19614 research outputs found
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Diversity, Equity and Inclusion in the Implementation of Indigenous Relations and Leadership Competencies in Leadership Competitions at the BC Office of the Auditor General
No full textThe BC Public Service is working towards improving diversity, equity, and inclusion (DEI). The BC Office of the Auditor General (BC OAG) is also making commitments to improve DEI in the organization. The purpose of this thesis is to assist with these improvements, focusing on how DEI can be better incorporated into hiring practices for leaders at the BC OAG. Specifically, this thesis is seeking to determine how the BC OAG implements competencies in leadership competitions in a way that aligns with these DEI commitments. To assess this, the researcher undertook a qualitative mixed methods research approach, consisting of a cross-jurisdictional scan of Canadian audit offices, structured interviews with BC OAG staff members who had been panelists on leadership competitions, and a document review of leadership competition files. From the cross-jurisdictional scan, the key finding is that Canadian audit offices value and plan around DEI quite differently from one another. The key finding from the structured interviews is that DEI is not a requirement in competency implementation at the BC OAG, nor is it a requirement for panelists to utilize a DEI lens in their role on leadership panels. The key finding from the document review is that the competencies the BC OAG utilizes in leadership competitions have the potential to incorporate DEI, but this incorporation is inconsistent. From these findings, an option was presented to the BC OAG to develop its own explicit DEI competency that is tested for in every leadership competition.Graduate2025-01-1
Doing well and Feeling Well: Investigating the Contributions of Two Stress Related Appraisals and Regulatory Practices on Student Success Outcomes
No full textStudent success is facilitated by effectively navigating academic demands and the inevitable stress that is experienced in the academic context. Appraisals and beliefs about stress impact coping, however they have been underexamined in academic settings. Stress Optimization and Self-Regulated Learning (SRL) theory inform the understanding of stress responses and learning processes respectively. Despite the importance for student success of managing both stress and academic demands, there is a paucity of research examining their combined contributions. This two study dissertation examined: (a) the predictive capacity of two stress appraisals, coping self- efficacy (CSE) and stress mindset (SM), on student success outcomes which were comprised of student academic experiences (e.g., academic wellbeing, motivation challenges, social emotional challenges) and performance (GPA) and (b) the mediating role of regulatory practices (e.g., metacognitive monitoring and adapting, academic social engagement) on the relationship between stress appraisals and student success. First, a case is made for an integrated theoretical framework that incorporates stress optimization and SRL. Second, a literature review delineates research expectations. Third, paper one utilizes regression to examine CSE and SM as predictors of student success outcomes. Fourth, paper two utilizes structural equation modeling to examine associations between stress appraisals, regulatory practices, and student success outcomes. Findings show: (a) CSE and SM predicted student success outcomes directly, (b) CSE was a stronger predictor of student success than stress mindset, and (c) regulatory practices can promote student success beyond what is provided by stress appraisals alone. This research is important for understanding adaptive responses to stress in academic contexts.Graduat
Vectron: A dynamic programming auto vectorization framework
Full text linkDynamic programming (DP) is a fundamental algorithmic strategy that decomposes large problems into manageable subproblems. It is a cornerstone of many important computational methods in diverse fields, especially in the field of computational genomics, where it is used for sequence comparison. However, as the scale of the data keeps increasing, these algorithms are becoming a major computational bottleneck, and there is a need for strategies that can improve their performance. Here, we present Vectron, a novel auto-vectorization suite that targets array-based DP implementations written in Python and converts them to efficient vectorized counterparts that can efficiently process multiple problem instances in parallel. Leveraging Single Instruction Multiple Data (SIMD) capabilities in modern CPUs, along with Graphics Processing Units (GPUs), Vectron delivers significant speedups, ranging from 10% to more than 20x, over the conventional C++ implementations and manually vectorized and domain-specific state-of-the-art implementations, without necessitating large algorithm or code changes. Vectron's generality enables automatic vectorization of any array-based DP algorithm and, as a result, presents an attractive solution to optimization challenges inherent to DP algorithms.Graduat
Optimal Embedding of the Phase Unwrapping Problem onto the Quantum Annealers
Full text linkQuantum computers and algorithms are undergoing rapid development, offering promising solutions to complex computational problems. This study focuses on harnessing the potential of quantum annealing to address the challenging phase unwrapping problem. Specifically, we employed D-Wave’s quantum annealers, currently among the most powerful in existence. To effectively utilize these systems, it is crucial to embed the problem onto their underlying structure, the Pegasus graph in the case of the D-Wave Advantage system. A shorter chain-length in the embedding process generally correlates with improved results.
In the course of this thesis, we devised an algorithm for efficiently embedding the phase unwrapping problem onto the D-Wave Advantage system. Our approach yielded promising results when compared to D-Wave’s automatic embeddings. Notably, our introduced embedding boasts the minimum chain-length and utilizes the native structure of the target graph.
