance of the proposed system over different system parameters is studied. Additionally, to improve the effectiveness and validity of this mechanism, the system's performance has been evaluated for some irregular conditions, such as road traffic and unbalanced energy demand over the service area. Furthermore, numerical simulations show that the proposed ADM mechanism increases the system profits besides the number of served EVs in comparison with other EV charging coordination approaches including conventional charging at fixed charging stations (FCS), Nearest-Job-Next assignments (NJN), First Come First Served assignments (FCFS) and Earliest Deadline-First (EDF).
Assessing the Relationship Between Motivation and Foreign Language Anxiety: The Role of Variance
(2023-11) Boktor, Mary; McCarthy, Philip; Almonte, Paul
The interaction between motivation and foreign language anxiety exhibits a complex nature in shaping learning experiences. The current study explores the varying levels of motivation and foreign language anxiety experienced by the learners and identifies whether a significant correlation exists between the two variables. Two contrasting hypotheses were developed in this study. The first hypothesis is that with higher anxiety levels, learners’ motivation levels diminish. The second hypothesis is for, a positive correlation between learners’ motivation levels and anxiety; thus, where higher motivation levels are associated with higher anxiety levels. A cross-validation method was adopted to address these hypotheses. The University Achievement Bridge Program at the American University of Sharjah serves as the chosen context for this study.The study employed two adapted questionnaires, the Motivation Questionnaire by Gardner in 2004, and the Foreign Language Anxiety Scale by Horwitz et al. in 1986. Both questionnaires used a 6-point Likert scale to measure the levels of motivation and anxiety in the context of language learning. A total of 50 participants were initially included; however, the study ultimately included 33 participants because of common issues of withdrawals and incomplete surveys. Participants were randomly categorized into two groups, and each group received the questionnaires in a distinct sequence of elements. Correlation was measured between motivation and anxiety within each group. Results demonstrated a positive correlation between motivation and anxiety, initially indicating higher motivation levels are associated with increased anxiety levels, affirming their interdependence. Participant evaluation mostly centered around responses 3 and 4, reinforcing the importance of considering the role of variance in correlation analysis and understanding the intricate link between the two variables.
Classical and Modified Fractional Differential Transform Methods: Applications to Health-related Problems, including covid-19 and Bromsulfthalein (BSP) Studies
(2023-11) Ansar, Abrar; Abukhaled, Marwan
The present thesis employs the Fractional Differential Transform Method (FDTM) to address two health-related problems. The FDTM was initially applied to the linear fractional human liver model, resulting in a semi-analytic approximate solution. Secondly, it was successively applied to a non-linear fractional COVID-19 model over a prolonged time span with the goal of observing the convergence of the approximate solution. In both applications, the results obtained by the FDTM demonstrated a high level of precision when compared to the fourth order Runge-Kutta (RK4) method for the integer order α = 1. Furthermore, we analyzed the effects of the FDTM solution on various fractional values of α and found that the hereditary properties of fractional differential equations are preserved. In addition, this thesis discusses some important properties of the solution for both models.
Partition of Unity Finite Element Method for Solving Phase Change Problems
(2023-11) Kayyani, Abdulla Hussain; Belhamadia, Youssef
This thesis presents a comprehensive study of the Partition of Unity Finite Element Method (PUFEM) and its applications in solving phase change problems. First, it introduces the Finite Element Method (FEM), the classical counterpart to PUFEM, and a pivotal tool in simulating phenomena described by PDES. The limitations of FEM when handling problems with steep gradients are highlights. The thesis is dedicated to the exploration and development of PUFEM, and how it is considered as an advanced variant of FEM that incorporates enrichment functions to enable more accurate numerical solutions. The development of PUFEM is presented for the Poisson Equation, Heat Equation, and Bistable Equation showing its superior accuracy and efficiency compared to the standard FEM. Finally, the application of PUFEM for phase change problems is illustrated with detailed numerical results showing its superiority to the standard FEM offering avenues for future research.
Neuro-Linguistic Programming in the ESL Classroom in the UAE
(2023-11) Prakharenka, Natallia; McCarthy, Philip
Neuro-Linguistic Programming (NLP) is a practical approach to language learning and teaching that focuses on understanding the thought and behavior patterns underlying communication and skill acquisition (Day, 2008). Despite the global success of NLP in various contexts, its implementation in the UAE remains understudied (Hejase, 2015). NLP has formed a unique theoretical nomenclature and introduced such concepts as pillars of NLP, sensory acuity, and a map of reality. This research explores whether participants unconsciously employ NLP techniques in their daily lives and academic pursuits and whether the pillars are a valid construct. The data for the study is based on a Likert scale questionnaire administered to 163 undergraduate students at a major university in the Gulf region. The statistical procedure of factor analysis is employed to analyze the survey results. The findings of this research contribute to the existing literature on NLP in education, particularly the discussion of such key constructs as NLP pillars, facilitating cross-cultural comparisons and inspiring further investigations into implementing NLP strategies in ESL classrooms.
