19614 research outputs found
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Multi-responsive calixarene hosts for the detection of drug molecules
Get PDFThe field of host-guest systems is advancing toward a wide variety of biologically relevant applications such as drug delivery, encapsulating agents, sensing target molecules of interest, and acting as therapeutics themselves. Calixarenes are a family of macrocyclic hosts that have been able to fill many of these roles, including novel systems that can act as therapeutic drug reversal agents and powerful chemosensors. In this work we present a new series of Stilbene-calix[4]arene (StiCx) molecules that can act as fluorescent sensors for small cationic drug molecules. The reaction by which these are constructed was optimized to synthesize a small library of compounds, by introducing a phenylacetylene “arm” with different substituents onto the calixarene scaffold, followed by in situ semi-reduction to obtain the corresponding stilbene. We also studied the ability of these molecules to bind and detect small drug molecules, with detection based on the change of fluorescent intensity of the host upon binding. We find that the new StiCx system can have either a turn-on or turn-off response depending on the identity of the target, which has the advantage of giving information-rich outputs of a mixture. This system is also capable of differentiating different analytes based on the fluorescence response using multivariate statistical treatment of the data. The mechanisms of guest binding and sensing by StiCx sensors were studied using different NMR and optical spectroscopy techniques. This work provides novel chemo-sensors for future use in drug detection, as well as providing insight into their mechanisms of action.Graduate2025-04-2
Thriving and affirmed: Supporting positive 2SLGBTQI+ care experiences in British Columbia
Get PDFMegan Webber and Connie Epp, project sponsors, British Columbia Ministry of Children and Family Development (MCFD). Dr. Alison Gerlach, course instructor, University of Victoria. Thais Amorim, course organizer, University of VictoriaGraduat
Unveiling the black box: A unified XAI framework for signal-based deep learning models
No full textCondition monitoring (CM) is essential for maintaining operational reliability and safety in complex machinery, particularly in robotic systems. Despite the potential of deep learning (DL) in CM, its ‘black box’ nature restricts its broader adoption, especially in mission-critical applications. Addressing this challenge, our research introduces a robust, four-phase framework explicitly designed for DL-based CM in robotic systems. (1) Feature extraction utilizes advanced Fourier and wavelet transformations to enhance both the model’s accuracy and explainability. (2) Fault diagnosis employs a specialized Convolutional Long Short-Term Memory (CLSTM) model, trained on the features to classify signals effectively. (3) Model refinement uses SHAP (SHapley Additive exPlanation) values for pruning nonessential features, thereby simplifying the model and reducing data dimensionality. (4) CM interpretation develops a system offering insightful explanations of the model’s decision-making process for operators. This framework is rigorously evaluated against five existing fault diagnosis architectures, utilizing two distinct datasets: one involving torque measurements from a robotic arm for safety assessment and another capturing vibration signals from an electric motor with multiple fault types. The results affirm our framework’s superior optimization, reduced training and inference times, and effectiveness in transparently visualizing fault patterns.This research received the financial support of NTWIST Inc., Edmonton, Canada and Natural Sciences and Engineering Research Council (NSERC) Canada under the Alliance Grant ALLRP 555220-20, and collaboration of Fraunhofer IEM, Düspohl Gmbh, and Encoway Gmbh from Germany in this research.FacultyReviewe
Institutional inquiry: Campus responses to gender-based violence in British Columbia
Get PDFSexual assault is among the most common forms of violence perpetrated against women and gender-diverse people; in Canada, university and college campuses are among the most prevalent sites of this violence (MacKenzie, 2019). Since 2016, campuses in Canada have become increasingly responsible for responding to and preventing gender-based violence, including sexual violence related to their campuses. Responses have come in the form of sexual violence policy and, at some post-secondary institutions, the establishment of designated sexual violence response offices and support staff (Albert & Perry, 2024). This research focuses on the experiences and institutional processes of student survivors who seek institutional support for campus gender-based violence at their post-secondary institution in British Columbia, and the support staff who assist them with these processes. To understand these experiences, data come from eleven qualitative semi-structured interviews, six of which were with survivors who have been through the process of seeking support on campus, and five of which were with individuals in the role of supporting survivors on campus. Interview data are contextualized alongside a consideration of relevant post-secondary policies to facilitate a robust analysis of institutional support processes that both survivors and support staff engage with, and the policies that they are both organized and coordinated by. Findings indicate that campus response is critical for survivors to receive support to continue their education and feel supported by their institution. Experiences also showed that academic accommodations were one of the most helpful resources campuses can provide. Yet, the process of reporting gender-based violence is intertwined with institutional betrayal for both survivors and those who support them. The harm caused through institutional betrayal is upheld by policy documents that tend to state institutional values, often performatively, in the response process rather than outlining the entirety of the process. This leads to confusion, betrayal, and a lack of clarity for survivors, demonstrating a diffusion of responsibility for those seeking to support them. I conclude by highlighting promising practices such as providing less punitive responses when desired by the survivor; moving to a policy framework that centers collective safety not just individual safety; decreasing dismissals of disclosures; and, increasing transparency in the reporting process.Graduate2025-10-2
