Engineering Commons^™

Molecular Simulations Of Mechanical Effects Of Adsorption In Gas And Liquid Phases, Alina Emelianova

Dissertations

Fluids adsorbing in nanoporous solids cause high pressures that affect the solids and the properties of the fluids themselves. This work explores the mechanical response of solids to the adsorption of gas and liquid. Chapter 1 outlines the current achievements in investigating the mechanical response of adsorbents to the adsorption process, specifically, the computational approaches to predict adsorption-induced deformation. Chapter 2 introduces the theoretical and computational methods employed in this work. Chapters 3-7 address the design and application of the computational techniques for such prediction in different systems: mesoporous silica, microporous zeolites, and metal-organic frameworks, and Chapter 8 focuses on …

Electrodialysis Process For The Simultaneous Treatment Of Reject Brine And Capture Of Carbon Dioxide, Jawad Mustafa

Dissertations

Desalination plants are highly efficient in producing desalinated water for potable applications. However, the process also generates reject brine, which is highly concentrated saline water. In order to produce 1 liter of potable water using desalination, approximately 1.5 liters of reject brine is generated. The disposal of reject brine presents a significant challenge for the desalination industry. Typically, this waste product is discharged into the sea, causing environmental damage, and polluting marine life. Moreover, certain countries heavily rely on desalination to meet their potable water needs, which results in the emission of greenhouse gases. As desalination plants consume a significant …

Upgrading The Performance Of Steel Beams Using Fastened Advanced Composite Materials, Omnia Ragab Abouelhamd

Dissertations

Bonding Fiber-Reinforced Polymers (FRP) is commonly used to enhance the structural performance of existing steel beams; however, the brittle failure at the adhesive risks the effectiveness of the bonded system. Fastening FRP is recently proposed to avoid the brittle failure of the adhesive. Promising outcomes of few studies on fastened FRPsteel system provoked the interest to investigate the effects of various fastening parameters on the performance of fastened FRP-steel beams.

This study investigates, experimentally and numerically, the impact of various fastening parameters on the performance of fastened FRP-steel beams. The proposed method involves fastening carbon-glass hybrid FRP (HFRP) strips to …

Intelligent And Explainable Solution To Predict Infant Birthweight And Preterm Birth In The United Arab Emirates, Wasif Khan

Dissertations

Adverse pregnancy outcomes such as Low Birth Weight (LBW) and Preterm birth (PTB) are complex pregnancy challenges that can lead to high perinatal mortality and long-term morbidity for infants. Early prediction of such adverse outcomes can be useful for averting catastrophic outcomes for the mother and her baby. With advances in machine learning (ML)-based algorithms, several models have been proposed for both PTB and LBW predictions. However, the risk factors associated with these outcomes are still unknown, particularly in the United Arab Emirates (UAE). Furthermore, existing ML-based prediction models work in a black-box manner and lack proper interpretations for clinicians, …

The Influence Of Diamine Curing Additives On The Network Architecture Of Phthalonitrile Thermosetting Polymers, Tyler J. Richardson

Dissertations

Phthalonitrile monomers undergo diamine-promoted polymerization by a complex reaction mechanism involving two competitive cure pathways, forming two primary network architectures: linear polyisoindoline chains and branched triazine crosslinks. The influence of the diamine curing additive on the polymerization pathway, and the influence of the resulting network architectures on cured network properties, have not been adequately explored within the phthalonitrile field. Two structurally different diamine curing additives, bis[4-(3-aminophenoxy)phenyl] sulfone (mBAPS) and 1,3-phenylenebis((4-(4-aminophenoxy)phenyl)methanone) (AEK-134), were studied for the polymerization of resorcinol phenylphosphate phthalonitrile (RPPhPN), where the influence of diamine structure and concentration on the polymerization behavior, network architecture, and bulk thermal and thermomechanical …

Structural Integrity Assessment Of Shell And Tube Heat Exchanger Tube-To-Tubesheet Joints Fabricated Using Conventional And Nonconventional Manufacturing Processes, Dinu Thomas Thekkuden

