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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Novel Channel Based Relay Attack Detection Protocols in the Physical-Layer
(University of Waterloo, 2025-01-03) Abubaker, Radi
A relay attack is a set of simple, but powerful attacks that can be used to circumvent entity authentication protocols. Through the forwarding of legitimate and real-time messages, relay attacks can cause unexpected authentications to occur, despite the use of modern cryptographic methods, such as digital signatures and message authentication codes. Relay attacks are primarily used by attackers to fraudulently authenticate cyber-physical systems, leading to undesirable responses in the real-world. For example, the relay attack can be used to start vehicles, activate payment systems, and illegally access secured areas. Current solutions are not widely tested and come with trade-offs, which motivates the use of the wireless channel of the physical-layer of communications in wireless systems as a novel prospect for detecting relay attacks. This thesis details the theory, motivation, design, and implementation of four physical-layer protocols that can individually detect either a decode-and-forward or an amplify-and-forward relay attack, occurring in a wireless challenge-response entity authentication protocol. Each protocol utilizes different aspects of the wireless channel to perform the detection. The first proposed protocol leverages channel reciprocity to detect a decode-and-forward relay attack. The second proposed protocol generalizes detection to non-reciprocal channels to detect decode-and-forward relay attacks under stronger adversarial conditions. The third proposed protocol utilizes the change in distribution of a relayed channel as a feature to detect amplify-and-forward relay attacks. The fourth proposed protocol utilizes a deep neural network to generalize feature selection to detect an amplify-and-forward relay attack. Each protocol builds on work from prior physical-layer research, but introduces novel ideas to handle attacks that have not been well explored. To evaluate the performance of these protocols, this thesis performs Monte-Carlo simulations, hardware implementations on software defined radios, and theoretical performance evaluations. These results show practically that physical-layer based relay attack detection using the wireless channel is capable of robustly detecting relay attacks, while simultaneously providing ubiquitous high-throughput data communications, which other potential solutions lack. The thesis is organized into nine chapters. The first three chapters detail the background and motivations of the relay attack. The next five chapters focuses on the protocol designs and their theoretical performance evaluations. The last chapter details the hardware implementation on embedded software-defined radios and the novel processes required to implement the protocols.
How hard was that? Context effects on judgments of effort
(University of Waterloo, 2025-01-03) Ashburner, Michelle
How do individuals make judgments of effort? Despite cognitive effort being a central construct in scholarship, as well as an influential concept in day-to-day life, we have a limited understanding of how individuals determine the effort associated with cognitive acts. Recent work has demonstrated that judgments of effort can be influenced by the context in which they are made (i.e., the judgment context). I employed a reading task and stimulus set that has produced a reliable dissociation between judgments of effort and cognitive demand to further investigate contextual influences on effort judgments. Specifically, I manipulated the context (i.e., the evaluation context) in which individuals read and judged the stimuli; collected individuals’ reasons for their effort judgments; and measured objective demand (i.e., reading times, error counts). In Experiments 1-4, I determined that this context manipulation did not seem to eliminate the dissociation between judgments of effort and objective demand; however, I revealed that evaluation context has a robust effect on judgments of effort. Furthermore, individuals’ reasons varied just as markedly across evaluation contexts. In Experiments 5-7, I extended this work by manipulating the context in which individuals read the stimuli (i.e., the stimulus context) while holding the judgment context constant. Individuals’ reasons for judgment suggested that the cues used to make effort judgments are influenced by the stimulus context, with both judgments and reasons exhibiting notable changes across stimulus contexts. Implications of these results, including how they guide our understanding of the effort judgment process, are discussed.
A Real-Time Autonomous Path Planning Framework for Space Satellites Using Improved Interfered Fluid Dynamic System (IFDS)
(University of Waterloo, 2025-01-03) Patel, Aditya Hetalkumar
In the vast expanse of space, a critical challenge threatens the sustainability of satellite operations and future exploration: space debris. The accumulation of inactive satellites and small debris has elevated the risk of cascading collisions, known as the Kessler Syndrome, which could render critical orbital paths unusable. This scenario would significantly impact our ability to deploy and maintain satellites essential for global communication, weather monitoring, navigation, and scientific research. Addressing the urgent need for advanced space traffic management solutions, this research proposes an autonomous satellite navigation system designed to optimize collision avoidance maneuvers and minimize fuel consumption, contributing to more sustainable space operations. Our system integrates the Interfered Fluid Dynamic System (IFDS) with Machine Learning (ML) models, leveraging real-time predictive capabilities to enhance satellite safety and reduce human intervention. Using the Nutcracker Optimization Algorithm (NOA), optimal parameters are generated to train the predictive model, enabling efficient dataset generation. XGBoost, trained on this dataset, is then employed within the IFDS framework to predict optimal collision-avoidance parameters in real time. This two-step approach enables satellites to autonomously adjust trajectories, maintaining safe distances from debris with minimal fuel consumption. XGBoost achieved an 92% success rate in predicting the optimal reaction parameter of the IFDS Algorithm such that the collision is avoided with a minimum of 2000 m, proving its effectiveness in dynamic orbital environments. Our work also compares NOA with Particle Swarm Optimization (PSO) for tuning IFDS parameters. Our results show NOA’s superior convergence rate and computational efficiency, reducing processing time by approximately 47% compared to PSO. This efficiency accelerates dataset generation and model training. Simulations were conducted using the orekit library to assess the system’s operational effectiveness. The IFDS algorithm, guided by XGBoost-predicted parameters, effectively executes preemptive collision avoidance maneuvers, achieving minimum fuel consumption while ensuring safe separation from debris up to one hour in advance of a potential collision. In conclusion, this research introduces a framework for autonomous satellite collision avoidance that enhances the safety and efficiency of space operations. By reducing reliance on ground intervention, conserving fuel, and enabling safe, independent navigation, this system supports more effective and scalable space traffic management, paving the way for future advancements in satellite operations.
Perceived Differential Parenting and Childhood Physical-Mental Multimorbidity
(University of Waterloo, 2025-01-02) Barclay, Christopher
Background: Physical-mental multimorbidity (herein multimorbidity), the co-occurrence of a chronic physical and mental illness, affects a substantial proportion of children, compromising their quality of life, causing hardship for families, and burdening the healthcare system. Research investigating the potential causal mechanisms of childhood multimorbidity is scarce. Child-perceived differences in parenting style within families (i.e., differential parenting) may be one of these mechanisms. Objectives: 1— What is the direction and strength of the association between perceived differential parenting and psychopathology in children with chronic physical illnesses (i.e., multimorbidity)? 2— Do birth order and concordant/discordant child-sibling sex pairs moderate this association? 3— Does child self-concept mediate this association? Methods: Data come from the Multimorbidity in Children and Youth across the Life-course (MY LIFE) study. Perceived differential parenting was assessed using the Sibling Inventory of Differential Experiences (SIDE), and child psychopathology was assessed using the Emotional Behavioural Scale (EBS). linear mixed models were used to determine the directionality and strength of the association between perceived differential parenting and multimorbidity. Stratified models were computed to determine the moderating effect of birth order and child-sibling sex pairs. The product of coefficients approach was used alongside generalized linear modelling to determine the mediating effect of child self-concept. Implications: Health professionals can utilize findings to foster better communication within families and refer those at-risk to appropriate resources. Findings can also inform the integration of physical and mental health services to promote healthy child self-concept and nurturing family environments to reduce the incidence of childhood multimorbidity.
