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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Characterization of Cardiac Mitochondrial Alterations in Mice Deficient in Phospholipase A/Acyltransferase-1 (Plaat1)
(University of Waterloo, 2026-04-28) Berdeklis, Antonia
Phospholipase A and acyltransferase 1 (PLAAT1) is a phospholipid remodeling enzyme that belongs to a paralogous family with orthologues documented in more than 500 species of vertebrates. In humans, Plaat1 is most highly expressed in the heart, brain, skeletal muscle, and testes. PLAAT1 can catalyze O-acyltransferase, N-acyltransferase and phospholipase A1/2 reactions in vitro, however its enzymatic and physiological functions in vivo are poorly understood. Our group discovered that mice deficient in Plaat1 (Plaat1-/-) have a significant ~1/3 reduction in cardiac cardiolipin content, suggesting PLAAT1 is a critical regulator of this lipid in the heart. Cardiolipin is an important phospholipid for mitochondrial morphology and is required for appropriate mitochondrial function, so the loss of cardiolipin in the Plaat1-/- heart has significant implications for mitochondrial content, morphology, and function. Research outside of our group has additionally shown that whole body PLAAT1 loss has significant implications for lipid metabolism, whereby PLAAT1 appears to be necessary for the development of non-alcoholic fatty liver and body weight gain associated with a high fat diet in mice. The purpose of this thesis project was to characterize Plaat1-/- cardiac mitochondria with a specific focus on examining parameters typically affected by the loss of cardiolipin content. We found that despite the reductions to cardiac cardiolipin content, there was no evidence of differences in mitochondrial content, electron transport chain supercomplex stability, inflammation, fibrosis, or expression of genes required for cardiolipin biosynthesis. However, Plaat1-/- cardiac mitochondria possess a mild increase in fused phenotype compared to their wildtype counterparts without changes to the content of various mediators of mitochondrial morphology. We additionally performed a preliminary investigation of outcome variables related to lipid metabolism in the heart, given previous research that has shown PLAAT1 to be important for lipid storage and metabolism. This investigation revealed an ~35% reduction in cardiac triacylglycerol concentration in male Plaat1-/- hearts, suggesting that PLAAT1 is also critical for normal cardiac neutral lipid metabolism in male mice, although the mechanism underlying this change was not investigated in the current thesis. Our finding of a significant perturbation in another important cardiac lipid with the loss of PLAAT1 indicates a novel role for this enzyme in the heart that merits further investigation for potential implications in cardiac health and disease.
Envisioning the Impact of Mass Migration, Geopolitical Conflict, Climate Change, and Trust on Foodborne Illness Risk Among Refugees in Canada in 2045
(University of Waterloo, 2026-04-28) Gulasingam, Ashvini
Background: Refugees in Canada have faced significant food safety challenges, including limited familiarity with local food systems, communication barriers due to language and cultural differences, and restricted access to resources. These challenges are anticipated to intensify by 2045 due to mass migration, geopolitical conflicts, and climate change, which will likely disrupt food supply chains, and undermine trust in food safety systems. This research aimed to explore how these four drivers may shape foodborne illness risk behaviors and potential mitigation strategies for refugees in a future Canadian context. Methods: In this thesis I used a future-oriented scenario-based approach to develop two plausible scenarios for Canada in 2045 using the Shared Socioeconomic Pathways and geopolitical disruption-based scenarios created by the Pardee Center for International Futures, reflecting different trajectories of climate change, migration, conflict, and social trust. The ‘Worst Case’ scenario portrayed a plausible 2045 future characterized by intensified climate impacts, heightened geopolitical instability, increased migration pressures, and declining social trust, resulting in compounding and systemic challenges. In contrast, the ‘Typical Case’ scenario described a plausible future that more closely resembled current social, political, and environmental conditions, with challenges unfolding in ways consistent with existing trends and institutional capacities. I conducted 16 individual audio-recorded interviews with food safety and refugee resettlement experts in which participants explored potential foodborne illness risks and mitigation strategies for refugee populations under these scenarios. I analyzed the data thematically using inductive and deductive coding, informed by Ecosocial Theory, Critical Theory, the World Health Organization’s “5 keys for Safer Food”, and Fight BAC!’s “Four Core Practices of Food Safety” to identify key themes related to foodborne illness risks and mitigation strategies. I interpreted the findings through these frameworks to explore how these interviewees perceive foodborne illness risks among refugees, integrating both verbal and non-verbal data for a comprehensive understanding. Results: Findings revealed that foodborne illness risk for refugees in Ontario in 2045 may not be experienced primarily as an issue of individual food handling behavior, but rather as the outcome of interacting structural, environmental, and social systems. Participants described food safety as a multi-scalar phenomenon shaped by global governance dynamics, climate change, infrastructure stability, and the material conditions of resettlement, including housing, food access, and resource constraints. Communication barriers, culturally embedded food practices, and varying levels of institutional trust may further influence how food safety knowledge will be interpreted and applied in everyday contexts. While established food safety frameworks were recognized as important, their implementation may be constrained by these broader systemic conditions. Conclusion: Foodborne illness risks for refugees in Canada in 2045 will likely not be driven solely by individual behaviors but emerge from dynamic interactions across environmental, infrastructural, social, and institutional systems. This study reframes food safety in 2045 as an emergent property of complex socio-ecological systems, shaped by climate change, migration processes, infrastructure stability, settlement conditions, communication, cultural practices, and trust. The findings highlight that the feasibility of safe food practices will be structurally constrained, underscoring the limitations of individual-level interventions alone. Addressing these risks will require integrated, multi-level public health approaches that strengthen system resilience, support culturally responsive strategies, and build institutional trust. Such approaches are essential for promoting equitable food safety outcomes and enhancing the resilience of food systems in an increasingly uncertain global context.