Fault Classification Using Formal Modeling and Mutation Testing with Deep Learning
(2023-01) Kaddoura, Yara; El Fakih, Khaled; Zualkernan, Imran
Development of High-Strength Conductive Concrete Mix Using Locally Available Materials
(2024-01) Othman, Obida; Yehia, Sherif; Qaddoumi, Nasser; Elchalakani, Mohamed
Smart Farming Using Artificial Intelligence and IoT
(2023-08) Al Barri, Lina Adnan; Mukhopadhyay, Shayok; Al-Ali, Abdulrahman
Evaluating the factors affecting microplastic removal from synthetic leachate via electrocoagulation
(2023-11) Al-Dawood, Zahraa; Mortula, Maruf; Fattah, Kazi Parvez
The ubiquitous presence of microplastics in the environment has raised concerns over their impact on human, environmental and ecological health. Landfill leachate, which is the highly toxic wastewater produced in landfills, is a primary source of microplastics into the soil and groundwater. Though microplastics have been detected in landfill leachate, studies on their presence and removal remain limited. Electrocoagulation has recently emerged as a viable and efficient wastewater treatment process. Although microplastic removal using electrocoagulation has been studied, the impact of the inter-electrode distance, the interaction between the variables, and the sample preparation technique need to be investigated. This study evaluates the effectiveness of electrocoagulation in the removal of microplastics from synthetic leachate sample to address these important gaps. Polyvinyl chloride samples were weathered using UV light before microplastics were leached from the samples to synthesize a wastewater sample containing weathered secondary microplastics. The electrocoagulation experiments were conducted across two phases whilst varying the inter-electrode distance (1-3 cm), initial pH (5-9), electrode material (aluminum and iron), current density (2-10 mA/cm2) and electrolysis time (15-45 minutes). The results show that electrocoagulation is effective in removing microplastics, with removal efficiencies exceeding 85% reported for all the experiments conducted. The analysis of the results show that the effects of the pH and inter-electrode distance were not significant for the removal of microplastics using aluminum electrodes but that the pH2 interaction was significant for the iron electrodes at a confidence level of 90%. The paired t-test results show that both electrode types were effective in removing microplastics and were statistically indifferent. In the second phase of the experiment, the current density and electrolysis time were found to be statistically significant with p-values of 0.0001 and 0.000, respectively. The optimal variables were consequently determined as: aluminum electrodes, inter-electrode distance of 2 cm, current density of 6 mA/cm2, and electrolysis time of 45 minutes, yielding a removal efficiency of 98%. The important findings of this study can be used in future studies to assess the removal of microplastics using electrocoagulation from real wastewater samples.
A Formal Assisted Approach for Modeling and Testing Security Attacks in IoT Edge Devices
(2023-11) Bhanpurawala, Alifiya; El Fakih, Khaled
With the rapid growth in the number of IoT devices being added to the network, a major concern that arises is the security of these systems. As these devices are resource constrained, safety measures are difficult to implement on the edge. We propose a novel approach for the detection of IoT device attacks based on the use of formal modelling and mutation testing. Namely, we model the behaviour of small IoT devices such as motion sensors and RFID card reader as state machines with timeouts. We also model basic IoT attacks; namely, battery draining, sleep deprivation, data falsification, replay, and man in the middle attacks, as special mutants of these specifications. We also consider tests for detecting actual physical device manipulation. Mutation testing is then used to derive tests that distinguish these attacks from the original specifications. The behaviour of these mutants is tested in real environment by running the tests on the data collected while the edge device is still running. Our experiments show that derived number of attack mutants and tests is small and thus these tests can be executed many times with limited overhead on the physical device. Consequently, our approach is not deterred by related high costs of traditional mutation testing. Furthermore, we demonstrate that the tests generated by our method, encompassing the considered IoT attacks, do not adequately cover mutants derived through conventional mutation code-based operators. This highlights the necessity of employing our method. A framework that implements our approach is presented along with some other relevant case studies.
