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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Aluminum-Air Batteries Across Scales: A Multiscale Framework for Electrochemical Characterization, Materials Optimization, and Electric Vehicle Integration
(University of Waterloo, 2026-06-10) Shabeer, Yasmin
Aluminum-air (Al-air) batteries have emerged as a promising energy storage technology due to their exceptionally high theoretical energy density, material abundance, and potential for low-cost deployment. However, their practical implementation remains constrained by challenges related to electrochemical performance, parasitic corrosion, electrolyte management, and system-level integration. This thesis presents a comprehensive multiscale investigation of Al-air batteries, integrating techno-economic analysis, experimental characterization, data-driven modeling, and system-level simulation to evaluate their viability as advanced energy storage systems and range extenders in electric vehicles (EVs). The study begins with a techno-economic assessment of metal-air batteries in EV applications. A comparative framework is developed to evaluate the performance of Al-air systems relative to conventional lithium-ion batteries, incorporating key metrics such as gravimetric energy density, vehicle energy consumption, and cost. The analysis shows that Al-air batteries, with practical energy densities of approximately 700-900 Wh kg⁻¹, significantly outperform Li-ion systems (150-250 Wh kg⁻¹), offering strong potential for range extension. Simulated vehicle scenarios indicate that Al-air integration can extend driving range by a factor of 2-5, depending on system configuration and operating conditions. However, these benefits are offset by trade-offs related to system complexity, auxiliary components, and cost, highlighting the need for integrated evaluation frameworks. Experimental investigations are conducted to examine the electrochemical performance of Al-air batteries under varying electrolyte compositions and operating conditions. Using a novel galvanic generator-type Al-air system with a rotating electrode configuration, multiple prototype units provided by AlumaPower were evaluated. The rotating electrode design enhances mass transport, reduces passivation, and promotes uniform anodic dissolution, enabling improved discharge stability compared to conventional static systems. Systematic experiments reveal the alkaline electrolytes, particularly in the range of 6-8 M concentration, provide optimal performance by balancing ionic conductivity and electrochemical kinetics. Peak power densities exceeding 500 mW cm⁻² are achieved under controlled conditions, while discharge tests at moderate current densities (~80-100 mA cm⁻²) exhibit stable voltage profiles in the range of 1.0-1.2 V. The results further demonstrate that increasing electrolyte concentration beyond optimal levels accelerates parasitic corrosion and hydrogen evolution, leading to reduced efficiency and highlighting the importance of electrolyte optimization. To address the critical challenge of aluminum corrosion, a data-driven predictive modeling framework is developed. Artificial neural networks (ANNs) are trained on experimental datasets to model the relationship between electrolyte composition, temperature, and electrochemical variables with corrosion metrics such as corrosion potential (Ecorr) and corrosion current density (Icorr). The ANN models achieve high predictive accuracy, with coefficient of determination (R²) values exceeding 0.99, demonstrating their capability to capture complex nonlinear relationships in electrochemical systems. To further enhance system performance, genetic algorithms (GA) and multi-objective optimization (NSGA-II) are integrated with the ANN framework to identify optimal operating conditions. The optimization results reveal trade-offs between maximizing Ecorr and minimizing Icorr, enabling the identification of optimal electrolyte conditions that balance performance and degradation. This integrated modeling approach represents a significant advancement over conventional empirical methods by enabling predictive and systematic optimization of corrosion behavior. At the system level, the thesis develops a comprehensive modeling framework for integrating Al-air batteries within EV architectures. Using MATLAB and Simulink, a dual-energy storage system is implemented in which Al-air batteries function as range extenders for lithium-ion battery packs. The system incorporates experimentally informed battery models and employs state-of-charge (SOC)-based control strategies to manage power flow between energy sources. Simulations conducted under standard driving cycles, including UDDS, WLTP, and HWFET, demonstrate that Al-air integration can significantly mitigate SOC depletion and extend vehicle range, particularly in reduced-capacity Li-ion configurations (e.g., 50% and 35% baseline energy). The results highlight the importance of control strategy design, power limitations, and system configuration in achieving optimal performance. Collectively, the findings of this thesis establish a comprehensive framework linking electrochemical behavior, corrosion kinetics, and system-level performance of Al-air batteries. The integration of experimental characterization, data-driven modeling, and vehicle-level simulation provides new insights into the practical feasibility of Al-air systems and identifies key design and operational parameters governing their performance. The use of industrially relevant prototype systems further enhances the applicability of the research and bridges the gap between laboratory studies and real-world implementation. This work demonstrates that Al-air batteries, supported by optimized electrolyte conditions, predictive corrosion modeling, and intelligent system integration, represent a viable pathway for next-generation energy storage and EV range extension, while advancing both scientific understanding and practical development.