PERSONA: A Tool for Generating Algorithmic Personas for Reflective Annotations
(University of Waterloo, 2025-01-02) Frasheri, Kris
The domain of machine learning (ML) has grappled with the challenge of curating subjective datasets, where there can be many equally valid labels due to differences in perspectives and a significant technical gap remains in how we can effectively incorporate multiple subjective viewpoints into the labelling process. We contribute PERSONA, a dataset labelling tool that presents LLM-generated personas with diverse labelling per- spectives to encourage annotators to consider different human values during the dataset labelling process. We studied how interactions with these personas affect the annotator’s decision-making patterns. Based on a two-part user study, our evaluation shows that PERSONA enriches the labelling process by prompting the annotators to reflect on differ- ent viewpoints, showing the potential value of integrating LLMs in machine learning data generation pipelines.
Sparse and Scalable Modular Arithmetic
(University of Waterloo, 2025-01-02) Chen, Benjamin
Modular arithmetic is a crucial concept in computer algebra and finds extensive use in applications such as cryptography, polynomial GCD computations, and linear system solving. This thesis investigates methods to enhance the efficiency of modular arithmetic by focusing on choosing sparse and scalable moduli. A contribution of this work is the exploration of a balanced binary representation, which provides the sparsest way to represent integers. Techniques that convert to and from RNS (Residue Number System) using special form moduli (e.g. Mersenne type, Fermat type, and ``trinomial'' type) are also studied, demonstrating significant speedups over conventional division methods. The thesis also explores different schemes to generate scalable moduli sets. One important result is the discovery of a close relation between the inverses of the moduli in sparse balanced binary form and the inverses under a polynomial setting. This relation allows for the generation of scalable moduli sets with Fermat type numbers. We test the proposed improvements on modular arithmetic on a two-layer modular arithmetic scheme that leverages scalable moduli to improve the efficiency of RNS (Residue Number System) conversion and modular inverses to show the effectiveness of modular arithmetic with sparse and scalable moduli. Benchmark results demonstrate significant computational advantages achieved through these methods, offering scalable solutions for large integer operations.
Erosion Risk Modelling: An Improved Screening Tool for Urban Watershed Management
(University of Waterloo, 2025-01-02) Thirimanne, Hettige Dona Thiruni Dulara
Urbanization alters hydrological responses by increasing impervious surfaces, leading to elevated runoff, altered streamflow regimes, and heightened flood risks (Paul & Meyer, 2001; Walsh et al., 2012). The impact of land-use changes is a crucial consideration for urban watershed management (Bochis-Micu & Pitt, 2005; Walsh et al., 2012). SPINpy 2 is a screening tool that utilizes digital elevation model (DEM)-based methods of stream power mapping from Vocal Ferencevic and Ashmore (2012) to integrate land-use data into its modelling framework. This study presents the development of two of SPINpy 2's Land Use (LU) based analyses: i) the No Stormwater Management (NSM) Scenario and ii) the Engineered Stormwater Management Pond (ESM) Scenario. Incorporating Nature-based Solutions (NbS), such as stormwater management ponds, into SPINpy 2 can model methods to alleviate the adverse effects of urbanization by promoting infiltration and stabilizing stream banks. This feature is particularly valuable for urban watersheds at high erosion risk, where NbS can help reduce the effects of impervious surfaces, lower flood risks, and stabilize channels. SPINpy 2 facilitates the modelling of NbS, assessing their effects on stream power, discharge, and erosion sensitivity and providing a decision-support tool for urban watershed managers. It helps evaluate the long-term benefits of NbS in reducing runoff and enhancing ecosystem resilience. By modelling the effects of reducing peak flows on erosion risk, SPINpy 2 simulates how stormwater management measures can mitigate erosion and offers insights into effective strategies for enhancing channel stability. The model was applied to urbanized watersheds such as Cooksville Creek to assess its utility in high-risk environments. The simulation results provide insights into the potential of NbS to reduce flood risks and improve channel stability. The application of SPINpy 2 demonstrated that incorporating NbS significantly mitigates the impacts of urbanization. Comparisons between scenarios with and without NbS interventions highlighted the importance of infiltration-based solutions in stabilizing stream channels and reducing sediment transport. SPINpy 2 also provided spatially explicit maps showing locations of high erosion risk and areas where NbS would be most effective. The findings underscore the potential of SPINpy 2 as a decision-support tool for urban watershed managers. By simulating the impacts of land-use changes and NbS interventions, SPINpy 2 offers a proactive approach to addressing hydrological and geomorphological challenges posed by urbanization. The ability to model diverse NbS scenarios enhances the tool's applicability in high-risk watersheds, such as Cooksville Creek, where impervious surfaces dominate and flood risks are heightened. The results demonstrate that NbS can substantially reduce runoff and stabilize channels, promoting ecosystem resilience and sustainable development. Overall, SPINpy 2 serves as a screening tool for decision-makers, enabling them to simulate and evaluate the impacts of land-use changes and NbS interventions, promoting sustainable development and environmental stewardship in urban environments. Its comprehensive approach allows watershed managers to tackle the unique challenges posed by urbanization and supports the development of cost-effective and environmentally sound infrastructure and policies. This proactive, integrative approach positions SPINpy 2 as a key resource for managing urban watersheds.
Rust-based Path Coverage-Guided Fuzzing
(University of Waterloo, 2025-01-02) Kim, Yunji
Coverage-guided fuzzing is one of the most effective approaches for library testing. While edge coverage has proven successful in finding many bugs, security-critical projects often require higher granularity to thoroughly examine complex execution paths. Path coverage offers a promising alternative, but it is hindered by path explosion and the overhead of path handling. In this thesis, we propose Bounded Path coverage, an advanced coverage metric that mitigates path explosion by leveraging a configurable loop unrolling parameter. For that we propose two algorithms: DAGification and Path reduction. To balance thorough path exploration with resource efficiency, we use the Rust compiler toolchain’s MIRI component with minimal instrumentation overhead for both static and runtime analyses. Our prototype fuzzer successfully generated bounded path coverage, uncovered one unknown bug and one discrepancy from real-world Rust projects, and showcase the potential of superior path exploration compared to traditional edge coverage.
