UWSpace

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Recent Submissions

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    Techno-Economic Analysis of Bio-Methanol to Sustainable Aviation Fuel Process
    (University of Waterloo, 2026-05-26) Erdmann, Oliver
    There are many sustainable aviation fuel production methods that are currently in use today, like the ethanol alcohol-to-jet process and Fischer-Tropsch. Demands from international aviation bodies and organizations are calling for more development and sources for producing sustainable aviation fuels, and the methanol-to-jet process is a pathway of interest. Currently, there exists no techno-economic work on such a plant model and insights on its profitability, break even costs, and most expensive contributing factors of running a methanol-to-jet plant are unknown. Extensive research and development work on a techno-economic tool from the German Aerospace Center called PyTEEA granted the option of doing economic calculations on an ASPEN plus simulation of the methanol-to-jet plant. The results found that both the cost of bio-methanol fuel or the production of bio-methanol to be the leading expense in running such a plant, with up to 75.95% of the break even cost of 3.26 EUR/kg being attributed to bio-methanol fuel costs alone. Although the methanol-to-jet pathway shows technical promise, it is currently less cost-competitive than other sustainable aviation fuel routes, such as ethanol-to-jet, largely due to the limited availability and high price of bio-methanol. As production capacity and market supply of bio-methanol expand, the methanol-to-jet pathway may become a more viable option for large-scale sustainable aviation fuel deployment.
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    Inherited Scripts and Contested Narratives: Arab Identity in 18th and 19th Century America
    (University of Waterloo, 2026-05-26) Altaher, Ayesha
    This dissertation examines the construction and negotiation of Arab and Muslim identity in the United States from the eighteenth through the early twentieth centuries, tracing how cultural representation, legal discourse, and immigrant self-articulation shaped the terms of visibility, belonging, and exclusion. Challenging narratives that frame Arabs and Muslims as recent or peripheral to American history, the project demonstrates that their presence in the Atlantic world predates the nation-state itself and that their identities were forged through a long process of representational struggle rather than belated arrival. Drawing on Edward Said’s theory of Orientalism and Leonard Cassuto’s concept of the racial grotesque, the dissertation analyzes how Barbary captivity narratives by Thomas Pellow, John Foss, and Robert Adams produced enduring ideological frameworks that cast Muslims as cruel, despotic, and civilizationally opposed to the West. These narratives, I argue, provided an early cultural architecture through which American identity was defined against a racialized and religiously marked “Arab Other.” The project then shifts to the late nineteenth and early twentieth centuries to examine how Arab and Syrian immigrants encountered and contested these inherited representations through print culture, legal advocacy, and public performance. Using an interdisciplinary American Studies methodology that combines literary analysis, visual culture studies, archival research, and translation of Arabic-language sources, the dissertation foregrounds immigrant newspapers such as Al-Hoda as sites of negotiation over race, gender, language, and citizenship. It further analyzes the 1893 Chicago World’s Fair as a pivotal moment in the institutionalization of Orientalist spectacle, where visual regimes reinforced racial hierarchies even as immigrant subjects carved out limited spaces of resistance. Ultimately, this dissertation argues that Arab and Muslim identity in the United States emerged through continuous negotiation between imposed representations and immigrant agency. Neither wholly excluded nor fully accepted, Arabs and Muslims shaped American cultural and political life while contesting the narratives that sought to marginalize them. By situating Arab and Muslim histories at the center of American racial and cultural formation, this project redefines their role in the making of American identity itself.
