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Exploring Women’s Experiences in Far-Right Circumstances – through Theological Assessments and Test Case: Giorgia Meloni
Full text linkThis thesis explores women’s experiences in far-right societies using multiple theological assessments and prime minister of Italy, Giorgia Meloni, as the main test case. Elected in 2022 but having held right-wing leadership positions for most of her life, Meloni is opposed to issues like abortion, same-sex marriage, and surrogacy, and aims to limit these rights as well as the rights of women and other minorities in Italy with the goal of reverting back to Italy’s traditional, Christian lifestyle. This thesis delves into issues such as feminism, fascism, and the far-right – situating and defining Meloni’s beliefs and political actions and detailing how they are impacting Italian women’s experiences. This research determines if Meloni is adhering to some type of feminism, if her government can be considered fascist, and it pinpoints how much of her politics is taking place online, as well as certain repercussions that come with this. Using a comparative methodological approach, this thesis compares Meloni and her party to other female right-wing politicians in Europe, as well as other right-wing politicians in North America, at present. This research draws on political theology since religion has made its way into her politics. Thus, it is essential to perform theological assessments on Meloni to determine her method as well as if her views are rooted in accurate theological ideas. Here, contextual theology, Lonergan’s notion of the human good, and feminist theological approaches are vital. Through these theological assessments, this thesis concludes that Meloni is afraid for her country’s cultural identity and believes she is doing good by women and minorities but is allowing for oppression in Italy to continue
Efficient Mapping and Navigation System for Weed Removal Robot in Confined Garden Spaces
Full text linkAutonomous navigation and mapping technologies are reshaping how robots
interact with their surroundings, enabling a wide range of applications. This the-
sis introduces an autonomous mapping and navigation system for a mobile robot
tailored for weed control in confined, outdoor garden environments. Unlike indoor
robots that often rely on joystick-based manual mapping, the proposed system is
fully automated, delivering a seamless, user-friendly setup experience optimized
for backyard use. The solution leverages Google Cartographer for real-time
SLAM and AMCL for adaptive localization. To optimize exploration coverage,
the robot uses a combination of random exploration for initial mapping and
structured exploration to target unexplored areas effectively. The integrated
A* algorithm ensures efficient path planning and reliable obstacle avoidance
throughout navigation. Extensive simulations and real-world testing demonstrate
the robot’s ability to autonomously map and navigate complex backyard lay-
outs with minimal human intervention. The system shows resilience to dynamic
obstacles, sensor limitations, and uneven terrain, confirming its robustness and
practical utility. A significant contribution of this research is the development of
a fully autonomous, modular navigation framework that removes the need
for manual setup while ensuring high-accuracy mapping. By simplifying navi-
gation in small-scale, unstructured outdoor environments, this work provides a
functional and scalable solution for backyard maintenance and extends the ap-
plicability of autonomous robotics beyond controlled indoor settings. This thesis
highlights how integrating SLAM, adaptive exploration, and planning can provide
effective autonomy for lawn care, contributing to innovation in outdoor robotic
systems
Automatic Handwriting Analysis for Classifying Multi-Label Personality Traits using Transformer OCR
Full text linkHandwriting analysis, or graphology, studies an individual’s psychological traits through handwriting patterns and features. It is used in forensic science, criminology, and disease diagnosis.
Previous studies have evaluated the correlation between psychological questionnaires and manual handwriting analysis, but results were inconsistent due to its limitations and human error. This research addresses these challenges by developing an automated handwriting analysis system using deep learning to predict multi-label personality traits based on the Big Five Factor Model (BFFM).
The proposed model is built on the Transformer OCR (TrOCR) architecture, pre-trained on diverse datasets, including handwritten texts like IAM. In this study, the text generation function is replaced with a classification approach to predict levels (Low, Average, High) of BFFM traits from handwriting samples. The model uses Focal Loss to handle class imbalance and Binary Cross-Entropy with Logits for accurate classification.