A Study on High-Frequency Induction Welding of TRIP 690 Tubes using Mechanical Tests and Computer Simulations
(University of Waterloo, 2025-01-02) Okoroafor, Sydney
The High-Frequency Induction Welding (HFIW) process is increasingly being adopted for manufacturing tubes used in hydroformed Advanced High Strength Steel (AHSS) components, specifically TRIP 690, in the automotive industry. This trend is driven by strict government climate legislation on automobiles that promotes the development of lightweight materials (AHSS) and efficient manufacturing techniques in the industry. Despite its advantages, the HFIW of TRIP 690 faces significant challenges, particularly the recurring issue of oxide inclusion defects. These defects are often undetectable by conventional tube mill inspection technologies and can only be identified through destructive mechanical testing. These defects also lead to poor mechanical properties and potential failures during complex loading scenarios like tube hydroforming. These oxide inclusion defects have not been explored in literature, leading to a critical knowledge gap in the HFIW of TRIP 690 that reduces the yield of high-quality TRIP 690 tubes during the HFIW process. This research aims to bridge the knowledge gaps associated with the HFIW of TRIP 690. It first investigates the influence of welding parameters and oxide inclusions on the mechanical properties of TRIP 690 tubes. Key findings indicate that the Ring Hoop Tensile (RHT) test yields reliable mechanical property data, revealing notable discrepancies when compared to traditional flat sheet data. The study also establishes that welding power and speed significantly affect Ultimate Tensile Strength (UTS), Uniform Elongation (UE), and fracture toughness. Optimal operating regions are identified through mechanical properties-process mapping, linking these properties to the mechanical properties-thermal process map for heat input and temperatures at the vee apex experienced in the vee region. Furthermore, the presence of oxide inclusions is shown to detrimentally impact mechanical performance, resulting in substantial reductions in UTS, elongation, and toughness. A preliminary mesoscale Finite Element Analysis (FEA) model demonstrates the potential to predict failure behavior in samples containing oxide inclusions through simulations. In addition, this research explores the thermal dynamics of the vee region during the HFIW process. A numerical model developed in COMSOL Multiphysics integrates the thermal modeling with experimental validation from tube mill trials, providing a comprehensive analysis of how critical parameters—such as heat flux, coil-to-weld point distance, and the thermophysical properties of TRIP 690—affect weld quality. Through this study useful tools have been developed that can aid in the optimization of HFIW of TRIP 690.
Multi-Physics Smoothed Particle Hydrodynamics Implementation to Enhance Vertebral Fracture Prediction in a Finite Element Model of a Lower Cervical Spine Segment Under Compression
(University of Waterloo, 2025-01-02) Ngan, Sophia
Events such as vehicle rollovers can lead to compression of the spine and vertebral fractures. Bone fragments from vertebral fracture displace, or occlude, the spinal canal, deforming the spinal cord and leading to the potential of a spinal cord injury (SCI). Finite element (FE) human body models (HBMs) provide an opportunity to predict vertebral fractures and investigate SCIs. Models such as the Global Human Body Models Consortium (GHBMC) model use strain-based element erosion to model hard tissue fracture by removing elements from the simulation upon reaching threshold strains. While strain-based element erosion allows for the prediction of fracture initiation, the method results in the loss of hard tissue material. Under compression, the loss of hard tissue material limits the post-fracture predictive ability of the model due to the loss of structural support and absence of fractured material that may occlude the spinal canal. The objective of the work in this thesis was the implementation of a multi-physics modelling approach to combine strain-based element erosion with smoothed particle hydrodynamics (SPH) to preserve hard tissue material and simulate the movement of fractured material under central compression in a C5-C6-C7 cervical segment FE model. The implementation of SPH was then assessed by comparing the response of the segment model to experimental results and by evaluating SPH particle dependency. Finally, a parametric study was conducted using the model with the SPH implementation to investigate the response of the FE segment model under varied impact severities, aged hard tissue material parameters, and eccentric loading. The model with the SPH implementation was numerically stable and was found to improve the prediction of the trend and magnitude of the force-displacement response, with the area under the curve compared to the experimental response improving from a 34% difference to a 4% difference. Additionally, the implementation of SPH allowed for modelling the flow of hard tissue material and, consequently, occlusion of the spinal canal. The prediction of maximum occlusion in the model compared to the experiments improved from a 137% difference to a 5% difference. Increasing the number of SPH particles generated for each solid element showed numerical instability, illustrating a need for compatibility between the size of the solid element and the number of SPH particles. Varying the impact severity of the central compression load showed that the occlusion in the FE segment model appeared to have a greater dependency on the maximum displacement applied in compression rather than the maximum velocity of the impact due to the amount of fractured material in the simulation. Applying hard tissue material parameters representative of an older age group resulted in higher occlusion and a lower force-displacement response, in agreement with experimental data. The resulting multi-physics approach improved the model predictive capabilities in all cases. Future research will include a spinal cord in the FE segment model to more accurately assess changes in the spinal canal geometry and potential for SCI.