Topology Optimization for Additive Manufacturing: Towards efficient and manufacturable structures for multi-physics applications
(University of Waterloo, 2026-04-28) Orakwe, Joseph Nonso
The Additive Manufacturing (AM) paradigm has continued to revolutionize the way products and components are conceptualized, given its procedure of building parts from the ground up, in a layerwise manner, which brings benefits such as part consolidation, performance-driven products, and unparalleled design freedom to build complex geometries, albeit within some constraints. To fully exploit the capabilities of additive manufacturing (AM), Design for AM (DfAM) integrates process constraints early in the design stage, leveraging topology optimization (TopOpt), lattice design, and simulation-based engineering (SbE) as core tools. TopOpt is a gradient-based method that determines optimal material layouts within a domain under governing physics, objectives, and constraints, while multiphysics topology optimization (MTO) - such as thermal-fluid topology optimization (TF-TO) - extends this framework to coupled flow and heat transfer problems. Lattices are architected cellular structures used to tailor effective material properties, enabling lightweighting & heat dissipation, while SbE drives engineering design and decisions through computational simulation. A review of the MTO literature revealed two key opportunities for novelty. First, components subjected to severe thermo-mechanical loading (e.g., gas turbine hardware) often require the simultaneous design of coolant flow channels and lightweight load-bearing regions, which can be addressed using MTO with coupled fluid, thermal, and structural physics; however, challenges remain in handling multiple material phases while ensuring manufacturability. Second, thermal-fluid topology optimization (TF-TO) is increasingly applied to heat-sink design for high-power-density electronics, but remains accessible only through limited commercial tools, while full three-dimensional (3-D) implementations demand substantial computational expertise, resources, and manufacturability integration. Integrating MTO with lattice architecture offers further potential through SbE-based functional grading and AM compatibility, yet existing studies underutilize advanced design methodologies that could yield additional performance gains while lowering the barrier to adoption. In this research, two major TopOpt-based methodologies have been proposed. To address the first opportunity, an FTS-TO framework was created, which integrates load-bearing and fluid-delivery requirements within a unified Fluid-Thermal-Structural topology optimization using a cascaded two-stage formulation. Optimization Stage 1 (OS1) addresses the thermal-fluidic optimization, while Optimization Stage 2 (OS2) performs a design-independent thermo-structural TopOpt initialized from OS1, yielding structures satisfying flow, cooling, and structural constraints. Development focused on OS2, introducing a robust multiobjective TopOpt for stiff, conductive, lightweight designs with manufactured-feature-size uncertainty resilience, later extended from 2-D to 3-D with explicit minimum & maximum size control, and self-supporting constraints. Validating simulations indicate promise in designing structures that include flow, cooling, and structural needs, while incorporating various constraints, including AM. For the second research prospect, hybrid field-driven design techniques were developed, which integrate multi-scale Triply Periodic Minimal Surface (TPMS) lattices and airfoil-type fins with TopOpt-predicted flow fields. The first hybrid technique Lattice-TopOpt (LTO) method, conforms heat-dissipative lattices to optimal flow paths to obtain AM-suitable high-performance heat sinks. 3D printability was validated via pure copper on an EOS M290 LPBF machine, and experimental validation backed up predicted superior thermo-hydraulic performance, including a real-world application to an award-winning electrochemically printed cold plate. The Field-Oriented Fin Placement (FOFP) approach orients low-drag airfoil fins along TopOpt velocity vectors, yielding lightweight heat sinks with significantly enhanced mass-based thermo-hydraulic performance. Overall, this work advances the integration of manufacturability constraints into MTO for additively manufactured heat-sink and cooling applications, with a focus on low user complexity, and is expected to contribute to more efficient thermal management solutions in the energy sector.
Advances in similarity-based prediction modeling
(University of Waterloo, 2026-04-28) Kim, Minzee
Personalized predictive modeling has been growing rapidly in recent years, especially with the availability of Electronic Health Records (EHRs). This approach aims to improve a model's predictive performance by fitting a unique model to each individual. We train the model on a subset of the training data consisting of individuals that are similar to the individual we are predicting for, identified through some patient similarity metric. Studies have shown that using a personalized model trained on a customized subset of the data leads to better prediction than using a global model trained on all the available data in the training data. In this thesis, we discuss advancements in similarity-based prediction modeling through extensive simulation studies and data analyses. Longitudinal and time-to-event data are often analyzed in biomarker research to study the association between the longitudinal biomarker measurements and the event-time outcome, in which the longitudinal information contributes to the probability of the outcome of interest. An attractive feature of fitting a joint model on this type of data is that we can dynamically predict the survival probability as additional longitudinal information becomes available. In Chapter 2, we propose a new similarity-based method for the dynamic prediction of joint models where we consider training the model on only a targeted subset of the data to obtain an improved outcome prediction. Through a comprehensive simulation study and an application to intensive care unit data on patients diagnosed with sepsis, we demonstrate that the predictive performance of the dynamic prediction of joint models can be improved with our proposed similarity-based approach. Next, we develop a new patient similarity metric designed to improve the predictive performance of a personalized model for binary response data. Specifically, we introduce a weighted cosine similarity metric in Chapter 3 that extends the standard cosine similarity metric by assigning predictor-specific weights when computing similarity between participants. These weights are estimated using the relaxed adaptive group lasso. Results from our simulation study and an analysis of intensive care unit data involving patients with circulatory system disease show that although the proposed similarity metric leads to a slight deterioration in calibration, it produces substantial gains in discrimination. Overall predictive performance measured by the Brier Score improves because the increase in discrimination outweighs the loss in calibration; therefore, our proposed similarity metric more effectively identifies clinically similar patients, resulting in improved predictive accuracy. Finally, in Chapter 4, we conduct a comprehensive comparison of several similarity metrics to investigate how the choice of similarity metric influences predictive performance in personalized modeling, again in the context of binary response data. By fitting models using only a subset of training participants who are most similar to the individual of interest, prediction accuracy for that individual can be improved. Consequently, selecting an appropriate similarity metric that identifies the most relevant subset of data is critical. We compare a range of distance-based and cosine similarity measures alongside clustering-based approaches, an area that is not well explored in the existing literature. In addition, we perform an extensive simulation study to examine how different data-generating mechanisms and underlying dataset characteristics affect the relative effectiveness of each similarity metric. Finally, we end with a discussion chapter that summarizes the key contributions of the thesis along with highlighting some key areas of future work.