Security Analysis for Vehicle Area Networks Protocol Using AVISPA
No full textIn the era of smart transportation, Vehicle Area Networks (VANs) are critical in enabling secure communication between vehicles and infrastructure. This project examines the security robustness of the PUFGuard protocol, a physically unclonable function (PUF)-based authentication framework designed to protect Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communications in VANs. PUFGuard leverages the inherent uniqueness of PUFs for secure key generation and authentication, aiming to establish trust and resilience against adversarial attacks in dynamic, multi-hop communication environments. To validate PUFGuard’s resilience, this research employs formal verification tools—AVISPA and SPAN—to simulate and analyze its effectiveness against common network threats, including replay attacks, manin-the-middle attacks, and impersonation attacks. The protocol is modelled in the High-Level Protocol Specification Language (HLPSL), where each component of the V2I and V2V authentication processes is systematically represented. Results from the AVISPA tests highlight the protocol’s strengths and potential vulnerabilities, providing insights into the adequacy of PUFGuard’s security measures in real-world VAN applications. The findings of this study suggest refinements to fortify PUFGuard further, offering a framework for secure, authenticated communication in modern vehicular networks.Graduat
Earlier detection of Alzheimer’s disease: Investigating brain-based changes in older adults with subjective cognitive decline
Get PDFAlzheimer’s disease (AD) is an incurable neurodegenerative disorder with a late clinical diagnosis, which disproportionately affects women. Research in preclinical AD has begun to focus on individuals with subjective cognitive decline (SCD), who are considered earliest on the cognitive continuum between healthy aging and AD. This dissertation is comprised of three manuscripts each focused on investigating whether neuroimaging (across modalities) can detect brain-based differences between individuals with SCD compared to their healthy counterparts. Study 1 is an in-depth systematic review of neuroimaging studies on SCD. Search results identified 62 studies that examined the use of structural and/or functional neuroimaging techniques in the detection of brain-based differences between individuals with SCD and healthy controls. While significant differences were found within and across various neuroimaging modalities, inconsistencies were observed within and between studies, suggesting the need for standardized criteria and longitudinal investigations in future research. Study 2 utilized resting-state functional MRI to explore functional connectivity differences in multiple brain networks between healthy older women and those with SCD. Results revealed increased functional connectivity in the default mode network and frontoparietal network among women with SCD independent of demographic variables, lifestyle factors, and medical comorbidities. Study 3 utilized multi-modal neuroimaging approach to examine grey and white matter differences in women with SCD compared to healthy women. No significant differences were detected in grey matter volume or white matter microstructure between the two groups. The resulting findings of studies 2 and 3, revealed the detection of differences between groups in brain function but not structure. This finding suggests that women with SCD can be differentiated from healthy women, before any significant and irreversible brain atrophy has taken place. Together, these studies contribute to the understanding of SCD as a preclinical marker of AD. Ongoing research and advancements in the conceptualization of earlier detection of AD are expected to play an important role in the development and implementation of disease-modifying interventions at the earliest possible time point, thereby reducing the devastating effects of this neurodegenerative disorder.Graduate2025-06-1
QoS-oriented multipath protocol design in mobile networks
Get PDFEmerging applications demand stringent quality of services (QoS). Meanwhile, future networks are featured by ubiquitous mobility. How to meet users' QoS requirements in highly mobile environments remains an open issue, which motivates our research on QoS-oriented multipath transport layer protocol design in mobile networks.
First, multipath transfer is promising in tackling mobility issues for a seamless handoff. Scheduling packets across multiple paths, however, has the issue of out-of-order (OFO) arrival due to the heterogeneity of the paths. In this regard, we put forward a Mobility-Aware Multipath Scheduler (MAMS), ensuring that the reordering delay of each packet is minimized in various mobility scenarios and thus the QoS is significantly improved.
Enabling multipath transfer in the Integrated Terrestrial and LEO Satellite Network (ITSN) is promising. However, the existing multipath congestion control algorithms in ITSN suffer from bandwidth under-utilization or overshooting issues due to the high-speed network movement. Therefore, a novel Mobility-Aware COngestion control (MACO) algorithm is developed.
As applications are the driving force for protocol design, we investigate the performance of video streaming applications using multipath transfer. Assuming the QoS requirements of the application are known by the sender, we adopt a lightweight learning framework, a contextual multi-armed bandit (CMAB), to discover the underlying relationship between dynamic network states and QoS performance, which can intelligently select access networks and adapt FEC coding to trade off delay, reliability, and throughput.
