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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Beyond conventional P-values: Addressing statistical challenges in big data
(2026) Zhang, Jing; Zhang, Xuekui; Tsao, Min
Do larger sample sizes lead to higher false positive rates in statistical analysis? The answer provided by ChatGPT 4o is ’no’, which is a common opinion shared by many statisticians. However, empirical evidence from large datasets analyses, such as those from biobanks and single-cell genomics, challenges this conclusion. Com- mon practice assesses both p-values and effect sizes to mitigate the risk of identifying spurious effects in large samples. Nonetheless, the need to adjust p-values in these contexts is unaddressed, which motivated this investigation. We found that common beliefs and practices are incorrect in real-world data analysis, since theoretical assumptions are always violated. Growing sample sizes can amplify violation impacts, inflating false positive rates. Using a simulation study, we provide examples to support our statement and illustrate a permutation-based remedy. This work’s intended contribution is to heighten awareness within our community about the pressing need to reevaluate standard statistical methods in analyzing datasets with huge sample sizes, thereby inspiring further substantial efforts to tackle this emerging challenge of the big data era.
Design and simulation of a three-body self reacting point absorber wave energy converter using inertial control
(2026) Friedl, Luke; Buckham, Bradley Jason
Global energy systems are rapidly transitioning through the increasing introduction of renewable energy sources. Most renewable generation sources are intermittent, creating challenges for grid stability and reliability. Diversifying these generation sources can mitigate this issue. Integrating ocean wave generation sources into the energy mix represents a candidate strategy for this diversification. Despite their potential, ocean wave energy converters remain underutilized due to their comparatively high cost relative to other renewable generation technologies. Recent research has shown that significant increases in the power production of wave energy converters can be achieved through advanced control strategies that utilize a mechanical device called an inerter to tune the system into resonance with incident ocean waves. These studies assumed idealized inerters and system dynamics, and relied on frequency domain analyses, which neglect nonlinear hydrodynamic and mechanical losses. Additionally, limited consideration was given to the physical constraints required to integrate the inerter into an actual wave energy converter system. To address this gap in the research, this work develops a time-domain simulation tool to model the coupled dynamics of the wave energy converter and inerter system, incorporating both nonlinear mechanical and hydrodynamic forces. The inerter was then analyzed under design and operational objectives and reduced to its core design parameters. A genetic algorithm was applied to optimize the inerter design in order to satisfy these objectives. Finally, the simulation tool was used to model the wave energy converter coupled with the optimized inerter to evaluate the effects of the nonlinear mechanical and hydrodynamic forces on the power production of the system under optimal control. The optimized inerter design was able to achieve the effective mass response required for optimal power capture. However, when implemented with the optimal control scheme, the wave energy converter system exhibited unrealistic motion under low frequency wave conditions. To compensate for the unrealistic motion, an adapted control approach was applied, which led to substantial losses in power production below a wave frequency of 2.96 rad/s. The addition of friction forces in the inerter, end stop forces and viscous drag forces also led to significant losses in power production. This research developed a time-domain simulation tool to evaluate the realistic performance of coupled wave energy converter-inerter systems. By demonstrating the limitation of optimal control schemes when applied to realistic systems, the significant gap between idealized control models and physically achievable systems was revealed. This work also demonstrated that while the friction force induces a parasitic dissipation of power the primary source of loss is due to the control algorithms ignorance to the change in the system impedance that the friction force causes. This is the opposite for viscous drag where the loss in power from the dissipation of energy due to drag outweighed the control related loss. The insights gained in this research identify key mechanisms responsible for these losses and point towards strategies that could be used to mitigate them. Future challenges that must be addressed to advance this research area include linearizing the nonlinear forces and incorporating them into the optimal control algorithm as well as developing higher-fidelity inerter models to better capture real-world dynamics. Ultimately, future work should apply this research when constructing a real-world prototype device and experimentally validate the results and insights obtained in this research. Overall, this work established a foundation for future researchers to refine realistic control approaches, mitigate the effect of non-linear forces and advance the practical viability of inerter-based wave energy converter control methods.
