ETD (Electronic Theses and Dissertations)

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For information on how to submit your thesis to this collection, please go to our ETD website on the UVic Libraries Website.

Access to the full text of some theses may be restricted at the request of the author.

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

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    Single-Class Instance Segmentation for Vectorization of Line Drawings
    (2024) Vohra, Rhythm; Branzan Albu, Alexandra
    Images can be represented and stored either in raster or in vector formats. Raster images are the most ubiquitous and are defined as matrices of pixel intensities/colours, while vector images consist of a finite set of geometric primitives, such as lines, curves, and polygons. Since geometric shapes are expressed via mathematical equations and defined by a limited number of control points, they can be manipulated in a much easier way than by directly working with pixels; hence, the vector format is much preferred to raster for image editing and understanding purposes. The conversion of a raster image into its vector correspondent is a non-trivial process, called image vectorization. Creating vector images from a given raster image can be time-consuming and requires the expertise of a skilled graphic user. This thesis explores the effectiveness of a Deep Learning based framework to vectorize raster images comprising line drawings with minimal user interventions. To improve the visual representation of the image, each stroke in the line drawing is represented with a different label and vectorized. In this document, we present an in-depth study of image vectorization, the objective of our research, challenges, potential resolutions, and compare the outcomes of our approach on six datasets consisting of different types of hand drawings. More specifically, this thesis begins by comparing raster images with vector images, the importance of image vectorization, and our objective to convert raster images to vector-based representations by accurately separating each stroke from the line drawings. In further chapters of this thesis, a Deep Learning based segmentation methodology is introduced to perform Single-Class Instance Segmentation of hand drawings to process the input raster image by labeling each pixel as belonging to a particular stroke instance. This segmentation approach is able to leverage the spatial relationships between each stroke instance. A novel loss function specifically designed to optimize our highly imbalanced datasets by scaling the margins and adding a regularization term to improve its feature selection technique. The weighted combination of our proposed margin regularized loss function is combined with the Dice loss to reduce the spatial overlap and improve the predictions over infrequent labels. Finally, the effectiveness of our segmentation technique of line drawing vectorization is compared experimentally with the state-of-the-art and our reference method. Our method can successfully handle a wide variety of human drawing styles. The results are comparable in terms of accuracy and way ahead in terms of speed and complexity, with other methods.
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    Exploring spatiotemporal variability in secondary production off the west coast of Vancouver Island using biochemical approaches
    (2024) Hubbert, Liam; Dower, John F.; Sastri, Akash Rene
    Zooplankton production in marine ecosystems refers to the rate at which zooplankton biomass increases through a combination of somatic and reproductive growth. Despite its importance in understanding the flow of energy to higher trophic levels, in situ measurements of zooplankton production rates in marine ecosystems remain rare. In recent decades, biochemical methods of estimating zooplankton production have become increasingly popular, though there still exist critical knowledge gaps as to how effective these methods are at estimating in situ growth and production rates. Addressing these knowledge gaps is necessary to lay the foundation for the future integration of routine secondary production rate measurements as part of synoptic oceanographic surveys. Chapter 1 of this thesis introduces the global importance of zooplankton and reviews the methods currently used to assess zooplankton production rates. Specifically, two contemporary biochemical methods are discussed, as well as their advantages and limitations as compared to more traditional incubation methods. The first is the aminoacyl-tRNA synthetases (AARS) method, where the activity of in vivo AARS enzymes is utilized to derive a proxy measure of growth rate. The second is the chitobiase method, in which the rate of decay of dissolved chitobiase activity in water is used to estimate the growth and production rates of crustacean zooplankton assemblages. The chapter concludes with a description of the regional oceanographic setting in which these studies took place and outlines the primary objectives of this thesis. Chapter 2 focuses on the AARS method of measuring secondary production rates. Here, the efficacy of this method for mixed zooplankton assemblages was assessed by comparing growth and production rate estimates to those predicted from two widely used empirical models. Samples collected from eight stations off the West Coast of Vancouver Island (WCVI) in September 2021 were used to measure total AARS and protein-specific AARS (spAARS) activities. Total AARS showed strong positive correlations with production rates predicted by both models, whereas correlations with spAARS were weaker. Spatial variation in AARS activity showed that higher production rates were observed in the inshore regions of the WCVI, and lower rates were observed offshore. These results indicated that in situ AARS-based production rates are temperature-dependent and show significant variation with total zooplankton biomass. In Chapter 3, the chitobiase method of estimating secondary production rate was used to assess production rates in the waters off the WCVI in September 2022 and May 2023. Water samples were collected from the four distinct bioregions off the WCVI during each sampling period, along with zooplankton net samples for biomass and taxonomy analyses. The data gathered from these samples were used to better understand how production rates vary between regions with distinct oceanographic characteristics. Chitobiase biomass-production rate (BPR) and growth rate estimates (daily production to biomass ratio) varied with both season and region, though the trends in these rates did not align with trends in mixed-layer temperature and biomass. Higher chitobiase-based growth rates were observed in inshore regions during September 2022 and May 2023. Chitobiase BPR in September 2022 also followed this trend. Conversely, production rates in May 2023 were higher in the south, indicating a change in the regional drivers of production rate between seasons. The chitobiase-based growth and production rate estimates obtained during this study were also added to the growing time series of previous chitobiase measurements in this region and indicate that production rates have recovered since the low values measured following the 2014-2016 marine heatwave. Chapter 4 of this thesis presents general conclusions on how the AARS method can be used in future studies, as well as the ecological and methodological challenges faced during this study. This thesis concludes with suggestions of how these methods can be utilized in the future to gain a greater understanding of in situ zooplankton community production rates.
