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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Neuromodulatory Enhancement of BCI Critical EEG Features: Site Specific iTBS Effects on Movement Related Cortical Activity
(University of Waterloo, 2026-05-29) Wolfe, Paul James
Self-paced voluntary movement generation induces a complex cascade of activity in the brain to coordinate and execute the desired movement. This activity can be observed as the movement-related cortical potential (MRCP) in the time domain, and the event-related spectral perturbation (ERSP) in the time-frequency domain. The MRCP is generated primarily by the supplementary motor area (SMA) and primary motor cortex (M1) during self-paced movement. Similar networks drive the ERSP leading to event-related desynchronization (ERD) and synchronization (ERS) during movement preparation and execution. Brain computer interfaces (BCIs) leveraging the MRCP and ERSP have emerged as a therapeutic option for supporting neurorehabilitation. These devices rely on high detection accuracy to function effectively, a trait hampered by the inherently noisy data. While research has considered various methods to improve detection accuracy, limited work has investigated methods which improve the saliency of the signal itself. Transcranial magnetic stimulation offers non-invasive methods which transiently modulate the brain. Intermittent theta burst stimulation (iTBS) is one such method which increases cortical excitability in a targeted region of the cortex. This thesis proposes the application of iTBS to enhance the neural correlates of movement preparation and execution to ultimately improve the signal-to-noise ratio within the MRCP and ERSP. Different locations of iTBS stimulation were investigated targeting the primary generators of these signals to maximize the induced change. Three experiments were conducted differentiated by the location of iTBS stimulation, over M1, SMA, and both M1 and SMA sequentially. In each experiment, participants performed a self-paced ballistic gripping task before and after receiving the intervention. The MRCP was analyzed as mean amplitudes of four components: the readiness potential (RP), negative slope (NS′), motor potential (MP), and peak negativity (PN). In the frequency domain, ERSP was investigated through mean amplitudes of ERD/ERS in alpha and beta bands both before and after movement onset. Time windows of interest in the pre-movement phase were selected to align with MRCP components. Post-movement ERS was quantified over two windows of time to reflect early and late ERS. Results indicated that iTBS significantly modulated the MRCP and ERSP across the three experiments. Within the MRCP, the NS′ and MP were enhanced after sequential M1 + SMA iTBS and M1 iTBS, respectively. The difference between these outcomes suggests that sequential iTBS preferentially modulated intracortical connections between SMA and M1. Surprisingly, SMA iTBS suppressed the PN while not significantly modulating any other component. While the RP was not modulated across any of the experiments, beta ERD was amplified following SMA iTBS in this pre-movement time window. However, this effect was inverted after sequential iTBS which induced a reduction in beta ERD during the pre-movement period. Both alpha and beta bands following movement onset were facilitated by sequential iTBS while M1 iTBS trended towards facilitation of only beta band ERS. SNR of the MRCP was significantly enhanced by sequential iTBS but not following single site iTBS at either site. Conversely, alpha and beta SNR were not significantly adjusted by any of the interventions. Lastly, time to maximal effects differed between the experiments such that iTBS protocols involving SMA exhibited their strongest effects thirty minutes after stimulation compared to M1 iTBS which was most prominent ten minutes after stimulation. The findings underscore the influence iTBS has upon the neural correlates of movement preparation. Single- and multi-site iTBS significantly increased the size of the various components of the MRCP and the ERSP. Importantly, the interventions involving SMA iTBS are capable of modulating early movement preparation processes, a necessary trait to benefit many neurorehabilitation approaches. The ability of iTBS to modulate neural signatures of motor planning positions itself as a promising avenue for improving signal salience and ultimately BCI performance. Interestingly, the findings suggest potential advantages to each iTBS protocol rather than a generalized “one-size-fits-all” protocol. For example, BCIs operating in the frequency domain may find superior performance with the early beta ERD facilitation induced by SMA iTBS. Conversely, sequential iTBS may benefit MRCP-based BCI due to robust facilitation of the NS′. By examining three novel applications of iTBS, this thesis demonstrates that iTBS can modulate neural mechanisms underlying movement preparation, thereby advancing understanding of its potential for BCI and, by extension, neurorehabilitation.
Categorical Limits of Quantum Graphs and Possibilities Induced by Quantum Pseudometrics
(University of Waterloo, 2026-05-29) Zhu, Jennifer
Two monographs [Wea12] and [KW12] introduced new notions of quantum relations and quantum pseudometric spaces incorporating inspiration and techniques from a broad array of fields related to quantum theory. We begin by investigating quantum relations; namely, we find a new formulation of a morphism of quantum relations. Under the general principle that classical functions should be dualized to contravariant maps between associated algebras in quantum theory, we use some operator space theory to analogously dualize the complement of a subset of vertices. This framework yields a representation independent expression of a morphism of quantum relations that aligns with previously representation dependent ones under the appropriate assumptions. Under these morphisms, the categorical (co)limit of a subclass of quantum relations has an obvious candidate. We also define these morphisms on the level of bimodules We next investigate quantum pseudometrics with an emphasis on the quantum mechanical interpretation of the background and results. The motivational theorem (due to unpublished work by Farah and Weaver) is that pure states of a von Neumann algebra 𝓜 are in bijection with maximal filters in the projection lattice of 𝓜. Under the observation that the neighborhood filter of a point in a topological space is also a maximal filter and armed with a notion of distance 𝜌 between projections given by quantum pseudometrics, we investigate whether 𝜌 induces a notion of distance between pure states.
