Brock University Digital Repository
Brock University's Digital Repository is an online archive showcasing and preserving the Brock community's scholarly output as well as items from the Library's Archives & Special Collections. Researchers can disseminate their work by depositing it in this Open Access repository, which provides free, immediate access to users while also allowing Brock scholars to track downloads and views of their scholarship. The Digital Repository is also the home of the Brock University E-Thesis Portal.
For more information, see the repository's policies and procedures.
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Recent Submissions
The interplay of citizen-sourced, conventionally surveyed, and meteorological data in recreational fisheries
(Brock University) Azar Taheri Tayebi; Ramazi Pouria; Department of Mathematics
Traditional methods of collecting data on angler activity involve conventional surveys, such as creel surveys
and aerial surveys, which are often costly. A modern, cost-effective alternative is utilizing online platforms
and smartphone applications (apps) designed for anglers. Previous studies identified correlations between
data reported by citizens via these apps and data gathered from conventional surveys. However, it is
still unclear if the activities recorded by the two sources are directly related, or if other “intermediate”
variables are primarily related to the conventionally surveyed data. In my first study, I employed Bayesian
networks (BNs) to explore this question, focusing on two metrics: daily catch rate and daily fishing effort.
These metrics were sourced from creel surveys, aerial surveys, and Angler’s Atlas website with related
MyCatch app in Alberta and Ontario, Canada. I included additional factors, e.g., weather conditions, as
possible “intermediate” variables in the network. To study the uncertainty of the results, I measured the
strength of connections between variables using Bayesian model averaging. Waterbody webpage views were
directly related to daily and weekly-aggregated boat counts in Ontario (51% and 100% probability) and to
weekly-aggregated creel survey-reported fishing duration in Alberta (100%). This highlights the value of
citizen-sourced data in providing unique insights beyond meteorological factors, with online interest serv
ing as a potentially reliable proxy for angler pressure and effort. In my second study, I aimed to evaluate
three BN structure learning approaches: (i) expert knowledge, (ii) ChatGPT, and (iii) data-driven models,
in predicting angler activity as reported through aerial surveys on the Ontario dataset. The Friedman
test indicated no significant difference in prediction accuracy between the three models. These findings
underscore the potential of AI-driven approaches, as the ChatGPT-assisted model performed on par with
expert-based and data-driven models, demonstrating its viability for ecological predictions.
Dissecting the anti-allergic potential of carnosic acid during IgE and IL-33-mediated mast cell activation
(Brock University) Robert W.E. Crozier; Adam J. MacNeil; Applied Health Sciences Program
Mast cells are critical immune sentinels and essential regulators of inflammation, most notably recognized for their central role in allergic inflammation. Allergen-induced cross-linkage of FcεRI complexes initiates a biphasic mast cell response, characterized by the immediate degranulation of preformed mediators and the delayed production and release of pro-inflammatory cytokine and chemokines. As a result of mast cells being the main perpetrators of the inflammatory responses associated with allergy, identifying novel mast cell stabilizing compounds is an area of intense investigation worldwide in an effort to alleviate the severity of the allergy epidemic. Carnosic acid (CA), a major polyphenolic constituent of the Lamiaceae family herbs has been shown to exhibit potent anti-inflammatory effects on other cellular models, but it’s role as a potential modulator of mast cell activation is currently undefined. Therefore, the purpose of this dissertation was to dissect the anti-allergy potential of CA in a mast cell model. Here, study 1 identified that out of the 3 major polyphenols derived from rosemary, CA showed the most promise as an inhibitor of mast cell responses, impairing mast cell degranulation and cytokine/chemokine release at increased concentrations, warranting its further investigation. Study 2 followed up on our previous investigation, to fully uncover the therapeutic potential of CA and its underlying mechanism of action. Here, we found that CA significantly impairs critical inflammatory responses during both the early and late phase response of allergic inflammation by targeting and inhibiting the upstream tyrosine kinase Syk, a novel mechanistic finding. To further establish CA as a mast cell stabilizer, study 3 determined its inhibitory effects following IL-33-induced mast cell activation in the presence or absence of allergen and SCF activation. Here, we found that similar to study 2, CA treatment significantly impaired the secretory mechanisms responsible for pro-inflammatory mediator release as IL-33-activated signaling and gene expression of mediators was significantly increased despite a decrease in secretion. Finally, study 4 investigated the role of CA treatment during the mast cell differentiation process. We determined that the presence of CA during differentiation has a dramatic effect on the presence of key mast cell surface receptors, and differentially regulates the inflammatory response following activation with allergen and or IL-33. Collectively, the findings of this dissertation help to establish the potential therapeutic utility of CA in a mast cell model of allergic and IL-33-mediated mast cell activation. We expect that the data presented will help the progression of future research identifying novel anti-allergy compounds and will contribute to better understanding the mechanisms responsible for mast cell function in various pathological contexts.