Rethinking filtration performance assessment for public health protection
(University of Waterloo, 2026-04-28) Batista, Elyse
Minimizing acute health risks from waterborne pathogens such as Cryptosporidium is the paramount goal of drinking water treatment. Because oocysts of the protozoan Cryptosporidium resist chlorine-based disinfectants and require complex and expensive detection methods, removal by physico-chemical filtration (CAF) is critical. Regulatory frameworks such as the US EPA suite of Surface Water Treatment Rules (SWTRs) and their Canadian analogs prescribe treatment credits based on filtered water turbidity, assuming the achievement of turbidity targets must reflect well-operated treatment and ≥3-log oocyst removal. However, in systems reliant on low turbidity (<2 NTU), low dissolved organic carbon (DOC; <2 mg/L) source water, raw water turbidity often meets or exceeds filtered water targets, obscuring whether coagulant dosing is resulting in sufficient particle destabilisation. As a result, treatment plants may unknowingly operate at coagulant doses that fail to achieve the particle destabilization required for pathogen removal. This challenge is further compounded because low turbidity, low DOC waters have fewer interactions between particles (as particle concentrations are low) resulting in the need for higher doses of coagulant to increase contact opportunities by precipitating additional particles. Here, pilot-scale CAF investigations confirmed that oocyst removal was dependent on sufficient particle destabilization. Filter challenge studies using Cryptosporidium oocysts were run using low turbidity, low DOC source water and various coagulant doses. The experiments were replicated at similar operational conditions over several years and consistently demonstrated an increased risk of oocyst passage when insufficient coagulant was added and inadequate particle destabilization occurred. The results incontrovertibly demonstrated that turbidity is inadequate as a sole indicator of particle destabilization necessary for ensuring sufficient oocyst removal by CAF, particularly for low turbidity, low DOC source waters. Zeta potential analysis proved to be a useful tool for indicating sufficiency of particle destabilization. Zeta potential values within ±5 mV of zero were required to consistently achieve ≥3-log removal of Cryptosporidium oocysts by filtration of low turbidity, low DOC Lake Ontario source water; in these cases, ≥4-log removal of oocysts was often achieved. As expected, higher coagulant doses than those typically practiced for low turbidity, low DOC source waters—specifically, coagulant doses that led to aluminum hydroxide solid precipitation—were required to achieve these levels of Cryptosporidium removal by CAF. These findings highlight the dangers of sole reliance on turbidity as an indicator of post-filtration water quality and treatment performance and underscore the need for complimentary monitoring tools to ensure protection of public health in systems reliant on higher quality source waters. Integrating zeta potential monitoring into routine coagulation control could provide operators—especially those dealing with the challenge of determining coagulant dose in the absence of substantial turbidity—with an indication of sub-optimal particle destabilization and associated poor filtration performance. These insights also point to broader implications for regulatory policy, as current turbidity-based treatment credits may not always adequately reflect true pathogen removal performance. Further operational guidance for zeta potential operationalization alongside turbidity analysis is needed to help ensure sufficient Cryptosporidium removal by CAF.
Post-Training Large Language Models as Software Engineering Agents
(University of Waterloo, 2026-04-28) LYU, Zhiheng
Large language models (LLMs) have demonstrated remarkable capabilities in code un- derstanding and generation, yet a significant gap remains between static code generation and interactive software engineering. This thesis investigates the post-training of LLMs as software engineering agents, focusing on three interconnected challenges: infrastructure, data, and training methodology. First, we contribute to VerlTool, a unified framework for agentic reinforcement learn- ing with tool integration (ARLT). The author’s contributions center on the training orches- tration layer — the stateful environment protocol, environment server architecture, and SWE agent post-training pipeline — which make tool-augmented RL training practical and accessible for researchers. Second, we address the critical bottleneck of training data and evaluation infrastructure. SWE-Next provides a scalable, Ray-native pipeline for synthesizing verifiable software engineering tasks from open-source repositories (ongoing work with intermediate results reported). For SWE-QA-Pro, a representative benchmark for code question answering, the author contributes the data sourcing and synthesis pipeline. Third, we investigate the post-training design space for software engineering agents, spanning supervised fine-tuning (SFT), rejection fine-tuning (RFT), RL from AI feed- back (RLAIF), and RL with verifiable rewards (RLVR). Through three complementary case studies—code question answering (SFT + RLAIF), web-based information retrieval (SFT + RFT), and repository-level bug fixing (RLVR)—we demonstrate that the opti- mal training recipe depends on task characteristics such as reward verifiability, exploration complexity, and data availability. Our experiments show that task-specific post-training of smaller open-weight models can be competitive with larger proprietary models, and that matching the training method to the task structure is more important than uniformly applying all stages.
Audio-Visual Feature Fusion through Transformers for Automated Depression Screening in Social Media Content
(University of Waterloo, 2026-04-28) Haque, Md Rezwanul
Depression has become a critical public health concern, with the World Health Organization reporting that over 280 million people worldwide are affected by it. The rapid growth of social media, particularly video blogs, has drawn research attention toward analyzing user-generated audiovisual content for signs of depression. These videos capture natural facial expressions, voice characteristics, and speech patterns that may reveal more about a person's emotional state than verbal self-reports alone. However, extracting useful features from such noisy, unstructured data and combining audio and visual information in a way that preserves their complementary nature remain open problems in this domain. The thesis is organized into two main contributions. In the first part, we propose MDD-Net, a multimodal depression detection network that uses a mutual transformer to fuse acoustic and visual features. The acoustic branch employs a global self-attention network to process 25 low-level descriptors including loudness, Mel-Frequency Cepstral Coefficients, and spectral flux, capturing both content-based and positional relationships. The visual branch applies hierarchical multi-head self-attention on 68 facial landmarks extracted from each video frame. The mutual transformer then operates bidirectionally: audio queries attend to visual keys and values, and visual queries attend to audio keys and values. We also design a composite loss function that combines binary cross-entropy, focal loss, and L2 regularization to handle the noisy labels and class imbalance that are common in social media datasets. In the second part, we introduce MMFformer, a multimodal fusion transformer network that takes a different approach to the same problem. For video, a pre-trained vision transformer augmented with residual connections extracts high-level spatial patterns from facial data. For audio, a transformer encoder built on the audio spectrogram transformer paradigm models temporal dynamics in speech signals through patch and positional embeddings. On the fusion side, we propose and compare three distinct strategies: late transformer fusion, intermediate transformer fusion, and intermediate attention fusion, each operating at a different level of the processing pipeline. We evaluate both architectures on the D-Vlog dataset, a publicly available collection of 961 YouTube vlogs from 816 individuals annotated for depression. MMFformer is additionally tested on the LMVD dataset, a larger corpus of 1,823 vlogs collected from four different social media platforms. MDD-Net reaches an F1-Score of 77.07% on D-Vlog, which is an improvement ranging from 1.82% to 17.37% over previously reported methods. MMFformer achieves 90.92% on D-Vlog and 90.48% on LMVD, surpassing the best existing results by 13.92% and 7.74% respectively. Cross-corpus validation between D-Vlog and LMVD further confirms that the developed architectures generalize across different platforms and populations.