Autonomous Driving System Rule Learning Using Expert-Defined Causality
(University of Waterloo, 2026-06-10) Bouchard, Frédéric
An increasing number of road users are travelling freely in urban environments. Each of them has their own motion preferences but is expected to comply with the traffic laws. To cope with the motion discrepancies, autonomous vehicles require highly sophisticated reactive decision-making that can adapt their motion given the surrounding environment and the applicable traffic laws. Such decision-makers must be trustworthy, since each mistake can lead to a fatality, and performant, since they must estimate, at a high frequency, which behaviour to implement. This thesis describes how such functionality can be achieved with a practical rule engine learned from expert driving decisions and a precise notion of requirements. We first demonstrate the feasibility of planning the motion of an autonomous vehicle by implementing a prototype that, given a curated training suite of driving examples, can create and maintain a two-layer rule-based theory. Assuming perfect perception, we then design a method that learns the rules based on a precise notion of requirements. An expert anticipates that the decision-maker can enter a state for which a requirement is unmet and therefore specifies with a set of template rules the cause of each anticipated violation. For each template rule, its antecedent entails a notion of causality, and its consequent specifies the behaviour to implement. The set of template rules are used as a labelling function. Namely, each time the decision-maker fails to satisfy a requirement, an associated template rule is used to address the misbehaviour. The rules of the rule-based theory are based on templates. The antecedent of such rules are automatically learned and may have been significantly altered to include new relevant constraints that are expected to cope better with the requirements. Finally, considering that autonomous vehicles rely on sensor capabilities, we thereafter extend our method to compete in the Carla Leaderboard operational design domain. Using the same computer vision as the best performer for which there is code available, we demonstrate that our system can learn a policy that is explainable while performing better than our competitor on the set of provided requirements. This thesis has been divided into three phases, each of which strongly correlates with a paper submitted to a conference or journal for publication. In the first phase, we assess the feasibility of the proposed rule-based architecture by implementing/deploying a rule engine prototype in a level-3 autonomous vehicle driving for 110 kilometres of field tests in an urban environment of the city of Waterloo. Namely, the prototype has an algorithm to create and iteratively refine a rule-based behaviour planner, using a two-layer rule-based theory. The first layer determines a set of feasible parametrized behaviours, given the perceived state of the environment. From these, a resolution function chooses the most conservative high-level maneuver. The second layer then reconciles the parameters into a single behaviour. Based on the set of traffic rules described in a driver handbook, an expert produces a set of training examples expressing the relevant change of behaviours. The algorithm presented in that paper performs hierarchical rule-based machine learning. In the second phase, we formalize the construction of the training suite of driving examples that inevitably comes with the iterative development of the autonomous driving technology infrastructure of software and hardware. Namely, we explore how to extract knowledge from counterexamples encountered while driving in a city generated by the CARLA simulator. For that, we convert the requirements of the CARLA Autonomous Driving Leaderboard into a specification that is used to learn a rule-based policy. We assess the generalization of the learned rule-based policy by evaluating its performance on an unseen city generated by the same simulator. We then compare our performance with InterFuser, a state-of-the-art competing approach, and demonstrate that our method outperforms their method. In the third phase, we use the computer vision and tracker of InterFuser and create our own path generator inspired by the route planner of TransFuser to demonstrate that our method can cope with sensor noise while achieving state-of-the-art performance. In this phase, we use the six official towns that form the CARLA Autonomous Driving Leaderboard as the training towns and attempt to generalize to two unseen towns. Although our initial goal was to become an entry on the CARLA Autonomous Driving Leaderboard, the evaluation infrastructure has become unavailable. Therefore, to be convincing that our approach achieves state-of-the-art performance, we create our own challenge by randomly generating novel routes both on the six official towns and two additional unseen towns that have been released by the same officials. Although we demonstrate that our method outperforms a state-of-the-art end-to-end approach, we list in Limitations a number of issues that have not yet been addressed and constitute limitations to the results presented in this thesis. Thereafter, we speculate on how our method can be extended to mitigate some of these limitations.
Exploring Structure, Agency and Equity in Cross-Sector Partnerships for Advancing Sustainability and Climate Goals
(University of Waterloo, 2026-06-10) Samuel, Naima
The Sustainable Development Goals (SDGs) position partnerships as a central mechanism for advancing sustainability objectives by enabling coordinated efforts across multiple actors and sectors, particularly in addressing complex challenges like climate change. Despite the prominence of partnerships, there remains limited understanding of how cross-sector partnerships function in practice, particularly in local contexts where implementation occurs across municipal, community, and private-sector actors. Existing research has often emphasized formal structures, early phases of collaboration, or normative commitments, providing relatively limited insight into how partnerships are enacted during implementation, how they deliver outcomes, and how equity is embedded and sustained over time. Organized as a three-paper thesis, this dissertation combines a systematic review of sustainability partnerships situated within the SDGs with a comparative qualitative analysis of twelve Canadian local climate action partnerships to examine how cross-sector partnerships are structured, enacted, and adapted, and how effectiveness and equity emerge through the interaction of partnership structures and partner agency. Drawing on document analysis and semi-structured interviews, the analysis is informed by structuration theory, which provides a lens for examining how structures enable and constrain action and how partners reproduce or adapt these arrangements through practice. The findings show that the effectiveness of cross-sector partnerships in local climate action depends on more than formal design alone. Outcomes are shaped through the interaction of partnership structures and partner agency, as structural arrangements influence coordination, participation, and resource allocation while partners interpret, enact, and adapt these arrangements over time. Equity is similarly shaped through these dynamics, not through representational diversity or stated commitments alone, but through deliberate adjustments to decision-making, engagement, and resourcing structures. Co-design emerges as a central practice through which partners collectively reshape partnership arrangements and sustain equity over time. The dissertation contributes an integrated understanding of how cross-sector partnerships support effective and equitable action toward sustainability goals, using local climate mitigation as a site of implementation within the broader sustainability agenda. It extends structuration theory by showing how structure–agency dynamics are enacted through collective practice in multi-actor implementation contexts, highlighting the role of co-design, the influence of partnership arrangements and lifecycle dynamics, and the importance of aligning structures and agency to support both effectiveness and equity. It also offers practical insights for understanding, designing, and adapting partnerships to better support coordinated, inclusive, and effective local climate action.
Effect of Construction Contaminants on the Bond-Slip Behaviour of GFRP Reinforcing Bars
(University of Waterloo, 2026-06-10) Deng, Quanxiang
Glass fibre-reinforced polymer (GFRP) reinforcing bars are an attractive alternative to conventional steel bars in reinforced concrete (RC) bridge construction due to their non-corrosive properties. With advances in production and quality control, GFRP reinforcing bars are an economical alternative for steel reinforcement in many bridge components, particularly in the substructure and at joints. However, gaps remain in the design and construction of GFRP reinforced concrete. including the impact of surface contamination on GFRP reinforcing bars. During construction, construction activities are frequently carried out in close vicinity to exposed GFRP reinforcing bars, which can damage and contaminate their surface. Existing standards, such as CSA S807 and CSA S806, do not provide guidance on how contamination affects performance or how to treat contaminated GFRP reinforcing bars. In Ontario, exposed GFRP reinforcement contaminated by concrete splatter is required to be replaced, leading to costly delays and waste. This study evaluates the impact of surface contamination on GFRP bond behaviour using pullout specimens. 13M and 20M ribbed GFRP bars, and 12M and 20M sand-coated GFRP bars were tested with surface contamination from two common materials used in concrete placement: form oil and concrete splatter. Additionally, non-destructive inspection methods are used to investigate potential differences in ultrasonic pulse velocities associated with different surface contaminants and corresponding bond strength outcomes, and to establish the potential correlation between the contamination effects identified in the pullout and tests those obtained from the non-destructive testing measurements. The results demonstrated that the form oil contamination reduced bond strength and UPV considerably. This suggests that UPV is an effective method for predicting bond loss caused by the form oil contamination. Nevertheless, the concrete splatter contamination exhibited no clear correlations between the pullout test and the Ultrasonic test. Therefore, preventive measures should be applied for the form oil contamination from GFRP reinforcement, and immediate removal is required if detected. Concrete splatter contamination should be removed as a precautionary measure. Furthermore, comparison to the test data showed that the mBPE model yielded a slightly conservative estimate of experimental bond behaviour, while the CMR model tended to under-predict the bond slip response. The experimental bond stresses exceeded the ACI 440 and CSA S806 predictions for all bar sizes and contamination groups.
