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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Flexible integration of classical and quantum techniques in the evolutionary path to quantum utility
(2025) Angara, Prashanti Priya; Müller, Hausi A.; Stege, Ulrike
This dissertation addresses fundamental challenges in solving constrained combinatorial optimization problems using hybrid quantum-classical computing approaches. Quantum computing shows promise for tackling computationally intractable problems, current approaches face significant limitations. The path forward in computing involves combining quantum and classical computing approaches. Quantum processing units (QPUs) will play a key role in accelerating algorithms in optimization, simulation, and machine learning applications beyond what classical computers can achieve on their own, similar to how graphical processing units (GPUs) have become integral to general-purpose computing. The current generation of quantum devices is limited by noise and circuit depth constraints, making it challenging to gain practical utility. Hybrid quantum-classical approaches, which combine quantum computing resources with classical computing resources, are essential for addressing these limitations on near-term devices. In the area of combinatorial optimization, current practical state-of-the-art hybrid approaches are variational in nature, relying on parameterized quantum circuits to encode solutions and classical optimization techniques to find optimal parameters. While the ultimate goal of solving computationally intractable problems is to find provably optimal solutions, practical constraints of real-world scenarios often necessitate focusing on efficiently obtaining high-quality, near-optimal solutions. The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical approach for tackling these challenging problems that are encoded using quadratic and higher-order unconstrained binary optimization problems (QUBO and HUBO). QAOA alternates between two operators: a problem-specific cost Hamiltonian that encodes the optimization objective, and a mixer Hamiltonian that explores the solution space. The algorithm's parameters are iteratively optimized using classical methods to maximize the probability of sampling high-quality solutions. However, these methods often struggle with solution feasibility, especially for problems where the solutions must satisfy specific constraints. Penalty-based encodings require careful parameter tuning and risk producing infeasible solutions, while feasibility-preserving methods demand deeper quantum circuits that are challenging to implement on near-term quantum devices. In this dissertation, we present novel strategies that reduce or omit dependency on penalty parameters while maintaining solution quality, effectively capturing both optimal and near-optimal solutions, and balancing the trade-offs between circuit depth and solution feasibility. We present SCOOP, a novel QAOA-based framework for solving constrained optimization problems. SCOOP transforms a constrained problem into an unconstrained counterpart, forming SCOOP problem twins. QAOA operates on the unconstrained twin to identify potential optimal and near-optimal solutions. Effective classical post-processing reduces the solution set to the constrained problem space. Our SCOOP approach is solution-enhanced, objective-function-compatible, and scalable. We demonstrate the effectiveness of the SCOOP framework on selected NP-hard problems, some of which can be encoded quadratically (such as Minimum Vertex Cover and Maximum Independent Set), and some that can be encoded using higher-order terms (such as Minimum Dominating Set, Minimum Maximal Matching, and Minimum Set Cover). We also apply our framework to constrained combinatorial optimization problems that can be solved in polynomial time, such as Minimum Edge Cover and Maximum Matching. Experimental results on full statevector simulators and tensor network simulators show that SCOOP significantly improves the probability of finding high-quality feasible solutions across various problem classes, on problems that can be encoded quadratically as well as problems that can be encoded using higher-order terms. We also demonstrate the effectiveness of SCOOP on real quantum hardware, specifically IBM Quantum Heron R2 processors with 156 qubits, showing that SCOOP can be applied to practical problems with current quantum hardware limitations. This work makes significant contributions toward achieving quantum utility by enabling systematic investigation of constrained optimization problems on quantum devices. While current quantum computing research primarily focuses on unconstrained problems like MaxCut, real-world applications typically involve constraints. SCOOP provides a scalable framework for encoding constrained problems, extending quantum computing's practical utility beyond simple unconstrained cases. By eliminating dependency on penalty parameters and providing natural problem formulations, SCOOP makes it feasible to explore quantum solutions for constrained problems where classical exact methods become intractable. This capability is crucial for identifying promising avenues for demonstrating quantum utility across diverse constrained optimization problems, advancing the practical applicability of quantum computing approaches.
