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    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.
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    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.
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    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.
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    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.
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    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.