Theses

Permanent URI for this collectionhttps://uwspace.uwaterloo.ca/handle/10012/6

The theses in UWSpace are publicly accessible unless restricted due to publication or patent pending.

This collection includes a subset of theses submitted by graduates of the University of Waterloo as a partial requirement of a degree program at the Master's or PhD level. It includes all electronically submitted theses. (Electronic submission was optional from 1996 through 2006. Electronic submission became the default submission format in October 2006.)

This collection also includes a subset of UW theses that were scanned through the Theses Canada program. (The subset includes UW PhD theses from 1998 - 2002.)

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    Assessing the prevalence and youth-directed marketing power of outdoor food and beverage advertisements around schools in six cities across Canada.
    (University of Waterloo, 2025-01-24) Morielli, Amanda
    Recent policy initiatives in Canada propose to restrict the commercial advertising of foods containing sugars, sodium, or saturated fat to youth on digital and broadcast media. While there is abundant research on youth’s exposure to food and beverage advertising on digital and broadcast media, there is limited research exploring youth’s exposure to outdoor food and beverage advertisements (e.g., freestanding billboards, restaurant exteriors, bus shelters). To address this research gap and inform policy decisions, Manuscript 1 of this thesis describes the prevalence, content, and youth-directed marketing power of outdoor food and beverage advertisements near schools. Manuscript 2 of this thesis explores the association between outdoor F&B advertisement prevalence, food outlet density, degree of urbanization, neighbourhood deprivation, and ethnocultural composition near schools to understand how the built environment and neighbourhood characteristics influence outdoor advertising environments. For this research, data on outdoor advertisements and food outlets within 1000 m of elementary and secondary schools in six cities across Canada (Vancouver, BC; Calgary, AB; Winnipeg, MB; Ottawa, ON; Quebec City, QC; and Halifax, NS) was analyzed, along with Statistics Canada data on deprivation and ethnocultural composition (from the Canadian Index of Multiple Deprivation). Descriptive statistics, chi-square tests, and negative binomial regression models were used to analyze the data. Most (64.5%) outdoor F&B advertisements near schools promote “unhealthy” food and beverage products. The most common marketing techniques used to target youth were youth product/convenience (39.4%), sense of urgency/limited time offer/seasonal (18.4%), and price promotion/discount (13.1%). School areas with high food outlet counts contained 7.429 times more advertisements than those with low counts (CI: 4.805 – 11.486, p < 0.05). The mean count of outdoor advertisements on food outlet exteriors (M = 23.22, SD = 35.52) was 10.6 times higher than the mean count of freestanding outdoor advertisements (M = 2.18, SD = 3.94), revealing that most outdoor F&B advertisements around schools are located on food outlets. Measures for deprivation and ethnocultural composition were not found to have notable patterns of significance with outdoor advertisement, except for residential instability. School areas with a high degree of residential instability contained 1.707 times more advertisements than the school areas with a low degree of residential instability (CI:1.029 - 2.832, p < 0.05). These findings suggest outdoor F&B advertisements near schools primarily promote unhealthy food choices and advertisement prevalence is influenced by features of the built environment, such as food outlet density. Future research should explore the impact of planning and public health policy interventions to reduce outdoor food and beverage advertising to youth. Opportunities for these professions (as well as other relevant disciplines) to collaborate to create healthier food environments for youth should also be identified.
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    “AnnoTools”: Extending AnnoTree and AnnoView for Database-Wide Genome Annotation, Visualization, and Comparison
    (University of Waterloo, 2025-01-24) Tan, Huagang
    Genomic analysis has revolutionized our understanding of the biology and evolutionary history of bacterial and archaeal microorganisms, leading to numerous applications in biotechnology, medicine, and environmental sciences. One of the fundamental aspects of genomic analysis is protein functional annotation, which involves assigning biological functions to protein-coding sequences identified within genomes. These annotations are widely used to support analyses, such as examining gene or function distributions across the tree of life and comparing gene neighborhoods across taxa. By combining these analyses, researchers can comprehensively explore gene functions and the mechanisms of given genes or gene clusters. In this thesis, I will introduce a pipeline that supports genomic analysis. The project consists of three parts: data annotation, visualization, and the language model. The first part of the pipeline is the generation of protein function annotations. Raw protein sequence data is downloaded from the Genome Taxonomy Database (GTDB) and submitted to two tools: Kofamscan and DIAMOND. Kofamscan assigns KEGG ORTHOLOGY IDs to each input sequence, while DIAMOND assigns Uniref IDs, which are then mapped to InterPro IDs. Combining these IDs provides comprehensive and reliable annotations. The data is filtered for quality and stored on a remote server as an annotation database for further analysis. The second part of the pipeline involves updating two user-friendly, web-based visualization tools, AnnoTree and AnnoView, which utilize the annotation database. AnnoTree displays the distribution and taxonomy of different protein annotations across GTDB using a tree of life representation, offering insights into biological and evolutionary patterns through species phylogenies and supporting genome-wide co-occurrence analysis. AnnoView focuses on comparing and exploring gene neighborhoods, identifying functionally related genes clustered together in genomes as "gene clusters," thus emphasizing window-based co-occurrence analysis. The new annotation database not only provides more comprehensive and accurate annotations, enhancing the databases that both visualization tools rely on, but also extends their functionalities for fast data retrieval and new features. The last part of the pipeline involves the application of the Word2Vec language model, which treats genome contigs as sentences in natural language and trains the model using the annotation database. After training, the updated model can encode each annotation from a specific protein family into high-dimensional vectors with continuous number, allowing researchers to explore annotations that share similar genomic contexts. This allows protein functions prediction based on this comparative gene neighborhood analysis. Finally, I will use one protein domain in the Type VI Secretion System (T6SS) as a case study. T6SS is a cell envelope-spanning machine that translocates toxic effector proteins into eukaryotic and prokaryotic cells. Besides the conserved essential core components, there are various effector and accessory proteins in the system. Some proteins are annotated as Domains of Unknown Function (DUF) and are poorly explored. In this case, I will focus on PF20598 (DUF6795), which shares a similar genomic context with one of the T6SS proteins. Using the visualization tools AnnoTree and AnnoView, I will demonstrate that this DUF is part of the T6SS cluster, supporting the hypothesis that it may function as an adaptor protein in T6SS. In summary, the AnnoTools pipeline integrates all components to enhance comparative genomic analysis with a large-scale annotation database. The user-friendly web-based tools enable researchers to visualize data both genome-wide and at a window-based scale. The ultimate goal of this thesis is to provide researchers with a comprehensive and easy-to-use method for predicting functions of genes or gene clusters of interest.