Toward quantum computational biomolecular structure prediction
(2025) Zaborniak, Tristan; Stege, Ulrike; Numanagić, Ibrahim
Biomolecules and their interactions form the material and processual basis underlying all biological phenomena, from photosynthesis to Alzheimer’s disease. Studying these systems is therefore central to the purview of all biological sciences. Computational biomolecular structure prediction (CBSP) supports this effort by leveraging computers to determine, model, and engineer biomolecular structures, properties, and processes—offering a powerful complement to laboratory-based methods. However, many core CBSP problems—such as finding minimum free energy or conformationally-stable structures given sequence information—are computationally challenging. These problems are typically NP-hard in their general form, while their corresponding decision variants are NP-complete. As a result, both formulations are resistant to efficient exact solution at large scales. Quantum computing, a developing computational paradigm leveraging quantum mechanics, offers a potential path forward, given recent evidence suggesting that certain quantum approaches may reduce resource demands for certain NP-hard problem families. Approaches include fully quantum algorithms, quantum-inspired classical heuristics, and hybrid quantum-classical frameworks, all of which may help address long-standing computational bottlenecks in CBSP. This dissertation offers a preliminary investigation of the practical potential of quantum computing for three core CBSP challenges—RNA folding, multi-body molecular docking, and protein design—that, despite their diverse applications, share structural features well suited to exploration by quantum optimization methods. Specifically, we cast each problem as a cost function network (CFN), and develop transformations of these CFNs to quadratic unconstrained binary optimization (QUBO) models in order to render them compatible with current quantum and quantum-inspired hardware. We argue that these transformations not only broaden the range of solvable CFNs across quantum platforms, but in some cases possess intrinsic features which may offer optimization advantages over native CFN formulations. Using a current‑generation superconducting flux‑qubit quantum annealer, we: (a) demonstrate its use for tuning free QUBO parameters against biomolecular structure data, and (b) benchmark solution quality and resource usage against optimized classical Monte Carlo methods, finding comparable performance. Finally, we package these methods into the Masala Quantum Computing Plugins library, an open‑source, modular CBSP platform that supports CFN construction, multiple QUBO encodings (one‑hot, domain‑wall, approximate‑binary, hybrid), and execution on both classical and quantum backends. Our contribution lays the groundwork for extensible, state‑of‑the‑art, quantum-compatible CBSP workflows.
The creation, validation, and comparison of a novel computer vision-assisted image monitoring method for assessing long-term underwater diversity
(2025) Rimmer, Talen; Juanes, Francis
As marine ecosystems face rapid change, there is a growing need for accurate and efficient tools to support long-term biodiversity monitoring. This thesis evaluates the application of computer vision (CV) methods for monitoring epipelagic macrofauna using unbaited remote underwater video collected via FishCams, a novel low-cost camera system developed for nearshore environments. A ten-step workflow was developed to generate an annotated object detection training dataset, which included salient motion filtering, image classification, bounding box annotation, and iterative model training and validation. Using this method, we trained a YOLOv8 object detection model on over 240,000 annotations spanning 54 pseudo-taxonomic categories (representing both taxa and visually similar groups of fauna) from footage collected at two kelp farms and adjacent reference sites (N = 4 sites) on the west coast of Vancouver Island, British Columbia. Model performance was highest for species- and genus-level pseudo-taxa with distinctive morphology, while broad taxonomic groups exhibited lower accuracy and systematic misclassification. A re-training experiment found that increased annotation effort improves model accuracy, though performance improvements were more with taxonomic resolution (e.g., species-, genus-, or family-level) than the type of organism (e.g., fishes, gelatinous zooplankton). Another goal of this thesis was to compare census methods across sites over a sevenmonth period. CV-assisted monitoring (YOLOv8 object detection model) results were compared to conventional census approaches (diver transects, Remotely Operated Vehicle (ROV) surveys, and manual annotation of FishCam footage) across seven months of sampling. The CV method that included training annotations (YOLO With Annotations) consistently recorded the highest taxonomic richness (34 unique pseudo-taxa), substantially exceeding that of ROV (17), dive (23), and the FishCam Subset (22). Monthly richness comparisons indicate that sampling frequency likely plays a substantial role in pelagic biodiversity detection; hourly sampling methods (YOLO With Annotations, and YOLO without annotations (YOLO Only)) captured higher richness than the low-frequency FishCam Subset, despite using the same camera system. Proportionally, YOLO-based methods and dive surveys observed a broader range of fish and gelatinous pseudo-taxa, while ROV surveys primarily detected small gelatinous zooplankton. Community composition varied significantly by both site (farm versus reference) and census method. Across all methods, richness was significantly higher at the kelp farm than the reference site (p = 0.024), driven by an increased proportion of solitary fish pseudo-taxa at the farm site. Together, our results suggest that CV-assisted underwater video monitoring has the potential to outperform dive and ROV surveys in an epipelagic environment, though differing sampling frequencies prevent equal effort comparisons across methods.
