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I Saw You in the Archive
No full textI Saw You in the Archive is a multidisciplinary exhibition that reveals the history of eugenic practices in Kitchener-Waterloo in the mid 20th century. The exhibition questions how the framing of history impacts our personal understandings of each other’s identities. Mixing visuals associated with institutional archives and rubber factories, the artworks examine the former Kaufman Rubber Company and its owner A. R. Kaufman’s attempts to contain certain types of people, particularly those deemed ‘feeble-minded’. The exhibition embodies my experience parsing through this complicated history as a queer and neurodiverse woman—digging through a cacophony of propaganda pamphlets, shoe sale reports, instruction manuals for birth control use, blueprints of the factory, and photography of the workers. Seen through video documentation, I reintroduce remnants from the archive to the contemporary condominium that was formerly the rubber factory, connecting past rhetoric to today’s circumstances. The complicated layers of eugenics, birth control access, disability rights, and feminism seep into each other and spill into the gallery. Through use of performance and site-specific interventions, I challenge the internal shame many who have been othered experience and resist systems of containment that aim to erase our identities
Holistic face processing across neural and behavioral measures
No full textHolistic face processing is a fundamental aspect of human visual perception, distinguishing face processing from other visual stimuli processing. It involves the integration of various facial features into a whole face percept. This thesis investigates the cognitive and neural mechanisms underlying holistic face processing through a series of behavioral and electrophysiological experiments. Using event-related potentials (ERPs), this research explores the effects of stimulus orientation and fixation location on face and object processing. In addition, using accuracy and reaction time measures, the thesis explores the effects of face size on face recognition and gender discrimination.
The first two ERP studies focus on the face inversion effect and its modulation by fixation location for faces and houses. A gaze-contingent paradigm ensures precise control over where participants fixate on upright and inverted faces and houses. Results reveal that fixation location significantly influences ERP responses, particularly the N170 component, a key marker of early face processing, but the strongest effects were seen at earlier and later time periods. Notably, holistic processing appears flexible, with fixation on specific facial features exerting differential effects on neural activity. Importantly, we found a strong house inversion effect and similar modulations of the neural activity to houses with fixation location. The findings challenge the traditional view of holistic processing as a rigid, fixation-independent mechanism only at play for faces and not objects.
A third behavioural study examines the role of face size in recognition accuracy and reaction times across orientations and tasks. Behavioral results indicate that holistic face processing varies with size, orientation, and task with optimal processing occurring within a specific range between extreme face sizes. These results provide new insights into how face size modulates perception and recognition efficiency.
Together, these studies demonstrate that holistic face processing is dynamic and influenced by multiple factors, including fixation location, orientation, and size. By employing advanced statistical techniques such as mass univariate analysis of ERP data, this research provides a more nuanced understanding of the spatiotemporal dynamics of face perception. The findings have implications for models of face recognition, cognitive neuroscience, and applications in artificial intelligence and biometric technologies
Efficient Algorithms for RDV graphs
No full textIn this thesis, we study the maximum matching and minimum dominating set problem in RDV graphs, i.e., graphs that are vertex-intersection graphs of downward paths in a rooted tree. A straightforward implementation of these algorithms would require time. We improve their efficiency by transforming the question about the neighborhood of into a type of range query amid a set of horizontal and vertical line segments. Our algorithms run in time, presuming a -sized intersection representation of the graph is given. In addition, our techniques can also be used to obtain faster algorithms for maximum independent set and perfect -clique packing in RDV graphs
Statistical developments for network meta-analysis and methane emissions quantification
No full textThis thesis provides statistical contributions to solve challenges in Network Meta-Analysis (NMA) and the quantification of methane emissions from the oil and gas industry.
NMA is an extension of pairwise meta-analysis which facilitates the simultaneous comparison of multiple treatments using data from randomized controlled trials. Some treatments may involve combinations of components, such as one or more drugs given in different combinations. Component NMA (CNMA) is an extension of NMA which allows the estimation of the relative effects of components. In Chapter 2, we compare the popular Bayesian and frequentist approaches to additive CNMA and show that there is an important difference in the assumptions underlying these commonly used models. We prove that the most popular Bayesian CNMA model is prone to misspecification, while the frequentist approach makes a less restrictive assumption. We develop novel Bayesian CNMA models which avoid the restrictive assumption and are robust, and demonstrate in a simulation study that the proposed Bayesian models have favourable statistical properties compared to the existing Bayesian model. The use of all CNMA approaches is demonstrated on a published network.