Improving OOD Detection, Recognition, and Understanding via Multi-Modal Feature Alignment
(University of Waterloo, 2026-05-29) Wang, Yimu
The deployment of artificial intelligence (AI) systems in real-world scenarios, such as autonomous vehicles encountering novel road conditions and medical AI analyzing rare pathologies, requires robust handling of out-of-distribution (OOD) data—inputs that differ from the training distribution due to semantic shift (e.g., novel object categories) or covariate shift (e.g., changes in lighting or sensor noise). Achieving this robustness requires that AI systems progress from detecting OOD data, to recognizing novel categories within OOD data, and ultimately understanding OOD scenarios to answer user queries—an essential capability for safety-critical applications where understanding unfamiliar situations is required. This motivates the use of increasingly advanced vision-language capabilities, yet current models face technical barriers in multi-modal feature alignment that limit practical deployment in OOD detection, recognition, and understanding. The challenges and applications of multi-modal feature alignment for these tasks have not been fully explored. This thesis makes three key contributions to advance the understanding and application of multi-modal feature alignment in vision-language models (VLMs). First, we address the limitations of VLMs in OOD detection. We observe that the modality gap between image and text features causes high false positive rates, as OOD samples can exhibit high similarity to in-distribution (ID) text prototypes. To overcome this limitation, we propose a novel few-shot OOD detection method that incorporates ID image prototypes alongside ID text prototypes. Our method introduces the Bias Prompt Generation module to enhance image-text fusion and the Image-Text Contrastive module to reduce the modality gap. This multi-modal prototype approach significantly improves OOD detection accuracy across multiple benchmarks. Next, we tackle OOD recognition through 3D open-vocabulary semantic segmentation, which leverages VLMs to generate point-wise recognition results for novel object categories. Due to the lack of large-scale 3D-language data, current methods distill knowledge from pre-trained 2D VLMs into 3D models. However, this distillation is supervised by misaligned 3D-scene-image-to-text data pairs, leading to suboptimal performance. To address this issue, we propose an aligned 3D open-vocabulary semantic segmentation framework with two novel modules: a CLIP-Rewarded Alignment Module that generates high-quality, well-aligned 3D-scene-image-to-text pairs through temperature-based generation and CLIP-rewarded sampling, and an Adaptive Segmentation Module that introduces trainable tokens within the text encoder to adapt it to 3D contexts. This approach significantly outperforms previous methods on representative benchmarks. Finally, we explore efficient multi-modal feature alignment for OOD understanding. In real-world applications such as autonomous driving and medical diagnosis, AI systems must not only detect and recognize OOD data but also generate appropriate responses by understanding the scenario—for instance, determining safe actions when encountering an unexpected obstacle or providing diagnostic insights for rare pathologies. Multi-modal large language models (MLLMs, as a class of generative VLMs) offer strong generalization capabilities for such understanding tasks, but their substantial computational requirements limit practical deployment. To address this, we propose an efficient MLLM that incorporates a novel conditional token reduction module to consolidate visual tokens based on their similarity to text tokens and learnable queries, and a novel mixture of multi-modal experts module with a router that takes both text and visual tokens as input for better switching between different low-rank adaptation (LoRA) experts. The proposed method achieves competitive performance while using significantly fewer visual tokens, enabling efficient OOD understanding without sacrificing effectiveness. This thesis demonstrates that systematic improvements in multi-modal feature alignment can address multiple complex OOD challenges, from detection through recognition to understanding. These contributions establish a foundation for the deployment of AI systems in open-world environments, enabling more reliable and scalable AI systems that can robustly handle novel scenarios.
Design and Biomechanical Evaluation of a Self-Centering Dual Mobility Concept for Reverse Total Shoulder Arthroplasty
(University of Waterloo, 2026-05-29) Ombogo, Mercy
Reverse total shoulder arthroplasty (RTSA) remains limited by restricted range of motion, inferior impingement leading to scapular notching, and persistent trade-offs among mobility, constraint, and stability. This thesis investigated whether dual mobility principles established in total hip arthroplasty could be translated to RTSA in a biomechanically coherent manner. The central objective was not simply to introduce a second articulation in pursuit of range of motion gains, but to adapt dual mobility into RTSA in a way that would increase functional range of motion without compromising the established biomechanical benefits of the current RTSA design. For this translation to be mechanically meaningful, motion at the primary glenosphere-liner articulation and the secondary liner-humeral articulation had to be partitioned in a controlled sequential manner, such that the inner articulation remained dominant through mid-range motion while the outer articulation was recruited in a near the end range. This requirement motivated a three-stage methodological approach. First, a standardized computational framework was developed and validated to evaluate how geometric design parameters specific to RTSA influenced impingement-free ROM under controlled and repeatable conditions, thereby enabling consistent comparison of different implant design geometries. Second, structured concept generation and screening methods rooted in classical design frameworks were used to identify a biomechanically coherent dual articulation strategy for an RTSA implant. Third, the selected concept was embodied, computationally evaluated using a full factorial iv parametric study in which compressive load, friction, and radial clearance were varied. The embodied design was then qualitatively assessed experimentally through benchtop testing. The final implant concept employed a deliberate geometric offset (eccentricity) between the centers of rotation of the liner’s inner and outer surfaces, such that the applied joint compressive force generated a restoring moment about the liner’s center of rotation (COR), thus biasing the mobile component toward alignment with the load line and thereby promoting self-centering. The computational framework used a CAD-to Simulink pipeline to prescribe motion, detect impingement, and quantify articulation behavior. The principal embodiment variables were compressive load, inner-articulation friction, outer-articulation friction, and radial clearance at the outer articulation. The evaluated metrics were kinematic surrogates, including liner-shell misalignment and measures of articulation hierarchy via motion contribution metrics. The embodied design incorporated an eccentric liner, a humeral shell, and an inferior end-stop that limited liner excursion. Computational parametric evaluation showed that friction at the primary articulation was the dominant driver of liner-shell misalignment, whereas friction at the outer articulation had a smaller and less consistent effect. Radial clearance further modulated the load-dependent self-centering response: 0 mm clearance favored tighter tracking (better self-centering) under lower loads, 0.5 mm clearance increased geometric freedom but also increased sensitivity to loading, and 0.25 mm clearance exhibited the most balanced overall recentering behavior within the tested design space. Benchtop experiments provided qualitative support for the proposed v mechanism by reproducing the predicted articulation sequence and self-centering tendency under applied compressive load, while also confirming the computationally predicted impingement-free ROM. Outside of the parametric investigation, the embodied DM-RTSA concept demonstrated meaningful improvements in impingement-free ROM relative to a contemporary RTSA configuration. Within the scapular plane of elevation, the DM-RTSA implant increased adduction ROM by approximately 65%, delaying inferior impingement by 32° past the arm-at-side position through activation of the secondary articulation. Experimental evaluation qualitatively reproduced the predicted articulation sequence, self-centering tendency, and delayed inferior impingement behavior observed computationally, supporting the biomechanical feasibility of the proposed mechanism. Within the modeled and experimental scope, the thesis therefore demonstrates biomechanical and kinematic feasibility for a self-centering dual mobility RTSA concept and established a structured basis for future design refinement and preclinical evaluation. More broadly, this work provides a structured biomechanical foundation for the future refinement, preclinical evaluation, and eventual clinical translation of dual mobility principles within shoulder arthroplasty.