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    Legal borderlines: Theorising rupture in the realm of interlegality - The potential for radical legal change in the face of ecological collapse
    (2024) Llorca, Katherine; Johnson, Rebecca
    The IPCC has issued increasingly stark warnings that climate breakdown and ecological collapse are inevitable if radical action is not taken in the coming decade. To date, the legal academy seems dangerously impervious to this warning. And yet, any “radical action” will also demand radical legal change. Is it possible for law to do more than simply edge forward with piecemeal legal reform? Is radical legal change possible in a legal order that values stability above all else? And if it is, what might it look like? I start by considering radical change in the form of ruptural events and the ways in which such events question the foundations on which our legal systems are built (Chapter 1). I then consider the origins of ruptural events. It seems that they emerge in the spaces of friction between legal orders, understood in the broadest, pluralist sense. But when the meaning of legal order is understood so broadly – as it is among legal pluralists – it is easy to lose one’s footing: what distinguishes one legal order from another (Chapter 2)? what is specifically legal about each order (Chapter 3)? and, crucially, what does it mean for legal orders to overlap (Chapter 4)? These detours through legal theory are not accessory; we cannot begin to envisage radical legal change without clarifying law’s potential. Together these chapters provide one possible understanding of the “distinctness”, “legalness”, and “intersectingness” of legal orders. With these theoretical tools in hand, I then consider how they help us grapple more constructively with the potential for change in the form of ruptural events (Chapter 5). The result is an experiment in legal theorising: How might we think about law’s role in times of ecological crisis? And what are the consequences of this thinking for our understanding of what the law can do? My conclusion is that ecological collapse changes the way we should be thinking about law. Indeed, it may not be the only modern development that will push us to reconsider the potential for change in the context of law...
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    Monitoring Earth Using the Software Defined Radio (SDR) Earth Imager
    (2024-02-23) Sharif, Radwan N. K.; Herring, Rodney A.
    The ionosphere, which is the highest region of Earth's atmosphere, contains waves created from both space and Earth disturbances. The ionosphere is considered the largest sensor on Earth and has been the subject of study since the 1920s, primarily through the use of ionosondes. A Software Defined Radio (SDR) Earth Imager has been devised to obtain information about these Earth disturbances. This research is divided into four stages: 1) engineering of the SDR Earth Atmosphere Imager, 2) imaging of waves that exist within the ionosphere, 3) determining the location of the earth disturbance that created the waves, and 4) measuring the power of the ionospheric waves. The Earth Imager device functions similarly to a camera by utilizing an antenna array to create images of the ionosphere and its waves. The radio wave, i.e., the carrier wave of the ionosphere information, is transmitted up through the atmosphere at a near-vertical incidence from the Earth's surface. It reflects off the ionosphere back down to the Earth's surface, where it is detected by an antenna array to produce a phase image of the ionosphere. The proof of concept of the SDR Earth Imager occurred at the University of Victoria, Victoria, BC, Canada, and was initially constructed at the Dominion Radio Astrophysical Observatory (DRAO), Penticton, BC, Canada. From the DRAO data analysis, two types of waves were found: one with a constant frequency, possibly originating from power losses in transmission lines, and another with a single sharp spike, potentially caused by earthquakes or lightning. Further experiments at the University of New Mexico, utilizing Long Wavelength Array (LWA-1 and LWA-SV) antennae arrays, served as a high-resolution radio wave camera. The datasets from the LWA-1 and LWA-SV sites provided results showing the wavevector directions of one set of ionospheric waves, i.e., the strongest sets of waves, which have a spatial frequency of 0.06 cycle/m. The wavevectors were used to identify the location of the generation of the ionospheric waves and, thus, the likely source of the disturbance. This Ph.D. research thesis shows a correlation between the waves in the ionization layer and Earth's disturbing events, including man-made disturbances such as the electromagnetic radiation emitted by power lines and electrical grids, which generate waves within the ionosphere. Further, this research illustrated how the phase image, not the amplitude image, determined from Fourier analysis, is critical to characterizing these waves. The phase image enables the characterization of these waves by providing information about their phase shifts, frequencies, and wave vectors. This research demonstrates a clear relationship between waves within the ionosphere and disturbing events occurring on Earth. One significant finding of this dissertation is the deduction that all power generated and consumed by humans is not completely dissipated but rather transformed and captured by the Earth's ionosphere. This fact may assist climate modelers in gaining a better understanding of climate change.