Parcel-Level Land Valuation under Planning Policy Change: A Spatially Based and Segmented Modeling Framework
(University of Waterloo, 2026-06-10) Wu, Zekai
Planning policies shape land markets by defining development rights, regulating land use intensity, and signaling future growth expectations. However, in current practice, land and parcel value estimation remains highly dependent on expert judgment and manual comparison of recent transactions. While professional appraisal methods are effective for individual assessments, they rely heavily on subjective interpretation and lack systematic mechanisms to identify how planning policy changes influence parcel values across large datasets. This limitation is particularly evident in growing suburban municipalities, where frequent policy updates are used to respond to development pressure and evolving growth objectives. As a result, both municipal decision-making and real estate analysis increasingly require data-driven models capable of automatically evaluating the valuation impacts of planning policy changes. This thesis addresses this gap by developing a spatially explicit, data-driven framework to examine how planning policy changes are capitalized into parcel-level land values in the Town of Aurora, Ontario. The research integrates GIS-based spatial analysis with segmented regression and machine learning modeling to move beyond manual valuation approaches. Parcel-level datasets are constructed by combining transaction records with Official Plan designations, zoning regulations, and adjacent based spatial variables. Parcels are further segmented by size into two datasets to distinguish between house driven and land driven valuation mechanisms, enabling the model to identify the conditions under which planning signals emerge in observed prices.
Multi‑Frequency (X‑, C‑, and L‑Band) F‑SAR Analysis of Scattering Behavior of Freshwater Ice on Noell Lake, Northwest Territories
(University of Waterloo, 2026-06-10) Kharoud, Sukhdip
Lake ice plays a critical role in Arctic and sub-Arctic environments, influencing physical processes within lake systems while also supporting a range of socio-economic activities in northern communities. However, despite its importance, the interaction between lake ice and microwave signals remains inadequately understood, particularly in terms of backscatter behavior during the simultaneous acquisition of active microwave wavelengths. This study utilizes the fully polarimetric FSAR (Flexible Synthetic Aperture Radar) system developed by the Deutsches Zentrum für Luft- und Raumfahrt (DLR), which simultaneously acquired repeat-pass imagery at C-, X-, and L-bands over Noell Lake in the Northwest Territories to investigate the dominant scattering mechanisms associated with lake ice. The results of this study indicate that single-bounce scattering is the dominant scattering mechanism over Noell Lake at all frequencies, but with variable intensities proportional to the wavelength. Furthermore, the influence of tubular bubbles is observed in X- and C-bands but is not detectable in the L-band, aligning with recent research suggesting that tubular bubbles do not significantly increase backscatter, but influence the roughness at the ice-water interface, and is therefore wavelength dependent. These coincident observations at X-, C-, and L-bands improve the understanding of microwave interactions with freshwater ice and the role of ice structural variability in influencing wavelength-dependent scattering responses. Additionally, the investigation of ice property retrieval provides a theoretical foundation for future SAR missions, including support for the NASA–ISRO (NISAR) and TanDEM-L L-band sensors.
Long-term Durability of Iron-based Shape Memory Alloys (Fe-SMAs) and their Performance in Structural Strengthening Applications
(University of Waterloo, 2026-06-09) Carofilis, Wilson
A wide range of repair and strengthening techniques have been developed to address vulnerabilities in aging civil infrastructure. Nevertheless, most conventional approaches rely on passive mechanisms that contribute resistance only when external demands are applied. Active retrofitting strategies, by contrast, introduce permanent restorative forces, such as prestressing, that proactively enhance structural performance, reducing damage accumulation and extending service life. Shape memory alloys (SMAs) offer a unique advantage in this context due to their heat-activated shape memory effect, which enables prestressing without bulky equipment or invasive procedures. Among them, iron-based shape memory alloys (Fe-SMAs) have emerged as a cost-effective and promising solution for structural applications. Despite their growing implementation, the long-term durability and sustained mechanical performance of activated Fe-SMAs remain insufficiently understood, limiting their reliable implementation under environmental exposure and repeated loading. This thesis addresses this critical gap through an integrated experimental–numerical investigation of the durability, fatigue behaviour, and seismic retrofit applications of activated Fe-SMA systems. The experimental program quantifies the evolution of mechanical and functional properties under corrosion and repeated loading. Accelerated durability tests were conducted on activated Fe-SMA dogbone specimens exposed to a sodium chloride solution for varying durations to characterize corrosion induced degradation. Likewise, fatigue tests were performed on pre-cracked reinforced concrete (RC) beams strengthened with Fe-SMA strips to evaluate structural-level performance and service-life implications under cyclic loading. Complementing the experimental work, the numerical investigation evaluates an innovative SMA– based seismic retrofitting strategy. Advanced nonlinear static and dynamic time-history analyses were utilized to quantify global seismic response, identify governing SMA material parameters for retrofit design, and determine critical structural demand parameters influencing performance. Additionally, a resilience-based assessment framework incorporating post-earthquake recovery time was implemented to extend evaluation beyond conventional seismic demand metrics toward functional performance. Experimental results demonstrate that activated Fe-SMAs experience progressive reductions in recovery stress, ultimate tensile strength, and deformation capacity as corrosion develops, while retaining reactivation capability at reduced prestress levels. At the structural scale, RC beams strengthened with Fe-SMA exhibit enhanced fatigue resistance, characterized by reduced crack growth, lower deflections, and decreased of steel reinforcement and concrete strain accumulation. Numerical simulations indicate that the proposed retrofit strategy significantly reduces lateral displacements and residual deformations under seismic loading, although potential increases in floor accelerations highlight important design considerations for non-structural components and functional recovery. Overall, this research establishes a multi-scale understanding of activated Fe-SMA performance by explicitly linking material degradation, structural fatigue behaviour, and system-level seismic response. The findings provide new experimental evidence on long-term durability, performance, structural validation under repeated loading, and application-oriented SMA-based retrofit solutions evaluated through resilience metrics. Together, these contributions support the development of more reliable, durable, and resilient active retrofitting strategies for strengthening deficient and seismically vulnerable infrastructure.