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    Data-Based Modeling of Electrochemical Energy and Thermal Systems: Fuel Cell and Lithium-Ion Battery
    (University of Waterloo, 2025-01-24) Legala, Adithya
    As a solution to combat climate change and environmental pollution, electrochemical energy systems such as Proton Exchange Membrane Fuel Cell (PEMFC) and Lithium-Ion Battery (LIB) are being developed as the replacement for fossil fuel-powered combustion engines, especially for ground transportation and aviation applications. These electrochemical energy systems must be able to operate independently and in conjunction with each other by complementing their advantages and limitations, such as efficiency, range, thermal behavior, aging, and operating environment. This interoperability requires accurate real-time computational models to control, diagnose, and adapt according to field requirements. A typical electrochemical energy system model needs to incorporate effects related to reactant concentrations, system overpotentials, thermodynamics, porous media mechanics, membrane dynamics, gas diffusion, electrode degradation, electrolyte status, ion transport, and chemical kinetics across various operating conditions, all of which result in complex interactions affecting the accuracy and reliability of the system. Today, both PEMFC and LIB use complex computational physics-based fluid dynamics models in the product development phase, which requires enormous computational power and long lead times for iterative prototype improvements. On the other hand, both PEMFC and LIB rely on simple lookup tables and semi-empirical equations as plant models that require intensive calibration activity to determine the mode of control and diagnosis for automotive applications. However, considering the present-day automotive propulsion systems, which operate in widely varied applications and geographic locations and have short product development cycles, these approaches are not able to comprehend the complexities, hindering the ability of these systems to operate at their full potential and leading to catastrophic failures (e.g., Thermal runaway). Data-based modeling techniques are one of the potential solutions, which is quite in contrast with other empirical or physics-based models where the entire input-output relations of the model are established primarily based on the data. Data-based models use aspects of statistics, probability, and network architecture, avoiding the complexities of physics-based models and intensive calibration, providing better accuracy in most cases, primarily where the complex mechanisms can’t be modeled using specific governing equations, and fast, efficient computation with much less computational resource requirement. This thesis focuses on data acquisition (identifying and collecting the relevant data) and data-based model development by incorporating machine learning algorithms and regressors to predict the system's performance, thermal behavior, aging, and faults in real-time (on-board diagnostics). Data for these models is acquired through two approaches: experimentation by utilizing Fuel Cell and Green Energy Lab facilities such as the Automated Battery Test Station (ABTS), G20 fuel cell automated test station, and by partnering with the relevant industry. In the second approach, data is generated by simulation of physics-based models (CFD, Semi-empirical, equivalent circuit models) that are experimentally validated in the literature and developed within the research groups of UWaterloo. Development of a data-based model includes the identification of feature vectors (inputs), prediction attributes (outputs), state estimates (internal parameters), non-linearity of the systems, correlation factors of various system entities, and application of machine learning techniques such as feed-forward artificial neural network, support vector machine classifier - regressor, along with their respective adaptations and calibration processes. The primary objectives of this study are to develop data-based models for three main application areas: (i) Prediction of PEMFC performance, internal states of the membrane, cell voltage degradation, and system outputs. (ii) Prediction of LIB heat release rate during discharge and thermal dynamics of an open system during an exothermic reaction. (iii) Prediction of fuel cell battery hybrid electric vehicle’s system dynamics and thermal behavior. During this study, various data-based models were developed to tackle the problems encountered in fuel cell-battery hybrid systems, such as predicting the fuel cell performance, fuel cell voltage degradation, PEMFC membrane dynamics, lithium-ion battery thermal dynamics, thermal behavior during exothermic reactions and dynamics of fuel-cell battery hybrid system. The results presented in this study proved the data-based model’s applicability in surrogate modeling, real-time system monitoring, controls, and diagnostics of electrochemical energy systems both at the component level and system level. Additionally, the results implicate that the data-based model can serve as a complement and alternative to the traditional computational fluid dynamics models as well as complex physics-based and empirical models to predict thermal gradients and system internal states during multifaceted reactions.
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    Can We Achieve ‘High-Quality’ Weight Loss Through Anabolic and Weight Loss Supplementation in Combination with Exercise in Overweight and Obese Males and Females?
