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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    Cyclicité de la crise dans le roman abitibien : Jocelyne Saucier, Louis Hamelin, Biz
    (University of Waterloo, 2025-03-31) Mitchell, S. James
    Dans le sillage des théories de Naomi Klein (La doctrine du choc, 2007) et de David Harvey (L’énigme du capital et les crises du capitalisme, 2010), cette thèse analyse les cycles de boom and bust qui caractérisent le capitalisme en général, mais dont les conséquences sont souvent démultipliées dans des contextes de régions marginalisées et donc plus vulnérables comme l’Abitibi. Je retrace les représentations des effets de cette cyclicité économique à travers trois romans qui caractérisent l’Abitibi au cours du siècle dernier : Jeanne sur les routes (2006) de Jocelyne Saucier, Autour d’Éva (2016) de Louis Hamelin et Mort-Terrain (2014) de Biz. En effet, la longue histoire des opérations minières dans la région a produit un contexte idéal pour l’utilisation de récits de crise. Mes recherches mettent l’accent sur les caractéristiques historiques et géographiques de l’Abitibi, notamment sa position frontalière au Québec. Les divers types de distance créés par ces circonstances placent la région en position de faiblesse face aux forces puissantes qui agissent sur elle. Les récits sur la récurrence des crises économiques sont utilisés pour justifier la mise en œuvre de mesures d’austérité au nom d’une plus grande centralisation économique. Dans son ensemble, le corpus dépeint le déclin à long terme de la région tout en soulignant les circonstances particulières des périodes clés. Par ailleurs, chacun des trois romans éclaire une dimension sociopolitique différente. L’univers de Saucier met l’accent sur les considérations de solidarité ouvrière et suggère un parallèle entre idéologie politique et religieuse. Le roman de Hamelin propose des descriptions étoffées de l’environnement naturel et met en scène un conflit entre les partisans du développement touristique et les partisans de la préservation, tandis que Mort-Terrain de Biz met en lumière la façon dont la santé publique est liée aux questions économiques.
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    Development of Novel Surface Finishing Processes for Additively Manufactured Metal Parts
    (University of Waterloo, 2025-03-31) Sun, Manyou
    Poor surface quality is one of the drawbacks of metal parts made by various additive manufacturing (AM) processes. They normally possess high surface roughness and different types of surface irregularities. Post-processing operations are needed to reduce the surface roughness to have ready-to-use parts. Among all the surface treatment techniques, electrochemical surface finishing methods have the highest finishing efficiency. However, there are challenges with electropolishing in terms of reducing surface roughness of metals parts made via AM. Firstly, parts made with AM have both small-scale surface roughness and large-scale surface waviness. Electropolishing is only suitable for the reduction of micro-scale surface roughness while it is difficult to use the method to remove meso- to macro-scale surface waviness. In addition, it is still challenging to use electropolishing to reduce the surface roughness of internal channels of additively manufactured parts, benefiting from the promising feature of AM to produce parts with complex internal geometries. Finally, how to improve process sustainability is another question that needs to be addressed, since hazardous and corrosive chemicals are always used for the technique. To address the aforementioned problems, novel approaches were developed, incorporating both modeling and experimental investigations. Analytical and numerical models were constructed to explore the mechanisms of electropolishing and to understand the surface evolution during the process. The results offer valuable insights that can guide the design of experiments and foster the development of novel processes. The first experimental study focuses on using hybrid surface finishing technique to reduce meso-/macro- surface waviness. A novel surface finishing technique combining electrochemical polishing, ultrasonic cavitation and abrasive finishing was designed. Experiments were conducted on both electropolishing and hybrid finishing and the results were compared. While similar optimal arithmetic mean height values (Sa ≈ 1 μm) are achieved for both processes, the arithmetic mean waviness values (Wa) obtained from hybrid finishing are much less than those from sole electropolishing after the same processing time. The second experimental investigation aims at electropolishing internal channels. For doing this, a novel cathode tool was invented and fabricated using polymer 3D printing. Electropolishing was conducted on both straight and curved channels with different curvatures. Preliminary experiments demonstrated a maximum surface roughness Sa reduction, from 10.86 ± 0.50 μm to 1.44 ± 0.46 μm. Apart from this, electropolishing failure mechanisms were explained and design optimization was conducted through numerical simulation. The investigations show that the method is promising in reducing surface roughness of internal channels. In addition, experimental trials were also conducted to improve the sustainability of the surface finishing processes, including using greener electrolytes for electropolishing, and developing shear thickening polishing. Both alcohol-salt electrolyte system and deep eutectic solvent electrolyte were investigated, demonstrating effective surface roughness reduction. Shear thickening polishing using the corn starch slurry was also explored. In spite of some promising results, the process was not repeatable due to numerous influencing factors.
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    Assessing Critical Metal Incorporation in Ca-Carbonate Minerals using Cyanobacteria: Application to Mine Site Environments
    (University of Waterloo, 2025-03-28) Lei, Benjamin
    Meeting the current global market demand for critical metals will require an increase in the number of mining operations in Canada. While mining will generate mine tailings, which can be an environmental concern, tailings also present an opportunity for carbon sequestration and metal recovery. Carbon sequestration in mine tailings can be implemented by using divalent cations from the tailings to form stable carbonate minerals. Ultramafic mine tailings typically contain an abundance of divalent cations, including various transition metals, that can be incorporated into carbonate minerals. During this process, critical metal enrichments are possible, and thus tailings may become valuable sources of metals as high-grade ore deposits continually become less accessible for mining. Microorganisms, including cyanobacteria, can contribute to carbonate mineral precipitation, however, this process is understudied with respect to transition metal incorporation into biogenic carbonate minerals. This thesis explores the application of these processes to ultramafic mine sites through two laboratory experiments using a pure culture of cyanobacteria, 𝘚𝘺𝘯𝘦𝘤𝘩𝘰𝘤𝘰𝘤𝘤𝘶𝘴 𝘭𝘦𝘰𝘱𝘰𝘭𝘪𝘦𝘯𝘴𝘪𝘴. In the first experiment, a biosorption study explores the metal sorption abilities of 𝘚. 𝘭𝘦𝘰𝘱𝘰𝘭𝘪𝘦𝘯𝘴𝘪𝘴 in a nutrient limited environment. The second experiment examines the incorporation of transition metals into precipitates during microbial mineral carbonation. In Chapter 2, the ability of 𝘚. 𝘭𝘦𝘰𝘱𝘰𝘭𝘪𝘦𝘯𝘴𝘪𝘴 to remove Co²⁺ and Ni²⁺ from solution via biosorption in a nutrient limited environment what tested in a lab experiment. Comparison between nutrient enriched conditions and a nutrient deficient condition (simulating an ultramafic mine site solution) was conducted in mono-metal and di-metal systems. The results revealed that measured nickel and cobalt concentrations were lowest in the first 3 days of the experiment, which indicates a fast metal removal rate. The biosorption of nickel and cobalt was upwards of 34.2–49.4% removal of metal from solution. Imposing nutrient limitations caused increased production of extracellular polymeric substances (EPS), which can increase metal sorption, and resulted in a decrease in measured nickel concentrations in solution in the di-metal system. The findings from this experiment indicate that inducing additional stress through metal exposure and nutrient limitations can increase the metal biosorption capacity of 𝘚. 𝘭𝘦𝘰𝘱𝘰𝘭𝘪𝘦𝘯𝘴𝘪𝘴. In Chapter 3, a microbially induced carbonate precipitation experiment was conducted to test the incorporation of nickel and cobalt into biogenic calcium carbonate mineral precipitates. These results are preliminary due to an experimental failure that occurred. Nevertheless, the measured concentrations of dissolved cobalt and nickel in solution indicated metal(s) removal success of up to 89.5% and 94.5% in the first day after metal addition. Observation of the biofilms using scanning electron microscopy (SEM) revealed nanometer-scale amorphous calcium carbonate (ACC) precipitates. This preliminary result suggests that inducing calcium carbonate precipitation may remove dissolved metals solution. The results from Chapter 2 and Chapter 3 together reveal that 𝘚. 𝘭𝘦𝘰𝘱𝘰𝘭𝘪𝘦𝘯𝘴𝘪𝘴 can quickly remove metals from solution, which could be applied to both metal recovery and remediation projects. Data from this research could be applied to the development of photobioreactors at ultramafic mine sites. The outcomes suggest that inducing nutrient limitation can enhance metal removal by increasing metal binding through enhanced EPS production. The research presented in this thesis will contribute to the development of sustainable mine operations, with the aim of recovering metals from tailings, and lowering CO₂ emissions, thereby working towards net-neutral mining operations.