A critical examination of participatory mapping for Indigenous-led salmon habitat monitoring
(2025) Palmer, Carly; Tremblay, Crystal; Bone, Christopher
Pacific salmon (Oncorhynchus spp.) are foundational to Indigenous food systems, governance, and cultural identity across the North Pacific Rim, yet their habitats continue to be degraded by industrial development, altered hydrology, and cumulative impacts of colonial land use. Indigenous Nations have long monitored and stewarded salmon habitats through lived experience, intergenerational knowledge, and cultural practices, but these perspectives remain marginalized in dominant management frameworks that prioritize narrow biophysical indicators. This thesis examines the potential of participatory mapping (PM) as a tool to support Indigenous-led salmon habitat monitoring, with a case study in Tsawwassen First Nation territory. Chapter 2 of this thesis describes a systematic literature review of PM applications in Indigenous habitat research, analyzing 33 publications to examine geographic distributions, participation levels, and knowledge representation. Through this analysis, significant gaps in Indigenous leadership and underrepresentation of relational knowledge dimensions were identified. The significance of these findings is explored, as well as their implications for research and practice. Chapter 3 describes a case study conducted in partnership with Tsawwassen First Nation exploring long-term salmon habitat changes through integration of PM, community testimony, historical and scientific records, and spatial analysis. Extensive habitat transformations were documented by all knowledge sources, while revealing both convergences and divergences between Indigenous observations and scientific records. Chapter 4 provides a critical analysis of the benefits and limitations of PM as a method for Indigenous-led salmon habitat monitoring. Particular attention is paid to the political constraints that prevent Indigenous knowledge from meaningfully influencing environmental governance despite extensive documentation efforts. Suggestions for addressing these limitations through Indigenous-led frameworks are discussed, and a brief assessment of PM’s potential role within salmon habitat monitoring is put forward. Chapter 5 summarizes findings from the previous chapters and reflects on implications for research and practice emerging from this research, including recommendations for the development of frameworks that center Indigenous knowledge systems and governance authority.
From interpretable penalized LDA models to end-to-end deep learning for cell type annotation in single-cell RNA-seq
(2025) Bai, Kailun; Zhang, Xuekui; Shao, Xiaojian
This dissertation presents a systematic exploration of scalable, interpretable, and high-accuracy computational frameworks for automated cell type classification in single-cell RNA sequencing (scRNA-seq) data. Motivated by the increasing scale, dimensionality, and heterogeneity of modern scRNA-seq datasets, this work focuses on methods that balance statistical interpretability, computational efficiency, and predictive performance across diverse biological and technical settings. The research spans three major contributions, each addressing different trade-offs between simplicity, interpretability, and predictive power: 1. PCLDA (Penalized Component-wise Linear Discriminant Analysis) introduces a highly interpretable and statistically grounded annotation tool. 2. scSorterDL expands on this foundation by combining penalized LDA with ensemble learning and deep neural networks. 3. CellAnnotatorNet represents the culmination of this research by integrating a categorical autoencoder with the Swarm-pLDA framework into a unified, fully differentiable architecture. Together, these three contributions provide a progressive development from classical interpretable statistical models to fully integrated deep learning pipelines for large-scale single-cell analysis, offering a coherent and extensible framework for automated cell type annotation in single-cell genomics.
Creating representative skeletal cohorts through statistical modelling and synthetic CT image generation
(2025) Beagley, Aren; Giles, Joshua W.
Finite Element (FE) analysis is an important tool for orthopaedic research that allows studying the effects of orthopaedic devices, such as joint replacements, in ways that would be difficult or impossible to investigate experimentally. High fidelity FE studies are typically performed using a cohort of subject-specific FE bone models created from Computed Tomography (CT) images to ensure realistic bone shapes and material properties. However, this limits sample sizes because acquiring CT images exposes subjects to harmful radiation and such exposure should be avoided unless medically necessary. Unfortunately, small sample sizes create significant limitations on the applicability and generalizability of the insights provided by most FE studies. Ideally, FE studies should use sufficiently large and diverse cohorts to ensure that the resulting insights generalize to the broader population. If the population-level distribution of shape, size, and stiffness for a bone was known prior to conducting the FE study, subjects could be systematically chosen to create a representative cohort; however, even the process of choosing these subjects can be a challenge. Statistical Shape and Intensity Models (SSIMs) are an established tool for characterizing the population-level variance of size, shape, and material properties, and these models can be created from pre-existing, medically necessary, CT images. Furthermore, SSIMs can generate new instances that are representative of the population. Unfortunately, previous methods of developing SSIMs do not capture sufficient detail and produce models incapable of generating new instances suitable for use in FE studies. This thesis describes the development and validation of a method for creating high resolution SSIMs capable of generating new instances that contain data comparable to that of CT images. An associated method for converting SSIM-generated instances into SSIM-derived synthetic images, that are comparable to CT images, was also developed and validated. In combination, these two methods result in a model capable of systematically sampling a population-level distribution to create representative cohorts suitable for use in FE studies. To determine how representative the combined method is, generalization of both SSIM-generated instances and SSIM-derived synthetic images were assessed by comparing shape and material properties against real subjects. SSIM-generated instances were assessed to have Mean Generalization Root-Mean-Square (MGRMS) errors of 2.15 mm and 228.1 Hounsfield Units (HU) for shape and material properties respectively, and an average surface distance (ASD) of 1.145 mm. After converting SSIM-generated instances to synthetic images, the MGRMS error for shape could not be assessed, but for material properties it increased to 286.2 HU (+58.1) and the ASD showed improvement by decreasing to 1.028 mm (-0.117). These results demonstrate that the high resolution SSIM presented in this work has similar generalizability as previously published SSIMs while capturing significantly greater variance and that the conversion to synthetic images introduces minimal additional error. As such, the combined high resolution SSIM and conversion algorithm developed for this thesis represent a viable method of improving generalization for FE studies by overcoming the current barriers to systematically producing representative cohorts. This work set the stage for future work investigating the impacts of conducting FE studies with SSIM-derived synthetic images and addressing a range of clinically relevant biomechanical questions.
