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Corporate language in social media: A corpus-based linguistic case study of target group-oriented corporate communication on Instagram and LinkedIn
No full textThis thesis investigates how corporate language can be used to address different target groups in social media communication. Using the engineering company FC-Gruppe as a case study, it investigates how linguistic strategies on Instagram and LinkedIn can be tailored to audiences such as employees, potential trainees and students, experienced professionals, and business partners. A corpus of company posts was compiled and analyzed according to selected linguistic criteria, including pronoun use, sentence structure, jargon, emotionality, gender-inclusive language, and modality. To support the analysis, a Python-based prototype employing the NLP library spaCy was developed for automated text evaluation. The findings show that each target group requires distinct linguistic features and communicative approaches, and that combining several groups within one post is only effective in limited cases. The study contributes a practical framework for aligning corporate language with target group needs and demonstrates how corpus linguistics and NLP can be applied to optimize social media communication
The Mediating Effect of Functional Social Support in the Pathway between Memory and Depressive Symptoms: A Longitudinal Mediation Analysis of the Canadian Longitudinal Study on Aging
No full textBackground: Cognitive function and emotional wellbeing are essential for healthy aging. Cognitive impairment and depressive symptoms can lead to severe morbidity and mortality in aging adults. Strong, positive associations exist between memory impairment – memory is a subdomain of cognition – and depressive symptoms. Evidence also suggests one’s perceived level of functional social support (FSS) may affect the emergence of depressive symptoms in aging adults with memory impairment. However, few studies have explored whether FSS mediates the association between memory and depressive symptoms.
Methods: This research utilized an analytical sample drawn from 21,241 participants between the ages of 45 and 85 years who were enrolled in the Tracking Cohort of the Canadian Longitudinal Study on Aging (CLSA) at baseline. The thesis examined three aims: the association between memory and depressive symptoms across three time points of data (baseline, three-year follow-up, and six-year follow-up), controlling for health, lifestyle, and sociodemographic covariates; the potential mediation effect of FSS on this association; and whether moderated mediation was present by age group and sex.
Results: Overall, memory function was inversely associated with depressive symptoms (β ̂ = -0.08; 95% confidence interval [CI]: -0.11, -0.04). The indirect (mediated) effect of memory on depressive symptoms through FSS was statistically significant, though minimal (β ̂ = -0.02; 95% CI: -0.02, -0.01), and most of the effect was direct (β ̂ = -0.07; 95% CI: -0.10, -0.04). No evidence existed for statistically significant moderated mediation by age group or sex.
Contribution: This novel research suggested that functional social support may mediate the association between memory and depressive symptoms. Further research is required to advise health practitioners who deal with memory-impaired individuals as to whether interventions promoting functional social support (e.g., social prescribing) can help minimize symptoms of depression
Punctuated Ethos: Addressing Trust, Credibility and Expertise in Times of Crisis
No full textTrust, Communication, and Crisis: Rhetorical Lessons from COVID-19
Trust, the earning, sustaining, and loss of it, is at the center of public responses during a health crisis like the COVID-19 pandemic. This dissertation explores how trust functions not merely as a social or institutional ideal, but as a rhetorical construct negotiated through language, ethos, and public discourse. Drawing on rhetorical theories of ethos, from Aristotle’s character-based model to Hyde’s concept of ethos as dwelling, the project introduces the concept of “punctuated ethos” to analyze how rhetorical credibility is constructed, fractured, and recalibrated at key moments of crisis. Through a rhetorical analysis of Canadian responses to COVID-19, grounded in a corpus of local news media coverage, this study investigates how political and public health authorities communicated protective measures such as lockdowns and vaccination campaigns, and how acts of resistance, such as the Trinity Bible Chapel (2020-2021) defiance and the “Freedom Convoy” (2022) protest, contested institutional credibility and reshaped public narratives of trust.