Resource Allocation in Time-Varying Satellite QKD Networks
(University of Waterloo, 2026-05-29) Park, Sun Gyu
Quantum Key Distribution (QKD) is a foundational technology for future secure communications, and several QKD networks have already been deployed and tested around the world using optical fibers. However, these networks cannot scale in size due to the strong exponential decay of signal efficiency in fiber with increasing distances, making satellite networks a major candidate for the global deployment of QKD networks. Despite the advantages of free-space QKD via satellites, such networks face challenges due to changes in satellite-ground links caused by orbital motion and atmospheric fluctuations. Therefore, resource allocation schemes must account for these time-varying conditions. In this work, we investigate the problem of resource allocation in satellite QKD networks, taking into account the changing key generation rates per time slot, reflecting the evolving weather conditions and satellite visibility. We formulate a MILP model to allocate resources in satellite QKD networks, which decides both link assignments (i.e., determining the ground node assignment for each satellite) and the appropriate routing paths for sharing end-to-end keys. In addition, the MILP models multiple time slots and considers keys stored in the QKP, allowing keys generated to be used in later periods. To improve computational efficiency while approaching the near-optimal total served keys of the MILP model, we propose a two-stage approach, Genetic Algorithm-Cumulative Key Reservoir (GA-CKR). In the first stage, satellite QKD links are assigned using a genetic algorithm (GA)-based heuristic. In the second stage, routing and key management (RKM) are performed using Cumulative Key Reservoir (CKR). The proposed approach achieves solutions consistently within 15% of the MILP result while reducing computation time by several orders of magnitude in the most demanding topology.
Computational Prediction and Mapping of Protein Folding Pathways
(University of Waterloo, 2026-05-29) Cotra, Filip
Protein structures are dictated by their sequences, but the mechanisms underlying folding remain ambiguous. Various computational approaches exist to investigate protein folding, but they are often “black-box” tools that only predict native structures. Here, we introduce StepFold, a tool to rapidly explore the fold space by traversing contact maps. By representing 3D structures on a 2D grid, contact maps offer dimensional simplicity through which probabilistic calculations can be performed upon structures. StepFold integrates empirical statistics from experimentally derived structures to predict folding as a series of residue interactions influenced by their local contexts. By incorporating the blob-based model, StepFold generates grounded folding pathways and gives insight into how contacts beget complex folds. The results of this paper show that StepFold can rapidly and efficiently recreate native contact maps through blob-based folding. While its capacity for de-novo structure prediction is limited, StepFold can reproduce structures with an accuracy of over 91% for predicted contacts, while capturing over 62% of those in the native structure. StepFold is both rapid and scalable to large sequences, with a mean runtime of approximately 173 seconds per 1000 folding steps under default conditions. While improvements to the underlying probabilistic model are needed to improve prediction performance, StepFold can already give insights into how local folds cumulatively create complex tertiary structures.
Synthesizing Parameterized Protocols from Local Temporal Specifications
(University of Waterloo, 2026-05-29) Zhang, Ruoxi
Reactive systems, such as controllers, web services, communication protocols, and hardware circuits, are computational entities that continuously interact with the environment. Reactive synthesis aims to automatically generate such systems from their temporal specifications. This dissertation focuses on the synthesis of parametric, distributed reactive systems composed of copies of interacting processes that collectively satisfy global correctness specifications, under the assumption that the scheduler fairly selects processes for execution. The number of component processes thus serves as a natural parameter for such scalable systems that can handle increasing workloads by structurally adding more components without requiring the redesign of the entire system. Parametric systems induce local symmetries, as they are constructed from a small, finite number of process types instantiated across potentially large underlying networks. To alleviate state explosion, we synthesize a representative process for each process type from its local specification instead of synthesizing the global product machine. We express the local specification as a temporal formula that describes the behavior of the process in its neighborhood. We begin by considering fixed neighborhood topologies, such as token rings, meshes, and tori, and then extend our approach to protocols with parametric neighborhood configurations, including hypercubes and fully connected networks. We manually formulate the local specifications and introduce a specification rewriting transformation based on counter abstraction that approximates parametric neighborhoods while preserving the context required for concretization of abstract models. The rewriting step supports both local safety and local liveness properties parameterized by the number of neighbors. A key challenge in synthesis from local specifications is that the neighboring processes are unknown before the representatives are constructed. To address this challenge, we propose a local, iterative synthesis methodology that incrementally infers interference caused by the neighbors based on representative transitions constructed so far. Our approach adapts a tableau-based decision procedure for Fair CTL specifications and a game-theoretic approach for LTL specifications. We show that the iterative construction eventually converges to a fixpoint, at which no further interactions can be added. The approach then prunes the resulting structure to extract a representative model that can be instantiated at the corresponding network nodes to form system instances of arbitrary network sizes and neighborhood configurations, with synthesis cost independent of these parameters. We evaluate the local synthesis approach on various example protocols, including the dining philosophers, leader election, producer-consumer, and others.
Modeling and Control of Thermo-Electrical Microgrids Considering Uncertainties
(University of Waterloo, 2026-05-29) Verdugo Rivadeneira, Pablo
Global decarbonization targets for 2050 have accelerated the development of low-carbon energy system solutions. Consequently, numerous initiatives have been proposed to reduce carbon emissions, such as the deployment of high-efficiency heating and cooling systems based on Heat Pumps (HPs) and latent Thermal Energy Storage Systems (TESSs); the electrification of transportation systems, including Electric Vehicles (EVs) and Electric Aircraft (EA) operations in airports; and the integration of Renewable Energy Sources (RESs). To support the integration of these technologies, Microgrids (MGs) have emerged as a key architectural solution for coordinating renewable generation, electrical and thermal resources, and loads while delivering technical, economic, and environmental benefits. This thesis develops detailed models to represent the Thermo-Electrical (TE) operation of building-integrated MGs, with a focus on residential and airport hangar applications, considering uncertainties and multi-zone building thermal dynamics with their associated thermodynamic and physical properties. Based on these models, Energy Management System (EMS) formulations are proposed to optimize the coordinated operation of electrical and thermal resources under practical operational constraints. The first part of the thesis develops and validates an optimization-based EMS for a residential TE-MG that integrates an enthalpy-based model of a Phase-Change Material (PCM) TESS capable of operating in both active and passive modes. The proposed framework is formulated to minimize operating costs while maintaining indoor thermal comfort, with uncertainties in demand and environmental conditions addressed through a Model Predictive Control (MPC) approach and with explicit consideration of battery degradation. The EMS is applied to a real-world residential MG corresponding to the Energy Smart Home Lab (ESHL) at the Karlsruhe Institute of Technology (KIT) in Germany. Simulation results demonstrate the effective integration of the PCM system within the TE-MG operation and highlight its contribution to cost-effective and reliable energy management under various environmental conditions. The second part of this thesis discusses the modeling of an airport hangar MG and an optimization-based EMS to coordinate the dispatch of the MG's TE resources, using an MPC approach to address uncertainties, and including a detailed building thermal model, HPs for heating and cooling, and battery degradation. The proposed mathematical model of the EMS is applied to a detailed model of an actual MG under development at the Waterloo Wellington Flight Centre (WWFC) in Ontario, Canada. The presented results demonstrate that the proposed framework enables reliable and cost-effective operation while ensuring multi-room thermal comfort, and achieves significant reductions in operational costs and CO2 emissions compared to a baseline scenario without a MG and to a MG configuration employing simplified single-zone thermal modeling. As the energy management of TE-MGs becomes increasingly challenging for model-based approaches due to detailed component modeling requirements and uncertainty in renewable generation and environmental conditions, the final part of this thesis proposes a model-free Reinforcement Learning (RL) framework, based on Deep Reinforcement Learning (DRL) methods, for the operation of multi-zone airport hangar MGs. Constraint satisfaction is ensured through the incorporation of physics-based dynamic constraints and a discretization of the coupled multi-zone thermal dynamics, which enables stable representation of inter-zone heat exchanges. Simulation results based on the operation of an actual Canadian airport MG are benchmarked against the proposed optimization-based approach, demonstrating that the physics-based RL framework achieves near-optimal performance. Furthermore, compared to conventional reward-based RL approaches, the proposed framework is shown to yield significantly faster convergence and more stable training behavior.