Local convergence of grounded lipschitz functions on d-ary trees
(2025) Butler, Nathaniel; Ray, Gourab
We consider the uniform sampling of grounded M-Lipschitz functions on the d-ary tree with n levels, with special interest as n → ∞. In the case M = 1, it was shown in [2] that this sampling converges weakly (in the infinite d-ary tree) iff 2 ≤ d ≤ 7. We continue this work by putting the computations into a form that a computer can handle, and we use this to confirm convergence for several other values of M and d. As in [2], the main idea is use the recursive structure of the d-ary tree to reduce the problem to studying the fixed points of a certain function on ℓ∞(N). In [2], the authors also showed an even-odd phenomenon for M-Lipschitz functions on any infinite bipartite graph with ‘rapid expansion’ (i.e. with sufficiently large Cheeger constant). Specialized to our original problem of grounded M-Lipschitz functions on Tn d, this shows that the samplings for n even and n odd both converge, but to separate limits when d ≫ M logM. We reproduce this proof here.
Machine-learning framework to identify and validate biochemical regime clusters in the global blue carbon ecosystem
(2025) Singh, Bhan; Popli, Navneet; Sima, Mihai
The Earth’s climate system is undergoing profound transformation, driven by changes in natural and anthropogenic stressors that disrupt environmental balance across land, air, and sea. Among these domains, the ocean stands as both a stabilizer and a sentinel, absorbing excess heat and carbon while revealing the earliest signs of ecological stress. Yet, the ocean itself is changing, shaped by interacting forces such as temperature, salinity, oxygen depletion, depth stratification, and biological productivity. Understanding how these stressors combine to reshape marine ecosystems requires not just observation but intelligent pattern recognition. This thesis approaches the problem as one of learning structure within complexity. Rather than relying on political boundaries or fixed geographic regions, it asks: can we allow the data itself to define the ocean’s natural divisions? Using in-situ observations from the World Ocean Database (WOD), a machine-learning framework was developed to uncover underlying biogeochemical regimes, clusters of ocean states defined by their physical and chemical signatures. Through careful preprocessing and hierarchical spatialtemporal imputation, the dataset was refined to reflect true environmental variability rather than sampling noise. The analysis employed multiple clustering algorithms to let ocean data “self-organize,” followed by classification models that validated and explained the separability of the discovered regimes. This hybrid approach revealed five coherent and interpretable patterns corresponding to familiar yet dynamically interconnected oceanic systems: productive coastal upwellings, oligotrophic gyres, polar waters, oxygen-minimum zones, and transitional open-ocean regimes. Together, these patterns tell a story of a living ocean, one organized not by political maps, but by the natural language of its own chemistry and biology. By combining unsupervised discovery with supervised validation, the research demonstrates how global ocean observations can be transformed into quantitative, interpretable indicators of ocean health. The resulting framework contributes to the emerging vision of a digital twin ocean, a system where data, models, and machine learning work together to monitor, predict, and ultimately safeguard the resilience of the planet’s largest ecosystem.