    (University of Waterloo, 2025-01-24) Ocampo, Gabriela
    Introduction: In 2022, 32.6% and 29.4% of the Canadian adult population from 18 to 49 years of age were considered overweight and obese, respectively, and therefore may become predisposed to developing a myriad of serious health problems and diseases, as well as psychological problems from discrimination and stigmatization. A traditional method to achieve weight loss is to impose an energy restricted diet, however this method has proven to be problematic as it reduces lean body mass (LBM). The loss of LBM can impede ability to perform daily physical activity, increase risk of injury, and increase risk of sarcopenia and thus it is important to implement exercise and/or increase protein intake to promote high-quality weight loss. Many seek alternatives, such as over-the-counter appetite suppressants, herbal products, or weight-loss supplements, to aid in the process. Purpose: To determine if the consumption of a fat oxidizing, TRIM7, and anabolic, MUSCLE5, supplement while performing a mixed-mode training for 12 weeks can promote a high-quality weight loss in the absence of an energy deficit diet. Furthermore, sex and aerobic fitness and strength outcomes will be examined to observe other differences. Methods: Seventy-four overweight/obese, sedentary males (n=35) and females (n=39) were recruited and randomized into group A and group B (active or placebo supplementation) and performed a 12-week mixed-mode exercise intervention. Prior to training, participants underwent anthropometric, body composition (dual energy x-ray absorptiometry), aerobic fitness (VO2max test) and strength (3-5 repetition max test) assessments. Training consisted of 3 weekly sessions involving 30-minutes of aerobic and 30-minutes of resistance training. Supplementation consisted of one group consuming TRIM7 and MUSCLE5 while the other consumed a placebo, every day for 12 weeks. Results: Analysis was completed for group A (n = 11 males and n = 13 females) and group B (n = 10 males and n = 13 females) who had completed the trial by August 2024. There was no change in body mass in either group (p=0.24) after the 12-week intervention. Group A had a significant increase in percent change of LBM after the intervention, with males gaining 1.2% and females gaining 1.7%, while group B had a slight decrease in LBM post intervention by 0.8% for males and 0.4% for females (p=0.05). There was no difference between the sexes in how the intervention influenced any other body composition measurements (all p≥0.34). Both group A and group B improved aerobic fitness (p=0.003) and strength (all p≤0.05), with no difference between groups, sexes, or interactions. Conclusion: The addition of TRIM7 and MUSCLE5 to a 12-week mixed-mode exercise routine did not elicit a high-quality weight loss in overweight/obese males and females. Furthermore, there were no sex differences observed in body composition measures. Group A did have an increase in LBM, thus surmising group A’s consumption of the active supplements based on the increase in protein intake as the trial remains unblinded.
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    A Broken History: Examining the Events, Experiences, and Narratives of the High Arctic Relocations, 1950-2010
    (University of Waterloo, 2025-01-24) Hossack, Sam
    In 1953, the Canadian government moved thirty-five Inuit from Inukjuak in Northern Québec to the High Arctic with promises of better hunting opportunities and the ability to return to their communities within two years if conditions were not to their liking. Two years later, twenty-nine additional Inuit were sent to join them. Since these High Arctic Relocations, government officials, lawyers, and academics have questioned the federal government’s motivations for and responses to the relocations, focusing on the question of whether the government was justified in undertaking an ill-fated humanitarian mission or if the government coerced Inuit into staking Canadian claims to the Arctic. This dissertation explores the legacy and ongoing influence of the relocations in Canadian history by tracing the documentary, experiential, and political narratives surrounding the High Arctic Relocations from the 1950s to the 2010s. This includes critically re-examining the archival evidence from the 1950s; analyzing Inuit testimony of experiences and contemporary storytelling about the relocations; and examining Inuit, government, and academic political narratives from the 1980s through the 2010s. By examining the narratives of the High Arctic Relocations and framing these narratives using the event, experiences, and memory of relocation over the course of seven decades, this study parses the evolving themes and foci as Inuit struggled to secure recognition and compensation for their suffering. This dissertation re-assesses the government’s motivations for relocating Inuit in the early 1950s and includes analysis of the complexities and limits of government decision-making. It also explores the effects of those decisions on Inuit relocatees through an examination of remembered experiences in the 1990s. Finally, this dissertation analyzes the academic and government framing of the narratives of relocation since the 1990s, investigating how these narratives affect contemporary perceptions of government actions. This dissertation demonstrates that the intentions of government officials in the 1950s (the event) and vigorous debate about the perceived motivations of government have superseded the outcomes (experience) of the relocations. This evolving discourse has produced generally-accepted conclusions in Canadian history about the alleged motivations for the relocations that find little grounding in the archival record but which have become a key part of the meta-narrative about state sovereignty, deceit, and coercion in the twentieth century Canadian Arctic.
