Electronic Theses and Dissertations (ETD)

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All theses from 2011 to the present are in this collection, as well as some from 2010 and earlier years.

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Are convertible bonds efficiently priced in the Chinese market? Insights from a simulation-based pricing model
(2025) Long, Shuyi; Zhang, Xuekui
This study investigates the pricing efficiency of Chinese convertible bonds and presents evidence of systematic mispricing. To support this analysis, we develop a pricing framework based on the Least Squares Monte Carlo (LSM) method, tailored to reflect contractual features unique to the Chinese market. Using this model, we simulate fair values over the full lifespan of 154 convertible bonds issued between 2015 and 2019 and compare them to observed market prices. The model-predicted price curves generally align well with observed price patterns, demonstrating the robustness and practical value of our approach. However, we also find that trading prices occasionally deviate from model-implied values by more than 10%, with these deviations exhibiting consistent patterns rather than random fluctuations. Furthermore, we demonstrate that simple trading strategies—both at the individual bond level and at the portfolio level—can exploit these discrepancies to generate substantial excess returns. These findings suggest that the Chinese convertible bond market is only partially efficient and highlight persistent arbitrage opportunities, underscoring the importance of market-specific valuation models in emerging financial markets.
When empathy becomes a crime: The repression and criminalization of animal activism in Canada
(2025) Zavitz, Tayler; Weiler, Anelyse
This dissertation explores the historical and contemporary repression and criminalization of animal rights activists in Canada. While the current literature on social movements is rife with the documentation and analysis of many historical and current social movements, the animal liberation movement is often missing in these considerations, especially within the Canadian context. There is a notable absence of comprehensive, historical examinations of how animal activism has been repressed in Canada, and activist perspectives are often underrepresented in the existing literature. My dissertation therefore directly responds to this gap in the literature, bringing the animal liberation movement into greater visibility within academic discourse on social movements. Employing a mixed-methods approach, the dissertation combines archival research with original qualitative data gathered through oral history interviews with animal activists, animal lawyers, and legal scholars. This triangulation of data allowed for the construction of a detailed timeline of the animal rights movement in Canada and an analysis of the strategic and collaborative efforts used by state and private actors to repress dissent and protect the animal industrial complex. The research findings reveal that the repressive tactics used against Canadian animal activists closely parallel those seen within the United Kingdom and the United States, pointing to a transnational pattern of ideological and structural repression of the animal rights movement. Further, this dissertation also explores activists’ emotional responses to their repression, specifically pride, hope, fear, and frustration, and the ways in which these emotions impact their engagement in activism. Rather than deterring advocacy, repression has, in many cases, deepened activists’ commitment to their cause. This dissertation contributes to understandings of social movement repression, the role of emotions in activism, and the broader dynamics of advocacy work. By centering activist voices that are often excluded from academic discourse, it serves as both a scholarly contribution and a resource for activists, advancing critical discussions on repression, resistance, and social justice advocacy in increasingly hostile political climates.
Global sensitivity analysis for terrestrial carbon cycle simulations under present and future climate conditions
(2025) Suruli Nagarajan, Raj Deepak; Seiler, Christian; Monahan, Adam Hugh
In this dissertation, I assessed the sensitivity of the land surface carbon, water, and energy fluxes to variations in model input parameters when simulated by a land surface model (LSM). The terrestrial biosphere currently uptakes approximately 30% of anthropogenic CO2 emissions. LSMs project that the biosphere will continue to take up carbon till early to mid 22nd century, making it a net carbon sink. These carbon sink projections are important for improving the future carbon predictions and informing mitigation strategies. But, there are substantial uncertainties in the strength of the simulated sink. For instance, the spread in the inter-model carbon sink is 1 to 3.2 PgC yr-1 during 2014-2023 (Global Carbon Budget), and 2 to 7 PgC yr-1 for the end of the 21st century (Intergovernmental Panel on Climate Change's Sixth Assessment Report). Some of the mentioned uncertainties in the simulated carbon sink arises from parameter uncertainties. While parameter tuning can help reduce these uncertainties, optimizing all input parameters in a complex, non-linear LSM is computationally prohibitive. Identifying influential parameters and understanding their influence on the model output(s) is an essential step before tuning the parameters. The influence of parameter uncertainties on the terrestrial carbon cycle output variables can be assessed using global sensitivity analysis (GSA). In this dissertation, I apply a two-step GSA to the output variables simulated by an LSM, the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC). This research is divided into three parts, each applying GSA to CLASSIC output variables under different conditions. The questions asked are: (1) Is there a common set of parameters that substantially influence the majority of ecosystem output variables simulated at an eddy covariance site?, (2) Which parameters substantially affect the uncertainty of the historical carbon sink for different biomes?, and (3) Which parameters substantially affect the uncertainty of future carbon sink projections for different biomes? Through GSA's first step, a coarse sampled screening test, I found that only 15–17% of input parameters show appreciable influence on any of the simulated output variables. Through the second fine sampled quantitative analysis, I further narrowed this subset, and identified between two and 15 parameters as the most influential for different output variables and statistical measures. The influential parameters varied depending on the meteorological forcing used. The maximum rate at which CO2 is used during photosynthesis (vmax) and the loss of light along the canopy depth (kn) are the most recurring influential parameters across all forcing scenarios, and statistical measures. Additionally, other photosynthetic parameters, as well as those related to rooting and phenology, play an important role when CLASSIC is forced using reanalysis and Earth system model data. The sensitivity of the terrestrial carbon sink to the uncertainty in $vmax$ reduces by the end of the 21st century. In many cases the analysis is unable to rank the most influential parameters because of large sampling variations in the sensitivity indices. GSA is a stepping stone before performing model optimization. However, the computational demands of GSA are substantial. In this study, performing GSA for just seven grid cells required approximately 25 CPU years. Scaling such analyses to a global level using the full model would be computationally prohibitive. However, advancements in machine learning and emulator-based approaches present a promising alternative for GSA and optimization efforts, drastically reducing computational costs by requiring fewer input-output simulations than the full model. These innovations could enable large-scale assessments of parameter uncertainty, ultimately leading to more robust predictions of the terrestrial carbon sink, which will help in the shaping of better mitigation efforts.