In early 2020, Canadian acceptance of public health measures was initially high. However, prolonged lockdowns, pandemic fatigue, and vaccine controversies fractured public trust, leading to increased polarization and protest. Emerging communication technologies further complicated trust-building by amplifying mis/disinformation and undermining traditional media authority. This dissertation applies a rhetorical approach to health risk communication frameworks (Leiss, 2004; Witte, 1992), alongside theoretical tools such as Huiling Ding’s epidemic rhetoric (2014), Stephen Katz and Carolyn Miller’s rhetorical model of risk communication (1996), and rhetorical analyses of appeals, topoi, and public argumentation (Fahnestock, 1998; Miller, 1989; Perelman, 1982; Sontag, 1978, Bitzer, 1968; Goodnight, 1982; Burke, 1969). These frameworks support an examination of how the public validates expertise (Mehlenbacher, 2022) and how trust becomes rhetorically shaped, disrupted, or re-established in moments of crisis.
Chapter 2 offers a historical context for Canada’s public health communication, from the 1918 Spanish influenza pandemic through SARS (2003) and H1N1 (2009), showing how trust was constructed, destabilized, and unevenly distributed across racialized and marginalized communities. Chapter 3 surveys relevant rhetorical, medical, and communication literatures, framing trust as a contingent rhetorical achievement rather than a stable condition. Chapters 4, 5, and 6 form the core case studies, analyzing pandemic rhetoric, vaccine rhetoric, and protest rhetoric, respectively, each applying grounded theory and rhetorical analysis to trace how communicators used strategies like fear, hope, and ethos to shape audience responses. These chapters also identify the shifting roles of local media as amplifier, skeptic, or translator of public health messages. The final chapter proposes a symbolic formulaic framework to model how emotional appeals, perceived efficacy, and media functions interact rhetorically to either sustain or fracture public trust.
Key findings highlight the importance of localized, community-centered messaging, the strategic use of emotional appeals, and the need for credible, transparent communication. Public health communicators must anticipate rhetorical outcomes by aligning emotional resonance with timing (kairos), community values (topos), and credible ethos. Authorities, professionals and communicators must develop critical literacy practices to prepare for future crises, including audience analysis, myth debunking, and media testing. Policy considerations, such as regulating mis/disinformation and enhancing journalistic integrity, are essential to supporting effective communication frameworks.
This research underscores that rhetoric is not an afterthought in crisis communication, it is the mechanism through which trust is built, challenged, or lost. Grounded in rhetorical theory and applied to contemporary media and health contexts, this dissertation offers actionable strategies for health professionals, communicators, educators, journalists, and policymakers to design resilient, trustworthy communication in times of crisis
An Introductory Undergraduate Experiment on Second Harmonic Generation
No full textThis project is part of a comprehensive revision of the Undergraduate Physics Laboratory Curriculum at the University of Waterloo, supported by the Dean's Undergraduate Teaching Initiative, Waterloo Science Endowment Fund (WatSEF), and the Sinclair Foundation. I have designed an introductory-level experiment on Second Harmonic Generation (SHG). SHG is a nonlinear optical process in which photons interact to produce light at twice their original frequency. The experiment is taught using inquiry-based instruction. Students investigate whether SHG depends on pulse energy, peak power, or laser intensity using a Titanium Sapphire femtosecond laser (850±50nm, 100±50fs pulses) and a Beta Barium Borate (BBO) crystal. The experiment will be implemented in the new Gee-Whiz Lab Course (GWLC), where students will conduct contemporary physics experiments without requiring prior subject mastery. This approach encourages students to revisit these beyond-introductory-level topics throughout their undergraduate education and explore how experimental investigations contribute to progress in physics in ways distinct from theoretical approaches. Preliminary work suggests the experiment is both technically feasible and pedagogically effective, providing a foundation for future introductory-level curriculum development in nonlinear optics and other topics typically reserved for upper-year or graduate study
Looking Back or Looking Ahead: Metamotivational Beliefs About Progress Framing in Goal Pursuit