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    TEST ETD Submission for CATS
    (2024) Test, Student; Example, Supervisor
    Lorem ipsum dolor sit amet, consectetur adipiscing elit. Proin id sapien gravida, tempor nisl vitae, condimentum dui. Sed venenatis eleifend suscipit. Donec aliquet orci id cursus blandit. Mauris semper odio elit, eget laoreet diam posuere vitae. Curabitur pharetra porta turpis. Mauris vel nibh sit amet turpis dignissim pellentesque. Vestibulum mollis lobortis lacus, quis consectetur lectus porta vitae. Nunc rhoncus lacus at purus aliquet, quis convallis libero blandit. Class aptent taciti sociosqu ad litora torquent per conubia nostra, per inceptos himenaeos. Donec vestibulum nisi non turpis euismod efficitur. In hac habitasse platea dictumst. Mauris consectetur varius nisi, id molestie velit auctor et. In sed lacinia nisl. Sed elementum aliquam commodo. Vivamus congue diam nec blandit placerat.
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    Innovations in In-Shoe Plantar Pressure Measurement Technology for Field Based Quantification of Running Gait
    (2024-02-16) Blades, Samuel; Klimstra, Marc D.; Hundza, Sandra R.
    Although substantial progress has been made in the field of running biomechanics, a significant portion of this research has been confined to laboratory settings. Data collection within the laboratory, while controlled, often lacks the ecological validity necessary to capture the complexities of athletes' performances in their natural training and competition environments. Given this need, in-shoe plantar pressure measurement technology is of primary importance due to its location of measurement and its unique capacity to deliver continuous measurements of both kinematic and kinetic biomechanical data. However, most commercially available in-shoe plantar pressure measurement systems (PPMS) are designed primarily for use in research settings and are thus unsuitable for field-based use due in part, to their high cost, low durability, and cumbersome hardware designs that can interfere with natural running gait. These limitations restrict researchers, athletes, coaches, and footwear designers from using PPMS to acquire valuable biomechanical data in training and competition environments. The development of a wearable, field-appropriate, in-shoe PPMS capable of providing lab-quality pressure data and its derivative biomechanical signals could address the current gap in measurement technology enabling significant advancements in running biomechanics research. The development of such a technology, however, is highly demanding due to many competing requirements such as low weight, high durability, imperceptible form factor, and cost-effectiveness while still providing lab-quality data. The purpose of this dissertation is to present research that could aid in the development of a wearable, field-appropriate, in-shoe PPMS through the following research objectives. The first research objective was to determine the accuracy and performance of a low-cost, fully integrated pressure sensing insole relative to a research-grade PPMS using laboratory-standard equipment on bench-top and in-situ performance tests (Chapter 2). The second research objective was to determine the optimal sparse sensor layout and plantar pressure distribution estimation method capable of measuring the complete plantar pressure distribution with lab quality accuracy (Chapter 3). The final research objective was to develop and determine the optimal foot contact event detection algorithms for use with plantar pressure data to enable highly accurate gait phase analysis (Chapter 4). The results presented in this dissertation demonstrate the feasibility of the development of a wearable, field-appropriate, PPMS that can provide accurate kinematic and kinetic data. The application of these findings can aid in the further development of wearable PPMS, leading to advancements in the field of running biomechanics and the sport of running.
