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Investigating the Potential Role of Pic2 protein in Mitochondrial Copper Transport using Saccharomyces cerevisiae
No full textCopper (Cu) is an essential metal required for the assembly and activity of two key mitochondrial enzymes: cytochrome c oxidase (COX) and superoxide dismutase (SOD1), which are responsible for energy metabolism and antioxidant defence, respectively. For the past decade, Pic2 has been generally accepted as the main mitochondrial copper transporter, delivering copper to COX and SOD1, both of which cannot function without this essential metal. However, this model of copper transport in mitochondria had many inconsistencies and required revision due to conflicting data in the literature and more recent findings suggesting that Pic2 is not involved in copper transport.
In this project, I hypothesized that Pic2 is not the primary copper transporter for COX and SOD1 and used various approaches, comparing wild-type yeast with a Pic2 deletion strain in the W3030 background, to test the role of Pic2 in copper transport. Growth assays under varying copper concentrations revealed no differences in growth phenotypes between strains and enzymatic assays further demonstrated that loss of Pic2 does not lead to reduced COX or SOD1 activity. Considering that the activity of both enzymes depends on sufficient copper levels in mitochondria, these results suggest that Pic2 is not essential for mitochondrial copper transport. Additionally, measurement of aconitase activity showed that Pic2 deletion had no effect on oxidative stress, thus further supporting the notion that copper homeostasis in mitochondria is maintained in the absence of Pic2.
Together, my findings show that Pic2 cannot be the main copper transporter and that its role needs to be reevaluated, given that a better understanding of the mechanism of copper transport into mitochondria could provide new insights into both a subset of mitochondrial and copper-dependent disorders
Intra- and Inter-Rater Reliability of Features of Movement Commonly Associated with Movement Competency
No full textMovement screening tools, such as the Functional Movement ScreenTM and the Movement Competency Screen, are commonly used to assess fundamental movement patterns. These movement patterns, such as squatting and lunging, serve as the foundation for human movement, essential for daily living and athletic performance. By assessing an individual’s ability to control their joints during fundamental movements, screens help identify potential areas for improving performance and reducing injury risk. However, existing screens are limited. They do not adequately assess movement competency, the ability to move competently, without limitation or impairment, under different task demands (e.g., varying loads or speeds).
The Physical Literacy ScreenTM (PLS) was developed to more comprehensively assess movement competency. Unlike previous screens, the PLS considers how different task demands (e.g., varying load or speed) influence how movers control key movement features, to inform exercise, education, and coaching recommendations based on a more thorough understanding of an individual’s movement abilities. Because PLS feature scoring relies entirely on visual observation, the reliability of judging these features must be established before the framework can be applied with confidence. Therefore, the purpose of this study is to determine the intra-rater and inter-rater reliability of visually assessing key movement features commonly associated with movement competency.
This study examined the reliability of raters visually judging three key movement features, knee control, back control, and shoulder control, using standardized video recordings of athletes performing PLS-derived tasks. Twenty-two kinesiology students independently assessed pre-recorded videos of varsity volleyball athletes and re-scored the same videos at least 14 days later. Intra-rater reliability was measured using Cohen’s Kappa, and inter-rater reliability using Fleiss’ Kappa.
Results showed moderate intra-rater reliability for Knee (κ = 0.42) and Back (κ = 0.47) features and fair reliability for Shoulder (κ = 0.28). Inter-rater was poor across all features, with Fleiss’ Kappa values below zero.
The study provides initial reliability evidence for the visual judgment of key movement features using PLS-derived tasks. These findings highlight the importance of refining feature definitions, improving rater training and calibration procedures, and clarifying scoring criteria to enhance consistency. Strengthening these elements may support more reliable use of visual movement assessments in sport, education, and health contexts
Two-sample Inference, Order Determination, and Data Integration for Functional Data
No full textFunctional data analysis has gained increasing prominence in modern statistics, largely due to advancements in data collection technologies. It provides a nonparametric framework for analyzing discrete observations obtained from realizations of a continuous random function, often defined over time or space. In this thesis, we focus on three distinct problems, each reflecting a different aspect of functional data analysis.
