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Denoising speech and audio signals by using models of human auditory perception
No full textThe full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat
Hey, ChatGPT, look at my work : using conversational AI in requirements engineering education
No full textThe emergence of conversational AI tools in late 2022 practically changed the face of software engineering and software engineering education. Contemplating the question of how to best prepare and evaluate students in this new reality, we experimented with systematically introducing a conversational AI tool, ChatGPT, into the 2023 offering of an upper-level undergraduate project-based course on Software Engineering. In this course, 20 groups of four students each had to design and implement a project of their choice, with an Android-based mobile client and a Node.js-based cloud server. This thesis discusses our goals, approach, and lessons learned from introducing ChatGPT into the first phase of the project development: scoping the work and defining the project requirements.
Our experience shows that students can achieve comparable results using a variety of ChatGPT interaction modes and the success of each mode largely depends on students’ preferences, learning styles, and the invested effort. Yet, in any of the modes, with moderate effort, students can produce artifacts of a mid-range quality level of around 80%. Moving above this range requires substantial investment, which can be spent on brainstorming, crafting high-quality prompts, or critically assessing ChatGPT’s output.
We also observe low prompting proficiency of the students: students can improve their prompting strategies by providing a more adequate description of their course and project setup, examples, and expected output format for their requests. Interestingly, students can often be “swayed” by ChatGPT’s projected confidence, even when their original ideas are, in fact, more appropriate than the proposed refinements. In a follow up study, we further evaluate the latest version of ChatGPT on the original students’ prompts, and discover that while ChatGPT can produce artifacts that are better in some attributes, the results are still mediocre and would need substantial effort to get 95%+ scoring artifacts. We hope that our experience and lessons learned will help spark further discussions on how to best embed AI tools into the software engineering curriculum and work practices.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat
By the bootstraps : teachers, grassroots computing, and educational culture in British Columbia, 1966 to 1986
No full textThis dissertation is a history of computer culture in British Columbia’s (BC) education system from 1966 to 1986, a period of teacher-led, grassroots computer adoption, integration, and interpretation. As cultural artifacts, computers were more than instruments—they were symbols that held meaning for people. I first discuss what they meant in the context of the 1960s computer counterculture, where computers were interpreted as a transformative force leading to personal empowerment. This ethos sparked various computer-based cultural initiatives in the San Francisco Bay Area, including the People’s Computer Company and Community Memory, an early social network. Beliefs in accessible and convivial technologies transferred to Vancouver, BC, where activists formed a computing organization called INFACT and their own Community Memory system. By the late 1970s, INFACT members were involved in the early computer hobbyist movement, promoting microcomputers as devices for everyone to access, understand, and control. Some BC teachers were also hobbyists, and computers held a similar significance for them. Just like members of the countercultural computing movement, teachers were immersed in the social, political, and cultural currents of the Sixties. As teachers increasingly adopted a social justice orientation and progressive pedagogy, they viewed computers as a means to achieve both social and professional change. Computers would support their transformation into facilitators and curricular leaders, and lay the foundation for greater social equity. Rather than opposing computers in the classroom, as other scholars suggest in an American context, teachers led computer adoption and innovation in BC. Computers represented an opportunity to reshape the philosophy, practice, and business of education. This progressive, teacher-led culture of computing began with William Goddard in 1966, who encouraged “computers for the whole school,” and continued through the Instructional Uses of Microcomputers Pilot Project in 1980; it concluded with the Provincial Advisory Committee on Computers in 1986. This cultural history of educational computing in BC draws on a variety of primary sources, including archival documents and reports, contemporary newsletters and magazines, newspaper articles, conference recordings, and oral interviews. It is a history rooted in teacher agency and local reinterpretations of global ideas about education, technology, and power.Education, Faculty ofEducational Studies (EDST), Department ofGraduat
Advancing equity, diversity, and inclusion in arthritis research : a mixed-methods exploration of strategies for improvement and integration
No full textThe full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires.Pharmaceutical Sciences, Faculty ofGraduat
The effect of fatigue from different types of repetitive maximal-effort concentric elbow extensions on cortical and motoneuronal excitability