Investigating the Impact of the Current ADHD Diagnostic Process on Women in Canada
(Brock University) Henderson, Rachel; Dr Jan Frijters; Center for Applied Disability Studies
This thesis investigates the impact of Canada’s ADHD diagnostic process on women, focusing on systemic, gendered, and sociocultural factors that lead to delayed or missed diagnoses. Through a review of literature and interviews with women diagnosed in adulthood, the study examines seven themes: self-blame, systemic ableism, misogyny, reaching a breaking point, community importance, grief, and self-acceptance (Mowlem et al., 2019; Quinn & Madhoo, 2014). Using Interpretative Phenomenological Analysis (IPA), feminist, and critical disability theories, the research explores intersectional barriers affecting women’s access to ADHD diagnosis and support in Canada (Smith et al., 2009). Findings indicate that current diagnostic criteria, designed around male presentations, often overlook women’s unique ADHD symptomology, resulting in underdiagnosis and misattribution to other conditions (American Psychiatric Association, 2022; Agnew-Blais et al., 2016). This often leads to emotional distress and inadequacy, compounded by societal expectations (French et al., 2019). The study advocates for gender-sensitive diagnostic criteria and support resources. Practical recommendations include healthcare education reforms, inclusive diagnostic tools, and peer support networks. This Canadian-focused research aims to enhance diagnostic practices and improve experiences for women with ADHD (Espinet et al., 2022).
The Utilization of Carbene Catalysis and Different Synthetic Strategies for Sydnone Construction
(Brock University) Ryan Dol; Department of Chemistry
Here, the synthesis of benzoin was conducted using carbene precatalyst bis(amino)cyclopropenylidene (BAC-H) with multiple different bases that are used in literature. It was determined that the combination of bases sodium hydride and KHMDS provided moderate yields of benzoin. Further optimization studies were then commenced to increase the yield followed by the investigation of the scope. Further, different catalytic strategies for the cyclization of sydnones were also investigated including the utilization of the Vilsmeier –Haak reagent and triphenylphosphine oxide to provide fast reactions and excellent yields. Both of these catalytic methods were optimized, and broad substrate scopes were demonstrated in high yields. These findings are supported by DFT studies and the calculations of the procedure’s green metrics.
Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models
(Brock University) Wallace, Tom; Ombuki-Berman, Beatrice; Ezzati-Jivan, Naser; Department of Computer Science
Advancements in Natural Language Processing are heavily reliant on Transformer architectures, whose improvements come at substantial resource costs due to ever-growing model sizes. This study explores optimization techniques, including quantization, knowledge distillation, and pruning, focusing on energy and computational efficiency while retaining performance. Among standalone methods, 4-Bit quantization significantly reduces energy use with minimal accuracy loss. Hybrid approaches, like NVIDIA’s Minitron approach combining KD and structured pruning, further demonstrate promising trade-offs between size reduction and accuracy retention. A novel optimization framework is introduced, offering a flexible framework for comparing various methods. Through the investigation of these compression methods, we provide valuable insights for developing more sustainable and efficient LLMs, shining a light on the often-ignored concern of energy efficiency.