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Visual analytics : a time series imaging paradigm for process monitoring
No full textIn the era of big data, driven by the advent of the Internet of Things, process industries face the challenge of analyzing massive and complex data to extract relevant information for effective process monitoring. Leveraging these data is critical for ensuring safety, optimizing performance, and maintaining competitiveness. As a result, data-driven process monitoring has received significant research attention and achieved remarkable progress. However, limitations such as limited interpretability, reliance on extensive labelled datasets, and challenges in translating research findings to practical settings persist.
This dissertation introduces a novel paradigm called visual analytics. Visual analytics transforms temporal process data into visual formats to uncover hidden patterns. It bridges the gap between traditional process monitoring methods and modern data-driven approaches by reframing process monitoring problems as computer vision tasks, such as image classification. This paradigm emphasizes interpretability, allowing process experts to relate visual patterns to operational conditions and, consequently, supporting more informed decision-making. This dissertation explores three pathways within the visual analytics paradigm: (i) feature engineering, where predefined mappings are used to convert time series data into visual representations; (ii) architecture engineering, which develops neural network architectures to directly learn visual representations; and (iii) data engineering, which employs contrastive learning to highlight differences and similarities in the data without relying on annotations.
The proposed methodologies are evaluated using two benchmark datasets. The first is the simulated continuous stirred tank heater dataset, which provides a controlled setting for testing the proposed methods. The second is the industrial Arc Loss dataset, which includes one year of operating data from a 60,000 ton/year pyrometallurgical plant, used to demonstrate the robustness and scalability of the proposed methods in real-world scenarios. We conduct comprehensive experiments to compare the proposed methods with state-of-the-art techniques from both traditional and advanced process monitoring approaches. The evaluation focuses on fault detection performance, qualitative interpretability, and challenging scenarios, such as limited labelled data and transfer learning. Experimental results highlight the effectiveness of the proposed visual analytics frameworks, demonstrating competitive performance across all criteria. In addition, the proposed approaches provide informative visual representations, which enhance interpretability and facilitate improved process monitoring and decision-making.Applied Science, Faculty ofChemical and Biological Engineering, Department ofGraduat
Mycelium as an advanced functional material
No full textThis thesis explores the engineering of pure mycelium materials as sustainable, functional alternatives to conventional synthetic materials for pollution control. By systematically investigating the influence of fungal species and bioprocessing methods, this work demonstrates that mycelium networks can be tailored to yield materials with tunable network morphology, mechanical strength, and surface chemistry. Compared to liquid state fermentation, solid-state fermentation produced dense mycelial mats with high chitin content and superior mechanical properties. Submerged fermentation methods enabled the formation of porous sheets with high strength values relative to static fermentation methods and a high mannoprotein content. These findings reveal the tunability of fungal material properties and underscore the importance of bioprocess control in designing materials for specific applications.
Building on these insights, mycelium-derived materials were applied to address pressing environmental challenges in air and water remediation. Mycelium-modified mask layers achieved high particulate filtration efficiency (up to 97% for PM2.5) with breathability comparable to commercial masks and exhibited asymmetric hydrophobicity for improved moisture management. In water treatment, mycelium membranes demonstrated high adsorption capacities for heavy metals (up to 73.2 mg/g for Cu(II)), outperforming other low cost, biobased adsorbents, and retained performance over multiple regeneration cycles. Hybrid membranes incorporating cellulose nanofibrils further balanced water flux and metal rejection. Additionally, mycelium-based membranes with immobilized laccase were produced for the effective degradation of phenolic pollutants, maintaining high catalytic activity and stability over repeated use. Application of the enzyme-mycelium membrane to water contaminated with azo dye Congo Red resulted in a reduction of 92% of dye using optimal immobilization conditions.