A commonly reported item in an NMA is a list of treatments ranked from most to least preferred, also known as a treatment hierarchy. In Chapter 3, we present the Precision Of Treatment Hierarchy (POTH), a metric which quantifies the level of certainty in a treatment hierarchy from Bayesian or frequentist NMA. POTH summarises the level of certainty into a single number between 0 and 1, making it simple to interpret regardless of the number of treatments in the network. We propose modifications of POTH which can be used to investigate the role of individual treatments or subsets of treatments in the level of certainty in the hierarchy. We calculate POTH for a database of published NMAs to investigate its distribution and relationships with network characteristics. We also provide an in-depth worked example to demonstrate the methods on a real dataset.
In the second part of the thesis, we focus on some problems in the quantification of methane emissions from the oil and gas industry. Measurement-based methane inventories, which involve surveying oil and gas facilities and compiling data to estimate methane emissions, are becoming the gold standard for quantifying emissions. However, there is a current lack of statistical guidance for the design and analysis of such surveys. In Chapter 4, we propose the novel application of multi-stage survey sampling techniques to analyse measurement-based methane survey data, providing estimators of total and stratum-level emissions and an interpretable variance decomposition. We also suggest a potentially more efficient approach involving the Hajek estimator, and outline a simple Monte Carlo approach which can be combined with the multi-stage approach to incorporate measurement error. We investigate the performance of the multi-stage estimators in a simulation study and apply the methods to aerial survey data of oil and gas facilities in British Columbia, Canada, to estimate the methane emissions in the province.
In Chapter 5, we introduce a Bayesian model for measurements from a methane quantification technology given a true emission rate. The models are fit using data collected in controlled releases (CR) of methane for six different technology types. We use a weighted bootstrap algorithm to provide the distribution of the true emission rate given a new measurement, which synthesizes the new measurement data with the CR data and external information about the possible true emission rate. We present results for the measurement uncertainty of six quantification technologies. Finally, we demonstrate the use of the weighted bootstrap algorithm with different priors and data
Saturation-Dependent Thermal Conductivity of Southern Ontario Soils
No full textSoil thermal conductivity is an important parameter in geotechnical and environmental engineering applications, influencing the performance of underground energy storage, ground heat exchangers, and other subsurface thermal systems. Through geotechnical characterization and laboratory measurements, this study investigates the thermal conductivity of 20 soil samples collected from seven locations in Southern Ontario. The key soil properties, including texture, moisture content, and bulk density, were analyzed to understand their impact on thermal conductivity. Measured thermal conductivity values were compared with published regression-based and normalized models to assess their predictive accuracy across diverse soil types. A statistical evaluation incorporating root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²) was performed to identify the best-performing models. The results indicate that Lu et al. (2014) and Yoon et al. (2018) describe the most reliable regression-based models, demonstrating strong correlations with measured data, minimum bias, and low error margins. Among normalized models, the Côté and Konrad (2005) model exhibited superior adaptability and lower prediction errors, while Johansen’s (1975) model performed well but required calibration for extreme soil compositions. The results emphasize the significant influence of soil texture and moisture content on thermal conductivity, with silty and sandy soils exhibiting higher values due to their mineral composition and structural properties. The best-performing models effectively captured these variations, highlighting their applicability in geotechnical and environmental engineering
A Study of the Eroding Dry-Band Arcing on Silicone Rubber Insulation Using Ultra-High Frequency Detection Technique
No full textErosion of silicone rubber housing material is a major cause of failure in outdoor insulators used on overhead power distribution and transmission networks. Dry-band arcing, resulting from leakage currents on the insulator surface, is the main culprit of the erosion of silicone rubber housing material. The inclined plane tracking and erosion test, standardized in IEC 60587 and ASTM D2303, has been a key tool for testing outdoor insulating materials under dry-band arcing at the material development stage, enabling efficient material ranking for field applications. Extensive research on the erosion mechanism of silicone rubber has been carried out in the standard test, and accordingly, the reliable assessment of erosion has facilitated the development of formulations with acceptable performance for outdoor insulation applications. The degree of erosion of silicone rubber varies depending on the severity of dry band arcing during the inclined plane test, with highly severe arcing—referred to as critical or eroding dry-band arcing—often leading to deep erosion.
