21793 research outputs found
Sort by
Human/Nature (dis)Connectedness Beyond the Built Environment
No full textThis thesis explored the dynamics of Human/Nature Connectedness and Disconnectedness within and beyond the built environment, examining how design, systemic barriers, and daily choices shape our relationship with the natural world. It challenged the notion that Human/Nature Connectedness is accessible to some while others remain disconnected. The study reframed Human/Nature Connectedness as an essential, embodied practice rooted in reciprocity and resilience. Grounded in critical theory and interdisciplinary methodologies, including autoethnography, heuristic inquiry, and embodied phenomenology, the research wove a personal narrative through global, experiential inquiry. Comparative analysis of Canada’s cool, climatic constraints and Egypt’s warm, sunlit landscapes revealed the psychological, spatial, and socio-economic dimensions of Human/Nature (dis)Connectedness. The research also compared dystopian realities with utopian possibilities and envisioned a future realigned with nature’s seasonal rhythms; where time is guided by the sun, and cycles follow the moon. The work revealed a resistance to the unnatural, relentless pace of productivity-driven systems that distance us from nature, eroding our health and well-being. The work then encouraged a more balanced, sacred experience with nature, reclaiming the fact that we are of nature. Inspired by Indigenous stewardship, the work honoured intergenerational wisdom while advocating systems that prioritize deep-rooted tradition over shallow innovation. It called for generational interconnectedness, broadening our vision beyond instant gratification to respect future generations, grounded in reciprocal coexistence with nature. As a contribution, the thesis proposed S.E.E.D.S. (Standard for Environmental and Ecocentric Design Specifications) as an actionable design framework that empowers individuals to transcend structural constraints. Through daily choices, practices and ecocentric values, a convergence towards a holistic lifestyle emerges. More than a guide, it is an act of rebellion where small choices collectively reseed our bond with nature, harmoniously intertwining people and planet
Machine Learning based Memory Load Approximation
No full textModern computing applications demand ever-increasing performance and energy efficiency. However, conventional processor architectures frequently stall while waiting for data retrieval from memory, creating a bottleneck known as the memory wall. Over the past decades, various approaches such as speculative prefetching, load value prediction, and hardware caching have been proposed to mitigate this limitation. While these techniques yield moderate gains, they often rely on rigid hardware logic or simple pattern matching, which struggle with the irregular, data-driven workloads typical of contemporary multimedia and machine learning applications.
This thesis propose to use Machine Learning (ML) to speculate load values and reduce memory accesses. The proposed method is grounded in the principles of Approximate Computing (AC), where minor inaccuracies are accepted in exchange for improvements in performance or efficiency. To this end, we introduce an ML-based Load Value Approximation (ML-LVA) approach, which predicts the values of memory loads to reduce access latency. The ML-LVA is trained offline to generate a compact predictor that captures patterns in image and audio data, enabling accurate value prediction during runtime without the need for continual retraining. By learning spatial correlations among adjacent data values, the proposed ML-LVA effectively anticipates memory contents, thereby reducing stalls and improving overall system performance in online deployment.
We have implemented the proposed ML-LVA framework both in software and hardware. The software variant targets existing processors lacking reconfigurability, as well as systems with tight area or power constraints that prohibit adding custom hardware. It operates as a callable subroutine designed for seamless integration without modifying the processor architecture. The software implementation was tested on an x86 processor in the GEM5 simulator. On the other hand, the hardware-based implementation integrates the proposed ML-LVA as a dedicated accelerator accessed via a custom instruction, offering tighter pipeline integration, lower latency, and enhanced efficiency for newly designed systems. The hardware-based ML-LVA was implemented in CVA6, which is an open source RISC-V processor. The synthesis results conducted in Cadence Innovus showed that the overhead of the added accelerator is marginal.