Usability testing of a mobile health physical activity application for people with an inflammatory bowel disease: Mixed methods study
(2025) Trim, Cameron; Liu, Sam
Background: Living with an Inflammatory bowel disease (IBD) implies a lifelong burden of physical and mental health complications to diagnosed individuals, even when in a quiescent disease state. The prevalence of IBD within industrialized nations is increasing worldwide, and the associated economic cost is substantial. Physical activity (PA) has the potential to improve systemic symptoms for people with an IBD without risk of exacerbating disease activity. Despite this, the recommended amount of PA is often not met within the IBD population. Fatigue is a common systemic symptom of IBD which can affect participation in PA. Just-in-time adaptive interventions (JITAI) can be delivered using mobile health (mHealth) apps and can provide tailored support for behaviour change. These types of interventions have the potential to offer a scalable solution to help increase PA levels for people with an IBD while tailoring to a person’s fatigue level. Using the IDEAS framework, a mHealth JITAI app with the aim of increasing PA levels of people with an IBD has been developed; however the usability and acceptability of the app remains unclear. Objective: This study aims to evaluate the usability and acceptability of a JITAI app, IBD-Move, among individuals living with an IBD. Methods: This mixed-methods study involved two cycles of 4 participants (n = 8) and was conducted at the University of Victoria. Participants were asked to complete five tasks, 1) login and read through the introductory module, 2) read through the Chapter 1 module, 3) add a PA goal, 4) complete a PA session, and 5) track the PA using the app. During the study, participants 1) completed the baseline questionnaire on demographics (e.g., age, sex, social economic status) and experience with smartphones, 2) attempted to complete the five goal-oriented tasks, listed above, while performing the Think-Aloud protocol, and 3) responded to the mHealth app usability questionnaire (MAUQ) and answered semi-structured interview questions. Usability measures included task completion rates, efficiency (i.e., completing a task with minimal steps), and qualitative use feedback. The framework analysis method and usability problem taxonomy were used to identify themes of usability problems, as identified from Think-Aloud task completion and interview. Results: Quantitative results showed that task completion rates improved from Cycle 1 to Cycle 2, though Tasks 3 (Add Goal) and 4 (complete PA Session) sustained relatively lower rates of completion in Cycle 2 and demonstrated the highest inefficiencies of screen transitions. Usability scores improved in both Ease of Use (MAUQ) (Cycle 1 (M[SD]) = 1.93 [0.52], Cycle 2 (M[SD]) = 1.45 [0.21]) and Interface and Satisfaction (MAUQ: Cycle 1 (M[SD]) = 1.98 [0.42] to Cycle 2 (M[SD]) = 1.15 [0.14]). Usefulness scores, used to measure acceptability, also improved (MAUQ: Cycle 1 (M[SD]) = 2.88 [0.82] to Cycle 2 (M[SD]) = 1.67 [0.85]). Constructs of the Theoretical Framework of Acceptability were met with unanimous agreement in Cycle 1, however the constructs Perceived Effectiveness and Self-efficacy were not fully agreed during Cycle 2. Qualitative analysis identified 12 consolidated usability problems for Cycle 1 and 13 for Cycle 2, with the most severe problems in Task 2 (read Chapter 1), Task 3 (Add Goal), and Task 4 (Complete PA Session), primarily related to Visualness and Task-mapping classifications of UPT. Conclusion: IBD-Move demonstrated high usability and acceptability, with its tailored approach to physical activity and content well-received. Key refinements were made from Cycle 1 to Cycle 2; text was adjusted, instructions were added, and technical errors were fixed, though challenges in Tasks 3 and 4 remained. Further adjustments to the app will be made in preparation for a future feasibility study, evaluating IBD-Move’s effectiveness for improving physical activity levels and health outcomes.