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    Translanguaging - New Approaches for Teaching German as a Second Language?
    (University of Waterloo, 2025-03-28) Hoelderle, Leonie Theresa
    Given the great diversity of students, there are numerous hurdles to overcome in teaching German as a Second Language in Germany – one of which is the large number of different language backgrounds of the students. The concept of (pedagogical) translanguaging, which has become increasingly popular in the US in recent years, opens a new approach to integrating these heterogeneous language skills into German Second Language lessons in a beneficial way in German schools. To this end, this thesis examines the theoretical, political, descriptive, and pedagogical aspects of translanguaging, in particular based on García and Li (2014), García, Johnson and Seltzer (2017) and García and Kleifgen (2020). The aim is to create a basis for the subsequent analysis of the BAMF-Rahmencurriculum and the educational documents of the three federal states of Baden-Württemberg, Bavaria and Hamburg regarding their orientation to multilingual education. The analysis revealed a generally positive attitude towards multilingualism in the documents, which gives room for the inclusion of pedagogical translanguaging. However, in terms of language policy, due to their nature as ideologized educational instruments, these documents still fall short of the language policy expectations of pedagogical translanguaging.
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    A Study of Statistical Methods for Modelling Longevity and Climate Risks
    (University of Waterloo, 2025-03-27) Guo, Yiping
    In recent years, two pivotal risks have emerged and taken a significant position in modern actuarial science: longevity risk and climate risk. Longevity risk, or the risk of individuals living longer than expected, poses a severe challenge to both private insurance companies and public pension systems, potentially destabilizing financial structures built on assumptions of life expectancy. On the other hand, climate risk, associated with fluctuations and extreme conditions in weather, has substantial implications for various sectors such as agriculture, energy, and insurance, particularly in the era of increasing climate change impacts. The Society of Actuaries (SOA) has recognized the growing importance of these risks, advocating for innovative research and solutions to manage them effectively. Furthermore, statistical modelling plays an indispensable role in understanding, quantifying, and managing these risks. The development of sophisticated and robust statistical methods enables practitioners and researchers to capture complex risk patterns and make reliable predictions, thereby informing risk management strategies. This thesis, composed of four distinct projects, explores statistical methods for modelling longevity and weather risk, contributing valuable insights to these fields. The first part in this thesis studies the statistical methods for modelling longevity risk, and in particular, modelling mortality rates. In the first chapter, we study parameter estimation of the Lee-Carter model and its multi-population extensions. Although the impact of outliers on stochastic mortality modelling has been examined, previous studies on this topic focus on how outliers in the estimated time-varying indexes may be detected and/or modelled, with little attention being paid to the adverse effects of outliers on estimation robustness, particularly that pertaining to age-specific parameters. In this chapter, we propose a robust estimation method for the Lee-Carter model, through a reformulation of the model into a probabilistic principal component analysis with multivariate t-distributions and an efficient expectation-maximization algorithm for implementation. The proposed method yields significantly more robust parameter estimates, while preserving the fundamental interpretation for the bilinear term in the model as the first principal component and the flexibility of pairing the estimated time-varying parameters with any appropriate time-series process. We also extend the proposed method for use with multi-population generalizations of the Lee-Carter model, allowing for a wider range of applications such as quantification of population basis risk in index-based longevity hedges. Using a combination of real and pseudo datasets, we demonstrate that the superiority of the proposed method relative to conventional estimation approaches such as singular value decomposition and maximum likelihood. Next, we move onto parameter estimation of the Renshaw-Haberman model, a cohort-based extension to the Lee-Carter model. In mortality modelling, cohort effects are often taken into consideration as they add insights about variations in mortality across different generations. Statistically speaking, models such as the Renshaw-Haberman model may provide a better fit to historical data compared to their counterparts that incorporate no cohort effects. However, when such models are estimated using an iterative maximum likelihood method in which parameters are updated one at a time, convergence is typically slow and may not even be reached within a reasonably established maximum number of iterations. Among others, the slow convergence problem hinders the study of parameter uncertainty through bootstrapping methods. In this chapter, we propose an intuitive estimation method that minimizes the sum of squared errors between actual and fitted log central death rates. The complications arising from the incorporation of cohort effects are overcome by formulating part of the optimization as a principal component analysis with missing values. Using mortality data from various populations, we demonstrate that our proposed method produces satisfactory estimation results and is significantly more efficient compared to the traditional likelihood-based approach. The third part of this thesis continues our exploration of the efficient computational algorithm of the Renshaw-Haberman model. Existing software packages and estimation algorithms often rely on maximum likelihood estimation with iterative Newton-Raphson methods, which can be computationally intensive and prone to convergence issues. In this chapter, we present the R package RHals, offering an efficient alternative with an alternating least squares method for fitting a generalized class of Renshaw-Haberman models, including configurations with multiple age-period terms. We extend this method to multi-population settings, allowing for shared or population-specific age effects under various configurations. The full modelling workflow and functionalities of RHals are demonstrated using mortality data from England and Wales. Lastly, we turn to modelling climate risk in the final chapter of the thesis. The use of weather index insurances is subject to spatial basis risk, which arises from the fact that the location of the user's risk exposure is not the same as the location of any of the weather stations where an index can be measured. To gauge the effectiveness of weather index insurances, spatial interpolation techniques such as kriging can be adopted to estimate the relevant weather index from observations taken at nearby locations. In this chapter, we study the performance of various statistical methods, ranging from simple nearest neighbor to more advanced trans-Gaussian kriging, in spatial interpolations of daily precipitations with data obtained from the US National Oceanic and Atmospheric Administration. We also investigate how spatial interpolations should be implemented in practice when the insurance is linked to popular weather indexes including annual consecutive dry days (CDD) and maximum five-day precipitation in one month (MFP). It is found that although spatially interpolating the raw weather variables on a daily basis is more sophisticated and computationally demanding, it does not necessarily yield superior results compared to direct interpolations of CDD/MFP on a yearly/monthly basis. This intriguing outcome can be explained by the statistical properties of the weather indexes and the underlying weather variables.
