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Assessment of Arctic sea ice properties during advanced melt using C- and L-band polarimetric synthetic aperture radar
Get PDFOver recent decades, there have been significant changes in Arctic sea ice, marked by a shift from thicker to thinner ice, and reductions in extent and volume. These changes, along with extended melt seasons, have accelerated sea ice melt and the ice-albedo feedback. This dissertation leverages Synthetic Aperture Radar (SAR) to analyze sea ice dynamics and thermodynamics at various scales.
Focusing on the advanced melt season, this dissertation examines the relationships between sea ice geophysical properties and SAR backscatter signatures at C- and L-bands. Our analysis uses RADARSAT-2 (C-band) and ALOS-2/PALSAR-2 (L-band) imagery in different polarimetric modes to assess sea ice type separability and the effects of incidence angle and melt ponds on backscatter. We discover that C-band SAR is more effective early in the melt season, while L-band SAR provides clearer delineation of ice features later in the season. Additionally, the co-pol ratio (VV/HH) was found to be a consistent indicator of the melt ponds for FYI, despite environmental changes, such as wind-roughened melt ponds, for both frequencies and incidence angles in the near and far range.
Utilizing our findings, we developed a dual frequency, dual-incidence angle sea ice classification approach using a random forest classifier. We achieved over 70% accuracy in sea ice type classification, validated by airborne measurements. This research underscores the value of a dual-frequency approach in improving sea ice classification, particularly for first-year and multi-year ice during advanced melt stages, while considering the evolution of deformed ice types in the advanced melt season. The findings contribute significantly to climate studies and operational services and are pertinent to future dual-frequency SAR missions.Graduat
Implementing voice assistant for visually impaired using LLMs and Vision Language Models
Get PDFAs a result of population aging, the number of visually impaired people is growing. Unfortunately, there is limited accessibility measures to help improve the quality of life of these people. The recent technological development in Artificial Intelligence (AI), especially Large Language Models (LLMs), should offer effective and efficient solutions. Recognizing the limitation of existing products, we design and implement a user-friendly and privacy-safe voice assistant for visually impaired people. Using LLMs and Vision Language Models, the assistant can recognize and identify objects through low-latency speech-to-speech interactions. The assistant can be deployed on offline edge computing devices with camera/microphone/speaker, with easily extendable functionalities. In this report, we present the design, adopted technologies, and adjustment that we applied to arrive at the final implementation.Graduat
In vitro and in vivo synergetic radiotherapy with gold nanoparticles and docetaxel for pancreatic cancer
Get PDFThis research underscores the potential of combining nanotechnology with conventional therapies in cancer treatment, particularly for challenging cases like pancreatic cancer. We aimed to enhance pancreatic cancer treatment by investigating the synergistic effects of gold nanoparticles (GNPs) and docetaxel (DTX) as potential radiosensitizers in radiotherapy (RT) both in vitro and in vivo, utilizing a MIA PaCa-2 monoculture spheroid model and NRG mice subcutaneously implanted with MIA PaCa-2 cells, respectively. Spheroids were treated with GNPs (7.5 ?g/mL), DTX (100 nM), and 2 Gy of RT using a 6 MV linear accelerator. In parallel, mice received treatments of GNPs (2 mg/kg), DTX (6 mg/kg), and 5 Gy of RT (6 MV linear accelerator). In vitro results showed that though RT and DTX reduced spheroid size and increased DNA DSBs, the triple combination of DTX/RT/GNPs led to a significant 48% (p = 0.05) decrease in spheroid size and a 45% (p = 0.05) increase in DNA DSBs. In vivo results showed a 20% (p = 0.05) reduction in tumor growth 20 days post-treatment with (GNPs/RT/DTX) and an increase in mice median survival. The triple combination exhibited a synergistic effect, enhancing anticancer efficacy beyond individual treatments, and thus could be employed to improve radiotherapy and potentially reduce adverse effects.This work was supported by the Kuwait Foundation for the Advancement of Sciences (KFAS) under project code CB21-63SP-01; the NanoMedicines Innovation Network Strategic Initiative fund (NMIN-SI), a member of the Networks of Centers of Excellence Canada program; the John R. Evans Leaders Fund (JELF) from the Canada Foundation for Innovation (CFI); the British Columbia Knowledge Development Fund (BCKDF); the Discovery Grant from the Natural Sciences and Engineering Research Council of Canada (NSERC); the National Institutes of Health (NIH) of the United States of America under grants code R01CA257241, R01DE028105, R21CA252156, and R01CA274415; and a collaborative health grant from the University of Victoria.FacultyReviewe
Pseudoku: A Sudoku Adjacency Algebra and Fractional Completion Threshold
Get PDFThe standard Sudoku puzzle is a 9 × 9 grid partitioned into 3 × 3 square boxes and partially filled with symbols from the set {1, 2, ..., 9}, with the goal of the puzzle being to complete the grid so that each symbol appears once and only once in each row, column, and box. We study generalized Sudoku puzzles, set on an n × n grid with cells partitioned into n boxes (sometimes called cages) of height h and width w such that hw = n. Throughout this work, these generalized Sudoku are referred to as (h, w)-Sudoku when h and w are significant, but as simply Sudoku otherwise. The goal of solving a partially filled (h, w)-Sudoku puzzle remains the same; complete the Sudoku by assigning placements in the grid to each symbol from {1, 2, ..., hw} so that each symbol appears once and only once in each row, column, and box.