Permutation in regression revisited: the residual route proven optimal theoretically
(2025) Kim, Soojeong; Zhang, Xuekui
The assumptions for classical linear-model are never met in practice. Recent evidence shows that such violations inflate Type I error as sample size grows, while simple permutation tests can restore control in single-predictor regressions. Yet in multiple regression, practitioners face a confusing menu of residual- and raw-data shuffling schemes, with little theory to guide the choice. We develop the first closed-form, finite-sample comparison of six widely used permutation strategies for a coefficient of interest in the presence of nuisance covariates. We derive exact means and variances of the permuted estimator, and we establish its asymptotic distribution. Based on this, we discuss Type I error and power of each permutation strategies, as well as how are these affected by corelation between covariances and the focal predictor. The analysis reveals that (i) the three residual-based schemes—permuting response residuals, predictor residuals, or both—are identically distributed; they match the true null up to second moments in finite samples and match in distribution as n → ∞, guaranteeing valid Type I error control. (ii) Raw-data permutations behave unpredictably: shuffling the response is overly conservative, shuffling the predictor is liberal when covariates are correlated, and shuffling both can be unstable. Closed-form results quantify how predictor–covariate correlation, error variance, and sample size drive these patterns and specify the Monte-Carlo sample size needed for accurate p-values. Extensive simulations confirm the theory: residual permutations maintain nominal error and retain power comparable to the classical linear model when assumptions hold, whereas raw-data schemes either inflate or deflate Type I error and sacrifice power. The work reconciles decades of ad-hoc practice, provides actionable guidelines, and equips analysts with a principled, computationally feasible framework for exact inference in large-sample regression.
Characterization of cholinergic mediated α7 and α4β2 nicotinic acetylcholine receptor responses in layer1 interneurons of the medial prefrontal cortex
(2025) Abazari, Mohammad Foad; Nashmi, Raad
The medial prefrontal cortex (mPFC) is a brain region responsible for a variety of cognitive functions including attention and working memory. Cholinergic neurons, which release acetylcholine (ACh), are known to enhance attention and their pathophysiology is associated with disorders such as Alzheimer’s disease and epilepsy. Nicotinic acetylcholine receptors (nAChRs) are activated by the cholinergic system and modulate neuronal excitability. Therefore, understanding nAChR mediated synaptic neurotransmission will allow us to better understand how the activity of neurons is precisely controlled. Using whole cell recordings of layer 1 neurons of mouse mPFC and optogenetic stimulation of ACh release resulted in two nAChR mediated currents, one having a rapid rise and decay kinetics and sensitive to inhibition by the α7 nAChR antagonist MLA. The second nAChR current was long lasting and inhibited by the α4β2 nAChR antagonist DHβE. The α4β2 current was significantly inhibited by the calcium chelator EGTA-AM, while there was no effect on α7 currents. This suggests that ACh release eliciting α7 nAChR responses is mediated by tight coupling of the presynaptic calcium source and the calcium sensor, while that of α4β2 responses is a looser coupling. Following stimulation of ACh release, there were delayed asynchronous miniature excitatory postsynaptic current (mEPSC) responses. DHβE eliminated both the α4β2 currents and the asynchronous current events. However, MLA eliminated α7 responses but did not impact the asynchronous events, thus confirming that the asynchronous responses were mediated by α4β2 nAChRs. The α4β2 mediated asynchronous activity was also inhibited by EGTA-AM. The spontaneous excitatory postsynaptic activity prior to light stimulation was insensitive to either nicotinic competitive antagonist DHβE or MLA but was inhibited by EGTA-AM and the L-type Ca2+ channel blocker nifedipine. The substantial reduction in the number of spontaneous events after the application of nifedipine highlights a unique role for L-type calcium channels in regulating spontaneous activity. This could have implications for understanding presynaptic calcium dynamics and spontaneous neurotransmitter release mechanisms.
Associations between frailty risk and gendered ageism: A nurse practitioner shift towards health equity for 'old' women
(2025) Woodley, Jinelle; Bruce, Anne; Newton, Lorelei
This dissertation advances understandings of associations between gendered ageism and frailty risk for women to generate priority considerations for shifts in NP knowledge development and practice. Healthcare of older adults is regarded as a healthcare priority related to a rapidly growing older adult population and escalating rates of frailty, particularly amongst ‘old’ women. Nurse Practitioners (NP) are uniquely positioned to serve as most appropriate care providers for frailty risk and gendered ageism because of their mandate to optimize health from perspectives of holism, complexity, and associated health inequities. However, a paucity of knowledge exists to inform NP approaches to the healthcare of ‘old’ women in regard to frailty risk and gendered ageism, with particular gaps in understanding from intersectional, qualitative, nursing, and critical feminist angles of vision. The overall purpose of the study was to describe and interpret associations between frailty risk and gendered ageism. Data sources included 14 published autobiographical and 12 semi-structured in-depth participant interview narratives of critical feminist ‘old’ women. The exploration was oriented by a critical feminist gerontology lens, qualitative study design, interpretive description (ID) methodology, and narrative thematic analysis methods. Data collection and analysis occurred concurrently with a focus on potential themes or patterns. Generated understandings of associations between gendered ageism and frailty risk are themed as: 1) shroud of conflation of ‘old’ and ‘frail’; 2) self-inflicted typification; and 3) socially imposed typism. Extended interpretation situates priority generated understandings in existing literature. This study invites next logical steps for NP knowledge development and practice shifts to improve health and health equity for ‘old’ women by considering generated understandings of associations between gendered ageism and frailty risk to broaden the angle of vision from individualistic to systemic and create safer spaces for women to claim ‘old’ and ‘frail’ free from discriminatory assumptions and responses.