In Chapter 2, we address the problem of comparing mean functions between two groups of sparse functional data within the framework of a reproducing kernel Hilbert space. The proposed method is well-suited for sparsely and irregularly sampled functional data. Traditional approaches often assume homogeneous covariance structures across groups, an assumption that is difficult to justify in practice. To circumvent this limitation, we first develop a novel linear approximation for the mean estimator, which naturally leads to its desirable pointwise limiting distributions. Furthermore, we establish the weak convergence of the mean estimator, enabling the construction of a test statistic for the mean differences. The finite-sample performance of our method is demonstrated through extensive simulations and two real-world applications.
In Chapter 3, we study the problem of determining the number of eigenpairs to retain in functional principal component analysis---a problem commonly referred to as order determination. When a covariance function admits a finite representation, the challenge becomes estimating the rank of the corresponding covariance operator. While this problem is straightforward when the full trajectories of functional data are available, in practice, functional data are typically collected discretely and are subject to measurement error contamination. Such contamination introduces a ridge in the empirical covariance function, obscuring the true rank. We develop a novel procedure to identify the true rank of the covariance operator by leveraging the information of eigenvalues and eigenfunctions. By incorporating smoothing techniques to accommodate the nonparametric nature of functional data, the method is applicable to functional data collected at random, subject-specific points. Extensive simulation studies demonstrate the excellent performance of our approach across a wide range of settings, outperforming commonly used information-criterion-based methods and maintaining effectiveness even in high-noise scenarios. We further illustrate our method with two real-world data examples.
In Chapter 4, we investigate the integration of multi-source functional data to extract a subspace that captures the variation shared across sources. In practice, data collection procedures often follow source-specific protocols. Directly averaging sample covariance operators across sources implicitly assumes homogeneity, which may lead to biased recovery of both shared and source-specific variation structures. To address this issue, we propose a projection-based data integration method that explicitly separates the shared and source-specific subspaces. The method first estimates source-specific projection operators via smoothing to accommodate the nonparametric nature of functional data. The shared subspace is then isolated by examining the eigenvalues of the averaged projection operator across all sources. If a source-specific subspace is of interest, we re-project the associated source-specific covariance estimator onto the subspace orthogonal to the estimated shared subspace, and estimate the source-specific subspace from the resulting projection. We further establish the asymptotic properties of both the shared and source-specific subspace estimators. Extensive simulation studies demonstrate the effectiveness of the proposed method across a wide range of settings. Finally, we illustrate its practical utility with an example of air pollutants data
A Study on the Impacts of Wide-Bandgap Devices on Turn-to-Turn Insulation Performance in Hairpin Winding for Electric Vehicle Traction Motors
No full textElectric Vehicles (EVs) are one of the important levers of transportation electrification. However, charging time and limited mileage per charge remain significant barriers to widespread EV adoption. Continuous advancements in EV technology aim to mitigate these challenges. Three major improvements addressing these issues include increasing the operating voltage level, replacing random-wound motors with hairpin winding, and utilizing wide-bandgap (WBG)-based power converters to drive the motors. These advancements, however, raise concerns regarding motor reliability, particularly in the winding insulation system. Therefore, it is crucial to study and characterize the effects of higher voltage levels and WBG device-based drives on hairpin winding insulation.
Turn-to-turn insulation is the most vulnerable point in motor stators. Power electronic converters employ pulse-width modulation (PWM) techniques to generate AC output waveforms, producing pulses with fast (short) rise times, overshoot, high frequency, and variable duty cycles. These PWM-driven systems subject insulation to greater electrical stress than conventional AC-fed machines. The adoption of WBG device-based drives exacerbates this stress due to their inherently fast switching characteristics and high-frequency components. Increased electrical stress may lead to partial discharge (PD) activity, which accelerates insulation degradation. Consequently, evaluating turn-to-turn insulation under WBG device-based drive operation and PD exposure is critical.