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    Framing reciprocal contributions across Indigenous and artisanal fisheries: Exploring cases, conflicts, and future pathways
    (2024-02-14) Ojeda, Jaime; Ban, Natalie
    Throughout human history, Indigenous and local communities have been stewards of nature. Their practices often embody the value of reciprocity, fostering positive contributions of humans with other components of nature. Yet, during colonization, and more specifically with the rise of neoliberal policies and the dominance of market worldviews, we have become distanced from this nature-people reciprocity. Concepts like "ecosystem services" provide a framework for comprehending the vital functions that ecosystems play in many facets of human existence. However, they also inadvertently narrow the discourse to a unidirectional relationship: nature serving people. This perspective can obscure our responsibilities to care for and sustain the environment. In this dissertation, I unpack the nature-people reciprocity, exploring its theoretical and practical relevance for social-ecological systems. I frame this work in one of the oldest biocultural interactions: marine fishing practices. This dissertation has five chapters. In the introductory Chapter, I outline the rationale and objectives of the study, highlighting the gaps in current understandings of nature-people reciprocity. In the second Chapter, I introduce the concept of “reciprocal contributions,” which encompasses actions, interactions, and experiences between people and other components of nature that result in positive contributions and feedbacks that accrue to both directly or indirectly across different dimensions and levels (Chapter 2). Following this conceptual chapter, I draw on two case studies to understand how reciprocal contributions can emerge with a bi-hemispherical approach in diverse fishing settings. First, in Haida Gwaii (North America), I partnered with the Council of Haida Nation, Haida Fisheries, to research the ancestral relationships between Haidas and abalone, examining their reciprocal contributions. Here, I interviewed Haida knowledge holders who have lived through the tragedy of the commercial abalone fishing boom and subsequent decline. In this chapter, I discuss the harms of overfishing on reciprocal contributions to review the past and rethink future abalone management strategies (Chapter 3). Second, working with artisanal fishers in Patagonia (South America), I investigated the reciprocal contributions between these individuals and marine life, especially seabirds. Employing both ethnographic and ecological methodologies, I explore the intricate relationships between fishers and seabirds and discuss how these reciprocal contributions can serve as tools for studying the complex interactions between humans and nature within an ecosystem-based management framework (Chapter 4). In the concluding Chapter, I reflect on the theoretical and practical implications of reciprocal contributions through the themes of nature-people relationships and fisheries management. Ultimately, I hope that this dissertation serves as work to resituate the importance of reciprocity.
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    Hardware Architecture for Accelerating Frequency-Domain Ultrasound Image Reconstruction
    (2024-02-09) Navaeilavasani, Pooriya; Rakhmatov, Daler N.
    Ultrasound is a widely employed biomedical imaging modality enabling non-invasive, low-cost, and real-time diagnostics. In a typical ultrasound system, a multi-channel transducer emits sound waves into the medium and then records returning echo signals that are subsequently converted into an image of the subsurface structure. Coherent plane-wave compounding (CPWC) is one of the latest ultrasound imaging techniques that involves emitting multiple plane-wave pulses at various angles and then combining angle-specific reconstructed image data into a final frame. This approach offers high data acquisition rates (e.g., hundreds or even thousands of raw data frames per second) that are crucial for capturing fast-changing phenomena in the imaged medium. High data acquisition rates should be matched with fast data processing to increase the frame rate of reconstructed, or beamformed, image frames. One example of highly efficient plane-wave beamforming methods is the Temme-Mueller algorithm that operates in the Fourier domain. This thesis describes a novel pipelined hardware architecture for accelerating the execution of this algorithm. The proposed design has been coded in VHDL and implemented on a modern Xilinx® field-programmable gate array (FPGA), taking advantage of Xilinx® intellectual property (IP) core reuse to reduce development time. Our architecture is capable of producing over 1,300 beamformed frames per second, where each frame contains 256K complex-valued data points using the 32-bit floating-point representation for both real and imaginary parts. The correctness of our FPGA-based beamformer has been verified by comparing its output to the reference software-based implementation of the Temme-Mueller algorithm. This verification was done on an experimental ultrasound dataset available as part of the public-domain PICMUS evaluation framework. Our evaluation results demonstrate that the proposed design provides a promising alternative to the conventional GPU-based approach to high-frame-rate ultrasound image reconstruction, paving the way for future algorithmic and architectural enhancements.
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    Learning-based Ultra-Wideband Indoor Ranging and NLOS Identification
    (2024-02-09) Li, Xin; Dong, Xiaodai
    The need for precise indoor positioning has become increasingly important with the rise of Internet of Things (IoT) technology, robotics, and autonomous vehicles. Indoor positioning has a wide range of applications, including asset tracking, indoor navigation, and location-based services. To achieve high positioning precision for these applications, accurate and reliable indoor ranging is a key factor when using techniques like time of arrival (ToA), as it enables the calculation of distances between different objects in the indoor environment. In this thesis, we focus on machine learning-based approaches for indoor ranging and non-line-of-sight (NLOS) identification. The first part of the thesis concentrates on reducing ranging errors through machine learning with the improvement of the resolution of channel impulse response (CIR) data. We collect a dataset of 412, 172 traces of CIR data across 12 indoor Line-of-Sight (LOS) scenarios. This dataset is used to train and test three machine learning models, including long short-term memory (LSTM), gated recurrent units (GRU), and multi-layer perception (MLP), to predict the range between the anchor and tag directly through the CIR data. The results demonstrate that LSTM and GRU models outperform traditional meth-ods and the device built-in algorithm in terms of mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE), thereby showing the effectiveness of machine learning techniques for indoor ranging applications. On the other hand, indoor ranging accuracy can be significantly affected by NLOS conditions, where the direct path between the transmitter and receiver is obstructed, and the signal has to travel through multiple reflections and diffractions before reaching the receiver. In this thesis, we propose a quantitative approach to differentiate between Soft and Hard NLOS based on the ranging error percentage. We develop machine learning models to identify and classify NLOS conditions. Our study shows that when NLOS is classified into Soft NLOS and Hard NLOS, the accuracy of LOS identification is achieved better than using binary classification. Compared to traditional methods such as leading edge detection or search back window for ranging and positioning, our method exhibits superior performance in noise, multipath, and NLOS environments.