Spatial-Temporal Computer Vision Methods for Automated Vision-Based Visual Inspection
(University of Waterloo, 2026-06-08) Midwinter, Max Xuhao Xue
The objective of this thesis is to investigate how spatial and temporal context can be leveraged to enhance automated vision-based visual inspection (AVVI). The prevailing paradigm in AVVI is the single-shot supervised deep semantic inference model, where an image is processed independently and the resulting semantic prediction is compared against labeled data to generate a supervision signal. While these methods have demonstrated strong performance for defect detection tasks, they often neglect the spatial and temporal context in which inspection data are collected. In practice, engineers rarely make decisions based on a single observation in isolation; instead, they rely on contextual information such as multiple viewpoints of a region of interest, geometric cues for estimating defect scale, and comparisons with previous inspection records. This thesis therefore explores how contextual information inherent in inspection workflows can be incorporated directly into the inference process. Three research challenges are investigated in my thesis: leveraging multi-view imagery to improve defect segmentation, developing and evaluating spatial inference models for defect quantification in civil infrastructure, and enabling visual change detection between unordered sets of inspection data. In Chapter 3, multi-view spatial relationships between inspection images are used to refine segmentations from an unsupervised feature-clustering semantic segmentation model through a novel iterative stochastic consensus algorithm. In Chapter 4, a civil infrastructure RGB-D dataset is created using a custom handheld Light Detection and Ranging scanner, consisting of five short- to medium-span overpass bridges used to benchmark monocular metric depth estimation methods for defect measurement. In Chapter 5, synchronized pairs of novel view synthesis models are constructed to generate pixel-aligned renders of the same structure across inspection events, enabling visual change detection. Finally, Chapter 6 discusses the implications of this research for industrial inspection workflows and possible directions for future work.
From Mock Environments to Ownership-Aware Compilation: Practical Advances in Low-Level Program Reasoning
(University of Waterloo, 2026-06-08) Priya, Siddharth
Automated reasoning can improve both the correctness and the performance of systems software. In practice, however, such reasoning matters only when it fits how developers build software and when it scales to realistic codebases. In verification, one workflow challenge is that the cost of modeling the surrounding environment can exceed the cost of verifying the system itself. Another practical difficulty is scaling precise low-level reasoning and optimization to larger bodies of code. To address these problems, this work develops verification and optimization tools built over a low-level intermediate representation, LLVM-IR, integrating directly with existing compiler toolchains. The thesis first addresses developer workflow friction in verification. vMocks introduces testing-style mocks to code-level formal verification, bringing a familiar testing idiom for specifying environments to a setting where environment modeling is a significant barrier. Instead of building full environment models, developers specify unit-local behavior through SeaMock, a compile-time C++ library compatible with symbolic execution. On the Android Trusty TEE communication layer and the mbedTLS cryptographic library, this approach substantially reduces unit-proof development effort relative to full environment models. The verify-rust case study extends this workflow-oriented perspective to mixed safe–unsafe Rust programs, where the type system alone cannot enforce whole-program properties such as panic freedom and memory safety. This case study builds on SEABMC, a bit-precise bounded model checker for LLVM-IR introduced in prior work. Because SEABMC operates on LLVM-IR, it verifies these properties directly on the bitcode that the Rust compiler already produces. This lets the approach fit into existing Rust toolchains used in production without requiring a custom frontend. The thesis then addresses scale by preserving and exploiting ownership semantics in low-level reasoning and optimization. Cache-at-Pointer and SeaUrchin show that ownership information from high-level languages can remain useful after lowering to low-level representations. Cache-at-Pointer develops ownership semantics for an LLVM-like IR. It then uses a pointer cache to simplify low-level memory reasoning by modeling some memory accesses directly at the pointer rather than through a shared address space. SeaUrchin maps Rust’s ownership discipline to LLVM-IR, preserving semantic structure that is normally discarded during compilation and making it available to optimization. As a case study, ownership-aware loop-invariant code motion shows that the preserved ownership information improves LICM efficacy on realistic Rust benchmarks. Together, these two phases form a single arc: low-level automated reasoning becomes more useful when it better fits developer workflows and when it preserves enough semantic structure to scale. The thesis therefore advances formal verification and compilation not as isolated techniques, but as parts of software engineering practice.