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    Advancing Photometric Odometry to Dense Volumetric Simultaneous Localization and Mapping
    (University of Waterloo, 2025-03-25) Hu, Yan Song; Zelek, John
    Navigating complex environments remains a fundamental challenge in robotics. At the core of this challenge is Simultaneous Localization and Mapping (SLAM), the process of creating a map of the environment while simultaneously using that map for navigation. SLAM is essential for mobile robotics because effective navigation is a prerequisite for nearly all real-world robotic applications. Visual SLAM, which relies solely on the input of RGB cameras is important because of the accessibility of cameras, which makes it an ideal solution for widespread robotic deployment. Recent advances in graphics have driven innovation in the visual SLAM domain. Techniques like Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) enable the rapid generation of dense volumetric scenes from RGB images. Researchers have integrated these radiance field techniques into SLAM to address a key limitation of traditional systems. Although traditional SLAM excels at localization, the generated maps are often unsuitable for broader robotics applications. By incorporating radiance fields, SLAM systems have the potential for the real-time creation of volumetric metric-semantic maps, offering substantial benefits for robotics. However, current radiance field-based SLAM approaches face challenges, particularly in processing speed and map reconstruction quality. This work introduces a solution that addresses limitations in current radiance fields SLAM systems. Direct SLAM, a traditional SLAM technique, shares key operational similarities with radiance field approaches that suggest potential synergies between the two systems. Both methods rely on photometric loss optimization, where the pixel differences between images guide the optimization process. This work demonstrates that the benefits of combining these complementary techniques extend beyond theory. This work demonstrates the synergy between radiance field techniques and direct SLAM through a novel system that combines 3DGS with direct SLAM, achieving a superior combination of quality, memory efficiency, and speed compared to existing approaches. The system, named MGSO, addresses a challenge in current 3DGS SLAM systems: Initializing 3D Gaussians while performing SLAM simultaneously. The proposed approach leverages direct SLAM to produce dense and structured point clouds for 3DGS initialization. This results in faster optimization, memory compactness, and higher-quality maps even with mobile hardware. These results demonstrate that traditional direct SLAM techniques can be effectively integrated with radiance field representations, opening avenues for future research.
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    Transformer-based Point Cloud Processing and Analysis for LiDAR Remote Sensing
    (University of Waterloo, 2025-03-24) Lu, Dening; Li, Jonathan; Xu, Linlin
    The processing and analysis of Light Detection and Ranging (LiDAR) point cloud data, a fundamental task in Three-Dimensional (3D) computer vision, is essential for a wide range of remote sensing applications. However, the disorder, sparsity, and uneven spatial distribution of LiDAR point clouds pose significant challenges to effective and efficient processing. In recent years, Transformers have demonstrated notable advantages over traditional deep learning methods in computer vision, yet designing Transformer-based frameworks tailored to point clouds remains an underexplored topic. This thesis investigates the potential of Transformer models for accurate and efficient LiDAR point cloud processing. Firstly, a 3D Global-Local (GLocal) Transformer Network (3DGTN) is introduced to capture both local and global context, thereby enhancing model accuracy for LiDAR data. This design not only ensures a comprehensive understanding of point cloud characteristics but also establishes a foundation for subsequent efficient Transformer frameworks. Secondly, a fast point Transformer network with Dynamic Token Aggregation (DTA-Former) is proposed to improve model speed. By optimizing point sampling, grouping, and reconstruction, DTA-Former substantially reduces the time complexity of 3DGTN while retaining its strong accuracy. Finally, to further reduce time and space complexity, a 3D Learnable Supertoken Transformer (3DLST) is presented. Building on DTA-Former, 3DLST employs a novel supertoken clustering strategy that lowers computational overhead and memory consumption, achieving state-of-the-art performance across multi-source LiDAR point cloud tasks in terms of both accuracy and efficiency. These Transformer-based frameworks contribute to more robust and scalable LiDAR point cloud processing solutions, supporting diverse remote sensing applications such as urban planning, environmental monitoring, and autonomous navigation. By enabling efficient yet high-accuracy analysis of large-scale 3D data, this work fosters further research and innovation in LiDAR remote sensing.
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    Freaking Fans: An Oral History of Disability in Fan Spaces
    (University of Waterloo, 2025-03-24) Vero, Eric; Milligan, Ian; Dolmage, Jay
    This oral history, under the lens of critical access studies, provides case studies that illustrate the long and interconnected history of disability and fan communities. Through interviews of eight disabled fans from varying communities, I have discovered a key understudied theme in the shared history of disability and fandoms. I argue that a fan community is a relational space where fans share access with each other. To be a fan is to offer room in this shared space for people of similar body/minds. For disabled fans, their identity founded on lived experiences facilitates relationships or fosters barriers within these fan spaces, constituting “access.” Disability activism within fan spaces consists of disabled fans finding empowerment through creating inclusive spaces that further empower other disabled individuals who share in this space. Disabled fans seek inclusion through providing extra room in spaces for others that they see themselves in. In this way, relationships form and sustain fan spaces. Rather than conceiving of fandom as a textual relationship between fan and creator, I advocate for considering how fans construct their own spaces, their opening or closing of which reveals whom they identify with among their fan communities. This reveals a perceived hierarchy based in historical forces in fandom that excludes marginalized groups, not just the disabled community. However, “fannish” acts and practices of inclusion resist the exclusion present in fan spaces where historical forces such as ableism encourage fans to share space at the expense of the marginalized. This is a novel and useful paradigm to conceive of fan communities as inclusive and exclusive spaces, as it reveals the hidden lives of my interviewees who shared with me their practices of inclusion, accessibility, and access. This study is also activist for stressing the importance of joy and pleasure in the disabled experience to complement the more common disability narratives of marginalization and activist struggles.