By bridging fungal biology, materials science, and environmental engineering, this thesis highlights mycelium as a next-generation sustainable technology. This work demonstrates that materials can be grown, not manufactured, providing solutions for pollution mitigation while reducing reliance on petrochemicals.Applied Science, Faculty ofChemical and Biological Engineering, Department ofGraduat
Numerically efficient and accurate modeling strategies of multilevel multimodule solid-state transformer for accelerated electromagnetic transient simulation
No full textMultilevel multimodule voltage source converter (VSC) emerges as an innovative enabler, contributing to integrate renewable energy sources and distributed generators into the modern power systems. Multilevel multimodule solid-state transformer (SST), also known as power electronic transformer, has become an advanced technology in applications of smart distribution systems, EV charging stations and renewable energy systems. Various electromagnetic transient (EMT) simulation tools are wildly adopted for fast control prototyping of SSTs. However, conventional EMT simulation of the SST detailed model (DM) requires tremendous computational efforts due to detailed representation of massive switches and discrete circuit components. Thus, the simulation time step has to be sufficiently small to guarantee simulation accuracy at expense of simulation efficiency. The prior-art works represent the submodules (SMs) by Resistive Switch Model (RSM) or L/C-Associated Discrete Circuit (L/C-ADC). However, these models either feature time-variant conductance (G)-matrices or incur fictitious numerical oscillations.
This thesis focuses on developing numerically accurate and efficient modeling strategies i.e., switching-function-based detailed equivalent model (SFB-DEM) and switching-function-based average value model (SFB-AVM) for EMT simulation of the SST. Compared to the state-of-the-art equivalent modeling approaches, the proposed models realize constant G-matrix, circuit decoupling through DC-link capacitors, significant node reduction, and flexible representation of SMs’ deblocking and blocking modes. Simulation studies have demonstrated that the proposed equivalent models can significantly enhance the numerical efficiency, compared to the DM and variable G-matrix DEM (VG-DEM). Taking a three-stage SST with 60 SMs for example, the proposed SFB-DEM achieves speedup by 211 and 6 folds while the SFB-AVM achieves speedup by 2127 and 56 folds, compared to the DM and VG-DEM respectively. Additionally, it has been proved in the case study that the use of combined implicit-explicit multi-step solvers contributes to improve numerical accuracy of the SFB-DEM without incurring numerical oscillations, compared to the prior-art DEMs employing Trapezoidal Rule + Forward Euler methods. Meanwhile, the switching interpolation technique has been implemented and integrated with the multi-step solvers for accurate representation of intra-time-step switching events. Simulation studies have verified that it can effectively improve modeling fidelity of the SFB-DEM in particular when carrying out large-time-step simulation.Applied Science, Faculty ofEngineering, School of (Okanagan)Graduat
A conjugation-ready fluorescent molecular rotor dye toward the development of a cellular microviscosity probe
No full textThe full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires.Science, Faculty ofChemistry, Department ofGraduat
Bringing healthy birth back to Quw'utsun : examining determinants of an elevated rate of preterm birth in Quw'utsun (Cowichan Valley) territory, British Columbia, Canada
No full textIndigenous people in Canada have a higher rate of preterm birth, which is birth before 37 weeks of gestation, compared to other residents. Though research has linked health disparities between Indigenous and non-Indigenous people to colonial legislations and actions, there lacks evidence of specific determinants of preterm birth among Indigenous people. As part of the Quw'utsun Preterm Birth Study that Cowichan Tribes initiated to address the elevated rate of preterm birth in Quw'utsun territory (Cowichan Valley, British Columbia), I examined the determinants of health that are associated with preterm birth and that could be amenable to intervention among Indigenous people.
The research team and I created a research framework, Nuts'a'maat shqwaluwun (one heart, one mind), to adhere to Quw'utsun standards for ethical research: (a) self-determination of Quw'utsun people in research and (b) respect for the snuw'uy'ulh, which are the “ways of life” that Elders share with younger generations to guide them through all aspects of life, including pregnancy and birth. Using this framework enabled adherence to these standards throughout this study.
We interviewed Indigenous women in Quw'utsun to understand the factors that impacted the health of their pregnancies and preterm births specifically. Social and environmental challenges negatively affected health in pregnancy. In addition, the snuw'uy'ulh were viewed as foundational to the promotion of health in pregnancy.
