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Robust Haptics with Nonlinear Impedance Matching for Robot-assisted Laparoscopic Surgery
Get PDFThe integration of haptic feedback into robot-assisted minimally invasive surgery (RAMIS) has been constrained by challenges in accurately rendering forces while maintaining system stability and safety. Addressing these limitations, this research introduces the Nonlinear Impedance Matching Approach (NIMA), a novel force-rendering method designed to accurately model complex tool-tissue interactions. Building on the Impedance Matching Approach (IMA), NIMA incorporates nonlinear dynamics to enhance the precision and reliability of force feedback systems. The experimental results demonstrate that NIMA achieves a mean absolute error (MAE) of 0.01 ± 0.02 N, representing a 95% reduction in error compared to IMA. Notably, NIMA eliminates haptic ”kickback” by ensuring that no residual force is applied to the user’s hand when releasing the haptic device, significantly improving both user comfort and patient safety. Furthermore, its ability to account for the nonlinearities of tool-tissue interactions allows for high fidelity, responsiveness, and precision across diverse surgical conditions. This research advances the development of robust, high-performance haptic systems, offering a transformative solution to the challenges of force rendering in teleoperated surgical robotics. By providing a realistic and reliable interface for robotic-assisted surgical procedures, NIMA has the potential to enhance surgical precision, optimize patient outcomes, and set new standards for haptic feedback in RAMIS
Affordances of The Digital Academic Article
Get PDFAcademic articles in their digital form are skeuomorphic: a linear translation from paper to screen. This linear translation makes engagement with digital text awkward and also creates a boundary between the form of the article and the form of the digital reader. Attempts to solve this problem retain the PDF form and enhance it by revising either the ISO standard, or readers that can read a certain type of PDF formats with plugins.
Based on the assumption that the existing forms and tools for academic reading and publication are saturated, this research re-imagines and speculates on a \textit{digital-first form of the academic article} that breaks the constraints imposed by PDFs. With a Research through Design (RtD) approach -- iterative prototyping and design journaling, this project calls for a paradigm shift: from Article as Files to Article as Software. It re-imagines the form of the digital academic article as a digital-first artifact, and explores the design space of the digital-first academic article (DFA). Instead of setting it up for printing and archival, the goal is to establish a unique identity of the digital research article that affords enhanced reading and learning from academic text.
Prototypes can be found at https://prose.shahrom.dev. It is assumed that this thesis will be read on a computer, so you can explore the live version for yourself
Where Geragogy Meets Dementia Care: Mapping Connections and Disconnections in Teaching Artmaking with Older Adults Living with Dementia
Get PDFThe purpose of this research is to investigate how practicing art educators facilitate artmaking in community programming for adults living with dementia. This project addresses intersections between the fields of gerontology, art education, arts-based pedagogies, geragogy, as well as dementia and Alzheimer’s disease research and care. Through interviews with art educators, I investigate experiences and approaches to adult art education for those living with memory loss using interdisciplinary perspectives. This study highlights various approaches to teaching and learning in recreation education programming within clinical and community settings. This study has two core objectives: 1) Examine the structures and approaches used in art education for individuals with memory loss through interviews with art educators; 2) Map connections and gaps between art educators’ experiences and existing literature on teaching, learning, and artmaking with individuals living with dementia
Data-Driven Methodology for Model Order Reduction to Predict and Manage Building Energy Flexibility in Smart Grids
Get PDFThe evolving energy landscape, driven by rising demand, electrification, and renewable energy integration, necessitates a shift from traditional “follow-the-load” model to demand-side management. This transition requires accurate prediction of building energy demand, effective demand response participation, and quantification of energy flexibility.
This thesis develops a methodology for predicting and optimizing building thermal energy demand using data from smart thermostats and monitoring infrastructures. Multi-zone buildings and schedule-based operations are modelled using resistance-capacitance (RC) thermal networks. An automated model order reduction approach identifies dominant thermal zones in multi-zone buildings, while control-oriented RC archetypes capture key dynamics in schedule-based operations. Calibration follows a Model Predictive Control Relevant Identification (MRI) process, ensuring models accurately predict thermal dynamics up to 24 hours ahead.