No full textWe are often told that if we keep our eyes on the prize, we will achieve our goals. However, research on the dynamics of self-regulation has established that whether it helps to focus on the starting line or the finish line depends on how committed people are to their goals: if commitment is strong, focusing on remaining progress (“to-go” information) is more motivating, whereas if commitment is weak, focusing on accumulated progress (“to-date” information) is more motivating. Yet research has not systematically examined whether people recognize and leverage these progress framing strategies based on their commitment strength. Across seven studies (N = 2,792), I applied a metamotivational approach to examine the nature and normative accuracy of people’s beliefs about progress framing and whether these beliefs manifest in or are related to behavioural and self-regulatory outcomes. Studies 1 and 2 found that people’s beliefs about progress framing aligned with normative effects observed in the literature on average, though with substantial variability. Studies 3-5 explored whether beliefs manifest in consequential choices. Study 3 found that people made differential progress framing choices as a function of their own commitment levels for personal goals. However, Studies 4 and 5 failed to replicate this pattern when commitment was experimentally manipulated in lab contexts or when making recommendations for others. Studies 6 and 7 investigated links between beliefs and outcomes, finding no relationship with goal progress (Studies 6 and 7) or life satisfaction (Study 6), though more normatively accurate beliefs were associated with experiencing less distress and difficulty during goal pursuit (Study 7). These findings demonstrate that while people possess a nuanced understanding of progress framing strategies, translating this knowledge into improved self-regulatory outcomes remains complex. By examining the nature and implications of people’s progress framing beliefs, this research offers novel contributions to the field of motivation science with valuable insights for goal pursuit and motivation regulation
The Effects of Sequence Variations in the Structural Dynamics and Ligand Interactions of Main Proteases in Betacoronaviruses
No full textThe main protease (Mpro) of betacoronaviruses is an essential enzyme for viral replication and a premier target for antiviral drug discovery. The high conservation of its active site across the genus makes it an ideal candidate for developing pan-coronavirus therapeutics to combat future outbreaks. In response to the Coronavirus disease 2019 (COVID-19) pandemic, numerous large-scale screening campaigns were initiated to identify Mpro inhibitors. However, these efforts were frequently hindered by the fundamental pose classification problem, which is the inability to distinguish between correct and incorrect binding poses. Compounding this issue, the research response generated an unprecedented volume of Mpro structural data, yet existing bioinformatics platforms lack the integrated tools for its systematic, comparative analysis. These combined challenges hinder the rational design of next-generation inhibitors.
In this work, we address these challenges by developing a novel, integrated computational toolkit. We first present CoviProDigy, a web-based platform designed for the comprehensive and comparative analysis of Mpro-ligand interactions, featuring specialized tools for subpocket occupancy analysis and elucidating common and unique interactions across different ligands. This functionality is designed to support scaffold hopping and medicinal chemistry optimization based on the molecular-level insights gained from known structures. To overcome the major issue of inaccurate pose selection in virtual screening, we then developed and validated a fine-tuned machine learning model that demonstrates enhanced accuracy in classifying ligand binding poses for the Betacoronavirus Mpro family. Together, these contributions provide a robust, end-to-end framework that accelerates the discovery of potent inhibitors, presenting a valuable resource for pandemic preparedness and the ongoing search for broad-spectrum antiviral agents
Experimental Study on the Vibration Response of a Jackleg Hammer Drill
No full textThis thesis presents an experimental investigation into the response of mechanical vibration in jackleg hammer drills during underground rock drilling operations. While previous studies have primarily focused on vibration exposure at the handle or operator interface, this work analyzes vibration transmission through the full structure of the drill to better understand internal component behavior under realistic working conditions. Vibration data were collected using uniaxial accelerometers mounted on four key components-the fronthead, main cylinder, backhead, and handle, with measurements recorded along three spatial axes. Testing was conducted in operational environments, capturing variations across distinct drilling phases, including collaring, sustained drilling, and retraction. The acquired data were processed using time and frequency domain methods, including Fast Fourier Transform (FFT) and Root Mean Square (RMS) analysis.
