VictorJuiz/thesis huggingface dataset:
Repetitive Peripheral Magnetic Stimulation for Phasic Heat Pain Modulation
Keywords: acute pain
hyperalgesia
pain management
peripheral magnetic stimulation
phasic heat
TENS

Existing pain management tools have limited efficacy and can be associated with significant side effects. Repetitive peripheral magnetic stimulation (rPMS) is a novel non-pharmacological technique that could induce non-invasive analgesia. The aim of this thesis was to determine whether rPMS reduces experimental phasic heat pain (PHP), compared to TENS. Seventy-seven healthy participants (41 males, 36 females) attended two sessions counterbalanced across participants: control and stimulation. Participants were randomly allocated to three stimulations groups: TENS or one of two rPMS paradigms (intermittent or continuous theta burst stimulation). PHP stimuli were applied in each session, and differences in three outcome measures across the two sessions were collected: (1) pain intensity, (2) pain unpleasantness, and (3) secondary hyperalgesia. There were significant reductions in secondary hyperalgesia (p=.002) between stimulation groups, but not pain intensity or pain unpleasantness (p=.100, p=.355, respectively). These results show that rPMS may be a suitable modality for non-invasive pain management.
Open and Closed-Book Assessments in Secondary School Mathematics
Keywords: assessments
closed-book
education
mathematics
open-book
secondary school

This qualitative research study investigates teachers’ perspectives on open and closed-book assessments in secondary math classrooms. The covid-19 pandemic forced many teachers in Ontario to change their assessment style from closed-book to open-book. After teaching a full year with open-book assessments (2021/2022), three secondary math teachers were interviewed based on their experiences. The following year (2022/2023), the same teachers transitioned their classrooms back to closed-book and participated in another interview. Based on their interviews, this study had twelve major findings pertaining to open and closed-book assessments within the themes; student learning, grades, assessment practices, learning skills, higher order thinking, anxiety and equity. Some of the findings include; (1) teachers stated that some students struggled with time management on open-book assessments, (2) the teachers stated that students assumed open-book assessments would be easier and two teachers stated that some students spend less time preparing for their assessments as a result, and (3) the teachers stated that students engage in last-minute preparation for their closed-book assessments.
Evaluation of a RANS-based Computational Framework for Predicting Sooting Turbulent Jet Flames
Keywords: Radiative Heat Transfer
RANS
Soot
Turbulent Combustion

A Reynolds-averaged Navier-Stokes (RANS)-based computational framework for predicting soot formation in turbulent flames using the commercial computational fluid dynamics (CFD) software, Ansys Fluent, is evaluated for two experimental turbulent, non-premixed, sooting jet flames. The computational framework includes modelling for turbulent combustion through the Steady Laminar Flamelet Model (SLFM) and Flame-Prolongation of Intrinsic Low-Dimensional Manifold (FPI) tabulated chemistry approaches coupled to a presumed conditional moment (PCM) method. Modelling of soot is treated via a semi-empirical two-equation model for the transport of soot mass fraction and number density with reaction mechanisms governing soot inception, surface growth, coagulation, and oxidation. Radiative heat transfer is modelled via three strategies: (i) the optically thin approximation (OTA), the P1 spherical harmonics closure, and (iii) the discrete ordinates method (DOM). Non-gray radiation is treated via the Statistical Narrow Band Correlated-κ (SNBCK) method for evaluation of the gas phase absorption coefficients and soot absorption is estimated using Rayleigh scattering theory. Turbulence-radiation interaction (TRI) is evaluated from tabulated thermochemical quantities for the emission TRI, whereas absorption TRI is treated via the optically thin fluctuation approximation (OTFA). Results for the proposed framework were evaluated for three commonly-used two-equation turbulence models: the 1998 and Shear Stress Transport (SST) k-ω models, and the standard k- model with some tuning of model constants applied. The capabilities of the proposed framework to predict temperature, soot volume fraction, axial velocity, fuel mass fraction, and normalized OH concentration is assessed via comparisons to experimental data. A discussion of the relative capabilities to other modelling options is also provided.
The Professional Ballet School ‘Bubble’: Former Students’ Experiences in (Potentially) Harmful Environments
Keywords: child maltreatment
children's rights
classical ballet education
professional ballet schools
safe sport
vocational ballet schools