The definition of head and the syntactic structure of verbs in the composition of Yorùbá serial verb constructions
(2025) Ariyo, Oluwabukola Oluwaseun; McGinnis, Martha
I investigate serial verb constructions (SVCs) in Yorùbá within the Minimalist framework, addressing two fundamental questions: (i) which verb serves as the syntactic head of the extended projection associated with the SVC in Yorùbá, and (ii) what hierarchical relation exists between verb phrases in these constructions. Focusing specifically on SVCs in which both verbs are transitive and select distinct internal arguments, this dissertation employs multiple empirical and syntactic diagnostics to establish the head of these complex predicate formations. The analysis shows that the first verb (V1) functions as the head of the extended projection in Yorùbá SVCs. Three primary lines of evidence support this determination: (i) verb nominalization and clefting operations, which systematically target V1, while excluding the second verb (V2); (ii) adverbial modification patterns, wherein manner adverbs scope exclusively over V1, despite the ability to modify any verb in simple Yorùbá clauses; (iii) the distribution of functional categories including aspectual markers, negation, and modals, which appear only before V1. Drawing on Chomsky's bare phrase structure theory (1995, 2000) and Stepanov's late adjunction hypothesis (2001, 2007), I establish the structural properties of the verbs in the SVC. I show that VP2 is an adjunct to the VP1, rather than a complement. This conclusion is substantiated through the examination of extraction asymmetries, where wh-movement and focus movement proceed freely from V1. and its complements (including DP, PP, infinitival CP, and finite CP complements), and extraction from VP2 is categorically blocked regardless of the syntactic category or structural position of the displaced element. This extraction behavior suggests that VP2 is in an adjoined position, making it inaccessible to syntactic movement. Additional evidence from adjunct placement possibilities and reflexive binding across V1 and V2 shows that the object DP of V1 cannot be an antecedent to the object DP of V2, corroborating this structural analysis. This work contributes to the cross-linguistic understanding of SVCs by demonstrating that the mono-clausal properties characteristic of SVCs, including unified event structure, shared external arguments, and single tense, aspect, and mood specifications, can derive from adjunction configurations rather than being exclusively derived from the complement structure. This research advances both the descriptive understanding of Yorùbá syntax and theoretical discussions concerning headedness, complement/adjunct distinction, and complex predicate formation.
Sustaining student motivation and well-being: Academic and non-academic pressures, supports, and coping
(2025) Garavellos, Victoria; Cunningham, Bart
The purpose of this report is to gain a better understanding of how academic and non-academic pressures affect student well-being and how these pressures influence their academic performance. A literature review was conducted to explore the meanings of stress and student well-being, the effects of stress and stress management, and university wellness supports available to students. From the literature review, a conceptual framework was created to capture academic and non-academic pressures, motivations and demotivations, and faculty and peer support. This framework was applied to the interview guide. Semi-structured interviews were conducted with current and former university students to gather insight into their academic and non-academic needs. Interview data were analyzed to identify key themes from participant responses. Participants described several challenges and supports they encountered during their time in university, including heavy workloads, financial pressures, and the value of peer and personal relationships. The study highlights the importance of understanding the diverse stressors students face and offers recommendations to improve institutional support and promote student well-being.
Tracking uncertainty in knowledge graphs using Kalman filtering
(2025) Tkachenko, Alina; Thomo, Alex
Knowledge graphs (KGs) represent structured knowledge as networks of entities and relations, forming a foundation for reasoning in artificial intelligence. To make these symbolic structures usable by machine learning systems, knowledge graph embeddings (KGEs) map entities and relations into continuous vector spaces. However, traditional KGE models are typically static and deterministic. They treat all facts as equally certain and require full retraining when new data arrive, making them unsuitable for evolving, uncertain knowledge. This thesis introduces a new framework that reframes knowledge graph embedding as an online state estimation problem. By integrating the Kalman filter, a recursive algorithm that updates beliefs under uncertainty, into KGE training, the proposed approach enables continuous and uncertainty aware learning of entity and relation representations. The framework treats each embedding as a probabilistic latent state, updated incrementally as new triples arrive, blending prior knowledge with new, possibly noisy, observations. Two models instantiate this framework. KalmanKG2E extends the probabilistic Gaussian embedding model KG2E with Kalman-based online updates of means and covariances. KalmanComplEx adapts the non-probabilistic, complex-valued ComplEx model to a dynamic, uncertainty-tracking setting. Together, these demonstrate the frameworks generality across fundamentally different embedding architectures. Extensive experiments on six benchmark datasets show consistent improvements over static baselines. The Kalman-based models converge faster, achieve higher predictive accuracy, and exhibit greater robustness in sparse, evolving graphs. These results validate Kalman filtering as a principled and efficient mechanism for online knowledge graph learning. Overall, this work bridges classical state estimation and modern representation learning, advancing knowledge graph embeddings from static snapshots to dynamic, continuously adaptive systems that better reflect the evolving nature of real-world knowledge.
