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Changes to Youth Suicide Rate Trends by Province in Canada (1950-2019) as Indicative of Major Social Structural Shifts
Get PDFThe study of suicide remains predominantly psychocentric and individualistic, often overlooking social dimensions. This thesis presents a sociological analysis of suicide as a crucial complement to individualistic approaches. Male and female suicide rates are analysed for the period spanning 1950 to 2019, by Canadian province, with a particular focus on youth suicide. A descriptive analysis highlights the simultaneous emergence of youth suicide across all provinces as of the 1960s. While rates for both sexes rose in unison, female youth suicide rates continued to rise through to 2019, whereas rates for males generally plateaued at a new ‘normal’. Previously nearly non-existent, youth suicide has since matched or exceeded rates in traditionally higher-risk age groups.
An age, period, and cohort (APC) analysis was subsequently conducted to model temporal trends using the APC-Interaction model. The findings indicate notable estimated cohort effects, with increased suicide risks for males born between 1960-1974 and females born after 1985. These results underscore the need for a sociological perspective in suicidology, demonstrating how social forces shape suicide trends. Without a sociological perspective, suicide is framed as almost exclusively an individual act, overlooking broader socio-historical influences. By mapping youth suicide trends across all Canadian provinces over seven decades, this study addresses a critical gap in the literature
Developing a Droplet Microfluidic Platform for Cultured Meat Production
No full textThe field of cultured meat consists of evolving primary muscle cells into mature muscle tissues through different three-dimensional growth configurations. The aim is to provide an eco-friendlier alternative to traditional animal agriculture due to both environmental and ethical concerns. A major challenge in the cultured meat field is the need for supplementing expensive growth factors into cell culture media, which make up over 90% of the production cost. Studying endogenous growth factor production in these heterogeneous cells is important in determining whether increasing endogenous production through genetic engineering can provide an alternative to media supplementation. In my thesis, I will describe the development of a droplet microfluidic platform for culturing single primary muscle cells in droplets. My platform provides an enhanced growth environment where single cells have improved growth compared to traditional culture conditions. The platform can also be used to analyze the effects of genetic engineering by culturing single cells in different growth factor supplemented medias to determine how it effects their growth. Finally, I also show how endogenous growth factor production in mammalian cells can lead to finding alternatives for growth factor media supplementations
The Preventive Effect of High-Intensity Interval Training in a Mouse Model of Essential Tremor
Get PDFEssential tremor (ET) is the most prevalent movement disorder characterized by involuntary, rhythmic shaking. The effects of exercise have not been widely studied for their potential benefits in ET while this has been growing for other neurodegenerative disorders. We studied a mouse model of ET, which shows tremor after receiving the drug harmaline. The objective of this study was to examine the effects of High-Intensity Interval Training (HIIT) and Resistance Training (RT) on tremor characteristics (power within certain frequencies) in the harmaline mouse model of ET. We employed a repeated measures design with three groups: a HIIT group, an RT group, and a sedentary (SED) control group. Each group received a harmaline injection before and after their respective exercise regimens. Tremor power within two frequency bands (8-14 & 14-29 Hz) was assessed using accelerometry through a smartphone application to measure kinetics, complemented with video analysis for kinematics. We found a statistically significant decrease in the 8-14 Hz band, and increase in the 14-29 Hz band, in the HIIT group compared to pre-protocol measurements, which we did not find in the other groups. We found a change in the BDNF growth factor between groups, potentially mediating an exercise effect on the brain. These results indicate that HIIT shifts tremor predominance in this ET animal model, potentially through mechanisms involving neuroplasticity in tremor networks. The findings support physical exercise as a potential therapeutic/preventive intervention of essential tremor, with translational implications for human studies
Becoming a Nature-Inspired Teacher: Pollination of the Mind Through Nature-Inspired Pedagogies An Autoethnographic Narrative Inquiry
No full textABSTRACT
Becoming a Nature-Inspired Teacher: Pollination of the Mind Through Nature-Inspired Pedagogies
James Watts, Ph.D.