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    Evaluation of Visual Function, Eye-Hand Coordination and Motor Ability in Typically Developing Children
    (University of Waterloo, 2025-01-24) McKee, Elena
    Many aspects of a child’s development contribute to thriving in everyday activities. For example, motor ability and visual function play a crucial role impacting social, physical and emotional development. While it is expected that better visual function would be associated with better motor performance, this association has not been directly assessed in school-aged, typically developing children. Thus, this study aims to characterize the visual function, motor ability, and hand-eye coordination in a typically developing cohort of children and to determine if there is any association between measures of visual function and motor performance. A cohort of 35 children aged 7-14 years (9.7 SD 2.1 years, 19 males) were tested during a one-time visit which included three standardized clinical tests, an assessment of vision and binocular function, and an experimental hand-eye coordination task. The clinical tests consisted of the Movement Assessment Battery for Children (MABC-2) to assess overall motor development with subtests including fine and gross motor skills, the Beery Butkenica Developmental Test of Visual-Motor Integration (Beery-VMI) to assess visuomotor integration, and the Test of Word Reading Efficiency – 2nd Edition (TOWRE-2) to assess reading and pronunciation ability. The optometric assessment included visual acuity, stereoacuity, fixation disparity, phoria, fusional vergence, vergence facility, accommodative facility, and amplitude of accommodation. Eye-hand coordination was assessed using eye tracking and hand motion tracking while children performed a bead threading task. Results from the optometric tests fell within expected ranges with the exception of vergence facility (13.5 SD 3.9) and binocular accommodative facility (9.2 SD 3.1). Performance on the TOWRE-2 subtests and the Beery-VMI aligned with the expected norms as well. The overall score for the MABC-2 was within the expected range (8.9 SD 2.1), however the manual dexterity subtest fell below the expected range (7.8 SD 2.8). A correlation analysis was performed revealing a relationship between the total MABC-2 score and vergence facility (ρ = -0.38, p = 0.04, 95% CI -0.65, -0.02) as well as accommodative facility (r = -0.48, p = 0.007, 95% CI -0.71, -0.05). The MABC-2 manual dexterity subtest score was associated with accommodative facility (r = -0.38 p = 0.037, 95% CI -0.71, -0.05). In addition, the amplitude of accommodation was associated with three kinematic measures from the bead threading task: the grasping interval (ρ = 0.63, p = <.001, 95% CI 0.35, 0.81), bead threading interval (ρ = 0.38, p = 0.041, 95% CI 0.02, 0.65), and total movement time (ρ = 0.42, p = 0.021, 95% CI 0.07, 0.67). The findings from this study provide preliminary information about visual and motor function measures obtained from the same cohort of typically developing children. In contrast to the hypothesis, a negative moderate association was found between the MABC scores and accommodative and vergence facility. Similarly, the association between accommodation amplitude and bead threading task measures was in the opposite direction to the hypothesis. A larger study is necessary to determine whether the associations found in this small cohort are reliable. An important contribution of this study is the creation of a normative database that includes both the visual and motor scores. These normative values will be used when comparing the performance of children with a coordination disorder in a subsequent study.
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    Investigating Abundances in Galaxy Clusters and Gas Motions in M87 using XRISM
    (University of Waterloo, 2025-01-24) Dizdar, Neo
    Galaxy clusters are the forefront of extragalactic diffuse X-ray astrophysics, yet there are still many questions about their formation and evolution. The creation of XRISM, a new X-ray imaging and spectroscopy mission, will study the metal abundance history of clusters and the conversion of jet energy into atmospheric kinetic energy. XRISM’s payload contains an instrument with the highest spectral resolution (5 eV) in the field of X-ray astronomy so far. With this resolution, we observed metal abundances and the broadening of metal lines through turbulent motions in the intracluster medium. In this thesis I present the conversion of data from the Chandra X-ray Observatory to XRISM’s high-resolution format. This includes the preparation and selection of clusters in Chandra, simulating selected clusters for XRISM and applying for proposals. Finally, we extracted abundance and velocity information from the Virgo cluster’s early XRISM data.
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    Impact of Mechanical and Electrical Tilting for Cellular-Connected Drones and Legacy Users
    (University of Waterloo, 2025-01-23) Elleathy, Ahmad
    Drones, also known as Unmanned Aerial Vehicle (UAV)s, have lately been employed for a variety of tasks in our daily lives, including surveillance, delivery, and rescue operations. High-performance, dependable two-way communication with cellular networks is necessary to expand UAV applications quickly. Supporting different UAVs into current fifth generation (5G) networks is challenging. One of these challenges comes from ground and aerial users having different channel properties. This thesis investigates how the performance of cellular-connected UAVs and legacy ground users in a cellular network can be improved by changing the antenna tilting angle or type, and we will consider mechanical, electrical, and hybrid tilting in the system. This study considers the case of single user Multiple-Input Multiple-Output (SU-MIMO) system featuring Uniform Linear Array (ULA) or Uniform Planar Array (UPA) antenna system with Third Generation Partnership Project (3GPP) parameters. This study illustrates the impact of antenna tilting in improving user throughput, making it easier to integrate UAVs into 5G and future networks. These conclusions are supported by simulation results, which also show how hybrid tilting may be used as a scalable way to enhance multi-user performance for next-generation networks.
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    Type-Safe Tree Transformations for Precisely-Typed Compilers
    (University of Waterloo, 2025-01-23) Magnan Cavalcante Oliveira, Pedro Jorge
    Compilers translate programs from a source language to a target language. This can be done by translating into an intermediate tree representation, performing multiple partial transformations on the tree, and translating out. Compiler implementers must choose between encoding transformation invariants with multiple tree types at the cost of code boilerplate and duplication, or forgoing static correctness by using a broad and imprecise tree datatype. This thesis proposes a new approach to writing precisely-typed compilers and tree transformations without code boilerplate or duplication. The approach requires encoding the tree nodes using two type indices, one for the phase of the root node and another for the phase of all children nodes. This tree datatype can represent partially transformed nodes, and distinguish between trees of different phases. In order to make this approach possible it was necessary to modify the Scala type checker to better infer types in ‘match’-expressions. Precisely-typed tree transformations make use of the default case of a ‘match’-expression; this case must be elaborated to a type which is the type difference of the scrutinee expression and all previously matched patterns. We demonstrate the viability of this approach by implementing a case study for a precisely-typed compiler for the instructional language Lacs. This case study modifies the existing implementation of Lacs to track which subset of tree nodes may be present before and after any given tree transformation. We show that this approach can increase static correctness without the burden of additional code boilerplate or duplication.
