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Effects of Aquatic Therapy versus Standard Care on Non-Specific Chronic Low Back Pain and Feasibility of mHealth Application Play the Pain: A Pilot Randomized-Controlled Trial
Get PDFIntroduction: The effects of aquatic exercise on psychological function associated with chronic LBP remains poorly understood and adherence to exercise-based interventions in CLBP is low. A promising solution to improve adherence is through the integration of mobile health application Play the Pain, which allows for continuous self-tracking of pain.
Objectives: The primary objective of my thesis was to compare the effects of aquatic therapy to standard care on CLBP in terms of pain, disability, and psychological factors. The secondary objective was to determine the feasibility of using Play the Pain in a CLBP clinical intervention in terms of adherence and satisfaction.
Methods: 34 participants with CLBP were randomized to the aquatic therapy (AT) group or the standard care (SC) group (AT, n=18; SC, n=16), while 12 participants tested Play the Pain. Outcome measures were pain (NPRS), disability (ODI), quality of life (SF-12), depression and anxiety (HADS), pain catastrophizing (PCS), kinesiophobia (TSK-11), insomnia (ISI) and adherence and satisfaction with the app.
Results: Both groups significantly improved pain, disability, pain catastrophizing and quality of life with no differences between groups. Twelve participants used Play the Pain and 6 completed the exit survey. The adherence to the app was at 41.6% and the user satisfaction was low.
Conclusions: Our results provide preliminary evidence on the efficacy of aquatic therapy to improve pain, disability and psychological outcomes associated with CLBP. We encountered many technical difficulties during the study that prevented our ability to adequately determine the feasibility of Play the Pain
Flash Boiling Atomization of Suspension for Application of Suspension Plasma Spraying
Get PDFSuspension Plasma Spraying (SPS) is a thermal spray technique used to deposit sub-micron and nano-sized particles. The liquid is evaporated by exposing the suspension to the plasma jet. Then, the particles are melted and directed in the plasma jet to impact the substrate and form a coating. SPS has lower solid feed rates and deposition efficiency compared to other thermal spray techniques. Increasing particle concentration in the suspension can enhance the feedstock deposition rate, but high viscosity and nozzle clogging are issues.
To address these issues, this study explores flash boiling atomization (FBA) as a novel injection method in SPS for high solids concentrations, up to 70 wt.%. FBA uses thermodynamic instability to break up a liquid jet. When superheated suspension is accelerated through a nozzle and its pressure drops below the saturation pressure, rapid boiling occurs. Vapor bubbles expand within the liquid jet, causing it to fragment into smaller parts. FBA has applications in various industries such as fuel injection, desalination, and pharmaceuticals. The main objective is to use FBA to inject high-solids suspensions into the plasma flow to create SPS coatings.
Suspension injection in SPS can be axial or radial. In axial injection, fragmentation occurs inside the torch, while in radial injection, the suspension is injected from outside the torch into the plasma flow. Conventional radial injection methods include spray atomization, which creates disintegrated droplets, and mechanical injection, which produces a continuous jet. This study compared coatings made with FBA to those made with mechanical injection, assessing microstructure, deposition weight per pass, deposition efficiency, and coating thickness. Results indicated improvements across these parameters.