Cultivating Home: Preserving Intergenerational Knowledge in The Chinese Food Garden
(University of Waterloo, 2026-04-27) Yang, Eva
The home food garden is more than a space for food production; it is a space that fosters memories and continually grows to intertwine with collections of personal stories. With a focus on personal and familial experiences, this research traces the exchange of gardening knowledge and practices across three generations of my family: my grandparents' farmlands in Guangzhou, China, their gardens in Scarborough after they migrated to Canada, my parents' garden in Richmond Hill, and my own garden in Cambridge. When my grandparents and her children lived in Guangzhou, cultivating food was deeply ingrained in their daily lives and was the main source of their diet. The gardens developed by each following generation became a point of connection to our ancestral roots. Drawing on theories of rhizomatic thinking and viewing the garden through the lens of ancestral plants, I base this thesis on the garden and follow my family’s migration to understand our agricultural heritage as it has been passed down, lost, or adapted to fit into host cultures. Learning from Atelier Bow Wow's approach to ethnographic research, I conducted interviews with collaborative drawings and gardened alongside each family member to gather information on the intergenerational transmission of knowledge. In Ontario, I had the opportunity to garden with my family. To ensure participation at each step of the gardening process, the collaborative gardening began with this year’s growing season in March 2025 and ended in October 2025. I visited my grandparents’ and parents’ garden at least twice a month and documented it through photography and videography. From these interviews and gardening together, I identified four categories to analyze the collected data: Water, Soil, Tools, and Cultivation. To translate my family members' memories and knowledge into visual representations, informed by the spatial research of Huda Tayob and Jan Rothuizen. This research is presented as a series of four interconnected drawings, each related to one of the four categories. Each series of drawings is further dissected into the three different generations, highlighting what knowledge was passed on, what knowledge was forgotten, and what was reinvented to fit the new societal norms of the diaspora. The final proposal synthesized the research into a design for my current garden space, as an attempt to reintroduce lost or unused knowledge while integrating values of the host culture. The purpose of this research was to further understand how the garden functions as a space for the Chinese diaspora to create a sense of belonging and preserve cultural identity across generations. It was also an opportunity to explore how migrant identities were constructed through food practices as a means of actively engaging with and adapting to a new landscape and cultural practices.
Exploring 3D Printing as an Innovative Approach for Phosphor Design in Next-Generation MicroLED Devices
(University of Waterloo, 2026-04-27) Ezekiel, Ubokobong
Micro–light-emitting diodes (microLEDs) have emerged as a leading platform for next-generation emissive displays and solid-state lighting, offering exceptional brightness, energy efficiency, and modulation bandwidth. However, realising high resolution full-colour microLED systems remains constrained by the lack of scalable, high-precision phosphor-deposition technologies capable of producing uniform, tunable, and geometrically precise colour-conversion layers at the micron scale. Conventional phosphor-coating approaches, such as spin-coating, inkjet deposition, and particle–binder lamination, struggle to meet the stringent spatial and colourimetric tolerances demanded by microLED pixels. This work addresses the development of a novel colour conversion approach using engineered phosphor inks, with a focus on their formulation, printability, and optical performance for advanced display applications. An experimental framework is established to investigate the feasibility of depositing these phosphor-based materials via stereolithography (SLA) 3D printing to form uniform thin films. The study evaluates the printability of high-loading phosphor composites, identifying critical process limitations such as scattering-induced lateral curing and ultraviolet (UV) dose interactions, which define a practical feature resolution of 125– 150 µm for 25 vol% formulations. To enable consistent film fabrication, mechanical modifications to the printing platform including tilt compensation and enhanced structural rigidity are implemented, resulting in high-uniformity blanket films with controlled thicknesses of 86.8 ± 8 µm and 132.2 ± 7 µm. In parallel, the optical properties of the engineered phosphor ink and printed films are systematically characterized. Raw phosphor analysis confirms stable silicate amber emission centered at 595 nm, while polymer embedding significantly enhances emission intensity by more than 40× due to improved optical extraction. The printed films are further evaluated through colourimetry, spectral analysis, brightness measurements, and accelerated UV ageing tests. A full-factorial colour-point study comprising 63 remote-phosphor samples and 21 direct-print microLED samples quantifies the influence of thickness, solid loading, phosphor concentration, and yellow dopant level on CIExy chromaticity. Statistical analysis reveals that brightness is dominated by phosphor loading (βph ≈ 7.08, p ≈ 0.014), with yellow doping as a secondary contributor (βy ≈ 2.38). Coefficients of variation (0.40–0.48) highlight moderate spatial non-uniformity driven by residual thickness variation and particle aggregation. Accelerated UV-weathering tests show that small-milled particles exhibit significantly improved chromatic stability (∆E < 2 at 3500 kJ/m2), while unmilled and high-doping samples show marked degradation. A physics-based simulation framework is developed to replicate microLED excitation of printed phosphor layers, accurately predicting chromaticity drift as a function of film thickness and phosphor loading. Optimal colour conversion is identified at approximately 15 vol% phosphor and thicknesses exceeding 200 µm, demonstrating strong agreement between simulation and experimental results. These findings are supported by integrated simulation-driven validation and experimental measurements, enabling systematic analysis of performance trends and verification against industry-defined colour and reliability targets. Collectively, this work demonstrates that SLA 3D printing enables precise, tunable, and mechanically robust phosphor architectures, establishing additive manufacturing as a viable and scalable pathway for next-generation microLED colour conversion technologies.
Probabilistic Assessment of Heatwaves and Building Energy Demand under Changing Climate
(University of Waterloo, 2026-04-27) Khan, Kunwar Aneeq
Climate change is expected to increase building cooling demand not only by raising average temperatures, but also by intensifying extreme heat conditions that produce short duration peaks and prolonged periods of elevated cooling use. This thesis investigates these effects for Toronto using an integrated framework that combines future climate projections, building energy simulation, and probabilistic modeling of extremes. The study begins with a review of the literature on climate change impacts on buildings, future weather datasets, and probabilistic approaches for assessing building energy performance. A bias-corrected future climate ensemble is then used to generate EnergyPlus simulations for a prototype building over the period 2003--2094. From these simulations, annual and hourly cooling demand metrics are derived and analyzed together with heatwave characteristics identified using Environment and Climate Change Canada heat-warning criteria. The probabilistic component of the thesis applies non-homogeneous Poisson processes, Weibull models, maximum value distributions, and Gumbel models to characterize both climate and building response extremes. These models are used to examine changes in heatwave occurrence, cumulative heat exposure, annual extreme heatwave severity, annual peak cooling load, and cooling demand during heatwave periods. The results show that future warming leads to more frequent and more severe heatwaves, with upward shifts in cumulative heat exposure and annual heatwave extremes. The building energy analysis shows a corresponding intensification of cooling demand. Annual cooling energy use increases, annual peak cooling loads rise, and cooling demand during heatwaves becomes progressively larger, especially toward the upper tail of the distribution. The analysis also shows that stationary models are generally less suitable than non-stationary formulations for representing these future changes. The thesis demonstrates that future cooling related building risk cannot be understood adequately using deterministic summaries or stationary assumptions alone. By linking evolving heatwave behavior to changes in simulated building demand within a probabilistic framework, it provides a rigorous basis for assessing climate-driven cooling extremes in buildings.