Concordia University, 2025
This autoethnographic narrative inquiry traces my journey toward becoming a nature-inspired educator, using the concepts of becoming, plateaus, and lines of flight from Gilles Deleuze and Félix Guattari to frame my investigations. I draw on the metamorphosis of a caterpillar into a butterfly as a metaphor to encapsulate key life experiences—my plateaus—that have shaped my teaching philosophy. These experiences include my religious upbringing, global travels, engagement with Indigenous epistemologies, analysis of adolescent nature narratives, and a 30-year tenure at the alternative high school I founded. Through autoethnographic narrative inquiry, I describe, analyze, and interpret how these personal and scholarly experiences shaped my evolving role as an educator and led me to question conventional teaching models. The theory I propose, Nature-inspired Pedagogy of Becoming (NIPB), contends that being a nature-inspired educator is not a fixed identity but an ongoing evolution shaped by a series of plateaus and lines of flight. Similarly, I operationalize pedagogy as Deleuze and Guattari treat identity; always in flux, always evolving, always becoming. This process unfolds through dynamic interactions between teachers, students, and the natural environment, where each participant develops unique narratives and engages in their own forms of becoming. I aim to offer fellow educators an alternative pathway beyond traditional, teacher-centred approaches, advocating for a nature-inspired, holistic, and immersive teaching model. By challenging the notion of a fixed teacher identity, this work offers new insights into how educators and students co-evolve through shared experiences in natural environments
Inelastic Light Scattering from Collective Modes in Multiband Superconductors
No full textInelastic light scattering (ILS) is an important experimental technique that has helped characterize superconductors (SCs) and study their collective modes, whose proper accounting may give us hints on the superconducting pairing interactions and mechanisms. However, despite decades of efforts on modelling the fermionic contributions to the ILS response from different SCs, interpretations of experimental data remain loosely qualitative, as the models are applied in the isotropic Fermi-surface limit and usually consider decoupled energy bands, with the inclusion of pairing-interaction effects – essential to account for collective modes – being limited. In fact, most known SCs are multiband, with anisotropic Fermi surfaces having possibly non-trivial topologies. As these effects go mostly unaccounted for in ILS models, the interpretation of experimental data of more complex systems is hindered.
To help improve the interpretation of ILS data from SCs, in this thesis, we develop an encompassing theoretical framework to compute the non-resonant ILS response, in the presence of phase collective modes in any polarization channel, from fermionic degrees of freedom in two-dimensional multiband spin-singlet SCs with possibly non-trivial Fermi-surface topologies. Using a diagrammatic approach, we renormalize the ILS vertex with multiband pairing interactions and write a practical set of formulæ using the pairing-interaction eigenbasis. The framework allows us to compute the response from different systems more easily, whilst accounting for material-specific properties self-consistently.
With our framework, we analyse the line spectra of multiband systems having different ground states and Fermi surfaces, with our analyses showing that the ILS response is almost always composed of collective-mode induced features. We also demonstrate that the response from multiband SCs is sensitive to the sign of interactions, which leads to new many-body-induced physics selection rules. Moreover, we identify the conditions for collective modes to become ILS-active with distinct spectral weights. Furthermore, in addition to modelling experimental data, we explore the effects of non-trivial Fermi-surface topologies and of anisotropic interactions in the ILS line spectrum in different collective-mode scenarios, establishing direct relations between spectral features and properties of SCs
Emulation of Faults in Permanent Magnet Synchronous Machines
No full textPermanent magnet synchronous machines (PMSMs) are used in electric vehicles because they are compact, highly efficient, and have high dynamic performance. High reliability and fault tolerance are required for these applications. When exposed to mechanical stress, moisture, and high temperatures, electric machines can suffer from various faults. One of the more common and primarily catastrophic faults in PMSMs is the inter-turn short circuit fault in a stator coil. Hence, PMSM behavior should be studied in the event of this fault.
It is essential to have a machine model that is accurate enough to study such fault scenarios. An advancement of this idea is to develop a power electronic setup that emulates the PMSM machine fault in real-time. Power hardware-in-the-loop (PHIL) simulation, also called emulation, is the real-time simulation of a device under test (DUT) involving rated voltage, current, and power levels of the DUT. For the emulation of an electric machine, a power electronic converter is used to mimic the machine in real-time for all static and dynamic conditions. An emulator can replace a physical machine during the testing and development stages of the drive inverter. PHIL emulation provides a safer and more flexible development environment. It gives the advantage of testing the drive inverter before the machine is prototyped. In addition, the emulator can simulate various loading conditions. Thus, the drive inverter can be tested at different power levels without a prototype of the machine. Since no mechanical drive train is needed for such a test, an emulator setup can reduce the cost of testing in terms of space, safety enclosures, and maintenance requirements.