Water and ethanol are common suspension solvents. Water-based suspensions face challenges with atomization and evaporation resistance, impacting coating properties. FBA can improve fragmentation and prevent clogging by reducing viscosity and surface tension in the superheated state. The effect of high solids concentration and plasma power on coating microstructure, thickness per pass, deposition weight per pass, and deposition efficiency was investigated, showing that a dense coating microstructure with high solids deposition can be achieved using 70 wt.% suspension and a high power torch
Genomics-based Mixed-stock Analysis of Brook Trout Reveals Cryptic Population Structure and Complex Lake Migrations
Get PDFEffective fishery management relies on knowing the contributions of genetically distinct populations to mixed-stock harvests. We investigated population genomic structure and harvest contributions of lake-migratory brook trout inhabiting three large Quebec lakes (Mistassini, Mistasiniishish, Waconichi). These brook trout support fisheries important to the Cree Nation of Mistissini and their tourism outfitting industry. Together with local partners we collected 1063 samples from spawning sites and feeding areas between 2020-2022. We then used a GTseq (Genotyping-in-Thousands by sequencing) panel of 393 single nucleotide polymorphisms to: i) infer population genetic structure and test for unknown populations; ii) assign individuals to their population of origin, and iii) determine harvest contributions of genetically distinct populations. Our results revealed population structure in two of three study lakes and extensive movements of brook trout, with some individuals travelling over 100km away from spawning rivers. In the largest lake (Mistassini), two of three populations contributed over 90% of the lake’s harvest and exhibited distinct spatial distributions that were stable across years. In Mistasiniishish Lake, over 80% of harvested trout originated from a single, previously known population; the remaining trout originated from a cryptic, unsampled population with a strongly overlapping spatial distribution. No population structure was detected in Waconichi Lake. We also detected low levels of migration from Mistasiniishish Lake into Mistassini Lake through a waterfall historically reported to be a dispersal barrier. Our results illustrate the precision afforded by GTseq to inform insights into the ecology and genetics of lake-migratory salmonids, thereby facilitating local management for sustainable fisheries
Public Perception of Automated Shuttles for the Last-Mile Connectivity in Montreal
Get PDFThis thesis investigates the public perception and acceptance of automated shuttle services for last-mile connectivity in Montreal. Through a comprehensive survey, the study examines key factors influencing acceptance of the autonomous shuttle, including experience, awareness, comfort and safety level, trust in technology, benefits and barriers, and potential integration into urban transportation systems. A survey of Montreal residents (n=52) reveals key insights into demographic trends and attitudes towards autonomous vehicles (AVs). Results indicate a moderate familiarity with AVs (38.6%) compared to the US (70.90%), UK (66%), and Australia (61%). Despite this, Montrealer’s expressed positive sentiments towards AVs (54%), slightly higher than the UK and US. Concerns about safety (49% very concerned), legal liability (47.10% very concerned), and data privacy (63.50% very concerned) were prominent. Comfort levels with autonomous technology varied, with 38.45% having heard of autonomous shuttles but only 13.46% having boarded one. Respondents showed preference for level 3 automation (56%) over higher levels. Concerns about interactions with other vehicles, pedestrians, and bikers were noted. Overall, Montreal residents are open to AVs but harbor significant concerns, highlighting the need for targeted interventions to address safety, security, and privacy issues in deploying automated shuttle services effectively.
Keywords: Automated Shuttles, Comprehensive Survey, Urban Transportation, Demographic
Three Essays on Ownership Structure
No full textThis thesis examines three topics in ownership structure. In the first essay, we document a convex relationship between leverage and ownership of a firm’s largest individual blockholder. This convex influence of ownership on leverage is largely driven by the bankruptcy risk. Ownership has a concave impact on leverage through the threat of the market for corporate control and blockholder’s empire building desires. Further, we show that the ownership-leverage relationship differs depending on the blockholder’s identity (from a convex one for family firms to a concave one for non-family insider firms). Our results are robust to controlling for the endogeneity of ownership and alternative definitions of leverage.
In the second essay, we investigate the timing and determinants of investment in organization capital (OC). Using a comprehensive sample of U.S. IPOs, we document a significant decline in the investment in OC after going public. This decline is positively related to the dilution of the largest individual blockholders (a proxy for change in agency problems) and negatively related to IPO offer size (a proxy for access to capital). We also find that OC investment is positively related to family ownership and negatively related to venture capitalist presence, possibly because of their different investment horizon.