Combustion and Emission of Additional Fuels in Laboratory Non-premixed Flames
(University of Waterloo, 2026-05-29) Shin, Cheolhee
Additive strategies are widely used in combustion and post-combustion systems to modify emissions, oxidation behavior, and heat-release characteristics. Their reported benefits, however, are often obtained under limited configurations and may not transfer directly across changes in mixing, temperature field, loading level, oxygen access, particle history, or heterogeneous contact. Rather than treating additive performance as an intrinsic property of the additive alone, this thesis examines how additive-induced responses depend on the reacting configuration in which the additive acts. Four selected case studies are used to evaluate this problem across gas-phase fuel addition, dispersed metal-particle combustion, flame-scale effective metal addition, and catalytic heterogeneous oxidation. The first case study examines NOx formation in ethylene-ethanol dual-fuel counterflow flames using combined experiment and detailed chemical-kinetic analysis. Relative to the ethylene-only reference flame, ethanol addition increases and broadens the high-temperature region, modifies intermediate-species distributions, and increases NO formation under the investigated conditions. Pathway-isolation, sensitivity, and rate-of-production analyses show that the baseline ethylene flame is primarily prompt-NO-influenced, whereas ethanol addition strengthens the thermal-NO contribution by extending the high-temperature region and modifying the radical environment. These results show that the NOx consequence of ethanol addition cannot be interpreted from fuel chemistry alone, but must be understood through the coupled changes in flame structure, temperature field, and pathway balance. The second case study investigates dispersed micro-sized aluminum particles in a non-premixed methane-air flame using high-speed imaging, two-color pyrometry, thin-filament pyrometry, and supporting numerical simulation. Individual particles exhibit substantial heterogeneity in trajectory, radiative temperature history, optical-emission lifetime, fragmentation behavior, molten aluminum ejection, and continued oxidation after breakup. Particle temperatures frequently exceed the surrounding gas temperature, and particle behavior correlates with local temperature gradients, oxygen availability, and transient particle-level processes rather than with bulk flame properties alone. This study provides the central experimental evidence that aluminum-particle combustion in a non-premixed flame is strongly particle-history-dependent. The third case study extends the aluminum investigation to a bounded flame-scale numerical analysis of higher effective aluminum loading in a non-premixed methane flame. Aluminum is represented as an effective gaseous reactive component, so the model is not intended to reproduce discrete-particle shell rupture, molten ejection, or fragmentation. Instead, it focuses on how stronger aluminum participation can alter the reacting field. The results show that increasing effective aluminum loading shifts and broadens the high-temperature region, redistributes methane-flame and aluminum-bearing species, modifies the energy-normalized CO2 response, and affects the modeled NO field under the adopted gas-phase mechanism. This analysis provides a controlled numerical bridge between individual-particle observations and flame-scale aluminum-addition effects. The fourth case study examines ceria-catalyzed carbon oxidation as a heterogeneous catalytic analogue for post-combustion particulate treatment. By comparing bulk soot-ceria powder oxidation measured using TGA/DSC-DTG with local carbon-ceria contact oxidation observed using in-situ ESTEM, the study shows that the apparent catalytic response depends on contact geometry, oxygen delivery, particle morphology, and the physical configuration in which carbon, ceria, and oxygen interact. Bulk measurements reflect ensemble powder behavior and oxygen transport through the packed sample, whereas ESTEM observations resolve local interface-controlled oxidation near evolving carbon-ceria contacts. The same catalytic system therefore produces different apparent oxidation behavior depending on the contact and oxygen-access conditions. Across the four case studies, the dissertation shows that additive-induced changes in emissions, combustion, or oxidation behavior are configuration-dependent responses rather than intrinsic additive properties. The principal contribution is the development of an experimentally and numerically grounded basis for identifying when additive effects remain physically interpretable and engineering-relevant as mixing, temperature, loading, oxygen access, particle history, and heterogeneous contact vary. The dissertation also provides structured experimental and numerical datasets, including dual-fuel flame measurements, individual aluminum-particle trajectory and temperature histories, methane-aluminum numerical flame fields, and ceria-catalyzed soot-oxidation data, that can support future model validation, comparative additive studies, and data-driven or AI-assisted analysis of reacting systems.
Applied Machine Learning with Kernel Features for Variational Partial Differential Equations
(University of Waterloo, 2026-05-29) Le, Phuong Dong
This thesis develops mesh-free approximation methods for linear elliptic and free-boundary partial differential equations within variational formulations. It consists of two main contributions. First, we construct radial basis function approximations for linear elliptic partial differential equations and free-boundary value problems. Classical problems, including the Poisson and reaction–diffusion equations, are reformulated as minimization tasks, with boundary conditions imposed through penalty methods. For obstacle problems, we consider a variational formulation with a non-smooth ℓ¹-regularized objective. To address the ill-conditioning of the discrete linear systems, a truncated singular value decomposition is employed as a stabilization mechanism. Numerical experiments demonstrate that the proposed method achieves high accuracy and efficiency, and that it outperforms competing approaches, including finite-difference methods, Galerkin methods, and neural network-based solvers. The second contribution extends this variational framework to high-dimensional problems by means of random feature approximations. The resulting formulation yields scalable convex optimization problems that are well suited for high-dimensional settings. Numerical results indicate that the proposed approach surpasses other mesh-free methodologies, particularly physics-informed neural networks and the Deep Ritz method, while preserving high accuracy. In general, these contributions provide effective variational mesh-free algorithms for linear elliptic, obstacle-type, and high-dimensional partial differential equations. The findings highlight the potential of radial basis function networks and random feature models as accurate, stable, and scalable methods for the numerical approximation of variational partial differential equations.