The role of the internet in providing reproductive health information to female youths in the Middle East
(2025) Pahlavan, Fattaneh; Kakuru, Doris; White, Jennifer
This systematic review aims to identify the role of the Internet in providing reproductive health information to female youths in the Middle East, utilizing the “Cyberfeminism” framework and the Transtheoretical model of change. I imported 27 eligible studies (from 2005 to 2024) into the Covidence platform, extracted data, and analyzed them using Reflexive Thematic Analysis. My research questions are as follows: How does the Internet affect female youths’ access to reproductive health information in the Middle East? What are the internet-related barriers to female youths’ access to reproductive health information? How can female youths’ access to reproductive health information be improved? I found some themes that helped me to answer research questions: The first theme was “The effect of the Internet on access to reproductive health”. It shows that the Internet and social media are useful tools for providing female youths with reproductive health services and information in the Middle East. However, I found other themes as barriers that prevent female youths from accessing reproductive health information using the Internet and social media, including trust issues, technical barriers, governments, and low digital literacy. Based on my findings, accessible resources and a safe online space, education, high digital literacy, and female youths' empowerment would be facilitators that could help female youths access reproductive health information in the Middle East. In conclusion, the Internet and social media positively affect female youths’ reproductive health information in the Middle East. While some barriers prevent female youths from accessing that information, we can utilize facilitators to enhance their reproductive health information using the Internet and social media.
Personalized font generation using keystroke dynamics
(2025) Sayah Dehkordi, Narges; Nacenta, Miguel
Before the advent of digital communication, personal correspondence was often handwritten, allowing people the opportunity to express themselves in their own unique style. As digital communication has become more common, typed text often lacks the personal touch that handwriting conveys. Despite the wide variety of styles in modern font design, digital font uniformity limits individual identity in typed communication. The act of typing itself, however, is a nuanced activity with distinct patterns unique to each individual. This study explores how these unique typing patterns can be leveraged to generate personalized fonts, offering a form of digital self-expression similar to handwriting. We present a system that analyzes keystroke dynamics, such as Keydown-Keydown time, Flight Time, and the spatial distribution of keys, to create customized fonts that are stable for individual participants yet unique across different participants. Using datasets from multiple universities, we preprocess and analyze typing behaviours, extracting features that are both highly discriminative and consistent. These features are then used to generate personalized fonts that visually reflect each participant’s distinct typing style. Our system demonstrates the feasibility of personalized digital communication through typing behaviour-driven font generation, offering an innovative way to enhance individuality in electronic communications.
A Machine learning approach to surrogate development for Canadian power system toward decarbonization
(2025) Jahangiri, Zahra; McPherson, Madeleine
As Canada works toward a net-zero emissions economy by 2050, understanding optimal strategies for power sector expansion and decarbonization is crucial. To address this challenge, this thesis uses machine learning, specifically neural networks, to conduct a detailed sensitivity analysis, uncertainty analysis and provincial analysis. We developed a supervised learning surrogate model for a capacity expansion model, reducing computation costs by five orders of magnitude. Using this model, we perform sensitivity analysis to evaluate how changes in input variables, such as generation technology capital costs, electricity demand, and carbon taxation, impact model outputs. Additionally, we perform an uncertainty analysis to explore the behavior of the model’s outputs in response to variability, uncertainty, and potential fluctuations in these inputs. This approach allows for a more advanced exploration of the design options for Canadian national and provincial power systems. This model reduces computational time from 11–72 hours to milliseconds with minimal resource requirements. The computational efficiency enables integration into various platforms and tools for decision-making. It’s essential because it makes the model accessible to users who may not have technical expertise, such as stakeholders and decision-makers. By reducing the need for extensive technical resources, these users can leverage the model's outputs to inform real-time decisions without relying on advanced computing power. The study in chapter 4, uses unsupervised machine learning and statistical techniques to identify key factors influencing system outcomes. These include the increasing importance of gas combined cycles in a low-carbon system and the strong potential of wind energy in Canada's decarbonization. Our methodology identifies key patterns in power system outcomes. For example, it uncovers critical correlations like that between variable renewable energy capacity factors and transmission expansion. The results in chapter 5, underscore the importance of flexible grid systems and offering a province-specific roadmap. This thesis introduces the use of machine learning for large-scale energy system planning. It contributes by developing analytical frameworks for model usage and offering a detailed discussion of the results. These insights provide a foundation for strategic planning and policy formulation, particularly in supporting Canada’s transition to a sustainable energy future.