To compare preterm birth rates between First Nations and other residents, we undertook a population-based retrospective cohort study of deliveries in the Cowichan Valley (2011-2022) by using a linkage of the First Nations Client File and Perinatal Data Registry. The rate of preterm birth for First Nations (22.6%) was three times higher than for other residents (7.2%; risk ratio 1.91, 95% CI 1.65, 2.22, adjusted for maternal age, parity, and timing of first prenatal visit).
Finally, we conducted knowledge-translation activities to identify the determinants that were amenable to intervention. We identified early and consistent attendance at prenatal care visits and prenatal care that supports pregnant people to follow the snuw'uy'ulh.
In summary, the determinants of preterm birth among Quw'utsun people are multifactorial and require comprehensive intervention studies, informed by the findings of this study.Medicine, Faculty ofPopulation and Public Health (SPPH), School ofGraduat
Much the same or radical change? : the case for a multisectoral approach to quality physical education
No full textThe purpose of this research is to better understand the complex relationships and collaborative efforts of interest groups involved in a multisectoral partnership (MSP) aimed at promoting and developing physical literacy (PL) and physical activity (PA) in school settings. This dissertation examines how such partnerships are conceptualized, operationalized, and sustained, and how cross-sector collaborations can support equitable access to movement experiences, PL, and lifelong engagement in PA, while also addressing broader educational and health priorities.
Grounded in a social constructivist paradigm (Bruner, 1960; Jonassen, 2012; Perry, 1981) and guided by the Operational Model of Collaboration (OMOC) (Roberts et al., 2016), the research explores the relational, structural, and political dynamics that shape collaboration among education, health, recreation, community, and sport sectors. Using a qualitative case study research design, the dissertation includes a literature review, a scoping review of peer-reviewed scholarship, and a case study of a provincial PL initiative (here after called the PL Project). Data were collected through semi-structured interviews, focus groups, and pertinent documents, and analyzed thematically. The analysis explores how governance structures, trust, mutuality, and policy alignment impact collaborative processes, sustainability, and outcomes.
Findings highlight persistent tensions between contractual imperatives, deliverable-based frameworks and the relational work necessary for meaningful, sustained collaboration. Power asymmetries, particularly between education and health, along with jurisdictional complexity and resource constraints further challenge partnership equity and long-term viability. At the same time, relational trust, shared purpose, adaptive leadership, and community-driven prototyping were identified as critical enablers of effective collaboration.
The dissertation contributes to intersectoral collaborations by extending the OMOC through a critique of governance arrangements and their implications for equity and power-sharing. By foregrounding MSPs, the research demonstrates the extensive effort required to create successful and sustainable partnerships. Practically, it offers recommendations for policy alignment, capacity-building, and more integrated, relationally grounded approaches to PL and PA promotion in schools. The research contributes to intersectoral collaboration scholarship by reframing MSPs as dynamic, and socially constructed spaces that are adaptive and evolving rather than fixed, mechanistic entities. The research has implications for policymakers, educators, and practitioners seeking to strengthen collaborative approaches to school-based health promotion.Education, Faculty ofCurriculum and Pedagogy (EDCP), Department ofGraduat
Real-time safety and mobility assessment using extreme value theory modeling : an investigation using real-world self-driving vehicles’ dataset
No full textThe increasing integration of autonomous vehicles (AVs) in urban traffic systems presents an opportunity for more advanced and proactive road safety management. However, the use of AV-generated data for real-time safety analysis presents several challenges due to the current low AV penetration rates, including data scarcity and spatial heterogeneity as well as non-stationarity of traffic conditions. This thesis addresses these challenges by developing a real-time road safety assessment framework using real-world AV data, with a focus on predicting crash risk and assessing vehicle exposure to hazardous conditions. To address the problems of data scarcity and imbalanced data, the framework employed an Extreme Value Theory (EVT) model with Bayesian hierarchical spatial random parameter (BHSRP) structure. The model estimates two key safety metrics, namely Risk of Crash (RC) and Return Level (RL), using Modified Time-to-Collision (MTTC) and Post-Encroachment Time (PET) as conflict indicators. A novel model comparison and validation criterion independent of crash records and sample size was introduced.