AI-Driven Multi-Purpose Platform for Smart Healthcare Systems
(University of Waterloo, 2025-01-02) Islam, Md. Milon
The significant increase in the number of individuals with chronic ailments has dictated an urgent need for the development of an innovative model for healthcare systems. The design of smart healthcare platforms, a subject of recently growing interest, has become technologically feasible due to the emergence of modern technologies, including smartphones, wearable sensors, 5G communication networks, Internet of Things (IoT), cloud computing, and Artificial Intelligence (AI), particularly machine learning and deep learning. Together, these technologies enable expanded levels of data storage, computation, and secure communication for devices and servers, thus drastically increasing the degree of mobility, security, and available functionality. The thesis is focused on the development of AI-driven data fusion architecture in smart healthcare platform for a scalable, intelligent, and easily deployable remote monitoring system, aimed at providing cost-effective quality healthcare services in assisted living centers. The contributions of the thesis are two-folds: we mainly focus on two major aspects of human behavior, including Human Activity Recognition (HAR) and emotion recognition to monitor the elderly in smart healthcare. In the first part, we propose two AI-enabled multimodal fusion approaches for HAR in ambient assisted living. We present a deep learning-based fusion approach for multimodal HAR that fuses the different modalities of data to obtain robust outcomes. Initially, Convolutional Neural Networks (CNNs) retrieve the high-level attributes from the image data, and the Convolutional Long Short Term Memory (ConvLSTM) is utilized to capture significant patterns from the multi-sensory data. Afterwards, the extracted features from the modalities are fused through self-attention mechanisms that enhance the relevant activity data and inhibit the superfluous and possibly confusing information by measuring their compatibility. Later, we propose a multi-level feature fusion technique for multimodal HAR using multi-head CNN with Convolution Block Attention Module (CBAM) to process the visual data and ConvLSTM for dealing with the time-sensitive multi-source sensor information. The architecture is developed to be able to analyze and retrieve channel and spatial dimension features through the use of three branches of CNN along with CBAM for visual information. The ConvLSTM network is designed to capture temporal features from the multiple sensors’ time-series data for efficient activity recognition. The second part of this thesis focuses on multimodal emotion recognition in connected healthcare to monitor the patient’s health status. To achieve this goal, we introduce two feature fusion approaches through AI tools for emotion recognition. We propose a novel model-level fusion technique based on deep learning for enhanced emotion recognition from multimodal signals to monitor patients in connected healthcare. The representative visual features from the video signals are extracted through the Depthwise Separable Convolution Neural Network, and the optimized temporal attributes are derived from the multiple physiological data utilizing Bi-directional Long Short-Term Memory. A soft attention method fused the high multimodal features obtained from the two data modalities to retrieve the most significant features by focusing on emotionally salient parts of the features. We exploited two face detection methods, Histogram of Oriented Gradients and CNN-based face detector (ResNet-34), to observe the effects of facial features on emotion recognition. Lastly, we introduce a Multi-Stage Fusion Network (MSF-Net) for emotion recognition capable of extracting multimodal information and achieving significant performances. We propose utilizing the transformer-based structure to extract deep features from facial expressions. We exploited two visual descriptors, local binary pattern and Oriented FAST and Rotated BRIEF, to retrieve the computer vision-based features from the facial videos. A feature level fusion network integrates the extraction of features from these modules, directing the output into the triplet attention technique. This module employs a three-branch architecture to compute attention weights to capture cross-dimensional interactions efficiently. The temporal dependencies in physiological signals are modeled by a Bi-directional Gated Recurrent Unit (Bi-GRU) in forward and backward directions at each time step. Lastly, the output feature representations from the triplet attention module and the extracted high-level patterns from Bi-GRU are fused and fed into the classification module to recognize emotion. Finally, we conduct a series of extensive experiments to demonstrate the performance against State-of-the-Art (SOTA) approaches. The findings from the experimental results reveal that the developed multimodal fusion networks surpass the existing SOTA methods in terms of multiple performance metrics. We deployed an IoT system to test the developed feature-fusion networks in real-world scalable smart healthcare application. The developed multimodal predictive analytics frameworks residing in the cloud and trained on large datasets are continually analyzed the nature of the ingested data, making any appropriate notifications to the patients themselves or the healthcare provider through a user-friendly human-machine interface.
Explaining Water Conservation Behaviour with the Big Five Personality Traits
(University of Waterloo, 2025-01-02) Bland, Autumn
Climate change has led to increased levels of water scarcity around the globe and heightens the attention paid to the importance of water conservation. Water conservation behaviour, just like other behaviours, is likely to be affected by personality psychology, specifically the Big Five personality traits. However, the current scholarly understanding of how peoples’ intentions toward water conservation are affected by their personality traits is limited. The current research helps close this knowledge gap by clarifying how people vary in their intentions toward water conservation and how differences in their personality traits are related to the observed variation in their water conservation behaviour intentions. In pursuit of this goal, the current study examined the relationship between the Big Five personality traits (i.e., openness, conscientiousness, extraversion, agreeableness and neuroticism) and water conservation intent and, used the Theory of Planned Behaviour as an analytical framework. Data were collected with an online survey of students at the University of Waterloo, Ontario, Canada. The results suggest significant relationships between various Big Five personality traits and several of the Theory of Planned Behaviour constructs. These relationships were tested for demographic effects (i.e., program of study and gender) on their strength or direction. However, the results indicate that most of the relationships were not affected by demographic variables, suggesting that the identified relationships are universal based on the survey population and investigated demographic variables. The results from this study further our understanding of the factors that affect water conservation behaviour.
Facing the Heat: Utilizing the Internet of Things (IoT) to Enhance Preparedness Against Extreme Heat Events in Canadian Households
(University of Waterloo, 2025-01-02) Oetomo, Arlene
\textbf{Background} Heat waves pose a severe and growing health risk in Canada, with an estimated 17 million people likely to experience extreme heat in the coming decades. These risks are heightened in urban areas, where the urban heat island effect exacerbates dangerous temperatures. Extreme heat events are increasingly frequent, intense, and widespread, with record-breaking heat waves affecting almost every region globally. Despite increasing awareness, many of the dangers associated with extreme heat are only now being fully realized. This work addresses data gaps related to indoor heat vulnerability needed to create safe indoor conditions. Education and early warnings are critical to protecting the population, especially vulnerable groups. Extreme heat is a climate justice issue, disproportionately affecting marginalized groups with fewer resources to cope. \textbf{Methods} This study used smart thermostats (ecobee) to collect real-time indoor temperature data, developing a hyperlocal alert system for indoor heat risks. First, we analyzed ecobee's "Donate Your Data" dataset to identify homes without air conditioning and to explore indoor and outdoor temperature differences during the 2018 Quebec heat wave. Next, a scoping review assessed the use of IoT or similar devices, focusing on climate-related impacts. Canadians were surveyed through Google Opinion Rewards to evaluate their understanding of heat waves and preferences for receiving heat-related information. Online discourse about heat waves was analyzed using Latent Dirichlet Allocation (LDA) topic modelling on tweets from Twitter (now X), via natural language processing (NLP), and manually using an inductive approach to observe emerging narratives and themes. Finally, in the summer of 2022, a full-scale deployment of smart thermostats was conducted in community housing without air conditioning in collaboration with housing and public health organizations. Indoor temperature data was collected from 70 British Columbia and Ottawa homes, with results shared via daily emails and an elastic dashboard. Three surveys captured details on building types, resident behaviours, and heat-related habits. \textbf{Results} The scoping review found that using IoT and low-cost sensors for indoor heat monitoring is an emerging field that could strengthen early warning systems and heat response efforts. The survey results showed that most Canadians preferred to receive heat wave information online, with income correlating to the usage of mobile devices. Older demographics still preferred traditional media like TV, radio, and newspapers. Online discussions centred around activities, personal experiences, advice, and warnings related to heat waves. Emerging trends of climate denial were present in both survey and tweet samples. Indoor temperature data revealed that homes frequently exceeded emerging threshold recommendations of 26°C and 31°C, with some homes recording temperatures as high as 38°C on non-heat wave days. Participants in British Columbia were better prepared and had higher heat safety awareness than other regions. This is the first Canadian study to explore the use of IoT smart thermostats in near-real-time indoor temperature monitoring to protect vulnerable populations from extreme heat. Significant differences were observed between indoor and outdoor temperatures ($P <0.001$). \textbf{Discussion and Conclusion} This work shows the potential of IoT devices and social media to improve real-time monitoring and inform evidence-based public health interventions for extreme heat. The widespread adoption of smart thermostats in Canadian homes and the prevalence of social media provide valuable opportunities to address existing data and knowledge gaps. However, the persistence of climate misinformation and denial challenges public health communication efforts. Indoor heat is a public health and equity issue related to poverty, substandard housing, urban planning, and energy access. These interconnected issues must be considered in policy frameworks to protect vulnerable populations. Low-cost sensors and solutions, such as natural ventilation mobile alerts, could support the response to heat health by addressing a data gap. Rapidly analyzing these areas is important as digital spaces evolve to support public health communication. Policymakers must take immediate action to prevent unsafe indoor conditions and protect marginalized communities from the growing threat of extreme heat.