No full textAlthough fatigue is known to be task-dependent, the neuromuscular mechanisms underlying fatigue from repetitive, dynamic contractions remain less understood than those associated with isometric tasks. This dissertation examined the effects of fatigue on cortical and motoneuronal excitability using three types of maximal-effort, repetitive, dynamic contractions of the elbow extensors: unconstrained velocity with a resistance of 30% maximal voluntary isometric contraction (MVC) torque (Chapter 2), as well as 40°/s (Chapter 3) and 240°/s (Chapter 4) isokinetic contractions in females and males. Fatigue was characterized by reductions in peak power and MVC torque. Neuronal excitability of the triceps brachii was assessed using responses recorded with surface electromyography: motor evoked potentials (MEPs), cervicomedullary motor evoked potentials (CMEPs), and maximal compound muscle action potentials (Mmax). Cortical (MEP/CMEP), motoneuronal (CMEP/Mmax), and peripheral excitability (Mmax area) were evaluated pre-fatigue (PRE), during the fatigue task and at task termination, as well as throughout 10 min of recovery. Despite evidence of reduced power and MVC torque, cortical excitability was elevated immediately following the fatiguing task (Chapters 2) or during recovery (Chapters 3 and 4). Motoneuronal excitability was either unchanged (Chapters 2) or reduced (Chapters 3 and 4) during the fatigue task and did not recover in some cases (Chapters 3 and 4). Peripheral excitability remained mostly unchanged across protocols (Chapters 2 and 3). In some instances, neuronal excitability was altered differently in females than males by fatigue and recovery (Chapter 4). These findings indicate that fatigue induced by repetitive, concentric elbow extensions resulted in divergent time courses of cortical and motoneuronal excitability adaptations. Despite a fatigue-related increased cortical excitability, motoneuronal excitability was blunted. Collectively, this dissertation highlights the complexity of fatigue-related alterations in neuromuscular function and neuronal excitability for dynamic contractions. Additionally, this dissertation emphasizes the importance of considering the task-dependent nature of fatigue, especially as it relates to biological sex, contraction type, velocity, and recovery when evaluating and interpreting corticospinal excitability assessments.Health and Social Development, Faculty of (Okanagan)Health and Exercise Sciences, School of (Okanagan)Graduat
Towards point-of-care echocardiography aortic stenosis screening with deep learning
No full textAortic stenosis (AS) is a life-threatening heart valve disease affecting approximately 5% of individuals over the age of 65, with rapid progression making timely detection critical for effective treatment. However, current diagnostic procedures rely on comprehensive echocardiographic evaluations, which are typically available only in well-resourced hospitals, limiting accessibility to
patients and increasing costs for healthcare systems. To address this gap, we propose a series of deep learning–based techniques for automated AS severity assessment from standardized echocardiogram views. Key challenges in automating AS assessment include robust classification of standard-plane views, interpretable prediction of disease severity, and managing the noise and heterogeneity present in echocardiographic data. We introduce methods which address these challenges while preserving the strong predictive performance and flexibility of deep neural networks. We present a lightweight classifier for detecting relevant standard-plane views and filtering out nonstandard views. We also propose an interpretable prototypical part neural network that follows a transparent decision-making process based on similarity with existing examples. Furthermore, we propose architecture-agnostic training strategies that mitigate the limitations of partial anatomical information, which is inherent to 2-D imaging of the 3-D patient anatomy. Additionally, we introduce a calibration approach to improve performance in under-represented subgroups. We validate our methods on both public and private datasets, demonstrating competitive performance relative to existing state-of-the-art approaches.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat
Evaluating the impact of extensional rheology on agricultural spray retention
No full textAgrochemicals play a vital role in enhancing crop productivity, yet poor retention and unintended spreading during spraying pose environmental and economic risks. This research addresses these challenges by investigating strategies to improve the effectiveness and stability of agricultural spray formulations through a comprehensive assessment of spray application parameters, polymer solution extensional rheology, the influence of surfactant addition, and the effects of stabilizing agents on storage duration. The findings reveal that nozzle pressure and translation speed jointly influence droplet deposition and retention behavior, with deviations observed at higher nozzle speeds.
Beyond application mechanics, molecular characterization highlights the critical role of extensional relaxation time (λₑ), the time required for a polymer solution to recover its original structure after stretching, as a key determinant of spray deposition. This parameter serves as a practical and rapid predictor for assessing formulation performance through extensional rheology tests. The study further demonstrates that ionic surfactants alter the rheological response of polymer solutions, whereas nonionic surfactants primarily act as interfacial agents without significantly affecting relaxation dynamics.