The two inclined plane test standards specify different testing methods, range of test voltages, and failure criteria for the assessment of erosion on silicone rubber. However, the testing methods specified in the standards have often been used interchangeably in different studies, and there is no general agreement on which method is most appropriate for the reliable evaluation of erosion on silicone rubber. In addition, there are wide discrepancies in the selection of test voltages, and no unified agreement exists on the most suitable voltage level for testing silicone rubber materials, leading to inconsistencies in test outcomes. Another crucial aspect of the inclined plane test in evaluating the performance of insulating materials is identifying erosion failure, for which different criteria are specified in the two standards. However, the failure criteria specified in the standards are also applied inconsistently across various studies, with different criteria being used. While some criteria may be effective for certain materials, they are not always applicable to SR—particularly under DC voltages. These inconsistencies in testing methods, voltage selection, and failure criteria largely stem from a limited understanding of the erosion failure mechanisms of silicone rubber, highlighting a critical research gap that needs to be addressed.
Additionally, the erosion mechanism of silicone rubber has been extensively studied in the inclined plane test based on leakage current techniques. While these methods correlate leakage current with dry-band arcing severity during the test, the onset of erosion or material failure could not be reliably detected with these techniques. In particular, no significant focus has been given to the erosion failure mechanisms on silicone rubber insulation under the eroding dry-band arcing. Consequently, the underlying physics of the eroding dry-band arcing driving erosion failure of silicone rubber remains inadequately understood, presenting a critical technical gap. This highlights the critical need for a reliable detection method of the eroding dry-band arcing to identify deep erosion failure during the inclined plane test.
This thesis provides a mechanistic understanding of the erosion failure mechanisms of silicone rubber particularly through a reliable detection of the eroding dry-band arcing in the inclined plane test. To achieve this, ultra-high frequency detection of the eroding dry-band arcing is introduced as a reliable technique to identify erosion failure of silicone rubber in the AC and DC inclined plane tests. This detection method will serve as a foundation for improving the elucidation of the mechanisms of eroding dry-band arcing that drive erosion failure in silicone rubber. Moreover, it is a critical step towards improving the standards by introducing a clear erosion failure criterion for silicone rubber in the inclined plane test
Advanced Laser Weld Brazing of Zinc-Coated Automotive Steels: Process Optimization for AI-Enhanced Inspection, Tailored Intermetallic Formation, and the Evolution of Mechanical Behavior
No full textLaser weld brazing (LWB) is a key joining process in automotive manufacturing, offering minimal substrate melting, reduced Zinc (Zn) coating burn-off, and superior joint appearance. However, challenges such as sub-surface defect formation and intermetallic compound (IMC) embrittlement hinder its optimization and broader adoption. Due to LWB's relative novelty for Zn-coated steels, limited research has explored brazing defects, segregation behavior, and IMC formation, as well as their direct impact on mechanical performance. The potential for maximizing braze joint mechanical performance is limited. Even with the elimination of interfacial premature failures caused by the brittle IMC layer, the extent of mechanical performance improvement in defect-free joints remains confined to the strength of the brazing filler material. Moreover, the role of IMCs and any second phases forming within the braze matrix in either degrading or improving mechanical performance within the literature is inconclusive. This work systematically investigates LWB across Zn-coated steel grades using Si-bronze filler wire, advancing the understanding of laser-wire-substrate interactions that dictate microstructure and mechanical behavior, to maximize mechanical performance of LWB joints. Additionally, it establishes a novel LWB optimization approach that enables modification of microstructure of LWB joints as well as improvement of an in-line non-destructive testing (NDT) method assisted by artificial intelligence (AI) for real-time defect inspection and precise braze geometry measurement.
Comparative analysis with gas metal arc brazing (GMAB) confirms that LWB significantly enhances wettability of molten filler by refining the surface morphology of substrates and alters elemental segregation. This study introduces a novel wire-adjusted heat input (HI) approach, a systematic optimization strategy that directly correlates developed HI-related parameters with defect formation, overcoming inconsistencies observed in conventional nominal heat input methods. This approach enables accurate distinction between defect-free and defective seams, including lack of adhesion (LoA), pores, base metal melting (BMM), and non-conforming geometry. Process window validation through three-dimensional (3D) X-ray micro-computed tomography (μCT) using semi-automated pore segmentation and statistical analysis of pore evolution, supports the robustness of the optimization strategy. The braze joints made within the process window successfully enabled acquisition of reliable UT signal feedback for defect detection. The two-dimensional (2D) slices of μCT data, enriched AI training data base, effectively contributed into AI predictions of the seam geometry from the UT data.