Experimental results conducted on audio and image processing workloads demonstrate that the proposed ML-LVA accelerates memory access by over 6×, resulting in application speedups up to 2.45×. Additionally, even when predicting up to 95% of loads, the output fidelity remains within perceptual thresholds. Subsequently, the proposed ML-LVA outperforms state-of-the-art LVAs in terms of performance and quality. The ML-LVA achieves these results with only a 5% area overhead and less than 1% power increase in silicon
Exploring the Roles of Cultures and Cultural Competencies in the Fields of Learning and Development and Technical Communication
No full textThis dissertation examines the roles of culture and cultural competence in the related professional disciplines of Learning and Development and Technical Communication. It first explores the portrayal of culture and cultural competence through in-depth content analyses of peer-reviewed articles published over a decade and a half in Performance Improvement Quarterly and the Journal of Business and Technical Communication. Building on these analyses, the dissertation then explores empirical evidence of the roles of culture and the competencies needed in the two lines of work through separate Developing a Curriculum (DACUM) studies. In both studies, focus groups of individual contributors and managers elicited the evidence.
All studies explored culture broadly, in terms of personal backgrounds (national, societal, and demographic) and in terms of the organizations in which individuals practice (organizational, occupational, and technological cultures). Participants in the two professions identified overlaps in cultural competencies, including inclusivity and bias reduction, but also differences in focus. Workplace and societal cultures received the most attention, with Learning and Development emphasizing workplace culture more than Technical Communication.
Learning and Development participants identified a wide array of competencies spanning cognitive, behavioral, and affective dimensions, addressing both learner adaptation and cultural complexities within their work. Technical Communicators emphasized behavior-based competencies centered on audience-focused communication, with less attention to workplace cultural dynamics.
The study proposes a cross-field model of cultural competence that includes both shared and field-specific dimensions, and offers recommendations to support culturally responsive practices
Robust Integrated Tactical Planning in Hybrid Multi-Echelon Manufacturing Systems
No full textThe rise of Industry 4.0 has intensified demand for hybrid production systems delivering both standard and highly customized products. Yet, current production planning models largely overlook modular-structured products in multi-echelon job-shop environments, particularly under uncertainty. This study develops a robust tactical planning framework that captures defect risks, variable processing times, and capacity uncertainty for customized items. A mixed-integer programming model based on the cardinality-constrained robust optimization approach is formulated to jointly optimize order acceptance, machine activation, procurement, production, and inventory decisions, with the objective of maximizing profit across the manufacturing network.
To mitigate the effects of volatile manufacturing conditions, the framework allows the flexible activation of additional machine capacity modules within a budgeted uncertainty set. Computational experiments are conducted using literature-driven data to evaluate the model’s behavior under a range of uncertainty realizations. Sensitivity analysis confirms that the selected uncertain parameters have the largest impact on profit. Complexity analysis shows that the model remains computationally efficient and scalable across different problem sizes.