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    The Soundtrack of Dissent: Analyzing the Cultural Polarization of 1960s America through Antiwar and Pro-War Protest Movements
    (University of Waterloo, 2025-01-23) Trivino, Christine
    The Vietnam War was a seminal event that perpetuated shifting notions of American culture in a period of significant societal transformation. Its influence extends far beyond the 1960s, providing a foundational context for the evolution of cultural and political discourse in subsequent decades. The cultural dimensions of the Vietnam War are frequently underexamined, despite the numerous cultural contributions that emerged in response to the conflict. Notably, music became a powerful tool for articulating dissent, shaping American society during the era, and mobilizing a generation of young people toward activism. This thesis examines the cultural conflicts in the 1960s and early 1970s, focusing particularly on their relationship to the Vietnam War. It examines both the antiwar and pro-war movements, analyzing their roles in shaping the broader cultural and ideological divisions of the era – a type of phenomenon that could properly be considered a precursor to the modern culture wars of the late 20th and early 21st centuries. Through an in-depth examination of music, youth social movements, and the prevailing narratives of Vietnam War dissent, this study seeks to understand the underlying causes of these cultural tensions. It argues that the intensity of the conflict was fueled by widespread misunderstandings, mutual hostilities, and the era’s increasing openness to new social and political ideas. Understanding these factors is essential for comprehending the broader historical and ideological shifts of the era.
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    Online Likelihood-free Inference for Markov State-Space Models Using Sequential Monte Carlo
    (University of Waterloo, 2025-01-23) Verhaar, Tyler
    Sequential Monte Carlo (SMC) methods (a.k.a particle filters) refer to a class of algo- rithms used for filtering problems in non-linear state-space models (SSMs). SMC methods approximate posterior distributions over latent states by propagating and resampling par- ticles, where each particle is associated with a weight representing its relative importance in approximating the posterior. Through iteratively updating particles and/or weights SMC methods gradually refine the particle-based approximation to reflect true posterior distribution. A key challenge in SMC methods arises when the likelihood, responsible for guiding particle weighting, is intractable. In such cases, Approximate Bayesian Computa- tion (ABC) methods can approximate the likelihood, bypassing the need for closed-form expressions. The particle SMC method of interest in this thesis is Chopin’s SMC2 frame- work Chopin et al. [2013] which uses a nested SMC approach. An “outer” operates on the parameter space θ, and an “inner” particle filter estimates the likelihood for a fixed param- eter θ, facilitating joint inference over the parameters and states. The framework proposed by Chopin required closed-form likelihoods and was intended for offline learning. This thesis proposes an ABC-SMC2 algorithm for online-inference in SSMs with intractable likelihoods. Our method uses Approximate Bayesian Computation (ABC) in the inner particle filter to approximate likelihoods via an ABC kernel, thus enabling inference with- out closed-form observation likelihoods. To address the challenges of online learning, we introduce an adaptive ε-scheduler for dynamically selecting the ABC kernel’s tolerance lev- els and a likelihood recalibration mechanism that retroactively refines posterior estimates using previously observed data. We validate our approach on three case studies using com- partment models governed by an ODE system: a toy linear ODE system, the non-linear Lotka–Volterra equations, and a high-dimensional SEIR model with real-world covariates. In these experiments, ABC-SMC2 outperforms fixed and adaptive ε-schedulers in terms of credible interval coverage, posterior accuracy, RMSE.
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    Modulation Strategies of Cu-based electrocatalysts for Enhancing Electrocatalytic CO2 Conversion
    (University of Waterloo, 2025-01-23) Wang, Lei
    Electrocatalytic CO2 reduction (ECR) into value-added chemicals and fuels using renewable energy contributes to global decarbonization, offering an elegant solution for achieving carbon neutrality and fostering sustainable development of human society. However, this strategy highly relies on the rational design of catalysts to enhance product selectivity and activity. To advance CO2 conversion technology, systematic and comprehensive studies on ECR are urgently needed to demonstrate the origins of catalytic activity, elucidate the relationship between structural defects of catalysts and catalytic activity, and reveal the dynamic evolution of active sites under ECR reaction conditions. In this thesis, mechanistic studies and functional catalyst design are extended from lab scale to large scale. The regulation of grain boundaries structures and local microenvironments is employed to stabilize oxidized copper species, thereby enhancing the selective production of desired products. Firstly, at the lab scale, we introduce oxidation and alloying strategies into grain boundaries systems. Low-loading Ag and water oxidation induce oxygen enrichment at the grain boundaries, leading to a grain boundary oxidation effect. In situ characterizations indicate that the grain boundaries and grain boundary oxidation effects contribute to strengthening resistance of the oxidative Cuδ+ species to the electrochemical reduction. Experimental and theoretical results demonstrate that in intricate grain boundaries assemblies, the oxidation state of copper plays a crucial role in the C2+ product pathway, while the nanoalloy effect tends to the formation of CH4 product. Secondly, to achieve the industrial-scale ECR to multi-carbon products with high selectivity using membrane electrode assembly (MEA) electrolyzers, we introduce activated carbon black with different functional groups to modulate the interfacial microenvironment of Cu nanoparticles, enhancing CO coverage to suppress hydrogen evolution reaction (HER). In situ multimodal characterizations consistently reveal that in situ generated strongly oxidative hydroxyl radicals can create a locally oxidative microenvironment on the catalyst surface, stabilizing the Cuδ+ species and leading to an irreversible and asynchronous change in morphology and valence, yielding high-curvature nanowhiskers. The well-stabilized Cuδ+-OH species serve as active sites during MEA testing. By comprehending this mechanism, we achieve selective ethylene production with a Faradaic efficiency (FE) of 55.6% for C2H4 at a current density of 316 mA cm-2. The insight of these reaction mechanisms bridges the gap between lab-scale studies and industrial-scale implementation, contributing to the development of sustainable and carbon-neutral industries.