The dataset includes 873 French and 181 English handwriting samples from CENPARMI, originally labeled for Extraversion and Conscientiousness. It has been expanded to cover all five BFFM traits: Extraversion, Neuroticism, Agreeableness, Conscientiousness, and Openness to Experience, totaling 1,054 samples. Each sample is segmented into individual lines to improve generalization.
The model's performance is compared with ResNet50 and Vision Transformers (ViT Base 16 - 224 and 384). Results show that TrOCR outperforms them in accuracy and overall performance. For two personality traits, it achieves 90.05% accuracy, AUROC of 0.97, and F-Score of 89%. For all five traits, it reaches 89.01% accuracy, AUROC of 0.95, and F-Score of 87%. Extraversion shows the weakest performance (AUROC of 91), while Agreeableness performs best (AUROC of 97). These results highlight the model's effectiveness in classifying BFFM traits despite class imbalance
In Support of Sustainability: Teaching Future Circus Artists in Québec
Full text linkThis qualitative-interpretative research presents the experiences and observations of thirteen circus artists around the concept of professional sustainability, as they have come to understand it in their careers. All thirteen had worked with me as their teacher in circus discipline classes during their post-secondary education at the École nationale de cirque (ENC). They graduated from the school’s three-year professionalizing program between the years of 2011 and 2021. In semi-directed interviews, they spoke of their personal understandings of sustainability following years of professional experiences. They related their experiences of autonomy support within the learning environment we shared in classes. The observations and memories of these circus artists, taken in dialogue with my reflexive analysis of own teaching behaviors, demonstrate an alignment between balanced career longevity and self-determination theory (Ryan & Deci, 2000). The professionalizing education of circus artists today as performers, creators and athletes demands preparation not only for immediate employment, but also for long-term health and artistic growth. Circus artists creatively adapt to employment instability, variable working demands-conditions, injury and repetition. Autonomy-supportive physical and creative learning environments within a high-performance professionalizing circus education can facilitate the development of intrinsic motivation, which will allow them to persist in a career in circus arts.
Keywords: Circus education, autonomy support, career sustainability, Québec circus, performing arts pedagog
Low-Power Class-D Amplifier for Industrial Applications
Full text linkThis dissertation investigates the design and development of fully differential switching (class-D) amplifiers optimized for high efficiency, linearity, and compact integration, tailored to low-power applications such as industrial servo valves, hall effect sensors, and low-power actuators. These loads, commonly employed in automotive and other critical power systems, require differential sine wave inputs at frequencies ranging from several kilohertz to 10 kHz. Traditional linear amplifiers (Class A, B, and AB) are constrained by low efficiency and significant thermal management requirements, while switching amplifiers, despite their inherent efficiency advantages, pose challenges in mitigating nonlinearities and distortions.
The first major contribution is the development of a low-power Selective Harmonic Elimination Pulse-Width Modulation (SHEPWM)-based full-bridge inverter, featuring a novel FPGA hardware implementation. Unlike conventional SHEPWM systems focused on high-power, fixed-frequency applications (50 Hz–60 Hz), this work extends SHEPWM to low-power systems operating at high fundamental output frequencies (4 kHz–10 kHz). A unique FPGA-based architecture enables real-time configurability of output amplitude and frequency, offering flexibility without excessive computational or storage demands. Experimental results demonstrate harmonic elimination up to the 34th order, achieving total harmonic distortion (THD) below 5.1% and efficiency improvements of up to 17.3% compared to natural PWM (NPWM). By integrating this design into a compact system-in-package (SiP) utilizing Gallium-Nitride (GaN) power transistors, the inverter minimizes the printed circuit board (PCB) footprint compared to conventional discrete implementations. This integration offers a robust and versatile solution for next-generation low-power industry applications.