Ecology and ecophysiology of autumn migration in Silver-haired bats (𝘓𝘢𝘴𝘪𝘰𝘯𝘺𝘤𝘵𝘦𝘳𝘪𝘴 𝘯𝘰𝘤𝘵𝘪𝘷𝘢𝘨𝘢𝘯𝘴)
(University of Waterloo, 2026-05-29) Nogueira e Figueira, Beatriz
The Silver-haired Bat undertakes long-distance latitudinal migrations, in which several aspects of its biology are not understood. Energy is an important constraint on migration, and many aspects of bat migration may be considered in the context of selection pressure to conserve energetic stores. Bats can conserve fat stores to fuel migratory flight by reducing energy spent on other activities. Migration consists of alternating periods of nocturnal flight and daytime stopovers wherein bats must select a temporary diurnal roost. I propose an optimization trade-off framework for selection, wherein animals must balance the value of a roost with the cost of securing it as well as its availability on the landscape. Given that bats use torpor-assisted-migration during daytime inactivity to drastically reduce energy expenditure, there may be little benefit to searching for roost sites that are rare on the landscape but provide multiple benefits. Alternatively, energy conservation is a principal concern during migration, and may drive bats to locate specific beneficial structures. To assess this, I caught and tracked bats to their diurnal roost at a stopover site on Long Point, Ontario, Canada, during fall migration in 2023 and 2024, and evaluated tree as well as surrounding habitat characteristics to evaluate selection on a local- and landscape-scale. Bats roosted in trees that were large and present in dense tree patches, taking opportunities to conceal themselves with a variety of features when convenient. However, bats were not selective for other tree or plot features, suggesting that the size of a roost and tree density are characteristics that aid in reducing the cost (energy, time, opportunity, risk) of searching for a roost. The energy required by nocturnal migratory flights is fueled primarily by adipose fat stores, composed of fatty acids that have a trade-off between energy density and the speed at which they are mobilized to meet the demands of sustained activity. Fatty acids can be synthesized and modified by the body, but some fatty acids that are essential for bodily function are only obtainable from the diet. Because dietary inputs vary geographically, fatty acid profiles can also provide a signature indicative of regional variation in source populations. Thus, stored fatty acids can serve as biomarkers for physiological performance and spatiotemporal structure in a migratory population. I analyzed adipose fatty acid profiles from migrating Silver-haired Bats caught at the same stopover site on Long Point, Ontario in autumn of 2024. My goals were 1) to assess whether fatty acid composition varied within the migratory population, 2) whether these patterns suggest performance trade-offs, and 3) whether fatty acid profiles suggest variation in the origin of migrants arriving at a stopover site. Migrating bats had similar profiles with higher proportions of fatty acids with higher mobilization rate, and similar proportions of essential fatty acids. Interestingly, male bats had a unique mechanism for storing excess fat, wherein heavier bats had higher proportions of energy-dense fatty acids. There was also clear temporal clustering within the population, suggesting distinct geographic origins of two waves of migrants arriving early and late in the migration period. Fatty acid profiles, therefore, offer valuable insights into the physiological strategies and spatiotemporal dynamics of migratory bats. My work highlighted the importance of integrative migration biology, considering multiple aspects of biology across disciplines to inform a holistic understanding of the phenomenon. As well, the use of tree roosts at a common stopover site by several populations of an endangered species with critical energy demands highlights the importance of large-scale ecosystem conservation.
Sinter-based Additive Manufacturing: Densification and Geometrical Control using Impurity-Containing Steel Powders
(University of Waterloo, 2026-05-29) Yang, Mingzhang
Sinter-based additive manufacturing (SBAM) has emerged as a promising route for net-shape production of metallic components. These processes decouple shaping from densification, enabling high build rates and batch-type thermal processing for high-throughput manufacturing. However, their broader adoption remains limited by challenges in achieving full densification, while maintaining geometrical fidelity, as well as the reliance on high-cost, highly refined feedstocks. This dissertation employed multiple sinter-based additive manufacturing routes, including binder jetting, MoldJet printing (paste-based additive manufacturing), and gel-casting, to systematically investigate densification and deformation behavior across a range of steel feedstocks. These include conventional atomized pre-alloyed powders, non-refined pre-alloyed powders, and pre-mixed oxide precursors. A combination of characterization techniques, including microscopy, thermal analysis, compositional analysis, and thermodynamic calculations, was used to elucidate the governing mechanisms of densification, phase evolution, and deformation during sintering. For pre-alloy powder systems with low as-printed density, it was established that solid-state sintering alone was insufficient for densification and that supersolidus liquid phase sintering (SLPS) can be leveraged to attain high final density. Deformation was found to be governed by pore structure evolution prior to liquid formation and mitigated through modified heating profiles. An inherent trade-off was identified between achieving near-full density and retaining fine features. A processing window was subsequently established to balance densification and geometrical fidelity. To address the complexity of sintering shrinkage under phase transformations, a phenomenological modeling framework based on continuum sintering theory was developed. By incorporating multi-stage kinetics and a variable bulk modulus, the model captures densification behavior under varying heating rates and phase evolution, providing improved predictive capability for shrinkage compared with conventional approaches. Lastly, to achieve densification and shape control while reducing material cost, this dissertation investigated the utilization of chemically non-refined powder system in SBAM. Un-annealed water-atomized powders with high carbon and oxygen contents were shown to be directly processable through in-situ chemical refinement during sintering. In addition, mixed ore-derived oxides (metal precursors) were transformed into dense metallic components with 316 stainless steel composition via H2-driven redox reactions during sintering, enabling rapid densification at reduced temperatures without macroscopic deformation. The sequential oxide reduction and alloying within the mixed system are rationalized through thermodynamic analysis, establishing a pathway for designing oxide precursor compositions as alloying sources. Collectively, these results demonstrate that densification, geometrical control, and feedstock cost reduction can be addressed simultaneously, broadening the manufacturing and materials design space of SBAM.