Neurovascular Coupling in healthy human retina evaluated with Optical Coherence Tomography
(University of Waterloo, 2024-12-21) Dhaliwal, Khushmeet
Retinal neurodegenerative diseases such as glaucoma, age-related macular degeneration (AMD), diabetic retinopathy, and retinitis pigmentosa affect millions of people worldwide and pose a significant burden on public health and the economy. Glaucoma, impacting approximately 80 million people globally, is a leading cause of irreversible blindness. In 2019, an estimated 19.8 million Americans (12.6%) were living with AMD, of which about 1.49 million people faced vision-threatening conditions. An estimated 9.6 million people were living with diabetic retinopathy in 2021, with about 1.84 million of them experiencing vision-threatening stages. In the United States alone, vision impairments- including those resulting from these retinal diseases- cost an estimated $139 billion annually. Retinal neurodegenerative diseases not only cause progressive damage to the retinal morphology and vascular network, but also cause acute and transient metabolic, physiological, and blood flow changes at the early stages of the disease development which become permanent and chronic at the advanced stages of the disease. Neurovascular Coupling (NVC) refers to the transient vasodilation and increased retinal blood flow resulting from the increased metabolic activity of retinal neurons in response to visual stimulation. Over the past few decades, a range of imaging techniques from clinical ophthalmoscope and confocal microscopy to adaptive optics scanning laser ophthalmoscope and optical coherence tomography (OCT) have been used ex vivo and in vivo to study components of the neurovascular coupling and its underlying mechanisms. Techniques such as Laser Doppler Velocimetry, Optical Coherence Tomography Angiography (OCTA), and Doppler Optical Coherence Tomography (D-OCT) have been used to observe the vascular responses of the retina caused by visual stimulation. Additionally, Electroretinography (ERG) has been widely used in clinical settings to evaluate the electrical activity of the neuronal retina. More recently, an optical equivalent to ERG, Optoretinography (ORG) was developed and OCT technology, imaging protocols, and image processing algorithms were designed to conduct OCT-based ORG studies in the human and animal retina. However, most of the Doppler OCT, OCTA, and ORG studies have examined components of the neurovascular coupling separately, potentially overlooking the dynamic interactions and comprehensive responses inherent in neurovascular coupling. OCT, which acquires simultaneously both intensity and phase information, is particularly well-suited for investigating neurovascular coupling in the retina, as it enables a completely non-invasive approach for simultaneous monitoring of retinal blood flow dynamics and neuronal responses. The integration of a commercial ERG system with a research-grade OCT modality adds further value by offering easy control of the visual stimulus, use of clinically established ERG protocols designed to elicit responses from specific types of retinal neuronal cells, and using the ERG recordings to validate the visually-evoked neuronal responses. The main objectives of this PhD thesis were: 1. To develop a combined OCT+ERG imaging system to conduct in vivo and simultaneously morphological and functional imaging that can be utilized for investigating neurovascular coupling in the human retina. 2. To evaluate the performance and capabilities of the OCT+ERG system, imaging protocols, and image processing algorithms by conducting a pilot study on healthy human subjects. 3. To utilize the OCT+ERG technology to explore the neurovascular coupling mechanisms in the healthy human retina by extracting vascular and neuronal responses from different retinal layers simultaneously. 4. To examine the effects of different wavelengths and flicker frequencies on the dynamic retinal blood flow changes evoked by visual stimulation, providing deeper insights into the mechanisms of neurovascular coupling. Results from this PhD research have been summarized in three manuscripts that are either under review or under preparation for submission. Therefore, this PhD thesis was prepared in such a way that individual manuscripts represent separate thesis chapters.
From Fisher Economicus to Fisher Socialis: Investigating the Role of Fisher Behaviour for Effective and Equitable Governance
(University of Waterloo, 2024-12-20) Battaglia, Maria Bernadette
The purpose of this dissertation is to advance the emerging field of fisher behaviour. The applied aim is to enhance fisheries and oceans policy, and primarily within the context of small-scale fisheries sustainability. In this dissertation, fisher behaviour is defined as the actions (or inactions) taken by fishers in response to internal and external stimuli and it describes the multitude of ways in which fishers interact with their social and ecological environment. Further, fisher behaviour can manifest through physical action, verbal expression, emotional responses, and cognitive processes. Fisheries and oceans policy shapes and is simultaneously shaped by fisher behaviour because regulations and enforcement mechanisms that emerge from policy interventions signal what behaviours are allowed and which ones are not. Fishers, in turn, interpret these signals based on their values, needs, and perceptions of legitimacy over resource use. For this reason, when policy does not adequately reflect the diverse range of factors that shape fisher behaviour, regulations can become inefficient or inequitable and may result in poor social and ecological outcomes. Despite the importance of behaviour and its central role in the pursuit of fisheries and oceans sustainability, fisher behaviour is complex and hence still not well understood. Historically, predominant paradigms of behaviour have been based in neoclassical economics’ Homo Economicus. These models of behaviour predict that rational and self-interested individuals will always prioritize personal gain over the collective interest and, without external interventions, they will inevitably deplete shared resources. Yet, in the last few decades, empirical evidence has challenged these assumptions, and has shown that resource users, including fishers, are able and willing to engage in collective action to solve social dilemmas. The scope of this dissertation is to use the emerging field of fisher behaviour as a critical lens to strengthen fisheries and oceans policy. To achieve this overarching aim, this dissertation is guided by three interrelated research objectives: 1) To advance and understand fisher behaviour as an emergent and critical, yet understudied, field through the development of a comprehensive conceptual typology based on selected literatures; 2) To map and synthesize the complex interactions between social norms (as a particular manifestation of fisher behaviour), collective action problems, and fisheries policy and to further unpack the role of social norms as a catalyst of collective action in natural resource systems, including in fisheries systems; and 3) To empirically examine the role of social norms and social networks as two fisher behavioural approaches and further assess their implications for policy. The first objective provides the foundation of this dissertation and frames the context and significance of this research by presenting an overview of alternative behavioural approaches to understand fisher behaviour. The second and third objectives delve deeper into two behavioural approaches, and in doing so, they challenge one of the core assumptions of Homo Economicus: that humans are self-interested, hence incapable of solving collective action problems. The research methodology used in this dissertation is informed by deductive and inductive approaches and the integration of qualitative and quantitative methods, alongside a theoretical exploration. To further understand fisher behaviour in real world settings, a case study was conducted in Sardinia, Italy, in the context of three small-scale fishing communities adjacent to the Asinara MPA. Fieldwork was conducted both in-person and coordinated remotely in response to COVID-19 pandemic requirements. Research instruments were co-developed in collaboration with BMF, a not-for-profit organization working in the area, and further reviewed by key community informants to ensure coherence with the local context. Further, informal conversations with research partners and members of the local communities added additional depth and confirmed the direction of the research findings. In Chapter 2, a theoretical exploration of existing literature on fisher behaviour was adopted to provide a typology of key selected approaches that offer an interdisciplinary suite of entry points and complementary opportunities to advance the understanding of fisher behaviour. These approaches include theories, concepts, and perspectives from Critical Social Theory, Systems Approaches, Development Scholarship, and Institutional Scholarship. Two vignettes, one based in Italy, and one based in Canada, were included in this chapter to add further empirical weight to the chapter by delving deeper into two of the lenses presented in the typology. In Chapter 3, a systematic scoping review was used to map and synthesize existing literature on social norms in fisheries. However, given