The second contribution is the design and analysis of a Double Integral Sliding Mode Control (DISMC)-based class-D amplifier. Theoretical work forms the foundation of this research, involving a rigorous analysis of reaching and stability conditions to derive optimal controller gains. The proposed controller employs a double-loop strategy that uses the integrals of inductor current and output voltage tracking errors to ensure robust tracking and stability under varying operating conditions. The theoretical findings are validated through extensive simulation and experimental studies, demonstrating the DISMC's superior disturbance rejection, enhanced transient response, and reliability compared to conventional proportional-integral (PI) controllers.
By combining innovative control techniques such as SHEPWM and DISMC with compact and efficient hardware designs, this research advances the state-of-the-art in switching amplifier technology. The outcomes offer practical solutions for compact, high-performance systems, addressing critical requirements in modern industrial applications while paving the way for future advancements in power electronics
Enhancing CityGML with the Built Environment End-users’ Inputs
No full textEnhancing CityGML with the Built Environment End-users’ Inputs
Farzaneh Zarei, Ph.D.
Concordia University, 2025
Digitalization of infrastructure (also known as smart infrastructure or ‘smart city’) is based upon capturing and analyzing urban data and providing decision-makers with real-time analytics and insights. However, since some features of the built environment, such as citizens’ comfort, cannot be solely measured by physical sensors, there is a need for alternative tools and methods to evaluate such aspects and record the results. This exposes a key gap in current digital infrastructure models: the lack of integration between physical infrastructure data and the subjective experiences of service users.
To address this gap, this dissertation explores the following research questions:
1. How can end-user perspectives be systematically integrated into 3D city models to better represent the performance of urban infrastructure?
2. What kind of data schema and semantic structure are required to link social, technical, and spatial data in a standardized, extensible manner?
3. Can such integration support practical, socially-aware decision-making in urban planning?
With these questions in mind, the dissertation proposes a new Application Domain Extension (ADE) —the Social ADE — that enables the integration of urban data streams from both internal (e.g., decision-makers) and external (e.g., infrastructure users) stakeholders into 3D city models. The Social ADE is designed to be universal and applicable across various domains of the built environment. However, this dissertation focuses on two transportation-related case studies to validate the approach, primarily due to the availability of relevant, high-quality data.
The first case study examines acoustic comfort at bus stops by integrating static (geometrical and spatial), dynamic (noise levels via mobile phones), and subjective (survey-based comfort ratings) data. The second case study presents a GIS-based method for prioritizing pothole repairs by combining technical road condition data with citizen-reported concerns to uncover latent patterns in past decision-making.
The results demonstrate that the Social ADE enables meaningful linkage between the built environment and user experiences, supporting the generation of socially-informed, human-centered insights. This research contributes a novel data integration framework that enhances the capacity of city digital twins to support inclusive and adaptive urban infrastructure planning, especially in the transportation sector
Semantic Fusion of Health Data: Implementing a Federated Virtualized Knowledge Graph Framework Leveraging Ontop System
Full text linkData integration (DI) and semantic interoperability (SI) are critical in healthcare, enabling seamless, patient-centric data sharing across systems to meet the demand for instant, unambiguous access to health information. Federated information systems (FIS) highlight auspicious issues for seamless DI and SI stemming from diverse data sources or models. We present a hybrid ontology-based design science research engineering (ODSRE) methodology that combines design science activities with ontology engineering principles to address the above-mentioned issues. The ODSRE constructs a systematic mechanism leveraging the Ontop virtual paradigm to establish a state-of-the-art federated virtual knowledge graph framework (FVKG) embedded virtualized knowledge graph approach to mitigate the aforementioned challenges effectively. The proposed FVKG helps construct a virtualized data federation leveraging the Ontop semantic query engine that effectively resolves data bottlenecks. Using a virtualized technique, the FVKG helps to reduce data migration, ensures low latency and dynamic freshness, and facilitates real-time access while upholding integrity and coherence throughout the federation system. As a result, we suggest a customized framework for constructing ontological monolithic semantic artifacts, especially in FIS. The proposed FVKG incorporates ontology-based data access (OBDA) to build a monolithic virtualized repository that integrates various ontological-driven artifacts and ensures semantic alignments using schema mapping techniques