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    Slice Based Fuzzing of Software
    (University of Waterloo, 2026-05-26) Murali, Aniruddhan
    Modern software systems are increasingly complex, and static analysis tools are widely used to identify potentially vulnerable code by issuing warnings. However, these warnings often require manual inspection by developers to determine whether the reported issues are genuine, making validation time-consuming and error-prone. Similarly, ensuring that individual commits (i.e., code changes) do not introduce bugs remains a fundamental challenge in software maintenance. Directed fuzzing has emerged as a powerful automated testing technique for bug detection. Yet applying directed fuzzing to entire projects for each warning or modified code location is computationally expensive, often requiring days of execution while yielding only incremental coverage improvements. We present a unified framework for bug detection based on the construction and fuzzing of compiled code slices centered on either static analysis warnings or functions modified in code commits. Unlike prior approaches that extract slices from the program entry point or impose restrictive slice-size limits, our framework constructs slices of arbitrary size and compiles them into standalone, testable units. For static analysis warning validation, we directly fuzz slices of the function containing the warning, enabling rapid elimination of false positives. We implement this approach in a tool called FuzzSlice. The key insight that we base our work on is that a warning that does not yield the bug when fuzzed at the function level in a given time budget is most likely a false positive. Evaluation on the Juliet benchmark shows that FuzzSlice detects all 864 known false positives in the ground truth. For open-source repositories, developers from tmux and openSSH independently labeled reported warnings. In these projects, FuzzSlice automatically identified 33 of 53 developer-confirmed false positives, reducing false positives by 62.26%. These results demonstrate that FuzzSlice substantially reduces manual validation effort, achieving complete elimination of false positives in Juliet and significant reductions in real-world codebases. To further strengthen static analysis warning validation, we introduce SnipTest, a framework that heuristically identifies true positives among warnings. SnipTest employs a layer-by-layer slicing strategy that incrementally expands the slice context around the target location prior to fuzzing. Unlike FuzzSlice which tests only the function containing the warning, SnipTest can incrementally grow the boundary of the code slice to include more calling context, enabling the validation of potential bugs with progressively increasing precision. We evaluate SnipTest on a benchmark comprising 97 true bugs and 97 false alarms across three real-world projects. SnipTest triggered 53 true bugs (54.6%), consistently across three slice levels, while the remaining cases were determined to be unreachable. Compared to state-of-the-art directed fuzzers, unseeded SnipTest confirms more bugs than unseeded baselines and matches the effectiveness of seeded fuzzers. Moreover, SnipTest significantly improves efficiency, achieving a 5.5–10.6× speedup in fuzzing time relative to both seeded and unseeded directed fuzzers. We further demonstrate its practical relevance by discovering three previously unknown bugs in vim and libpcap, leading to the disclosure of CVE-2025-11964. Finally, we extend our slice-based approach to commit verification. We introduce CommitGuard, a novel commit-aware differential fuzzing framework for detecting bugs introduced by code changes. Rather than fuzzing entire program versions, our method automatically identifies functions modified by the code changes and generates dedicated slices for those functions. For each function that has been modified, we create two slices: one using the updated version after the code change, and another using the older version before the code change. These slices are fuzzed independently, and their runtime behaviors are systematically compared to uncover divergences indicative of commit-induced bugs. We show the practicality of commit-level differential fuzzing by detecting five previously unknown bugs across 300 commits in widely used projects, including openSSL, libpcap, and leptonica. Additionally, CommitGuard exhibits a low false positive rate, with only 2 false positives among these 300 commits. Finally, we demonstrate that CommitGuard is efficient, requiring 32 minutes per commit, while achieving up to 75.36% code coverage of modified functions on average. Overall, we aim to show that slice-based differential fuzzing is both effective and computationally efficient, making it well-suited for integration into modern, fast-paced software development workflows, such as Continuous Integration (CI).