Dissertations

Tubes and tubesheets, integral components of shell and tube heat exchangers, have an important role in the functioning of the heat transfer between the tube-side and shell-side fluids. Tube-to-tubesheet joints act as a barrier to prevent the mixing of the transfer fluids in addition to aiding the structural rigidity of the shell and tube heat exchanger. Tube expansion process and welding are the manufacturing processes used for fabricating structurally rigid tube-to-tubesheet joints. Many instances of tube-totubesheet joint failures leading to the complete collapse of the heat exchangers demand attention to assessing the mechanical and metallurgical characteristics of tube-totubesheet joints fabricated …

Integrated Experimental And Dft Study Of Palladium And Nickel Metals Supported On Reducible Metallic Oxides For Selective Hydrogenation Of Alkynes And Dienes Bonds, Toyin Daniel Shittu

Dissertations

This dissertation investigates the capacity of ceria (CeO₂) to serve as a stand-alone reactive catalyst, and to act as catalyst support for different catalyst configurations. Experimental and density functional theory (DFT) approach were used to critically predict and to design ceria-containing catalysts. Ceria owes the catalytic propensity to the ability to undergo switch in the oxidation state from the +4 to +3 states. Motivated by experimental studies involving CeO₂ catalysts, defects CeO₂ surface with an oxygen vacancy (CeO2(111)_Vo) was used to explore the decomposition chemistry of methanethiol based on DFT. The potential formation pathways revealed that …

Optimal Component Sizing And Control Of A Multi-Source Elecctric Vehicle Based On An Improved Optimization Algorithm And A Novel Energy Management System, Prasanthi Achikkulath

Dissertations

Electric vehicles (EVs) are considered the ultimate solution for a sustainable transportation alternative to reduce the global warming and energy crisis. The public's perception of EVs is still adverse despite advancements in EV technology because of issues with driving range, high initial costs, the added weight of the energy source, and an unpredictably short lifespan of the EV system. To administer the same performance as compared to modern fossil fuel-based vehicles, hybridization of multiple energy sources is significant for EVs. The most pressing issues with multi-source EVs are optimum source sizing and regulating power flow from hybridization sources with the …

Design, Simulation, Modeling, And Implementation Of Triboelectric Nanogenerator (Teng) Sensors For Automotive Applications, Sam Ali

Dissertations

Triboelectricity is a promising technique for energy harvesting in which mechanical energy converts to electricity for powering small electronic devices. This method of energy harvesting serves as an alternative to traditional battery power sources and can significantly benefit low-power sensing applications by generating unlimited electrical energy. This study focuses on the development of flexible triboelectric nanogenerators (TENG) and energy harvesting devices for various applications in the automotive industry.

The study is organized into four projects. In the first project, various polymeric materials were used to investigate the performance of different TENGs. Four designs (D1, D2, D3, and D4) of flexible …

Approximate Computing Based Processing Of Mea Signals On Fpga, Mohammad Emad Hassan

Dissertations

The Microelectrode Array (MEA) is a collection of parallel electrodes that may measure the extracellular potential of nearby neurons. It is a crucial tool in neuroscience for researching the structure, operation, and behavior of neural networks. Using sophisticated signal processing techniques and architectural templates, the task of processing and evaluating the data streams obtained from MEAs is a computationally demanding one that needs time and parallel processing.
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/="/">This thesis proposes enhancing the capability of MEA signal processing systems by using approximate computing-based algorithms. These algorithms can be implemented in systems that process parallel MEA channels using the Field …

Coordinated Location And Capacity Planning Of Fast Charging Stations For Electric Vehicles Using Multi Objective Approach, Asna Madathodika

Dissertations

With the growing concerns on the energy depletion and CO₂ emissions, electric vehicles (EVs) have marked a new paradigm in the transport sector. Installation of publicly accessible charging stations (CS), namely fast CSs are crucial for the large-scale deployment of EVs. However, randomly placing CSs without prior research affect EV owners, CS operators and grid operators. For instance, the location and size of charging stations affect the accessibility and convenience of EV users since users prefer charging stations that are easier to access without much traffic congestion in road networks. At the same time station should offer good service …

Capturing Maximum Photovoltaic Power With Machine Learning: Maximum Current Prediction, Mppt Methodology, And Adjustable Ml-Based Controller, Zahi Mohamed Mohamed