An out-of-sample performance evaluation demonstrates that the robust approach’s machine capacity module activation decisions outperform conventional deterministic planning by enhancing resilience and profitability under uncertainty, while maintaining operational efficiency with only marginal additional machine use. Most of the profit improvement is driven by reduced delays and cancellations, indicating a higher service level. These results highlight the model’s ability to safeguard profitability and operational stability in complex, customization-driven production systems
Mechanical Characterization and Finite Element Simulation of Carbon/PEEK Thermoplastic Composite Laminate Manufactured using Automated Fiber Placement (AFP) Process
No full textDespite fabrication difficulties, the utilization of thermoplastic composite laminates is expanding, especially in the aerospace industry, owing to their outstanding characteristics, such as high toughness and recyclability. Compared to established manufacturing procedures, such as hand layup autoclave process, automated manufacturing techniques, such as Automated Fiber Placement (AFP), offer the potential to economize time and costs. An advantage of manufacturing thermoplastic composite laminates using AFP lies in the possibility of in-situ consolidation, thereby eliminating the necessity of any secondary consolidation processes. However, short processing time during the AFP method leads to a significant contrast in the quality of in-situ-consolidated thermoplastic composite laminates in terms of interlaminar bond strength and other material properties when compared to that of their autoclave-reconsolidated counterparts. The present thesis focuses on this aspect and aims to develop an efficient micromechanical computational model based on the finite element method that can predict the interface strength and other material properties, including stiffness and strength, of in-situ-consolidated Carbon/PEEK thermoplastic composite laminate. Two batches of laminate samples are fabricated by AFP with in-situ consolidation. One of the batches is subsequently re-consolidated in an autoclave to serve as a reference for a comparative study (i.e., in-situ consolidated vs. autoclave re-consolidated). The Short-Beam Shear (SBS) test, due to delamination failure mode, is chosen to measure the Interlaminar Shear Strength (ILSS). The interface strength properties caused by AFP in-situ consolidation are computationally determined using the cohesive zone model and the SBS test results. The manufactured samples undergo micrographic study and thermoanalytical Differential Scanning Calorimetry (DSC) testing to gather the essential data for the computational model, including fiber volume fraction, interlaminar resin pocket, void content and degree of crystallinity. Then, realistic two-dimensional Representative Volume Elements (RVEs) are generated at a micro-scale based on the obtained information from micrographic examination and DSC analysis. These 2D RVEs were first used in the finite element simulation to predict the transverse tensile strength, resulting from the AFP in-situ consolidation process, using the Drucker-Prager law along with ductile failure criterion to take into account the plastic deformation of the matrix, as well as crack onset and evolution in the neat PEEK resin. Furthermore, the effective stiffness properties, such as transverse elastic and out-of-plane shear moduli, influenced by AFP in-situ consolidation were predicted by applying periodic boundary conditions and using the homogenization theory. The obtained results reveal that while the AFP in-situ consolidation manufacturing process reduces the transverse stiffness properties of Carbon/PEEK thermoplastic composite laminate 10% to 20%, the transverse tensile strength value may even decrease up to 44%, in comparison with the autoclave treatment. The outcomes of this thesis demonstrate that the mechanical performance of Carbon/PEEK thermoplastic composite laminates is significantly affected by the AFP in-situ consolidation process. The predicted interfacial strength and effective material properties provide essential input parameters for subsequent finite element modeling, analysis, and structural design of thermoplastic composite components produced through the AFP in-situ consolidation process
Les pratiques entrepreneuriales artistiques comme approche éducative. Trois études de cas collaboratives : de l’espace public aux espaces d’exposition.
No full textCette thèse examine les pratiques entrepreneuriales comme une approche artistique éducative, à travers l’analyse de trois projets collaboratifs menés par le collectif Stories of a Near Future (Montréal,Canada). Ancrée dans une posture de praticienne-chercheuse, la recherche explore comment la curation, la narration et l’entrepreneuriat culturel peuvent constituer des méthodes de transmission, d’engagement et de mise en récit dans des contextes situés. Les projets Corps Usés (exposition patrimoniale à Paspébiac, Gaspésie), De Passage (intervention sonore dans l’espace public au canal de Lachine, Montréal) et People Make Places (projet de médiation numérique au Centre PHI, Montréal) sont analysés comme des expériences critiques révélatrices des tensions entre autonomie, temporalité, institution et pédagogie. La recherche propose la notion de repreneuriat pour penser une posture curatoriale fondée sur la reprise. Elle avance également la notion de entrepreneurship as art pour désigner une manière de faire projet où l’acte d’entreprendre devient une pratique artistique à part entière : relationnelle, narrative et collective
Cumulative Power Spectral Density (CPSD) Feature for Separating Motor Intention from Overt Behavior in EEG Signals
No full textUnderstanding the decision-making processes behind voluntary and involuntary motor actions remains a central challenge in neuroscience. Conventional EEG markers such as event-related desynchronization/synchronization (ERD/ERS) have provided valuable insights into motor intention but suffer from persistent limitations—most notably the reliance on pre-stimulus baselines. These baselines are highly sensitive to inter-trial variability, transient state changes, and non-stationary noise, making absolute amplitude comparisons unreliable across trials, subjects, and recording sessions.