Methods to optimize resource use in drinking water quality monitoring
(2025) Gentleman, Alice Margaret; Dorea, Caetano; Chatterley, Christie
Drinking water quality monitoring (DWQM) is essential to achieving universal access to safely managed drinking water services (SMDWS). However, barriers such as insufficient funding and challenges in procuring material have contributed to persistent data gaps, with nearly half the global population lacking adequate water quality data. To bridge these gaps, low-resource methods and field kits have been developed. While many of these tools claim to align with international standards, they often lack formal validation, and their limitations remain poorly understood. This study evaluated three alternative methods for DWQM to assess their comparability to existing methods and their suitability within low- and medium- resource contexts. Chapter 2 evaluated the use of a yoghurt maker as a low-cost alternative incubator for DWQM. The results demonstrated that the yoghurt maker provided adequate incubation conditions provided that temperature was monitored throughout the incubation period. Chapter 3 assessed the Spritz method, a 70% isopropyl alcohol-based spray for decontaminating single-use filtration funnels. Laboratory validation found that the Spritz method achieved complete funnel decontamination while field testing demonstrated a comparable proportion of positive blanks compared to a practical baseline. Chapter 4 examined the efficacy of formaldehyde-based decontamination in portable laboratories. Under both challenge and practical conditions, the method was found to reduce but not completely inactivate residual contamination. The findings emphasised the necessity of routine quality control and independent validation of both new and existing DWQM methods. Together, these studies underscore the importance of validating alternative DWQM methods to ensure the reliability and accuracy of results. Improving the understanding of the strengths and limitations of these methods will support more informed, data-driven decisions and ultimately strengthen efforts to achieve universal access to SMDWS.
Inuktut vitality as a protective factor of suicide risk among Nunavut Inuit
(2025) Loggie, Alexander; Huang, Li-Shih; Rodriguez de France, Carmen
This thesis examines the relationship between Inuktut language vitality and suicide rates among Inuit communities in Nunavut, exploring whether cultural continuity—particularly through language—serves as a protective factor against suicide risk. While previous research has linked Indigenous language knowledge to improved mental health outcomes, including reduced suicide rates, limited quantitative work has focused specifically on Inuit populations in Nunavut. This study draws on data from the 2016 Canadian census and suicide incidence reports from 1999 to 2014, using multiple regression analysis to assess the association between community-level measures of Inuktut vitality and suicide rates, while controlling for socio-economic variables including income, education, and employment. The analysis finds a modest but consistent, inverse relationship between changes in the use of Inuktut as a main language at home and changes in suicide rates, with gains in the former significantly associated with declines in the latter. These findings contribute to the growing body of evidence linking language vitality to well-being and underscore the importance of Indigenous language maintenance and revitalization as integral components of public health and community wellness strategies. Additionally, the study highlights methodological challenges in operationalizing language vitality and demonstrates the value—and limits—of using census data to explore complex relationships between culture and health in Indigenous contexts.
Exploring the impact of recent immigration policy changes on international students
(2025) Sarwar, Shanzae; Piñán, Astrid V. Pérez
Amid concerns about the unsustainable growth of international students in Canada and the subsequent pressures on housing, the federal government introduced several changes to immigration policies to address these issues. Some of these changes include a cap on study permits, restrictions on the eligibility for work permits, and decreased permanent residency targets. This study uses a case study approach based on the University of Victoria to explore how these changes have impacted the lived experiences of international students. Guided by Interpretative Phenomenological Analysis based on semi-structured interviews with 14 undergraduate and graduate international students, findings show that while these policy reforms aimed to improve the integrity of the international student program and alleviate housing pressures, they have also produced various challenges for international students who arrived under earlier policies with the intention to settle in Canada. These immigration reforms reflect a complex wicked issue involving competing priorities among multiple groups, including various levels of government that are responding to economic pressures, post-secondary institutions that are reliant on international student revenue, and international students navigating uncertainty about their futures in Canada. The findings highlight the complex and multifaceted impacts of immigration policies on international students, emphasizing the importance of culturally responsive support services and timely, transparent, and accessible communication about policy changes to support them in making informed decisions about their futures.