the limited availability on empirical articles that focused solely on fisheries, the scope of the evidence synthesis was broadened to include other environmental contexts. As such, the systematic scoping review was conducted on peer-reviewed articles (n=69) to map and synthesize ways in which social norms are conceptualized, elicited, and measured in the empirical literature at the intersection of social norms and collective action problems in environmental settings. This evidence synthesis followed the PRISMA-ScR. Findings revealed that social norms can be conceptualized as collective or individual constructs, and they can be elicited or measured using a variety of qualitative and quantitative methods. Further, social norm definitions do not necessarily correspond to a unique elicitation method. These results suggest that what may initially appear as lack of coherence is instead an opportunity to study social norms from various angles and perspectives. In Chapter 4, structured surveys (n=81) were used to empirically examine the role of social norms and social networks in the Asinara MPA communities. Findings indicated that the existence of subgroups within networks does not necessarily hinder capacity for collective action, as analyses on the strength and distribution of social norms showed that cooperative behaviours within the Asinara MPA communities were still strong. Importantly, network analyses also elicited the presence of well-connected, central actors within each subgroup. This finding holds promising potential for collective action because central figures can leverage their positions to synthesize subgroup heterogeneity and generate innovative solutions to shared challenges. Findings and insights of this dissertation contribute to advancing the emerging field of fisher behaviour, while offering alternative approaches to the paradigm of behaviour based in Homo Economicus. Context-relevant knowledge on fisher behaviour can be operationalized in policy settings to catalyze change using insights on who fishers are and the reasons behind their actions. For instance, Challenge 10 of the United Nations 2021-2030 Decade of Ocean Science for Sustainable Development aims to identify barriers to behaviour change to achieve ocean health. However, this aim cannot be achieved using models of human behaviour that underserve and oversimplify the complexity of empirical reality. Findings from this dissertation translate into theoretical and empirical contributions by helping identify lenses and approaches to enhance fisheries and oceans policy, through a more comprehensive understanding of fisher behaviour. These insights can support policy in three complementary ways. First, knowledge on fisher behaviour can enhance coherence between policy and the social context within which policy instruments are embedded, which includes the behavioural elements of social systems, such as fishers’ values, needs, and beliefs. Second, aligning policy with contextual knowledge about fisher behaviour can improve policy equity by bringing recognition to pre-existing forms of organization (e.g., social networks) and informal rules of behaviour (e.g., social norms) that fishers have developed over long periods of time and persistence. Finally, this research reveals that there are currently untapped opportunities to generate new evidence about fisher behaviour. However, these efforts will require challenging the assumptions that have long underpinned fisheries and oceans policy, and cultivating collaborations across academia, policy practitioners, and fishing communities to inform the development of new methodologies and contextually-relevant understandings.
Computational study of cellular adhesion in metastasis: Implications for Circulating Tumor Cell Arrest, Extravasation, and Thrombosis Formation
(University of Waterloo, 2024-12-20) Rahmati, Nahid
Cancer metastasis is the process by which cancer cells spread from the primary tumor to distant sites in the body, forming secondary tumors. This process is responsible for the majority of cancer-related deaths, despite significant advancements in treating primary tumors. This thesis aims to enhance the understanding of metastasis mechanisms by exploring the roles of circulating tumor cells (CTCs), ultra-large Von Willebrand Factor (UL-VWF) multimers, and blood vessel configurations. This study focuses on the mechanical, biochemical, and hemodynamic factors that drive metastatic processes and cancer-associated coagulopathies, providing insights into the interactions between CTCs, VWF, and endothelial cells. Through computational modeling and simulations, first, we investigate the role of UL-VWF multimers in cancer-associated thrombosis. The computational model integrates the lattice Boltzmann method for simulating blood flow, a coarse-grained model for deformable cells to capture their mechanical behavior, and the immersed boundary method to handle fluid-structure interactions. Additionally, an adhesion model was developed to simulate the binding dynamics between cells. This multi-scale approach allows for a detailed analysis of how UL-VWF multimers interact with blood cells to initiate microthrombus formation and progression. The findings reveal that UL-VWF plays a dual role in thrombosis and metastasis, enhancing platelet adhesion and trapping red blood cells, which can lead to significant changes in blood flow dynamics, such as reduced velocity and increased shear stress near thrombus sites, leading to a pressure drop of up to six times compared to healthy conditions. The study also explores the impact of blood vessel architecture on CTC dynamics, focusing on how vessel tortuosity influences CTC adhesion and extravasation. The same computational methodology has been utilized to analyze CTC interactions with the vessel wall, incorporating adhesion dynamics between the CTCs and the endothelial surface while considering the effect of shear rate on adhesion strength. The results indicate that curved vessels create asymmetrical flow patterns, resulting in variable shear stress, a 25% decrease in the wall shear stress in low-shear regions and a 58.5% increase in the high-shear region, that significantly affects CTC behavior. Specifically, high-shear regions in curved vessels show a threefold rise in adhesion bond formation compared to straight vessels, enhancing the likelihood of CTC extravasation. Increasing the tortuosity index of the vessel led to a 50% increase in maximum wall shear stress ratio and a 15.3% decrease in minimum wall shear stress ratio, as well as a 58% increase in the transit time of CTCs through the vessel curvature. The adhesion force in these high-shear regions increased by about 171%, indicating a significantly higher risk of CTC adhesion and extravasation in vessels with higher curvature. Additionally, while softer CTCs in low-shear regions showed a higher likelihood of detachment, stiffer cells in high-shear regions exhibited a reduction of approximately 12% in adhesion force compared to their behavior in straight vessels. This study identified an optimal range of cellular stiffness for successful CTC extravasation, challenging the assumption that softer cells always extravasate more efficiently. In this thesis, we also employed a stochastic model to analyze the dynamics of CTC adhesion, a crucial factor driving metastasis, incorporating parameter uncertainties in cell mechanical properties and adhesion characteristics. This probabilistic approach realistically captures the biological variability inherent in CTC behavior by accounting for a wide range of possible cell adhesion scenarios. Our analysis revealed that incorporating parameter variability, with a coefficient of variation of 20%, led to a maximum uncertainty of 12% in cell velocity. This variability manifested in two distinct CTC behaviors: either the cells detached from the vessel wall or continued to roll in a semi-stable manner, emphasizing the non-linear and complex nature of the adhesion dynamics. To efficiently manage computational demands, we developed a Random Forest surrogate model, achieving a high level of accuracy with a maximum error of 4.36% for velocity and 0.63% for stretch ratio. This model enabled comprehensive sensitivity analysis using Sobol' and E-FAST methods, which identified the bond spring constant and rupture strength as the most influential parameters following the initial adhesion phase, while cell membrane elasticity played a critical role during the initial adhesion. We also observed significant interdependencies between bond formation and rupture properties, underscoring their combined impact on CTC dynamics. Furthermore, machine learning techniques, particularly XGBoost, validated the model's predictive capabilities by achieving a classification accuracy of 95.62% and an area under the curve (AUC) value of 0.99 in distinguishing between 'rolling' and 'detached' CTC states. These findings highlight the importance of focusing on key parameter interactions to refine predictive models for metastasis. This comprehensive approach builds on the computational frameworks developed in this thesis, enhancing our understanding of metastasis by offering predictive insights into CTC behavior under different conditions. By integrating these findings into a cohesive framework, the thesis supports the development of more targeted therapeutic strategies to prevent or disrupt cancer progression.
