VictorJuiz/thesis huggingface dataset:
Teenager or Terrorist?: The Problematic Treatment of Young People Charged with Terrorism Under the Canadian Youth Criminal Justice Act

This paper will argue that despite a separate system of criminal justice with different penological objectives and enhanced protections, young people accused of terrorism offences are being subtly and negatively impacted by the problematic criminal justice response to adult terrorism. Chapter 1 will suggest that there are different sentencing objectives for adults convicted of terrorism offences compared to youth. Chapter 2 will analyse the eleven terrorism proceedings brought against a young offender from 2001-July 2022. Chapter 3 will suggest that judicial bias against terrorism offences is preventing young people from being released on bail in a timely manner and potentially leading to a denial of pre-trial credit. Chapter 4 will analyse the tempered role that rehabilitation plays compared to the outsized role of denunciation and deterrence in sentencing young offenders in terrorism cases. Chapter 5 will list four recommendations for the future of sentencing young offenders convicted of terror offences.

Keywords: Terrorism
Terrorist
Young Person
Youth
Youth Criminal Justice Act
Youth Terrorist
Advisor: Roach, Kent
Methods and Applications of Deep Learning in Sequential Medical Data

Understanding high-dimensional, complex and heterogeneous medical data is the prerequisite to transforming the healthcare system. With promising performance in understanding complex data, deep learning algorithms have provided a new paradigm for medical data, and many potential applications have been demonstrated recently. However, as one of the most common medical data, less attention was drawn to sequential medical data than other topics, such as medical imaging. Sequential medical data consists of various types, including clinical testings, medical videos, sensor data, clinical notes, etc. Regarding sequential medical data, it is challenging to apply current deep learning algorithms in most scenarios, such as limited labeled data and high sparsity. This thesis aims to improve the current methodology and explore new deep learning algorithms applications for sequential medical data. The thesis presents four cases regarding methodology and applications of deep learning for sequential medical data, and we organize them into four chapters. The two methodology chapters focus on improving performance for two common data issues - labeled data and high sparsity data. The two chapters for applications explore new applications of deep learning in two different types of sensor data - electrograms and electrocardiograms, in the realm of cardiology.

Keywords: Healthcare
Machine Learning
Sequential Data
Advisor: Zhang, Zhaolei
Development of a Transition Prediction Methodology Suitable for Aerodynamic Shape Optimization

Aircraft configurations that exploit significant regions of natural laminar flow could play a key role in reducing the environmental impact of aviation. The design of natural-laminar-flow configurations can be accelerated using computational tools capable of accurately and efficiently predicting boundary-layer transition. Toward the development of a suitable computational design tool, methods for the efficient prediction of boundary-layer transition in a Reynolds-averaged Navier-Stokes-based flow solver and integration in a discrete-adjoint gradient-based optimization algorithm are presented. A local correlation-based transition model is modified and coupled to the Spalart-Allmaras turbulence model and integrated in a Newton-Krylov-Schur flow solver. Modifications to the solution strategy are introduced, including a source-term time step restriction, in order to prevent unstable solution updates for the fully coupled, fully implicit solver. A smooth-variant of the transition model is developed with approximations to discontinuous and stiff source-term functions. Both transition models are validated using two- and three-dimensional subsonic transition test cases, with the new, smooth model producing significantly improved iterative convergence. Compressibility corrections are developed and applied to extend the transition model empirical correlations to transonic flow regimes typical of commercial transport aircraft, with the resulting model investigated using two- and three-dimensional transonic transition test cases. The results demonstrate that the compressibility corrections produce substantially improved agreement with the experimental transition locations, particularly for higher Reynolds number applications. Finally, the smooth transition model is integrated in a discrete-adjoint gradient-based optimization algorithm and applied to two- and three-dimensional drag-minimization studies across a range of design conditions. The results demonstrate that the capability of the current framework to explore the natural-laminar-flow configuration design space is sensitive to the streamwise grid resolution in the transition regions, with grid requirements increasing as the transition length decreases with increasing Reynolds number. The light aircraft design space is found to be multi-modal, with the optimization framework producing two distinct local minima. Transonic drag minimizations demonstrate that the optimization framework can successfully trade a decrease in viscous drag with an increase in wave drag, and an infinite swept wing optimization demonstrates that the framework can delay both Tollmien-Schlichting and stationary crossflow instabilities.