Nitinol Stents: A Finite Element Analysis of their Interaction with the Wall of Blood Vessels
(2023-12) Saib, Mohammad Zubeir Allum; Abed, Farid
The recent decades have seen huge improvements in stent implantation in arteries affected by atherosclerosis. The latter entails the partial blocking or complete occlusion of the lumen. Self-expandable stents are being commonly utilized alongside traditional stents to provide scaffolding to stenosed arteries. Nitinol alloys are being widely used in the medical industry to produce such stents. However, a significant limitation hampering their efficacy is restenosis, triggered by neointimal hyperplasia and resulting in the loss of gain in lumen size, post-intervention. In this study, a nonlinear finite element model was developed to simulate stent deployment and its interaction with the surrounding vessel. The main aim was to determine contact pressures, forces, and shear stresses induced in an artery wall with plaque. This was followed by a parametric study of Nitinol superelastic properties as well as artery & plaque composition and thickness. The results demonstrate the drawbacks of plaque calcification, which triggered a sharp contact pressure surge at the interface, potentially leading to rupture and restenosis. A regression line was established to relate hypocellular to calcified plaques. Regarding the directionality of wall properties, contact pressure observations were not significantly different between isotropic and anisotropic arteries. Furthermore, the model having a thinner plaque experienced a lower peak contact pressure and radial force at the contact interface. In terms of frictional shear stresses, the device with the lowest martensite modulus was determined to be mechanically safer. It exhibited the lowest interfacial shear stress at the contact area and was deemed less likely to induce vascular injury, neointimal hyperplasia, and in-stent restenosis.
Sustainability Evaluation of 3D Concrete Printing in the U.A.E. Construction Projects
(2023-11) Hindieh, Mohammad; Tamimi, Adil; Kashwani, Ghanim
3D printing has revolutionized various industries and is gaining prominence in construction for its potential to enhance sustainability. The United Arab Emirates (UAE) has embraced 3D Concrete Printing (3DCP) in residential and commercial sectors to align with their sustainability goals. However, due to the diverse array of 3DCP technologies and the materials employed in the field, a critical gap exists in the aspect of conducting a comprehensive evaluation of their sustainability. Therefore, the study aims to address the gap by examining the economic, environmental, and social aspects of 3DCP technologies that can be adopted in the UAE industry. The research employs both qualitative and quantitative approaches, in which interviews, sustainability rating systems such as Leadership in Energy and Environmental Design (LEED) and Building Research Establishment Environmental Assessment Method (BREEAM), and literature reviews are conducted to obtain 3D Concrete Printing parameters and identify the Key Performance Indicators (KPIs). A quantitative approach includes a survey that uses pairwise comparison and the Delphi approach, along with the Analytic Hierarchy Process method (AHP), for the purpose of assessing and weighing the KPIs and ultimately determining the most sustainable 3DCP technology. The study's outcome aims to guide the UAE government and construction practitioners to identify the most suitable 3DCP technology. As a result, the environmental pillar of 3DCP technologies turned out to be the top priority to be considered when selecting the technology for the UAE’s construction projects, with the highest weight of 0.53 among the other pillars. Consequently, BOD 2 (COBOD) has been identified as the most sustainable printer in comparison with the other technologies due to its distinctive features that are suitable for the UAE market and contribute to its overall performance in the environmental, economic, and social sustainability realms. The study helps foster better sustainable practices in the construction industry, contributing to the UAE's sustainability objectives.
Cost Evaluation of 3D Concrete Printing in the UAE Construction Industry
(2023-11) Alhomsi, Mhd Amjad; Tamimi, Adil; Kashwani, Ghanim
The integration of 3D Concrete Printing (3DCP) in the construction sector is rapidly expanding, poised to revolutionize conventional processes and usher in disruptive technologies. Globally, the construction industry is actively exploring automated construction technologies, with a particular focus on the burgeoning capabilities of 3DCP. This technological shift not only enhances efficiency and reduces costs in construction projects but also extends its reach into realms beyond Earth, as evidenced by its application in building space habitats. This paper delves into case studies that meticulously demonstrate the invaluable impact of integrating 3DCP in the United Arab Emirates (UAE) construction industry. The discussion sheds light on diverse 3D Concrete (3DC) printers, each distinguished by unique attributes such as weight, price, building envelope, layer thickness, and more. While these factors remain immutable, the strategic selection of a 3DC printer at an optimal price point holds the key to substantial cost reduction in the final product. The primary objective of this thesis is to empower Small and Medium-sized Enterprises (SMEs) in the UAE to make informed decisions when selecting a 3DC printer. Three carefully chosen printers from different manufacturers, widely available and easily deliverable, undergo rigorous evaluation through a cost analysis using the Delphi-Analytical Hierarchy Process (AHP) method, a Multicriteria Decision-Making (MCDM) technique. The research addresses decision-making challenges through Google Form questions, employing the AHP approach on the Saaty scale of 0–9 points. Supplemented by a thorough literature review on the challenges faced by SMEs in adopting 3DCP and an insightful interview with a construction professional from an SME, the research identifies the most crucial criteria for the second level in the AHP method. In the culmination of this comprehensive analysis, the research unequivocally asserts that CyBe Construction Robot Crawler (RC) emerges as the pinnacle 3DC printer. Offering SME decision-makers a harmonious blend of cost-effectiveness and feature-rich capabilities, CyBe RC stands as the optimal choice in the dynamic landscape of 3DCP.
