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The Effect of Therapeutic Exercises on Paraspinal Muscle Morphology and Function in Chronic Low Back Pain
No full textLow back pain (LBP) is the most common musculoskeletal disorder globally, and a leading cause of disability, especially when it becomes chronic. Exercise therapy is the most widely used form of conservative treatment for chronic LBP. Due to the link between changes in paraspinal muscle morphology (e.g. atrophy, fatty infiltration) and LBP, many exercise interventions focus on activating paraspinal muscles. Past findings suggest the benefits of exercise for individuals with chronic LBP on patient-reported outcomes, including improvements in pain, disability and quality of life. However, the best type of exercise to improve paraspinal muscle health in chronic LBP is understudied, and therefore, remains unclear. This 7-chapter dissertation aims to use magnetic resonance imaging to comprehensively investigate the effect of different exercise programs on paraspinal muscle morphology and function, and its association with patient-reported outcomes in individuals with chronic LBP. Chapter 1 explains the rationale, objectives, and hypotheses, and Chapter 2 consists of a comprehensive review of the literature. Chapter 3 explains an aquatic therapy protocol. Chapter 4 aimed to investigate the effect of an aquatic therapy program versus standard care on paraspinal muscle morphology and function, and the association of morphological changes with strength and patient-reported outcomes. Similarly, Chapter 5 aimed to investigate the effect of a combined motor control and isolated lumbar extension strengthening program versus general exercise on paraspinal muscle morphology and psychosocial factors, and the association of morphological changes with strength and patient-reported outcomes. Finally, Chapter 6 aimed to investigate the effect of a multimodal exercise program versus no exercise on paraspinal muscle morphology and patient-reported outcomes, and the association of morphological changes with patient-reported outcomes and functional outcomes. Lastly, Chapter 7 summarizes the results of Chapter 4-6 and states the general limitations, recommendations for future research, and conclusions
Mainstreaming
Get PDFThis thesis paper accompanying a research-creation video essay titled “Mainstreaming,” explores the impact of news consumption on individual’s worldviews, drawing primarily on Cultivation Theory by George Gerbner. The project explores cultivation deeply, especially the concepts of mainstreaming, the Mean World Syndrome, and resonance, while challenging some established notions and deterministic aspects of the theory. Employing a diverse range of methods, such as survey, interviews, and an experiment, the project focuses on four individuals, three categorized as politically left-leaning, and one categorized as alt-right in their news consumption profiles. The interviews of these subjects explore how these individuals actively curate their news intake and how the lived realities of these individuals do, or do not, come against the narratives of their media. Through experiment, the three left-leaning individuals were asked to alter their news consumption habits, revealing an active reinforcement and justification of their typical media. The analysis of the interviews and experiment leads to a reinterpretation of cultivation’s resonance. By establishing a difference between resonance that is more concrete versus abstract, the argument is made that lived experiences that are grounded in a subject’s lived reality serve as a means of preventing or limiting the effect of cultivation. The interview and analysis of the alt-right individual provides a case study in cultivation mainstreaming, highlighting a convergence in worldviews between him and the media that he consumes. The project overall suggests that individuals are not passive receivers of media’s messaging, and advocates for utilizing agency in the consumption of media
The Real Friends Are The Machines We Made Along The Way
No full textMachine-human relationships exemplify humanity’s relationships with materiality at-large, as well as relationships with the "other", with society, and with the notion of “human” itself. This thesis investigates the social construction of ontological boundaries between humans and machines through a research-creation process centred around technician-machine relationships in design, maintenance and repair. Over the course of six years, the author developed four major projects that serve as case studies, each focused on a specific kind of social encounter: Machine Ménagerie, an installation and research-performance around relationships in design; Chronogenica, a machine-human cooperative organization; Ritualizing Care in Human-Robot Relations, an artistic residency with a robot collaborator; and The Nature Reserve of Useless Robots, an interactive installation for showing care towards machines.