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    The Aesthetics of Resistance in Australian-run Immigration Detention Centres on Manus Island: The Case Study of Behrouz Boochani
    (University of Waterloo, 2025-03-24) Mostolizadeh, Sayedali; Ilcan, Suzan
    The offshore detention regime for asylum seekers represents a contested model of border control, punishing those seeking refuge and violating their fundamental rights. This research, however, reveals a powerful counterpoint: the use of art and creativity as tools of resistance by detainees. Through the story of Behrouz Boochani, an Iranian-Kurdish journalist and former Manus Island detainee, this study illuminates the power of art and creativity in challenging this system. Despite his lengthy confinement, Boochani produced a remarkable body of creative work that exposed the harsh realities of detention, creating a counter-narrative that gained international acclaim. This research introduces creativity as a tool for political activism, challenging the invisibility inherent in offshore detention. The concept of ‘creative subjectivation’; is presented as an analytical framework to understand how creative practices facilitate the transformation of marginalized refugees into active political subjects. To explore this, the study investigates the structural, operational, and experiential dimensions of offshore detention, from macro-level border policies to micro-level dynamics within detention centers. Drawing on qualitative in- depth interviews with Boochani, his creative collaborators, journalists, human rights advocates, and former detainees, this study provides a multifaceted perspective on creative resistance within highly restrictive spaces of border enforcement. The dissertation comprises seven chapters that explore themes of border politics, the evolution of Australia’s offshore detention policies, the lived experiences within detention centers, and the transformative potential of creative resistance and includes the production of a documentary film that offers an immersive and sensorial exploration of creative resistance and migrant activism within the offshore detention regime. This project contributes to critical migration and border studies by illuminating the transformative potential of creative resistance in contexts of extreme marginalization. It offers new insights into refugee agency, migrant politics, border politics, and the role of art in contesting anti-asylum policies and practices.
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    Microstructure control and property enhancement of NiTi-stainless steel dissimilar joints
    (University of Waterloo, 2025-03-21) Zhang, Kaiping; Zhou, Y. Norman; Peng, Peng
    Dissimilar joining between Nickel-Titanium (NiTi) and stainless steel (SS) is of significance in many areas especially biomedical applications, however, achieving reliable NiTi-SS joints is highly challenging due to the formation of brittle intermetallic compounds (IMCs) in the fusion zone (FZ) or the interface. Two strategies can be summarized to address this issue: (1) restricting the mixing of molten metals and (2) replacing the most harmful Laves (Fe,Cr)2Ti with ductile phases. The former one poses large processing complexity and may lead to NiTi plastic deformation degrading the functional properties. The latter struggles to eliminate brittle IMCs entirely in the FZ and may introduce toxic elements. This research investigated both aspects to control the microstructure and properties of NiTi-SS joints by leveraging the flexibility of laser beam and the thermomechanical process of resistance welding. The combination of laser beam defocus and large offset enabled the laser weld-brazing of NiTi and SS wires. This approach successfully eliminated the IMCs network in the FZ, shifting the conventional and complex FZ brittleness issue to a focus on controlling the brazed interface. Additionally, laser welding mode significantly influenced the macrosegregation and porosity in the FZ of NiTi-SS joints. Low laser power density and long welding time mitigated the macrosegregation and porosity by weakening the laser keyhole effect and prolonging the molten pool duration. In NiTi-SS laser weld FZ, large pores were caused by the instability or collapse of the laser keyhole, while small pores originated from the Ni vaporization. Both IMCs control strategies were investigated in resistance spot welding (RSW) of NiTi and SS for the first time. The use of Nb interlayer resulted in a unique sandwich-structured joint, where two FZs were separated by solid-state Nb, suppressing the mixing of dissimilar molten metals. Nb-containing eutectics formed at both interfaces, enhancing the joint strength with a 38% increase in fracture load and a remarkable 460% increase in energy absorption. In another approach, increasing Ni concentration via a melted Ni interlayer effectively replaced Fe2Ti with relatively ductile Ni3Ti in the FZ. However, high Ni content also induced large pores and cracks, limiting the effectiveness of this strategy in NiTi-SS RSW. A novel processing approach leveraging interfacial liquid control was proposed, achieving a solid-state joined interface in NiTi-SS fusion welding (e.g., resistance microwelding) without any additional interlayers. The produced NiTi-SS joints showed superior strength, superelasticity and corrosion resistance compared to NiTi joints or base metal. The ultrathin reaction layer at the solid-state joined interface contributed to a strong metallurgical bonding, while Joule heating effects and interfacial reactions enhanced superelasticity and corrosion resistance of the joint. Notably, a face-centered-cubic (FCC) reorientated layer (ROL) was found between SS and IMC layer at the controlled ultrathin interface. The formation of this ROL was uncovered based on an epitaxial growth model. This ROL introduced a strong crystallographic mismatch with the textured SS, resulting in the fracture at this interface. These phenomenal findings offer valuable insights for studying material interface and controlling dissimilar-metal welding process.