BBAE: Bit-to-byte alignment with entropy analysis for binary protocol field identification
(2025) Zhang, Leijie; Wu, Kui
Protocol Reverse Engineering (PRE) is crucial for analyzing undocumented or proprietary network protocols, particularly in the fields of network security and the Internet of Things (IoT). To conserve network bandwidth, many protocols adopt a compact binary format that maximizes bit utilization. However, this compactness introduces significant challenges for PRE, because (1) the number of potential field boundaries grows exponentially, and (2) byte-oriented PRE tools become ineffective for these scenarios. To address these challenges, we propose Bit-to-Byte Alignment with Entropy (BBAE) analysis, an innovative approach designed to enhance boundary detection in bit-oriented protocols. BBAE leverages entropy analysis and bit-congruence calculations across multiple window sizes to identify field boundaries more effectively. In addition, it enables systematic verification of detected boundaries. We conducted extensive evaluations of BBAE’s performance in identifying field boundaries of binary protocols and compared its effectiveness with existing tools, including byte-oriented semantic inference tools like BinaryInferno and bit-oriented tools such as Auto-ETLV. Our experimental results disclose that BBAE achieves outstanding performance in reverse engineering binary network protocols.
An interpretive analysis of the effectiveness of non-traditional or ‘Structured Discovery’ blindness rehabilitation in Canada from the perspective of blind service recipients and teachers
(2025) Lalonde, Elizabeth; Wiebe, Sarah Marie
Blindness rehabilitation in Canada has traditionally emphasized maximizing residual vision and functional skills through prescriptive, vision-centered methods. While these approaches provide supports, they often reinforce dependency and limit adaptability for people facing progressive vision loss. Structured Discovery, an alternative model that originated in the United States, reframes blindness as a characteristic rather than a deficit and emphasizes non-visual skill development, problem-solving, and empowerment through the mentorship of blind instructors and peers. Despite its influence in the United States, Structured Discovery is largely absent from both practice and scholarship in the Canadian context. This thesis explores how blind Canadians experience Structured Discovery training as participants and teachers. Using Interpretative Phenomenological Analysis (IPA) and grounded in Critical Disability Theory, the study examines how individuals make sense of their training experiences, the skills and perspectives they gained, and how they compare Structured Discovery to more traditional rehabilitation services. The researcher conducted ten qualitative interviews with Canadians directly engaged in Structured Discovery programs, including those delivered through the Pacific Training Centre for the Blind (PTCB), one of the few Canadian organizations applying this model. This study makes several contributions. It represents the first academic examination of Structured Discovery in Canada and addresses a gap in both disability studies and rehabilitation research. It provides practical insights for rehabilitation practitioners by showing how empowerment-based, non-visual training can better prepare blind people for independence and social participation.
Reinforcement learning based resource allocation in fog computing
(2025) Mokhtari, Masoud; Ganti, Sudhakar
The Internet of Things (IoT) has revolutionized connectivity by enabling seamless data exchange among diverse devices, fostering intelligent services and informed decisionmaking. However, the rapid surge in data traffic has exposed the limitations of traditional cloud-based solutions, particularly in meeting the quality-of-service (QoS) demands of latency-sensitive applications. Fog computing has emerged as a transformative paradigm, extending computational resources closer to end-users and bridging the gap between centralized cloud systems and edge devices. This approach addresses QoS challenges by providing critical services and resources at the network’s edge. Despite its advantages, fog computing faces resource limitations at the node level, necessitating efficient resource allocation to optimize performance and meet application-specific QoS requirements. Deciding whether to process data at the fog or cloud level involves navigating complex trade-offs dictated by resource availability, offloading criteria, and diverse application scenarios. This thesis addresses these challenges through a comprehensive approach to resource allocation in fog and cloud computing environments. First, a reinforcement learning-based method is introduced to optimize resource allocation for a single fog node. By formulating the problem as a Markov Decision Process (MDP), the approach maximizes fog resource utilization while considering the number of resource blocks and delay tolerance for each request. Experimental evaluations demonstrate the superiority of the E-SARSA algorithm in terms of speed, utilization, and adaptability compared to Q-learning, SARSA, and a Fixed-Threshold approach. The study then extends to multi-fog/cloud systems, introducing a two-phase process. In the first phase, the optimal fog node for resource allocation is identified. In the second phase, reinforcement learning is applied to determine whether tasks should be processed locally or offloaded to the cloud. This method ensures efficient resource utilization, with experimental results highlighting the superior performance of the Selection-2 approach compared to Genetic Algorithms (GA), Round Robin (RR), and Random strategies, particularly in speed, utilization, and load balancing. Finally, the framework is further enhanced with a hybrid approach combining Genetic Algorithms and Reinforcement Learning (GA/RL) for dynamic resource allocation in integer-based multi-fog/cloud systems. This method applies the two-phase process, achieving significant improvements in speed, utilization, and load balancing compared to existing methods. By dynamically allocating fog resources and optimizing offloading strategies, this work addresses the limitations of traditional cloud computing systems and ensures seamless performance for latency-sensitive IoT applications. The proposed approaches advance resource allocation strategies in fog and cloud computing, offering scalable, efficient, and adaptive solutions for future IoT ecosystems.