Operational witnesses of non-classicality via Bell inequalities and contextuality
(University of Waterloo, 2026-04-27) Srivastava, Sanchit
This thesis investigates operational signatures of non-classicality in quantum systems, examining the relationship between Bell inequalities, contextuality tests, and discrete Wigner negativity across four case studies. The first analyzes multipartite entanglement and genuine multipartite nonlocality in multiqubit systems, deriving analytical expressions for Svetlichny inequality violations for generalized Greenberger–Horne–Zeilinger (GHZ) and maximal-slice states; the results indicate that the entanglement–nonlocality relationship depends on state structure rather than scalar entanglement measures alone. The second uses an optimized Bell inequality as a contextuality witness for the spin-1 quantum kicked top, revealing correlations between violation strength and the regular-versus-chaotic structure of the classical phase space. The third examines two qutrit Unruh–DeWitt detectors coupled to the Minkowski vacuum, showing that an initially noncontextual product state can develop contextual correlations through vacuum-mediated interactions, with contextuality onset coinciding with discrete Wigner negativity. The fourth constructs logical Bell inequalities for odd prime dimensions that connect single-qudit Wigner negativity to inequality violation. Each of these tests constrains a different class of classical model: locally causal hidden variables, noncontextual hidden variables, or positive Wigner representations. A recurring theme is that a violation of such an inequality certifies a property of the observed measurement statistics, rather than directly quantifying an underlying quantum resource.
Fabricating of Stable Thin Film Microdevices with UV Laser
(University of Waterloo, 2026-04-27) Menezes, Jace
Short-term and long-term stability remains a limiting factor in the practical deployment of micro-scale sensors and actuators, where small structural, thermal, or material changes can produce disproportionate performance drift over time. This thesis investigates drift mitigation strategies in two ultraviolet (UV) laser–fabricated micro-devices that operate in distinct but complementary domains: NiCr thin-film strain sensors for mechanical sensing and laser-induced graphene (LIG) microheaters for thermal actuation. Although these devices serve different functions, both exhibit degradation mechanisms rooted in microstructural instability, insufficient mechanical constraint, or poorly controlled thermal boundary conditions. For NiCr strain sensors, short-term resistance drift under constant mechanical load is addressed through the introduction of post-fabrication infill materials that mechanically encapsulate the laser-ablated traces. A systematic comparison of infill chemistries and viscosities demonstrates substantial reductions in noise, hysteresis, and short-term drift, supporting mechanical stabilization as the dominant mitigation mechanism. For LIG microheaters, long-term thermal stability is improved by incorporating an aluminum backing layer during fabrication, which fundamentally alters heat dissipation during UV laser processing. This substrate-mediated thermal boundary control produces denser LIG microstructures and enables stable Joule heating with minimal drift over 1000 thermal cycles and extended continuous operation. Across both device classes, this work demonstrates that stability can be engineered through deliberate control of mechanical constraint and boundary conditions, rather than relying solely on material substitution or complex control electronics. The results establish practical, fabrication-compatible strategies for improving short-term and long-term reliability in UV-laser-fabricated micro-devices and provide experimentally grounded hypotheses to guide future stability-oriented micro-device design.
A Bamboo Space Frame for a Low-Carbon Sustainable Community Market Hub
(University of Waterloo, 2026-04-27) Adikpe, Henry
In response to concerns about climate change, many accept that buildings need to reduce their substantial CO2 emissions during construction and operation. Steel and concrete, which are major construction ma-terials in recent years, require decarbonization with innovative, sustainable building materials as alter-natives to traditional construction methods, particularly for large buildings with space-frame designs. Bamboo, as a locally sourced, sustainable building material, offers a resilient opportunity for sustainable architecture with high CO2 sequestration capacity. This thesis examines the structural potential and environmental benefits of designing and constructing with bamboo for a space-framed design. In a case study of a community market hub in Makurdi, Benue State, Nigeria, the rapid growth of bamboo at the site is integral to mitigating environmental impacts and reducing embodied carbon in the building materials. The scope of this thesis is divided into three steps. First, mapping the site location for the community market hub to understand the climatic conditions, then using the data to inform the building orientation and its energy performance (using a design model), drawing on the literature. Second, the use of Rhino-Grasshopper software for building model design and Sun, Earth, and Tools to demonstrate the sun path in the market hub design. One building type was selected for sun path analysis conducted at four times a day, and the results apply to the six prototype buildings with similar orientations on the site. Lastly, A proposed bamboo-based space frame was developed using five and three-inch Bambusa vulgaris (com-mon bamboo), the species present in the research area, and a single building structure was replicated across six prototypes to enhance market usability. A schematic design of the structural system for the market hub demonstrates three load paths: lateral, gravity, and uplift forces, which are important for preventing building structural failure due to its complexity. For the load path, several designs were com-pared before selecting the best solution for the market hub, which uses a steel base plate, steel clamps, cast ball joints, gravel, bolts, and nuts for the connection.
Locality Optimizations and Analysis for Storage Hierarchies of Graph Databases
(University of Waterloo, 2026-04-27) Korkmaz, Zeynep
Graph database management systems (GDBMSs) are increasingly used to support traversal-heavy workloads. Graph queries often follow data-dependent paths through the graph, producing irregular or non-sequential accesses that do not align with the storage hierarchy, from data layout on disk to main-memory data pages. When storage and caching mechanisms are agnostic to graph structure, they can fail to exploit opportunities for locality. This thesis studies how the structural properties of graphs can be leveraged to improve locality across the storage hierarchy in disk-based GDBMSs. Although graph workloads are often characterized as having little locality, real-world graphs commonly exhibit strong connectivity patterns and community structure. These structural characteristics can provide useful information about which vertices and edges are likely to be accessed together during query execution. This thesis argues that incorporating graph topology into storage and cache-layer design enables the leveraging of locality benefits. The first part of the thesis focuses on main-memory page management. We study how graph topology affects page access behaviour during graph traversals and show that topology-aware data layouts increase the likelihood that related graph data are colocated within pages. Based on this observation, we develop a cache replacement algorithm that uses structural information to better anticipate future page accesses, improving cache hits for graph workloads. The second part of the thesis addresses data layout and serialization on disk. We investigate how graph data can be serialized to better represent connectivity patterns. We propose a topology-aware serialization strategy that captures real-world graph structure to support graph topology-aware cache replacement policies. We evaluate this strategy against existing approaches and demonstrate its effect on accepted metrics for judging locality quality. Finally, we address a significant problem observed while designing the storage and cache-layer optimizations: the commonly used serialization quality metrics do not always correlate with actual query performance. We systematically study why standard metrics fail to consistently predict query efficiency and we explore the reasons behind this irregularity. Overall, this thesis makes three contributions: (1) it demonstrates how structural properties of graphs can be exploited to guide topology-aware cache replacement decisions in disk-based GDBMSs, (2) it proposes and evaluates a topology-aware serialization strategy for improving locality on disk, and (3) it provides an analysis of serialization locality quality metrics, exposing their limitations in representing query performance.