This thesis introduces cumulative power spectral density (CPSD) in the beta and gamma bands as a robust, adaptive alternative. Unlike ERD/ERS, which requires frequent baseline recalculations (e.g., every 2 seconds from the preceding 0.5 s), CPSD avoids fixed baseline subtraction by using a sliding-window accumulation approach that updates its max–min reference only when a new extreme PSD value is detected. This method preserves temporal dynamics while minimizing the instability introduced by fluctuating baselines.
The approach leverages data-driven, sliding-window extraction of cumulative power spectral density (CPSD) features in the beta and gamma bands, enabling fine-grained characterization of motor intention and cognitive control. Comprehensive analysis across 109 healthy subjects shows that optimal classification performance is achieved with short accumulation windows (0.05–0.20s), particularly around movement cue onset. Beta and gamma CPSD features exhibit distinct temporal and spatial dynamics, with beta generally favoring slightly longer integration windows. These results highlight that motor intention signatures emerge within narrow, task-specific temporal windows, and that beta–gamma CPSD provides a stable and interpretable alternative to traditional ERD/ERS measures for decoding voluntary motor control.
Beyond classification, CPSD revealed coordinated beta–gamma dynamics underlying selective attention, response inhibition, and intention-to-execution transitions, offering a richer, more interpretable representation of motor control processes than conventional ERD/ERS measures.
The results establish that the cumulative beta--gamma power within task-specific windows serves as a core neural marker for voluntary motor control. This method allows for precise, automated classification of ME/MI states by referencing adaptive CPSD thresholds, providing a simultaneous measure of motor intention strength. Their interplay encodes the core neural processes for voluntary control. These findings position CPSD as a paradigm-shifting neural feature for next-generation real-time brain–computer interfaces (BCIs) and clinically relevant neuro-technologies, providing a principled foundation for decoding conscious versus unconscious motor actions. By replacing fixed cutoffs with adaptive, data-driven thresholds, CPSD advances the precision of motor intention classification and deepens our understanding of brain dynamics in human motor control, potentially redefining EEG-based motor intention research
Toward Predictable Tendon Driven Soft Robots: Methods in Friction, Design, and Simulation
No full textSoft robots have unique properties such as inherent compliance, safety, and adaptability, which make them attractive for applications in unstructured and human-centric environments. However, their nonlinear mechanical properties and geometric complexity pose significant challenges in modelling, design, and simulation. In addition, the unique qualities of cable-driven grippers which mimic muscle tendons, provide high force density and greater control, bring their own challenges. This thesis addresses three critical interrelated challenges which limit the scalability and reliability of tendon-driven soft robotic grippers: (1) the lack of accurate cable friction modelling for elastic surface contacts, (2) the cost and inefficiency of the design process for task-specific grippers, and (3) the absence of real-time simulation tools that accommodate hyperelastic and multi-material behaviour.
First, a novel friction model is proposed that captures the asperity behaviour between cables and elastic surfaces. Unlike existing friction models developed for rigid systems, this formulation accounts for deformation-dependent force transmission and can be calibrated with as few as nine data points. When validated experimentally, the model reduced tip prediction error from 16.1% (baseline) to 2.8% for a soft robotic finger with three joints.
Second, a grasp-based product classification framework is introduced. The frame-work maps food items to a small set of human-inspired grasp types. This classification supports a modular and reconfigurable gripper design strategy that balances versatility with task-specific performance. A streamlined design pipeline integrates human demonstration data, kinematic modelling, stiffness and cable placement optimization to rapidly generate custom gripper configurations. The resulting modular gripper was validated across 15 diverse food items, achieving trajectory tracking accuracy exceeding 97%, with a reconfiguration time under five minutes and full fabrication cycles under 24 hours.