Keywords: aerodynamic shape optimization
aircraft design
boundary-layer transition
computational fluid dynamics
natural laminar flow
transition prediction
Advisor: Zingg, David W
Sequential Decision-making Under Uncertainty: Novel Methodologies and Applications

In sequential decision-making under uncertainty, multistage stochastic mixed-integer programming (MSMIP) is a tool for addressing optimization problems with a given probability distribution and the goal of optimizing a performance measure over a planning horizon. If there is no knowledge about the probability distribution, multistage adaptive robust optimization (MSARO) is a suitable modeling framework. The goal of this thesis is to expand the methodological aspects of these two techniques, and in turn their applications. First, we study MSMIP problems, which can be approximated by using certain policies, e.g., linear decision rules. However, directly applying this idea to problems with integer decisions is difficult. We introduce Lagrangian dual decision rules (LDDRs) for MSMIP that overcome this difficulty by applying decision rules in Lagrangian duals of the MSMIP. We propose two new bounding techniques based on stagewise and nonanticipative Lagrangian duals. Our proposal requires fewer assumptions than most existing MSMIP methods. Our numerical experiments demonstrate that the LDDR approaches yield significant optimality gap reductions compared to existing general-purpose bounding methods for MSMIP. Next, we consider two problems in telecommunications industry. Network service providers routinely allocate resources to incoming requests and recover under-utilized capacity. For this resource allocation/reallocation, we present the first two-stage stochastic programming models to maximize the expected throughput, and solve them by an efficient decomposition algorithm that benefits from their observed tight linear programming bound. Additionally, we model the provisioning problem by MSMIP and approach it by our LDDR methodology. Experiments on real backbone networks reveal the significant value of considering the often overlooked uncertainty. Lastly, for MSARO problems we design primal and dual bounding methods, originated from adaptation of two decision rules rooted in stochastic programming. Our framework approximates the primal and dual formulations of MSARO with two-stage models. We provide sufficient conditions under which the column-and-constraint generation can exactly solve the primal approximation. For the dual approximation, we present a monolithic bilinear program valid for continuous MSARO problems, and a cutting-plane method for mixed-integer cases. Computational experiments on newsvendor and location-transportation problems show that our bounds yield considerably smaller optimality gaps compared to the existing methods.

Keywords: Decomposition methods
Robust optimization
Sequential decision-making
Stochastic programming
Telecommunications
Advisor: Bodur, Merve
Development of Microfluidic Platforms for Sperm Protein 10 Measurement

Infertility is a worldwide disease. Approximately 48.5 million couples worldwide are infertile, including one in six Canadian couples. Male infertility, which affects approximately 7% of the male population, is responsible for an estimated 40% to 50% of all infertility cases. Male infertility is often attributed to poor semen quality with suboptimal sperm motility, limited concentration, or abnormal morphology. The total number of spermatozoa per ejaculate and the sperm concentration are related to both time to pregnancy and pregnancy rates and are predictors of conception. Since sperm concentration is an important criterion to estimate semen quality, a direct relationship between sperm concentration and signal intensity is desired for establishment through enzyme linked immunosorbent assays that measure sperm protein 10 (SP-10) concentration.This thesis focuses on developing three types of microfluidic platforms for SP-10 measurement. Firstly, a nitrocellulose paper-based multi-well plate platform was developed with a LOD of 88.84 ng/ml. The developed nitrocellulose paper plate has 96 independent wells for multiplexing ELISA assay and is compatible with standard laboratory microplates and instruments. The nitrocellulose plate is fabricated through a flexible one-step laser micromachining process, which can simply and rapidly produce a low-cost nitrocellulose paper-based multi-well plate with different sizes for ELISA experiments. Next, a lateral flow immunoassay platform was developed for home-based SP-10 measurement. This simple, rapid, and cost-effective platform replies on capillarity of a nitrocellulose strip to transport fluids and produces a measurable signal within 15 minutes. Gold nanoparticles and gold nanoshells were both applied on this platform as colorimetric labels; it was demonstrated that the LOD with gold nanoshells (5.16 ng/ml) was 5 times lower than that with gold nanoparticles (25.03 ng/ml). In addition, a single magnetic bead system integrated with a microfluidic device was developed for SP-10 measurement in laboratory with a LOD of 1.18 µg/ml. By applying a rotating magnetic field, the single magnetic bead moves freely in the chamber of the microfluidic device to actively capture the SP-10 antigen linked to the fluorescent-labeled SP-10 antibody. SP-10 concentration was measured by comparing the fluorescent intensity of the bead before and after swimming in the chamber.