Architecture, Capitalism, and Social Good
(University of Waterloo, 2026-06-05) Mahvash, Kourosh
This study aims to critically investigate the extent of meaningful contributions to social good through architecture given the political economic context within which it operates. It examines the capacity of architecture as a profession as well as the agency and ability of architects as individuals to make such contributions under the capitalist relations of producing space. These relations are defined within a theoretical framework comprised of four core concepts. Gramsci’s theory of hegemony, the notion of urbanization under capitalism, and Lefebvre’s concepts of the “lived space” and “the Right to the City” are the four theoretical foundations which along with a historical examination of the relationship between architecture and capitalism help the research establish its own four central organizing concepts of Agency, Aesthetics, Governance, and Activist Architecture. These four concepts are then used to form a thematic schema for research design. Adopting semi-structured interviews as the instrument of implementing its qualitative method, the process included the recruitment of thirty-six participants - thirty licensed architects based in Toronto, Canada and six key informants who are closely associated and intimately familiar with architecture. The participants’ responses were first subject to deductive thematic analysis before being further discussed and dissected using ‘suspicious interpretation’ method. The results illustrate the limited extent of contributions by architecture and architects to social good while revealing several paths to maximize such contributions within those limits. Architecture may not have a leading or central role in moving towards meaningful social reforms. Nonetheless, it could make meaningful contributions within its own domain of influence by adopting a purposeful social agenda, prioritizing social good over profit in its practices, distancing itself from exploitative labour processes within both creative and construction processes, reclaiming its political capacity, empowering end-users by allowing their active participation in the design process, and replacing entrenched professional privilege, elitism, and egoism with humility. This would allow architecture to contribute its fair share to the struggles for a socially, economically, and politically just future.
Super-Queeros: Transformations of Queer Feminist Representation in the DC Pride Comics Run 2021-2025
(University of Waterloo, 2026-06-05) Grafton, Laura
This thesis analyzes the narrative and visual strategies used in DC Comics’ DC Pride anthology series from 2021 to 2025, examining how these texts construct and evolve a “model Super-Queero” across this time period. Drawing on visual rhetoric, semiotics, narratology, and queer and feminist theory, this project argues that the DC Pride run does not simply represent queer identity, but actively produces a shifting model of acceptable queerness that reflects broader sociopolitical conditions in the United States (USA). Across the five-year run, I argue that the anthology moves from an emphasis on visibility, celebration, and reader identification toward increasing normalization, containment, and disidentification. Early issues position queer characters as sites of pride, and community, using visual and narrative techniques that invite readers, particularly queer readers, into processes of identification. However, as the series progresses, these same formal elements are reoriented to privilege legibility, safety, and social acceptance, encouraging distance from more disruptive or visibly queer expressions of identity. Through close analysis of recurring formal patterns and focused case studies of the DC Pride issues covers, opening stories, and the #Harlivy stories in the issues, this thesis demonstrates how mainstream comic media negotiates the boundaries of queer representation. While these characters have the potential to expand dominant models of queerness, their depiction within the DC Pride run often reinscribes normative expectations through stylistic containment and narrative framing. Overall, I argue that the model Super-Queero constructed across the anthologies reflects a broader cultural shift toward regulating queer visibility, highlighting the role of popular media in shaping not only how queerness is represented, but how it is understood, performed, and then made (un)acceptable within contemporary culture.
Vector bundles on toric stacks
(University of Waterloo, 2026-06-04) Singh, Yash Vardhan
This thesis is concerned with a generalization of Klyachko's classification of toric vector bundles to toric stacks. The work of Klyachko gave an elegant method of studying toric vector bundles through filtrations of a vector space. We extend these techniques to vector bundles on a toric stack and generalize the aspects of Klyachko's work to a more geometric setting. In particular, we show that the category of reflexive sheaves on a toric stack is equivalent to a category of filtered reflexives sheaves of its largest Deligne-Mumford substack. We then combine this with an equivariant version of Gubeladze's result on the splitting of vector bundles on toric varieties to prove a classification theorem for vector bundles on toric stacks. As an application we reprove a known result on the splitting of rank-$2$ bundles on $[\bP^n/\bG_m]$ for a particular $\bG_m$ action. Our methods involve an extension of Cox's construction of homogeneous coordinates to toric stacks and we incorporate ideas from the classical Rees construction. We also study the Chow ring of toric stacks, and give a presentation of the Chow ring of a smooth toric stack.
Information Extraction for Low-Resource Schemas
(University of Waterloo, 2026-06-04) Xu, Justin
Information Extraction (IE) is a set of important tasks in the study of creating structured data such as knowledge graphs from unstructured data such as text. The past paradigm of IE focused on models with specialized neural network architectures, usually based on transformer encoders. These models typically focus on a single subtask of IE, following a single schema of entity and relation types, and are trained via supervised learning on large datasets of annotated texts. Meanwhile, the current paradigm of IE, called Universal IE (UIE), involves large language models which can generalize across IE subtasks and to completely unseen schemas, but which lack other abilities such as entity grounding and calibration. We first discuss structural consistency, a new measure of robustness in information extraction based on compositionality. We present structural consistency post-training (SCPT) as a data augmentation method to boost structural consistency for a wide range of model architectures. Besides greatly improving robustness, SCPT significantly reduces the amount of labelled data needed to achieve the same level of performance when training specialized IE models. Second, we use reasoning-based data augmentation techniques to gather AdaIE, a very large collection of human-annotated information extraction schemas. We diverge from UIE and align the dataset with a new task we call Guided Information Extraction (GIE). GIE emphasizes the tight grounding and schema-following requirements which have been largely neglected in UIE. Evaluations of state-of-the-art UIE models reveal that state of the art UIE methods can be surpassed by recent commercial large language models (LLMs). Although those LLMs achieve below human performance on AdaIE, they are rapidly advancing. Overall, we hope that both works presented will steer the IE research community towards unifying the strengths of the old and new IE paradigms, while casting light on their weaknesses.
Exploring the Preferred Knowledge Mobilization Formats to Enact Climate Action Among Canadian Municipal Staff
(University of Waterloo, 2026-06-03) Dion, Karlyn
Canadian municipalities are central to climate action but often face capacity, resource, and knowledge barriers that limit implementation. This study examines which knowledge mobilization formats are preferred among Canadian municipal staff to determine how to best enact climate change by integrating survey data with semi structured interview data. Results show a strong preference for collaborative, people centred formats, such as peer to peer discussions, coaching, and interactive webinars, alongside practical, implementation ready tools including templates, worksheets, guidebooks, and repositories. Capacity constraints significantly shaped feasibility, with staff favouring flexible, low burden formats that fit limited time and capacity. Several previously undocumented formats emerged, including concierge style support, monthly calls, and resource sharing networks, extending current knowledge mobilization literature. Statistical analyses revealed no meaningful differences across municipality sizes, suggesting broadly shared learning needs. These findings highlight the importance of hybrid models, community specific tailoring, and sustained human support in strengthening municipal climate capacity and offer new directions for researchers and practitioners designing knowledge mobilization strategies.