Advancing cell-type annotation and deconvolution in human bronchoalveolar lavage through single-cell transcriptomics and benchmarking protocols
(2025) Hu, Yushan; Zhang, Xuekui; Shao, Xiaojian
Bronchoalveolar lavage (BAL) provides a unique view to analyze immunological aspects of the human lung. Single-cell RNA sequencing data (scRNA-seq) of BAL offers great potential for immunotherapy of lung diseases. Despite promising challenges, persist in identifying disease-relevant high-resolution sub-cell types, standardizing annotations across studies, and accurately interpreting bulk RNA-seq data. This dissertation is approached from three perspectives. Chapter one serves as the introduction, while chapter two provides the background. Chapter three presents scRNA-seq data utilized to characterize macrophage and monocyte populations in chronic obstructive pulmonary disease (COPD). The analysis identified dysfunctional alveolar macrophages and hyperinflammatory monocytes, indicating potential therapeutic targets and emphasizing the modulatory effects of inhaled corticosteroids. The fourth chapter presents a standardized atlas of human BAL cells through the synthesis of multiple scRNA-seq datasets, with ensemble auto-annotation tools and reliable cross-study markers. This atlas deals with discrepancies in previous studies and provides a foundation for BAL research. Chapter five introduces the first true-paired benchmarking study of cellular deconvolution in BAL. Including 30 human BAL samples, each divided into bulk RNA-seq and matched single-cell libraries. After systematically evaluating 15 popular algorithms across multiple references and cell-type resolutions, this study demonstrates a modestly designed pairing strategy substantially improves both benchmark realism and practical accuracy. Our true-paired data, comparative analyses, and three-step protocol provide a blueprint for future deconvolution studies. Together, these chapters deliver disease insights, community resources, and methodological frameworks that advance the study of lung immunity through both single-cell and bulk transcriptomics.
Understanding the role of insulin signaling in female reproductive ageing
(2025) Athar, Faria; Templeman, Nicole M.
Preserving female reproductive health is crucial for maintaining the survival and sustenance of a species as well as for overall health and well-being. Reproductive health is mutable and has a strong life-history basis; two of its important regulators are chronological age and nutrition. Insulin signaling is an evolutionarily conserved mechanism for interpreting nutrition levels. To better understand the role of this pathway, I leverage its conservation to study impacts on reproductive function in a cross-species approach using human, mouse and Caenorhabditis elegans data. Analysis of longitudinal data from the Study of Women's Health Across the Nation (SWAN) revealed that women with higher fasting insulin levels at mid-life experienced an earlier onset and longer duration of vasomotor symptoms, independent of body mass index. To test the causal role of insulin in female reproductive ageing, I conducted longitudinal analysis of a mouse model and found that reducing endogenous levels of insulin production protects against high-fat, high-sucrose-induced reproductive dysfunction. Insulin-reduced dams maintained higher pregnancy rates and ovarian reserve compared to hyperinsulinemic littermates, despite similar levels of glycemia. In C. elegans we showed that glucose enrichment accelerates reproductive ageing by compromising oocyte quality and altering mitochondrial dynamics. However, reducing insulin signaling through mutation of the daf-2 insulin-like receptor protected against reproductive decline, even though it did not mitigate somatic ageing. I also found that insulin signaling in the intestine and body wall mediates reproductive ageing through non-autonomous mechanisms. Together, these studies provide evidence of insulin signaling being an evolutionarily conserved regulator of reproductive ageing.