Another major breakthrough of this study is developing a novel IMC category, termed surrounded interface-IMCs (SI-IMCs), formed under controlled elevated HI, distinct from conventional interface-IMCs (I-IMCs). A critical HIRelative threshold of 32 J/mm is identified for defect-free brazing in roof joints of GI-IF steels, with an additional 12.44 J/mm required to promote SI-IMC formation, occupying up to 38.2 ± 16.9% of the braze cross-sectional area. These SI-IMCs, consisting of a shell-like Fe-Si layer and a (Fe-rich)-Cu eutectic phase, enhance mechanical properties. Increasing SI-IMC area fraction from 1.2 ± 2.4% to 38.2 ± 16.9% results in a 14% increase in tensile peak load, a 350% increase in displacement, and improvement of joint toughness by 525%.
The complex geometry of roof joint coupons, combined with the low strength and high ductility of galvanized coated interstitial free (GI-IF) steel substrates, makes it difficult to fully attribute the improved mechanical performance to the presence of SI-IMCs. To fully elucidate the contribution of SI-IMCs to joint mechanical performance, this study conducts the first comprehensive investigation into a wide range of SI-IMC structures formed at different HIRelative levels during LWB in a lap joint configuration for GI-coated Gen 3 Q&P980 steel. It provides entirely novel insights into characterization of SI-IMC structures, the Cu-rich braze matrix, and how they impact mechanical performance. SI-IMCs formed at lower ∆HIRelative (2.3–7.5 J/mm) improve mechanical performance, increasing peak load by 91.7%, displacement by 319.8%, and toughness by 1200%, whereas those formed at higher ∆HIRelative (13–17.5 J/mm) degrade properties. Systematic elemental and texture analyses identify five SI-IMC types (α, β, γ, δ), with fine α-, β-, and γ-SI-IMCs delaying failure, while coarse α- and δ-SI-IMCs accelerate it. A strong {100} texture alignment between SI-IMCs and the Cu-rich braze matrix enhances strain accommodation and crack resistance. The newly introduced GND.Ratio (Cu/Fe) quantifies dislocation density, revealing that when GND.Ratio (Cu/Fe) > 0.67, SI-IMC-induced twinning activates, locally rotating the Cu-rich matrix orientation to and forming twins near crack paths. This transition shifts fracture behavior from brittle to ductile, enhancing mechanical performance. These findings redefine the role of IMCs and second phases that are formed in the braze interior, demonstrating their potential to optimize strain distribution, strain-hardening behavior, and crack propagation. Tailoring SI-IMC structures strategically can further enhance the mechanical performance and reliability of multi-material automotive structures
Enhancing YOLO through Multi-Task Learning: Joint Detection, Reconstruction, and Classification of Distorted Text Images
No full textRobust recognition of alphanumeric text mounted on vehicle surfaces may present significant challenges. These real-world challenges include conditions such as motion blur, out-of-focus imagery, variation in illumination, and compression artifacts. Existing automatic license plate recognition (ALPR) pipelines usually separate detection, enhancement, and recognition into distinct stages, either relying on explicit deblurring networks or extensive augmentation for generalization, each incurring latency, error propagation, or a performance ceiling on severely degraded inputs.
This study introduces YOLO CRNet, a unified end-to-end multi-task framework built upon the YOLO object detector, designed to simultaneously localize characters, enhance text regions, and perform optical character recognition (OCR) within a single network. We integrate two specialized heads into the YOLO backbone: a reconstruction head that restores degraded text regions, and a classification head that directly recognizes alphanumeric characters. Shared feature representations are extracted from multiple depths of the core YOLO network for synergistic learning across complementary tasks.
To inform feature selection for the classifier head, we extract per‑character embeddings from five different layer combinations of the YOLO network (ranging from early backbone to deep neck layers) and visualize class separability via t‑SNE. This analysis reveals that Configuration A which comprises of early backbone layers (1,2,4) with neck layers (10,13,16) yields the most distinct clusters for the alphanumeric character classes. The YOLO CRNet classifier head trained on Configuration A achieves 95.2% accuracy and a 94.97% F1‑score on a held‑out set of 10,100 sharp character crops, outperforming alternative layer configurations by up to 18%. Extensive experiments on blurred text datasets demonstrate that combined reconstruction followed by classification of YOLO CRNet significantly outperforms both the baseline YOLO detector and the YOLO CRNet classification head. In particular, the combined reconstruction followed by classification configuration achieves a 23.5% relative improvement in classification accuracy (from 44.5% to 68.0%) and a 15.5% gain in F1-score (from 0.550 to 0.705).