A Community-Based Exploration on the Impacts of Land-Based Learning in an Urban Indigenous Community
(University of Waterloo, 2024-12-20) Bui, Danica
Background Connection to the Land is essential for Indigenous Peoples’ health and well-being. Indigenous Food Sovereignty works towards regaining control over traditional food systems. There is a need for sustainable, Indigenous-specific, and accessible urban community Land-based practices. Done in partnership with White Owl Native Ancestry Association (WONAA) and the Wisahkotewinowak Collective (WC), four seasonal Land-based learning events were held. Each event involved a workshop, meal, and gathering at the University of Waterloo’s North Campus Community Gardens. Aims Guided by the interests of project partners, WONAA and WC, the overall purpose of this thesis is to explore and evaluate the impacts of Land-based learning on the local Indigenous and university communities and the PEG project’s effectiveness in establishing networks to facilitate ongoing research and learning to establish a permanent Land-based learning space on campus. The overarching research questions that guided the research are: 1. What are participants’ and facilitators’ perceptions of the Land-based engagement process? 2. What is the effectiveness of Land-based workshops in identifying and prioritizing collaborative Land-based opportunities for learning and research? 3. To what extent can the seasonal workshops establish a local network to facilitate ongoing research and learning in support of the development of an urban Indigenous Land-based learning space on the UW campus? Methods Appreciative Inquiry and Community-Based Participatory Research approaches were used for this thesis. Following each of the events, participants and project collaborators were recruited to participate in interviews. The data for this evaluation was collected through 17 semi-structured interviews with project collaborators and community partners (n=6), urban Indigenous community members (n=6), Indigenous youth and students (n=2), and UW staff (n=3), two focus groups with WONAA and WC staff members (n=5) that were involved with the workshops, and a document review. Reflexive thematic analysis was completed using NVivo for the interview and focus group transcripts, and the documents were analyzed to determine if the budget and timelines were met. Results Overall, participants, collaborators, and facilitators of the Land-based learning events had positive perspectives of establishing a Land-based learning space on campus. The three key themes from the interviews about the Land-based learning events and their potential for building connections to establish a permanent Land-based learning space include: (1) participants’ growing support of Land-based learning (2) creating connections and building relationships and (3) institutional support and project sustainability to support of the development of a permanent urban Indigenous Land-based learning space. The findings from the outcome evaluation, including the interviews, focus group, and documents reflect these themes and include (1) workshop feedback (2) project partners' goals, and (3) identifying opportunities to move forward. These findings show that the community, including project partners, participants, and collaborators, found the Land-based learning events to be a welcoming and positive experience that should become integrated into university curricula to support the university and local community. Conclusion Participants enjoyed the Land-based learning events and supported the establishment of a permanent Land-based learning space at the North Campus Community Gardens. Attending the events and being involved in the project generated ideas for learning and research opportunities, with many participants sharing future workshop ideas and identifying ways to integrate Land-based curricula and research opportunities. The events also connected the local urban- Indigenous community to the university community and helped to strengthen pre-existing relationships between the attendees. Through these connections, the desire for the North Campus Community Garden to become a space for Indigenous learning, teaching, and research, strengthened. Overall, the interview and focus group participants had positive perspectives of the events and the establishment of a permanent Land-based learning space at the North Campus Community Gardens, shared that Land-based learning was a need in the urban Indigenous and university community, and emphasized that further support from UW, longer-term funding, and physical and social infrastructure was needed for the project’s sustainability.
A Two-Effects Model of Explanation on Exposing the Illusion of Understanding
(University of Waterloo, 2024-12-20) Meyers, Ethan
People often overestimate their understanding of how things work. For instance, people believe that they can explain even ordinary phenomena such as the operation of zippers and speedometers in greater depth than they really can. This is called the illusion of understanding (originally known as the illusion of explanatory depth). Fortunately, a person can expose the illusion by attempting to generate a causal explanation for how the phenomenon operates (e.g., how a zipper works). This might be because explanation makes salient the gaps in a person’s knowledge of that phenomenon. However, recent evidence suggests that people might be able to expose the illusion by instead explaining a different phenomenon. Across six preregistered experiments and one secondary data analysis, I examined whether explaining one phenomenon (e.g., how a zipper works) leads individuals to lower their self-assessed knowledge of unrelated phenomena (e.g., how snow forms). My findings demonstrated that participants consistently revised their understanding downwards, not only for the item they explained but also for other items they did not explain. For instance, participants reported reduced understanding of speedometers after explaining helicopters or zippers. Contrary to prior research, participants did exhibit the illusion for familiar movie plots (Experiment 4), but consistent with prior research, participants did not exhibit the illusion for common procedures (Experiment 5). Additionally, when common procedures were included in the experimental design used in Experiments 2 and 3, participants showed no illusion whatsoever (Experiment 6). Finally, an analysis of explanation quality using ChatGPT to code the explanations revealed that the reduction in perceived understanding after explaining (compared to before) correlated with the difference between how well the participant thought they understood the item and how well they actually explained it, but only for explained items. These findings challenge the common framework of how the illusion of understanding operates. Throughout the thesis I evaluate alternative models of the illusion and ultimately find the most support for a two-effects model of explanation, wherein failing to explain a phenomenon temporarily makes people recognize the gaps in their knowledge of the item they explained and makes them feel less knowledgeable about most other things.