Ecology of nitrifiers within engineered freshwater systems
(University of Waterloo, 2026-04-27) Umbach, Alexander
Home aquarium systems and wastewater treatment plants (WWTPs) are engineered freshwater systems that rely on nitrifying microorganisms to remove ammonia and nitrite, which are harmful to aquatic life. In aquaria, inadequate nitrification can lead to ammonia/nitrite accumulation and fish mortality. Similarly, WWTPs must ensure removal of these nitrogen species prior to effluent discharge to prevent eutrophication of downstream streams and lakes. Despite their importance, nitrifying microbial succession in aquarium biofilters is poorly documented in peer-reviewed literature, and the diversity of WWTP designs and operations complicates broad generalizations about nitrifier activity within these engineered systems. Understanding how nitrifiers establish and persist within aquarium biofilters can provide insight into nitrifier ecology but may also be useful for developing effective home aquarium nitrifying supplements and guiding best practices for aquarium ammonia management. Likewise, examining nitrifier activity in engineered wastewater systems, including tertiary treatment processes such as rotating biological contactors (RBCs), can guide strategies to enhance nitrogen removal and optimize treatment performance. Newly established home aquarium systems have an increased risk of ammonia and nitrite accumulation, and associated fish toxicity, because they lack an established nitrifying guild. This problem can be further exacerbated by increased fish loads, because more fish will produce more metabolic waste and ammonia. Thus, the first goal of this thesis was to investigate how fish load influences microbial community succession and associated nitrifying populations. Analyzing aquarium biofilter media and water samples obtained previously, ammonia and nitrite concentrations were significantly higher in tanks with higher fish loads, although biofilters associated with all treatments depleted ammonia within similar timeframes. Filter microbial community profiles were influenced by fish load, with aquaria under higher fish loads showing increased relative abundances of biofilm-associated taxa, including Planctomycetes members, whereas aquaria under low fish loads contained more abundant Gammaproteobacteria populations. After six months, aquarium microbial communities differentiated based on fish load, with low fish-load aquarium microbial community composition resembling the early- to mid-stage timepoints of the high fish-load aquaria. Nitrifying populations were similar overall, with high relative abundances of Nitrospira spp. dominated by comammox Nitrospira. Aquaria with increased fish loads contained more abundant populations of ammonia-oxidizing bacteria (AOB) compared to aquaria with fewer fish, likely perhaps resulting from the increased ammonia load. Ammonia-oxidizing archaea (AOA) were undetected in all filter microbial community profiles following data processing, which likely may reflects reflect a lack of inoculation given their commonplace abundance in most home aquarium biofilters. Together, these results formalize how fish loads affect ammonia accumulation in home aquarium systems and how nitrifier establishment and succession is linked to the number of fish housed within freshwater aquarium systems. Many home aquarists minimize ammonia and nitrite accumulation in newly established aquaria by using nitrifying supplements, which help to augment filter-associated nitrifiers. These supplements contain nitrifying populations, but research on their microbial community composition and in situ effectiveness is lacking. The second goal of this thesis was to test enrichment cultures of comammox Nitrospira (CX) and the AOA Ca. Nitrosotenuis aquarius (AQ) for use as aquarium nitrifying supplements and to evaluate their efficacy alongside an existing conventional nitrifying supplement (CS). Experiments involving new aquaria investigated how supplements altered early and post-establishment ammonia accumulation and oxidation rates. The results demonstrate that maximizing supplement dosage was important for ensuring effective oxidation of ammonia to nitrate. Treating aquaria with CX, either as the sole supplement or in combination with AQ or CS, mitigated ammonia and nitrite accumulation and maintained ammonia and nitrite at low concentrations when aquaria were treated with the highest dose. Whereas Nitrosospira sequences associated with CS were rarely detected within biofilter profiles, those associated with CX and AQ established early and persisted temporally. Treating aquaria with CX also provided extended protection against nitrite accumulation during acute ammonia-loading events. Overall, these results highlight potential benefits of using CX as a home aquarium nitrifying supplement and demonstrate how nitrifying guilds might be differentially adapted for home aquarium conditions. The Guelph WWTP contains a tertiary treatment system composed of RBCs that was previously shown to host abundant Nitrospira populations, and less abundant but temporally persistent populations of AOA Ca. Nitrosocosmicus hydrocola. The third goal of this thesis was to investigate the microbial activity transcriptional activity of this system, focusing on nitrifier-associated transcriptional expression to identify nitrifying contribution and potential novel metabolisms. Metatranscriptomic and 16S rRNA gene surveys of the Guelph WWTP RBCs reinforced dominance of abundant and active Nitrospira populations. Transcripts associated with ammonia and nitrite oxidation were among the most abundantly expressed for Nitrospira, in addition to moderate expression of putative ureases, urea transporters, and agmatinases/guanidinases/arginases, suggesting that Nitrospira may generate ammonia from multiple nitrogenous compounds in ammonia-limited environments. Some of the most abundantly expressed transcripts affiliated with Ca. N. hydrocola were associated with a putative guanidinase, suggesting that AOA may be capable of degrading nitrogen rich compounds, such as guanidine, agmatine, or arginine, to release urea and ammonia. These results lay the foundation for future research to confirm whether Ca. N. hydrocola is able to supplement nitrification with guanidine-derived ammonia. Investigating nitrifiers within engineered freshwater systems often relies on broad microbial community profiling using the 16S rRNA gene. Ensuring that data generated using such techniques accurately represent microbial community and nitrifying guild composition is essential for testing hypotheses regarding nitrifier distributions. The fourth goal of this thesis was to investigate how processing 16S rRNA gene amplicon sequencing data influences final reported relative abundances of nitrifiers. Processing five engineered freshwater datasets as merged paired-ends (“paired”), single-end using only the forward reads (“forward”), or single-end using only the reverse reads (“reverse”) revealed that the relative abundances of phylum Nitrospirota and genus Nitrospira were reduced when 16S rRNA gene amplicon sequencing data were processed as paired or reverse reads. When processed as forward reads, relative abundances of phylum Nitrospirota increased by as much as 21.9 times compared to paired, and detailed analysis of an aquarium biofilter dataset showed a 2.6- to 15.0-fold increase in genus Nitrospira relative abundances. Sequence analysis suggested that a higher frequency of chimera generation in reverse reads contaminates high quality forward reads when reads are merged during paired-end processing. These results serve as a warning for nitrification researchers to investigate their 16S rRNA gene amplicon sequencing datasets for potential Nitrospira-specific bias based on analysis artifacts.