Research on professional ballet schools typically focuses on the ballet training context, and little research addresses student experiences beyond the ballet studio. Many students reside at their ballet schools, and recent cases of child maltreatment at international ballet schools raise concerns for student welfare across the entire school context. The purpose of this exploratory study was to understand the nature of former students’ experiences across dance, academics, socializing, and living, and any perceived contributing contextual factors to those experiences. Fifteen former students from six countries completed semi-structured interviews, which were analyzed using reflexive thematic analysis and a social constructionist approach. Interpretations included themes about complex positive and negative experiences and maltreatment; perceived effects of experiences; and perceptions of contributing contextual factors. Developed themes and accompanying participant recommendations for safer, healthier, and more inclusive ballet schools suggest that many environments and student experiences were misaligned with children’s rights, welfare, and development.
Investigating the Isoform-specific Functions of the Liver X Receptor during Fasting and Refeeding
Keywords: cholesterol
fasting
glucose metabolism
lipid metabolism
liver x receptor
sex differences

The liver x receptors (LXRα and LXRβ) are key transcription factors known to be important for cholesterol and triglyceride homeostasis under the fed state but their importance in fasting has not been examined. Using whole-body LXR knockout mice, we demonstrate that mice with one LXR isoform can compensate using the other isoform to maintain liver transcriptome and cholesterol homeostasis when fed a chow-diet. Liver transcriptomic and metabolite data did not find LXR isoform-specific functions after 24 hr fasting. With 24 hr refeeding, we show LXRα is the dominant isoform mediating the induction of lipogenic genes (from plasma and liver triglyceride data) consistent with prior literature. We observed sex-specific and LXR-dependent effects in liver triglyceride, plasma free fatty acid, and plasma cholesterol levels within LXR-/- mice, however the molecular mechanisms governing these effects is as yet unclear and could be due to LXR expression in other metabolic tissues apart from liver.
Leveraging Cancer Mutation Data to Predict the Pathogenicity of Germline Missense Variants
Keywords: Bioinformatics
Cancer mutations
Databases
Statistics
Supervised learning models
Variant interpretation

This thesis explores the potential of leveraging cancer mutation data as an additional line of evidence to enhance the interpretation of germline missense variants. The challenges in interpreting these variants often result in their classification as variants of uncertain significance (VUS). This study identified overlapping missense cancer mutations from the Cancer Hotspots database and germline variants from ClinVar. The analysis demonstrates a significantly higher likelihood of pathogenicity among the overlapping variants. By incorporating comprehensive data from the Cancer Hotspots database, the study employs supervised learning models to predict the pathogenicity of germline missense variants, showcasing robust and reliable predictive abilities on a separate test dataset. Incorporating cancer mutation data was identified as moderate-level evidence for pathogenicity, highlighting the potential upgrade of a substantial proportion of VUS to “likely pathogenic”. Overall, this research underscores the significant value of cancer mutation data in improving the interpretation of germline missense variants.
Implementing a GPU-Assisted LDPC Decoder for 5G New Radio
Keywords: 5G New Radio
GPU Computing
LDPC

This thesis presents a study on implementing a low-density parity-check (LDPC) decoder on a graphics processing unit (GPU) for 5G New Radio (NR). The proposed modifications include distributed execution configuration, compressed parity-check matrices (PCM) and message passing matrices to save memory space and applying the optimal iteration limit to the decoder. The performance of the LDPC decoder is evaluated using various testing cases with different algorithms, code rates and transport block sizes. The results show that the proposed decoder has more than 5× decoding speedup improvement on the short codes and more than 20× decoding speedup improvement on the long codes, while maintaining the error-correction performance. The optimal iteration limit is found to depend on the specific code rate and transport block size. The layered scheduling is a viable option on short codes over noisy channels.
Therapeutic Alliance: A Predictor of Emotional Intelligence in Addiction Therapy for Youths with Attention-Deficit Hyperactive Disorder?
Keywords: adolescents
attention-deficit hyperactive disorder
emotional intelligence
substance use disorder
therapeutic alliance

