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Patriarchy, Power and Protest: Women’s Agency in South Asian and African Literature
No full textThis dissertation explores the marginalization of women and their agency in the context of both literature reflecting the Partition of the Indian Subcontinent and African novels, examining the evolution of patriarchal structures and the ways in which women navigate and challenge these systems. Through an analysis of key texts, including What the Body Remembers, Cracking India, The Joys of Motherhood, Things Fall Apart, and Woman at Point Zero, this study highlights the patriarchal manipulation of religion, tradition, and women's roles as wives and mothers to enforce patriarchal control. Despite the doubly marginalized position of women, these narratives reveal how women have created voices and "mini-narratives" that puncture the overarching patriarchal structures. The research also delves into contemporary examples of women's oppression, such as widow immolation, honor killings, and female genital mutilation, analyzing the patriarchal discourses that circulate in these accounts and contextualizing these within ongoing global patriarchal trends. Additionally, this dissertation examines the political implications of the Western representation of Muslim women, particularly through the discourse on the veil, and argues that the Western stance on such symbols mirrors patriarchal tactics of marginalization. The study asserts that, despite the silencing forces of patriarchy, women consistently carve out spaces for agency and resistance through storytelling, both in historical and modern contexts
Systematic Methodology for Enhanced Performance Prediction in Designing Large Active Phased Array Antennas for Satellite Communication
No full textThe deployment of 5th generation (5G) and the development of 6th generation (6G) networks have increased the demand for global connectivity at high data rates. Traditional terrestrial infrastructure, including base stations and optical fiber, faces challenges in remote or sparsely populated regions, such as mountainous terrain and oceans. Satellite communication (SATCOM) provides a complementary solution, enabling direct wireless links between users and satellite constellations, removing the need for implementing optic fiber cables. Low earth orbit (LEO) satellites have been proposed as they reduce latency and power requirements compared to traditional geostationary satellites, but their constant motion necessitates rapid beam tracking. At the same time, Ka-band operation (27.5–31 GHz) offers wide bandwidths for high data rates but requires high-gain antennas to overcome propagation losses. Electronically steerable Phased Array Antennas (PAAs) have emerged as the leading solution to address these requirements by enabling fast beam steering without mechanical components. Achieving the necessary gain, however, demands large arrays, which introduces significant design challenges.
This thesis presents a systematic design methodology for large 26.5–40 GHz (Ka)-band active PAAs and their feed networks. The methodology leverages industry-standard eletromagnetic (EM) simulation models to guide the design of antenna elements and feed networks, ensuring wideband and scalability. Using this approach, a 16 × 16 active PAA is designed, integrating 64 beamforming integrated circuits (BFICs). Critical system level considerations, including DC power delivery, digital signal integrity, and thermal management, are then analyzed to ensure reliable operation
Design and Evaluation of a Novel Framework Inspired by Affect Control Theory to Create Social Robot Affective Identities
No full textDeveloping agent personalities and behaviours that can be adjusted based on individual factors such as personal preferences, context, and lived experiences is important to improve user acceptance and engagement with social robots in varying scenarios, such as for assistive robots. Current approaches used in the design of social robot personalities (e.g., extroversion manipulation-based) often do not encompass the entire personality (e.g., Big 5) as only a subset of dimensions can be manipulated. We propose a novel framework using Affect Control Theory (ACT) that enables the manipulation of all aspects of an agents' affective identities, as a proxy for personality. We evaluate this framework in two studies, one online and one in-person, involving an "at-home" medication sorting task to determine (1) whether participants are able to identify the manipulations in the social robot behaviours, and (2) whether this framework can be used for personalizing social robot identities, in other words, if participants will prefer to interact with specific robot identities considering their own affective identities. The results confirmed the design and manipulation of two out of three ACT dimensions and showed that participants preferred interacting with robots that are perceived to have an affective identity closer to them. Proposed framework and results can have significant contributions to the personalization of social robot behaviours in assistive robots and beyond
Approximation Algorithms for Relative Survivable Network Design Problems
No full textThe Survivable Network Design (SND) problem is a classical and well-studied graph
connectivity problem. Given a set of source-sink pairs and demands between them, SND
asks one to compute a subgraph such that the number of paths between each pair meets
their demand. SND is primarily interesting in modeling fault-tolerance; we can see the
problem as requiring certain nodes to be connected even if some edges ”fail”. It is well
known that a 2-approximation algorithm for SND exists, using the method of iterative
rounding.