This study develops a high-voltage SiC-MOSFET pulse generator to investigate the impact of WBG device-based drives on turn-to-turn insulation. A comprehensive analysis is conducted by examining the effects of pulse rise time, overshoot, frequency, and duty cycle. Three rise times (40 ns, 500 ns, and 800 ns) are considered to assess the influence of fast-switching transients inherent to WBG devices. Overshoot effects are examined using 10% and 20% overshoot pulses, while frequency effects are evaluated at 5 kHz and 10 kHz. Additionally, the impact of duty cycle is studied at 20% and 50%. Since traction motors operate under elevated thermal conditions, this study also evaluates the effect of increased temperature on insulation degradation to more accurately replicate in-service stress conditions.
To assess turn-to-turn insulation performance, back-to-back test samples replicating hairpin winding structures are developed using actual flat wires employed in EV motors. Two wire types, corona-resistant and non-corona-resistant, are evaluated and compared. Experimental tests are designed based on Design of Experiment (DOE) principles, with samples subjected to 24-hour aging under high-voltage pulses generated by the SiC-MOSFET pulse generator in the presence of PD activity. Insulation performance is assessed before and after aging by measuring partial discharge inception voltage (PDIV) and conducting dielectric frequency response (DFR) analysis. Wire surface temperature is continuously monitored during the aging process, and PD activity is confirmed through the detection of PD electromagnetic wave emissions using a UHF antenna. Furthermore, optical microscopy, atomic force microscopy (AFM), scanning electron microscopy (SEM) images, and EDX analysis are utilized to examine wire coating integrity before and after aging.
Results indicate that corona-resistant wires exhibit superior performance under PD conditions compared to non-corona-resistant wires. Additionally, frequency is identified as the dominant factor influencing PDIV drop, whereas overshoot has the most significant effect on the increase in dissipation factor after aging. Microscopy, AFM, SEM, and EDX analysis reveal clear evidence of PD-induced wire coating damage. The combined impact of thermal and electrical stress is examined, with findings compared to room-temperature test results.
This research provides critical insights into the reliability of turn-to-turn insulation in hairpin-wound EV motors under WBG device-based drive operation, offering valuable guidance for motor reliability improvement in next-generation EV powertrains
Machine Learning Approaches for Thermoelectric Performance Predictions
No full textThe area of thermoelectric (TE) research suffers from an affordable pathway to achieve high performance TE materials. This is because the merit of the experimental approach, although sacrosanct and irrefutable, often, resorts to a trial- and-error method approach. This approach is achieved through training, experience, and observed knowledge. Additionally, while working to find an effective solution through this approach, the target TE material is kept in mind. This introduces biasness in TE materials discovery.
In TE research, recent studies have demonstrated the potential for accelerated materials discovery through artificial intelligence (AI) driven methods. Building on these advances, the thesis aims to assist experimental researchers in predicting the properties for high-performance thermoelectric (TE) materials. The objective in the thesis is realized with the developed and tested machine learning (ML) models to predict TE properties.
The developed models are based on extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM) algorithms. These algorithms, which form part of the ML framework, were trained using curated datasets. The models achieved good predictive accuracy of TE properties. The interpretability of the model predictions through SHapley Additive exPlanations (SHAP), provided interesting and chemically meaningful insights between TE material compositions and the TE properties. In one of our studies, the predictive models were validated experimentally for new doped SnSe systems to observe near consistency between predicted and measured κ values.
Subsequently, we developed an end-to-end web application embedded with these ML models, hosted on Git-Hub and Microsoft Azure cloud to deliver rapid TE property predictions. The application is made accessible to TE researchers worldwide. The researchers can upload any set of compositions to the web interface and receive immediate thermoelectric (TE) property predictions.