Atomistic Modeling of Metal Oxide Behaviors in Nanothermite Reactions and Lunar Water Production
(University of Waterloo, 2026-06-02) Zhichao, Liu
This dissertation addresses two challenges in heterogeneous redox reactions of metal oxide materials, namely the reaction mechanism of Al-based energetic nanothermites and the in-situ production of water using lunar oxide materials. Although extensive theoretical and experimental efforts have been focused on both topics, a complete mechanistic understanding of the reaction mechanisms in both topics remains lacking. The first part of this work focuses on the fundamental reaction mechanisms of Al-based nanothermites, with emphasis on the typical system of Al/CuO. Despite extensive experimental and theoretical studies, the exact ion transport pathways and rate-limiting steps remain unclear because nanothermite reactions involve coupled redox processes, phase transformations, heat release, and ion transport across dynamically evolving condensedphase interfaces. In particular, the naturally formed amorphous alumina layer on Al, as the rate-limiting phase, is expected to play a crucial role in governing oxygen transport, interfacial redox chemistry, and ignition behavior. However, the mechanistic contribution of the surface amorphous alumina layer is still not fully understood. This dissertation establishes a mechanistic framework for heterogeneous redox reactions in Al-based nanothermites through systematic investigations of oxygen migration across representative bulk phases and key interfaces. The results identify the rate-controlling and exothermic steps that govern oxygen transport and ignition, and connect the proposed atomistic mechanisms to experimentally observed preignition behavior, reaction kinetics, and the wide range of reported activation barriers. The second part of this work addresses the data-driven discovery of lunar oxide materials for Solar-Wind (SW)-derived H retention and subsequent water production, especially in sunlit lunar regions. Although increasing evidence suggests that hydrogen implanted by SW can be retained in lunar mineral rims and converted to molecular water, the underlying mechanisms that govern H retention and H₂O formation remain poorly understood. In addition, the large elemental space of lunar-based oxides makes the experimental identification of promising materials highly challenging. To address this issue, this dissertation develops a data-driven materials discovery framework that integrates Density Functional Theory (DFT) calculations, materials databases, and Machine Learning (ML) models to evaluate local reaction energetics and materials screening criteria, which include H insertion energy, H₂O formation energy, H diffusion length, and the fugacity ratio of H₂O to H₂ to identify promising candidate materials. By applying these criteria, eleven high-/mixed-valence Fe-bearing oxides, together with lunar magnetite, are identified as promising materials for SW-implanted H retention and in-situ H₂O production, while most of the Fe²⁺-bearing lunar minerals are found to be intrinsically unfavorable for in-situ H₂O production. Overall, this dissertation provides new mechanistic insights into heterogeneous redox reactions in energetic and planetary oxide materials systems. The findings advance the fundamental understanding of nanothermite ignition and combustion, while also establishing a predictive framework for discovering lunar materials capable of supporting the future In-Situ Resource Utilization (ISRU).
Aluminum Nitride Photonic Integrated Circuits with Applications to Cold Atoms
(University of Waterloo, 2026-06-02) Videnov, Nikolay
Quantum sensing has become foundational to many modern technologies. Precision time keeping is core to the global positioning system (GPS), magnetometry is core to mineral discovery for mining. The ability to measure with unparalleled sensitivity has driven many major technological improvements across diverse fields. From simple neutral atom vapour cells to sophisticated ion traps, atoms are a preeminent quantum sensing platform. However, these systems remain difficult to make field-deployable, owing largely to the complexity and fragility of their optical systems. As the invention of the laser and commercialization of external cavity diode lasers (ECDL) enabled increasingly complex trapped atom experiments this thesis aims to take a step toward the next stage: scalable, robust, and portable trapped-atom-based quantum sensors. I argue that a primary limitation to achieving this goal lies in the reliance on bulk optical systems, which exhibit poor size, weight, and power (SWaP) characteristics, are prone to misalignment, and require specialized assembly. To overcome these limitations, I propose the use of photonic integrated circuits (PIC), leveraging fabrication tools and techniques from the semiconductor industry to create a versatile PIC "toolbox" for the trapped-atom community. The unique requirements of such systems motivate the choice of aluminum nitride (AlN) as the waveguiding material—a high-index, ultra-wide band gap, and electro-optically active medium that meets the optical and material needs of trapped-atom applications but has received relatively little attention compared to other established platforms. This thesis therefore details a reproducible nanofabrication process for AlN waveguides that achieves state-of-the-art propagation losses through the use of atomic layer deposition and rapid thermal annealing. Rather than treating this process as proprietary, the complete recipe is shared here for the benefit of the broader AlN research community. I also present the first demonstration of a hybrid ECDL incorporating an AlN photonic integrated circuit, an important milestone toward realizing fully integrated on-chip light sources. These hybrid ECDL operate near 852 nm and 650 nm, addressing optical transitions in cesium and barium ions. Finally, I describe a novel dual-mode phase shifter that combines electro-optic and thermo-optic tuning within a single fabrication layer, enabling both high-speed modulation and large index changes. Collectively, the work presented in this thesis represents a significant step toward fully integrated, chip-scale optical systems for trapped-atom experiments—paving the way for the next generation of compact, deployable quantum sensors.