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    The Interplay of Information Theory and Deep Learning: Frameworks to Improve Deep Learning Efficiency and Accuracy
    (University of Waterloo, 2025-01-23) Mohajer Hamidi, Shayan
    The intersection of information theory (IT) and machine learning (ML) represents a promising, yet relatively under-explored, frontier with significant potential for innovation. Despite the clear benefits of combining these fields, progress has been limited by two main challenges: (i) the highly specialized nature of IT and ML, which creates a barrier to cross-disciplinary expertise, and (ii) the computational complexity involved in applying information-theoretic concepts to large-scale ML problems. This dissertation seeks to overcome these challenges and explore the rich possibilities at the intersection of IT and ML. By leveraging powerful tools and concepts from IT, we aim to uncover novel insights and develop innovative ML algorithms. Given that deep neural networks (DNNs) form the backbone of modern ML models, the integration of IT principles into ML requires a focus on optimizing the training and performance of DNNs using information-theoretic frameworks. While DNNs have a broad range of applications, this thesis narrows its focus to two key areas: classification and generative DNNs. The objective is to harness IT principles to enhance the performance of these models. • Classification DNNs. For classification DNNs, this dissertation targets improvements in three critical areas: (i) Improving classification accuracy. The performance of classification DNNs is traditionally measured by classification accuracy, but we argue that conventional error metrics are insufficient for capturing a model’s true performance. By introducing the concepts of conditional mutual information (CMI) and normalized conditional mutual information (NCMI), we propose a new metric for evaluating DNNs. The CMI measures intra-class concentration, while the ratio of CMI to NCMI reflects inter-class separation. We then modify the standard loss function in deep learning (DL) framework to minimize the standard cross entropy function subject to an NCMI constraint, yielding CMI constrained deep learning (CMIC-DL). Then, via extensive experiment results, we show that DNNs trained within CMIC-DL achieves a higher classification accuracy compared to the state-of-the-art models trained within the standard DL and other loss functions in the literature. (ii) Enhancing distributed learning accuracy. In the context of distributed learning, particularly federated learning (FL), we tackle the challenge of class imbalance using informationtheoretic concepts to improve the accuracy of the shared global model. To this end, we introduce new information-theoretic quantities into FL and propose a modified loss function based on these principles. This leads to the development of a federated learning framework, Fed-IT, which enhances the classification accuracy of models trained in distributed environments. (iii) Reduce the size and training/inference complexity. We introduce coded deep learning (CDL), a novel framework aimed at reducing the computational and storage complexity of classification DNNs. CDL achieves this by compressing model weights and activations through probabilistic quantization. Both forward and backward passes during training are performed using quantized weights and activations, significantly reducing floating-point operations and computational overhead. Furthermore, CDL imposes entropy constraints on weights and activations, ensuring compressibility at every stage of training, which also reduces communication costs in parallel computing environments. This leads to models that are more efficient in both training and inference, with lower storage and computational requirements. • Generative DNNs. For generative DNNs, this dissertation focuses on diffusion models and their application to solving inverse problems. Inverse problems are common in fields like medical imaging, signal processing, and physics, where the goal is to recover an underlying cause from corrupted or incomplete observations. These problems are often ill-posed, with multiple possible solutions or high sensitivity to small changes in the data. In this dissertation, we enhance the performance of diffusion models by incorporating probabilistic principles, making them more effective at capturing the posterior distribution of the underlying causes in inverse problems. This approach improves the model’s ability to accurately reconstruct signals and provides more reliable solutions in challenging inverse problem scenarios. Overall, this dissertation demonstrates the powerful synergy between IT and ML, showcasing novel methods that improve the accuracy and efficiency of both classification and generative DNNs. By addressing key challenges in training and optimization, this work lays the foundation for future research at the intersection of these two fields.
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    Assessing Cervical Spine Response to Head-First Impact Using Vertebral Segments, Head-Neck, and Full-Body Computational Human Models
    (University of Waterloo, 2025-01-23) Morgan, Maxwell
    Head-first impact (HFI), which can occur in automotive rollovers and sports collisions, is associated with a high risk of cervical spine injury. Cervical spine injuries from HFI such as fracture-dislocations frequently lead to severe spinal cord injuries and in some cases death, as reported in field data and epidemiology. Experimentally, isolated motion segments have been tested in compression and bending to mimic loads incurred in HFI, while cadaveric head and necks with torso surrogate masses (TSMs) and full body (FB) cadavers have been inverted and dropped to investigate HFI. However, isolated segment tests are limited in producing the complex kinematics of HFI, and TSM response has not been quantified with respect to FB testing. Recently, computational human body models (HBMs) have been developed to simulate humans in injurious loading conditions, but have only seen limited application in HFI. In this study, computational models were applied to investigate HFI, using an isolated vertebral segment model, an isolated head and neck with a TSM, and a contemporary FB human model. First, an existing and validated cervical motion segment model was loaded in combined compression and flexion relevant to HFI, to investigate the loads and moments associated with fracture-dislocation failures. Next, TSM and FB head-first impacts were modelled using a contemporary HBM in three postures (flexed, neutral, extended) at three impact velocities. Finally, the FB model was compared with a unique set of experimental full body cadaver HFI tests. In isolated segment loading, combined compression and flexion produced hard tissue failure patterns reported in fracture-dislocations. Fracture-dislocation was achieved by simultaneously rotating and translating the superior vertebra anteriorly. Comparing the isolated head and neck TSM and FB models, the TSM condition demonstrated higher neck forces, internal energy, and a larger volume of hard tissue failure compared to the FB models under the same impact conditions. Despite similar head contact forces between TSM and FB, the compliant thorax of the FB model reduced the neck forces by half, which significantly reduced corresponding energy stored in the neck tissues. The neutral and extended neck postures predicted higher neck forces due to facet joints engaging, while neck flexion in the flexed posture reduced neck forces by misaligning the spine from the impact. Finally, it was found that the FB model had similar head impact forces and comparable T1 rotation to cadaveric HFI experiments, but a high sensitivity to initial posture was identified. This study identified forces and moments that can create a fracture-dislocation in a motion segment using prescribed boundary conditions. The TSM and FB simulations demonstrated compression loads and moments of a similar magnitude to the motion segment, but differed in timing, generating higher axial loads leading to the onset of fracture in the spine. The neck loads were higher using the TSM boundary condition compared to the FB condition. Both TSM and FB models identified the importance of neck posture on response, showing that an initially extended neck posture leads to higher neck forces compared to a flexed posture. This study identified the importance of full body boundary conditions for the simulation of HFI, the complex dependency of kinematic and kinetic response on neck posture, providing model results in agreement with the small number of full body experiments. Further experimentation was recommended to provide detailed measurements necessary for model assessment and validation. Future computational studies will integrate the motion segment and FB results to improve the understanding of fracture-dislocation.