Disaster, Recovery, and Resilience: Linking Social Capital and Tourism in Post-disaster Recovery in Nepal
(University of Waterloo, 2026-05-29) Bishwokarma, Dipak
This research examined the interlinkages between social capital and tourism in a post-disaster recovery context. The research is based on the assumption that disaster impacts and subsequent recovery efforts filter through a broad spectrum of tourism and community social capital networks. The relative strengths and weaknesses of these networks determine the overall impact of the disaster and the success of recovery. Taking the case of devastating aftereffects of the 2015 Nepal earthquake, this study focused on post-earthquake recovery efforts in Gosainkund Rural Municipality (RM) – one of the popular tourist destinations in Nepal. Employing mixed methods, data collection in Gosainkund RM involved a total of 288 household surveys, 15 key informant interviews (KIIs), and 3 focus group discussions (FGDs) at the local level and 17 expert interviews (EIs) at the national level during the field visit from September 2023 to May 2024. The findings revealed that bonding, bridging, and linking social capital were crucial in post-earthquake recovery. However, the degree of significance varied across the recovery continuum (i.e., immediate, medium-term, and long-term recovery phases) with changing priorities in recovery activities in each phase of the continuum. The recovery activities, such as search and rescue, performing rituals, debris clearance, and building temporary shelter, were priority recovery activities at the immediate and medium-term recovery phase, while house reconstruction was a priority at the long-term recovery phase. With such changed priorities in recovery activities, the role of bonding and bridging social capital was significant at the immediate and medium-term recovery phase, while linking social capital played a crucial role in the long-term recovery phase. Tourism and social capital have an interactive relationship that creates a positive feedback loop. The tourism-specific social capital (TSSC) also plays a crucial role in post-earthquake recovery. The TSSC was pivotal in accessing resources and information sharing in support of recovery activities. The role of linking tourism-specific social capital, which emerged with a close-knit relation with the international tourists, was crucial in the long-term recovery phase. Hospitality traditions in Nepal are culturally rooted. This study offers an interesting illustration of how the strong host-guest relationship between Nepalese tourism entrepreneurs, and their overseas networks of clients made a difference in post-earthquake recovery efforts. As part of a large-scale disaster recovery effort Nepal has experienced in this century, the Gosainkund RM study offers critical lessons on the importance of better preparations in the event of a future disaster of similar magnitude and scale. Lessons for post-earthquake recovery include the need for a holistic and integrated recovery approach that recognizes and builds on inclusive, consultative processes at the local level instead of a top-down one-size-fits-all approach. Given the perceived economic power of tourism, the study suggests that stakeholders involved in post-earthquake recovery see tourism as playing important roles in facilitating access to resources and information, influencing policies to make them inclusive, and advocating and lobbying for sustainable and resilient tourism. This dissertation advances theoretical and empirical understanding of the least researched aspect of the linkages between tourism, social capital, and post-disaster recovery, along with offering a working definition of tourism-specific social capital (TSSC) in the post-disaster context. Furthermore, it offers policy recommendations for strengthening post-disaster recovery efforts and identifies avenues for further research.
Fast Kubernetes Orchestration for Dynamic and Ephemeral Workloads
(University of Waterloo, 2026-05-28) Abbasi Alaei, Ali
In recent years, Kubernetes has become the primary choice for orchestrating software deployments on distributed clusters. It abstracts the complexities of the underlying infrastructure, enabling developers to focus on application logic rather than low-level resource management. In addition to traditional long-running services, Kubernetes is increasingly used to manage short-lived and latency-sensitive applications, such as serverless functions that demand fast startup and high deployment churn. The problem with using Kubernetes for these use cases is that it sacrifices performance for high reliability and strong consistency, making it less suitable for performance-critical and dynamically scaled environments. This work identifies etcd, the central storage in Kubernetes, as a critical factor influencing performance due to its persistent and strongly consistent operation. A design change for the Kubernetes control plane is proposed and implemented that employs a complementary metadata store, called Sharded Transient Etcd (STE), alongside the persistent etcd in the Kubernetes cluster. STE stores short-lived entities, such as Pod objects, which are critical yet ephemeral resources, in fast, in-memory storage, while retaining persistent etcd for durability-critical data. This design preserves reliability and failure recovery while significantly improving orchestration performance and scalability. An experimental evaluation shows that the prototype doubles deployment throughput, reduces Pod creation latency by 80%, and improves resource efficiency of Kubernetes components by up to 20% without compromising stability or failure recovery. In addition, the results show that the cold start latency of a widely used Kubernetes-based serverless orchestrator is reduced by more than 60%. While this work is focused on Kubernetes, the resulting insight is applicable to orchestration systems in general.
A Convex Optimization Approach to the Paulsen Problem
(University of Waterloo, 2026-05-28) Al-Hellawi, Layth
A frame $\{v_1, \dots, v_n\} \subseteq \R^d$, $n \geq d$, is a spanning set of vectors. Like a basis, it allows every vector in the space to be reconstructed from its inner products with frame vectors. A frame is Parseval if this reconstruction works without any rescaling, similar to computing the coefficients for an orthonormal basis. A frame is equal-norm if every vector in the collection has the same length. An equal-norm Parseval frame combines both properties. The Paulsen Problem is a problem in frame theory that asked, given an $\epsilon$-almost equal-norm Parseval frame, can one find an equal-norm Parseval frame such that the distance between the frames is bounded above by a polynomial in $d$ and $\epsilon$? This was answered affirmatively using two different algorithmic techniques: one using operator scaling, and another that uses convex optimization to find a linear transformation that will place the frame vectors in radial isotropic position (a geometric condition that causes this transformed frame to be an equal-norm Parseval frame). We investigate the latter and its relationships to convex analysis in various ways. We come to understand more clearly the structure of the basis polytope and problems related to it, how the functional analysis underpinning radial isotropic position may be understood with the lens of convex analysis, and analytic information regarding the Paulsen Problem and the radial isotropic position problem. We implement the algorithm as well, and using this analytic information, run experiments to attempt to observe an upper bounding polynomial in $d$ and $\epsilon$.