These studies aimed to investigate the impact of therapeutic alliance (TA) dimensions (Bond, Task, Goal) on emotional intelligence (EI) within the context of therapeutic addiction treatment and the differences in this relationship for adolescents with and without ADHD. TA was measured mid- and late-treatment using the WAI-YR, and EI was measured mid-, late- and post-treatment using the TEIque-SF. Study 1 (n = 70) regressions between TA and EI during treatment were stronger for the ADHD group. However, MANOVA found no significant group differences in mid- or late-treatment EI or TA scores, or in score changes between treatment points. When observing the total sample, Bond was the most significant predictor of EI, promoting its importance in building emotional intelligence in youths with addiction in general. Study 2 (n = 41) found that late-treatment Bond predicted post-treatment EI, but this may only apply to adolescent patients with stronger Bond-EI relationships during treatment.
Cross-Modal Data Fusion for 3D Instance Segmentation of Indoor Scenes
Keywords: Artificial Intelligence
Computer Vision
Fusion
Robotics
Segmentation

Tremendous strides have been made in image-based scene understanding over the past decade, thanks to larger datasets and enhanced model capacity. However, 3D-based understanding still struggles, in part because 3D data are so costly to annotate. Top 3D instance segmentation models, trained exclusively on 3D data, outperform models using both 2D and 3D data, suggesting untapped potential in merging 2D data to enrich 3D pipelines. Interestingly, while instance segmentation has not yet benefited from 2D-3D data fusion, the sequential fusion of outputs from 2D and 3D models that are trained separately does improve object detection. This thesis applies sequential fusion to instance segmentation and investigates what and where to fuse. We demonstrate that current 2D models do not perform well enough compared to 3D models to enhance instance segmentation results, but that future, higher-performing 2D models should show performance gains using the sequential fusion method.
A Quantitative and Qualitative Analysis of Household Organic Waste Diversion in Toronto, Canada
Keywords: Carbon Emissions
Household Organic Waste
Sustainability Transition
Waste Diversion
Waste Management

Improving waste diversion is identified as one of the important strategies for municipalities to reduce carbon emissions. Toronto has developed a set of climate goals and one goal aims to reach higher residential diversion rates. However, it has not yet reached the target in the single-family homes and multi-residential buildings sectors. However, the current organic waste diversion situation indicates that there is potential to reach those targets. Considering that there will be a growing population and the low diversion rate in this sector, an analysis that focuses on improving household organic waste diversion in both sectors was conducted. Using quantitative and qualitative methods, the research evaluated the carbon emissions of Toronto's household organic waste, identified effective strategies to increase household organics diversion, explored their applicability in Toronto, and recommended six strategies to help Toronto reach its climate targets, increase organic waste diversion, and facilitate low-carbon transition.
Maternal Antibody Transfer and Atopic Sensitization in Offspring
Keywords: antibodies
atopic sensitization
birth cohort
breastmilk
IgE
oral tolerance

Maternal antibody transfer in pregnancy and breastmilk is implicated in the development of allergen-specific responses in children. Human studies investigating the effect of maternal allergen-specific IgG (sIgG) on allergic diseases in offspring are limited. Maternal sIgG in breastmilk and serum was measured in a subset of the CHILD birth cohort using an allergen macroarray and compared against sIgE of the child at 12-months (n=266). Hierarchical clustering of breastmilk sIgG was used to analyze the impact on atopic outcomes via multivariable logistic regression models. Breastmilk sIgG did not directly correlate with 12-month outcomes regardless of maternal atopic status. Higher breastmilk sIgG was a significant risk factor for skin prick test positivity at 12-months to food (OR 1.90 [1.07-3.41], p=0.030). Mean breastmilk/serum sIgG were significantly lower among IgE-sensitized children for cashew and Ara h 2, and higher for almond and ovalbumin. Relative abundance of breastmilk/serum sIgG may affect IgE-sensitization in offspring.
The Effects of Antipsychotics on Central Lipid-Mediated Regulation of Glucose Metabolism
Keywords: Antipsychotics
Glucose homeostasis
Hypothalamus
Nutrient sensing
Schizophrenia