In the third essay, we examine the prevalence and importance of voting convertible preferred equity (CPE) in a comprehensive sample of US firms and find that around a half of CPE is voting and around seven percent have additional board election rights. In firms with voting CPE, holders of such shares control, on average, around 20% of votes (both on general corporate matters and on board elections). In firms with additional board control rights, CPE holders can elect more than a third of the board. We also investigate the determinants of the voting rights held by CPE holders and find that they are negatively related to individual common blockholder ownership and positively related to a firm's financial distress. We further show that additional board election rights have a positive impact on a firm’s survival. This positive influence is primarily driven by private equity firms as the largest CPE holder
Deindustrialization Along the Littoral: Shifting Capitalist, Social, and Environmental Relations in the American Fishing Industry, 1976-2007.
Get PDFAt the margins of the industrial economy of the mid-twentieth century, economic change in the American commercial fishing industry challenged its very market, social, and environmental relations. The issue of overfishing became a national one in the 1960s and by 1976, the U.S. Congress passed the Magnuson-Stevens Act, nationalizing waters within 200 miles of its coast. And yet, it wasn’t until the 1990s, with the collapse of fisheries across the Northwest Atlantic, that the notion of the unchangeable ocean would lose its hold over politics. This project considers the industrial decline in fisheries through the lens of neoliberalism and deindustrialization, an approach rarely used in fisheries history. Using the archive of the Point Judith Fishermen’s Cooperative Association (1947-1996), Rhode Island, USA, this thesis considers the history of industrial decline from the experiences of fishermen themselves. Instead of fishermen’s jobs going overseas, Point Judith fishermen experienced consolidation and atomization, relying on the free market to access economic security while experiencing the squeeze of global free trade. Industrial decline in fisheries highlights the contradictions within late 20th century America, in which economic nationalism and neoliberalism went hand-in-hand. Not only did neoliberalism impact how the fishing industry was governed and financed, but it shaped how fishermen were treated as workers. This thesis strikes a path to excavate the history of fishermen’s class consciousness at the nexus of ecological and economic pressures in an era of industrial decline
Integrating Handwriting Analysis and Machine Learning for Enhanced Personality Trait Prediction
No full textThis thesis presents an in-depth exploration of graphology and its integration with machine learning to analyze personality traits through handwriting. The motivation for this research stems from the brain's ability to express personality traits through neuromuscular movements, particularly in handwriting. This study bridges the historical graphological methods, tracing back to the 19th century, with contemporary machine learning techniques.
This research utilized a dataset of 1,108 handwriting examples. CENPARMI contributed 234 of these, while the remaining 874 were procured through a business-oriented graphology expert. The data used comprises a diverse set of handwriting samples, analyzed using machine learning algorithms such as KNN, Random Forest, Logistic Regression, and specifically the VGG16 model for transfer learning. The research employs techniques like SMOTE for data balancing and ensemble methods for classification, including Majority Voting and Stacking Method.
Experimental results demonstrate a significant improvement in the accuracy of personality trait predictions after using SMOTE, with the highest accuracy exceeding 90% for traits like "Agreeableness" and "Open to Experience" using the Ensemble method (Stacking Method). Thus, this integrated approach produces better results.
The main contributions of this research lie in its innovative integration of graphology and machine learning for personality assessment, methodological advancements in handling imbalanced datasets, and the application of transfer learning in handwriting analysis. The improved accuracy in personality trait prediction illustrates the potential of this interdisciplinary approach in fields such as psychology and personalized services, offering new insights into personality psychology and opening avenues for future research in this domain
Simplifying Interpretation of Ultrasound Imaging: Deep Learning Approaches for Phase Aberration Correction and Automatic Segmentation
No full textMedical ultrasound imaging is a widely used diagnostic tool in clinical practice, offering several advantages, including high temporal resolution, non-invasiveness, cost-effectiveness, and portability. Despite these benefits, ultrasound modality often suffers from lower image quality compared to other modalities, such as magnetic resonance imaging, which complicates image interpretation and poses diagnostic challenges, even for experienced clinicians. Given its unique advantages, simplifying the interpretation of ultrasound images can profoundly impact the accessibility and affordability of healthcare. This thesis aims to enhance the interpretability of ultrasound images using deep learning (DL)-based approaches on two parallel fronts.