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    Influence of Absorbency and Additives on Performance of Battery-Free IoT Water Leak Sensors
    (University of Waterloo, 2025-03-19) MacGregor, Oluwadamilola Solomon; Zhou, Norman
    Leak detection is a reliable solution for controlling the potentially destructive outflow and wastage of water. Several types of devices are used in domestic and industrial spaces; however, most have their power sources run out, and thus require battery change. The associated costs add to overhead expenditure of the user. This necessitates the use of leak detectors that are self-powered, having no use for external sources of power. Integrating water leak detection systems with Internet of Things (IoT) technology such as Bluetooth low energy (BLE) and long-range (LoRa) protocols provides advantages such as real-time monitoring, which informs incidents and ultimately saves huge cost. The use of IoT-enabled sensors and cloud-based data analytics offers pre-emptive control mechanisms for prompt identification and containment of localized leaks. This helps reduce wastage of water and damage to property, both of which reduce costs as remote access through IoT networks guarantee instant notifications for preventative measures. Scalability fosters effortless deployment in residential, commercial, and industrial environments. In a self-powered IoT water leak device, parameters such as capillary action and electrochemical reactions directly impact power generation and beacon activation. Energy generation and harvesting happen as water interacts with active materials within the sensor device. There must be a cathode and an anode, to interact with the leaking water which would be the electrolyte. Therefore, the materials selected to play such roles in the device are crucial for the desirable chemical interactions, once in contact with the leaking water. In a water leak detector where the most crucial feature is sensitivity to water, capillary action is one of the most significant parameters to consider. Both the design of the sensor casing and channels through which the water travels, are to foster a seamless flow. Also, within the sensor chamber, each material in the stack must demonstrate capillarity. Therefore, porosity is key, as their pore sizes determine what material passes through and what might otherwise be trapped to impede the flow of the water being transmitted. Therefore, capillary action is explored for absorbent materials and the sensor casing. Both filter paper (FP) and fabric materials are examined, to ascertain which one gives optimally combined advantages for absorbency and repeatability. FP showed superior performance, due to its pore size. This advantage becomes particularly useful where additives are considered for the powder mixture. Without additives, the stacked materials have only water to interact with. While this is sufficient to power BLE, it is not enough for LoRa technologies which require higher power. To account for this, additives can be included in the materials within the sensor stack. Salts are among such additives that can provide active ions when interacting with water. Subsequently, these ions facilitate electricity generation due to increased current. Therefore, the power output of the device can be increased when additives are introduced. In previous similar works, it was shown that pure materials without any additives produce an open-circuit voltage (OCV) of 2 V and short-circuit current (SCC) of only 10 mA. This combination was able to power the sensor for beacon activation through 7 cycles of wetting-drying rounds of repeatability, but only for the BLE protocol. To solve for this limitation, NaCl was added in varied proportions. 10 wt.% NaCl was found to outperform other samples. After several rounds of repeatability, the values of current and voltage were observed to diminish. A sensor without NaCl typically lasts 7 rounds of repeatability, sensors containing NaCl last only about 3 rounds. The primary concern with the use of such additives may be an imminent trade-off between the increased power generation and possible corrosion which compromises shelf life. One of the downsides of using additives to enhance power generation is the corrosion of metallic materials in the sensor. To study the effect of NaCl on the corrosion of the metallic material, and thus the shelf life of the sensor, electrochemical corrosion tests were performed. As expected, it was observed that higher salt content resulted in higher corrosion rate. Therefore, repeatability was significantly reduced in higher salt contents, thereby limiting the overall shelf life of the sensor. Ultimately, the use of salts should be limited and be specific to the target use case.
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    Multi-Object Tracking using Mamba and an Investigation into Data Association Strategies
    (University of Waterloo, 2025-03-19) Khanna, Dheraj; Zelek, John
    Multi-Object Tracking (MOT) is a critical component of computer vision, with applications spanning autonomous driving, video surveillance, sports analytics, and more. Despite significant advancements in tracking algorithms and computational power, challenges such as maintaining long-term identity associations, handling dynamic object counts, managing irregular movements, and mitigating occlusions persist, particularly in complex and dynamic environments. This research addresses these challenges by proposing a learning-based motion model that leverages past trajectories to improve motion prediction and object re-identification, and we also investigate how to maximize the performance of trackers with data association. Inspired by recent advancements in state-space models (SSMs), particularly Mamba, we propose a novel learning-based architecture for motion prediction that combines the strengths of Mamba and self-attention layers to effectively capture non-linear motion patterns within the Tracking-By-Detection (TBD) paradigm. Mamba's input-dependent sequence modeling capabilities enable efficient and robust handling of long-range temporal dependencies, making it well for complex motion prediction tasks. Building on this foundation, we explore hybrid data association strategies to improve object tracking robustness, particularly in scenarios with occlusions and identity switches. By integrating stronger cues such as Intersection over Union (IoU) for spatial consistency and Re-Identification (Re-ID) for appearance-based matching, we enhance the reliability of object associations across frames, reducing errors in long-term tracking. Fast motion and partial overlaps often lead to identity mismatches in object tracking. Traditionally, spatial association relies on IoU, which can struggle in such scenarios. To address this, we enhance the cost matrix by incorporating Height-based IoU to handle partial overlaps more effectively. Additionally, we extend the original bounding boxes with a buffer to account for fast motion, thereby improving the robustness and accuracy of the spatial association process. Additionally, we study the impact of dynamically updating the feature bank for Re-ID during the matching stage, culminating in a refined weighted cost matrix. To further address challenges in identity switching and trajectory consistency, we introduce the concept of virtual detections in overlapping scenarios and explore its effectiveness in mitigating ID switches. Developing a robust and accurate MOT tracker demands a critical interplay between accurate motion modeling and a sophisticated combination of stronger and weaker cues in data association. Through extensive experimental evaluations on challenging benchmarks such as DanceTrack and SportsMOT, the proposed approaches achieve significant performance gains, with HOTA scores of 63.16% and 77.26% respectively, surpassing multiple existing state-of-the-art methods. Notably, our approach outperforms DiffMOT by 0.9% on DanceTrack and 0.06% on SportsMOT, while achieving 3- 7% improvements over other learning-based motion models. This work contributes to advancing MOT systems capable of achieving high performance across diverse and demanding scenarios.
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    Low-power and Radiation Hardened TSPC Registers
    (University of Waterloo, 2025-03-19) Maheshwari, Yugal; Sachdev, Manoj
    Battery-operated systems require power and energy-efficient circuits to extend their battery life. Flip-flops (FFs) are a basic component of digital circuits, and their power consumption and speed significantly impact the overall performance of a digital system. A clock network in a complex System-on-Chip (SoC) consumes a substantial amount of power. Additionally, often pipelines are used to enhance the system throughput, which puts additional burden on the clock network. Arguably, a flip-flop with fewer clock transistors will reduce its power burden on the clock network. This research proposes three very low-power Single-edge Triggered (SET) True Single-phase Clock (TSPC) FFs with only two and three clock transistors. Moreover, a scan-chain of 256 FFs and AES-128 encryption engine were designed as a benchmark to further investigate the power savings of the proposed FFs. Additionally, we have also designed three very low-power Dual-edge Triggered (DET) latch-multiplexer type TSPC FFs with only eight and ten clock transistors to sample the data at both positive and negative clock edges. Furthermore, high-performance computations in Integrated Circuits (ICs) are increasingly needed for space and safety-critical applications. ICs are subjected to high-energy ionizing particles in the radiant space environment, which will cause the device performance to degrade or even fail. A Single Event Upset (SEU) occurs in the logic circuit when an ion strikes a device’s sensitive node, changing the output from 0 to 1 or from 1 to 0. In radiant applications, ICs contain storage cells like FFs, latches, or Static Random Access Memories (SRAM), and always experience SEU. Although package and process engineering can minimize alpha particles, cosmic neutrons cannot be physically blocked. Therefore, for high reliability systems, soft error tolerant circuit designs are crucial. Traditional Radiation Hardened By Design (RHBD) techniques have some trade-offs between area, speed, power, and energy consumption. Thus, new designs are required to reduce these penalties. This research proposes two high-performance, low-power, low-energy, and low-area RHBD TSPC FFs with only four and five clock transistors suitable for space and safety-critical applications.