By integrating detection, enhancement, and recognition into a single network guided by t‑SNE based feature selection, YOLO CRNet reduces latency, mitigates error propagation, and explicitly handles image distortions. This work lays a foundation for real‑time, robust vehicle text detection and illustrates the power of multi‑task learning and data‑driven feature analysis in fine‑grained text recognition tasks
Coordinated human-exoskeleton locomotion emerges from regulating virtual energy
No full text© 2025 Nasiri et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Lower-limb exoskeletons have demonstrated great potential for gait rehabilitation in individuals with motor impairments; however, maintaining human-exoskeleton coordination remains a challenge. The coordination problem, referred to as any mismatch or asynchrony between the user's intended trajectories and exoskeleton desired trajectories, leads to sub-optimal gait performance, particularly for individuals with residual motor ability. Here, we investigate the virtual energy regulator (VER)'s ability to generate coordinated locomotion in lower limb exoskeleton. Contribution: (1) In this paper, we experimented VER on a group of nine healthy individuals at different speeds (0.6m/s-0.85m/s) to study the resultant gait coordination and naturalness on a large group of users. (2) The resultant assisted gait is compared to the natural and passive (zero-torque exoskeleton) walking conditions in terms of muscle activities, kinematic, spatiotemporal and kinetic measures, and questionnaires. (3) Moreover, we presented the VER's convergence proof considering the user contribution to the gait and introduced a metric to measure the user's contribution to gait. (4) We also compared VER performance with the phase-based path controller in terms of muscle effort reduction and joint kinematics using three able-bodied individuals. Results: (1) The results from the VER demonstrate the emergence of natural, coordinated locomotion, resulting in an average muscle effort reduction ranging from 13.1% to 17.7% at different speeds compared to passive walking. (2) The results from VER revealed improvements in all indicators towards natural gait when compared to walking with a zero-torque exoskeleton, for instance, an enhancement in average knee extension ranging from 3.9 to 4.1 degrees. All indicators suggest that the VER preserves natural gait variability and user engagement in locomotion control. (3) Using VER also yields in 13.9%, 15.1%, and 7.0% average muscle effort reduction when compared to the phase-based path controller. (4) Finally, using our proposed metric, we demonstrated that the resultant locomotion limit cycle is a linear combination of human=intended limit cycle and the VER's limit cycle. These findings may have implications for understanding how the central nervous system controls our locomotion.NSERC Discovery Grant, RGPIN-2018-04850 || New Frontiers in Research Fund - Exploration, NFRFE2018-01698 || New Frontiers in Research Fund - Exploration, NFRFE2022-620 || John R. Evans Leaders Fund Canadian Foundation for Innovation || Ontario Research Fund (ORF) || NSERC, CGS-M
Temporal effect of docetaxel on bone quality in a rodent model of vertebral metastases
No full text© 2025 Akens et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.This study investigates the effects of the anticancer drug docetaxel (DTX) and its timing of administration on tumor development and resultant bone quality in a rodent model, considering both healthy animals and those with osteolytic bone metastases secondary to intra-cardiac injection (d0) of HeLa cells. Health and tumor-bearing rats were treated with DTX on d7 or d14 and compared to the control (no treatment) and an additional cohort treated with Zoledronic acid (ZOL). Notably, DTX administration on d7 markedly curtailed tumor growth, as evidenced by bioluminescence and histological analysis, indicating its effectiveness in reducing bone metastases. Bone metastases were more established in animals treated with later DTX administration and ZOL, but still reduced compared to no treatment. When considering bone quality, we found that both the organic and mineral phases of bone are impacted by DTX treatment. Tumor-bearing animals exhibited decreased hydroxyproline/proline ratios reflecting change in collagen metabolism compared to healthy controls, but these decreases were only significant with no treatment of DTX administration on d14. This suggests a positive impact of early DTX treatment similar to ZOL on bone quality from an organic perspective. As well, increased CaMean and CaPeak reflecting the degree of calcification was found in healthy rats treated early with DTX, similar to that seen with ZOL compared to the tumor-bearing treated groups. Overall, early docetaxel administration reduced tumor formation and improved bone quality, suggesting its potential benefit in managing bone metastases.Canadian Institutes of Health Research (CIHR), #156175