Equity, decolonization, and the urban forest: Exploring Indigenous-led urban forest planning practices in the Capital Regional District
(2025) Stoltz, Sydney; Wiebe, Sarah Marie
Equitable access to urban green spaces is vital for citizen health, climate change mitigation, and reconciliation. However, the urban forest planning processes in British Columbia’s Capital Regional District (CRD) currently do not adequately support Indigenous inclusion, knowledge, and self-determination. This deficiency in planning impedes efforts to achieve urban forest equity, decolonization, and reconciliation. Addressing this issue is essential to ensuring that urban forest management is inclusive, equitable, and respectful of Indigenous perspectives. This Master’s thesis examines potential barriers, best practices, and approaches to collaborative urban forest policy within the CRD in order to advance greenspace equity, decolonization, and reconciliation for all residents. Promoting collaborative urban forest planning policy is supported under B.C.'s Declaration on the Rights of Indigenous Peoples Act Action Plan, which outlines a framework for the province and municipalities to fulfill the goals of the United Nations Declaration. While the CRD facilitates regional decision-making and positive relationships with local Indigenous communities, it currently lacks specific policies for Indigenous participation in greenspace policy and planning. Using interpretive policy analysis, thematic analysis, and a critical policy lens, this thesis reviews findings from jurisdictional scans, a literature review, and eight interpretative interviews with Indigenous and non-Indigenous community members to determine potential pathways towards collaborative urban forest planning. The collective findings suggest that there are several approaches that the CRD (or the municipalities within the region) could adopt in order to increase Indigenous inclusion in local urban forest planning. Participants emphasized the need for shared priorities, engaging early and often, relationship-building, and clear communication. Key barriers included considerations around working within ongoing colonial systems, such as honoring Indigenous cultures and traditions in ways that are non-extractive or appropriative, ensuring continuity in work and relationship-building, and working within potential funding constraints. Preferred approaches emphasized proper engagement (such as through establishing protocols in the early stages), ensuring that all voices and concerns are heard equally, and an emphasis in bringing knowledge together in a relational way rather than an extractive one. Through analysing these findings, this thesis presents several short, medium, and long-term recommendations to increase education and capacity-building within government, continue to build relationships with local First Nations, and create ongoing spaces for co-governance in urban forest planning at the regional level in order to foster improved collaboration and equity. This thesis advances local regional efforts towards reconciliation, sustainability, and environmental equity by identifying existing barriers and proposing potential ways forward through collaboration built on trust and partnership.
How a hot topic affects governance in British Columbia: An examination of extreme heat exposure emergency management planning
(2025) Emenike, Jumai; Wiebe, Sarah Marie
This study explores how British Columbia (BC) is addressing extreme heat under the new Emergency and Disaster Management Act (EDMA, 2023), with a focus on equity and planetary health. The research analyzes provincial policy documents and draws on interviews with actors from health, emergency management, housing, and climate adaptation at both provincial and municipal levels. Using interpretive policy analysis, the research explores how EDMA provisions are understood across the four phases of emergency management (preparation, response, mitigation, and recovery) and how existing extreme heat measures address heat vulnerability and adaptation. Findings reveal notable advances in preparedness and public health response, but significant gaps in mitigation, recovery, and cross-ministerial coordination. While policy documents officially acknowledge equity through tools such as heat vulnerability maps and the Population Environmental Risk Characteristic (PERC) file, implementation is limited by built environment constraints and institutional fragmentation. Planetary health considerations, including but not limited to ecological impacts and nature-based solutions, are recognized but remain marginal. The study concludes that EDMA provides policy opportunities, but effective heat governance requires a coordinated, multi-sectoral approach.
Memories from the land of amnesty: Historical narratives of the armed right in Brazil
(2025) Santana Bertho, Ana Paula; Milton, Cynthia E.
In 2018, Brazilians elected the far-right candidate Jair Bolsonaro, a retired military captain, as President. His open praise of the military dictatorship (1964–1985) and the support it received among civilians called into question the hegemony of victim-centered memories about that period. This thesis dialogues with this context and aims to investigate the role of the armed right memories in contemporary Brazilian democracy, focusing on how these narratives have shaped public discourse and national identity. Drawing on Ksenija Bilbija and Leigh A.Payne’s concept of “memory market,” the study analyzes two memory products: the commemorations of March 31st (chosen by the military as the date of the military coup d'état of 1964) between 2014 and 2022 and the Army’s Historical Museum and Fort Copacabana in Rio de Janeiro. Based on the examination of newspaper coverage, government documents, interviews, and exhibitions, this study argues that the military has sought to refashion its role in the new democracy, reaffirming its authoritarian saviour role while attempting to engage with the era of human rights speech. The rise of “uncivil groups” after 2013 empowered the authoritarian nostalgia in the public sphere, mobilizing symbols of the past to justify authoritarian projects in the present. Bolsonaro emerged as a spokesperson for these actors. His government represented the institutionalization of the right armed narratives, paradoxically generating tensions with some military sectors while instrumentalizing these memories as fuel for the storming of Brasília in January 2023.