New Techniques for Lower Bounds in Graph Streaming and Distributed Computing
(University of Waterloo, 2026-06-02) Sundaresan, Janani
In this dissertation, we study lower bounds in the streaming and distributed computing models for graph problems. Both models arise naturally in the design of algorithms for the massive graphs that are ubiquitous in practice today. In the streaming model, the edges of the graph arrive in an arbitrary order and must be processed without storing the entire graph. Of particular interest is the semi-streaming setting, in which the algorithm is allowed space that is near-linear in the number of vertices. The stream may be scanned multiple times, and the goal is to minimize the number of passes needed to solve the problem. In distributed computing, each vertex acts as a computational agent and initially knows only its immediate neighborhood. Vertices communicate by sending messages along the edges of the graph. Communication proceeds in synchronous rounds: in each round, every vertex can send a (possibly different) message to each of its neighbors, receive all incoming messages, and then perform local computation. In the CONGEST model, each such message is restricted to a length logarithmic in the number of vertices. The goal is to minimize the number of rounds required to solve the problem. We highlight three results from this dissertation for graphs with n vertices: 1) The number of passes required to find a maximal independent set in semi-streaming is Ω(log log n). Our result is tight, matching a prior algorithm. This is the first multi-pass lower bound for this problem. 2) The number of passes required to find any (1-ε)-approximation of maximum matchings is Ω(log (1/ε)) in semi-streaming. This is the first lower bound on the number of passes with a dependence on ε for any constant ε. 3) We prove an Ω(log log n) lower bound on the number of rounds required to detect a triangle in CONGEST. This is the first multi-round lower bound for this problem. Lower bounds in both models are proven via communication complexity: the input graph is split among multiple players who send messages to each other over a limited number of rounds. A common theme underlying the highlighted results is to build on and extend the round-elimination technique from communication complexity literature. Our main technical contribution is to adapt this method for a range of different settings to obtain the first nontrivial multi-pass and multi-round lower bounds for these problems.
A multi-methods investigation of a long-term care staffing policy in Ontario
(University of Waterloo, 2026-06-02) El Hajj, Karen
Introduction: Long-term care is an important sector in the care continuum of older adults in Ontario. The challenges facing long-term care include, but are not limited to, lack of funding, complicated funding structures, limited workforce availability, and growing resident acuity (Long-Term Care Staffing Study Advisory Group, 2020). In 2021, the Ontario government introduced and funded a series of programs to increase the hours of care and the number of beds available in long-term care (Office of the Premier, 2020). The policy response came at a time of growing public concerns on the state of long-term care in Ontario, and excess resident mortality during the pandemic (Office of the Premier, 2020). Through this policy initiative, the Ontario government aimed to achieve an average of four hours of direct care per resident per day, and an average of 36 minutes per resident per day of allied health care, by March 31, 2025 (Office of the Premier, 2020). In addition to the average care hour policy, the Government of Ontario (2022) announced a goal of creating 30,000 new beds by 2028. Given the complex nature of the challenges facing the long-term care sector, there is a need to understand the potential impacts of the policy initiative on Ontario’s long-term care sector and workers. Study Aims and Methods: To understand the potential impacts of the increase in hours of care policy, and its associated programs, a multi-methods investigation was conducted. Study 1 aimed to understand the impact of the staffing policy on the Government of Ontario budget. To do this, a budget impact analysis model was developed (Mauskopf et al., 2017), quantifying ministry programs and announcements. Study 2 aimed to understand the impact of the staffing policy on the long-term care sector and identify any potential effects on other parts of Ontario’s health system. Study 2 explored the grey literature published on long-term care (Godin et al., 2015); selected documents were examined using the document analysis method (Bowen, 2009) and analyzed thematically (Braun et al., 2019). The thematic analysis informed the subsequent health policy analysis using Walt and Gilson’s (1994) policy framework. In addition to the policy analysis, a theory of change (Knowlton & Philips, 2009) was developed to map the ministry’s strategies to implement the staffing policy. Study 3 aimed to understand the perspectives of long-term care staff on the introduction of the policy, identify potential barriers and facilitators to the policy’s implementation across long-term care homes and suggest additional policy initiatives that could support or enhance the policy’s implementation. For study 3, a qualitative descriptive study, as described in Sandelowski (2000) was done using semi-structured interviews. Fourteen participants were interviewed to understand their perspectives on the policy’s effects and implementation. Interview data were analyzed using qualitative content analysis (Sandelowski, 2000). Results: After creating a budget impact model for the government policies, Study 1 provided cost estimates for the ministry programs and a bed funding database based on announced ministry projects, from 2021-2022 to 2029-2030. Study 1 provided estimates for the number of direct care workers required under the new policy until 2029-2030. In a no intervention scenario, ministry spending is estimated at $7.4 billion in 2029-2030. Compared to a baseline scenario where no policy or programs are introduced, the staffing policy would increase ministry spending to $13.32 billion in 2029-2030. The policy analysis provided insight into the staffing policy’s implementation and sustainability. Competing efforts and laws from the Ontario Government were identified as a potential factor that could limit the effects of the policy. A dynamic labour market, where competition is found between operators, health sectors, and direct care workers was identified as a challenge to ensure an increased number of hours and workers across long-term care homes. In Study 3, participants provided insight on their experiences in long-term care and their opinions on the staffing policy. Participants discussed limited staff availability, limited wage compensation, the demanding nature of long-term care work and competition across health sectors as some of the challenges in recruiting and retaining long-term care workers. Facilitators of this policy’s implementation included educational programs for personal support workers, and training workers for their roles. Participants identified potential efforts for the government to consider such as regulations and initiatives that could create a better work environment for workers and improved care for residents. Conclusion: It is evident that addressing the current staffing challenges in long-term care requires efforts beyond adding more long-term care workers. The complex relation among factors including funding, work environment and rules and regulations has a direct impact on the recruitment and retention of long-term care workers. On a larger scale, better coordination between government agencies and ministries should be considered to enhance the effect of policies on the health care labour force. The introduced policy efforts coupled with supplementary policies targeting the health sector labour force and working conditions are some of the ways to ensure that the policy can influence change in long-term care. The increase in care hour policy, and the increase in beds in long-term care are important steps to start resolving the issues in long-term care. Ensuring that the policy achieves its aims has proved to be much more challenging, requiring ministry consideration of potential consequences that might affect long-term care and the larger health sector in Ontario.