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    Migration, Gendered Translocality, and Rural Wellbeing in the Western Highlands of Guatemala
    (University of Waterloo, 2026-05-26) Kocsis, Emily
    Background: Rural out-migration is highly prevalent in the Western Highlands of Guatemala. In response to a confluence of factors, including economic poverty, exclusionary state policies, gang violence, and the effects of climate change, men and boys are leaving rural spaces in pursuit of opportunity elsewhere. While Guatemalan women are increasingly engaging in various forms of mobility, they are still the dominant group remaining at home while spouses, older children, and their parents emigrate. For this so-called “left behind” population, male out-migration shifts socio-spatial relations, altering livelihoods, health, and community life in complex ways. Understanding these evolving dynamics requires an attentiveness to translocality – the connections between mobile and immobile actors sustained through the flow of resources, knowledge, and ideas in migration-induced translocal networks. A growing number of studies have used the lens of translocality to examine how migration reworks household and community relations across space; however, few have integrated a gender perspective. Accordingly, this research positions gender as constitutive of everyday translocal negotiations, and in doing so, explores how gendered translocality shapes various aspects of rural wellbeing in a high out-migration context. Methods: This doctoral thesis takes a translocal perspective by considering the relational ties that exist across places and scales. Semi-structured qualitative interviews were conducted across two data collection phases during May-June 2023 and October-December 2023 in Tojchoc Grande, a community in the Western Highlands of Guatemala. Data collection included interviews with women and men representing various migrant typologies, as well as with local health providers and community leaders. This thesis employs translocal Feminist Political Ecology, systems thinking, and social resilience to explore the multiscalar dimensions of how rural wellbeing was produced across connected places. Further, analysis moves from the household to the community scale to capture how translocal relations operate across everyday practices and institutional settings. Results: Findings show that contemporary rural wellbeing in Tojchoc Grande is both deeply translocal, and gendered, with women playing a critical, but overlooked role in mediating connections between households, migrants, and community institutions. Engaging in labour, care, and community organization within this context necessarily involves negotiating entrenched gender norms that privilege male authority, decision-making, and access to resources even from abroad. Within the translocal household, male out-migration leads to partial shifts in women’s roles and responsibilities, but changes rarely translate into the feminization of agriculture. Further, gendered care roles, remittance flows, and declining trust in public services interact to produce reinforcing patterns of psychosocial and physical strain for women. At the community level, gendered translocal social relations shape who participates in institutions, who makes decisions, and how community organizations function under conditions of demographic upheaval. Conclusion: This thesis underscores the gendered character of migration-induced translocality, illustrating how those left behind confront new socio-spatial realities as they are embedded into translocal networks at various scales (e.g., individual, household, community). It reveals that in Tojchoc Grande, translocal ties modify women’s everyday negotiations within agricultural production, household health, and community organization. However, these reconfigurations tended to reproduce, and in some cases, intensify inequalities in labour, responsibilities, and access to resources, rather than evening them out. In this way, rural wellbeing was marked by constraint: everyday life is still profoundly influenced by, and responsive to patriarchal gender norms and practices that transcend place. For rural communities to adapt and build resilience in response to migration, policy and research must recognize that translocal networks are not gender neutral. They can extend gender inequalities as readily as they enable transformative social change.
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    A Surrogate Modelling Framework for Time Resolved Energy Prediction in High Performance Office Buildings
    (University of Waterloo, 2026-05-26) Akbari, Ehsan
    High-performance buildings are an important component of the transition toward low-carbon and net-zero energy systems. However, evaluating building energy performance across a wide range of design and operational conditions typically requires large numbers of detailed simulations, which can be computationally expensive. This research investigates the energy behavior of high-performance office buildings and develops a surrogate modelling framework capable of predicting time-resolved energy consumption using machine learning techniques. The study follows a four-phase methodology. In the first phase, operational data from a high-performance office building located in Waterloo, Ontario are analyzed to examine daily energy-use behavior and identify the primary operational drivers of electricity consumption. This analysis provides empirical insight that informs the selection of key parameters used in subsequent modelling stages. In the second phase, a parametric physics-based building energy model is developed in EnergyPlus to represent office buildings operating under Waterloo’s climate conditions. The model incorporates variations in building geometry, envelope properties and configurations, and internal loads to represent a range of plausible design and operational scenarios. In the third phase, the parametric model is used to generate a synthetic dataset through systematic sampling of the input parameter space. The resulting dataset contains hourly energy simulation outputs across a wide range of building configurations and serves as the training and evaluation data for surrogate models. In the fourth phase, machine learning algorithms are developed, trained, and evaluated to predict building energy performance, including hourly electricity consumption and annual performance indicators. The trained models are also used to examine the relative influence of key building parameters on predicted energy outcomes. Three surrogate architectures were evaluated: a fully connected artificial neural network (ANN), a convolutional neural network (CNN), and a hybrid ANN–CNN model. The ANN showed stronger performance for annual energy prediction but limited accuracy in reproducing hourly temporal patterns, while the CNN captured hourly variations more effectively but produced less accurate annual energy estimates. The hybrid ANN–CNN model combined these complementary strengths, achieving comparable hourly prediction accuracy to the CNN and similar annual prediction performance to the ANN. The hybrid model achieved an hourly RMSE of approximately 12.89 kWh and an annual energy prediction RMSE of 1.08 kWh/m²·yr, providing the most balanced overall performance among the evaluated architectures. However, the surrogate models showed a tendency to underestimate peak energy demand, indicating limitations in capturing short-duration peak loads.