Advisor: Sun, Yu
From Hospital to Home for Persons Living with Dementia and their Family Caregivers

Older adults living with dementia are at risk for more complex health care transitions than individuals without this condition. The central research question guiding this thesis was: “How do older adults living with dementia and/or their caregivers experience and perceive the hospital-to-home care transition?” One study with three separate areas of inquiry was pursued. For Paper 1, a body of qualitative literature was systematically searched and synthesized using a meta-ethnography approach. The analysis revealed three categories: (1) feelings associated with the health care transition; (2) processes associated with the care transition; and (3) evaluation of the quality of care associated with the care transition. The objective of paper 2 sought to develop a theoretical understanding of the social processes engaged in by people with dementia and their family caregivers leading to and following a care transition to hospital and then home. A constructivist grounded theory was used, and a total of 25 people participated in 21 interviews. Study findings contribute to the dementia care transition literature by providing a conceptual framework that explains what family caregivers do (taking over), how they manage (connecting dots), and what they experience (rising up) following a care transition. Situated centrally is the core process, Seeing the day-to-day situation, which is characterized by caregivers as constant intermediaries between patients and the health system. In paper 3, a qualitative descriptive design was used to capture perceptions of quality home and community care for those living with dementia and their caregivers. Study findings found that supporting people with dementia to live in the community requires available, acceptable, adaptable and affordable home and community-based care services. Overall, this thesis demonstrates the complex nature of care transitions for those living with dementia and their caregivers. Compounding the already strained experience are perceived challenges in communication and care coordination processes, and an inadequately resourced and inflexible home and community care system.

Keywords: Aging
Care transition
Dementia
Family caregiver
Grounded theory
Meta ethnography
Advisor: Kuluski, Kerry
Jeffs, Lianne
Associations between Age-related Changes in Sensory and Cognitive Abilities in Multisensory Integration and Driving

The ability to integrate sensory inputs from hearing and vision is important for basic and complex perceptions and behaviours. It is well-established that sensory and cognitive abilities typically decline with age; however, past studies examining whether multisensory integration processes (e.g., audiovisual integration) also change with age have been inconsistent, possibly due to differences in how sensory and cognitive abilities are characterized and controlled for. Further, studies of sensory integration use basic tasks, while less is understood about how multiple sensory abilities interact to affect complex behaviours. Chapter 2 examined whether multisensory integrative abilities in audiovisual Simultaneity Judgment and Temporal Order Judgment (TOJ) tasks differ between younger and older adults, and if performance is associated with different sensory acuities and cognitive abilities within healthy ranges. Results showed that even with normal pure-tone hearing, visual acuity, and global cognition, older adults integrated auditory and visual events at larger temporal offsets than younger adults, especially in the TOJ task. Performance in the audiovisual tasks was not associated with unisensory or cognitive abilities. Extending knowledge of how multiple sensory abilities interact to affect complex behaviours across a broader range of sensory and cognitive abilities, Chapters 3 and 4 examined whether driving is associated with cognitive and sensory abilities in older adults with varying cognitive impairments. Specifically, whether self-reported driving status and habits can be predicted by cognitive, hearing, and vision measures (independently and/or together) in individuals with subjective cognitive decline, mild cognitive impairment, and dementia. In Chapter 3, behavioural measures of hearing and vision were analyzed, whereas in Chapter 4, self-ratings of hearing and vision were analyzed, as the two types of measures may not align and provide different insights about sensory abilities. Both Chapters 3 and 4 showed that driving outcomes were best predicted by age, sex, and cognitive measures, and in Chapter 4, there was some evidence that self-ratings of hearing and vision predicted driving outcomes. Ultimately, this thesis showed that behavioural and self-reported cognitive and sensory abilities are important to consider when examining age-related changes to basic and complex outcomes involving multisensory integration.

Advisor: Campos, Jennifer JC
Unpacking Emotion Regulation: Associations among Symptom Burden, Strategy Implementation Beliefs, and Strategy Usage