Subsurface indicators of active faulting in the central Strait of Georgia, British Columbia and implications for hazard and risk
(2025) Podhorodeski, Anna A.; Leonard, Lucinda; Schaeffer, Andrew
This thesis investigates whether previously unidentified active faulting occurs beneath the central Strait of Georgia, a region located between Metro Vancouver, Nanaimo, and British Columbia’s Sunshine Coast. It also aims to evaluate the implications of such faulting for seismic and tsunami hazard in southwestern British Columbia. To address these objectives, ~2200 km of seismic reflection data are systematically interpreted to identify and catalogue subsurface evidence of active faulting across the study area. Fault interpretation is based on eight objective criteria: reflection offsets, reflection discontinuities, abrupt lateral changes in seismic unit, reflection truncations, associated folding and deformation, abrupt changes in dip, fault shadow, and fault plane reflections. Some criteria appear highly localized and unlikely to delineate structures, whereas others exhibit consistency and lateral continuity across multiple seismic reflection profiles and along linear trends. This analysis enables the delineation of the Central Salish Sea fault zone (CSSFZ; referred to previously as the Fraser Delta fault) beyond its previously mapped surface expression to a length of 12 km, with a possible extension up to 25 km. Subsurface evidence of active faulting is also present beneath a seafloor scarp offshore Bowen Island and a seafloor lineament near Gabriola Island. Given the CSSFZ’s proximity to densely populated areas of British Columbia, deterministic seismic hazard and risk modelling is conducted herein for various rupture scenarios. The CSSFZ’s main strand is modelled with an average dip of ~75 degrees southwest and strike of ~123 degrees. Based on the fault's strike relative to the orientation of local maximum horizontal compressive stress (SHmax), the fault is presumed to accommodate oblique right-lateral slip with a reverse component, although scenarios ranging from pure right-lateral to pure reverse slip are considered. The most impactful modelled scenario – a magnitude 6.7 daytime oblique or reverse rupture – is projected to result in 1,300 deaths, 5,600 uninhabitable buildings, and $18.5 billion CAD (2019) in economic losses across southwest British Columbia due to ground shaking and building damage alone. These hazard and risk results establish a baseline assessment for a fault rupture offshore of Metro Vancouver; secondary hazards (e.g., aftershocks, liquefaction, fires, slope failures) and damage to critical infrastructure could result in further damage and casualties. In addition, the proximity of the CSSFZ to the Fraser River delta, an area prone to submarine slope failure, along with its potential for right-lateral oblique slip, suggests that a rupture may be tsunamigenic. Empirical relations indicate that seafloor displacement during a magnitude 6.7 rupture could generate damaging tsunami runups, with potential impacts for several coastal communities around the central Strait of Georgia. The results in this thesis have demonstrated hazard and risk implications for southwest British Columbia. It is therefore recommended that the CSSFZ be incorporated into future seismic and tsunami hazard and risk assessments.
Three-dimensional multiple object tracking and its effects on functional, cognitive, and biological outcomes in TBI survivors: A patient-oriented study
(2025) Morrison, Jamie; Christie, Brian R.
Traumatic brain injury (TBI) is a leading cause of disability worldwide; however, accessible interventions to support recovery at chronic time points are limited. Cognitive training is a promising therapeutic avenue due to its low cost and accessibility. Three-dimensional multiple object tracking (3D-MOT) is a visuospatial cognitive training task that engages working memory, distributed attention, and complex motion integration – functions that are commonly impacted after TBI. This study explored the effects of a five-week, at-home 3D-MOT intervention for moderate to severe TBI survivors. This research was conducted in a patient-oriented manner with the Victoria Brain Injury Society. Thirty participants were randomized into the intervention or control group. Estimation statistics were used to report mean differences, confidence intervals, and effect sizes, aligning with a patient-oriented approach to emphasize clinical relevance. Self-reported functional outcomes, neuropsychological assessments, and telomere length as a biomarker of aging were assessed at baseline, post-intervention, and one-month follow-up in the 20 participants who completed the study. The intervention group exhibited medium to large effect size improvements in daily life challenges, TBI symptom severity, perceived stress, attention as measured on the Digit Span Forward, and executive function as measured on the Verbal Fluency FAS Test, that persisted at follow-up. Improvements in short- and long-term verbal memory and retrieval, as measured by the California Verbal Learning Test – Second Edition, were observed at one-month follow-up, but not immediately post-intervention. No change in telomere length following 3D-MOT intervention was observed. Control participants did not show meaningful improvements on any of these outcomes. Participant feedback highlighted the acceptability and perceived benefit of 3D-MOT, supporting its potential to be used as a therapeutic tool for TBI recovery.
“Come here, let’s take care of you”: Indigenous nurse wellness and intergenerational mentorship with/in community
(2025) Chakanyuka, Christina Marie; Bourque Bearskin, Lisa; Pauly, Bernie
This study examines how intergenerational Indigenous nurse mentorship and traditional wellness practices strengthen Indigenous nurse identity, belonging, knowledge, and wellness. Indigenous nurses—including First Nations, Inuit, and Métis—hold a unique role in drawing from both their nursing knowledge and lived experiences as Indigenous Peoples to co-create culturally safe environments that foster healing through respectful and trusting relationships. Despite ongoing recruitment and retention initiatives, Indigenous nurses continue to face systemic racism in nursing education and remain underrepresented in health care systems. This research responds to calls to address the distinct health needs of Indigenous Peoples in Canada, while affirming the rights of Indigenous nurses to self-determine wellness, mentorship, and professional development within their own communities and Nations. Guided by Indigenous Research Methodologies, this study engaged eight Indigenous nurses through visiting and circling practices to generate an evidence base for sustainable, relational strategies that protect and promote Indigenous nurse wellness and mentorship. Grounded in the principle of “nothing for us – without us,” the findings highlight the power of relational accountability and affirm Indigenous nurses’ self-determining role in co-creating retention and wellness strategies grounded in traditional knowledge. This study underscores the importance of (w)holistic approaches that integrate Indigeneity, relationality, Indigenous Knowledges, nursing praxis, and traditional wellness practices to create culturally safe health care experiences. The findings provide strategic imperatives for advancing intergenerational Indigenous nurse mentorship in nursing education, while emphasizing the interconnectedness of individual, community, and environmental well-being.