This thesis is specifically concerned with establishing conditions which guarantee a fractional Sudoku completion. A fractional Sudoku completion is an assignment of a set of weights to each symbol-cell incidence, representing the proportion of the symbol for that specific cell. The total weight of symbols for each cell must sum to one, and the sum of the weights for each symbol must be exactly one across the cells from each row, column, and box. These conditions still require a balanced distribution of symbols throughout the grid, but with considerably more flexibility than the typical Sudoku conditions.
In order to apply graph theoretic techniques to the problem, we develop a 4-partite graph representation, GP , for a partial Sudoku, P . The 4 parts correspond to the rows, columns, symbols, and boxes of P , and the edges of GP indicate the conditions for a completed Sudoku that remain unsatisfied in P . We then introduce the concept of a tile: a 4-vertex subgraph of GP , which represents a valid symbol placement in P . Completing P is equivalent to decomposing the edges of GP into these tiles. We then use an edge-tile inclusion matrix to relate the existence of such a decomposition to the existence of an solution vector with {0, 1} entries for a specific linear system. It is here that we move to the fractional setting through a relaxation of what constitutes an acceptable solution to the linear system - specifically, we are satisfied with solution vectors for which all entries are non-negative.
To find conditions that guarantee such a solution exists we study the Gram matrix of the edge-tile inclusion matrix for the empty (h, w)-Sudoku, denoted M. We show that M is symmetric and that each element of M corresponds to a pair of edges in the graph representation Ghw of the empty (h, w)-Sudoku grid. We then leverage the inherent symmetry of equivalence relations between these edges to establish a Sudoku adjacency algebra which contains M . This allows us to explicitly construct a generalized inverse for M . This generalized inverse, along with some applied perturbation theory, is used to show that given large enough h and w, the linear system for any sufficiently sparse partial (h, w)-Sudoku is a minor perturbation of the linear system for the empty (h, w) Sudoku, and therefore allows a fractional completion.
After presenting this main result, we take a brief detour to consider the unique case of Sudoku puzzles with thin boxes, examining how fixing the box width variable w while allowing height h to grow asymptotically influences the density conditions necessary for fractional completion. We also give an overview of our exploratory use of the Schur complement for matrix decomposition. Although this method didn’t directly feed into our primary results, it was instrumental in the discovery of the equivalence relations we used to construct our Sudoku adjacency algebra. Finally, we explore the potential applicability of our methodologies to certain Sudoku variants and acknowledge the limitations inherent in our approach.