The methodology of the overall ML pipeline explained in the chapters are open for diversification with other Deep Learning (DL) algorithms or Generative AI (Gen AI) models. Furthermore, the ML models can be retrained by modifying the data in the existing dataset, in the direction of improving model accuracy. With this, the models can be used for experimental or first-principle based computational validation. The scope of this research offers more questions than answers leading to an extensive scope of hypothesis generation. Therefore, this opens unlimited opportunities for future investigations
FROM THE MOUTHS OF HUNTERS: HUNTER PERCEPTIONS IMPROVE UNDERSTANDING OF THE RELATIONSHIP BETWEEN HUNTERS AND CONSERVATION IN CANADA
No full textAs a group of nature-based outdoor recreationists, hunters in Canada are not well understood, particularly in the context of their role in conservation. Hunters in Canada are not typically equated with being conservation actors, however there is a dearth of current academic literature that addresses the relationship between hunting and conservation in Canada and thus hunters may not accurately be represented within conservation circles. Collaboration between different groups of conservationists could be improved, and gaining a better understanding of how hunters perceive their place in conservation may contribute to greater unity around issues of conservation concern.
The research question I explored was “Do hunters in Canada perceive they contribute to conservation, and if so, in what ways?”. I used an anonymous online questionnaire to survey hunters across Canada using Qualtrics as the survey tool. The survey link was distributed through six provincial and territorial hunting-conservation organizations affiliated with the Canadian Wildlife Federation: Ontario Federation of Anglers & Hunters, Manitoba Wildlife Federation, Saskatchewan Wildlife Federation, Alberta Wildlife Federation, B.C. Wildlife Federation, and Yukon Fish & Game Association. The survey consisted of 23 questions and was conducted over a six-week period in the fall of 2023. 4022 valid responses were received from every province and territory, with the majority from Ontario.
Four key themes emerged from the survey results of hunters in Canada: hunters identified more strongly as conservationists than as hunters, hunters identified numerous ways in which they participate in and support conservation, hunters are political actors, and hunters can be allies for conservation. Focusing on hunter perceptions was a necessary first step in exploring the relationship between hunters and conservation in Canada. The breadth of these results highlight opportunities for further empirical research and the need for more research to be conducted in Canada on this topic
Multiple Model Adaptive Control with Blending in State Space
No full textAdaptive Control (AC) provides a systematic framework for handling uncertainty in linear
and non-linear systems. Single-model adaptive schemes such as Model Reference Adaptive
Control (MRAC) and Adaptive Pole Placement Control (APPC) face inherent limitations
when applied to systems with large parametric uncertainty, such as slow convergence rates
and limited noise robustness. This has motivated researchers to investigate multiple-model
strategies that employ several candidate plants to represent different regions of the operating
space.
In this thesis, we develop Multiple-Model Adaptive Control (MMAC) methodologies
based on the blending of signals from multiple fixed models. We consider uncertain plants
with known, compact, convex polytopic uncertainty. Our starting point is the design of a
Multiple-Model Parameter Identification (MMPI) scheme that quickly and robustly identifies
the uncertain plant parameters. In combination with a Model Reference Control
(MRC) framework, this leads to a Multiple-Model Reference Adaptive Control (MMRAC)
with blending for Linear, Time-Invariant (LTI), non-square (different number of inputs
and outputs), multi-input systems, with full-state feedback. Under an exact matching
condition, the parameter estimates are used to design a control input such that the plant
states asymptotically track the reference signal generated by a state-space reference model.
A procedure is provided to select the corner models based solely on the polytopic uncertainty.
The proposed MMRAC guarantees the boundedness of all closed-loop signals
and the asymptotic convergence of the state-tracking error to zero. Statistical analysis
demonstrate improved tracking speed and robustness to noise compared with single-model
approaches.