Coloniality of pain and decolonial pain care: Examining the effects of colonial violence on chronic pain through Kwakwaka’wakw women’s experiences
(2025) Sakai, Hiroko; Moosa-Mitha , Mehmoona
Recently, pain studies have begun to acknowledge the biopsychosocial nature of chronic pain with an increased emphasis on the social and structural factors contributing to pain. In particular, studies have identified relationships between chronic pain and intersecting structural oppressions such as systemic violence and racism created by heteropatriarchal settler-colonialism. However, few research studies on pain have examined how systemic colonial violence affects Indigenous women’s experiences of chronic pain, and its correlation to pain care inequities. Centring the experiential knowledge of five cisgender Kwakwaka’wakw women participants and situated within Indigenous decolonial feminist theories, this qualitative community-based participatory research study addressed the questions: how does colonial violence and trauma interact with physical pain? What were participants’ recommendations for decolonizing pain care? The participants described reciprocal interactions between pain and colonial violence and trauma; emotional pain; fatigue; and substance use. Their stories illustrated how their experiences of systemic gendered and racialized violence were closely connected to the onset and development of chronic pain. Participants also explained how chronic pain experiences were exacerbated by anti-Indigenous racism and the white-centric healthcare practices that discredited and pathologized their narratives of pain. Participants recommended decolonizing pain care by addressing structural injustices and care inequities. Cultural revitalization was considered foundational to decolonizing pain care by fostering healing that affirmed Indigeneity and the right to self-determination at multiple levels, while enhancing relational approaches to health and healing. Addressing anti-Indigenous racism is an urgent step towards decolonial pain care. Finally, decolonial pain care should encompass biopsychosocial as well as cultural and spiritual aspects through multidisciplinary care provisions.
Balancing energy and ecosystems: Exploring the spatiality of renewable energy development for a low-carbon future
(2025) Willard-Stepan, Maya; Hoicka, Christina; Bone, Christopher
Fossil fuel energy production, as one of the most significant drivers of climate change, is causing extreme social and environmental harm worldwide. These circumstances necessitate a transition to low-carbon energy sources. A key factor in the expansion of low-carbon energy systems is the potential impact this development may have on other benefits provided by the environment, such as food or materials, commonly referred to as ecosystem services. There is currently limited knowledge beyond the regional scope of how energy development is impacting these services; an important consideration, as local studies cannot consider the full spectrum of global environmental impacts. The research outlined in this thesis uses an exploratory methodology to examine the spectrum of environments in which renewable energy projects are constructed in, and which ecosystem services are most likely to be impacted by the expansion of renewable energy globally, both for single-technology and clustered renewable energy power plants. First, in Chapter 3, I analyse the land cover and associated ecosystem services surrounding global power plants. In Chapter 4, I reproduce this analysis on a growing global dataset of renewable energy projects that utilize multiple types of resources, known as clusters. These results are compared with those discussed in Chapter 3 to assess how the configuration of energy systems influence the land they are constructed on. I find that hydropower and wind power show the highest occurrence in ecosystem service rich environments, creating the largest risk of ecosystem service loss from renewable energy production, while clustered energy systems are placed in areas which decrease the risk of ecological trade-offs. As renewable energy continues to develop, incorporating other land considerations will be critical in ensuring the energy transition minimizes harm to the natural environment for which we all rely on.
Unsettled futures: Pathways for Indigenous solidarity on Haida Gwaii
(2025) Weder, Julia; Rowe, James K.
Haida scholars and community leaders have made it clear that non-Haida people can (and should) contribute to the collective protection, well-being, and resilience of Haida Gwaii. There is a lack of clarity among many settlers, however, around their agency in the anti-colonial movement and methods for addressing settler colonial logics in the community. To address this gap, I reviewed literature on settler colonialism and non-Indigenous agency in collective social liberation, and conducted 13 interviews with Haida and non-Haida community members. I explored three research questions. (1) What approaches, practices, and tools have been successfully used by other communities and social practitioners/organizations to support settler (un)learning and transformation? I found that helping people foster deeper relationships with land and ancestry, exploring shared place-based histories, engaging in reading, discussion, and embodiment-based courses, and using art as a tool for knowledge-sharing are examples of effective social/educational tools. (2) What past or current spaces/movements on Haida Gwaii have fostered dialogue around settler responsibilities and conceptions of Haida sovereignty? A key finding was that Haida Gwaii has a rich history of alliances between Haida and settler peoples – in support of Haida title and resurgence, to protect Haida Gwaii’s lands and waters, and in resistance to corporate industrial invasion – which have been powerful sites of personal transformation and solidarity-building. (3) What approaches, practices, and tools might be effective for settlers in Daajing Giids with various perspectives to critically interrogate and transform mindsets around settler identity and Haida sovereignty? How can passive allies or more neutral residents be brought more into the fold of anti-colonial action? I found that among local community organizers, a politics based on relationships and shared interests (such as a connection to place and the health of the community’s air, water, and food sources) was favoured over a politics of identity, shame, and deference; the latter of which risks homogenizing or unnecessarily burdening the Haida community. Neutral or passive allies could be engaged by creating opportunities for in-person relationship-building and supporting residents in witnessing Haida business at potlatches and other political/cultural events. Ultimately, I saw great interest among participants to strengthen networks and practices of mutual aid, support one another in subverting settler colonial habits and structures, and continue to build popular social power that aligns with the interests of the Haida Nation.