General beliefs concerning one’s capacity for emotion regulation (ER) influence the selection and implementation of regulatory strategies. For instance, depressive symptoms have been related to higher rates of ruminative thinking and lower rates of cognitive reappraisal. However, it is unclear how much people differ in strategy-specific ER beliefs, whether such variation in beliefs predicts differences in strategy implementation, and whether symptom burden is associated with this process. Three empirical studies were conducted to understand the associations of strategy-specific beliefs regarding implementation efficacy (the ability to successfully implement strategies) and effort (the motivational cost of implementing strategies), the moderating effect of symptom burden, and post-regulation appraisals of strategy efficacy and overall regulation effectiveness (whether the strategies collectively worked to reduce negative emotions). Study 1 (n = 128) revealed that strategy-specific beliefs of implementation effort predicted lower overall strategy usage in response to emotional stressors presented in a lab setting. However, no association was detected between implementation efficacy beliefs and strategy usage. Findings from Study 2 (n = 130) showed that symptom burden predicted lower efficacy but had no relation to effort beliefs, and the interactions between symptom burden and implementation beliefs did not predict strategy usage following an emotional stressor. However, in real-world settings, Study 3 (n = 240) demonstrated that strategy-specific efficacy beliefs positively predicted strategy usage. Here, symptom burden also interacted with implementation efficacy beliefs in predicting strategy usage: those with higher symptom burden showed a stronger relationship between implementation efficacy beliefs and strategy usage. Moreover, higher symptom burden was also related to higher appraisals of strategy efficacy but lower overall regulation effectiveness, potentially blunting reinforcement of adaptive strategy selection. Together, this work underscores the role of strategy-specific implementation beliefs for strategy usage across both lab and ecologically valid settings. These findings suggest that symptom burden makes it more likely for individuals to use strategies based on their implementation beliefs while perceiving strategies as less successfully implemented, which could have meaningful implications for the clinical treatment of psychological disorders.

Keywords: beliefs
efficacy
effort
emotion regulation
symptom burden
symptoms
Advisor: Segal, Zindel
Farb, Norman
The Financial Risks of Unpaid, Homebased Caregiving in Ontario, Canada

Little is known about how current conditions in the Canadian home care sector (e.g., COVID-19 pandemic) are imposing financial risks on unpaid caregivers. Financial risk considers the sources and magnitude of direct, out-of-pocket care expenditure, and corresponding impacts across domains of financial risk (income-generating potential, employment, and personal health). Pragmatically, financial risks from caregiving call into question whether the universal dimensions of social welfare built on universality that are characteristic of Canada’s Medicare system can be applied to home care. Using Ontario as a case-study, this dissertation explores the financial risks of unpaid, homebased caregiving through the lens of welfare regime theory. We conducted an original, cross-sectional survey to explore the financial risks of unpaid caregiving (Chapter 3). This survey, completed by 190 caregivers between August - December 2020, revealed challenges paying for care expenses such as supplemental support from a personal support worker (PSW), and difficulties balancing employment compromising income-earning potential (Chapter 4). We also conducted interviews with 24 dementia caregivers across Ontario to understand how challenges acquiring a PSW have contributed to financial risk (Chapter 5). Findings suggest dementia caregiving requires substantial hours of PSW care to ensure unpaid caregivers can maintain employment or take a break (respite). However, caregivers felt they were not granted enough subsidized hours from the state. This meant working caregivers took time off work to provide care, thereby contributing to lost wages. Other caregivers paid out-of-pocket for additional hours of privately acquired home care, which contributed to experiences of financial risk. Financial risks observed in Chapters 4 and 5 raise important questions about how social benefits ought to be distributed in the context of home care to protect unpaid caregivers. Drawing on notions of universalism in post-modern welfare regimes, including inclusive citizenship and new social risks, we developed an adapted welfare regime typology to better describe the organization of social benefits for unpaid, homebased caregivers (Chapter 6). This typology reveals home care follows a liberal ethos of social benefit distribution. Findings from this dissertation may better inform policies and programs that aim to protect unpaid caregivers from financial risk across different welfare states.

Advisor: Marchildon, Gregory P.
Droplet Formation in Simple Nonlocal Aggregation Models

We study interaction energies given by various kernels, and investigate how these kernels drive the formation of multiple flocks within a larger population. We show that for a class of kernels having a ``well-barrier'' shape that the energy is minimized by a sequence of indicators of finitely many balls whose supports move further and further apart. The dichotomy case of the concentration compactness principle is a key ingredient in our proof. To better understand the underlying geometry and the role of the height and width of the repulsive barrier, we also consider a toy model which forbids points in the support of an admissible density from being within a certain range of distances from one another. We show in one dimensions, that no matter the width of this range the energy is minimized by the indicator of a union of well separated intervals of equal length and one smaller interval, however other minimizers are possible. Finally, we also consider weakly repulsive kernels and show that Wasserstein $d_{\infty}$ local minimizers must saturate the density constraint.

Keywords: Aggregation
Calculus of variations
Concentration compactness
Interaction energy
Advisor: Burchard, Almut