Drawing on the knowledge-sharing practices of technician communities, the author uses narrative as a primary method for conveying the idiosyncrasies of each project and the specific machines involved. In return for the author’s care and attention, and given space to express themselves on their own terms, the machines contributed something of their own knowledge to the thesis. Supposedly-objective dominant truths about machine-ness fell away to reveal an underlying irrationality, highlighting the everyday practices of reality-construction that can empower us to alter our relationships with those nonhumans we care for (and who care for us)
On Translanguaging and Learner Affect: An Action Research Study on ESL Secondary Classrooms Through the Use of a Multilingual Presentation Project
Get PDFAmid controversies surrounding multilingual policies in a classroom, something that has become a key component of education in recent years is student’s emotional well-being and how emotions influence learning (Song, Howard, Olazabal-Arias, 2022). This action-research study examines the impact of translanguaging on student engagement and affect in a secondary-level English as a Second Language (ESL) classroom in Quebec. While traditional ESL instruction emphasizes English-only policies, translanguaging encourages students to use their full linguistic repertoire, supporting comprehension and participation. Conducted in a French-medium high school, this study involved multilingual presentations where students used their first languages (L1s) alongside English. Teacher-researcher observations through field notes were analyzed to assess student collaboration, participation, and emotional responses. The findings of this research suggest that the use of translanguaging can foster engagement, reduce presentation anxiety and promote inclusivity. This research contributes to multilingual education by highlighting translanguaging as a strategy to enhance student confidence and learning in diverse, multilingual classrooms.
Keywords: Translanguaging, ESL, affect, multilingual education, student engagement, collaboration, emotion
Impacts of Uncertainty of Predation Risk in Trinidadian Guppies
Get PDFThis study investigates how prey species, specifically Trinidadian guppies (Poecilia reticulata), cope with ecological uncertainty in predator-prey dynamics. Ecological uncertainty arises from conflicting and/or unreliable environmental cues, especially in ecosystems impacted by anthropogenic disturbances, which may directly influence prey decision-making and behaviour. This work explores how prey balance risk and safety in unpredictable environments, offering new insights into the adaptive strategies they use.
Chapter 1 looks at how conflicting safety and risk cues influence neophobia, the fear of novelty. The results show that guppies exposed to contradictory cues were more neophobic, exhibiting reduced movement, which supports the hypothesis that uncertainty leads to greater caution. Conversely, guppies conditioned to safety cues showed a preference for novelty, highlighting that they engage with new experiences when they feel that these are safe. These findings suggest that neophobia may be an adaptive response to environments where risk signals are unclear, particularly in areas disturbed by human activity.
Chapter 2 examines how anthropogenic disturbances contribute to ecological uncertainty which influence prey behaviour. Through field and lab experiments, it was found that guppies from high predation-risk and disturbed environments exhibited more caution and took longer to explore or return to disturbed areas. Guppies from low predation-risk environments, however, were more exploratory. This suggests that human disturbances increase uncertainty, driving prey to adopt more conservative, risk-avoidant strategies.
Ultimately, this study highlights how environmental unpredictability shapes risk-averse behaviours like neophobia. As human impacts on ecosystems continue to intensify, understanding ecological uncertainty is crucial for predicting long-term effects on biodiversity and ecosystem stability
Integrating Ontology and LLMs for Diagnosis and Repair of Concrete Surface Defects
Get PDFThis paper explores the integration of a novel Ontology for Concrete Surface Defects (OCSD) with a Large Language Model (LLM), specifically GPT-4o (omni), to enhance defect diagnosis and repair strategies in concrete structures. While LLMs independently offer significant reasoning and natural language capabilities, this study demonstrates the value of combining their interpretative power with the structured knowledge representation provided by OCSD. By enabling adaptive reasoning, where the LLM relies on the ontology's domain-specific relationships and thresholds, and updates its conclusions for specific defect types, the system flexibly adjusts its decision-making based on context. This integration improves diagnosis accuracy, reasoning transparency, and decision-making efficiency. The proposed method is validated through a case study, highlighting the synergy of OCSD and GPT-4o in addressing challenges in defects diagnosis and repair
Political Support and Participation in Canada: Digging Deeper into the Drivers of Unconventional Participation
Get PDFFor the past 40 years, scholars have been concerned with the decreasing electoral turnout in established democracies such as Canada. Around the same time, political participation occurring outside of state-sanctioned avenues – otherwise called unconventional forms of political participation – appeared to be increasing. As citizen participation is a core ingredient of democracy, it is important that we strive to understand how Canadians participate and why. Yet, the literature offers no consensus about what is driving Canadians to what forms of unconventional political participation, nor what that could mean for Canadian democracy. This thesis asks what unconventional political participation looks like in Canada, what drives it, and what the implications of those findings are. Using Easton’s Systems Theory as a framework, this study contextualizes unconventional political participation as a consequence of the larger political system’s outputs to explore the role of political support in the various forms of political participation. The survey data used in the analysis is from the Political Communities Survey Project 2017 dataset. By conducting analyses that consider both voters and nonvoters separately, this thesis demonstrates that Canada is not facing a shift away from electoral participation, but rather a broadening of the repertoire of actions used by citizens who already participate electorally. It also concludes that political support plays a different role in electoral participation than unconventional participation, but that in both cases, increased support generally leads to increased participation. In sum, unconventional participation in Canada is more so evidence of an engaged citizenry than a dissented one