The emergence of novel disturbance in Jasper National Park – evaluating the causes and implications of 100 years of landscape change using repeat photography
(2025) Tricker, James; Higgs, Eric
Recurring disturbance has a strong influence on the bounds of ecosystem variability. The concept historical range of variability (HRV) describes these bounds, providing a sense of the range of ecosystem characteristics exhibited in response to disturbance and recovery over time and space. Altered and novel disturbances can drive changes in ecosystem composition and configuration that depart from the HRV and lead to regimes shifts. In Jasper National Park, a systematic set of historical and repeated oblique photographs depict montane landcover in the aftermath of extensive fires in 1915 and a mountain pine beetle (MPB) outbreak in 2020/22. However, the MPB disturbance is historically unprecedented, and raises important questions about whether the characteristics of this event are within the HRV of the montane ecosystems. The focus of this dissertation is to apply a new workflow for deriving landcover maps from oblique photographs to evaluate the landcover changes that have occurred in the park’s montane ecoregion over the last 105 years. The workflow comprises a deep learning algorithm that automates the classification of landcover evident in grayscale and color oblique photographs and a georeferencing tool that incorporates these data into a GIS. I report on the accuracy of the data produced by the workflow (Chapter 2) and quantify the changes in composition and configuration of broad landcover types after the two disturbance events for a study area in the montane ecoregion (Chapter 3). A scenario planning exercise is then undertaken to evaluate the uncertainty surrounding the implications of these changes and the potential for future novel disturbance events (Chapter 4). Georeferencing accuracy using root-mean-square error for a subset of 7 images was 4.6 m and overall classification accuracy for the landcover map produced from oblique photographs using the new workflow was 68%. The change analysis in the montane ecoregion indicated that the MPB outbreak has returned a version of heterogeneity evident in 1915 to the landscape by reducing the dominance of mature conifer (both in composition and configuration) across the landscape. Four scenarios then describe alternative futures in the park based on different levels and combinations of ecological novelty and management intervention. The value of this research is to validate the development of a new workflow for analyzing historical and repeat photographs, increase the temporal depth of ecological monitoring in the park, and allow managers and restoration practitioners to develop a better understanding of how and where novel disturbance is altering ecological processes and could reoccur in the future.
Investigating the function of eosinophils in mucosal immunity
(2025) FitzPatrick, Rachael D.; Reynolds, Lisa A.
Eosinophils are a highly abundant immune cell type in the gastrointestinal (GI) tract at steady-state, where they have recently been reported to contribute to tissue homeostasis in response to nutrient and bacterial microbiota-derived signals. Eosinophils are also elevated in the GI tract of some individuals with inflammatory bowel disease (IBD) who are also more susceptible to enteric bacterial infections. Further, therapies to treat hypereosinophilic syndromes have been designed to deplete eosinophils from the human body rendering some people completely devoid of this cell type. Together, these observations emphasize the need to gain a deeper understanding of the role of eosinophils in the GI mucosa. In this thesis, I examine the function of eosinophils under three different contexts within the murine intestinal tract: 1) steady-state secretory immunoglobulin A (sIgA) production, 2) enteric bacterial infection, and 3) the development of oral tolerance. We find that contrary to previous reports, eosinophils are not essential for the maintenance of sIgA in the GI tract at steady-state. Instead, our findings emphasize the importance of optimally controlling rearing and housing conditions throughout life between mice of different genotypes when their phenotypes are being assessed. Further, we determine that eosinophils are responsive to an enteric infection with the bacterial pathogen Salmonella enterica serovar Typhimurium (S. Typhimurium) but not essential for controlling S. Typhimurium colonization within the GI tract. Finally, we established a mouse model to investigate the contribution of eosinophils to oral tolerance development in early life. Using this model, we uncover immune responses to dietary antigens unique to the early life period and determine that eosinophils are not an essential cell type contributing to oral tolerance in early life. Collectively, these results contribute to our understanding of eosinophils within the GI mucosa, which ultimately will help inform treatment strategies for people living with elevated or depleted levels of eosinophils. Further, our findings lay the groundwork for future well-controlled and robust studies of eosinophils as well as oral tolerance development during the early life period.
Bandwidth tomography
(2025) An, Jianwei; Wu, Kui
Bandwidth tomography—inferring the bandwidth of internal network links from end-to-end path bandwidth measurements—is a long-standing open problem in network tomography. The core challenge arises from the fact that no existing mathematical framework directly addresses the inverse problem formulated as a set of min-equations. To systematically tackle this challenge, we design a polynomial-time algorithm that accurately determines the bandwidth of all identifiable links and derives the tightest possible error bounds for unidentifiable links based on a given set of measurement paths. Furthermore, when additional information on link correlations is available, we leverage the extra information to refine our error bounds. Specifically, we explore two key types of link correlations: fairness constraints and total capacity constraints among a node's adjacent links. We provide theoretical guarantees on how these correlations enhance the precision of bandwidth tomography and develop algorithms to address two fundamental challenges in refining these bounds: (i) the impact of synchronous vs. asynchronous updates and (ii) the cascading effects during bound updates. Having developed algorithms to derive the tightest possible performance bounds for a given set of measurement paths, we then tackle the next major challenge: constructing optimal measurement paths that minimize the global error bounds for unidentifiable links. We prove the hardness of this problem and, in response, propose a reinforcement learning (RL) approach for measurement path construction. Our solution leverages domain-specific knowledge in bandwidth tomography and integrates both offline training and online prediction to build suitable measurement paths. We evaluate our proposed methods using real-world ISP topologies and simulated networks. Experimental results show that compared to existing path construction methods—Random and Diversity Preferred—our RL-based approach significantly reduces the average error bound of inferred link bandwidths. In addition, our performance bound computation algorithms improve the state-of-the-art techniques by substantially tightening the performance bounds in bandwidth tomography.