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    The Elders speak about the best interests of a Stó:lō child: family, connection and culture
    (2024-02-05) Mussell, Dayna Gawi-neh; Wright Cardinal, Sarah
    In response to recent legislative changes by the Government of Canada many Indigenous nations are engaged in the development of legal and practice frameworks to regulate culturally safe and equitable child and family services. To support this process there is a need to define the best interests of the child according to the nation based on cultural knowledge and traditions. Storywork, an Indigenous storied approach, is used to examine the question, “How do Stó:lō people define the “best interests of the child” based on the cultural, linguistic and governance structures of their nation?” Drawing on Indigenous literature and the history of child welfare in Canada affirms that culture is central to developing Indigenous based services. A series of sharing circle discussions with Stó:lō Elders from the Coqualeetza Cultural Education Centre and the Fraser Valley Aboriginal Children and Family Services Society were held to gather their life-experience stories. The Elders’ unique worldview and understanding of the teachings of a good Stó:lō life were central to mobilize community-based Indigenous knowledge on child-rearing in the past and present that centers the teachings of our ancestors. Thematic analysis was then used as a way to make meaning from the Elders’ life-experience stories to create new knowledge informing what is in the best interests of the Stó:lō child. As a result, a Longhouse Framework was created using four-story poles representing new stories of child well-being. These story poles include: 1) How children experience and understand shxwelí (life spirit); 2) Children learn the ways of co-reliance; 3) Families and communities care for their children; and 4) Raising children in healthy Stó:lō ways. This knowledge will be used to inform better practices for those working in the field of Indigenous child welfare and offer recommendations for communities which are moving towards self-determination in the area of child welfare.
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    Paternalism, Capitalism, and Political Suppression: Case Studies of Settler-Colonialism on the Grand River
    (2024-02-02) Wilcock, Cory; Cook, Peter
    The Haudenosaunee of the Grand River have received immense attention as objects of study by academics, but agents and systems of colonialism have been overlooked. As such, this thesis applies a settler colonial framework to the Grand River to examine how the interplay between individual settlers, corporations, and the colonial government unfolded. Because the end point of settler colonialism is acquiring Indigenous land, there are often similarities in the process across geographic and temporal boundaries. However, the goal of this thesis is to identify unaccounted structures and processes in order to demonstrate the distinct ways that settler colonialism developed on the Grand River. This is done through two case studies that take place during two different centuries in order to identify the through lines of how settler colonialism operated as both a structure and a process on the Grand River. This thesis focusses on the Grand River Navigation Company of the 1830s, the 1924 coup d’état at the Ohsweken Council House, and the conclusion briefly discusses the 2006 Kanonhstaton land dispute in order to thematically unite the cases. Over the course of three centuries settlers, corporations, and governments used paternalism, capitalism, and political suppression as tools to dispossess the Haudenosaunee.
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    EONS: A new biogeochemical model of Earth's longterm evolution
    (2024-01-31) Horne, Julia; Goldblatt, Colin
    I present Earth’s Oxygenation and Natural Systematics (EONS): a new, fully coupled biogeochemical model of the atmosphere, ocean, and their interactions with the geosphere, which can reproduce major features of Earth’s evolution fol- lowing the origin of life to the present day. The model includes an interactive biosphere, cycles of carbon, nitrogen, phosphorus, and oxygen, and climate. A nominal model run initialized in the Eoarchean resolves emergent surface oxy- genation, nutrient limitations, and climate feedbacks. The modelled atmosphere oxygenates in stepwise fashion over the course of the Proterozoic; a nearly billion year lag after the evolution of photosynthesis at 3.5 Ga is followed by a great oxi- dation event (GOE) at 2.4 Ga, which appears to be caused by the gradual buildup of organic matter on the continents imposing nutrient limitation on the biosphere by removing key nutrients from the ocean system. The simple climate system shows significant temperature shifts punctuate the oxygenation process, implying that major biological transitions possibly destabilized Earth’s climate. I expand upon this finding by adapting the climate system to include non-linearities such as ice-albedo and supergreenhouse feedbacks in order to investigate potential causes of Paleoproterozoic Snowball Earth events. My preliminary findings suggest that Paleoproterozoic glaciations may have preceded the GOE, and are more likely a result of perturbations to atmospheric CO2 than from declining CH4. This work demonstrates that forward modelling the entirety of Earth’s history with relatively few imposed boundary forcings is feasible, that the Earth system is not at steady state, and that our understanding of coupled C-N-P-O cycling as it functions today can explain much of the Earth’s evolution.