The empathetic engineer: Strategies to enhance social competence in engineering for wicked problem solving
(2025) Wilson, Eric; Mukhopadhyaya, Phalguni
Is engineering design education in North America adequately preparing students to tackle the major issues of our time? In today's political and social climate, engineers are essential members of multi-disciplinary teams addressing complex problems like poverty, climate change, the housing affordability crisis, resource depletion, and water shortages. These problems are "wicked"—complex, dynamic, and interconnected. To effectively address issues at the intersection of technology and society, engineers must have a deep understanding of both technical skills and human factors, including empathy. Given today’s challenges, incorporating social competencies and emotional intelligence (EI) into engineering education and practice is more crucial than ever, particularly in engineering design. However, literature indicates that some efforts to cultivate more empathetic engineers have backfired, causing cognitive dissonance and rejection of these essential concepts. The definitions of "engineering design" by the Canadian Engineering Accreditation Board (CEAB) have evolved over the years. However, the exclusion of non-technical competencies—such as empathy, communication, innovation, and creativity—remains a significant gap in engineering education and practice, hindering engineers' ability to address complex issues effectively. The engineering industry has noted deficiencies in the social competencies of engineering graduates, particularly in EI. This dissertation aims to explore how the social competencies of engineering students can be enhanced to better prepare them for addressing the 'wicked' challenges they will encounter as industry professionals. This was done by integrating design thinking and systems thinking frameworks into a participatory learning environment to enhance EI among fourth-year engineering students at the University of Victoria. The intervention, a course titled "Infrastructure Design with Indigenous Communities," was carefully re-designed based on theories of identity formation and best practices from educational psychology to avoid some of the pitfalls noted in the literature. The EQi2.0 inventory was used as the qualitative instrument to track changes in students’ EI. The EQi2.0 measures emotional intelligence on a scale from 60 points to 140 points. A low range is defined as having a score of less than 90 points. A mid-range is defined as a score between 90 points and 120 points, and a high range is 120 to 140 points. Statistical analysis of pre- and post-semester EI data from 17 students revealed statistically significant increases in overall EI. Results indicated an average overall EI increase of 5.4 points, with a calculated t-value of 3.105 and a p-value of 0.0034, thus rejecting the null hypothesis that the course had no effect on students' EI. Qualitative data from self-reflective papers supported the hypothesis that the course positively impacted students’ EI. Students attributed their positive changes to experiences such as cultural acumen training, experiential learning activities, and direct engagement with partner First Nation communities. Future research should include a control group to quantitatively validate that the intervention led to the increase in EI. Additionally, further examination of the EQi2.0 inventory is necessary to ensure its reliability in measuring EI, providing a more comprehensive understanding of the impact of such interventions on engineering education. Despite acknowledged limitations, this study suggests that carefully integrating design thinking and systems thinking into engineering curricula, along with cultivating engineering professional identity development, holds promise for elevating EI in students. This approach may better equip engineers to engage with contemporary engineering challenges.
Towards accessible devices for chemical research and education
(2025) Roberts, Nicholas J.; McIndoe, J. Scott
This thesis explores the role of inexpensive automated devices for the purposes of chemical research and education. The design of two novel devices and one device “update” are provided, each discussing device design, reliability, accuracy and application. The research broken up into three major projects, presented in Chapters 2, 3 and 4. Chapter 1 provides a broad overview of the topics found in the thesis, including automa- tion, additive manufacturing, programming and relevant chemistries. While a wide range of topics are covered, they are all brought into focus with this thesis’ main goal: automating chemistry. Chapters 2 and 3 investigate the design and use of liquind sampling devices. Explicitly, Chapter 2 discusses the design and application of an auto-sampling device for the study of reaction kinetics. Along with device specifications and design, the device was tested thoroughly for accuracy and reliability, and was demonstrated to be effective in the kinetics of a reaction. Chapter 3 discusses modifying a pre-existing auto-dispensing device found in the literature, with notable improvements in the design and user experience. Lastly, Chapter 4 covers a real-time device for monitoring the quality of air, targeted for educational purposes. Device specifications and use are covered, and instructions for novices on how to setup/use the device are included. This dissertation provides new and improved inexpensive automated tools to the chem- istry laboratory and classroom. The devices found herein can all be used to improve the workflow of researchers by obviating the need for manual labour, allowing them to focus more on results analysis and experimental design.
Bangla/Bengali geminates: Contributions to phonology, phonetics, and pedagogy
(2025) Ghosh, Sajib; Czaykowska-Higgins, Ewa; Bird, Sonya
While linguistic literature on geminates is growing, Bangla geminates remain underexplored, despite their high productivity in Bangla. This dissertation, therefore, investigates the phonology and phonetics of Bangla geminates and introduces a geminate pronunciation learning tool for heritage and/or L2 Bangla learners. In presenting a comprehensive phonological description of all the derived geminates in Bangla, the dissertation identifies a category of geminates that does not align with the cross-linguistically established categories: namely, lexical, assimilated, and concatenated. The dissertation offers phonological explanation and theoretical framework for this previously unexplored category, proposing the name “extended geminate” to distinguish it from other categories. Given the productivity and functional load of geminates in Bangla, the dissertation also explores their impact on Bangla’s rhythmic structure, based on phonetic analysis. The study proposes that Bangla rhythm is linked to pitch-based “rhythmic phrases,” and that geminates directly contribute to an increased number of these phrases. Finally, considering the recent growth in the number of heritage Bangla speakers in predominantly English-speaking contexts and challenges dominant English heritage Bangla learners face in acquiring accurate geminate pronunciation, this dissertation presents a pronunciation learning tool, designed specifically for learners seeking to improve their Bangla pronunciation. The tool focuses on geminates and is informed by the phonological and phonetic insights gained from the preceding studies.