Polymer-based solutions exhibit superior leaf retention compared with surfactant-only systems, primarily due to their inherent viscoelastic properties. To improve storage stability, isopropyl alcohol (IPA) and α-tocopherol were identified as effective stabilizers, particularly under refrigerated conditions. Overall, this work establishes a mechanistic link between spray operational factors, extensional rheology, and deposition outcomes. It provides a standardized framework for evaluating spray retention and introduces stabilizing strategies that extend formulation shelf life, contributing to the development of more efficient and sustainable agricultural spray technologies.Applied Science, Faculty ofChemical and Biological Engineering, Department ofGraduat
The memory remains : topographies of remembrance and belonging among the Italian minority in Fiume/Rijeka (1945-1991)
No full textThis dissertation investigates how the contemporary Italian-speaking community of Rijeka/Fiume interprets and transmits its collective past in the wake of the profound political and demographic transformations following the Second World War. The transition from Italian fascism to Yugoslav socialism dismantled the Italian populations’ prior economic and cultural prominence. While many Italian speakers departed amid fears of marginalization—the esuli—, those who remained—the rimasti—underwent a disorienting process of minoritization within the new socialist framework. Drawing on oral history interviews with members of the “Comunità degli Italiani di Fiume” (Italian community in Rijeka) alongside archival and press sources, the study examines how this borderland minority reconfigured its identity across shifting ideological regimes. It argues that the mnemonic practices of the Fiumani diverge from both Italian and (post-)Yugoslav historiographies, which often overlook locally rooted narratives of attachment. In a city marked by layered multicultural and plurilingual traditions, these practices give rise to a complex, non-linear sense of belonging.Arts and Social Sciences, Irving K. Barber Faculty of (Okanagan)Graduat
Performance-based seismic assessment of circular concrete bridge columns reinforced with ASTM A955 duplex stainless steel
No full textThis research investigates the low-cycle fatigue behaviour of reinforcing bars and the seismic performance of reinforced concrete bridge columns, considering ASTM A955 Grade 520 stainless steel and CSA G30.18 400W conventional reinforcement bars. The study first presents an assessment of both types of reinforcement bars under monotonic and cyclic loads, and analyzes their mechanical properties and low-cycle fatigue performance. Cyclic loading tests were conducted on deformed bars at various strain amplitudes and unsupported lengths to evaluate the impact of inelastic buckling. Results revealed that ASTM A955 Grade 520 bars outperformed the CSA G30.18 400W bars in terms of cycles to failure and energy dissipation, particularly at higher strain amplitudes. Building on these findings, four circular RC bridge columns were tested under lateral cyclic loading to assess their seismic behaviour. Two columns were reinforced with ASTM A955 Grade 520 stainless steel, and two with CSA G30.18 400W conventional steel, each with an aspect ratio of 3 and 6. The seismic performance was evaluated based on hysteretic response, ductility, energy dissipation, plastic hinge length, and performance damage states, including concrete cracking, cover spalling, stirrup yielding, bar buckling, and bar fracture. The column reinforced with stainless steel columns exhibited similar hysteretic behaviour to columns reinforced with conventional steel up to ductility demand levels of 4. At failure, the former demonstrated superior drift ratios and did not exhibit bar fracture. The performance damage states showcased material-specific responses and highlighted limitations of code-prescribed strain criteria for ASTM A955 Grade 520 reinforcement. Finally, seismic fragility and resilience assessment of RC bridge columns reinforced with ASTM A955 Grade 520 and CSA G30.18 400W steel were conducted, considering both crustal and subduction earthquakes. In both scenarios, the stainless steel reinforcement reduced the seismic vulnerability of RC columns across various damage limit states. Overall, the research underscores the excellent low-cycle fatigue performance and the potential benefits of stainless steel as seismic reinforcement. It provides essential data aimed at informing the development of performance-based seismic design guidelines for concrete columns reinforced with stainless steel. These advancements contribute to the development of more resilient infrastructure in high seismic zones.Applied Science, Faculty ofEngineering, School of (Okanagan)Graduat
Data-driven approaches to interrogate selectivity and generality in asymmetric catalysis
No full textThe pursuit of highly selective and broadly applicable catalytic transformations has driven
decades of innovation in synthetic chemistry. However, the design of new catalytic systems and
the a priori prediction of their performance remain elusive, relying heavily on empirical screening
and chemical intuition. In recent years, the fields of machine learning and data science have offered
powerful new paradigms for navigating such complex, high-dimensional problems. These data-
driven approaches hold the promise of uncovering subtle structure-activity relationships that elude
human intuition, leading to predictive models that pose to greatly accelerate catalyst design and
reaction optimization. This thesis details the development and application of tailored machine
learning frameworks to interrogate the fundamental principles governing selectivity in asymmetric
catalysis, with a focus on providing actionable insights for experimental chemists. The first section
of this thesis deals with expanding the use of techniques based on Linear Free Energy
Relationships (LFERs), specifically demonstrating how these models can be leveraged by
experimentalists to aid in target synthesis campaigns and how simple classifiers can be used to
accelerate mechanistic interpretation. I further show that the identification of LFERs themselves
can be a powerful approach to generate new mechanistic hypotheses from chemically
heterogenous datasets. Next, I establish principles for optimal data collection in data-limited
scenarios, demonstrating that a target-aware protocol is more efficient for building predictive
models than maximizing dataset diversity. Finally, I propose a workflow built on unsupervised
machine learning to quantify generality, the ability of catalysts to selectively catalyze diverse
reactions. I show that this data-driven approach can streamline the identification of general
catalysts, a process typically requiring decades of experimental effort, in addition to accelerating
reaction optimization. Overall, this thesis aims to bridge the gap between data-driven methodologies and experimental insights, providing researchers with predictive tools to make
catalyst and reaction design a more rational and efficient process.Science, Faculty ofChemistry, Department ofGraduat