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    Trade Openness and Inflation Dynamics: A Panel Data Analysis
    (2024-01-31) Parsi, Rouzbeh; Gabriel, Vasco
    This thesis focuses on the intricate empirical relationship between trade openness and inflation, challenging previous literature that suggests a straightforward negative correlation between the two. By employing recently developed dynamic heterogeneous panel methods and constructing a comprehensive panel dataset, which encompasses a spectrum of economic, political, and financial indicators, as well as two proxies for openness, we offer a nuanced perspective on the topic. Central to our findings is the critical role of allowing cross-sectional dependence in panel data, which has been frequently overlooked in past studies. Our analysis reveals a multifaceted relationship, where the influence of trade openness on inflation is dynamic and ambiguous in its direction. While traditional openness metrics remain useful, multidimensional proxies, such as the KOF trade openness index, have the potential to provide richer insights. Our results underscore the need for a thorough analysis and robust methodologies when exploring this economic relationship, suggesting that the dynamics of trade and inflation are more complex than previously assumed.
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    Microdroplets for drug discovery and delivery targeting neural models
    (2024-01-31) Forigua Coronado, Alejandro; Elvira, Katherine S.
    This dissertation explores the application of microdroplet technology in two pivotal areas of the pharmaceutical and biotechnological fields: drug discovery and drug delivery, with a focus on neural models. It presents a comprehensive study on the development and application of microfluidic devices for the fabrication of drug delivery particles and the modeling of cell membranes using Droplet Interface Bilayers (DIBs). The first part of this work, detailed in Chapters 2 and 3, describes the design and optimization of a Polydimethylsiloxane (PDMS) microfluidic platform for generating oil-in-water droplets. These droplets serve as precursors for polycaprolactone (PCL) microparticles, which have potential for controlled drug release. This platform showcases significant improvements in droplet generation and encapsulation efficiency compared to traditional batch processes and has been adopted for commercial-scale production. In the second part, Chapters 4 and 5 focus on the application of DIBs as a model for cell membranes. The research quantitatively analyzes the passive diffusion of memantine, an Alzheimer’s disease treating drug, and niacin, a common vitamin supplement used as a neuroprotective agent, and examines the impact of lipid formulation and droplet content on drug absorption and water transport. The findings highlight the advantages of using biomimetic lipid formulations for in vitro studies. This dissertation demonstrates the significant potential of microdroplet technology in enhancing the efficacy and precision of drug delivery systems and in providing more accurate models for cell membrane studies. The insights gained not only contribute to the academic understanding of drug interaction with cellular membranes but also pave the way for future innovations in neuropharmacology and biotechnology.
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    Algorithms for prediction of RNA secondary structure: coronavirus pseudoknots via Shapify & CParty
    (2024-01-30) Trinity, Luke; Jabbari, Hosna; Stege, Ulrike
    RNA molecules play a vital role in cellular processes, and many possess functional structures. Due to the complex nature of experimental methods to detect RNA structure, computational tools to predict RNA structure formation are invaluable for building comprehensive knowledge. We seek to predict RNA structure algorithmically, with a focus on the following concepts from the literature: (1) Minimum Free Energy (MFE) methods, (2) the hierarchical folding hypothesis, and (3) partition function ensemble approaches. The MFE framework is an RNA folding hypothesis stating that each RNA molecule folds into the structure with the minimum free energy. In conjunction with MFE, we employ the biologically motivated hierarchical folding hypothesis, stating that an RNA molecule will first fold once (initial fold), before a subsequent folding may occur that lowers the structure's free energy. The accuracy of MFE and hierarchical folding methods can be improved by effective incorporation of known RNA structure information such as experimental reactivity data. We introduce Shapify, an algorithm incorporating experimental data within hierarchical RNA folding prediction. Shapify receives SHAPE data as input to guide RNA structure prediction, allowing the unification of multiple experimental results to determine structure-function patterns. The time complexity of Shapify is O(N^3) time, where N is the RNA sequence length, enabling faster prediction compared with other methods that also handle a complex RNA structure class. We then consider the partition function model, based on the MFE approach, where we compute the sum of free energies for each possible RNA structure in the ensemble at equilibrium. The likelihood of any particular RNA structure occurring can then be determined based on the energy of the structure itself relative to the total energy in the system. Currently, partition function methods are restricted to predicting a limited set of RNA structures, because existing algorithms that allow complex RNA structures are too slow, at best O(N^5) time complexity. We introduce CParty, an O(N^3) time complexity partition function algorithm that includes complex RNA structures in the ensemble. The development of CParty's recursive decomposition schemes was non-trivial to integrate within the algorithmic implementation. By providing an input structure to algorithm CParty, we compute a `conditional' partition function, enabling probabilistic calculation that advances understanding of RNA structure formation patterns. In this dissertation, we (1) incorporate partial RNA structure information into hierarchical secondary structure prediction via Shapify to understand important secondary structure motifs affecting viral function, (2) design and implement CParty, a conditional partition function algorithm to handle complex RNA structures, and (3) apply these and other related algorithms to provide RNA structural information for COVID-19 therapeutic targets. Here, we pinpoint key secondary structure folding motifs in our quest to predict functional RNA structures. Our hierarchical folding algorithms push the frontier of prediction accuracy for functional RNA secondary structures, contributing to coronavirus treatments.