In 2022, Dinitz et al. introduced a problem that we refer to as Path-Relative Survivable
Network Design (PRSND), a natural extension of SND that addresses cases where the
underlying graph does not have the required connectivity; in this problem, we require
that the connectivity of our subgraph is ”as good as” it is in the original graph. Perhaps
surprisingly, this variation makes PRSND much harder to approximate than standard SND,
and outside of certain special cases no constant-factor approximations have been found.
In this thesis we introduce the Cut-Relative Survivable Network Design (CRSND) prob-
lem, another variant of SND that similarly aims to capture relative fault-tolerance. We
show that this problem admits a 2-approximation algorithm, matching the best known ap-
proximation factor for SND, via a decomposition technique. We explore some properties of
said approximation, as well as hardness and modeling properties of Cut-Relative Network
Design problems
Distributionally Robust Chance Constraints for Radiotherapy Treatment Planning under Geometric Uncertainty
No full textRadiation therapy is a popular treatment modality for cancer which benefits from optimization. Mainstream approaches define a margin around the tumor called the planning target volume (PTV) to account for changes in patient anatomy throughout the course of treatment. However, the PTV concept does not adequately address uncertainty in the position of the tumor or in the probability distribution of its shifts.
In this thesis, we develop a distributionally robust optimization framework for radiation therapy treatment planning under inter-fraction geometric uncertainty of the clinical target volume (CTV) where the underlying probability distribution of rigid shifts is not well known. To achieve this, we begin with a deterministic model, then augment it using chance-constrained optimization. Next, we consider an ambiguous chance constraint and apply its robust reformulation to treatment planning models.
We first apply these three frameworks to a basic fluence map optimization model. Then, we include machine deliverability constraints and formulate a direct aperture optimization model. Due to the large-scale combinatorial structure, these models cannot be solved efficiently at a clinical scale, making them unusable for modern treatment approaches such as volumetric-modulated arc therapy. Therefore, we extend a sequential convex programming algorithm to include robust and distributionally robust optimization.
Through experiments on two lung cancer patients, we show that the novel distributionally robust treatment planning model can achieve better tumor coverage than the robust model as well as better healthy tissue sparing than the standard PTV approach. We also show that adding robust and distributionally robust optimization does not degrade the quality of the convex approximation. We conclude with a discussion on limitations and present ideas for future research projects
The Quaternary stratigraphy of the Gods and Yakaw rivers area, northeastern Manitoba
No full textReconstructing the spatiotemporal dynamics of past glaciations provides a long-term perspective that is essential to understanding how Earth systems respond to climate change over different timescales. While most ice sheet reconstructions focus on the last glaciation, incorporating stratigraphic records from older glaciations enhances our understanding of past glacial-interglacial cycles. The western Hudson Bay Lowland (HBL) in northeastern Manitoba preserves thick Quaternary sediment sequences (20–70+ m) spanning multiple glacial-interglacial cycles, influenced by two major ice spreading centers of the Laurentide Ice Sheet (the Keewatin dome to the northwest and the Quebec-Labrador dome to the east).
This study revisits the Quaternary stratigraphy along the Gods and Yakaw rivers in the western HBL using contemporary techniques, including a detailed, multi-parameter approach to characterize tills, combining field observations, paleo-ice flow indicators (till fabric analysis, lodged boulder striations), and till composition (matrix geochemistry, clast-lithology counts). For this thesis, a hybrid lithostratigraphic-allostratigraphic approach was employed across ten sections to reconstruct local glacial dynamics and establish an updated stratigraphic framework.
Results confirm a complex, laterally variable stratigraphy. The revised framework includes 21 delineated stratigraphic units: 15 tills correlated using primarily paleo-ice flow indicators and stratigraphic position, and 6 sorted sediment units defined by allostratigraphy. Several key findings emerge from this framework, including: (1) A more detailed reconstruction of local glacial dynamics, (2) recognition of at least 4 sub-till sorted sediment units interpreted as ice-free intervals, and (3) a relative stratigraphy that extends the Quaternary record in the region, possibly back to MIS 12 or even older. Sediment provenance and ice-flow indicator analysis suggest that older tills (units 1t–11t) were dominantly sourced from a Quebec-Labrador dome, while younger tills (units 13t–20t) exhibit more complex signatures. This shift between units 11t and 13t may reflect changes in paleo-ice sheet configuration, bedrock availability to glacial erosion, or entrainment of pre-existing sediment. Although some till units appear superficially similar, they are interpreted as discrete tills deposited during separate ice-flow phases rather than products of glaciotectonic stacking. An alternative explanation invoking glaciotectonism is considered; however, variability of paleo-ice flow indicators, the absence of pervasive deformation structures, and a regional context unfavourable to large-scale glaciotectonism opposes this interpretation.