Constructing disability in policy: The discursive construction of disability in the Accessibility for Ontarians with Disabilities Act and the Accessible British Columbia Act
(2025) Fortin, Ashley; Wiebe, Sarah Marie
This research examines how cultural and political biases are embedded within Canadian accessibility legislation, focusing on the Accessible British Columbia Act (ABCA) and the Accessibility for Ontarians with Disabilities Act (AODA). It is guided by two research questions: 1. What biases are present in the AODA and ABCA, and how do they impact disabled individuals in these provinces? 2. What specific language and terminology in the AODA and ABCA express these biases? The study employs a qualitative, interpretive design that combines Critical Discourse Analysis (CDA) with a comparative case study of British Columbia and Ontario. Using Fairclough’s CDA framework, the research reveals the powers and ideologies embedded in accessibility legislation and examines how subsequent regulations and amendments may influence accessibility for disabled individuals. Five key biases were identified within the AODA: (1) structural and authoritative bias, (2) ambiguity and neoliberal governance, (3) symbolic inclusion, (4) economic framing, and (5) enforcement and compliance. Four primary biases emerged within the ABCA: (1) equality over equity, (2) tokenism and volunteerism, (3) technocracy, temporality, and optimism, and (4) ambiguity and authority. Each theme is illustrated with examples from the legislation and analyzed through a critical discourse lens. For practice, the findings underline the need for policymakers to shift from reactive, compliance-driven approaches to proactive, rights-based frameworks that anticipate and prevent barriers before they arise. Current systems often place the burden on disabled individuals to identify barriers and file complaints, rather than embedding accessibility as a baseline expectation. From a research perspective, the comparative analysis of the AODA and ABCA highlights the value of examining accessibility legislation across jurisdictions rather than in isolation. Expanding this approach to other provinces and territories could provide a more comprehensive understanding of how disability is discursively constructed in law across Canada.
Development of a secure underwater sensor suite for real-time environmental monitoring of blue carbon ecosystems
(2025) Singh, Rudra Pratap; Popli, Navneet; Dong, Xiaodai
The health of Canada’s blue-carbon ecosystems—kelp forests, seagrass meadows, and salt marshes—plays a vital role in marine biodiversity and long-term carbon sequestration. Yet these ecosystems are increasingly vulnerable to anthropogenic and natural stressors such as temperature variation, pH fluctuations, heavy-metal pollution, and hydrocarbon extraction. Traditional monitoring methods, relying on sporadic field sampling and manual analysis, fail to capture the temporal and spatial complexity of these changes. This thesis, Development of Machine Learning-Based Techniques for Monitoring and Analyzing the Effects of Natural and Manmade Stressors on Canada’s Blue Carbon Ecosystem Using a Secure Underwater Communication Suite, presents a comprehensive hardware-driven approach to address these gaps. The research involves the design, fabrication, and laboratory validation of a modular underwater sensor suite deployed via a Blue Robotics ROV platform to collect high-resolution oceanographic data. The integrated system measures temperature, salinity, dissolved oxygen, pH, turbidity, and chlorophyll concentrations through a network of calibrated probes, ensuring precise and repeatable environmental sensing. To support continuous operation, a secure underwater communication and data-handling framework was developed using a hybrid Ethernet-acoustic link and lightweight encryption protocols to preserve data integrity and mitigate cyber vulnerabilities within the Internet of Underwater Things (IoUT). Extensive laboratory testing in controlled aquatic environments demonstrated stable sensor calibration, minimal noise drift (< 0.05% FS), and consistent data throughput at depths up to 1 m. Complementary studies explored intrusion detection and federated-learning frameworks for distributed underwater nodes, strengthening the resilience of the proposed communication network. The system enables near-real-time environmental monitoring and data synchronization between underwater nodes and surface control units. By combining reliable hardware sensing with secure data transport, the work advances Canada’s capacity for sustained observation of blue-carbon habitats. The results contribute both an open hardware architecture for scalable underwater sensing and a validated communication protocol for secure marine data acquisition foundations that can inform future autonomous monitoring networks and adaptive management strategies for coastal ecosystems.

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