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    Investigating Dual Embodiment in Recurring Tasks with a New Social Robot: Designing the Mirrly Platform
    (University of Waterloo, 2025-03-19) Yamini, Ali; Dautenhahn, Kerstin
    In many contexts, including education, therapy, and everyday tasks, assistive robots have demonstrated considerable promise for augmenting human capabilities and providing supportive interactions. By designing and building a new tabletop social robot, Mirrly, as well as empirically examining how different robotic embodiments affect user engagement and task compliance, this thesis tries to contribute to this field. In light of advances in human-robot interaction (HRI) and child-robot interaction (CRI), I investigated a comprehensive set of mechanical, electronic, and software requirements. As a result of these requirements, Mirrly was developed, a low-cost, compact platform that could be deployed in schools, therapy centers, or personal homes and it is anthropomorphic enough for supporting social interactions with people. Following the design and implementation of Mirrly, I conducted a multi-session experiment to determine whether physical embodiment, virtual embodiment (mobile-based), or dual embodiment (both physical and virtual) promoted compliance with repetitive daily tasks, as relevant e.g. in clinical applications where patients need to comply with repetitive treatments. According to the results, physical presence is a strong motivator, leading to higher compliance and engagement, whereas dual embodiment enhanced participants' enjoyment (pleasure) of the interaction specifically. Interestingly, individual differences in the participant sample, such as personality traits and self-control, did not have a significant impact on adherence or user satisfaction. As at least within the short, relatively simple user tasks, these results emphasize the importance of design factors namely physical tangibility and interactive behaviors. As part of the thesis, a review of relevant HRI and CRI literature is conducted to contextualize Mirrly's design within the context of current robotics. Following a detailed description of utilized methodology, I present the experimental conditions, measures, and analytical methods for assessing compliance, engagement, and perceived enjoyment. Finally, I discuss the implications of the findings for building more adaptive, child-centered robots, especially in clinical, therapeutic and educational settings. Several future directions are also proposed, including extending task complexity, integrating advanced sensors for personalized feedback, and conducting longitudinal studies. As part of ongoing efforts in social and assistive robotics, this work introduces a novel robotic design. Moreover, in my study, I demonstrate that a robot with careful engineering, physical embodiment, and adaptability can significantly boost compliance. Consequently, this thesis lays a good foundation for future developments in CRI, highlighting how embodiment, anthropomorphism, and structured experimental design converge to support recurrent task compliance efficiently.
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    Chemo-rheological Characterization of Asphalt Binders Using Different Aging Processes
    (University of Waterloo, 2025-03-17) Sharma, Aditi; Baaj, Hassan; Tavassoti, Pejoohan
    The performance and longevity of asphalt pavements depend heavily on the properties of asphalt binders, which are affected by aging, binder modifications, and the incorporation of reclaimed asphalt pavement (RAP) materials. However, significant gaps exist in understanding the long-term chemical and rheological changes induced by aging processes (particularly with respect to differences between thermo-oxidative aging and UV exposure), and in the use/standardization of chemical analytical techniques such as Fourier Transform Infrared (FTIR) and Nuclear Magnetic Resonance (NMR) spectroscopy for binder characterization. Furthermore, the behaviour in RAP-virgin binder blends, along with the influence of bio-based rejuvenators and anti-aging additives under different aging conditions, remains underexplored. Addressing these gaps are crucial to developing more durable, sustainable pavements. This thesis bridges these research gaps through comprehensive investigation of chemo-rheological binder characterization, combining experimental testing with advanced analytical tools and varying aging methods. The findings offer essential insights into binder aging, rejuvenation strategies, and modification techniques, with significant implications for pavement durability and environmental sustainability. The first chapter presents an evaluation of Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectroscopy combined with functional group and multivariate analysis techniques to characterize asphalt binders. The research identifies challenges in repeatability across binder sources and aging states demonstrating the importance of standardized protocols for improving reliability. Repeatability as described by AASHTO standards is listed in the precision and bias statement as single operator precision. This is the allowable difference in two test results measured under the repeatability conditions (same asphalt binder, measured by the same operator, on the same piece of equipment in the same lab). Principal Component Analysis (PCA) and k-means clustering successfully classified binder types and aging states, with large quantity (LQ) sample preparation yielding more consistent results than small quantity (SQ) preparation. These findings underscore the need for uniform procedures in binder analysis, addressing inconsistencies prevalent in the current literature. The second part of the thesis investigates the impact of Styrene-Butadiene-Styrene (SBS) polymer modification on binder performance and oxidative resistance. Using Nuclear Magnetic Resonance (NMR) and ATR-FTIR spectroscopy, along with PCA and Partial Least Squares Regression (PLSR), the research highlights the ability of SBS to enhance high-temperature performance and slow thermo-oxidative aging. This work not only confirms previous findings on SBS but also provides new insights into the molecular interactions contributing to aging resistance. The study fills a gap in understanding how SBS-modified binders behave under various aging scenarios, offering a deeper perspective on polymer-modified asphalt technologies. The thesis also addresses a critical gap related to UV-induced aging, which has been underexplored in comparison to thermo-oxidative aging. A novel UV aging chamber was developed to simulate real-world environmental conditions, incorporating UV exposure, water spray cycles, and controlled heating at 70°C. Comparative analysis revealed that different additives exhibit varying effectiveness under UV and thermo-oxidative conditions. Zinc diethyldithiocarbamate (ZDC) showed strong resistance to thermo-oxidative aging but limited efficacy under UV aging, while ascorbic acid (Vit. C) accelerated aging under UV exposure, contrary to expectations. These findings emphasize the challenges involved in designing effective anti-aging strategies for asphalt binders, demonstrating the value of combining conventional rheological tests with spectroscopic techniques and further highlighting the need for more targeted approaches to additive selection and development. This thesis advances the understanding of asphalt binder behaviour and aging processes by integrating chemical, rheological, and multivariate analysis techniques. It offers critical contributions to the standardization of binder characterization protocols, the optimization of polymer-modified asphalt technologies, and the development of more effective anti-aging strategies. The research also demonstrates the potential of machine learning and artificial intelligence (AI) in predicting binder performance from spectroscopic data using multivariate analysis, paving the way for future innovations in asphalt binder characterization. In conclusion, the work in this thesis addresses significant gaps in the literature, providing new insights into aging mechanisms, additive/rejuvenation strategies, and RAP binder interactions. By combining chemical analysis, rheological testing, and multivariate techniques, this research contributes both to academic knowledge and practical pavement engineering, promoting the development of more sustainable, long-lasting asphalt pavements.