Swap-regret-minimizing bandits for distributed network optimization
(2025) Huang, Zhiming; Pan, Jianping
Modern networked systems—ranging from real-time communication platforms to distributed computing infrastructures—operate in increasingly dynamic and strategic environments, where traditional optimization methods often fall short. This dissertation develops a new algorithmic framework for distributed network optimization grounded in game-theoretic bandit learning. We model fundamental problems, such as congestion control and resource allocation, as repeated games involving strategic agents who receive only partial (bandit) feedback. Motivated by practical challenges in computer networks, we design and analyze algorithms that not only minimize regret but also steer collective behavior toward equilibrium. The contributions of this dissertation are threefold. First, we propose a new framework based on swap-regret minimization and online mirror descent, and establish high-probability regret bounds in multi-player bandit settings. These results guarantee convergence to correlated equilibria under decentralized, partial-information feedback. Second, we introduce optimistic learning techniques to accelerate convergence by leveraging predictability in the environment. Third, we apply our algorithms to real-world networking tasks, including TCP congestion control, and demonstrate improved stability, throughput, and fairness through extensive trace-driven emulations. Together, these contributions bridge the theoretical foundations of online learning and game theory with practical considerations in network protocol design, offering robust tools for decentralized decision-making in uncertain and adversarial environments.
Prescribed safer supply policymaking in BC: A qualitative analysis of problematizations, intervention, and evidence making during dual public health emergencies
(2025) Gudiño Pérez, Daniel; Pauly, Bernadette M.
In British Columbia (BC), the unregulated toxic drug supply has become the leading cause of premature death among people in their most active and productive years. Although the province declared a public health emergency nearly a decade ago, toxic drug deaths have continued to escalate, signaling the limitation of public health responses to date. The COVID-19 pandemic further intensified these harms: public health directives reduced direct care for people who use drugs and access to harm reduction services, while international border closures destabilized an already volatile unregulated drug supply. In the context of dual public health emergencies, BC introduced Risk Mitigation Guidance (RMG) in March 2020 to empower eligible prescribers to facilitate access to pharmaceutical-grade alternatives to opioids, stimulants, and benzodiazepines for individuals at risk of withdrawal, overdose, and/or COVID-19 infection. Some defined RMG as an initial model of prescribed safer supply. The dual public health emergencies created an opening for novel drug policymaking in BC. However, significant gaps remain in our understanding of how policymakers defined the problems and envisioned policy solutions to an increasingly toxic drug supply. In this research, I analyze the decision-making and policymaking processes during the implementation of RMG to understand how the province of BC a) interprets the current unregulated toxic drug emergency; b) intervenes with policy instruments to address identified issues; and c) uses policy instruments to respond to the public health emergency, from the perspective of people who use drugs in BC. The dissertation is organized into three papers and is guided by a qualitative interpretive research approach, informed by policy studies, and poststructural and critical theories. In paper one, I examine how health policymakers problematized the dual health emergencies and then critically interrogate how RMG came to be a policy response. I use Carol Bacchi’s (1999) “What is the problem represented to be?” framework to show that RMG was a contingency measure to COVID-19 that rendered the unregulated toxic drug supply a secondary issue to a communicable disease infection. Competing interpretations of RMG among policymakers reflected tensions about RMG’s aims and about the implementation process. In paper two I examine the diverse ways RMG was understood and implemented across different geographic areas in BC. I use the Evidence Making Intervention (EMI) conceptual framework developed by Rhodes and Lancaster (2019) to understand the local factors that shaped RMG’s implementation throughout the province. I identify that RMG resulted in dynamic and context-dependent interventions that were enacted either as a COVID-19 response or as a harm reduction tool to disconnect people who use drugs from the unregulated toxic drug supply. I argue for localized evaluations of prescribed safer supply, where evidence of the intervention is being generated as it is implemented in local contexts, producing different forms of evidence and contextual effects. In paper three I analyze the dynamic nature of unregulated drug markets and how people who use drugs navigate real-time shifts and changes in BC’s drug supply after being deprescribed. Through thematic analysis (Braun & Clarke, 2022), I identify user-defined experiences of safety and danger in local unregulated drug markets and highlight policy interventions such as scale up of safer supply, drug checking and drug regulation to support communities’ efforts in enhancing safety. Together, the three papers offer an integrated policy analysis of prescribed safer supply, foregrounding the perspectives of people who use drugs in assessing the effectiveness and relevance of policy interventions that impact their lives.