Results revealed significant directional dependence of vibration, with the axial (X-axis) component exhibiting the highest amplitudes during drilling. During collaring, when the drill bit lacks a guiding groove, vibration increased across all axes. A resonance condition was observed at approximately 142 Hz in the handle assembly, suggesting localized amplification potentially due to dynamic interaction between structural components. By characterizing dominant frequencies, directional behavior, and phase-specific amplification trends, this study provides a system-level understanding of vibration response in jackleg drills. The findings establish a foundation for future research aimed at developing targeted design improvements and vibration mitigation strategies to enhance operator safety and tool performance
Virtual Platform Design and Implementation for Magnetic Levitation Actuator Digital Twin With AI-Based Modeling and Control
No full textMagnetic levitation (maglev) planar actuators (MLPAs) utilize electromagnetic forces and torques between the stator array and movers to achieve frictionless and contactless precision motion. In this thesis, research works were developed and implemented for the existing MLPAs, specifically the Maglev floor (MagFloor) and the prototype (Testbench) at the University of Waterloo.
This thesis proposed a real-time magnet-coil role-switching force and torque (wrench) model for the levitation movement of disc-magnet movers (DMMs), through modeling the disc-magnet as a thin-walled conductor solenoid and the square coils as stacked coil-geometry magnets. The role-switching technique was achieved by utilizing equivalent magnetic dipole moments of the coil and magnet. The wrench model is the first online DMM wrench model in the literature, which computes the wrench between magnet and coil in 80 μs. A weighted pseudoinverse commutation law was proposed to extend the operating ranges of the DMMs. The single 4 inch DMM could be levitated with a maximum air gap of 70 mm and rotated with a maximum rotational angle of 45◦. The control resolutions were ±10 μm and ±20 mdegree for translations and rotations, respectively.
To further accelerate the implementation speed of the wrench model, a deep-learning residual-based model was established using an eight-million-point dataset generated from the above wrench model. Such a wrench model covered the extensive operating range of the above DMM, and computed the wrench results in 4.1 to 14.0 μs per coil-magnet pair without compromising the model accuracy. The 3σ error intervals of the model were equivalent to those of a lookup table with 20,645,504 mover poses. Furthermore, the wrench results were verified using measurements of the load cell and simulations. The deep-learning wrench model successfully controlled a 3 × 2 inch DMM, which could be levitated with a maximum air gap of 60 mm and rotated with a maximum rotational angle of 25◦. The same control resolutions were obtained.
The above wrench models were integrated into a novel virtual platform (VRP) for future digital twin (DT) applications, aiding research on MLPAs. This research proposed a VRP architecture that incorporated customized physics engines and uncertainties in physical replicas. Additionally, the virtual performance and motion results, considering uncertainties, were verified using physical experiments. The proposed VRP was fully open-source and constructed using PyBullet module and a parallel-operated graphic user interface (GUI) implemented with the PyQt5 module. The VRP simplified the processes of mover design, the wrench model comparison, and motion control verification. Furthermore, MLPA VRP was time-, material-, labor-, and cost-efficient to develop, which provided a virtual safeguard environment for the next stage of machine learning research and multiple magnet-mover motion control studies. Besides the advantages for MLPA development, the VRP could be embraced for remote operations and collaborative task research for FMSs. For future MagFloor and Testbench applications, the VRP system can be utilized as a training platform for researchers.
After establishing the VRP, a deep reinforcement learning (DRL) controller was implemented and trained for MLPAs, and its performance was verified using a DMM on the Testbench. The novel controller investigated the DRL approaches and verified the VRP for machine learning tasks. A linear controller was trained using proximal policy optimization (PPO) and soft actor-critic (SAC) models, which sampled at 455 μs, where the actions were continuous horizontal control forces. The remaining degrees of freedom were controlled using basic controllers. A reward function was proposed to minimize current saturation effects and power consumption while maintaining the dynamic responses. The model results were improved by using an additional sigmoid state machine to mitigate the oscillation issue of DRL policies when settling at references. After the successful demonstration of the DRL using the VRP for a single DMM, path planning for multiple movers could be considered.