Additionally, we leveraged D-Wave’s hybrid workflow, combining classical and quantum computing capabilities, to tackle larger image problems. Refinements to the hybrid method were implemented, resulting in enhanced performance. Experimental evaluations were conducted on actual quantum annealers, demonstrating that our refined algorithms outperform those provided by D-Wave.Graduat
Essays in high-frequency trading : insights of trading speed, systematic risk and market sentiment
Full text linkThe landscape of financial markets has undergone a profound transformation with the advent of high-frequency trading (HFT), fundamentally altering traditional notions of market dynamics. The dissertation explores the profound transformation of financial markets by HFT, examining its impact on trading speed, systematic risk, and market sentiment. Traditional methodologies are questioned, leading to an exploration of market microstructure and the integration of technologies like machine learning. Chapter 1 sets the stage by discussing the evolving nature of financial markets and the necessity of adapting research methodologies. Chapter 2 analyzes the regulation of trading speed, revealing trade-offs in market liquidity and price discovery. Chapter 3 focuses on detecting and mitigating mini flash crashes, leveraging machine learning to develop an Early Warning System. Chapter 4 examines the efficacy of speed bump mechanisms in reducing mini flash crashes, highlighting both benefits and unintended consequences. Overall, the dissertation enhances understanding of HFT’s effects on market dynamics, risk management, and regulation. This dissertation contributes to a deeper understanding of the ramifications of high-frequency trading on market dynamics, risk management strategies, and regulatory paradigms.Graduat
Numerical study of the structural performance of strong wood light-frame shear walls under large lateral loads
Full text linkThe motivation for this study comes from the increasing demand for safe, affordable wood-frame buildings in Canada over the past decade, primarily due to their low cost, high ductility, and ease of construction. In such buildings, wood-frame shear walls are commonly utilized as the main lateral load-resisting system to resist seismic loads. Wood-frame shear walls are typically comprised of timber framing members, sheathing panels such as plywood or oriented strand board (OSB), and fasteners like nails and bolts. The best performance of such walls is achieved when most of the energy is dissipated through shear deformation in the sheathing-to-framing connectors (i.e. nails) while the framing and anchorage systems remain in their elastic regime. This study presents the results of extensive numerical and analytical investigations into the behavior of "strong" wood-frame walls subjected to large monotonic and cyclic loads. A detailed 3D finite element (FE) model in ABAQUS software was employed for an in-depth analysis of shear wall components and to examine the impact of various parameters on their performance. The accuracy of the FE model for both the nail connectors and the wall assembly is validated by comparing its results with experimental data from the literature. Further analyses showed that the Canadian Standards Association (CSA), and the Special Design Provisions for Wind and Seismic (SDPWS) guidelines slightly overestimate the initial wall stiffness, with the discrepancy increasing at larger displacements. The numerical analyses conducted on strong shear walls with different hold-down systems show that discrete hold-down system can overstress the end studs, increasing the risk of wood crushing and brittle failure in the framing members.
In contrast, continuous steel rods maintain stresses within safe limits and shift the failure mode (nail yielding) from the end studs to the center of the wall, thereby enhancing the overall structural performance. The numerical results further indicate that, although the diameter of continuous rod hold-downs does not significantly affect the wall’s strength, it plays a critical role in delaying yielding in the anchorage system, thereby improving the overall wall performance and energy dissipation under lateral loads. Numerical results also show that thicker OSB sheathing panels or materials with a higher modulus of elasticity (MOE) improves energy dissipation while ensuring the frame members and anchorage system remain within their elastic range. Thicker panels help prevent edge tear-out and nail head pull-through by reducing the crushing of wood strands in the OSB, allowing the nails to deform more before ultimately withdrawing. This suggests that optimizing the mechanical properties of sheathing panels may improve shear wall performance and energy dissipation while minimizing the need for additional nails, providing a balanced approach to enhancing both strength and ductility in the design leading to more resilient shear walls under strong earthquakes.Graduate2025-11-1
Uniform, 1-dimensional polymer nanofibers for applications in nanomedicine
Full text linkPolymer nanomaterials have garnered increased attention over the past several decades due to their ability to perform in a variety of applications, depending on the chemical functionality of the material used. Of note, polymers have been used increasingly for biomedical applications, from drug and gene delivery vehicles, to contrast agents and therapeutics themselves. Living crystallization-driven self-assembly (CDSA) provides a novel pathway for the preparation of morphologically pure, length-controlled, 1-dimensional (1D) polymer nanofibers. In this thesis, the applications of these nanofibers for applications in nanomedicine is explored. Chapter 1 provides an introduction into polymer self-assembly, living CDSA, and a brief literature review of nanoparticles explored for biomedical applications. Chapter 2 describes the synthesis and self-assembly of biodegradable and cationic poly(fluorenetrimethylenecarbonate)-block-poly(dimethylaminoethylmethacrylate) (PFTMC-b-PDMAEMA) 1D nanofibers, and evaluates the length and shape dependence on antibacterial activity against Escherichia coli. A comparison to neutral 1D poly(ethylene glycol) nanofibers is made. Chapter 3 then investigates the antibacterial mechanism of action of 1D nanofibers relative to nanospheres of identical composition. This pathway is explored through the use of confocal laser scanning electron microscopy and flow cytometry, as well as transmission electron microscopy and scanning electron microscopy. Chapter 4 expands upon preliminary drug-loading results to explore the addition of the anticancer therapeutic paclitaxel to the core-corona interface of PFTMC-b-PDMAEMA seed nanofibers. These are then evaluated as a delivery vehicle in 2D and 3D cell models containing glioblastoma cells. Chapter 5 then extends the scope of antibacterial activity of 1D PFTMC-b-PDMAEMA nanofibers against gram-positive Staphylococcus epidermidis, as well as explores the ability of these nanofibers for treating the extremely drug-resistant organism Burkholderia vietnamiensis. Chapter 6 concludes this thesis with an outlook as well as proposes future directions that could expand on the projects presented herein.Graduate2025-08-1
Development and Implications of ISOL Target-Materials with High-Carbon content for Short-Lived Radioactive Isotope Beam Production
Full text linkIn the Isotope Separation On-Line (ISOL) method, a high-energy particle beam strikes a target, inducing nuclear reactions that produce isotopes. After releasing from the target by diffusion, the isotopes are ionized, and separated by mass.