The accurate emulation of the machine faults will pave the way for developing robust fault-tolerant control techniques for the inverter under test. In addition, the PHIL emulation helps avoid creating expensive physical damage to a motor when conducting studies regarding fault. This research proposes to do the PHIL emulation of a PMSM with an ITSC fault. The analytical model developed is used in the emulator setup. This research proposes using a proportional-integral resonant (PIR) controller instead of the traditional proportional-integral (PI) controller to increase the accuracy of the current drawn by the PHIL emulator in the event of a fault.
One application of this study is in electric power steering (EPS) motors. An EPS motor provides an assist torque for steering the vehicle based on the torque the driver applies to the steering wheel. A permanent magnet synchronous motor (PMSM) is the best fit for power steering because of the smaller size, lighter weight, and sinusoidal back-emf of a PMSM. These characteristics make PMSM suitable for reasonable torque control. However, a PMSM can eventually develop a stator inter-turn short circuit (ITSC) fault, which can cause a ripple in the assist torque produced by the EPS motor, thus leading to a possible catastrophe. Power hardware-in-the-loop (PHIL) emulation of this fault can help to replicate the scenario within the laboratory environment by drawing the rated current and rated power in real-time. This research proposes to study the PHIL emulation of the specific case of an EPS motor operating under torque control mode.
Another research area that needs to be focused on is the elimination or reduction of common-mode current when the drive inverter and the emulating converter are connected to a common DC power supply. In a conventional emulator setup, isolated DC power supplies are used for the drive inverter and the emulating converter to prevent circulating common-mode currents. The emulating converter side requires a power supply with bidirectional power flow capability. Generally, an active front-end converter (AFEC) is used to realize this power source. Suppose the emulating converter and the drive inverter are connected to a common DC power supply, the power circulates within these converters, and the DC supply can be a simple unidirectional power supply that provides for the losses. The complexity in the emulator hardware setup is reduced because the front-end converter and its associated control will not be needed in such a case. However, circulating common-mode currents will distort the emulator current while operating with the same DC bus. The elimination or reduction of this common mode current is one of the objectives of this thesi
Physics-informed neural network for beam deflection modeling in the context of structural health monitoring
No full textThe need for real-time, accurate monitoring of infrastructure is becoming increasingly urgent as aging structures and increasing loads put more stress on critical components. Traditional methods for structural health monitoring (SHM), which are either purely data-driven or solely based on physics, often face shortcomings. To address the limitations, this thesis investigates the use of Physics-Informed Neural Networks (PINNs), which combines data-driven approaches with physics-based principles. As part of a larger study, this thesis explores the practical application and effectiveness of PINNs in modeling the deflection of reinforced concrete (RC) beams in real world scenarios. The primary objective of the study is to evaluate PINNs performance considering complexities of a real RC beams in order to have a better insight regarding PINNs application in the real-world structural health monitoring and management as a modeling tool. To address this question, the research begins with the development and implementation of a PINN-based application for elastic beam models using numerical investigations with Finite Element Method (FEM) data for training. This initial phase establishes a foundation for integrating PINNs into structural modeling by addressing point loads and beam behavior under various conditions. Following this, the study extends the application to RC beams, incorporating practical considerations such as crack formation, reinforcement effects, and nonlinear load responses. Key contributions include the development of a practical PINN-based tool for structural deflection prediction, handling of real-world peculiarities in beam behavior, and the integration of point load effects. The thesis also investigates the impact of different loss weighting systems on model performance, providing insights into role of data and physics in training PINN in different stages of loading. Comparative analyses with FEM models highlight the strengths and limitations of PINNs, demonstrating their potential to complement traditional methods by capturing complex real-world phenomena such as crack, reinforcement interface, and nonlinearity.