Before initiating a machine learning approach for path planning in multiple mover control, a relative map path planning model was developed for operating a two-dimensional (2D) Halbach array mover (HAM) and a DMM. The model established an avoidance boundary for magnetic movers by analyzing the end effect of HAM and MLPA safety power consumption details, which determined mover operation speeds for the manufacturing process. Since the HAM experienced larger damping forces and required more power than the DMM, it was selected as the frame of reference to create the relative map. The optimal path obtained in the relative map was proven to preserve its optimality in the global frame for trajectory tracking. When no feasible optimal path existed, a speed-variant path was proposed. The algorithm was verified through 10,000 simulation cases and compared with the Lifelong Planning A* and Rapid Random Tree* methods, which demonstrated the fastest implementation speed (mean time of 0.05 s) and a 100 % success rate
Reparative Infrastructure: Reimagining Water Kiosks in Ulaanbaatar’s Ger Districts
No full textOver half of Mongolia’s population lives in Ulaanbaatar, with many settling in ger districts on the urban periphery. These areas, where some residents still live in traditional gers on self-claimed plots, resemble other informal settlements lacking basic infrastructure. Following the political reforms of the late twentieth century, many rural migrants relocated here seeking better opportunities, yet their living conditions remain poor. This thesis investigates how architectural interventions can enhance daily life, public space, and a sense of nomadic identity within these rapidly urbanizing areas. Focusing on the water kiosks system, it explores how these kiosks can serve as social and spatial anchors for future development. Based on literature review, secondary data, and remote site analysis, the thesis proposes two architectural upgrades in Bayangol District. The study ultimately frames a community-driven approach for informal settlements that promotes local agency and spatial justice through reparative infrastructure
Assessment, Mitigation, and Backtesting of Extreme Risks in Insurance and Finance
No full textIn this thesis, we focus on the quantitative assessment, mitigation, and backtesting of extreme risks in insurance and finance. We adopt tools from extreme value theory (EVT), dependence modeling, and statistical testing to address fundamental challenges in managing low-probability but high-impact events. The research contributes to three key aspects of extreme risk management: systemic risk assessment, catastrophe risk mitigation, and risk model evaluation.
We begin with the assessment of systemic risk under extreme scenarios. Systemic events are characterized by strong dependence among individual entities and heavy-tailed risk behavior. In Chapter 3, we develop an asymptotic framework for systemic risk measures, covering both Value-at-Risk-based and expectile-based risk measures. Second-order asymptotic approximations are derived to improve the accuracy of risk quantification beyond conventional first-order results. Special attention is given to expectile-based systemic risk measures, which provide a more conservative assessment of systemic risk.
The limitation of diversification for heavy-tailed risks has been well documented in the literature, especially for extremely heavy-tailed risks with infinite first moment. However, in practical insurance markets, catastrophic risks often exhibit extremely heavy tails but are also subject to truncation due to limited liability or policy design. In Chapter 4, we investigate the effectiveness of catastrophe risk pooling under these realistic constraints. Building on EVT, we characterize conditions under which diversification benefits remain achievable. The analysis incorporates tail heaviness, loss scaling, liability structure, and risk-sharing rules, providing theoretical foundations and practical guidance for catastrophe risk management.
In Chapter 5, we turn to the problem of risk model evaluation through backtesting. Conventional backtesting methods often rely on strict model assumptions and may fail under model misspecification, structural change or dependence uncertainty. Building on existing model-free testing approaches using e-values and e-processes, we extend these ideas to develop a backtesting framework for identifiable and elicitable risk measures, including Value-at-Risk, Expected Shortfall, and expectiles. The proposed framework delivers valid statistical inference for both standard and comparative backtests, and supports robust risk assessment across a wide range of risk levels, not limited to extreme events.
Throughout the thesis, theoretical results are complemented by extensive simulation studies and real-world applications. These findings contribute to advancing the theoretical foundations and practical methodologies for extreme risk management in modern insurance and financial systems