Stock Return Prediction In Emerging Markets Using Machine Learning
(2023-11) Rizny, Mohamed Shezan; Bahroun, Zied; Alshraideh, Hussam; Samet, Anis
Maintenance Decisions for Medical Equipment in UAE Medical Facilities
(2023-11) Elashi, Reham Adil Yousif Mohamed; Shamayleh, Abdulrahim; Awad, Mahmoud Ismail
Medical devices are crucial in the process of preventing, monitoring, diagnosing and treatment of diseases. Healthcare services greatly rely on medical equipment to properly deliver their desired services. Hence, the proper functioning and availability of those medical devices is crucial to healthcare services providers. Proper planned maintenance decisions lead to more cost efficient, reliable and non-disrupted service, particularly with the increased reliance on medical equipment in health care facilities to realize their services. Despite their importance and widespread usage in UAE, there is minimal information on maintenance practices. As such, it is crucial to understand the influential factors and key indicators that would lead to successful equipment maintenance decisions. The objective of this research study is to explore the current medical equipment maintenance practices implemented in the UAE within the medical facilities and explore the pivotal factors in maintenance decisions that would contribute to enhanced equipment performance. A qualitative study employing Confirmatory Factor Analysis (CFA) is carried out to investigate the relationship between various factors and equipment performance indicators. The main research tool used is a survey developed based on reviewed literature and feedback from five subject matter experts. Partial Least Squares Structural Equation Model (PLS-SEM) is applied for data analysis and the subsequent development of the final model. The results suggest that the main influential factors are strategic decisions and infrastructure. Additionally, the results suggest that there is an indirect influence of management on equipment performance. This influence is mediated through management's heightened awareness of maintenance decisions, and their participation in the strategic decisions related to maintenance. Maintenance practices in the UAE show discernible variations, especially with shifts in device functionality. The recommendation is to maintain existing decisions for critical devices while improving maintenance decisions for less critical ones. This involves careful planning of maintenance contracts, inclusive procurement procedures covering the equipment's entire life cycle, and the crucial use of IT tools for monitoring and analyzing performance data to inform corrective actions.
Artificial Intelligence to Enhance the Drilling of Composites
(2023-12) Al Alami, Ibrahim; Hussein, Noha
Advances in the study of fibre reinforced polymers have led to a huge interest in applying them to multiple fields as an alternative to more costly materials such as their metallic counterparts. However, if the machining of fibre reinforced polymers is done incorrectly this will lead to many defects. Such problems might lead to the underutilization of the fibre reinforced polymers; therefore, optimizing the drilling process is necessary to eliminate the defects. Drilled composite panels must be free of defects for them to succeed in their structural applications. Therefore, the objective of this study is to enhance the drilling process of composites by developing a machine learning mathematical model which will be able to predict the failure behaviour considering the delamination area and fibre pullout area as the response variables in terms of a set of process parameters. The proposed methodology consists of several steps to assess the quality of the drilled hole. Firstly, the composite material selection discusses the process of selecting a specific composite material taking into consideration the material’s properties. Secondly, the experimental setup describes how the experiments were conducted and what machines and tools were used in the process. Thirdly, different inspection techniques are proposed to monitor the quality of a drilled hole during the drilling process and after. Lastly, the modelling of the response variable in terms of the process parameters and the process monitoring variable. Based on a specific sample thickness and tool diameter for the composite panel the machine learning model developed was able to provide the optimum feed rate and spindle speed values needed to attain the minimum delamination area and fibre pullout area. In addition, the in-process monitoring identified a threshold value for the delamination area in terms of the force exertion.