In the appendices, we provide additional resources that complement the main body of our work. In Appendix A, we give a factorization of the Sudoku matrix M and its eigenvectors as Kronecker products for readers who wish to more directly compare our methodology to algebraic graph theory work done on Sudoku by other researchers. Appendix B presents a series of interactive and educational activities designed to introduce students to the basic principles of Latin squares in a fun spy-themed setting.Graduat
A comparison of Long Short-Term Memory, Convolutional Neural Network, Transformer, and Mamba models for sentiment analysis
No full textSentiment analysis is a critical task in Natural Language Processing (NLP) that helps decode the emotions and opinions embedded in text. With applications spanning from market research and social media monitoring to political analysis and customer feedback evaluation, sentiment analysis provides invaluable insights into public opinion and consumer behavior. This project studies the evolution of sentiment analysis models, focusing on the advancements made by deep learning techniques such as Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNNs), and transformer-based models like Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT). These models have set new benchmarks for accuracy, efficiency, and versatility. Additionally, this explores Mamba, a recent State Space Model (SSM) designed to overcome the computational challenges of transformers in handling long sequences and demonstrates state-of-the-art performance on language modeling tasks comparable to transformers twice its size. This study examines the strengths and limitations of these models, comparing their performance on sentiment analysis datasets to provide a comprehensive understanding of their applicability and efficacy in various contexts.Graduat
Molly Molly Oxenfree: Uncovering Queer Poets of the 19th Century
No full textThis project surveyed periodical poetry published in journals, newspapers, and magazines written by a small cohort of LGBTQ writers from the Victorian era in order to discover how queer poetry developed during the decade, and how more historical queer writers could be discovered. The project concluded that Victorian periodicals allowed for the establishment of a public-facing outlet in which poets of non-normative sexualities and gender identities could integrate with an oppressive culture to form a discoverable queer space and community in mainstream media by the end of the nineteenth century. Queer culture began to form in periodical poetry, peaking as it became more pronounced during the decadence and aestheticism movements, which enabled queer expression through their mode of cultural resistance. Decline of anonymous publication correlates with decadence and aestheticism, suggesting unity between public, artistic, and private identites. Queer poets become discoverable to historians through letters, diaries, and professional affiliations, all of which can continue to surface more voices.Jamie Cassels Undergraduate Research Awards (JCURA)UndergraduateReviewe
A study on the surrogate-based optimization of flexible wings considering a flutter constraint
No full textAccounting for aeroelastic phenomena, such as flutter, in the conceptual design phase is becoming more important as the trend toward increasing the wing aspect ratio forges ahead. However, this task is computationally expensive, especially when utilizing high-fidelity simulations and numerical optimization. Thus, the development of efficient computational strategies is necessary. With this goal in mind, this work proposes a surrogate-based optimization (SBO) methodology for wing design using a predefined machine learning model. For this purpose, a custom-made Python framework was built based on different open-source codes. The test subject was the classical Goland wing, parameterized to allow for SBO. The process consists of employing a Latin Hypercube Sampling plan and subsequently simulating the resulting wing on SHARPy to generate a dataset. A regression-based machine learning model is then used to build surrogate models for lift and drag coefficients, structural mass, and flutter speed. Finally, after validating the surrogate model, a multi-objective optimization problem aiming to maximize the lift-to-drag ratio and minimize the structural mass is solved through NSGA-II, considering a flutter constraint. This SBO methodology was successfully tested, reaching reductions of three orders of magnitude in the optimization computational time.The authors acknowledge Fundação para a Ciência e a Tecnologia (FCT), through IDMEC, under LAETA, project UIDB/50022/2020.FacultyReviewe
Degradation and Stabilization of Perovskite Inks for Solar Cells
No full textA photovoltaic cell (PV) converts photonic energy into an electrical current. A typical photovoltaic cell is a multilayer device consisting of an absorber layer, hole transporter layer (HTL), electron transporter layer (ETL), a transparent electrode and a back electrode. These devices are capable of directly translating a portion of the vast amounts of solar energy that collide with the Earth into usable electrical energy. Presently the vast majority of these devices use silicon for the absorber layer and positively or negatively doped silicon for the two charge carrier transport layers. The cost of manufacturing solar panels has dropped dramatically in the past decades, leading to their widespread adoption.
However, silicon is limited by its high manufacturing cost and indirect bandgap (the energetic and momentum difference between the valence band and conduction band in a semiconductor). Thus, there is a hunt for new semiconductor materials that do not possess the same intractable issues as silicon. One very promising emergent material is perovskite. Perovskite solar cells (PSCs) possess a number of advantages over traditional silicon devices. They can be deposited using standard wet chemical techniques and equipment, they require far lower processing energy, and they can be made thin enough to form flexible devices. These devices have seen dramatic improvement in efficiency over the few years.