Overall, the research advances the understanding of PINNs in structural engineering, showing their potential to enhance modeling accuracy and reliability in practical applications. The findings offer a valuable framework for future research and applications in structural health monitoring, paving the way for more effective and adaptable tools in the field
Artificial Intelligence versus Human Intelligence: Making Promotion Decisions
No full textAs artificial intelligence (AI) algorithms becomes increasingly embedded in workplace decision-making, its use in promotion decisions remains both promising and contested. While AI algorithms have been widely adopted in hiring, promotion decisions present distinct challenges: they are subjective, relational, and socially embedded. This thesis investigates how trust in AI algorithms unfolds in the context of promotion decisions by examining the perceptions of professionals across three roles: developers, users, and consultants. Through a mixed-methods approach combining qualitative interviews and quantitative surveys, the study explores both how AI algorithms are used and perceived in promotion contexts and what factors shape trust in its recommendations. Drawing on the Integrative Model of Organizational Trust, the study identifies ability, integrity, and transparency as antecedents of trust, while highlighting the influence of role-specific experience. The findings indicate that no single predictor of trust was sufficient to sustain trust in AI algorithms when they contradicted human judgment. Instead, participants preferred collaborative decision-making, where AI algorithms augment rather than replace human insight. Theoretical contributions include situating trust in AI algorithms within the underexplored context of promotion and offering a multi-role perspective. Practical implications point to the need for participative design, targeted training, and trust-preserving collaboration between human decision-makers and AI algorithms. Overall, this thesis provides conceptual and actionable insight into how organizations can responsibly integrate AI algorithms into promotion practices while maintaining trust
Quantitative Easing in an Open Economy Model with Financial Intermediaries
No full textThis paper models quantitative easing in a small open economy with unrestricted bond markets in both countries to examine its impact on long-term interest rates and stimulating production growth. Bonds with short-term and long-term maturities, as well as domestic and foreign bonds, are modeled as imperfect substitutes reflecting investor preferences to hold a diversified portfolio. Following a quantitative easing shock that lowers the market supply of domestic long-term bonds, this bond segmentation causes long-term domestic bond yields
to decrease, leading to higher domestic production. This model includes banks that receive deposits from households and invest them in domestic and foreign bonds. Households earn a rate of return that is a weighted average of the returns earned by banks. In contrast to models
that include households holding bonds directly, models with banks allow traditional monetary policies of lowering short-term interest rates to continue to be impactful when short-term interest rates are zero. The results show that quantitative easing leads to lower long-term interest rates, increased domestic production, and higher inflation
Energy-Aware Optimization and Machine Learning Frameworks for Sustainable Cognitive Networks
No full textThis thesis presents a unified framework for enabling sustainable cognitive networks through the integration of machine learning and energy-aware optimization. As networks evolve toward 5G and beyond, growing demands in performance, energy efficiency, and autonomous management call for intelligent, scalable solutions. This work addresses these challenges through a holistic approach spanning four layers: data generation, infrastructure optimization, distributed learning, and predictive control.
To enable AI-driven intelligence, techniques are developed at the data layer that leverage data-centric AI to generate high-quality synthetic 5G packet-level data and refactor real-world urban data into machine learning-ready, 5G-like flow-level traces. These methods mitigate data scarcity and heterogeneity, providing realistic and diverse data essential for robust network learning systems.
In the infrastructure layer, the placement of disaggregated 5G components, including Distributed Units, Centralized Units, and User Plane Functions, is formulated as a large-scale optimization problem. The proposed decomposition-based and heuristic approaches improve energy efficiency by up to 14\% while maintaining Quality of Service and responsiveness. Experiments in simulated 5G environments highlight the limitations of traditional peak-time-based planning.
For distributed learning, AFSL (Asynchronous Federated-Split Learning) and its energy-aware variant, AFSL+, are proposed to address client heterogeneity, achieving convergence time reductions of up to 13\%. These frameworks selectively engage participants, reducing energy consumption by up to 55\% without sacrificing accuracy and stability. To minimize communication overhead, Ada-AFSL is introduced, a dynamic compression technique that adapts to real-time bandwidth fluctuations. In realistic 5G and IoT scenarios, it achieves up to 82\% data reduction while preserving performance and enhancing generalization.
Lastly, ST-SplitGNN and ST-SplitGNN+ are developed as spatio-temporal split learning models for accurate traffic prediction and uncertainty-aware resource allocation. Learning process is partitioned between local and centralized components: at the edge, temporal encoders capture node-specific traffic patterns, while a centralized Graph Neural Network with a learnable adjacency matrix models time-dependent inter-node dependencies. These models enable proactive scaling policies that align reliability with sustainability goals.
Together, these contributions form a cohesive architecture for sustainable cognitive networks. By uniting optimization with machine learning, this thesis supports the development of intelligent, efficient, and environmentally responsible communication systems for the 5G era and beyond