The Impact of the SVB Collapse on Banking Industry
(2023-11) Xi, Ye; Samet, Anis; Gleason, Kimberly
Image-CNN Based Process Control of Profile
(2023-12) Zeinab, Zeinab Jihad; Alshraideh, Hussam
For quality inspection purpose, control charts have been widely adopted successfully in manufacturing industry throughout the years. Smart Manufacturing (SM) has emerged as a key concept for articulating the ultimate goal of manufacturing digitization as a result of the advancement of technologies like Artificial Intelligence (AI). For SM, an automatic process that can handle massive amounts of data from ongoing, concurrent processes is needed. In comparison, recognizing patterns in data and defect classification present challenges for typical control charts. To resolve these problems, Deep Learning (DL) algorithms proved to be an effective analytical tool that can aid in fault detection. The early classification of flaws and defects in machinery or manufacturing processes can be easily achieved by a detection monitoring system capability. In this thesis, a DL-based framework for monitoring profile generating processes is presented. The framework relies on the presentation of profile time series data as two-dimensional images, for which four transformation algorithms were explored including Gramian Angular Field (GAF), Markov Transition Field (MTF), and Recurrence Plots (RP). Proposed framework was evaluated through two case studies. In the first one, a tapping process is considered while a 3D printing process is considered in the second case. Proposed model achieved an accuracy level of 91.6% for the tapping dataset outperforming previous model performance reported in the literature of 84.04%. Similarly, the model showed an improved performance level over existing literature for the 3D printing process data with accuracy levels of 96.6% and 92.6% for the small and large versions of the data, respectively. Our proposed framework provides an automatic feature extraction step as it relies on DL technology providing a major advantage over existing models in the literature that assume a preexisting set of features to be used.
Machine Learning Model for a Sustainable Drilling Process
(2023-11) Alsaidi, Ola; Hussein, Noha
Drilling process is one of the most performed machining processes. Across many industries, drilling process directly influences the product quality as it is usually used in the final production steps before assembly. Drilling quality depends on the process parameters such as the spindle speed and feed. Improper selection of these parameters can lead to several defects like high surface roughness and burr formation. Consequently, the final product will not function properly, resulting in higher wastage of material, cost, and time. Additionally, reworking results in the loss of many resources. From a sustainability point of view, rework has a negative environmental impact as it increases electricity consumption and carbon emissions. Thus, the optimization of drilling process parameters is essential to produce high-quality products and make the process more cost-effective, efficient, and sustainable. Many experiments have been done to model the drilling process responses in terms of the input parameters. As a result, there is large amount of data available in the literature for drilling input parameters and their responses. This project aims to use big data analytics to make use of the data gathered from previous studies to model and optimize the responses. The collected data have some missing values because of the different input parameters and responses chosen for each experiment. To handle these missing values, deletion and imputation methods are used. For data analysis, various machine learning algorithms are used to model the process responses. The analysed responses are the surface roughness, thrust force, and drilling time. Further analyses are done including features selection and partial dependence. Moreover, several optimization runs are performed to assess different drilling cases. According to the results, the best algorithms for predicting the surface roughness, thrust force, and drilling time are bagged trees with 6 parameters, exponential GPR with 6 parameters, and fine trees with 5 parameters, respectively. The obtained accuracies are 92.91% for the surface roughness model, 81.33% for the thrust force model, and 90.61% for the time model. The obtained models can be used to find the optimal values of the input parameters that will give the minimum surface roughness, thrust force, or time without the need to conduct any experiments which leads to time, money, and resources savings.
Forecasting Emerging Stock Market Crashes via Machine Learning
(2023-11) Khan, Mohammad Osama; Alshraideh, Hussam; Bahroun, Zied; Samet, Anis
Stock markets indicate the overall health of an economy as they play a vital role in providing a way for companies to raise capital, create new opportunities and stimulate economic growth. However, stock markets are prone to crashes and the aftermath of such an event can cause far-reaching and long-lasting effects on the economy depending on the severity which induces a need to study stock market crashes. This work explores the idea of crashes in emerging stock markets leveraging a diverse array of machine learning models, while utilizing a comprehensive dataset comprising stock market data from 32 emerging market countries, with features derived from market data, along with several engineered liquidity features. A variation of the Artificial Neural Network model is identified as the top performer displaying high accuracy, about 96.66%, with high true positive rate and low false positive rate, outperforming existing models in the literature. In industry-specific analysis, the model consistently achieved strong true positive and false positive rates, indicating acceptable outcomes for the specific industries under consideration. Furthermore, it is found, using the SHapley Additive exPlanations framework, that return along with the attributes reflecting lag, mean, and standard deviation of liquidity indicators over the past week and month significantly contribute to the prediction of crashes suggesting that stock market crashes are typically gradual processes rather than abrupt occurrences. These findings hold profound implications for risk management and investment decision-making in emerging markets, offering valuable insights for both academia and industry practitioners.

Browse

Recent Submissions