Advancing characterization of spatiotemporal dynamics of dissolved organic matter after landscape disturbance to inform and protect drinking water treatability
(University of Waterloo, 2026-06-02) Klanderud, Carter
Surface waters are critical sources of drinking water globally. The quality and availability of water from these sources is threatened by landscape disturbances (e.g., urbanization, agriculture, resource extraction) including climate shocks (e.g., wildfires, intense precipitation). To ensure reliable and consistent supply of drinking water, offline reservoirs are often used for raw water storage prior to treatment. While largely designed for managing water quantity and availability, reservoir storage can also affect water quality. In addition to providing bypass capacity during contamination events (e.g., accidental spills/releases), hydrodynamic factors such as dilution, physico chemical and biological transformations occurring within reservoirs, including photodegradation, flocculation, and microbial primary production, can alter water quality relative to inflowing source water. Drinking water treatment needs and performance are driven by many aspects of source water quality, one of the most critical being organic matter. Landscape disturbances can substantially change organic matter concentration and character in source water because they alter hydrologic connectivity, mobilizing organic matter from the landscape and affecting its delivery to and transport within receiving waters. Rapid shifts in source water quality can challenge water treatment plants (WTPs), potentially increasing operational costs or leading to service outages. Raw water reservoirs can help attenuate such source water quality change; however, this is not typically done by design. Here, ultraviolet absorbance at 254 nm (UV254)—a real-time indicator of dissolved organic matter (DOM) concentration and aromaticity—was intensively monitored in a wildfire-impacted drinking water system with two off-line reservoirs in series to demonstrate how engineered storage can be leveraged to dampen landscape disturbance-associated changes in source water quality and increase treatment resilience. High-frequency sampling of UV254 did not show meaningful differences across depth, lateral position within the reservoir cells, and time of day. Notably, UV254 standard deviation was 1.6 and 2.2 times lower in the first and second reservoirs, respectively, than in the river source; respective peak UV254 values were 1.45 and 1.6 times lower, indicating effective attenuation of DOM from the river by the reservoirs in series. This attenuation also reduced polyaluminum chloride coagulant dosing by approximately 70 mg/L during a period of particularly deteriorated source water in 2023, helping reduce aluminum residuals and the potential for exceeding regulatory maxima in treated water. Accordingly, this work demonstrates that raw water reservoirs can be intentionally and strategically designed to attenuate disturbance-driven fluctuations in source water quality, thereby enhancing drinking water system resilience and supporting climate change adaptation. Watershed-scale source water quality monitoring is essential for assessing cumulative watershed effects to enable effective management of drinking water supplies and detection of threats to water treatability. However, it can pose substantial logistical challenges. Source water quality, including the concentration and character of organic matter, can change during the unavoidable period between sample collection and analysis, making effective sample preservation critical for maximizing return on monitoring effort. While a standard method is available for the preservation of dissolved organic carbon (DOC), a common surrogate for DOM, its implementation is not always feasible. Difficult access to remote or hydrologically critical headwater locations for sample collection, limitations on in situ sample preservation, and associated challenges in transporting collected samples to analytical facilities within prescribed holding times can compromise data integrity and representativeness. Several alternative preservation (e.g., filtration, freezing) techniques for organic carbon concentration and character are often used when adherence to Standard Methods is impractical; however, their efficacy is highly variable and has not been systematically evaluated. Thus, the direct and combined effects of temperature (e.g., refrigeration, freezing), filtration, and acidification were systematically investigated here using four natural water matrices of diverse quality. This analysis revealed that freezing effectively preserved DOC except when combined with acidification (which would be suggested by extension of Standard Methods); the combination of these methods caused significant shifts in DOC in some cases. Specific ultraviolet absorbance at 254 nm (SUVA) changed within 24 hours of sample collection in some water matrices; this was also reflected by change in the humic substances fraction of DOM, which was measured by size-exclusion chromatography using liquid chromatography-organic carbon detection (LC-OCD). DOC in unpreserved water samples remained stable (i.e., < 10% change) for up to seven days after collection. Accordingly, while Standard Methods remain the benchmark for preserving DOM in water samples, this work advances practical strategies to maximize the value and interpretability of monitoring data at conditions where adherence to these methods is not feasible. Collectively, these findings highlight the value of offline raw water reservoirs and robust sample preservation strategies for enhancing WTP resilience to source water quality change. Reservoirs can effectively attenuate rapid shifts in DOM concentration and character following climate shocks such as wildfires and substantially reduce extremes in coagulant demand. Understanding the stability of DOM during the unavoidable period between sample collection and analysis further helps maximize the value of raw water monitoring to inform water quality and treatability change without compromising data integrity. By integrating high-frequency reservoir monitoring with matrix-specific preservation practices, drinking water utilities can better anticipate source water variability, prepare for disturbance, and improve overall treatment performance and resilience.
Advancing ITR as a standard metric for real-time BCI performance assessment
(University of Waterloo, 2026-06-02) Li, Junsong
The current standard metric for assessing brain-computer interface (BCI) system performance is the information transfer rate (ITR). However, in our previous work using ITR to evaluate real-time BCI-controlled electric wheelchair performance, we obtained low ITR values while significantly outperforming prior studies in terms of task completion time. This led to the belief that ITR is an inconsistent metric for real-time applications, potentially indicating misleading results when comparing systems. The discrepancy in performance led to examining the limits of ITR and proposing an alternative metric, Jun’s Information Transfer Rate (JITR), aimed at addressing specific issues often overlooked in the current BCI system evaluations. In the literature, several key assumptions behind ITR are not met, including a memoryless system, independent choices, a constant update rate, and equally distributed choice probabilities. Common post-processing methods, such as weighted smoothing, feedback, and error correction, break these assumptions. As a result, ITR values become unreliable for comparison for real-time systems, motivating the use of JITR that maintains the basis of maximum channel bandwidth log2𝑁 while adding term in to penalized system delay and low accuracy. This approach affords opportunities to fine tune and optimize BCI configurations and predict task performance. To validate JITR, the update rate and weighted sum smoothing parameters were manipulated to find the configuration with highest transfer rate. Utility was tested in a simulated driving task, where performance was predicted using the JITR performance index. As a result, JITR was able to predict task completion time, if immediate pre-trial accuracy is given. JITR limitations, such as the need to model fatigue, learning effects, and false positives to improve precision and recall, and benefits are discussed, representing a step toward a reliable metric for assessing BCI systems.

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