Third, to support simulation-informed design and control, a novel geometry-based simulation framework is developed to efficiently model nonlinear, hyperelastic deformations. By embedding strain energy-dependent stiffness into element-wise parameters, the approach dynamically captures material behaviour without updating the global stiffness matrix, thereby maintaining the computational speed advantage geometry-based solvers have. This method enables rapid simulation of complex geometries with arbitrary amounts of materials. Thus, addressing current limitations which restrict existing geometry-based methods to two linear materials.
Collectively, the work presented in this thesis contributes new theoretical models, computational methods, and experimental frameworks that enable faster, more reliable, and more adaptable design of soft robotic grippers. These contributions address key bottlenecks in friction characterization, design scalability, and material simulation, and provide a pathway toward broader industrial adoption of soft robotics
Navigating my Way in, through, and out of PVE-Centered Instruction: Autoethnographic Reflections of Researching and Teaching PVE in CEGEP Literature Classrooms
No full textAs a college instructor who has previously researched, developed, and implemented Preventing Violent Extremism (PVE) curricula for my literature classes, I have identified several benefits, risks, and needs associated with teaching PVE in higher education. In this dissertation, I use critical autoethnography to elucidate my experience as a PVE researcher-practitioner from 2013 to 2016 at a CEGEP in the province of Quebec in Canada. I have done do so to improve my own practice as an instructor, to shed light on issues that may present barriers to effective PVE instruction, and to work toward socially just education. Autoethnography has been a useful method for understanding my experience as a PVE researcher-practitioner. It offers valuable insights into how the PVE-centered course I designed and taught both aligned with and diverged from recommendations in the literature. I found the experience to be paradoxically hopeful and despairing. On the one hand, the benefits of teaching PVE are promising, as they include fostering civic engagement and serving as a protective factor against radicalization. On the other hand, my research points to a number of potential drawbacks that, in my case, appeared to outweigh these benefits. These drawbacks include the potential risks that PVE poses to the students I teach and the negative experience I encountered while simultaneously researching PVE, designing timely and carefully designed PVE curricula, dealing with the emotionally charged content, and teaching those curricula. This resulted in a demanding workload, a heavy emotional and psychological toll, and a decline in my health and morale. These experiences prompted me to rethink and ultimately reconceptualize teaching my stand-alone PVE-centric course in favour of courses that focus primarily on teaching critical reading and critical thinking skills, since critical thinking can be beneficial in PVE and can bolster civic engagement—skills necessary for preventing violence in all forms. Additionally, I have found that balancing content that presents narratives of oppression with content that presents positive counter-narratives to be helpful in building resilience and instilling hope, motivation, and improved well-being
Ranking of AI-Based Criteria in Health Tourism Using Fuzzy SWARA Method
Full text linkHealth tourism, as a dynamic and rapidly growing sector of the tourism industry, plays a fundamental role in strengthening national economies, increasing international interactions and improving the quality of healthcare services. By integrating healthcare, wellness and recreational services, this field has become one of the key drivers for attracting foreign tourists. The emergence of artificial intelligence (AI) as a transformative technology offers unparalleled potential to optimize health tourism services. Using AI in trip planning, improving user experience and predicting the needs of health tourists has gained significant importance. This study aims to identify and rank AI-based criteria in health tourism. By reviewing and analysing previous studies, key criteria in health tourism influenced by AI were identified. Subsequently, these criteria were evaluated and ranked using Fuzzy SWARA method. The ranking results indicate that “healthcare service quality (C11)”, “competence and reputation of physicians (C12)”, “hospital equipment and facilities (C13)”, “political stability and security (C41)” and “access to medical information (C14)” were ranked first to fifth, respectively. These findings highlight the crucial role of AI in enhancing service quality and improving the experience of health tourists. The results of this study can be beneficial for policymakers and stakeholders in the health tourism sector for better planning and attracting more tourists