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    Imagery and Music Performance Anxiety in Elite Musicians
    (University of Waterloo, 2025-03-14) Finch, Katherine; Oakman, Jonathan
    Many musicians experience music performance anxiety (MPA) regardless of their level of expertise (Fernholz et al., 2019) and based on the level of distress and impairment it causes, MPA can be diagnosed as performance-only social anxiety disorder (SAD) (American Psychiatric Association, 2022). However, important phenomena theorized to fuel social anxiety, such as negative spontaneous self-imagery (NSI), have not been studied in musicians or integrated into existing MPA theory which might limit our conceptualization and treatment of this concern. Due to the distress and impairment associated with MPA, different treatments have been developed, many of which integrate intentional mental imagery manipulation to ameliorate anxiety (e.g., relaxation imagery). However, it is difficult to draw a clear picture regarding whether certain approaches (e.g., relaxation imagery) are more helpful than others due to methodological limitations of existing imagery-based MPA intervention research. Importantly, little is known about how musicians engage in mental imagery to manage MPA independent of existing intervention research, which could guide future imagery-based MPA research (e.g., examination of commonly used approaches). Thus, we conducted a series of studies to extend our understanding of musicians’ experiences along the imagery continuum (i.e., spontaneously experienced to intentionally generated) as they relate to MPA to inform future MPA theory, research, and treatment. In Chapter 1, we followed elite musicians (N = 40) for 7 days leading up to an important anxiety-provoking performance of their choice. After completing an initial battery of questionnaires including a measure of trait MPA, musicians completed nightly measures of NSI and state MPA. We theorized that similar to those with SAD, musicians would experience NSI related to musical performances, and that this phenomenon would be positively associated with the intensity with which musicians experience trait and state MPA. Consistent with our predictions, we found that NSI was highly prevalent amongst elite musicians. At the between-person level, musicians who experienced more frequent NSI also had higher trait MPA and state MPA intensity. Multi-level analyses further allowed us to explore the association between NSI and state MPA intensity at the within-person level amongst those who experienced NSI, and we found that state MPA intensity was significantly higher on days where musicians experienced NSI compared to days when they did not. Building on these findings and using the same methodology and participant data, Chapter 2 investigated the association between NSI and additional variables important to the conceptualization of MPA, including state MPA interpretation (i.e., whether musicians interpret anxiety as helpful or harmful to performances) and self-confidence. Contrary to our hypotheses, we did not find an association between NSI and these variables at the between-person level. However, on average within-person, we found that self-confidence was significantly lower on days where musicians experienced NSI than on days when they did not, although a similar association did not emerge between NSI and state MPA interpretation. Taken together, Chapters 1 and 2 indicate that cognitive theoretical models of SAD may be more applicable to MPA than previously thought (Osborne & Franklin, 2002), as an important phenomenon theorized to fuel social anxiety is associated with increased state MPA intensity and decreased self-confidence in anticipation of important anxiety-provoking performances. Further, similar to those with other forms of social anxiety, musicians experiencing MPA may benefit from imagery-enhanced cognitive behavioural therapy for SAD which specifically targets NSI to ameliorate anxiety. In Chapter 3, we turned our attention to the use of intentionally generated imagery to manage MPA. To lay important groundwork for future research, Chapter 3 investigated how elite musicians (N = 25) employ mental imagery to manage MPA, and whether they experience difficulty controlling such imagery through a semi-structured interview and Likert-style rating scales. Thematic analysis revealed that musicians engage in a variety of imagery approaches (e.g., mental rehearsal, relaxation) to manage MPA, several of which are similar to approaches in existing intervention research. Although musicians reported a high degree of overall control of intentionally generated imagery to manage MPA, themes emerged regarding difficulties which musicians experience controlling such imagery. Thus, our findings have implications for future MPA research, as well as clinical implications regarding the use of mental imagery to manage MPA. We conclude with a summary of our results, provide an overview of clinical implications of our novel findings, outline limitations of our program of research, and suggest directions for future research in this important area.
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    Symbols, Dynamics, and Maps: A Neurosymbolic Approach to Spatial Cognition
    (University of Waterloo, 2025-03-12) Dumont, Nicole Sandra-Yaffa; Eliasmith, Chris; Orchard, Jeff
    The discovery of various spatially sensitive neurons in the hippocampal formation, such as place, grid, and boundary cells, has provided valuable insights into the neural mechanisms underlying spatial representation and navigation. However, neural activity and connectivity data alone cannot fully reveal the brain’s algorithms. Bridging this gap requires computational models that not only explain the low-level activity of spatially sensitive cells but also link it to higher-level symbolic representations manipulable within a cognitive framework – models capable of binding spatial representations to discrete abstractions, while also supporting hierarchical and probabilistic structures that enable reasoning and decision-making. The Semantic Pointer Architecture (SPA; Eliasmith, 2013), in combination with the Neural Engineering Framework (NEF; Eliasmith et al., 2003), provides a mathematical and computational framework to represent symbols and implement dynamical systems in spiking neural networks. Spatial Semantic Pointers (SSPs; Komer et al., 2019), an extension to the SPA, encode continuous variables, such as spatial locations, while supporting the binding of spatial information with other features – continuous or discrete – into compressed, multi-domain representations. This flexibility allows SSPs to model diverse cognitive processes, ranging from spatial memory to abstract reasoning, offering a unified theory for how continuous variables might be represented and manipulated in the brain. In this thesis, we leverage these tools to model key components of spatial cognition, including path integration, cognitive map creation, and reinforcement learning. Our contributions include the development of SSP-PI, a SSP-based path integration model that combines velocity controlled oscillators with attractor dynamics to integrate continuous spatial variables. We also introduce SSP-SLAM, a biologically inspired spiking neural SLAM system capable of constructing semantic cognitive maps that bind and associate spatial and non spatial features. Furthermore, we propose spiking RL models that demonstrate how SSP embeddings can effectively represent successor features, reward distributions, and stochastic policies. Finally, we use the SPA and SSPs to construct state embeddings for deep RL networks, demonstrating their utility in tasks requiring mixed semantic-spatial representations. Our findings underscore the potential of SSPs to act as a unifying framework for understanding spatial representation in the brain while advancing biologically inspired approaches to navigation and learning in artificial systems. This work bridges theoretical neuroscience and artificial intelligence, laying the groundwork for future explorations of shared principles across spatial and abstract cognition.