Dissertations

The primary aim of this doctoral thesis is to examine the potential application of machine learning (ML) in optimizing Maximum Power Point Tracking (MPPT) and devising a flexible ML-based controller for photovoltaic (PV) systems. The lack of MPPT mechanisms leads to fixed voltage and current points for PV modules, resulting in substantial power loss when environmental conditions vary. The limitations of conventional and enhanced MPPT methods, as well as finding an alternative controller to classical Proportional-Integral (PI) controllers, have spurred the search for substitute approaches to maximize PV power. To address these limitations, this thesis proposes using ensemble ML techniques, …

The Effects Of Disinformation Upon National Attitudes Towards The Eu And Its Institutions, Alex Murphy

Dissertations

This work explores the effects of misinformation and disinformation upon national attitudes towards the EU. Several nations, in particular the Russian Federation, have been working for decades to spread narratives that debase the political processes of healthy democracies around the world. There is strong evidence to show that extensive efforts have been made to disrupt the inner workings and overall membership of the EU, to support disruptive policies in the United States such that political deadlock is maintained indefinitely. These efforts are largely based on the spreading of misinformation and disinformation across social networks that have done very little to …

Development Of A Hospital Discharge Planning System Augmented With A Neural Clinical Decision Support Engine, David Mulqueen

Dissertations

The process of discharging patients from a tertiary care hospital, is one of the key activities to ensure the efficient and effective operation of a hospital. However, the decision to discharge a patient from a hospital is complex, as it requires multiple interactions with nurses, family, consultants, health information records and doctors, which can be very time consuming and prone to error. This thesis descries how a neural network based Clinical Decision Support system can be developed, to help in the decision making process and dramatically reduce the time and effort in running the discharge process in a hospital. A …

A Computational Model Of Trust Based On Dynamic Interaction In The Stack Overflow Community, Patrick O’Neill

Dissertations

A member’s reputation in an online community is a quantified representation of their trustworthiness within the community. Reputation is calculated using rules-based algorithms which are primarily tied to the upvotes or downvotes a member receives on posts. The main drawback of this form of reputation calculation is the inability to consider dynamic factors such as a member’s activity (or inactivity) within the community. The research involves the construction of dynamic mathematical models to calculate reputation and then determine to what extent these results compare with rules-based models. This research begins with exploratory research of the existing corpus of knowledge. Constructive …

Exploring Gender Bias In Semantic Representations For Occupational Classification In Nlp: Techniques And Mitigation Strategies, Joseph Michael O'Carroll

Dissertations

Gender bias in Natural Language Processing (NLP) models is a non-trivial problem that can perpetuate and amplify existing societal biases. This thesis investigates gender bias in occupation classification and explores the effectiveness of different debiasing methods for language models to reduce the impact of bias in the model’s representations. The study employs a data-driven empirical methodology focusing heavily on experimentation and result investigation. The study uses five distinct semantic representations and models with varying levels of complexity to classify the occupation of individuals based on their biographies.

Evaluating The Performance Of Vulkan Glsl Compute Shaders In Real-Time Ray-Traced Audio Propagation Through 3d Virtual Environments, James Buggy

Dissertations

Real time ray tracing is a growing area of interest with applications in audio processing. However, real time audio processing comes with strict performance requirements, which parallel computing is often used to overcome. As graphics processing units (GPUs) have become more powerful and programmable, general-purpose computing on graphics processing units (GPGPU) has allowed GPUs to become extremely powerful parallel processors, leading them to become more prevalent in the domain of audio processing through platforms such as CUDA. The aim of this research was to investigate the potential of GLSL compute shaders in the domain of real time audio processing. Specifically …

Evaluation Of Text Transformers For Classifying Sentiment Of Reviews By Using Tf-Idf, Bert (Word Embedding), Sbert (Sentence Embedding) With Support Vector Machine Evaluation, Mina Jamshidian

Dissertations

As the online world evolves and new media emerge, consumers are sharing their reviews and opinions online. This has been studied in various academic fields, including marketing and computer science. Sentiment analysis, a technique used to identify the sentiment of a piece of text, has been researched in different domains such as movie reviews and mobile app ratings. However, the video game industry has received relatively little research on experiential products. The purpose of this study is to apply sentiment analysis to user reviews of games on Steam, a popular gaming platform, in order to produce actionable results. The video …