Three ethical dimensions of AI: Fairness in social recommenders, bias detection in LLMs, and privacy in NLP
(2025) Potka, Shera; Thomo, Alex
This thesis investigates three foundational challenges in the development of responsible Artificial Intelligence (AI): fairness in social recommender systems, demographic bias in large language models (LLMs), and privacy-preserving techniques for Natural Language Processing (NLP). Though these problems differ in technical scope and application domain, they share a common thread: vector-based representations—embeddings of users, words, and tokens—fundamentally shape how AI systems behave, make decisions, and affect people. Across these three dimensions, this work introduces new methods for measuring, interpreting, and mitigating risk, offering solutions grounded in both empirical analysis and practical utility.The first part of the thesis (Chapter 2) examines fairness in algorithmic link recommendation, with a focus on how structural minority communities—groups defined by network topology rather than identity—are represented in evolving social graphs. Standard recommenders tend to amplify popular users, reinforcing visibility gaps over time. We propose MinWalk, a fairness-aware algorithm that improves minority visibility while maintaining network stability. Simulations on real-world networks show that fairness- and diversity- aware algorithms vary widely in long-term impact, and that MinWalk offers a balanced, effective solution. This work underscores the importance of evaluating fairness dynami- cally and provides tools for designing more inclusive recommendation systems. The second part (Chapters 3 and 4) turns to demographic bias in LLM behavior. We analyze gender and race associations in contextual embeddings from five leading models developed by OpenAI, Google, Microsoft, Cohere, and BGE. Using the SC-WEAT metric and clustering techniques, we show that stereotypical associations persist and are amplified in modern embeddings. We also examine how these biases appear in real-world applications, focusing on consumer product recommendations. Using prompt engineering and computational linguistics methods—including Marked Words, SVM classification, and distributional divergence—we find that LLMs generate demographically skewed suggestions that reinforce social stereotypes. These findings highlight the risks of bias in LLM outputs and offer concrete tools for auditing fairness in generative systems. The final part (Chapter 5) addresses privacy in NLP, where the challenge lies in re- moving sensitive information from text without damaging meaning or fluency. Existing approaches either prioritize privacy but degrade text quality, or preserve fluency at the cost of weaker guarantees. To address this, we propose CluSanT, a flexible framework that uses token clustering and controlled replacement mechanisms to balance privacy and utility. Unlike prior methods, CluSanT retains strong privacy protection while producing more natural, semantically faithful text. We evaluate it using a range of metrics—including coherence, grammar, and semantic similarity—showing that it consistently improves over baselines on a legal benchmark dataset. Our results demonstrate that text sanitization can be both effective and intelligible to human readers. Taken together, this thesis presents a unified perspective on ethical AI through the lens of embeddings. In social networks, language generation, and privacy-preserving NLP, vector representations are not neutral—they encode power dynamics, preferences, and access. By examining how these embeddings influence visibility, bias, and confidentiality, this work contributes both practical algorithms and conceptual frameworks for designing fair, inclusive, and trustworthy AI systems.
Impacts of heatwaves and hypoxia on gene expression in the pacific oyster and the development of monitoring and mitigation tools for summer mortality
(2025) Bickell, Andrew; Pearce, Christopher Michael; Bates, Amanda
Marine heatwaves and coastal hypoxic events are increasing in frequency and intensity under anthropogenic climate change, resulting in widespread mass mortalities of the Pacific oyster (Crassostrea gigas). Those mortality events threaten the economic stability of global aquaculture, yet strategies to monitor oyster health and mitigate losses during periods of environmental stress are largely limited. Changes in gene expression of C. gigas in response to laboratory-simulated heatwaves and hypoxic events were assessed to identify candidate monitoring genes and explore artificial aeration as a potential mortality mitigation strategy. Two laboratory experiments were performed, exposing farmed C. gigas to simulated 10-day heatwave and hypoxic conditions similar to a 2021 marine heatwave that triggered farmed oyster mortality in Baynes Sound, British Columbia, Canada. Gill tissues were periodically sampled during the experiments and total RNA was extracted to explore patterns of gene expression via RNAseq and qPCR. Five candidate genes were consistently differentially expressed in both experiments— death-associated inhibitor of apoptosis 2 (A2I), high mobility group protein DSP1 (DSP1), high mobility group box 1 (HMGB1), heat shock protein 90 (HSP90), and peptidyl-prolyl cis-trans isomerase (PPCTI)—demonstrating potential for monitoring summer mortality. No significant differences in expression of the general stress marker genes heat shock protein 70 (HSP70) and heat shock protein 20 (HSP20) were detected, suggesting that genes related to immune function and regulation of transcription may be more appropriate for monitoring summer mortality. In addition, the presence of artificial aeration resulted in significantly lower HSP90 relative expression, suggesting some potential utility in stress mitigation during heatwaves. The present work provides insights into the role of heatwaves and hypoxia in Pacific oyster summer mortality and will inform effective monitoring and mitigation practices to support the adaptation of shellfish aquaculture to the growing impacts of climate change.