Experimental and Numerical Investigation of the Flexural Behaviour and Effective Flange Width of Mass Timber Composite Panels
(University of Waterloo, 2026-04-27) Hull, Tyler
The construction industry is increasingly adopting mass timber as a sustainable alternative for large-scale buildings, driven by the urgent need to limit global emissions. However, conventional mass timber floor solutions remain challenged in applications requiring long spans and open spaces (e.g., institutional, commercial, and industrial buildings). Compositely connecting cross-laminated timber (CLT) slabs and glulam beams to form ribbed or box type mass timber composite (MTC) panels has emerged as a promising solution for longer spans while maintaining the benefits of prefabrication and sustainability. Despite their potential, MTC floor systems have seen limited adoption due to an incomplete understanding of their structural behaviour. Key challenges include limited experimental guidance on practical shear connection solutions, uncertainty in predicting serviceability-level performance, and a lack of validated methods for accounting for shear lag and effective flange width (EFW) in the CLT flanges. Existing research has primarily focused on mechanically fastened or glue-pressed connections requiring large hydraulic presses, and limiting the practical manufacturability. Screw-gluing has been proposed as an alternative construction method; however, its structural viability and applicability to large-scale components remain insufficiently understood. Furthermore, while EFW concepts are widely used for composite systems, the current Canadian Engineering Design in Wood standard lacks guidance on the EFW of CLT, and the proposed draft update to Eurocode 5 provisions have undergone limited experimental verification. This thesis presents a comprehensive experimental and numerical investigation into the flexural behaviour of MTC panels, with emphasis on practical shear connections, serviceability (SLS) and ultimate-level (ULS) structural behaviour, and EFW at SLS. The research integrates connection level testing, full scale composite beam experiments, full strain field results using digital image correlation (DIC), and validated numerical modelling to improve understanding of MTC system performance. A central contribution of this work is the experimental characterization of screw glued timber–timber shear connections using gap-filling and non-gap-filling adhesives, and screws at larger, more economical spacings. Experimental results demonstrate that screw gluing, when combined with appropriate surface preparation and detailing, achieved high strength and stiffness, providing performance comparable to glue-pressed connections and meaningfully exceeding that of conventional mechanical fasteners. These findings demonstrate that screw gluing is a viable, practical, and scalable alternative to glue-pressing for manufacturing large-scale MTC panels and realizing full composite action throughout the response. Full-scale tests on CLT–glulam composite T beams further demonstrated that specimens incorporating screw glued connections exhibit highly consistent stiffness, linear elastic response virtually all the way to failure, and minimal connection slip, resulting in generally fully composite behaviour. In contrast, configurations with metal plate connectors exhibited increased slip, as well as SLS stiffness degradation and residual deformations when reloaded. Multiple governing failure modes were observed (particularly in shear), underscoring the need for comprehensive strength verifications in MTC system design. This thesis also evaluates commonly used analytical methods for predicting the flexural stiffness of MTCs. Comparisons with experimental results showed that accurate SLS predictions are governed more by consistent treatment of shear deflections and EFW than by the choice of analytical method alone. The Gamma Method proved to have good estimations of the experimental bending stiffness, as well as possessing a direct way of addressing rolling shear in the CLT perpendicular layer. The Rigidly Bonded Method also provided good estimations, but showed inconclusive results using its shear correction factor to account for longitudinal shear deflections. A validated finite element modelling method, supported by full field DIC results, was used as an interpretive tool to assess shear lag in the CLT flanges. The model demonstrated good capabilities for predicting the experimental results, but slightly overestimated the DIC EFW results, with shrinkage-induced separation of the edge-gluing deemed a primary cause. A major contribution of this work is the experimental and numerical demonstration that EFW in CLT is a governing, response-dependent design parameter. Results demonstrate that EFW is strongly influenced by load configuration, connection stiffness, and flange geometry (particularly the perpendicular CLT layer(s)), indicating that it is a response dependent parameter rather than a fixed geometric property. Existing design guidance was found to be generally overly conservative and, in some cases, non representative of observed flange participation, particularly for line load conditions. A new approach is proposed for combining layer level EFW from finite element modelling into a full depth CLT flange EFW, addressing a gap in current design guidance. The work also demonstrated that methods for capturing the full strain field are particularly beneficial over discrete strain gauges in order to mitigate the impact of local wood defects. Overall, this thesis advances the knowledge surrounding the behaviour of MTC floor systems by providing experimentally grounded insight into practical shear connections, system level flexural response, serviceability level modelling, and the effective flange width in CLT, ultimately supporting the reliable design of long span mass timber floors.
Adversarial Robustness of Modular Autonomous Driving Agents for Lane Keeping
(University of Waterloo, 2026-04-27) Dinashi, Kimia
Modular autonomous driving (AD) pipelines are widely used because their intermediate representations improve interpretability and facilitate targeted debugging. However, modularity does not necessarily imply robustness: adversarial perturbations can enter at multiple interfaces and propagate to downstream control. This thesis investigates adversarial robustness in a modular deep learning lane-keeping agent in the CARLA simulator, consisting of a learned lane detection module followed by a learned steering angle predictor that consumes RGB and lane-mask inputs. We evaluate white-box, digital, ℓ∞-bounded evasion attacks using Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD). Attacks are injected at different points in the pipeline to isolate perception-side (lane) and control-side (steering) vulnerabilities, including a leakage configuration that forwards the adversarial RGB to the steering module. Robustness is assessed using closed-loop safety metrics—attack success rate (ASR) and time-to-failure (TTF)—and complemented with offline steering-error analysis to separate numerical sensitivity from compounding vehicle dynamics. Experiments show that the steering predictor is the dominant point of failure: steering targeted perturbations consistently induce rapid behavioral failures, whereas lane-targeted attacks require substantially larger perturbation budgets to achieve comparable impact. Offline analysis confirms that gradient-aligned perturbations can amplify steering prediction error by orders of magnitude in the baseline model, while random noise of equal magnitude has negligible effect. Motivated by these findings, we apply adversarial training to the steering module as a targeted defense. The adversarially trained steering predictor substantially reduces sensitivity to gradient-based attacks and yields consistent improvements in closed-loop safety, demonstrating that module-specific hardening can mitigate the primary failure mechanism in modular lane-keeping systems.