These findings have broad implications for both ice sheet modeling and mineral exploration. In ice sheet modeling, incorporating records from older glacial-interglacial cycles provides key constraints to long-term ice sheet reconstructions, improving understanding of ice sheet response to climate change, which leads to better predictions of future changes. In mineral exploration, understanding glacial dynamics in thick-drift regions helps establish dispersal directions and sediment provenance, which may either 1) improve the interpretation of indicator mineral source, or 2) confirm a long history of inheritance and overprinting that requires the development of new techniques
Adaptive Live Streaming Strategies for Multi-homed Environments
No full textThe use of live video streaming applications over mobile, wireless networks continues to grow. In these applications, a sender (e.g., cameraperson) streams video to a receiver (e.g., video service such as Youtube or a television station), and the receiver disseminates the video to the viewers. Live video streaming over wireless (3G, LTE, 5G) linksis challenging since these links often experience fluctuating latency and available bandwidth. Furthermore, as the bandwidth demands of these applications continue to increase,a single, wireless network link may be unable to continuously stream video that satisfies the viewers’ Quality of Experience (QoE) requirements. Multi-homing is a potential solution that offers applications additional bandwidth through aggregation and the ability to circumvent congested paths. Although there are a number of commercial multi-homed, adaptive live streaming solutions, their proprietary nature makes it difficult to understand their design trade-offs and evaluate their effectiveness. Furthermore, many multi-homed transport protocols are not designed for latency-sensitive data, do not adapt the video bitrate to changing network conditions, or require significant adjustments if used in a different environment from their original design (e.g., number or type of links). In this thesis, we explore solutions to the challenges of multi-homed, adaptive live video streaming.
Firstly, accurate bandwidth measurements are important because they indicate the maximum video bitrate that the available links can send while avoiding congestion. A packet train is a common measurement technique that sends a series of packets and uses the packets’ inter-arrival time to measure the available bandwidth. However, packet train measurements are inaccurate due to kernel interrupts. In this thesis, we propose our PacketBurst bandwidth measurement technique to improve the accuracy of the packet trains. This technique detects packet inter-arrival times that are affected by kernel interrupts and would yield inaccurate bandwidth measurements. Our PacketBurst technique excludes these packets from the bandwidth measurement calculation to improve the accuracy of the packet train. In addition to accurate bandwidth measurements, video streaming protocols can also benefit from future link quality predictions so that they may preemptively reduce the video bitrate or change network paths to avoid video stream disruptions. This thesis evaluates various machine learning techniques for classifying or predicting link instability, which we define as sudden increases in application-level packet loss and latency, or decreases in available bandwidth over a short, pre-determined period of time.
Secondly, using both video bitrate adaptation and multi-homing can greatly improve the live video streaming application’s user QoE by avoiding congestion that results in excessive delay or by providing high video bitrates through link aggregation. In this thesis, we present Conflux: a modular, multi-homed, adaptive video bitrate protocol for live video streaming. Conflux uses a probabilistic link quality model that is used in conjunction with a user-specific utility function to determine the video bitrate and the rate at which to send on each link. By using a simple, yet general, probability-based link quality model, Conflux can easily support different requirements and environments such as the number of links by just maximimizing the expected utility. We evaluate Conflux in an emulated network environment where the available bandwidth comes from variety of wireless network traces, and the maximum available bandwidth is 38Mbps. Our evaluation shows that Conflux can obtain at least an 18% improvement when there are two available links, and 65% when there are five available links over its non-optimal, multi-homed comparison systems that have oracle-based knowledge of either each link’s available bandwidth or the total aggregate available bandwidth.