Unfortunately, these devices are presently too unstable under ambient conditions for widespread adoption. They react rapidly with moisture and oxygen in the air. Formamidinium lead iodide, the highest performing variant of perovskite used in solar cells, have an unstable crystal structure at room temperature. Not only is the device unstable, but so is the ink used to deposit the solar absorbent layer.
Perovskite precursor inks suffer various forms of degradation, such as iodide anion oxidation and organic cation breakdown, hindering reliable perovskite solar cell manufacturing. This thesis will show that benzylhydrazine hydrochloride (BHC) not only retards the buildup of iodine as previously reported but also prevents the breakdown of organic cations. Through investigating BHC and iodine chemical reactions, we elucidate protonation and dehydration mechanisms, converting BHC to harmless volatile compounds, thus preserving perovskite film crystallization and solar cell performance. This inhibition effect lasts nearly a month with minimal BHC, in contrast with control inks where organic cations fully react in less than a week. This enhanced understanding, from additive stabilization to end products, promises improved perovskite solar cell production reliability.
The thesis consists of 4 chapters:
• Chapter 1 will introduce the fundamentals (structure and optoelectronic properties) of perovskite solar cells. In addition, the objectives of the thesis will be discussed at the end of this chapter.
• Chapter 2 gives an overview of the methodology used in the course of my research and images of relevant devices.
• Chapter 3 contains the results of the research and its analysis focusing on the use of, and understanding of, BHC as a stabilizer.
• Chapter 4 is a conclusion and overview of future prospective research.Graduate2025-05-1
Prenatal couple conflict dynamics
No full textPsychology - Three Minute Thesis FinalistUVic Faculty of Graduate Studies Three Minute Thesis (3MT) research communication competition.Graduat
Optimizing the encapsulation of SN-38 in PCL-b-PEG polymer nanoparticles for cancer therapy
No full textBackground: In this study, we address challenges encapsulating the anticancer agent, SN-38, in polymer nanoparticles (PNPs) for improved cancer treatment. SN-38 is particularly difficult to encapsulate due to its poor solubility in water and many solvents.
Methods: PNPs were synthesized using both bulk and microfluidic nanoprecipitation, and characterized for physico-chemical properties, release kinetics, and cytotoxicity and tumor penetration in 2D cell culture and tumor spheroids. Different formulations of PNPs, including different ratios of SN-38 and curcumin (CUR), and block co-polymers with different PEG terminal endgroups were compared to find the optimal formulation for encapsulating SN-38.
Results: By co-encapsulating CUR with SN-38, we can achieve increased SN-38 encapsulation efficiencies in co-loaded SN-38/CUR-PNPs by up to ten-fold as compared to PNPs encapsulating SN-38 alone. Moreover, a two-phase microfluidic reactor demonstrates similar trends regarding SN-38 content with CUR co-encapsulation, compared to bulk nanoprecipitation methods. Our findings also reveal a decrease in PNP polydispersity from 0.34 to 0.07 as the initial CUR-to-polymer initial ratio increases from 0 to 10.
Our first cytotoxicity studies show adding CUR does not significantly affect SN-38 potency. However, we observed significant differences in the potencies of SN-38/CUR-PNP formulations depending on formulation. An optimized formulation exhibited sub-nanomolar cytotoxicity against A204 cells, surpassing the potency of free SN-38 or PNPs containing only SN-38.
We find that incorporating a thiol terminal end group onto the PEG in the PNP resulted in a doubling of SN-38 encapsulation efficiency from 10% in the reference SN-38/CUR-PNP-OCH3 to 21% in SN-38/CUR-PNP-SH, but that this increase is only seen when the SN-38/CUR drug mixture is used, and not when the drugs are encapsulated individually.
Confocal microscopy shows encouraging results regarding PNP penetration throughout tumor spheroids, but EC50 cytotoxicity results in both 2D and 3D culture models show limited efficacy in cell killing of our formulations in 3D models, and although the SH-PNP formulation shows the best results in 2D models, the reference OCH3 formulation shows better performance in the 3D models.
Conclusions: Our study presents a co-encapsulation strategy that significantly enhances SN-38 encapsulation efficiency within PNPs for improved cancer treatment strategies. These findings contribute to overcoming challenges associated with poor solubility of SN-38 and paves the way for the use of SN-38 in the clinic.
Graduate2024-12-1