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    Transparency, Accountability, and Accessibility: A Comparative Analysis of the Publication of Transition Documents through the Context of British Columbia, Manitoba, and Canada
    (2024-01-29) Olynyk, Madison; Speers, Kimberly
    This thesis sheds light on transparency, accountability, and accessibility efforts through the lens of British Columbia’s recently published transition documents. Using a multiple case study approach, with cases being the Government of British Columbia’s British Columbia’s website and published transition documents from 2020 and 2022, this research discusses similarities and differences between three of British Columbia’s ministry’s transition binders and compares findings to government transition documents and websites in the Government of Canada and Manitoba. Ultimately, one of the key findings from this research is that British Columbia meets more of the transparency, accountability, and accessibility criteria outlined in this thesis than the Governments of Manitoba and Canada. The research finds that the Government of British Columbia makes it easier for citizens to hold their governments accountable than the Governments of Canada and Manitoba. Regarding accessibility and transparency, British Columbia performs well: on par with the Government of Canada and better than the Government of Manitoba. Additionally, ministry-specific findings in British Columbia prove that ministries may be given some level of independence when supporting these initiatives. The thesis also identifies the areas where British Columbia has the potential to improve these metrics when publicizing its binders.
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    Data-driven Surrogate Models for Wind Turbine Design and Maintenance Applications
    (2024-01-29) Haghi, Rad; Crawford, Curran
    There is a gap between the current contribution of wind energy to the global electricity generation mix and its potential capacity. This discrepancy underscores the necessity for addressing social, economic, and technical hurdles that are impeding the broader integration and acceptance of wind energy. The research focuses on tackling the modelling challenges in wind energy by employing Surrogate Model (SM) techniques, combining probabilistic methods, machine learning, and simulation technologies. This dissertation aims to develop SMs capable of mapping wind time series to the power output as well as extreme and fatigue loads on wind turbines. In this dissertation, I try to answer a number of crucial questions: determining the most effective type of SM for this mapping, identifying the optimal sampling method for building these SMs, extending the applicability of the developed SMs with minimal effort, and leveraging publicly available simulation tools and wind turbine models for turbine health assessment. These objectives are essential for improving wind turbine design, operation, and maintenance, enhancing their efficiency and reliability. Throughout the dissertation, there is an effort to bridge the gap between theoretical research and practical application. The surrogate models developed are presented as a contribution to the integration of theoretical concepts with practical applications in the field of wind turbine design and maintenance. Central to this research is the development of SMs for effectively mapping wind time series to the extreme and fatigue loads experienced by wind turbines. The aim is to find the optimal SM type that balances accuracy with computational feasibility. As the wind turbine faces diverse conditions, I propose adaptable methodologies to optimize the SM performance across various settings. Additionally, I investigate the potential of combining publicly available wind turbine models with probabilistic data-driven models to assess turbine health. First, a non-intrusive Polynomial Chaos Expansion (PCE) is constructed based on the outputs from the NREL 5MW Blade Element Momentum (BEM) model, demonstrating the convergence of sectional statistics in the results. Subsequently, I utilize the SM to estimate thrust and torque on the rotor and perform a sensitivity analysis of the extreme loads to the number of Monte Carlo Simulations (MCS) in the SM. Transitioning from the PCE realm, I adopt a sequential Machine Learning (ML) method to map wind time series to the Damage Equivalent Load (DEL) of wind turbine loads. I demonstrate that the developed SM, based on a Temporal Convolutional Network (TCN)-Fully Connected Neural Network (FCNN) architecture, is capable of capturing the wind turbine structural dynamics. It demonstrates adaptability in digesting the upstream wakes and accurately estimating the DEL utilizing Transfer Learning (TL). Moving beyond purely synthetic data, I propose the development of a probabilistic data-driven model, integrating limited wind turbine measurements with synthetic data for wind turbine health assessment purposes. I illustrate that an Approximate Gaussian Process Regression (AGPR) trained on a year’s worth of Supervisory Control and Data Acquisition (SCADA) data, combined with simulation outputs from a publicly available wind turbine model, emerges as a promising probabilistic tool for wind turbine health assessment.