Weather variability is managed through clustering techniques that identify representative days, reducing computational complexity while enabling scenario-driven analysis. This approach bridges the gap between operational and design studies by integrating energy flexibility considerations early in building and community planning.
A distributed economic Model Predictive Control (e-MPC) framework optimizes thermal load management while maintaining occupant comfort and system constraints. It supports applications at both single-building and community scales, such as virtual power plants. Performance is assessed using energy flexibility Key Performance Indicators (efKPIs) against a reference scenario.
The methodology is validated through three case studies: (1) Residential buildings: 30 detached homes equipped with smart thermostats (data from Hydro-Québec); (2) Institutional building: The Varennes Net-Zero Energy Library, Canada’s first net-zero energy institutional building; (3) Community-scale system: A simulated hybrid photovoltaic-battery microgrid in Varennes serving residential and institutional buildings.
Findings highlight how varying building participation in demand response influences aggregated demand profiles, utility metrics (load shifting, peak shaving), and the sizing of grid-supportive technologies. At the single-building level, insights are provided for optimizing thermal load management across convective, radiant, and mixed heating systems. By integrating data-driven modelling, advanced control, and scalable design, this thesis provides actionable solutions for energy efficiency, flexibility, and resilience, supporting a sustainable energy transition
Lost in Transition: Exploring the Effects of a Sports Team's Rebranding on its Supporters and their Relationship with the Club
Get PDFChange never stops! Over the past two decades, the number of sports teams that have changed their logo and name has multiplied. This phenomenon, known as rebranding, is not always successful, with one out of two rebrandings generating negative reactions from fans. In this research, I investigate how fans may perceive rebranding as a betrayal and the subsequent impact on their relationship with the club. Previous research has already established the effects
of rebranding on fan identification and purchase behaviour, but the exploration of their relational trajectories remains understudied. To bridge this gap, I drew on the case of the Montreal Impact's rebranding in 2021. Through in-depth interviews with club supporters, podcasts, and forums, my findings revealed that the disruption of the club's identity and lack of fan involvement in the rebranding process were perceived as moral transgressions by fans. As such, their relationships with the club were divided into three possible trajectories: some fans accepted the rebranding and adapted their self-concept, others fought for the return of the previous identity, and others terminated their relationship with the club, going through a process of loss accommodation. This paper contributes to the consumer-brand relationship literature by demonstrating that a rebranding can act as a critical juncture in the relationship between fans and clubs. For practitioners, this research provides insights into managing a rebranding process, emphasizing the importance of transparent communication, incorporating fan opinion, and encouraging evolutionary image changes to maximize retention
Orbits & Animals
No full textOrbits & Animals is a poetic bildungsroman exploring a longing for genuine connection, gendered perspectives on mental illness, and the ways in which girlhood ruminates throughout the course of a woman’s early adulthood. The primary component of the collection is poems in which the sustained speaker addresses various “you” figures and considers the ways in which these relationships have affected her. While the reader is not made aware of who the different “you” figures are specifically, the context of each poem indicates the nature of the relationship. Sections from a crown of sonnets in which the speaker addresses the “you” figure meant to be her mother are interspersed throughout the collection, guiding the reader chronologically and emphasizing the ways in which the speaker’s earliest relationships have influenced her later relationships. Additionally, the collection explores the speaker’s relationship with depictions of mental illness often deemed “feminine,” such as unstable moods, a distorted self-image, and an intense fear of abandonment. Finally, the speaker often refers to a sense of disembodiment she feels in comparison to other people, which she emphasizes through relating to various animals. The project is inspired by confessional poets such as Sylvia Plath and Richard Siken, as well as the work of contemporary songwriters such as Phoebe Bridgers and Adrianne Lenker, all of whom explore the relationships they strive to have but feel are just out of their grasp
Beyond European Extractive Modernism: Appropriations of West- and Central-African Cultural Belongings in Paul-Émile Borduas' 1942 Gouaches