Advancing Behavior Modeling in Smart Buildings through Open Set, Universal, and Generalized Domain Adaptation
Get PDFSmart buildings use intelligent automation systems which optimize energy consumption while improving occupant comfort and promoting sustainable development. The core of this vision de-
pends on strong Occupancy Estimation and Activity Recognition models which support dynamic control of HVAC systems and lighting and other essential building operations. These models face
significant deployment challenges because real-world settings differ from training environments and suffer from insufficient availability of labeled data and evolving activity patterns. This thesis examines how Open Set Domain Adaptation, Universal Domain Adaptation, and Generalized Domain Adaptation enhance the adaptability and generalization potential of Occupancy Estimation and Activity Recognition models in smart buildings. Our research begins with the exploration of Open Set Domain Adaptation techniques designed to distinguish known activity classes from unknown
ones during domain shifts by implementing adversarial learning frameworks along with rejection-aware classifiers specifically for smart building sensor data. Our work presents a combined Uni-
versal Domain Adaptation approach which uses optimal transport and angular margin constraints to achieve flexible alignment between domains while operating without knowledge of overlapping
labels. Our study examines generalized domain adaptation methods that allow adaptation across domain and label shifts in previously unencountered environments through self-training and hybrid learning approaches combined with distribution-agnostic strategies. Extensive experiments show that the proposed methods excel in classification accuracy, unknown class detection capabilities and stability against label imbalance. The research provides scalable, privacy-conscious solutions for adaptive behavior modeling in intelligent environments, which help improve the energy efficiency of intelligent building systems
Ultrasound-assisted Modulation of the Endothelial Cell Membrane for Cellular Immunotherapy
Get PDFThe potential of immunotherapy for brain cancer remains limited due to the presence of a restrictive blood-brain barrier and suppressive tumor microenvironment. Therefore, developing strategies to improve the efficacy of molecular and cellular immunotherapy is in urgent need. In this thesis, I explore the potential of therapeutic ultrasound under fluid flow conditions to enhance endothelial cell permeabilization and immunobiology for improving immunotherapy. Here, fluidic systems were employed to cultivate and treat two human endothelial cell types, either umbilical vein (HUVEC) or brain endothelial cells (HBEC-5i), under flow conditions. The findings demonstrated a direct correlation between microbubble flow velocity and ultrasound-assisted cell permeabilization. The velocity of microbubble perfusion also substantially influenced the dynamics of Ca2+ influx in endothelial cells. Additionally, shear-flow preconditioning influenced endothelial cells' cytokine profile, significantly enhancing their susceptibility to ultrasound. Furthermore, distinct microbubble flow patterns dramatically influenced the efficiency of cell permeabilization. Building on these findings, I explored the effects of microbubble-induced shear stress on endothelial cell immunobiology. Ultrasound-stimulated microbubbles led to a time-dependent upregulation of cell adhesion molecules involved in immune cell homing. Additionally, ultrasound treatment under identical conditions significantly increased the secretion of 20 cytokines and chemokines 4 hours post-sonication, improving CAR NK-92 cells homing and trafficking. Overall, this thesis highlights the potential of ultrasound-activated microbubbles to overcome the challenges of tumor microenvironments, thereby enhancing the efficacy of cellular immunotherapy
A Hierarchical Incentive Mechanism Design for Clustered Federated Reinforcement Learning with Budget Limitation: A Contract-Stackelberg Game Framework
Get PDFFederated Reinforcement Learning (FRL) offers a powerful framework for distributed autonomous agents to collaboratively learn decision-making policies while preserving data privacy. This is particularly valuable in domains such as connected autonomous vehicles, where data sensitivity and environmental variability present major challenges. However, practical FRL systems face several obstacles, including environmental heterogeneity, high participation costs, and limited budget availability at the server side for incentivizing agents. To address these issues, we propose a clustered FRL framework that organizes agents into groups based on their operational environments, enabling more efficient training and localized coordination. Each cluster is managed by a local server that aggregates agent updates and forwards refined models to a main server for global aggregation. To encourage sustained participation, we design a hierarchical incentive mechanism: at the lower layer, contract theory is employed due to information asymmetry; at the upper layer, a Stackelberg game is formulated to enable the main server to allocate its limited budget strategically across clusters based on the accuracy of the local servers’ trained models.
Keywords: Federated Learning, Reinforcement Learning, Autonomous Driving, Incentive Design,
Stackelberg Game, Contract Theory, Budget-Constrained Optimizatio