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High-throughput compatible catalyst development towards sustainable direct alkenylation reaction discovery and optimization
Get PDFThe direct C–H functionalization of heteroaromatic compounds such as pyridine, furan, thiophenes, thiazoles, and others have been developed as effective methods for making Csp2–Csp2 linkages which are often found in biologically active compounds and π-conjugated functional materials. More specifically, the development of palladium catalysts that can selectively activate specific C–H bonds is key for late-stage functionalization of pharmaceutically-relevant compounds. Mechanistic studies of the catalytic system, reaction intermediates and evaluation of the reaction parameters allows chemists maximize the reaction performance.
This thesis explores direct C–H alkenylation reactions from both a catalyst and substrate perspective, and exploits modifications to the generally accepted direct alkenylation mechanism. Furthermore, this work shows how systematic, hypothesis-driven High-Throughput Experimentation of reaction conditions, palladium sources and ancillary ligands enables the development of new reactivity, optimization of catalytic systems and exploration of the chemical space of direct alkenylation of heterocycles.
Finally, this work also highlights the versatility of palladacyclic precatalysts in the selective C–H functionalization of challenging but pharmaceutically relevant heterocycles such as pyrazoles and thiazoles. New synthetic procedures have been described toward the development of single component precatalyst systems, and they have been used for the synthesis of two pharmaceutical compounds: GSK3368715, a PRMT1 inhibitor, and fatostatin, a lipid accumulation inhibitor.Graduate2025-08-1
Front-liners on the Sidelines: The credential recognition experiences of Filipino internationally-educated nurses (IENs) in Victoria, British Columbia (BC)
No full textThe impacts of the nursing labour shortage are being felt across Canada but especially in Victoria, BC where place-based realities have impacted internationally-educated nurses’ (IEN) professional pursuits. Rising inflation, housing costs, and living expenses create challenging contexts for IENs from the Philippines who aim to settle, integrate and complete professional recertification processes in order to become registered nurses in BC. As provinces across the country vie for nurses to alleviate strains on the health care system, this study explores Filipino IENs’ integration experiences and settlement barriers. The study examines to what extent these factors might have influenced their educational upgrading, professional recertification, and workplace acculturation experiences. This exploratory study rooted in an interpretivist paradigm examines the experiences of nurses from the Philippines who recently migrated to Victoria in the last ten years. The key findings of the study posit that financial barriers, time barriers, deskilling, and mental health challenges are the most prevalent obstacles encountered by Filipino IENs in Victoria, BC. These findings are further expanded upon in order to understand the impacts that migration pathways, post- and pre-arrival immigration processes, familial responsibilities, English-language requirements, workplace discrimination and professional recertification pathways have on the complex integration and settlement experiences of Filipino IENs in Victoria, BC. Nine recommendations are proposed including the creation of more efficient migration pathways, investing in accessible information supports, prioritising effective communication, designing equitable policies that account for familial responsibilities, supporting flexible English language requirements, developing local navigational supports for IENs, addressing deskilling, adapting professional recertification pathways, and increasing collaboration between clinical practice programs.Graduat
Games, an untapped resource for improving group relations
No full textPsychology - Three Minute Thesis FinalistUVic Faculty of Graduate Studies Three Minute Thesis (3MT) research communication competition.Graduat
Harnessing image-based deep learning for advanced malware classification
No full textThis thesis explores the application of image-based deep learning models for malware classification, leveraging a subset of the extensive MalNet-Image dataset, which includes around 87,000 binary images from a base of 1.2 million binary images based on Android APK files.
The core contribution of this work lies in the innovative use of multiple components that, as far as we know, have not been used before to tackle the malware classification problem. Harnessing the power of deep neural networks (DNNs), which have demonstrated exceptional capabilities in various classification tasks, we aim to enhance the accuracy and efficiency of malware detection.
These include Feature Pyramid Networks (FPN) to handle the file size scale issue when converting to images and the application of data augmentation techniques like MIXUP and TrivialAugment. We employ transfer learning with pre-trained models on ImageNet and optimize them using the AdamW Schedule-Free optimizer. Our experimental results show that the integration of
these techniques achieves remarkable improvement in classification accuracy, with our best model achieving an F1 score of 0.6927 compared to 0.65 reported on the provided split for MalNet-Tiny. This could be considered a step forward in the field of malware classification using image-based deep learning models.Graduate2025-08-2
Deep learning downscaling of climate variables to convection-permitting scales
No full textAdapting to the changing climate requires accurate local climate information, a computationally challenging problem. Recent studies have used Generative Adversarial Networks (GANs), a type of deep learning, to learn complex distributions and downscale climate variables efficiently. Capturing variability while downscaling is crucial for estimating uncertainty and characterising extreme events—critical information for climate adaptation. Since downscaling is an undetermined problem, many fine-scale states are physically consistent with the coarse-resolution state. To address this ill-posed problem, downscaling techniques should be stochastic, able to sample realisations from a high-resolution distribution conditioned on low-resolution input. Previous stochastic downscaling attempts have found substantial underdispersion, with models failing to represent the full distribution. I propose approaches to improve the stochastic calibration of GANs in three ways: a) injecting noise inside the network, b) adjusting the training process to explicitly account for the stochasticity, and c) using a probabilistic loss metric. I tested models first on a synthetic dataset with known distributional properties, and then on a realistic downscaling scenario, predicting high-resolution wind components from low-resolution climate covariates. Injecting noise, on its own, substantially improved the quality of conditional and full distributions in tests with synthetic data, but performed less well for wind field downscaling, where models remained underdispersed. For wind downscaling, I found that adjusting the training method and including the probabilistic loss improved calibration. The best model, with all three changes, showed much improved skill at capturing the full variability of the high-resolution distribution and thus at characterising extremes.