Exploring the Use of Managed Retreat in Canada's Policy Domain
(University of Waterloo, 2024-12-19) Cottar, Shaieree
The use of managed retreat is growing and will continue to evolve within Canada’s policy domain to better adapt to the realities of climate change. Climate induced managed retreat involves the strategic relocation of people, assets and critical infrastructure from high-risk areas via the use of government funded property acquisitions (buyouts). Amid increased flood risks and rising recovery costs, communities in Canada have recognized that conventional approaches to flood risk management (FRM) are no longer sustainable and will require the use of practical policy solutions, such as managed retreat, that are cost-effective, politically viable, and publicly accepted. Given the growing adaptation deficit in municipalities, there is an inherent need to identify the policy gaps and barriers to implementation, analyze the policy levers, and explore opportunities for future managed retreat policy and program development. Rooted in the climate change adaptation and disaster risk reduction literature, this dissertation explores the theoretical tenets of FRM, particularly the lack of alignment between flood risk governance, risk reduction and risk prevention through the empirical application of post-disaster managed retreat policies in Canadian communities. The current policy discourse in Canada focuses on the development of a subsidized national flood insurance program whilst provincial jurisdictions like British Columbia (BC) are contending with how to amend existing disaster and emergency management policies to effectively integrate community led managed retreat. Similarly, provinces like Quebec have significantly advanced in their progress and have implemented multiple buyout cycles as part of their disaster financial assistance programs. By analyzing multiple case studies across different timeframes during their recovery process, this dissertation investigates the complex post-disaster decision-making process amongst different levels of government and explores potential pathways towards building climate resiliency. Through three interrelated qualitative studies, this research documents the development, application and implementation of managed retreat buyout programming and its wider implications for communities. The findings suggest that flood disasters often act as focusing events and open policy windows during the post disaster recovery stage providing an avenue for renewed disaster recovery discussions including the use of managed retreat as a policy tool which may not be politically justifiable in a proactive context. A case study analysis critically documents the recovery process by analyzing municipal perspectives on managed retreat and flood mitigation signaling a shift from a hazards-based to a risk-informed approach in Merritt, Canada after the 2021 flood disaster. Moreover, existing path dependencies and outdated disaster policies can favour recovery decisions and limit the types of mitigation measures that are considered by jurisdictions. Likewise, the development of a buyout program for flood mitigation purposes should account for design considerations that include community led and pricing methodologies that follow an equity-based approach. While the use of buyouts is an important tool to become climate resilient, partially retreated communities must strategically develop land use plans that reconcile the benefits of floodplain restoration and provide recreational spaces for public use. A longitudinal study analyzes the institutional alignment of provincial buyouts policies and regulatory tools in Gatineau, Canada, five years after the Quebec Spring 2019 floods. In broader terms, this dissertation ascertains that in the face of surging disaster costs and conflicting governmental priorities, the use of managed retreat will continue to grow and evolve within the Canadian policy arena as a viable climate adaptation and risk reduction strategy. As a result, this critique presents novel approaches for evaluating managed retreat policies to minimize the negative impacts on communities whilst maximizing the co-benefits. Moreover, this dissertation makes key contributions to the managed retreat literature by linking the topics of flood risk governance, flood risk management, and applied policy. The research provides valuable insights for policymakers on buyout policy development, program implementation, and long-term land use plans for communities in Canada.
Modifications of Zein Biopolymer for Material Applications: Biopolymer Blends, Films, Bioactive Delivery Nanoparticles, and Nanofibers
(University of Waterloo, 2024-12-19) Tadele, Debela
The need for sustainable and high-performance materials is becoming increasingly urgent as society confronts escalating environmental challenges and pressing healthcare demands. Zein, a protein derived from corn, offers promising potential as a renewable, biodegradable, and biocompatible material for various applications. However, its utility is hindered by intrinsic limitations, such as poor processability, inadequate mechanical properties, and limited thermal stability. This research aims to address these challenges through chemical modifications of zein, blending with complementary polymers, and employing advanced fabrication techniques to develop nanoparticles and nanofibers for targeted applications. The first part of this study focused on chemically modifying zein via esterification using fatty acid chlorides of varying chain lengths (C6, C10, and C16). The findings revealed that fatty acid-modified zein exhibited significantly enhanced hydrophobicity, improved melt processability, and superior tensile properties compared to unmodified zein. The introduction of fatty acid chains also acted as internal plasticizers, facilitating the creation of a melt-processable biopolymer suitable for scalable production. In the second phase, the modified zein (mZein) was blended with poly(butylene adipate-co-terephthalate) (PBAT) via melt extrusion to fabricate biodegradable films. The study explored the effects of varying blending ratios on the compatibility, morphology, and physicomechanical properties of the films. It was observed that the esterification with decanoic acid (C10) significantly enhanced mZein's compatibility with PBAT, enabling the formation of films with balanced mechanical and barrier properties. A composition of mZein/PBAT at 30/70 wt% exhibited optimal performance, achieving a tensile strength of 10.88 MPa, elongation at break of 561.41%, and a modulus of 105.63 MPa. Furthermore, the blend demonstrated superior oxygen barrier properties, reduced water vapor permeability, and improved disintegration rates, making it suitable for food packaging and agricultural applications. The third component of this research examined zein as a biopolymer matrix for nanoencapsulation of bioactive compounds, specifically quercetin and α-tocopherol. Zein nanoparticles (ZNPs) were fabricated using a green antisolvent co-precipitation method, achieving encapsulation efficiency of 96% with particle sizes ranging from 50 to 320 nm. Co-encapsulation of quercetin and α-tocopherol in various formulations revealed distinct release dynamics, with the Zein/Que/Toc (20:1:1) formulation demonstrating controlled release rates over 8 hours. ATR-FTIR and fluorescence spectroscopy highlighted the hydrogen bonding and hydrophobic interactions critical to the encapsulation mechanism. In the final phase, α-tocopherol-encapsulated zein was integrated with polyvinyl alcohol (PVA) to produce nonwoven fiber mats via solution blow spinning. These mats exhibited uniform fiber morphology (diameter: 350–796 nm), excellent mechanical properties, and sustained release of α-tocopherol over 24 hours. Cytotoxicity assessments confirmed high cell viability (>90%) and enhanced cell spreading, suggesting the potential for biomedical applications such as wound dressings. Overall, this research contributes significantly to the fields of biodegradable polymers and bioactive delivery systems. By overcoming the intrinsic limitations of zein through chemical modification and advanced processing techniques, this work offers a scalable framework for the development of high-performance sustainable materials. These findings have broad implications for applications in food packaging, agriculture, pharmaceuticals, nutraceuticals, and biomedical sciences, paving the way for more environmentally friendly and functional material solutions.

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