The combination of MMPI with pole-placement techniques, allowed us to develop
Multiple-Model Adaptive Pole Placement Control (MMAPPC) for LTI, square (same number
of inputs and outputs), multivariable systems with full-state feedback, and for with
LTI, Single-Input, Single-Output (SISO) systems via an intermediate state estimation step.
The resulting controller again ensures the boundedness of all closed-loop signals, while also
asymptotically placing the closed-loop eigenvalues at designer-specified locations. Statistical
analysis shows a clear increase in robustness to noise relative to single-model schemes.
These improvements were validated in the context of motion control of lateral vehicle dynamics,
where multiple-model schemes consistently outperformed single-model approaches,
including cases with slowly time-varying unknown parameters
Perceptual Relationship and Representation Learning for 3D Understanding and Quality Enhancement
No full text3D modeling plays a crucial role in a wide variety of real-world applications such as autonomous driving, smart cities, entertainment, education, and video game development. Over the past decades, research has focused on enhancing 3D understanding and improving the quality of 3D representation and rendering. Despite these efforts, existing approaches often rely on either low-level features, such as object sizes and edges, or high-level semantics, like object categories, while neglecting the critical relationships between multiple objects. These relationships are essential for a comprehensive understanding of 3D scenes in natural environments. Moreover, contemporary deep learning-based methods for 3D modeling often overfit due to reliance on large-scale neural networks, limiting their efficiency and generalizability. This thesis addresses these limitations through four key contributions centered on extracting efficient perceptual representations.
In the first part, we propose a method to extract perceptual relationships that extend 3D understanding from local cues, such as shapes and semantics, to global cues, such as depth perception. To achieve this, we design a VRS framework with two main objectives: (1) identifying perceptual relationships that significantly contribute to 3D understanding and (2) quantifying their contributions. By integrating these spatialized relationship representations into monocular depth estimation tasks, we evaluate their effectiveness. Experiments on KITTI, NYU v2, and ICL-NUIM datasets validate the efficacy of this approach. Furthermore, incorporating the relationship spatialization framework into state-of-the-art depth estimation models results in marginal improvements across most evaluation metrics.
In the second part, we extend perceptual relationship representations from single to multiple viewpoints. This extension enables the integration of richer 3D information into novel-view-related tasks, such as NVS, which requires generating multiple novel views. To this end, we introduce a VRT framework that predicts perceptual relationships from unseen viewpoints, thereby overcoming the constraints of view dependency. By capturing transformed relationship representations, this framework enhances 3D understanding in novel view synthesis tasks.
In the third part, we aim to improve the perceptual quality of rendered novel views, by using HPP, which are sensitive to distortions in 2D images. To this end, we design a HuPPO framework to improve the quality of 3D renderings. The framework imposes ``human perception'' as guidance to learn perceptually satisfactory representations. At the same time, the human perception is formulated as a meta-learning objective function to regularize the training process. Evaluation in the novel view synthesis task demonstrates the strong effectiveness of the proposed framework.
In the final section, we extend the aforementioned methods, which primarily enhance the quality of 3D modeling from perceptual representations, with a focus on improving efficiency and generalizability. To this end, we propose an HCDM for learning 3D models, represented as neural radiance fields or point clouds, using a latent diffusion model. The designed HCDM}improves the quality of 3D modeling by representing 3D models as functional parameters that are much fewer than the size of 3D models, allowing for an efficient representation. The HCDM is designed to learn the distribution of these parameters, improving generalizability across data from multiple modalities. The significant performance improvement of the proposed methods is further validated on 2D images and 3D motion data
Perspectives of Graph Diffusion: Computation, Local Partitioning, Statistical Recovery, and Applications
No full textDiffusion describes the process of mass moving from one region to another. In the con- text of graph, the diffusing mass spreads from nodes to nodes along the edges of the graph. Broadly speaking, this includes a number of stochastic and deterministic processes such as random walk, heat diffusion, network flow and electrical flow on graphs. Graph diffusion is a highly important primitive, and has been shown to have a variety of surprising properties both theoretically and practically. In this thesis, we present several new perspectives of graph diffusion, with an emphasis on how diffusion algorithms uncover the local clustering structure of the input data without necessarily exploring the entire graph.