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    Statistical Power for Small Effect Sizes: An investigation of backward priming in Mandarin-English bilinguals
    (2024-01-29) Li, Xiao Xiao; Archibald, John
    Backward priming, or L2 to L1 priming, is a small but important effect for understanding the structure of the bilingual lexicon. A meta-analysis of priming in bilingual populations has shown that while the backward priming effect is quite small, it is qualitatively but not quantitatively different from the forward (L1 to L2) priming effect (Wen & Van Heuven, 2017). The empirical evidence for this view has come from various groups of bilinguals, including Japanese-English (Nakayama et al., 2016) and Korean-English (Lee et al., 2018) bilinguals, but not yet with Mandarin-English bilinguals: In this population, the effect is inconsistently significant. In response to this, researchers have raised the question of whether the existing studies were underpowered, given the small backward priming effect. Using a simulation-based power analysis, I show that this is most likely the case, as roughly 5400 observations per condition are necessary to detect a small backward priming effect. Previous work collected an average of 453 observations per condition, making it very unlikely for their statistical tools to be able to detect the effect. Based on this, I recommend that future work in this field conduct power analyses a priori, using the results as a guideline rather than a strict criterion for adequate power. Adopting this practice can help make experiments more replicable and future work in this direction is crucial for developing our understanding of the structure of the mental lexicon.
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    Log Message Anomaly Detection using Positive and Unlabeled Learning
    (2024-01-29) Seifishahpar, Fatemeh; Gulliver, T. Aaron
    Log messages are widely used in cloud servers and software systems. Anomaly detection of log messages is important as millions of logs are generated each day. However, besides having a complex and unstructured form, log messages are large unlabeled datasets which makes classification very difficult. In this thesis, a log message anomaly detection technique is proposed which employs Positive and Unlabeled Learning (PU Learning) to detect anomalies. Aggregated reliable negative logs are selected using the Isolation Forest, PU Learning, and Random Forest algorithms. Then, anomaly detection is conducted using deep learning Long Short-Term Memory (LSTM) network. The proposed model is evaluated using the commonly employed Openstack, BGL, and Thunderbird datasets and the results obtained indicate that the proposed model performs better than several well-known approaches in the literature.
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    Career-Life Exploration in Secondary Schools in BC: An Analysis of the Career Education Documents for Grades 10, 11 and 12
    (2024-01-29) Fallahi, Sara; Tobin, Ruthanne
    Excellent career education that guides young people to align their talents and competencies with their life-path, positively influences job satisfaction, individual’s mental wellness, resilience against stress, and overall life satisfaction, which in turn, affects the contributions that those individuals may make to society. A key component in helping young people forge a meaningful life-path is the direction and guidance that they receive in secondary school via the career education curriculum. In this study, I used critical discourse analysis to examine the British Columbia (B.C.) Ministry of Education’s Career-Life Education (CLE) curriculum for grades 10 and 11, and the Career-Life Connection (CLC) curriculum for grade 12, and additional supplementary documents that were available. While research and reports about high school career education was found, after an extensive review of literature, no published academic research directly linked to the provincial curriculum was evident. Therefore, the aim of my research was to uncover the nature of the B.C. Ministry of Education curriculum by exposing the pedagogical and theoretical frameworks underpinning it. In addition, I sought to query the intention of the career education curriculum and to identify the opportunities, strategies, and activities used to discover all students’ skills, interests, and strengths. I also examined if and how the curriculum discovered and responded to the needs of culturally, socially, and linguistically diverse students. The findings suggest that the curriculum is robust and coherent; however, there is a lack of cohesiveness and in-depth discussion of practical implications and applications of this curriculum in the classroom, school, and community. In addition, findings showed that while learning activities, strategies, and opportunities are clearly articulated in the curriculum, there is insufficient emphasis on an exploration of students’ unique talents. The findings also show a gap in addressing the need for differentiated support for learners of socially, culturally, and linguistically diverse backgrounds. This study contributes to the field of research on career guidance and exploration of life-path for young people in that it is the first study of the B.C. career education curriculum, and it points to the merits and shortcomings of this curriculum for exploring one’s life-path.
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