Chinese Mothers’ Perspectives on Supporting Young Children’s Language Development Through Dialogic Reading and Co-Viewing
No full textIn this study, 16 Chinese parents with children aged 3–6 years were asked to share their perspectives on language practices with a focus on two strategies recommended in the research literature to support expressive and receptive language: dialogic reading (DR) and co-viewing and conversing about television programs or videos (CVC). Data was gathered through a parent questionnaire and focus group discussions. While recognizing the benefits of DR and CVC for language and cognitive development, parents expressed concerns about the effects of these practices on their child’s engagement; potentially negative responses by their child to interruptions during book reading or co-viewing television/video; their own linguistic skills, particularly in English, a common second language in China and globally; and the demands of the practices on parents. These factors might prevent parents from implementing DR and CVC at home either in Mandarin, in a nonnative language, or both. We discuss the findings in light of sociocultural factors and recommend ways these language supports can be adjusted to address parental concerns, aiming to improve future practice and research
Characterization of Alkali-Silica Reactivity in Hybrid Alkali-Activated Cementitious Systems
Full text linkHybrid alkali-activated cementitious systems incorporating ordinary Portland cement (OPC), slag and alkaline activators enhance sustainability and performance. Several standardized alkali-silica reactivity (ASR) testing protocols, such as ASTM C1260, are available. The applied temperature is one of the main dominating factors that vary from one testing protocol to another. Hence, this study aims to evaluate the ASR for hybrid systems applying various temperatures and durations, while comparing with OPC and alkali-activated slag systems. The Experimental program was designed using the Taguchi method to explore the effect of three main factors on ASR-induced expansion, namely, slag content, activator dosage, and curing temperatures (60 °C, 80 °C, and 130 °C). Nine mixtures were cast and tested for expansion, compressive strength, UPV, TGA, XRD, and SEM to assess hydration, phase development, and microstructural changes. Results indicate that 60 °C reliably detects ASR-related expansion while minimizing confounding thermal effects observed at higher temperatures (e.g., 130 °C). Samples with 100% slag and 10% activator cured at 60 °C exhibited high compressive strength (49 MPa) and minimal expansion (–0.12%), indicating stable hydration products under this condition. In contrast, samples exposed to 130 °C experienced severe expansion and degradation due to accelerated hydration and the formation of expansive ASR gel. The findings suggest that 60 °C is suitable for evaluating ASR susceptibility in alkali-activated and hybrid systems, providing a more accurate method for assessing their durability without introducing thermal artifacts
Analyzing Public Sentiments on Urban Transportation in Montreal Using GPT-4o
Full text linkThis thesis investigates how people in Montreal feel about transportation by analyzing posts on
X (formerly Twitter) using a Large Language Model (LLM) called GPT-4o. Montreal is a unique
city with French and English speakers, making public opinion mining challenging. However, GPT-
4o can directly process both languages, making the analysis more accurate and efficient.
Unlike traditional methods that often struggle to capture the nuances of language, GPT-4o generates
precise sentiment analysis, helping us understand the emotions behind people’s opinions. this
tool was used to categorize tweets into transportation modes (e.g., bus, metro, and train), specify
aspects (e.g., safety, cost, and punctuality), and overall sentiment (positive, negative, or neutral).
Local terms like ”REM” and ”STM” were included to ensure the AI understood the context. AIgenerated
aspects were then grouped into standardized categories like reliability, cost, safety, and
environmental impact to enhance clarity and consistency.
This approach showcases the flexibility and scalability of LLMs for multilingual public opinion
mining. The study revealed significant differences in public sentiment across transportation modes
and aspects, such as safety concerns in cycling and punctuality issues for public transit. These insights
are valuable for transportation planners and policymakers seeking to improve urban mobility.
Future research could explore other public opinion sources or use this technology for real-time
sentiment tracking to aid urban infrastructure planning