Antipsychotics are associated with adverse metabolic side effects. Central fatty acids strongly regulate whole-body glucose homeostasis. We examined the effects of the antipsychotics olanzapine and haloperidol on central sensing of the fatty acid oleic acid and subsequent regulation of peripheral glucose metabolism. Pancreatic euglycemic clamps were used to assess changes in glucose kinetics. Male rats were co-treated with a continuous central infusion of oleic acid or vehicle into the third ventricle and a subcutaneous injection of olanzapine, haloperidol, or vehicle. Intracerebroventricular (ICV) oleic acid infusion caused an increase in peripheral glucose infusion rate compared to ICV vehicle which was prevented by co-treatment with olanzapine or haloperidol. ICV oleic acid suppressed hepatic glucose production and increased glucose utilization. Both effects were abolished by co-treatment with olanzapine or haloperidol. Olanzapine and haloperidol disrupt the ability of central oleic acid to regulate peripheral glucose kinetics, resulting in impaired whole-body glucose metabolism.
Object-centric Approaches for the Abstraction and Reasoning Corpus
Keywords: Abstraction and Reasoning Corpus
Few-shot Learning
Graph Representation
Heuristic Search
Large Language Models
Prompt Engineering

This thesis explores two object-centric approaches to solving the Abstraction and Reasoning Corpus (ARC), a benchmark dataset for Artificial General Intelligence. The first approach employs a search-based technique, introducing a novel framework, Abstract Reasoning with Graph Abstractions (ARGA), which represents images as graphs and performs a constraint-guided search in a Domain Specific Language based on the abstracted graph space. Our experiments show the promise of ARGA in tackling some of the complicated object-centric tasks of the ARC rather efficiently, producing programs that are correct and easy to understand. The second approach uses Large Language Models (LLMs), and explores their direct application to the ARC as well as their augmentation with ARGA as an external tool. We find that LLMs reason much better when provided with object-based representations. These contributions advance our understanding of the capabilities and limitations of current AI models in abstract reasoning tasks, provide valuable insights into the design of future models and benchmarks for general artificial intelligence, and align our methods with fundamental aspects of human cognition, potentially bringing us closer to the goal of achieving Artificial General Intelligence.
It’s time to go: Ecological and Social Drivers of Nest Emergence in Turtles
Keywords: Behaviour
Bioacoustics
Deep Learning
Evolutionary Ecology
Field Ecology
Non-avian reptile

At the hatchling life stage, turtles are hypothesized to coordinate digging activity to synchronize nest emergence and adapt to unpredictable environmental conditions. However, as the social life history of non-avian reptiles remains relatively incomplete, understanding the social and ecological mechanisms behind this behavior is crucial. In this study, we investigated whether acoustic communication facilitates nest emergence in hatchling snapping turtles (Chelydra serpentina). Through in-situ field experiments spanning two summers, we monitored subterranean nest behavior using acoustic recorders and emergence behavior with wildlife cameras. We found that snapping turtles showed some evidence of within-nest emergence synchrony but did not show evidence of among-nest emergence synchrony. Structural Equation Modeling revealed that hatchling vocalizations, temperature, and time of day primarily influenced movement within the nest. These findings offer evidence that hatchling turtles synchronize their behaviors with above-ground conditions, with vocalizations potentially playing a role in cueing synchronized nest emergence.
Pedestrian-Oriented Communities: Regenerating Critical Neighborhoods using GIS, Multi-Objective Optimization Design, and Simulation Tools to increase Social and Environmental Sustainability Performance
Keywords: GHGs
GIS
Multi-Objective Optimization
Parametric Design
Simulation
Sustainability