The thesis also studied the risk of crash associated with different mobility Levels of service. The highest chance of a high-risk crash condition occurred during LOS D and LOS F for intersections and segments, respectively. To further quantify individual vehicle risk, a Risk Exposure (RE) index is introduced, reflecting the likelihood of encountering extreme conflict conditions based on the time spent under various levels of service (LOS). The results show that vehicles face the highest crash risk under LOS E and LOS F at intersections and segments, respectively.
The thesis explored the transferability of real-time safety models to data-scarce regions. The transferred models performed reliably, demonstrating that safety models based on AV data can be adapted to other locations where data are limited.
Through a multi-dimensional analysis incorporating various temporal aggregations and different EVT’s approaches, structures, and covariates, the study demonstrated that bivariate Peak over threshold (POT) models significantly outperform univariate models, particularly in scenarios requiring high temporal resolution, by achieving more accurate and reliable crash-risk estimates. These findings offer practical tools for enhancing real-time traffic safety assessment and contribute to the broader understanding of AV data utilization in dynamic urban environments.Applied Science, Faculty ofCivil Engineering, Department ofGraduat
Unravelling crypto contracts : making honest formation matter
No full textThe full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires.Law, Peter A. Allard School ofGraduat
Investigating metabolic responses to refeeding following a short-term fast with or without daily exercise
No full textThe full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires.Health and Social Development, Faculty of (Okanagan)Health and Exercise Sciences, School of (Okanagan)Graduat
Tracing copper porphyry mineralization in Andean fluvial systems via multivariate unmixing of detrital zircon geochemistry
No full textTrace element geochemistry of detrital zircons from fluvial sediments can serve as a tracer for copper porphyry exploration and may be a useful novel method for identifying mineralization sources. However, this method has not been applied to a large data set in a geographically broad and geologically variable setting of a terrane-scale magmatic arc. I hypothesize that, even in this complex setting, detrital zircon samples from rivers downstream of ore bodies can be unmixed into multiple endmembers, one of whose geochemistry will be consistent with an ore-body source and whose mixing proportions will decrease with distance from the ore body. I test this hypothesis and exploration method by conducting geochemical analysis of zircons from modern river sands in southern Peru and northern Chile. I collected samples from three categories of catchments: (i) drainages hosting active copper porphyry mines, (ii) drainages with known but unmined copper porphyry centers, and (iii) adjacent drainages with no known mineralization. For each sample I calculated known copper porphyry indicator ratios, including Eu/Eu*, CeN/NdN, DyN/YbN, Ce/Ce*, Th/U, Ti and Hf. I used statistical methods, including Principal Component Analysis and clustering to identify the elements that account for the majority of the variability in the geochemical data set, and decomposed the multivariate dataset into endmembers using a Tucker-1 decomposition method implemented by DZgrainalyzer. Results suggested endmembers with geochemical distributions consistent with hydrous and oxidized (potentially Cu-permissive) and relatively anhydrous and unoxidized (likely Cu immobile) magmas.
Of the analyzed ratios, Eu/Eu* and CeN/NdN provide the greatest discrimination between potential Cu-permissive and Cu-immobile igneous sources. Modal ages from the Cu-permissive endmembers are consistent with known mineralized intrusions and include younger populations from 0 Ma to 10 Ma that have Cu potential in the area. The proportion of Cu-permissive endmembers is directly proportional to sample’s proximity to the Cu-porphyry system. We conclude that the Tucker 1- decomposition successfully distinguishes Cu-permissive from Cu-immobile sources located up to 40 km from the Cu-body. These results suggest that detrital zircons can be used to identify Cu-porphyry systems in inaccessible terrain where sampling is only possible tens of kilometers downstream of the source.Science, Faculty ofEarth, Ocean and Atmospheric Sciences, Department ofGraduat