Application Of Shallow Neural Networks To Retail Intermittent Demand Time Series, Urko Allende

Dissertations

Accurate sales predictions are essential for businesses in the fast-moving consumer goods (FMCG) industry. However, their demand forecasts are often unreliable, leading to imprecisions that affect downstream decisions. This dissertation proposes using an artificial neural network to improve intermittent demand forecasting in the retail sector. The research investigates the validity of using unprocessed historical information, eluding hand-crafted features, to learn patterns in intermittent demand data. The experiment tests a selection of shallow neural network architectures that can expedite the time-to-market in comparison to conventional demand forecasting methods. The results demonstrate that organisations that still rely on manual and direct forecasting …

Explaining Deep Q-Learning Experience Replay With Shapley Additive Explanations, Robert S. Sullivan

Dissertations

Reinforcement Learning (RL) has shown promise in optimizing complex control and decision-making processes but Deep Reinforcement Learning (DRL) lacks interpretability, limiting its adoption in regulated sectors like manufacturing, finance, and healthcare. Difficulties arise from DRL’s opaque decision-making, hindering efficiency and resource use, this issue is amplified with every advancement. While many seek to move from Experience Replay to A3C, the latter demands more resources. Despite efforts to improve Experience Replay selection strategies, there is a tendency to keep capacity high. This dissertation investigates training a Deep Convolutional Q-learning agent across 20 Atari games, in solving a control task, physics task, …

The Use Of Data Balancing Algorithms To Correct For The Under-Representation Of Female Patients In A Cardiovascular Dataset, Sian Miller

Dissertations

Given that women are under-represented in medical datasets, and that machine learning classification algorithms are known to exhibit bias towards the majority class, the growing application of machine learning in the medical field risks resulting in worse medical outcomes for female patients. The Heart Failure Prediction (HFP) dataset is a historical dataset used for the training of models for the prediction of heart disease. This dataset contains significantly fewer female patients than male patients, and as such it is expected that models trained using this data will inherit a gender bias to favour male patients. This dissertation explores the use …

Probability Expressions In Ai Decision Support: Impacts On Human+Ai Team Performance, Elias Spinn

Dissertations

AI decision support systems aim to assist people in highly complex and consequential domains to make efficient, effective, and high-quality decisions. AI alone cannot be guaranteed to be correct in these complex decision tasks, and a human is often needed to ensure decision accuracy. The ambition is for these human+ AI teams to perform better together than either would individually. To realise this, decision makers must trust their AI partners appropriately, knowing when to rely on their recommendations and when to be sceptical. However, research has shown that decision makers often either mistrust and underutilise these systems, or trust them …

Modelling And Mitigating Interlocutor Confusion In Situated Human-Avatar And Human-Robot Interaction, Na Li

Dissertations

Human-Robot Interaction (HRI) is an important but challenging field focused on improving the interaction between humans and robots, to make the interaction more intelligent and effective. However, building a natural conversational HRI is an interdisciplinary challenge for scholars, engineers, and designers. Achieving successful conversational interaction with a social robot necessitates not only observing a user’s active participation in the interaction but also being aware of their emotional and attitudinal states as the interaction progresses. On the topic of attitudinal states, one field that has received little attention to date is monitoring the user for possible confusion states. Confusion is a …

Carrier Transport Engineering In Wide Bandgap Semiconductors For Photonic And Memory Device Applications, Ravi Teja Velpula

Dissertations

Wide bandgap (WBG) semiconductors play a crucial role in the current solid-state lighting technology. The AlGaN compound semiconductor is widely used for ultraviolet (UV) light-emitting diodes (LEDs), however, the efficiency of these LEDs is largely in a single-digit percentage range due to several factors. Until recently, AlInN alloy has been relatively unexplored, though it holds potential for light-emitters operating in the visible and UV regions. In this dissertation, the first axial AlInN core-shell nanowire UV LEDs operating in the UV-A and UV-B regions with an internal quantum efficiency (IQE) of 52% are demonstrated. Moreover, the light extraction efficiency of this …