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    On the Initial Boundary Value Problem in Numerical Relativity
    (University of Waterloo, 2025-01-23) Dailey, Conner
    The principal goal of this thesis is to properly understand, characterize, and numerically implement initial boundary value problems in numerical relativity. Throughout the history of solving Einstein's field equations on computers, boundaries have been mostly dealt with in an approximate way. For example, boundaries might be placed far away from strongly gravitating sources, where approximations like linearized gravity are valid. It has become necessary however to place boundaries in the strong gravity regime of a dynamical spacetime to model complicated and interesting physics, which necessitates a complete understanding of the initial boundary value problem of Einstein's field equations. One motivation for this comes from a need to simulate black hole echoes. In classical general relativity, black holes are perfectly absorbing objects, where the mass of radially incoming wavepackets of matter or gravitational waves is absorbed by the black hole. Thus conclusive evidence of modifications to general relativity, such as quantum gravity, could include partial reflections of radially incoming wavepackets, called black hole echoes. To properly understand the modifications this would bring to detectable gravitational wave signals, we require simulations where reflecting boundary conditions are imposed close to the horizon of a black hole. Another motivation comes from recent advances in Cauchy characteristic matching, which combines state of the art numerical techniques to obtain physically accurate gravitational waveforms from simulations. This can allow numerical relativists to dramatically save on the computational cost of black hole merger simulations, but only if boundaries can be placed in the strong gravity regime. This thesis presents advances in simulating initial boundary value problems in numerical relativity. Starting with spherical symmetry, a framework for reflecting a scalar field in a fully dynamical spacetime is developed and implemented numerically using the Einstein-Christoffel formulation. The evolution of a wave packet and its numerical convergence, including when the location of a reflecting boundary is very close to the horizon of a black hole, is studied. Next, this approach is generalized to spacetimes with no symmetries and implemented numerically using the generalized harmonic formulation. The evolution equations are cast into a summation by parts scheme, which seats the numerical method closer to a class of provably numerically stable systems. State of the art numerical methods are demonstrated, including an embedded boundary numerical method that allows for arbitrarily shaped domains on a rectangular grid and even boundaries that evolve and move across the grid. As a demonstration of these frameworks, the evolution of gravitational wave scattering off of a boundary either inside or just outside the horizon of a black hole, is studied. Finally, a boundary condition framework designed to control quasi-local energy flux is proposed motivated by examples from electromagnetism.
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    Visual Change in the Urban Landscape: Taste, Gentrification & Displacement
    (University of Waterloo, 2025-01-23) Babin, Caleb
    Measuring the pace, characteristics and spatial distribution of gentrification is important to developing policies to mitigate its negative consequences, most crucially displacement. Typically, this is done through an analysis of census data on demographic, socioeconomic or housing change. However, this approach has numerous shortcomings, including the homogenizing effect on differences within neighbourhoods and the infrequency of census data collection. Visual analysis, particularly when examining multiple temporal views of the same location, has the potential to render visible fine-grained detail about spatial, economic and cultural changes within the urban landscape. Google Street View (GSV) is emerging as a source of repeat photography data. In this thesis, GSV is employed for analysis within a number of neighbourhoods and retail streets in Hamilton, Ontario. Coding and analyzing GSV images between 2009 and 2021 reveals an array of specific home upgrades, retail turnover as well as aesthetic changes that reflect middle-class tastes, values and lifestyles that suggest more upgrading than found within conventional statistics or dominant narratives about the city. Mapping these changes paints a complex, and fine-grained, block-by-block picture of gentrification that reveals why some areas are more conducive to gentrification than others and how retail gentrification can lead to both direct and indirect displacement. This analysis is important for critical visual methodologies, gentrification and neighbourhood change theories in addition to planning and policymaking.
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    Echoes of contamination: Investigating heavy metal exposure at Wadi Faynan 100, Jordan
    (University of Waterloo, 2025-01-23) Ruddell, Kasiah
    Located in southern Jordan, Wadi Faynan was once a center for copper mining, smelting, and trade during the Early Bronze Age (EBA). The legacy of pollution in Wadi Faynan is visible in the contemporary landscape in the form of spoil tips and over 250 copper mines. The largest and possibly most significant EBA site in Wadi Faynan is Wadi Faynan 100 (WF100), which dates to EBA Ib (3300-3000 BCE) and has clear evidence of copper production including copper ores, slag, and copper casting molds. This research employed laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) to measure trace element concentrations of lead (Pb), cadmium (Cd), and arsenic (As) in human enamel from WF100 to determine if copper production during EBA Ib introduced heavy metal toxicity into the population. The sample consisted of 29 human teeth divided into three groups representative of different early life stages: first molars, premolars, and third molars. Although seven samples were excluded from the main analysis, the others all had trace amounts of Pb, Cd, and As. The samples were categorized into four different groupings for Pb based on their pattern of exposure across the growth layers of enamel: stable exposure, variable exposure, increasing exposure, and decreasing exposure. For Cd and As, each sample was identified as having concentrations above or below their limit of detections. Examination of the distribution of these heavy metals revealed inter- and intra-individual variation in exposure providing insight into participation in copper production activities and possible mobility patterns practiced at WF100.