The first front focuses on improving image quality by addressing the phase aberration effect, a primary contributor to the degradation of medical ultrasound images. Phase aberration arises from spatial variations in sound speed within heterogeneous media, introducing artifacts such as blurring and geometric distortions. This effect hinders the accurate representation of tissue structures and complicates clinical interpretation. To tackle this, we propose two novel methods. The first involves training a convolutional neural network (CNN) to estimate the aberration profile from the B-mode image and employing it to compensate for the aberration effects. The second introduces an aberration-to-aberration approach combined with an innovative loss function to train a CNN that directly predicts corrected radio frequency data without requiring ground truth.
The second front focuses on the automatic segmentation of ultrasound images and explores the challenges associated with employing DL-based approaches. Manual segmentation, typically performed by expert clinicians, is time-consuming and prone to human error, and automating this process can simplify the interpretation of ultrasound images. While DL methods have demonstrated considerable potential, ultrasound image segmentation poses unique challenges due to artifacts such as shadowing, reverberation, refraction, phase aberration, and speckle noise. The scarcity of medical data further complicates these challenges, limiting the generalizability and robustness of models in clinical settings. To address these limitations, we investigate the shift-variance problem in CNNs and propose pyramidal blur-pooling layers to mitigate this issue. Furthermore, we tackle domain shift and data scarcity by employing a domain adaptation method and introducing an ultra-fast ultrasound image simulation technique based on frequency domain analysis
Nonlinear Estimator for a Class of Systems with Linear Dynamics and Noisy Quadratic Measurements
Get PDFThis thesis proposes a novel nonlinear estimator to estimate the state of a class of systems with linear dynamics and noisy quadratic measurements. It is shown that the error dynamics is described by a nonlinear Verhulst logistic equation. This observation unveils a link between population dynamics and state estimation for this class of systems. The stationary distribution of the estimation error converges to a zero-mean Gaussian with adjustable variance.
This estimator is used to estimate the physical state variables in energy harvesters by using the measurement of electrical energy. Furthermore, the problem of estimating the position of a quadrotor in waypoint navigation using noisy range measurements can be formulated in a way in which it can be solved by the proposed estimator. This application emphasizes the practical use of the estimator, which guides the quadrotor through various pipeline configurations. The quadrotor’s input is designed to maintain the piecewise affine trajectory within the thickness of the pipeline for the inspection task. The simulation results illustrate a stable estimation error that consistently converges to an area around zero with different initial conditions. In addition to evaluating the performance of the proposed estimator, a comparison is made with the Kalman filter for the augmented linearized system
Choosing Canada: The Role of Brazilian Immigrant Influencers in Shaping Destination Reputation and Migration Decisions
Get PDFThe widespread use of Information and Communication Technology (ICTs) has reshaped migration. Individuals with the agency to decide on a migration destination rely on social media platforms to guide their decision-making process. While scholars have highlighted the relevance of online spaces for migrants, there is a gap in exploring which digital actors facilitate migration and the type of information conveyed to aspiring migrants. This thesis studied the role of Brazilian immigrant influencers on Instagram in building Canada's destination reputation to shape co-national destination choices to fill this gap. For this purpose, this project relied on the content analysis of 30 Instagram posts from five Brazilian immigrant influencers and ten interviews with Brazilian newcomers residing in Canada. This thesis found that influencers convey an overtly positive representation of Canada, the 'Canadian Paradise,' by sharing partial and exaggerated information that compares life in Brazil and Canada. As a second finding, newcomers shifted their views after migrating and now believe that Brazilian immigrant influencers acted guided by economic motivations. These findings indicate that Brazilian immigrant influencers are digital migration intermediaries who rely on idealized representations of Canada to promote migration-related services, which reveals the emergence of a digital migration industry