Improving The Stimulation Selectivity In The Human Cochlea By Strategic Selection Of The Current Return Electrode, Ozan Cakmak

Dissertations

The hearing quality provided by cochlear implants are poorly predicted by computer simulations. A realistic cochlear anatomy is crucial for the accuracy of predictions. In this study, the standard multipolar stimulation paradigms are revisited and Rattay’s Activating Function is evaluated in a finite element model of a realistic cochlear geometry that is based on µ-CT images and a commercial lead. The stimulation thresholds across the cochlear fibers were investigated for monopolar, bipolar, tripolar, and a novel (distant) bipolar electrode configuration using an active compartmental nerve model based on Schwartz-Eikhof-Frijns membrane dynamics. The results suggest that skipping of the stimulation point …

Integrated Machine Learning And Optimization Approaches, Dogacan Yilmaz

Dissertations

This dissertation focuses on the integration of machine learning and optimization. Specifically, novel machine learning-based frameworks are proposed to help solve a broad range of well-known operations research problems to reduce the solution times. The first study presents a bidirectional Long Short-Term Memory framework to learn optimal solutions to sequential decision-making problems. Computational results show that the framework significantly reduces the solution time of benchmark capacitated lot-sizing problems without much loss in feasibility and optimality. Also, models trained using shorter planning horizons can successfully predict the optimal solution of the instances with longer planning horizons. For the hardest data set, …

Faceted Nanomaterial Synthesis, Characterizations And Applications In Reactive Electrochemical Membrane Filtration, Qingquan Ma

Dissertations

Facet engineering of nanomaterials, especially metals and metal oxides has become an important strategy for tuning catalytic properties and functions from heterogeneous catalysis to electrochemical catalysis, photocatalysis, biomedicine, fuel cells, and gas sensors. The catalytic properties are highly related to the surface electronic structures, surface electron transport characteristics, and active center structures of catalysts, which can be tailored by surface facet control. The aim of this doctoral dissertation research is to study the facet-dependent properties of metal or metal oxide nanoparticles using multiple advanced characterization techniques. Specifically, the novel atomic force microscope-scanning electrochemical microscope (AFM-SECM) and density functional theory (DFT) …

Hydrodynamic Investigation Of The Discharge Of Complex Fluids From Dispensing Bottles Using Experimental And Computational Approaches, Baran Teoman

Dissertations

The discharge of non-Newtonian, complex fluids through orifices of industrial tanks, pipes, dispensers, or packaging containers is a ubiquitous but often problematic process because of the complex rheology of such fluids and the geometry of the containers. This, in turn, reduces the discharge rate and results in residual fluid left in the container, often referred to as heel. Heel formation is undesired in general, since it causes loss of valuable material, container fouling, and cross-contamination between batches. Heel may be of significant concern not only in industrial vessels but also in consumer packaging. Despite its relevance, the research in this …

Microhydrodynamic, Kinetic And Thermal Modeling Of Wet Media Milling For Process Optimization And Intensification, Gulenay Guner

Dissertations

Nanoparticle production by wet stirred media milling (WSMM) is a common method for the formulation of poorly water-soluble drugs. While most of the studies in the WSMM literature focus on the formulation aspects to overcome the stability challenges, a thorough mechanistic understanding of the process is lacking, and the process is slow, costly, and energy-intensive. This dissertation presents experimental and modeling work with the ultimate goals of (i) gaining a deeper and more mechanistic understanding of the WSMM process and breakage kinetics of the particles using a microhydrodynamic model with various improvements and advancements, (ii) examining the heat dissipation during …

Bioremediation Of Petroleum Hydrocarbons In Coastal Sediments, Charbel Abou Khalil

Dissertations

The biodegradation of dispersed crude oil in the ocean is relatively rapid (a half-life of a few weeks). However, it is often much slower on shorelines, usually attributed to low moisture content, nutrient limitation, and higher oil concentrations in beaches than in dispersed plumes. Another factor may be the increased salinity of the upper intertidal and supratidal zones since these parts of the beach are potentially subject to prolonged evaporation and only intermittent inundation. Therefore, two laboratory experiments are conducted to investigate whether such an increase in porewater salinity results in additional inhibitory effects on oil biodegradation in seashores.

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