A deeper look: The development of global peat depth datasets and subsequent carbon stock estimates
(2025) Skye, Jade Erin; Melton, Joe; Goldblatt, Colin
Peatlands are important carbon stores which are being destabilised by anthropogenic activity and are sensitive to climate change. To faithfully assess the carbon stored in peatlands and to model their responses to future climate scenarios, it is essential to have accurate information on peat depth. Presently, however, observations of peat depth are insufficient for conducting these tasks at the global scale. Thus, the goal of my thesis is to accurately generate a global distribution of peatland depth and use that distribution to estimate how much carbon is stored within them. The first step was to create Peat-DBase, the largest database of harmonised peat depth measurements at the global scale. Peat-DBase was then used as the basis of training and testing data for PeatDepth-ML, a machine learning-based modelling framework designed to predict peat depths globally. I created PeatDepth-ML by adapting an existing modelling framework that was designed to predict peatland spatial extents by including new datasets of environmental variables that may drive or indicate peat formation, updating the cross-validation procedures used for model testing, and adding a custom scoring metric to the model to assist in predicting deeper peat depths. I then used PeatDepth-ML to produce a spatially continuous global map of peatland depths. Inspection of Peat-DBase revealed regional data gaps, such as in the Tropics, and potential sampling biases in peat depth measurements, e.g. the collecting of a single peat core to represent the depth of an entire peatland wherein depth could be varying significantly or the presence of multiple peat cores with highly varying depths over small spatial scales. The impact of Peat-DBases's regional biases on PeatDepth-ML's predictions was assessed by calculating a metric describing the predictions area of applicability. To test the sensitivity of PeatDepth-ML to some aspects of sampling bias, a bootstrapping method was developed to create multiple training datasets from Peat-DBase. Running PeatDepth-ML on the bootstrapped datasets showed that model behaviour could vary significantly in response to changes in the training data, particularly at the regional scale. When compared to other estimates in the literature, PeatDepth-ML achieved a similar or improved level of performance and is of better overall quality because of its global reach and continuous representation of peat and non-peat regions without the use of an independent peatland extent map. However, PeatDepth-ML demonstrated a tendency to predict towards the mean peat depth of its training data, which was relatively shallow possibly due to the inclusion of non-peat data, which was included to allow the model to predict over all regions. Performing simple carbon stock calculations using PeatDepth-ML’s results produced estimates that are in line with those previously published. Collectively, Peat-DBase and PeatDepth-ML are cohesive global datasets of peat depth that can aid future peatland research and policy endeavors.
Single mutation effects on protein secondary structure
(2025) Perez Martell, Raul Ivan; Stege, Ulrike; Jabbari, Hosna
Human diversity often manifests through single nucleotide polymorphisms (SNPs). Among these polymorphisms, SNPs that alter amino acids can modify a protein's three-dimensional structure. Such single amino acid mutations can impact the protein's function and potentially elicit diseases or affect drug interactions. Thus, understanding protein single point mutations is crucial for precision medicine, as it helps tailor treatments based on individual genetic variations. Protein tertiary structure prediction models like AlphaFold2 have revolutionized the field with unprecedented accuracy, yet predicting structural changes arising from single amino acid mutations remains a challenge. The complexity introduced by these mutations calls for models that can incorporate mutational information into their predictions. As atomic locations can be susceptible to any number of changes that might or might not affect function, we focus on the secondary structure to provide concrete results on possible protein structural deformation that may occur from single amino acid mutations. We assess state-of-the-art structure prediction methods regarding backbone deformations caused by single amino acid mutations. We categorize these deformations as local, distant, or global based on the proximity of structural changes to the mutation site. Our analysis utilizes a diverse dataset from the Protein Data Bank, comprising over 500 protein clusters with experimentally determined structures and documented mutations. Our findings indicate that single amino acid mutations can significantly affect the accuracy of structure prediction methods. These mutations often lead to predicted structural changes even when the actual secondary structures remain unchanged, suggesting that current methods overestimate the impact of single amino acid mutations. This issue is particularly evident in advanced prediction algorithms, which struggle to accurately model proteins with stable mutations. We also found that the addition of low-performing prediction methods during structural analysis can positively impact the results on some proteins, particularly those with low levels of homology. Furthermore, proteins that form complexes or bind ligands—such as membrane and transport proteins—are inaccurately predicted due to the absence of extra-molecular interaction data in the models, highlighting how single amino acid mutations can complicate accurate structure prediction. Due to these findings, we propose a novel refinement strategy for protein secondary structure prediction that leverages single amino acid mutational data. As part of this strategy, we introduce Mut2Dens, a model that not only yields more consistent predictions for mutational data but also maintains robust predictive performance on non-mutational datasets. These refined models take multiple predicted secondary structures and generate a mutation-aware secondary structure. In particular, Mut2Dens employs the extremely randomized trees algorithm to avoid overfitting and make effective use of the limited mutational data available from experimentally determined three-dimensional structures. By combining predictions from highly accurate structure prediction models, we create an ensemble that integrates their strengths while enhancing mutational capabilities. This refinement strategy also improves the non-mutational performance of state-of-the-art methods by addressing their most inaccurate and least confident predictions. Moreover, our refinement strategy reduces improbable outcomes in mutated protein structures—such as transforming π-helices into β-sheets—that can still occur in current prediction models. Finally, by using interpretable machine learning algorithms, we can reveal the underlying biological knowledge from the refinement model. The insights gained from Mut2Dens can be corroborated with known mutational outcomes, helping users pinpoint discrepancies across structure prediction models and make more informed decisions regarding the predicted structures.