Keeping it in the Family: A Two-Part Exploration of Views on Cousin Marriages and Genetic Counselling
(University of Waterloo, 2026-05-28) Abdulkarim, Sultan
A consanguineous marriage occurs between two people of close genetic descent. This practice is particularly common among Muslims in the form of cousin marriages, though it has historically taken place in many different communities, and in some cases, it still prevails in non-Muslim contexts. Though they take place among Muslims, there is no real religious basis for the practice; it is driven by social and cultural factors, as well as potentially misconstrued ideas of what the faith of Islam promotes. Reasons for practice vary across communities, with some specifying family ties, wealth, and ease of marriage. The ancestral closeness between cousins in marriages has been associated with increased likelihood for offspring to develop diabetes, blood disorders, and other conditions. Hence, there is potential for medical harm to occur to children conceived in this partnership. Genetic counselling is a process whereby a couple is screened to assess the risk to any potential offspring. Reception to this process has been mostly positive, though with some caveats; studies have shown that there may lie the belief that whatever happens to offspring is at the will of a greater power, or that people are fearful of stigma or judgment, among other reasons for aversion to any part of the genetic counselling process. The present work has two key aims and corresponding studies; a scoping review and qualitative analysis of views related to cousin marriages located on Reddit. First, given the social and cultural importance of cousin marriages and use of genetic counselling, study 1 is a scoping review conceived and carried out to determine the current research landscape for perspectives on genetic counselling for consanguinity. Following Arksey and O’Malley’s framework for scoping reviews, over 1300 articles were screened, ultimately leading to the extraction of data from 44 articles. Education, religion, and gender were found to be correlated with perspectives on genetic counselling for consanguinity. Furthermore, research gaps were identified: The vast majority of studies were quantitative in nature with very few being qualitative, leaving a gap in methodology and need to understand differences in perspectives. Locations of studies were mostly regionally focused to Muslim majority countries, leaving a gap related to geographic area and the practice of Muslims in non-majority countries where cultural norms may differ. Finally, only one paper incorporated theory in its explanation of perspectives, creating an opportunity to outline findings in relation to existing theoretical frameworks. To address these gaps, a social media-based qualitative study was devised (study 2). The primary aim of this study was to analyse how people discuss cousin marriages and genetic counselling and inform how awareness of genetic counselling practices can be promoted for cousin marriages, enabling couples who are cousins to make educated choices about conception of children. Basing the study in social media allows for potentially varied perspectives from Muslims around the world rather than a small concentration of countries, and to get an understanding of the drivers of perspectives around genetic counselling for consanguinity, briefly drawing on relevant theory at a structural level to explain some findings (i.e. medicalization). The study involved scraping data from Reddit using the following subreddits: r/MuslimNikah, r/MuslimLounge, r/MuslimMarriage, r/Islam, r/Progressive_islam, and r/Islam_ahmadiyya. After data collection, the collected posts (and respective comments) were manually screened for relevance to addressing the study aim. Data were then analysed inductively using discourse analysis. Discourse analysis tends to be particularly focused on wording, arguments, and phrasing, so applying it in this case aided highlighting users’ discussion points surrounding cousin marriages and genetic counselling, aligning with the aim of the project. The results illustrate that while some users were against the practice of cousin marriages (i.e. expressing that practicers were akin to gamblers and irresponsible), those who were supportive of its practice or practitioners of it themselves cited dichotomies between religion and science, as well as fears of stigma towards their perspectives on cousin marriages. Those against cousin marriage cited comparisons between practicing cousin marriage and being ‘anti-science’. This shows the diversity in perspectives and underscores why further education and genetic counselling promotion is needed. The data can be used to inform future efforts to tailor promotion awareness and education surrounding genetic counselling in ways that appeal to those who may not see the use of it or disagree with it due to cultural or religious beliefs. When aiming for behaviour change, there can be tensions between drivers of that change (i.e. health promotion) and factors such as ideology, religion, and culture. The data contain several examples of the way people think of such factors (i.e. equating scientific concern with being anti-God), and this can be of service in sensitive promotion of genetic counselling awareness.
Perception-Aligned Representation Learning for 3D Visual Content
(University of Waterloo, 2026-05-28) Tang, Sheyang
3D visual content is ubiquitous across applications such as digital humans, product visualization, film and gaming, and AR/VR. While advances in 3D acquisition, modeling, and rendering have greatly improved technical fidelity, the success of 3D experience is ultimately judged by human observers. However, most existing 3D representations are designed for geometric accuracy or rendering efficiency, treating perceptual objectives as external post- rendering signals rather than properties modeled within the representation itself. This thesis studies perception-aligned representation learning for 3D visual content, aiming to learn 3D representations whose features and structure are shaped by human-centric signals so that perceptual goals become measurable and optimizable in downstream applications. We explore this idea across three settings and representation types, covering the evaluation, generation, and presentation stages of the 3D visual content pipeline: quality assessment of colored 3D meshes, controllable 3D generation with Implicit Neural Representation (INR), and aesthetic camera viewpoint suggestion using 3D Gaussian Splatting (3DGS). The first part focuses on perceptual quality evaluation of colored 3D meshes. Human judgments of mesh quality depend not only on geometric distortions or texture degradations individually, but also on their interactions, which are largely overlooked by existing Mesh Quality Assessment (MQA) methods. To address this, we propose HybridMQA, which learns perception-aligned quality representations by integrating topology-aware geometric learning with appearance cues from rendered images to model geometry–texture interplay. Across diverse datasets and distortion types, HybridMQA achieves superior assessment accuracy and provides interpretable localization of perceptually meaningful regions. The second part studies controllable 3D content generation using INRs, where generating new content amounts to generating neural network parameters. Prior methods commonly use a single “flat” latent representation, which ignores the hierarchical structure of INRs and leads to entangled semantics and limited control during generation. We propose a representation learning framework that aligns hierarchical semantics with the layer-wise structure of INR through layer-wise representations and cross-layer dependency modeling. Experiments demonstrate improved generation quality and interpretable control compared to prior generative INR baselines across 3D content and additional modalities. The third part investigates aesthetic camera viewpoint suggestion for presenting 3D content, aiming to efficiently identify viewpoints with high aesthetic appeal. Existing approaches either provide limited adjustments for an anchor view without 3D understanding or rely on dense scene captures and costly exploration. We introduce the 3D aesthetic field, which distills 2D aesthetic knowledge into a feed-forward 3DGS representation for spatially grounded aesthetic reasoning given sparse input views. Combined with an efficient search pipeline, the proposed method identifies aesthetically appealing viewpoints with improved framing and composition quality in both quantitative and qualitative evaluations. Overall, this thesis shows that perception-aligned representation learning enables direct measurement and optimization of perceptual objectives across multiple stages of the 3D visual content pipeline. Through perception-aware quality assessment, controllable generation, and aesthetic viewpoint suggestion, the proposed methods demonstrate how such alignment can improve evaluation, generation, and presentation of 3D visual content.
Dynamic Modelling and Energy-efficient Trajectory Planning of an Electric Fixed-wing Aircraft
(University of Waterloo, 2026-05-28) Shum, Christopher
Electrification of air transportation is an emerging technology with the potential to sig- nificantly reduce global greenhouse gas emissions. The Pipistrel Velis Electro, an all-electric fixed-wing light aircraft, is the first battery-electric aircraft to receive type certification in Canada. This technology represents an opportunity to eliminate aircraft exhaust emis- sions and reduce the cost of operation. However, similar to other electric vehicles, electric aircraft are subject to physically-limited energy density compared to liquid fuels, which makes energy-efficient operation critical to maximize their benefits. Toward this goal, the development of computational models based on system-specific performance can enable more precise operational planning. In this thesis, a series of models are developed that represent the aircraft’s subsystems that contribute to energy flow and consumption. First, a set of physics-based models was developed using established governing equations. Analogous black-box data-driven models consisting of feedforward neural networks are then trained to approximate physical relationships for aspects of the system. A neural network is also trained to approximate the residual error of the physics-based system model compared to the measured observations, then combined with the output of the physics-based model in a hybrid architecture to improve accuracy and compensate for system-specific and environment-specific deviation. The models are tuned and validated using real-world data collected from flight testing. The best-performing model is then used to estimate the energy consumption of the aircraft as a function of the change in state and exogenous variables. By mapping the state trajectory to a directed graph, graph-based optimization methods can be utilized. The projected energy consumption is mapped to the graph as edge costs, and a shortest- path algorithm is applied to find the minimum energy path with respect to the decision variables.