Capacity building in neonatal nursing research in majority world settings: A Malawi case study
(2025) Amundsen, Miranda M. M.; Marcellus, Lenora
In this dissertation, I contribute to the scientific understanding of neonatal nursing-led research in Malawi, a majority world context. I conduct a single heuristic case study informed by participatory action research and postcolonial feminism perspectives to offer a comprehensive account of neonatal nursing-led research in this setting. Through this work, I uncover how neonatal nursing practice is problematized in the global south, draw attention to the underrepresentation of nurses in global health research, and expose the persistent neglect of nursing perspectives in efforts to advance neonatal health equity. I identify structural barriers that limit neonatal nursing-led research and propose actionable solutions grounded in the insights of Malawian nurses. My study is also the first to present a comprehensive definition of capacity building from the perspective of these nurses. Ultimately, I center the voices of Malawi’s neonatal nurses to challenge the dominant paradigms in global neonatal health research that continue to marginalize nursing contributions. This dissertation reflects my personal growth over the past five years as I immersed myself in this work. I engaged in the entire research process collaboratively with RC, my valued nursing colleague and co-conspirer in Malawi. Along the way, I deepened my understanding of research, critical theory, participatory approaches, and case study methodology. More importantly, I learned about equity, examined my own positionality, and began to meaningfully deconstruct colonial systems. While I know I still have much to learn, this dissertation marks a significant step forward in my journey.
High-level accelerator design for plane-wave ultrasound beamforming in Fourier domain
(2025) Babajan Rahaghi, Mahdi; Rakhmatov, Daler N.
This thesis presents the design and implementation of a high-throughput receive beamforming system for Fourier-domain multi-angle plane-wave ultrasound image reconstruction using high-level synthesis (HLS) on a AMD Versal™ adaptive system-on-chip device. The proposed architecture implements a customized Temme–Mueller migration algorithm entirely within the programmable logic fabric, avoiding off-chip memory and relying solely on on-chip resources. The system operates in a stream-based multi-stage pipelined fashion, with each stage — including temporal and spatial Fast Fourier Transforms (FFTs), dynamic phase delay, spectral remapping, multi-angle coherent compounding, and inverse FFTs— realized as modular, latency-aware HLS blocks. Unlike previous methods, the system performs remapping and interpolation without relying on large, precomputed lookup tables, instead computing all migration-related parameters on the fly. The architecture achieves a sustained processing throughput of over 500 frames per second (with five-angle compounding), enabled by four-lane parallelization and efficient pipelining across all stages. The system HLS specification is statically parameterized in terms of user-controlled FFT lengths, data frame sizes. and number of compounding angles. On the other hand, the key imaging parameters, such as speed of sound, sampling frequency, and probe geometry, are configurable at runtime. Extensive module-level simulation and architecture-level integration testing have validated the system’s correctness confirmed against a MATLAB-based reference model.
The Ramsey multiplicity problem
(2025) Lee, Jae-baek; Noel, Jonathan; MacGillivray, Gary
"Complete chaos is impossible'' is a core concept of Ramsey theory. Ramsey's Theorem formalizes this idea by showing that any sufficiently large graph, no matter how disordered, must inevitably contain a certain type of pattern. The Ramsey multiplicity problem extends this concept quantitatively: instead of asking whether a fixed pattern exists, it asks how many copies of a fixed pattern are guaranteed as the size of the graph increases. Combinatorial limit theory, one of the most significant developments in modern combinatorics, helps to understand these large discrete structures. It also provides a framework for viewing discrete structures as approximations of rich continuous objects, like measurable functions or measures, which facilitates the use of analytic tools to tackle the problems in extremal combinatorics including the Ramsey multiplicity problem. By solving such a problem, we want to deepen our understanding of large and seemingly chaotic graphs. A graph H is said to be "common" if the number of monochromatic copies of H is asymptotically minimized by a random colouring. It is well known that the disjoint union of two common graphs may be uncommon; e.g., K_2 and K_3 are common, but their disjoint union is not. In Chapter 3, we investigate the commonality of disjoint unions of multiple copies of K_3 and K_2. As a consequence of our results, we obtain the first example of a pair of uncommon graphs whose disjoint union is common. Our approach is to reduce the problem of showing that certain disconnected graphs are common to a constrained optimization problem in which the constraints are derived from supersaturation bounds related to Razborov's Triangle Density Theorem. We also improve the bounds on the Ramsey multiplicity constant of a triangle with a pendant edge and the disjoint union of K_3 and K_2. Fox and Wigderson recently identified a large family of graphs whose Ramsey multiplicity constants are attained by sequences of ``Tur\'an colourings;'' i.e. colourings in which one of the colour classes forms the edge set of a balanced complete multipartite graph. Each graph in their family comes from taking a connected non-3-colourable graph with a critical edge and adding many pendant edges. In Chapter 4, We extend their result to an off-diagonal variant of the Ramsey multiplicity constant which involves minimizing a weighted sum of red copies of one graph and blue copies of another. In Chapter 5, we focus on finding smaller graphs whose Ramsey multiplicity constants are achieved by Tur\'an colourings. While Fox and Wigderson provide many examples, their smallest constructions involve graphs with at least 10^{66} vertices. In contrast, we identify a graph on only 10 vertices whose Ramsey multiplicity constant is achieved by Tur\'an colourings. To prove this, we apply the method developed in Chapter 3 and used a powerful technique known as the flag algebra method, assisted by semi-definite programming.

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