Simultaneous Distributed Formation Tracking and Self-Localization for Constrained Nonholonomic Networks
(University of Waterloo, 2026-04-27) Mostafa, Ahmed Fahim
Motivated by the deployment of autonomous vehicle networks to ensure safe navigation in unstructured environments with insufficient prior terrain data, this thesis presents a comprehensive multi-vehicle coordination system for active area surveillance and mapping. To facilitate such missions, the proposed framework addresses the network coordination task through three primary objectives: (1) establishing robust formations to maintain inter-vehicle interactions, (2) guaranteeing cohesive, safe motion along a reference mission path, and (3) leveraging these formation interactions to localize vehicles and targets when global measurements are unavailable. However, establishing robust formations without relying on global positioning, full-state sharing, or centralized computation remains a fundamental control challenge. This thesis introduces distributed frameworks for bearing-based formation tracking control and simultaneous self-localization, tailored explicitly for networked vehicles operating under strict sensing, communication, and motion constraints. The proposed methodology leverages graph rigidity properties in conjunction with constrained optimal control to address four coupled formation objectives: geometric shape maintenance, time-varying trajectory tracking, collision avoidance, and relative localization. Unlike traditional schemes based on global or relative position tracking, this research develops distributed control architectures that rely primarily on local bearing measurements. The multi-agent framework exploits the infinitesimal rigidity of the sensing graph to ensure the formation shape is unique. The control laws are then designed to minimize inter-agent bearing errors to maintain the geometric structure, while simultaneously steering the formation along time-varying reference paths by achieving velocity consensus and orientation alignment. To accommodate the practical limitations of underactuated vehicles, the control design explicitly incorporates nonholonomic kinematic constraints and input saturation. Furthermore, the framework integrates Control Barrier Functions (CBFs) to strictly enforce safety-critical requirements, including inter-agent collision avoidance and sensor Field-of-View (FOV) limits. These safety and sensing formulations are incorporated into a distributed Quadratic Programming (QP) problem, which defines control-action filters to guarantee safety and ensure that neighboring agents remain within limited sensor coverage. This architecture prevents inter-agent collisions and preserves sensing graph connectivity during dynamic maneuvers. Concurrently, a distributed estimation scheme is introduced to resolve the scale ambiguity inherent in bearing-only formations. By exploiting the relative motion profiles of the agents and relative bearing measurements, the proposed nonlinear observer recovers the inter-agent distances and the formation’s global scale without requiring external anchors, distance measurements, or global references. The estimated scale and dynamics are fed into safety filters and control loops to maintain collision-free navigation and achieve a desired formation scale and orientation in large networks. The asymptotic stability and convergence of the closed-loop system over different network topologies are established through theoretical analysis. The performance of the proposed framework is validated through extensive numerical simulations and experimental implementations on mobile robot platforms, demonstrating robustness across diverse deployment scenarios.
A General Computational Framework for Restoring Cellular-Resolution in Optical Coherence Tomography Images of the Eye
(University of Waterloo, 2026-04-27) Abbasi Firoozjah, Nima
Achieving full-volume cellular-resolution imaging of the eye with Optical Coherence Tomography (OCT) is constrained by aberrations that limit the depth range over which sufficient lateral resolution and Signal-to-Noise Ratio (SNR) can be maintained. Among many solutions proposed for this problem, computational methods stand out as they offer the correction of these aberrations without compromising imaging speed or quality, and without introducing additional costs to the OCT setups. However, these solutions are only applicable to the data acquired with complex OCT systems featuring high phase stability. In addition, the in vivo application of these solutions in the human eye has been limited to the posterior layers, i.e., retinal tissues. The proposed research aims to address these challenges through innovative computational methods that do not rely on phase-stable OCT setups. Such development holds the potential to offer a system-agnostic approach for cellular resolution imaging of the human eye in full volumes, enabling seamless integration into any clinical OCT system for diagnostic purposes. This thesis proposes two computational methods for enhancing image reconstruction in cellular-resolution OCT. First, aberration- and noise-compensated reconstruction is addressed using non-local image priors within a Maximum a Posteriori (MAP) framework, yielding improved cellular-level detail across various tissue types, including the healthy human cornea. Building on this foundation, a Physics-Informed Diffusion Model (PIDM) is developed for super-resolved and defocus-corrected OCT image reconstruction. The model integrates non-local priors into the diffusion process, enabling conditioning of inputs and latent variables that guide reverse diffusion. By leveraging both the flexibility of diffusion models and the physical principles of OCT imaging, PIDM achieves high-fidelity correction of image degradations. Experimental validation against well-established ground truths demonstrates that PIDM outperforms baseline methods, including the MAP framework in the first project and a standalone super-resolution diffusion model. Quantitative evaluations highlight improvements in SNR, sharpness, and contrast, while qualitative assessments demonstrate its potential to facilitate key applications such as cell counting, segmentation, and dynamic cell studies in the human cornea. By addressing key limitations in cellular-resolution OCT imaging of the eye, this research has the potential to enable non-invasive, high-fidelity cellular imaging of the eye in full volumes. The proposed methods could facilitate widespread clinical adoption of cellular-resolution OCT, extending its diagnostic capabilities for early detection of eye diseases.
Defining Expertise in Requirements Elicitation
(University of Waterloo, 2026-04-27) Ewing, Rory
Eliciting high-quality Requirements in large, multi-stakeholder acquisition environments is a form of cognitive work that is not well captured in prescriptive process models. This thesis leverages qualitative methods and the Cognitive Work Analysis (CWA) framework for modelling work with complex socio-technical systems to examine how experts in Requirements elicitation approach their work, specifically in the context of the Canadian Armed Forces procurement system. Drawing on the semi-structured Critical Decision Method (CDM) interviews, I explored the experiences of four experienced Requirements elicitation professionals in specific Critical Incidents where their expertise was expressed by analyzing the perceptual cues, reasoning patterns, and adaptive strategies they employed. Thematic Analysis of the interviews revealed how experts negotiate ambiguity, manage competing frames, and stabilize emerging Requirement interpretations—behaviour that is largely absent from formalized descriptions of elicitation practice. To situate these findings within the broader system of constraints I identify the means-ends relationships between themes to construct an Abstraction Hierarchy to represent the domain’s functional purposes, values, and functions. I leverage this to construct a Strategy Flow Map to represent available strategies and How REPs apply them. I provide recommendations for novice Requirements elicitation professionals and the institutions they work within. Reducing overall project size and building relationships with stakeholders are valuable strategies, as is the strategic use of mock-ups and scenarios to support Requirements elicitation.

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