Furthermore, 360-degree videos are not only bandwidth-intensive but also highly sensitive to delays. Ensuring both high video quality and smooth playback experience remains a critical issue. Therefore, we introduce a QoE-oriented Deadline-driven (RIDE) algorithm for multipath scheduling at the frame level. RIDE employs a dependency tree to understand deadlines for different types of frames and considers the negative impacts of Field of View (FoV) changes on scheduling decisions. Utilizing an actor-critic framework to train the neural network enables the scheduler agent to adapt to dynamic environments, including network and FoV dynamics.Graduat
Investigating the Northeast Pacific Ocean Carbon Sink using a Machine Learning Approach
Get PDFImproving our understanding of how the ocean absorbs carbon dioxide (CO2) is critical to climate change mitigation efforts. The global ocean takes up nearly a quarter of anthropogenic CO2 emissions annually, but the variability of this uptake at regional scales remains poorly understood. In this dissertation I compiled an extensive collection of reported surface ocean air-sea CO2 exchange values within each of Canada’s three adjacent ocean basins. I go on to summarize current research and identify steps forward to improve our understanding of the marine carbon sink in Canadian national and offshore waters. I then developed advanced techniques for quantifying air-sea CO2 fluxes in the Northeast Pacific Ocean to improve our understanding of processes driving seasonal, interannual, decadal, and long-term variability, aiding in monitoring, reporting, and verification of future marine carbon dioxide removal, and helping inform carbon and climate policies. Utilizing a neural network approach to interpolate sparse observations, I created monthly gridded seawater partial pressure of CO2 (pCO2) data products from January 1998 to December 2019, at 1/12x1/12 spatial resolution, in the Northeast Pacific Ocean. The two data products, encompassing the open ocean and the coastal ocean, were created using non-linear relationships between pCO2 observations and a range of predictor variables representing processes affecting pCO2, at a spatial resolution four times greater than leading global products. Using an ensemble approach, I was able to produce robust pCO2 estimates, evaluated against independently withheld data, that represent regional variability with better overall performance compared to global products. I conducted a novel sensitivity analysis which identified that the parameters responsible for the neural network’s ability to capture regional pCO2 variability agrees with mechanistic processes. The regional open ocean and coastal products also reproduced pCO2 estimates well within the overlapping domain, with differences influenced by scarcity of observations. Using wind speed and atmospheric CO2, I calculated air-sea CO2 fluxes. In the open ocean, on sub-decadal to decadal timescales, I found that the upwelling strength of the subpolar Alaskan Gyre, driven by large-scale atmospheric forcing, acts as the primary control on air-sea CO2 flux variability. In the coastal ocean, I report an anticorrelation between annual air-sea CO2 flux and its seasonal amplitude with the relationship driven by regional processes. I estimate long-term surface ocean pCO2 increase at a rate below the atmospheric trend. The slowest rate of increase occurs where there is strong interaction with subsurface waters in the Alaskan Gyre and the West Coast upwelling zone. Basin-wide, my results suggest that the region is a net sink for atmospheric CO2 with trends indicating increasing oceanic uptake.Graduate2025-05-1
Practicing applied sociology on the ground, rather than in the tower: Exploring the legacy of community-engaged learning (CEL) course experiences
Get PDFThis exploratory qualitative study investigates the legacy of taking a singular community-engaged learning (CEL) course on alumni at the University of Victoria (UVic). Thirteen participant interviews with CEL alumni capture a range of lived experiences since course completion, from 1 month to 5 years with respect to their course deliverables (2018 to 2023).
This research reveals the long-term impacts of CEL post-course by exploring alumni’s reflections. Informed by previous research and literature, this thesis is guided by the following questions: How do sociology students describe their CEL experiences after course completion; what impacts, if any, does a CEL-applied sociology seminar course have on students post-course; to what degree do alumni CEL experiences change in relation to time since course completion; and, according to alumni, how effectively does CEL teach applied sociology? The research findings reveal that as a method for teaching applied sociology, a CEL course impacts how alumni practice sociology post-course and how they use, or apply, their sociology as a tool and perspective as they navigate and understand social realities. The data explores how and why CEL alumni continue to make meaning out of their experiences after course completion.
Findings suggest that alumni feel that their CEL experiences enhance their critical and relational thinking skills, inspire reflexivity, and increase feelings of self-efficacy. The research reveals that alumni feel more competent in their understanding of sociology and ability to apply sociology post-CEL due to courses’ engaged scholarship and experiential learning pedagogy.
Overall, this study suggests that understanding the legacies of CEL on alumni could impact how sociology is taught and practiced within and beyond academia.Graduate2025-03-122025-08-1
A time-domain Doppler estimation and waveform recovery approach with iterative and ensemble techniques for bi-phase code in radar systems
No full textThis paper presents a novel, cost-effective technique for estimating the Doppler effect in the time domain using a single pulse and subsequently leveraging the precise Doppler value to recover the radar waveform. The proposed system offers several key advantages over existing techniques, including the ability to calculate the target speed without any frequency ambiguity and the ability to detect a wide range of target speeds. These two features are not available in any existing techniques, including the conventional moving target detection (MTD) processor. To ensure improved accuracy and robust estimation, the system employs ensemble and iterative techniques by recursively and efficiently reducing the Doppler residues from the signal. Furthermore, the proposed system demonstrates effective signal recovery of a well-known bi-phase code shape at low signal-to-noise ratios in just a few iterations. The performance evaluation of the new algorithm demonstrates its practicability and its superiority over traditional radar systems. Implementation on software-defined radio (SDR) reveals that the proposed system excels in Doppler estimation and signal recovery at low SNRs, demonstrating promising results.FacultyReviewe