Finally, resending delayed data on alternate, non-congested paths and sending redundant data using Forward Error Correction (FEC) can enable a video frame to arrive on time in the presence of deteriorating link quality or link failures. However, determining the degree of redundancy while effectively making use of the available bandwidth so that users can have high QoE is challenging. In this thesis, we introduce extensions to Conflux that support retransmission and FEC. We present our method of calculating the probability of on-time video data arrival for a given redundancy level using Conflux’s probabilistic link quality model. This allows Conflux to select the degree of redundancy and corresponding video bitrate that maximizes the user’s expected utility. Our evaluation shows that using Conflux’s redundancy-specific user utility functions lowers the percentage of incomplete frames 11% to 0.02% in network environments that experience packet loss according to the Gilbert-Elliot loss model
Understanding Human Decision Variability and the Effects of AI System Data Modality on Trust and Acceptance in Human–AI Collaboration : A Human-Centred Approach to Designing AI Systems in Healthcare Contexts
No full textEffective decision-making is a complex cognitive process that plays a crucial role in high-stakes domains such as healthcare, where inconsistencies in judgment can significantly impact outcomes. It varies widely between individuals, particularly across levels of expertise [Rasmussen, 1983], often leading to variability and inefficiencies [Curran et al., 2022], a phenomenon well-documented in cognitive and decision sciences. As Artificial Intelligence (AI) becomes increasingly integrated into various domains, including finance, transportation, and healthcare, it presents new opportunities to enhance human decision-making. With recent advancements, AI has the potential to mitigate these decision-making challenges, such as inter-user variation, by providing standardized, data-driven recommendations. It can support novice reasoning by guiding decision processes and complement expert intuition when aligned with users’ strengths and limitations [Inkpen et al., 2023].
However, other research has shown that standalone AI systems cannot be fully relied upon due to inherent biases, limited contextual understanding, and an inability to adapt dynamically to the complexities of human decision-making. These limitations necessitate a shift toward human-AI collaborative systems, where AI serves as a complementary tool to enhance human performance. Effective collaboration, however, depends on calibrated trust and high acceptance of AI systems, influenced by numerous factors, including the underexplored effect of AI system data modality on user trust and acceptance.
This thesis investigates the variations in decision-making strategies between novices and experts and examines how AI systems can bridge these gaps through human-centric design. It further explores how data modality in AI systems, particularly unimodal versus multimodal data, affects human-AI collaboration. A two-phase study was conducted in the healthcare domain, focusing on glaucoma diagnosis as a specific case study, and employed a mixed-method approach combining qualitative interviews and quantitative evaluations.
The first phase examined variations in decision-making strategies between novices and experts. It was found that experts adopted more dynamic and efficient approaches by integrating a wider range of factors, emphasizing progression analysis, identifying complex patterns and correlations, and dynamically balancing positive and negative decision factors based on contextual severity. They demonstrated cognitive efficiency by filtering out extraneous information and prioritizing critical data points. In contrast, novices relied on more structured and analytical methods, often overemphasizing explicit indicators and struggling to balance conflicting evidence. Their decision-making factors also varied significantly across different scenarios. Furthermore, the impact of data availability was evident, with novices being more adversely affected by limited data compared to experts.
The second phase evaluated user interactions with unimodal and multimodal AI systems designed for glaucoma diagnosis, measuring trust and acceptance using statistical methods. Multimodal systems consistently outperformed unimodal systems by integrating diverse data sources that mirrored how clinicians process information, leading to better alignment with their workflow. This, in turn, led to significantly higher trust and acceptance for multimodal systems, compared to unimodal systems with significant differences. (p < 0.01). Decision-making performance of optometrists also improved, with multimodal systems achieving higher performance than unimodal systems. An interaction effect between user factors (expertise, gender) and system type was also observed, with notable differences in accuracy and confidence levels. These findings show the need for human-centric AI systems that support novice learning and expert decision-making, leveraging data modalities that align with cognitive processes to foster calibrated trust and high acceptance. By enhancing human-AI collaboration, such systems can improve decision-making consistency and optimize outcomes across diverse contexts.