In the first two parts of the thesis, we introduce a new class of graph diffusion methods that are provably better at extracting the local clustering structure of a graph or a hy- pergraph. Here, diffusion is formulated as a pair of primal and dual convex optimization problems, based on the idea of spreading mass in the graph while minimizing a p-norm net- work flow cost. The primal solution of the diffusion problem provides an intuitive physical interpretation where paint (i.e. mass) spills from the source nodes, spreads over the graph, and there is a sink at each node where up to a certain amount of paint can settle. The dual solution embeds the nodes on the non-negative real line and is considered as the output of diffusion. We will show that the dual variables nicely encode the local clustering structure around a given set of seed nodes. In particular, assume the existence of a cluster C of low conductance Φ(C), the sweep cut procedure on the dual variables returns a cluster whose conductance is not too much larger than Φ(C).
In the next two parts of the thesis, we introduce a weighted diffusion mechanism which allows any existing diffusion method to take into account additional node information such as node attributes and labels. The method weighs the edges of the graph based on the attributes or the labels of each node. Depending on the nature and availability of additional node information, two simple yet effective edge-weighting schemes are introduced and analyzed. Over contextual random graphs generated by a local variant of the stochastic block model with noisy node information, we will show that, if the additional information contains enough signal about the ground-truth cluster, then employing existing diffusion algorithms in the weighted graph can more accurately recover the ground-truth cluster than employing diffusion in the original graph without edge weights. In particular, statistical recovery guarantees in terms of precision and F1 score will be derived and compared.
All of the results are supplemented with extensive experiments on both synthetic and real-world data to illustrate the technical results and the effectiveness of the new methods in practice. The code is open-source on GitHub
Automated Generation, Evaluation, and Enhancement of JMH Microbenchmark Suites from Unit Tests
No full textEnsuring the performance of software systems is a cornerstone of modern software engineering, directly influencing user satisfaction and reliability. Despite its critical role, performance testing remains resource-intensive and difficult to scale, particularly in large projects, due to the complexity of microbenchmark creation and execution. Microbenchmarking frameworks like the Java Microbenchmark Harness (JMH) offer precise performance insights but require significant expertise, limiting their adoption. This thesis addresses these challenges by introducing ju2jmh, a novel framework that automates the transformation of JUnit tests into JMH microbenchmarks, bridging the gap between functional and performance testing.
The contributions of this thesis are threefold. First, ju2jmh automates the generation of high-quality JMH microbenchmarks from widely used JUnit test suites, enabling developers to adopt performance microbenchmarking with minimal manual effort. Results demonstrate that the generated microbenchmarks exhibit stability comparable to manually crafted ones and effectively detect real-world performance bugs. Second, the Performance Mutation Testing (PMT) framework is developed to systematically evaluate the robustness of microbenchmarks in detecting artificial performance bugs, achieving competitive mutation scores. Third, a clustering approach is proposed to optimize the execution of microbenchmarks by grouping functionally similar tests based on code coverage information. This strategy reduces execution time by 81.2% to 86.2% across three large-scale projects while preserving accuracy and reliability.
Evaluated on three diverse open-source Java projects, the proposed solutions address stability, detection capabilities, and scalability challenges in performance testing workflows. The findings highlight the potential of ju2jmh and its associated methodologies to transform performance microbenchmarking practices, providing developers with practical tools to integrate reliable and efficient performance testing into modern software development pipelines. These advancements pave the way for future research into extending automated performance testing across different programming languages and development ecosystems