ISAC, TRIUMF's ISOL facility, delivers RIBs to experiments on nuclear astrophysics, nuclear physics, particle physics, and material science.
The Advanced Rare IsotopE Laboratory (ARIEL) is under construction to expand TRIUMF's scientific capabilities with the development of two additional ISOL target systems for TRIUMF. This expansion entails a greater demand for target material, promoting the research and development of new targets tailored for enhanced isotope release and improved resilience under high-power beam irradiation.
This work presents the research and development towards reducing several limitations of the current ISOL-target paradigm.
A new method for synthesizing UCx targets has been developed. Now UCx targets are synthesized eight times faster than before while complying with the required micrometric particle size and high open porosity to promote isotope release.
The reduction in production time complies with ARIEL's future target material demand, and it has relieved personnel and equipment, allowing the development of a novel graphite-composite target.
Both targets have been characterized and submitted online for isotope delivery to experiments. Their performance has been studied and related to microstructure and thermal properties.
Both targets are now established and regularly operated at ISAC-TRIUMF and will be used in ARIEL.
Furthermore, temperature investigations of both targets, have resulted in an analytical and a finite element model to predict their temperature during operation.
Moreover, to further improve the performance of the targets, the implications of operating target materials with high carbon content have been investigated. Strategies are proposed for further learning about carbon penetration, the resulting target ovens' corrosion, and its prevention.Graduate2024-12-1
A study on the surrogate-based optimization of flexible wings considering a flutter constraint
Full text linkAccounting for aeroelastic phenomena, such as flutter, in the conceptual design phase is becoming more important as the trend toward increasing the wing aspect ratio forges ahead. However, this task is computationally expensive, especially when utilizing high-fidelity simulations and numerical optimization. Thus, the development of efficient computational strategies is necessary. With this goal in mind, this work proposes a surrogate-based optimization (SBO) methodology for wing design using a predefined machine learning model. For this purpose, a custom-made Python framework was built based on different open-source codes. The test subject was the classical Goland wing, parameterized to allow for SBO. The process consists of employing a Latin Hypercube Sampling plan and subsequently simulating the resulting wing on SHARPy to generate a dataset. A regression-based machine learning model is then used to build surrogate models for lift and drag coefficients, structural mass, and flutter speed. Finally, after validating the surrogate model, a multi-objective optimization problem aiming to maximize the lift-to-drag ratio and minimize the structural mass is solved through NSGA-II, considering a flutter constraint. This SBO methodology was successfully tested, reaching reductions of three orders of magnitude in the optimization computational time.The authors acknowledge Fundação para a Ciência e a Tecnologia (FCT), through IDMEC, under LAETA, project UIDB/50022/2020.FacultyReviewe
Escape criteria using hybrid Picard S-iteration leading to a comparative analysis of fractal Mandelbrot sets generated with S-iteration
Full text linkFractals are a common characteristic of many artificial and natural networks having topological patterns of a self-similar nature. For example, the Mandelbrot set has been investigated and extended in several ways since it was first introduced, whereas some authors characterized it using various complex functions or polynomials, others generalized it using iterations from fixed-point theory. In this paper, we generate Mandelbrot sets using the hybrid Picard S-iterations. Therefore, an escape criterion involving complex functions is proved and used to provide numerical and graphical examples. We produce a wide range of intriguing fractal patterns with the suggested method, and we compare our findings with the classical S-iteration. It became evident that the newly proposed iteration method produces novel images that are more spontaneous and fascinating than those produced by the S-iteration. Therefore, the generated sets behave differently based on the parameters involved in different iteration schemes.The author extends the appreciation to the Deanship of Postgraduate Studies and Scientific Research at Majmaah University for funding this research work through the project number (ICR-2024-949).FacultyReviewe