Get PDFThis thesis argues that the celebrated Québécois modernist Paul-Émile Borduas (1905-1960) appropriated West- and Central-African cultural belongings in five of his 1942 gouaches. Through popular literature including the surrealist magazine Minotaure, and exhibitions spaces such as the Musée d'ethnographie du Trocadéro and the Paul Guillaume Gallery in Paris, and the Exposition missionnaire and McGill Museums in Montréal, Borduas encountered the African cultural belongings he would eventually appropriate. The artist likely sought out these points of contact as a way to feed his interest in global Indigenous cultures. Using a post- and anti-colonial approach, this thesis highlights how influential literary and museological colonial channels were on the artist. The extractive nature of these channels places Borduas within a tradition of "extractive Modernism," pervasive in early twentieth-century European Modernism. While he was made aware of African cultural belongings in Paris, Borduas also took an interest in European art and likely adopted extractive Modernism as a way to assert his independence and originality in war time Québec. As well as engaging with global colonialisms, Borduas' appropriations can be placed in dialogue with local colonial legacies in Montréal. This thesis concludes that some of Borduas' 1942 gouaches are doubly transnational: first as they appropriate West- and Central African cultural belongings, and second as they inherit from European extractive Modernism. By placing Borduas at the intersection of these two themes, this thesis places Québec art history within global dynamics of colonialism
Assessment of Urban Microclimate and Its Impacts on Building, Community, and Urban Energy Performance
Get PDFWith global efforts aimed at reaching carbon neutrality by 2050, there is an increased focus on improving the energy efficiency of buildings. The interactions between constructions and their local microclimate significantly influence the built environment and building energy performance. This thesis examines the urban microclimate and its impact on building energy consumption from the individual building level to entire urban areas.
Building energy models (BEMs) are essential for understanding building energy consumption, forecasting building energy, and evaluating energy-saving measures. Meanwhile Urban Building Energy Model (UBEM) is an analytical tool for modeling buildings on city levels and evaluating scenarios for an energy-efficient built environment. However, building planners commonly overestimate cooling loads by relying on Typical Meteorological Year (TMY) data in BEM/UBEM simulations, neglecting local microclimate variations and the neighborhood effects of surrounding buildings. This research developed an integrated platform by coupling BEM/UBEM with an urban microclimate model, allowing local aerodynamic data to be exchanged between the two models at each time step.
Since these BEM/UBEM models usually come with a deal of computation cost and prior knowledge to work with. In recent years, Machine Learning (ML) techniques in specific terms have been proposed for predicting building energy consumption. A synthetic dataset from physics-based simulations can serve as a training and testing data source for the ML model during the design phase. Weather clustering techniques are implemented to enhance computational efficiency and feasibility avoiding the high computational costs of day-by-day simulations. By employing weather clustering to select representative days, the approach reduces database size for training ML-based building prediction models.
The study begins with a comprehensive review of the latest methods for incorporating urban microclimate data into urban building energy models, addressing both methodological approaches and practical issues. Subsequently, the research evaluates the effects of urban microclimate on building energy performance, considering both individual buildings and urban-scale contexts. To address the computational cost associated with BEM/UBEM, an ML-based hourly building energy prediction model was developed, leveraging weather clustering techniques. The conclusion summarizes the key contributions of this thesis and offers recommendations for future research directions
Vision-Based Construction Activity Recognition Using Supervised and Self-Supervised Methods
No full textTracking and monitoring the activities of construction entities, such as workers and equipment, on construction sites is crucial for assessing their performance and productivity. However, manual monitoring is demanding, time-consuming, and susceptible to inaccuracies. To this end, numerous automated computer vision (CV)-based methods have been developed to detect construction entities and classify their activities. Recently, single-stage activity recognition methods that simultaneously analyze spatial and temporal information have been proposed in the construction domain. While these methods have demonstrated improved performance over multi-stage approaches and can alleviate their limitations, they still suffer from a significant drawback: relatively low per-frame activity recognition and localization accuracy. This limitation necessitates additional post-processing to link the per-frame detection results and construct the corresponding action tubes, which in turn reduces the real-time applicability of these methods for simultaneous detection of the activities of multiple construction entities.