With cities generating 70% of global greenhouse gas (GHG) emissions and struggling to effectively manage population growth, housing affordability, social equity, and environmental justice, this thesis seeks to assess the capacity data-driven and climate-responsive urban renovations have to address climate change challenges and inequity present in Toronto neighborhoods when implementing green infrastructure and setting transit and pedestrian mobility targets. With the use of multi-objective optimization design algorithms and subsequent digital simulation tools, the intent of this thesis is to virtually simulate the effects of site redevelopment and to discuss the ways in which the domains of civil engineering, urban planning, and architecture are limited in addressing inequity while reflecting on the roles of monetary systems, upfront carbon, social structures, policy, and governance. The outcome is the generation of a decision-making process designed to support GHG emission reduction targets for a neighborhood while promoting its sustainable development.
An Automated Impedance Matching Network for Wireless Power Transfer
Keywords: Impedance Matching Network
Power Electronics
Wireless Power Transfer

Wireless power transfer (WPT) is an emerging technology that enables the transmission of electrical energy without the need for physical connections. The extensive applications and growing popularity of WPT make it a promising technology for convenient and efficient charging in various industries, such as consumer electronics, biomedical devices, and electric vehicles. However, the efficiency of WPT systems can vary due to changes in device types and distances, leading to impedance mismatches and energy losses. To address these challenges, this thesis introduces an innovative transmitter-side automated impedance matching network (IMN) that intelligently detects and adapts the transmitter's impedance to optimize power transfer, thereby enhancing the overall efficiency of the WPT system. Experimental validation confirms the effectiveness of the IMN design, showcasing efficiency gains of up to 12.4% across the operating range for a WPT system delivering up to 16 W at 250 kHz.
Social (Media) Justice Education: The Neoliberal Limitations of a Popular Feminist Instagram Account
Keywords: black feminism
Black Lives Matter
critical race feminism
Instagram
social media activism
Stop AAPI Hate

This study looks at the changes of popular social justice-oriented Instagram account @feminist between January 2020 and May 2022, with regard to the ways the murder of George Floyd on May 25, 2020, and the Atlanta Spa shooting on March 15, 2021, may have influenced these changes. Informed by critical race feminisms and anticolonialism (among other frameworks), this critical discourse analysis examines the content, language, frameworks and posting patterns of @feminist to discern whether the apparent changes in public discourse since the summer of 2020 have led to meaningful shifts towards more critical and radical social justice frameworks. Findings from this study demonstrate shifts in the subject matter and the format of @feminist’s content. Originally reposting more memes and artwork, @feminist began gradually including more informative slideshows and infographics on anti-racism in their content following the civil unrest of Spring 2020 and eventually shifted to include intersectional feminisms and broader social justice topics by the start of 2021, following an administrative change. However, despite these shifts in subject matter, analysis shows a continued neoliberal framework in @feminist’s content across the two years examined that point towards a reliance on individualism and social media activism that does not advocate for community and that does not incite more radical action for systemic change. This study brings into perspective a critique of the current state of social media activism and the limitations of social justice advocacy on Instagram.
Delisted Routine Eye Exams and the Increased Use of Family Physicians and Ophthalmologists for Glaucoma Diagnosis
Keywords: Delisting
Glaucoma
Health Policy
Routine Eye Examinations
Utilization of family physicians
Utilization of ophthalmologists

Purpose: To investigate if delisting routine eye exams in 2004 for Ontarians aged 20-64 was associated with increased use of family physicians (FPs) and ophthalmologists for new glaucoma diagnoses.Methods: The utilization of FPs and ophthalmologists for new glaucoma diagnoses from 1997-2019 was analyzed using administrative data and interrupted time series analysis. Results: In policy-affected groups, FP utilization for glaucoma diagnoses significantly increased post- versus pre-delisting: 14.6% (95% confidence interval [CI] 11.0%~18.3%) for the 20-39 group and 9.9% (95% CI 8.2%~11.5%) for the 40-64 group. Ophthalmologist utilization for glaucoma diagnoses increased substantially: 41.5% (95% CI 37.8%~45.3%) for those 20-39 and 40.3% (95% CI 35.7%~45.0%) for those 40-64. In the policy-unaffected 65+ group, minimal increases were observed: 1.9% (95% CI 0.6%~3.2%) for FPs and 4.4% (95% CI 0.4%~8.4%) for ophthalmologists. Conclusions: Delisting routine eye exams for individuals aged 20-64 was associated with increased use of FPs and ophthalmologists for glaucoma diagnoses.