Investigating the stochastic GAN framework with other variables, I show that it successfully downscales temperature, specific humidity, and precipitation. I also find that the stochastic framework substantially improves the downscaling of extreme precipitation. Next, I find that while multivariate downscaling can improve dependence structures between downscaled variables, it leads to blurry downscaling of individual variables. I demonstrate that including high-resolution topography as an input improves spatial structure for most variables. Finally, I test the generalisability of the GAN framework to a new location with a different climate, and show that while the GAN performs well for temperature and humidity, it fails for precipitation due to mismatches between the low- and high-resolution data. These results represent important techniques and insights towards operational GAN-based downscaling.Graduat
UVic Convocation November 14, 2023 – 10:00 am
No full textStudents from the faculties of Graduate Studies, Engineering and the Division of Continuing Studies.UndergraduateUnreviewe
Innovation, communication, and collaboration: Understanding family group decision making models in Canada and beyond
No full textSarah Hart and Ken Markley, project sponsors, British Columbia Ministry of Children and Family Development (MCFD). Alison Gerlach, course instructor, University of Victoria. Thais Amorim, course coordinator, University of Victoria.Graduat
Introducing the Let’s Face It! Scrapbook app: Social eye processing training for improving face-to-face social interactions in autistic youth
Get PDFInterpreting facial expressions, establishing and maintaining eye contact, and following the eye gaze of others are key social eye processing abilities. Deficits are associated with social dysfunction, clinical disorders, and particularly with autism spectrum disorder (ASD). A critical question is whether social eye processing abilities can be trained for improving face-to-face social interactions.
The current study utilized a pre-test/post-test control group switching replications design. In active training, 12 autistic youth received 4.5 hours of Let’s Face It! Scrapbook app: Social Eye Processing Training (LFI - SEPT) over 3 weeks. Active training included participation in weekly small group learning sessions where research facilitators introduced and modeled social eye processing abilities. The research assistants then facilitated autistic youth to record their own social eye processing abilities into the Let’s Face It! Scrapbook app. Over the remainder of the week, autistic youth played from the Let’s Face It! Scrapbook app games in designated gameplay sessions. In Control Training, autistic youth completed weekly small group learning activities and engaged in social gaming using educational apps.
The results revealed that relative to Control Training, autistic youth improved significantly after completing LFI - SEPT. Autistic youth experienced significant gains in interpreting subtle changes in facial expressions. In addition, autistic youth were shown to engage establishing and maintaining more eye contact in a story reading and conversation assessment. Parents reported further enhancements in social competency for understanding faces in the home environment. Collectively, the results provide optimism that social eye processing abilities can be improved through direct training using a mobile app.Graduat
Fire regime change in high-value temperate forested watersheds: a paleoecological investigation in the Greater Victoria Water Supply Area (GVWSA) on southern Vancouver Island, British Columbia
Get PDFClimate change is driving a global increase in wildfire that is disproportionately impacting temperate coniferous forests. These trends are forecast to continue with regional increases in area burned and extreme fire weather; however, the uncertainty associated with modelling the future extent and magnitude of change in complex fire systems remains a challenge for researchers. Examining historical fire regimes through paleoecological reconstructions of climate, vegetation, and fire can offer insights and can help validate models of future fire environments by characterizing potential analogues in the past. This study investigates the susceptibility of northern coastal temperate forests on Vancouver Island, Canada, to both past and future wildfire disturbance. Sediment cores were extracted from three lakes along a regional east-west precipitation gradient within a high-value forested water supply area. Supplemental data from a previously cored fourth lake within the water supply were also analysed. A comparison between warm-dry early- and cool-moist late-Holocene intervals was used to delineate spatio-temporal changes in fire regime. The results indicate that precipitation was lower in the past, with more open forests that were dominated by invaders and resisters – two important fire-related plant functional types. The wettest, western-most site experienced the greatest change and had frequent fires in the early-Holocene. This highlights the extent of fire regime shift and suggests that forests currently less predisposed to fire may become vulnerable in the future, the implications of which concern fire probability simulations and management actions to reduce wildfire risk to water supply.Graduate2025-12-1
New inequalities using multiple Erdélyi–Kober fractional integral operators
Get PDFThe role of fractional integral inequalities is vital in fractional calculus to develop new models and techniques in the most trending sciences. Taking motivation from this fact, we use multiple Erdélyi–Kober (M-E-K) fractional integral operators to establish Minkowski fractional inequalities. Several other new and novel fractional integral inequalities are also established. Compared to the existing results, these fractional integral inequalities are more general and substantial enough to create new and novel results. M-E-K fractional integral operators have been previously applied for other purposes but have never been applied to the subject of this paper. These operators generalize a popular class of fractional integrals; therefore, this approach will open an avenue for new research. The smart properties of these operators urge us to investigate more results using them.The author extends appreciation to the Deanship of Postgraduate Studies and Scientific Research at Majmaah University for funding this research work through project number (R-2024-992).FacultyReviewe