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    Rheology of Suspensions and impact of Cellulose Nanocrystal as an additive
    (University of Waterloo, 2025-01-23) Pattath, Karthika Prashanth
    Suspensions, as complex fluids, embody a fascinating interplay of solid particles within a liquid medium, presenting a diverse range of viscosity behaviors. Unlike simple Newtonian fluids, suspensions exhibit non-linear responses to applied forces, owing to interactions between dispersed particles and the surrounding solvent. Their viscosity can vary significantly with factors such as shape and size of particle, surface chemistry and concentration. Understanding the rheological properties of suspensions is crucial across industries like pharmaceuticals, cosmetics, paints, and food processing, where their flow behavior dictates product quality and performance. The research examines the consistent rheological characteristics of suspensions containing solid particles thickened by cellulose nanocrystals. Two distinct types and sizes of particles are utilized in preparing the suspensions: TG hollow spheres with a Sauter mean diameter of 69 µm and Solospheres S-32 with a Sauter mean diameter of 14 µm. The concentration of nanocrystals ranges from 0 to 3.5 wt%, while the particle concentration varies from 0 to 57.2 vol%. Additionally, the study investigates the impact of salt (NaCl) concentration upto 2 wt% and pH varying from 3 to 11 on suspension rheology. Generally, the suspensions display shear-thinning behavior, with a more pronounced effect observed in suspensions containing smaller particles. Experimental viscosity data conform well to a power-law model, with variations in flow behavior index and consistency index and under different conditions being thoroughly examined and discussed.
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    Melt-blowing of polymers for porous and functional air filters
    (University of Waterloo, 2025-01-23) Kalani, Sahar
    This thesis develops innovative, high-performance, melt-blown nonwoven materials for air filtration. The first chapter presents a two-step process to create nano-porous, compostable PLA nonwovens with high porosity for particulate capture. First, PLA is melt-blended with polyethylene glycol (PEG) of varying molecular weights to enhance melt flow index (MFI), producing blends with MFI values ranging from 56 g/10 min to 238 g/10 min. These blends are processed into microfibers, with diameters from 1.05 to 2.64 µm, using a twin-screw extruder. The second step involves boiling water etching to remove PEG and form nanopores (50–200 nm), achieving approximately 85% particulate capture efficiency for 0.3 µm NaCl particles. This eco-friendly method shows potential for air and water filtration and battery separators. The second chapter addresses the limitations of conventional face masks, which lack antibacterial or antiviral properties. To improve mask functionality, advanced melt-blown filters are created using polypropylene (PP) and Rose bengal (RB), a photosensitizer. The study investigates the impact of processing temperature on fiber morphology, filtration efficiency, and antibacterial properties. The optimized filters show superior antibacterial performance, particulate filtration efficiency, and breathability, offering significant improvements for personal protective equipment (PPE), with enhanced antimicrobial protection and durability.
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    From disaster recovery to whole-of-society resilience: The impact of the 2021 British Columbia atmospheric rivers event on flood risk management policy and governance
    (University of Waterloo, 2025-01-23) Watterodt, Felicia
    Flooding poses significant risks to the safety, well-being, and long-term security of many Canadian communities. In recent years, extreme weather events, as a result of a changing climate, have cost Canadians billions in insured and uninsured losses annually, and such losses do not encapsulate the various social, ecological, and health impacts that are difficult to quantify. In addition to climate change, misaligned land-use planning, intensified development in floodprone areas, fragmented risk governance, gaps in funding and policy, and an over-reliance on protective structures continue to place many Canadians in harm’s way, while creating barriers for proactive adaptations at the watershed scale. Major disasters, like the 2021 atmospheric rivers floods in British Columbia, underscore the need for transformative flood risk management [FRM] policy and governance by highlighting the systemic drivers of flood risk, namely a FRM system that was never designed to withstand the dynamic realities of the present day. Such focusing events—relatively rare, sudden, and impactful events like disasters—are often critical in generating significant public interest around a focal issue, garnering political will to advance policy agendas, and enabling governance actors to advocate for policy reform. In the post-disaster landscape, coalitions of policy actors can seek to leverage these emergent ‘windows of opportunity’ to advance a paradigm shift in how various public issues, like disaster risk reduction and climate change adaptation, are understood and managed, who is involved in decision-making processes, and what solutions are considered socially-acceptable and politically-feasible. Actors are most likely to be successful in advancing agenda items if enabled by the institutional environments that policy processes are embedded within, and if there is an existing foundation of collaboration among others within the broader policy community. This research, utilizing a case study of a major Canadian flood disaster, evaluates the ways in which policy champions, advocacy coalitions, and institutional actors have sought to leverage existing relationships, prior learnings, and post-disaster momentum to advance shifts in FRM policy and governance at the local, regional, and provincial scales. Semi-structured key informant interviews provide insights into how the disaster manifested as a focusing event, what enabling conditions contributed to the creation of a window of opportunity for policy change, and how recent shifts in British Columbia’s flood governance and policy regimes have been shaped by longer-term institutional developments and interjurisdictional partnerships. This research illustrates the transformational nature of adaptive learning and multi-scalar governance, and is intended to assist FRM decision-makers, policymakers, and practitioners in advancing resilience.