Another major disadvantage of the current state-of-the-art construction equipment activity recognition methods is their reliance on supervised learning. This approach requires large, labeled datasets for each type of equipment and activity, which can be costly and time-consuming to create. Particularly, for the task of activity recognition and localization that requires frame-level annotations. To address this challenge, many self-supervised deep learning methods have been proposed in the CV domain, which exploit the abundant unlabeled data to alleviate the data annotation cost by creating labels from the input data itself. However, the assumption of availability of abundant unlabeled data limits the applicability of the current self-supervised methods in the construction domain in which the number of videos for various activities of different construction equipment is also limited.
To overcome the aforementioned limitations, parallel frameworks are proposed in this research for developing a general construction entity activity recognition method. As such, the main objectives of this research are: (1) to tackle the data annotation requirements of existing supervised methods by utilizing Self-Supervised Learning (SSL) to leverage the information available in the unlabeled construction site videos; (2) to develop and apply an SSL method specifically tailored to limited-data scenarios in the construction domain, in contrast to the SSL methods developed in the CV domain that depend on the availability of abundant unlabeled data; (3) to develop a supervised, single-stage construction equipment activity recognition and localization method with high per-frame performance, thus, removing the need for a post-processing step; (4) to improve the activity recognition and localization performance for complex and fast-paced activities (e.g., excavator swinging) by incorporating the dynamic information present in the temporal gradient data modality of input videos, combined with knowledge distillation, to improve per-frame performance without increasing inference computation; (5) to enhance the multi-scale and generalization performance of the supervised method developed in the third objective through the development of a custom pyramid architecture and a novel anchor-free localization method; and (6) to provide accurate, real-time activity recognition and localization information of a diverse set of construction entities with significant size differences.
It is found that the developed SSL method results in an improvement of 10.1% over its supervised counterpart when using only 5% of the labels in the dataset. As such, the results clearly indicate the potential of the proposed SSL method for improving the model performance at no additional data annotation cost. Furthermore, the developed supervised single-stage method achieved per-frame excavator activity recognition and localization accuracies of 93.6% and 79.8%, respectively, thus eliminating the need for post-processing. This method was further enhanced to provide accurate, consistent, and real-time performance for simultaneous activity recognition and localization of construction entities with significant scale variations. Notably, it attained per-frame activity recognition accuracy of 94.67% and localization accuracy of 87.35% for simultaneous recognition of excavators and workers’ activities, despite their substantial size differences. Consequently, the obtained results demonstrate the effectiveness and applicability of the developed method at providing real-time monitoring information
Trauma-Responsive Integrative Art and DBT (TRIAD) as an Art Therapy Treatment Model for Adolescents with Complex Posttraumatic Stress Disorder (CPTSD): A Theoretical Intervention Research
Get PDFThis research paper explores the integration of Dialectical Behaviour Therapy (DBT) and art therapy in a trauma-informed approach as a theoretical intervention for adolescents (ages 13–17) with Complex Post-Traumatic Stress Disorder (CPTSD). Given the efficacy of DBT and art therapy as clinical psychiatric treatments on their own, as well as their shared therapeutic goals—emphasizing emotional regulation, distress tolerance, and self-exploration—this research proposes a DBT-informed art therapy model that may provide a comprehensive and multimodal approach for adolescents with CPTSD. The proposed intervention program incorporates Judith Black’s (2004) three phases of treatment for individuals with PTSD into a DBT-informed art therapy framework, offering a structured and phased approach to trauma recovery. This qualitative theoretical intervention research follows Fraser and Galinsky’s (2010) intervention research model, focusing on the initial stages: developing problem and program theories and designing program materials. By synthesizing existing literature and identifying gaps at the intersection of DBT, art therapy, CPTSD, and adolescent mental health, this study aims to lay the groundwork for a structured intervention program. The proposed framework seeks to address chronic stressors, trauma-related dysregulation, and maladaptive coping mechanisms in this vulnerable population, ultimately contributing to future clinical applications and research