Development of a performance measurement framework for the leadership fund
(2025) Matthewman, Spencer; Castle, David
Announced in Budget 2016 as part of the Pan Canadian Framework on Clean Growth and Climate Change, the Low Carbon Economy Fund aimed to advance Canada towards meeting its Paris targets by providing financial support for GHG reducing and clean growth projects. The client, Environment and Climate Change Canada’s Methodology and Evaluation Division, requested the development of a performance measurement framework for the Low Carbon Economy Leadership Fund (i.e., the Leadership Fund) to summarize key findings and general themes and support its recapitalization. The study employs a gap analysis to develop key performance indicators by assessing current performance mechanisms, defining a desired state based on available program data, and addressing the gaps between those states. Methods include a review of funding agreement and internal progress reports for each portfolio component across the three implementation stages. All components are classified by geographic region, component type, and economic sector to support the thematic analysis, with individual inclusion and exclusion criteria. A database is developed to support the analysis of data and key visualizations. While findings show the Fund is 39% less cost-effective from the original funding agreement execution to current data, the results narrative highlight key trends in average emissions reductions, cost-effectiveness, and project survivability across sectors, regions, and funding mechanisms. These trends underscore important successes and lessons learned across the portfolio and provide opportunities for improvement in the Fund’s recapitalization.
"Care is connection": How place shapes experiences of care for precariously housed adults nearing end-of-life
(2025) Stewart, Alexandra; Stajduhar, Kelli I.; Cloutier, Denise S.
As the social determinants of health literature highlight, housing is more than a physical space; it is a critical foundation for social connectivity and healthcare access. Stable housing supports the development of community connections, which are linked to enhanced well-being and a better quality of life. Furthermore, these connections fulfill a vital function in the context of end-of-life care. Conversely, for adults who are precariously housed, inadequate housing may disrupt the ability to engage with their communities, resulting in social isolation and adverse end-of-life care experiences. As such, housing stability plays a vital role in facilitating or limiting social connections. Drawing on observational fieldnotes and qualitative interviews, this study examined the role of ‘place’ in shaping experiences of care for unstably housed adults nearing end-of-life guided by a geographic and health equity lens. The findings reveal that social connection and supportive relationships were seen as central to participants’ sense of home and experiences of care. Meanwhile, displacement and frequent transitions illustrate how disrupted connections can impact quality of life and restrict access to social support at end-of-life. In conclusion, this study underscores the significance of social connection, community support and sense of place in fostering more equitable end-of-life care experiences for precariously housed adults.
Toward an extensible quantum platform-agnostic combinatorial optimization library
(2025) Ossorio Trochez, Jose; Muller, Hausi A.; Villegas, Norha M.
Combinatorial optimization (CO) problems are computationally challenging as evidenced in various industry and research domains. With recent advances in quantum computing hardware and algorithms, such problems represent an excellent case study for these technologies. Nevertheless, current software tools for CO lack platform-agnostic abstractions to enable researchers and practitioners to utilize quantum resources effectively. This thesis aims to validate and extend the QPLEX Python library, a platform-agnostic CO package built on DOcplex which integrates execution across multiple quantum providers using various algorithms. We focus on two key software quality attributes: completeness, examining the quantum providers QPLEX supports to look for features that could be added to our library, enhancing its capabilities for handling CO problems; and extensibility, making the library more adaptable for future expansions. We first compile a high-level workflow for solving CO problems to ensure that our elicited software requirements align with the actual process practitioners follow when solving these problems. Subsequently, we evaluate QPLEX through a comprehensive analysis of its completeness by comparing features against alternative solutions including platform-specific SDKs, and its extensibility by examining how easily new features can be integrated without disrupting existing functionality. Based on the identified functional and non-functional requirements, we design and implement several extensions to QPLEX, including support for Qiskit Runtime Sessions, integration with D-Wave's quantum solvers and implementation of the QAOAnsatz algorithm. Furthermore, we enhance the extensibility of the library through comprehensive documentation, automated testing, and CI/CD pipelines to ensure smooth integration of future open-source contributions. Validation results demonstrate that these enhancements successfully extend QPLEX's capabilities for solving CO problems using quantum resources, providing a more comprehensive suite of features for quantum-based CO while establishing robust foundations for future development. This work contributes to the evolving field of quantum software engineering by advancing an abstraction layer that shields practitioners from low-level quantum details, allowing them to focus on problem formulation. As quantum hardware and algorithms continue to advance, such platform-agnostic libraries will play a crucial role in broadening quantum computing adoption, enabling domain experts to leverage quantum resources without requiring deep quantum computing knowledge.

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