Fucoidan/Gelatin Microgel Formation with Droplet Microfluidics for Neural Spheroid Encapsulation
(University of Waterloo, 2026-05-28) Chenananporn, Pansit
Oxidized fucoidan (oFu) is a bioactive, sulfated polysaccharide that has demonstrated promising potential as a bio-inductive scaffold for neural engineering. Previous developments have characterized the anti-inflammatory properties of oFu; however, its capacity to direct the lineage commitment of neural progenitor cells (NPCs), which is critical for neuron regeneration, was not evaluated. The differentiation of NPCs desires a 3D culture that is more biomimetic to the natural in vivo microenvironment. Specifically, the structural similarity of fucoidan to heparan sulfate—a native extracellular matrix (ECM) component known for growth factor binding—suggests that fucoidan can similarly modulate the microenvironment to favor neuronal differentiation. Droplet microfluidics (DM), which can generate monodispersed emulsions of nanoliters at kHz rates by injecting fluid into another immiscible fluid in a microchannel network, offers a promising solution to precisely generate a high-throughput 3D culture environment when compared to conventional methods for differentiation culture, such as 2D surface coating. Therefore, this study was motivated by the implementation of an oFu-based matrix within DM architecture (Chapter 4). In line with enhancing neuronal differentiation, neural spheroid culture has shown to increase neuronal expression, thereby reducing the post-encapsulation duration needed. Therefore, we hypothesize that pre-formed neural spheroids within a droplet microfluidics-mediated oFu-based matrix would facilitate neuronal differentiation (Chapter 5). Following the introduction (Chapter 1) and materials and methods (Chapter 3), optimization of the operational parameters for incorporating oFu and other hydrogels into DM was performed through systematic experimental studies, where variables for gelation and degradation were identified and issues of spheroid-mediated channel adhesion, consistent droplet formation, and post-formation gelation were addressed (Chapter 4). Finally, the identified compatible gel systems were utilized to demonstrate neural spheroid encapsulation and evaluate their biological response (Chapter 5). Neural spheroids encapsulated in oxidized fucoidan (oFu)/gelatin methacrylate (GelMA) microgels were mostly viable and exhibited a significant upregulation of the early neuronal differentiation marker class III beta-tubulin. This study provides a foundational proof-of-concept for the integration of oFu/GelMA microgels with a droplet microfluidics platform.
Techno-economic assessment of sustainable aviation fuel production from captured CO2
(University of Waterloo, 2026-05-28) Rueda García, Fernando
In 2023, the aeronautics sector accounted for 2.5% of global energy-related CO2 emissions, having grown more rapidly in recent decades than rail, road, or shipping. While significant progress has been made in the electrification of vehicles and power generation, the aviation industry remains in search of a substitute fuel that can effectively reduce its carbon footprint. Among the technological pathways under consideration, the use of captured CO2 in the Reverse Water-Gas Shift (RWGS) process followed by Fischer–Tropsch (FT) synthesis is an attractive “alternative-to-oil” strategy for the production of Sustainable Aviation Fuel (SAF) This study evaluates the technical and economic performance of a Power-to-Liquids (PtL) plant designed to produce SAF from captured CO2. A central objective of this research was to maximize the kerosene fraction (C8–C16) while ensuring on-specification aromatic content. The system was assessed against key performance indicators, including jet fuel composition standards, energy consumption, carbon emissions, hydrogen and CO2 utilization efficiencies, and overall economic feasibility. Two primary cases based on the CO2 source were evaluated: 1. Large Point Source (LPS): CO2 captured from an industrial facility (specifically a steel plant) with an annual throughput of 1.88 million tonnes. 2. Direct Air Capture (DAC): CO2 captured directly from the atmosphere at a scale of 0.23 million tonnes per year. The study further distinguished two types of DAC: aqueous calcium looping- and solid sorbent adsorption- based DAC technologies. While the production scales differ, the DAC case being 8.2 times smaller, the underlying process design remains identical across both scenarios, comprising four main stages: water electrolysis, RWGS, Low-Temperature Fischer–Tropsch Synthesis (LT-FTS), and refining. To maximize SAF production, operating parameters for the two distillation columns in the refining section were judiciously selected, and wax yields were minimized via recycling into the hydrocracker. Consequently, the wax recycle fraction serves as a critical design variable. Furthermore, the refining section utilizes two distillation columns for which two reboiler heating methods were compared: conventional natural gas combustion and electrical heating. Results indicate that SAF production increases alongside the wax recycling fraction, reaching 447,000 and 54,000 tonnes per year for the LPS and DAC cases, respectively, at a 93% wax recycling rate. At a 50% SAF blend, the LPS production satisfies 30% of the annual jet fuel consumption at a major hub like Toronto Pearson, whereas the DAC case satisfies approximately 4%. In both cases, the natural gas-based configuration achieved the lowest Levelized Cost of Fuel (LCOF) at a 100% wax recycle rate. In contrast, the electrically heated configuration attained its minimum LCOF at 93% wax recycle rate in both cases. The LCOF values for the LPS case are consistently lower than those of the DAC case (e.g., 5.21 and 5.58 $ kg-1 SAF for natural gas- and electrically heated reboilers, respectively, in the LPS case, compared to 7.47 and 7.85 $ kg-1 SAF for the corresponding configurations in the DAC case at a 93% wax recycle rate). This research demonstrated that the environmental viability of producing SAF from captured carbon dioxide is governed primarily by the carbon intensity of the electricity grid, as well as by the carbon capture technology employed and the configuration of the downstream refinery system (i.e., wax recycle fraction and the method used to supply heat to the distillation column reboilers). The process has been engineered to maximize jet fuel production; however, co-products such as wax and heavy residues may act as carbon sinks, potentially enabling net-zero or even net-negative SAF carbon intensities under certain conditions. Climeworks DAC technology represents the most robust pathway for absolute decarbonization, consistently achieving the lowest carbon intensity (CI). In ultra-low-carbon grids such as British Columbia, it functions as a net-negative carbon sink (via carbon retained in waxes) across nearly the entire operating range, while maintaining a strong carbon-mitigation advantage even under intermediate grids (e.g., Ontario). In contrast, Carbon Engineering DAC operates primarily as a carbon-mitigation pathway rather than a net-negative solution, as approximately one-third of the carbon incorporated into SAF originates from natural gas used in the calciner. Nevertheless, it can still achieve well-to-wake (WtW) carbon intensities significantly lower than those of conventional fossil jet fuel. The LPS pathway consistently outperforms calcium-looping DAC and can achieve net-negative emissions under low-carbon electricity grids at low-to-moderate wax recycling fractions. Full electrification of reboiler duty is required except in ultra-low-carbon grids. Reliance on natural gas combustion for process heat, particularly at high wax recycle fractions, can cause both Carbon Engineering and LPS pathways to exceed the lifecycle carbon intensity of conventional fossil jet fuel (~3.85 tonne CO2 per tonne SAF), thereby rendering the synthetic fuel environmentally counterproductive.

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