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    Development of Novel Human Aggrecanse-2 Dual-Binding Bis-Squaramide Inhibitors
    (University of Waterloo, 2025-03-12) Ratto, Amanda; Honek, John
    Osteoarthritis (OA) is a degenerative joint disease that affects millions of individuals worldwide. OA is characterized by the breakdown of articular cartilage, including the proteoglycan aggrecan, which plays a crucial role in enabling cartilage to withstand compressive loads. A Disintegrin and Metalloproteinase with Thrombospondin Motifs-5 (ADAMTS-5; aggrecanase-2), has been reported to be the predominant aggrecanase in mice, and in vitro studies revealed ADAMTS-5 exhibits high efficiency at cleaving aggrecan. Although no disease modifying OA drugs have been developed, it is hypothesized that inhibitors against ADAMTS-5 could slow the progression of OA. Typical inhibitors of ADAMTS-5 include zinc-binding groups (ZBGs) that interact with the catalytic zinc. Recently, an exosite that inhibitors can target has been identified at a nearby domain, not within the catalytic site. Here we present the development of novel potential dual-binding inhibitors which aim to target both the catalytic site and exosite of ADAMTS-5. The inhibitors investigated in this thesis incorporate a squaramide nucleus, which is an excellent molecular scaffold due to its ease of derivatization, known synthetic pathways, and commercial availability. To identify potential dual-binding bis-squaramide inhibitors, a large in silico library was constructed, consisting of the squaramide nucleus linking potential exosite binding groups and ZBGs. Numerous computational techniques were utilized to identify inhibitors, including molecular docking to evaluate potential interactions with both the binding pocket and exosite of ADAMTS-5, as well as molecular dynamics simulations to assess inhibitor stability and predict binding affinities. The four bis-squaramide molecules identified from the computational screening were successfully synthesized using a one-pot, microwave-assisted synthetic approach, which facilitated a high-throughput process through reaction automation. A range of bis-squaramide compounds were enzymatically screened with micromolar IC50’s for ADAMTS-5.
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    Harnessing Exosomes from Human Dermal Fibroblasts and Pirfenidone-exosomes as Innovative Strategies for Scarless Tissue Repair in Wound Healing
    (University of Waterloo, 2025-03-11) Wang, Jin; Emmanuel, Ho
    The wound healing process often leads to scar formation that can negatively affect patients both physically and psychologically. The management and treatment of scars also place a considerable financial burden on healthcare systems. Significant efforts are being made to improve wound healing outcomes by accelerating closure while simultaneously minimizing scar formation. To facilitate scarless wound healing, developing an anti-scarring treatment that modulates dermal fibroblast activity is a promising strategy, with pirfenidone (PFD) showing potential due to its anti-fibrotic properties by targeting intracellular pathways that regulate collagen disposition. PFD, particularly when delivered via dermal fibroblast-derived exosomes, may further enhance therapeutic effectiveness and promote scarless healing. To achieve this goal, we began by isolating high-purity exosomes from in vitro cultured human dermal fibroblasts. Two common isolation methods—PEG precipitation and affinity-based techniques—were compared to identify the most efficient approach for obtaining high-purity and relatively homogenous exosomes. A range of characterization techniques, including transmission electron microscopy (TEM), atomic force microscopy (AFM), antibody arrays, and enzyme-linked immunosorbent assays (ELISA), confirmed the successful isolation of high-purity exosomes. The affinity-based method demonstrated superior performance, yielding well-dispersed and highly pure exosomes. Due to the difficulties in achieving efficient drug encapsulation in exosomes, the following chapter specifically focused on the encapsulation and formulation optimization of the antifibrotic compound PFD and explored the use of exosomes as a drug delivery platform. We optimized an active drug loading method using sonication to enhance encapsulation efficiency (EE%) and loading efficiency (LE%), ensuring that careful control of the sonication process maintained exosome integrity. The optimal formulation of PFD-exosomes achieved an EE% of 11.14% ± 1.27% and an LE of 10.01% ± 1.03%, with a particle recovery rate of exosomes at 64.21% ± 8.49%. Then, we investigated how to harness exosomes from dermal fibroblasts and PFD-exosomes as innovative strategies for achieving scarless tissue repair in wound healing. Our findings showed that exosomes enhanced fibroblast migration and proliferation through an autocrine mechanism, highlighting their potential as a stand-alone cell-free therapy for wound healing. Additionally, this study was ground-breaking in demonstrating that exosomes can improve the efficacy of PFD as a drug carrier, amplifying its anti-fibrotic effects in both in vitro and in vivo models. The in vivo results indicated that PFD-exosomes accelerated wound healing while organizing the extracellular matrix (ECM) by reducing excessive collagen deposition. Overall, PFD-exosomes present an innovative strategy for pre-scarring interventions, offering benefits of enhanced wound healing outcomes while minimizing scarring.
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    Perspectives of Graph Diffusion: Computation, Local Partitioning, Statistical Recovery, and Applications
    (University of Waterloo, 2025-03-06) Yang, Shenghao; Fountoulakis, Kimon
    Diffusion describes the process of mass moving from one region to another. In the con- text of graph, the diffusing mass spreads from nodes to nodes along the edges of the graph. Broadly speaking, this includes a number of stochastic and deterministic processes such as random walk, heat diffusion, network flow and electrical flow on graphs. Graph diffusion is a highly important primitive, and has been shown to have a variety of surprising properties both theoretically and practically. In this thesis, we present several new perspectives of graph diffusion, with an emphasis on how diffusion algorithms uncover the local clustering structure of the input data without necessarily exploring the entire graph. In the first two parts of the thesis, we introduce a new class of graph diffusion methods that are provably better at extracting the local clustering structure of a graph or a hy- pergraph. Here, diffusion is formulated as a pair of primal and dual convex optimization problems, based on the idea of spreading mass in the graph while minimizing a p-norm net- work flow cost. The primal solution of the diffusion problem provides an intuitive physical interpretation where paint (i.e. mass) spills from the source nodes, spreads over the graph, and there is a sink at each node where up to a certain amount of paint can settle. The dual solution embeds the nodes on the non-negative real line and is considered as the output of diffusion. We will show that the dual variables nicely encode the local clustering structure around a given set of seed nodes. In particular, assume the existence of a cluster C of low conductance Φ(C), the sweep cut procedure on the dual variables returns a cluster whose conductance is not too much larger than Φ(C). In the next two parts of the thesis, we introduce a weighted diffusion mechanism which allows any existing diffusion method to take into account additional node information such as node attributes and labels. The method weighs the edges of the graph based on the attributes or the labels of each node. Depending on the nature and availability of additional node information, two simple yet effective edge-weighting schemes are introduced and analyzed. Over contextual random graphs generated by a local variant of the stochastic block model with noisy node information, we will show that, if the additional information contains enough signal about the ground-truth cluster, then employing existing diffusion algorithms in the weighted graph can more accurately recover the ground-truth cluster than employing diffusion in the original graph without edge weights. In particular, statistical recovery guarantees in terms of precision and F1 score will be derived and compared. All of the results are supplemented with extensive experiments on both synthetic and real-world data to illustrate the technical results and the effectiveness of the new methods in practice. The code is open-source on GitHub.