This thesis makes interdisciplinary contributions to human factors, AI, and optometry by investigating cognitive variability in glaucoma diagnosis and examining how data modality influences trust, acceptance in human-AI collaborative environments. In the domain of human factors, it offers insights into how clinical expertise shapes diagnostic reasoning, revealing distinct approaches to data use, heuristic application, and uncertainty management between novices and experts. In the domain of AI, it proposes the design of human-centered AI systems that support optometrists with varying levels of expertise and also evaluates unimodal and multimodal decision-support systems, demonstrating that multimodal systems more closely align with clinicians’ workflows, resulting in higher trust, acceptance, and diagnostic performance. In optometry, the research examines how clinicians interpret glaucoma data, highlighting differences in reasoning strategies such as progression analysis and data prioritization. It informs the development of AI tools that align more closely with optometrists’ decision-making processes and integrate seamlessly into routine clinical workflows.
In conclusion, this thesis explores variations in human decision-making and demonstrates the value of human-centric AI systems in supporting both novices and experts. It identifies data modality as a key factor influencing trust and acceptance, providing a rationale for using the same data clinicians rely on to achieve better workflow alignment, leading to higher trust and greater system acceptance. These insights offer a strong foundation for future AI integration in optometry and other high stakes medical domains
Path Integral Monte Carlo Simulations of Flexible Water Clusters with Normal-Mode Sampling in Jacobi Coordinates
No full textThe primary focus of this thesis is on developing the normal-mode sampling algorithm, an importance sampling method specifically designed for path-integral quantum Monte Carlo simulations of flexible molecular systems. This work is motivated by the desire to include vibrational degrees of freedom in numerical studies of confined molecular lattices. Describing the dynamics of a molecular system using its normal modes naturally allows for the inclusion of molecular rotations, translations, and even vibrations.
We first introduce a novel density matrix factorization that arises from decomposing our system Hamiltonian into its harmonic and anharmonic terms. The normal-mode sampling algorithm is then constructed using this factorization. We also integrate Jacobi coordinates with our normal-mode sampling algorithm to allow for separability between translational and ro-vibrational degrees of freedom.
Finally, we validate our normal-mode sampling algorithm in the context of path-integral quantum Monte Carlo simulations of several flexible molecular systems, namely the water monomer, dimer, hexamer cage, and hexamer prism. For each of these systems, we calculate the ground-state energy and various structural properties, benchmarking our results against exact diagonalization, path integral molecular dynamics, and diffusion Monte Carlo studies from the literature
Analysis of integrated heating approaches for cold-start conditions in 21700 lithium-ion battery modules using thermal system simulation
No full textCold ambient conditions significantly reduce discharge capacity and slow the thermal response of lithium-ion cells, particularly at low state of charge (SOC). To address these challenges, this research studies the feasibility of heating strategies to improve cold-start performance in 21700 lithium-ion battery modules using thermal system simulation. Both experimental and simulation-based approaches were employed. At the cell level, experimental tests were conducted to evaluate thermal and capacity behavior under sub-zero temperatures. These results were compared against thermal system simulation simulations under convective or adiabatic conditions, revealing that experimental test setups introduce additional resistances not captured in idealized models. And adiabatic conditions could allow faster cell heating compared to convective conditions due to internal heat accumulation, which shows the effect of insulation. In fact, the temperature rise simulated under adiabatic conditions is approximately 2.3 to 2.9 times greater than under simulated convective conditions. Building on these findings, a module design was developed to enable system-level simulation of thermal strategies. The design considered safety, structural integrity, and thermal performance, balancing insulation with heat flow pathways.
Then the study focuses on evaluating the feasibility of external and battery-powered heating strategies. Four heating configurations were simulated, external heating, battery discharge, or combined configurations. Simulations were carried out across below zero ambient temperatures of -20 °C, -10 °C, and 0 °C and different initial SOC values of 80%, 50% and 20%. Results show that in the absence of heating, the battery was unable to complete discharge at low SOC, particularly at -20 °C and 20% initial SOC. Yet when external surface heating was applied, the module achieved a faster temperature rise enabling full discharge even under these extreme conditions. Furthermore, when external heating is applied without discharge, the heating rate slows down, highlighting the added benefit of internal heat generation during battery operation. Lastly, the study evaluated whether the battery could power its own heating system. At 20% SOC and -20 °C, the energy required for heating exceeded the battery’s usable output, rendering self-heating unfeasible. In contrast, at 0 °C and moderate SOC levels, it remained viable, with heating demands as low as 2 to 3% of the available capacity.
Overall, the findings support the integration of targeted heating strategies into electric vehicle (EV) thermal management systems, showing that a combination of external heating and internal heat generation enables reliable cold-start performance while minimizing energy consumption for battery heating in sub-zero conditions