Theses

Permanent URI for this collectionhttps://uwspace.uwaterloo.ca/handle/10012/6

The theses in UWSpace are publicly accessible unless restricted due to publication or patent pending.

This collection includes a subset of theses submitted by graduates of the University of Waterloo as a partial requirement of a degree program at the Master's or PhD level. It includes all electronically submitted theses. (Electronic submission was optional from 1996 through 2006. Electronic submission became the default submission format in October 2006.)

This collection also includes a subset of UW theses that were scanned through the Theses Canada program. (The subset includes UW PhD theses from 1998 - 2002.)

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English Identity After Britain: Restructuring Englishness in the 20th Century
(University of Waterloo, 2025-02-03) Cameron, Christopher
This dissertation explores how writers in early 20th-century Britain grappled with nationalism, particularly its relationship to English national identity. I analyze how authors during this pivotal moment in British history attempt to disentangle or redefine concepts of patriotism, nationalism, and national identity. I explore the tensions between ethnic and civic forms of nationalism and how authors navigated this divide. Grounded in critical nationalism studies drawing on the work of theorists like Benedict Anderson, Michel Foucault, Craig Calhoun, and Stuart Hall, I conduct close readings of both fiction and non-fiction, focusing on how writers engage with ideas of Englishness. Chapter One considers how George Orwell attempted to harness national identification for left-wing politics—with particular attention to his attempt to distinguish between “patriotism” and “nationalism”—and examines the difficulties of such an approach. In chapter two, I explore how Virginia Woolf rejected both patriotism and nationalism, in favour of a cosmopolitan project that retained national identity while also promoting a “global citizen” ethos. In chapter three, I discuss J.R.R. Tolkien’s focus on creating a national myth for England, separating Englishness from the larger imperial category of “Britishness.” This chapter also explores Tolkien’s use of fantasy to enact what he called a “recovery” for national identity, looking backward in the style of Romantic Nationalism but using it progressively. Lastly, chapter four turns to Scottish and Irish case studies via the work of Hugh MacDiarmid and James Joyce, in order to provide a point of comparison for the English writers’ projects and the complex relation between their versions of “Englishness” and anticolonial nationalisms from elsewhere in the home empire. I argue that while the English authors studied might not have been entirely successful in articulating an English national identity separate from imperial Britishness, their efforts demonstrate a potential for a progressive use of national identity. These writers were aware of nations as rhetorical constructs. They sought to use this understanding to cultivate an ethics of care at home rather than a defensive or expansionist attitude abroad. The work of these authors demonstrates how literature can shape, critique, and reimagine national identity. Completely separating national identity from its problematic aspects may not always be possible or desirable. While acknowledging that risk, this study shows that national identity can potentially be mobilized for progressive purposes and to foster an ethics of care.
Automated Generation, Evaluation, and Enhancement of JMH Microbenchmark Suites from Unit Tests
(University of Waterloo, 2025-02-03) JANGALI, MOSTAFA
Ensuring the performance of software systems is a cornerstone of modern software engineering, directly influencing user satisfaction and reliability. Despite its critical role, performance testing remains resource-intensive and difficult to scale, particularly in large projects, due to the complexity of microbenchmark creation and execution. Microbenchmarking frameworks like the Java Microbenchmark Harness (JMH) offer precise performance insights but require significant expertise, limiting their adoption. This thesis addresses these challenges by introducing ju2jmh, a novel framework that automates the transformation of JUnit tests into JMH microbenchmarks, bridging the gap between functional and performance testing. The contributions of this thesis are threefold. First, ju2jmh automates the generation of high-quality JMH microbenchmarks from widely used JUnit test suites, enabling developers to adopt performance microbenchmarking with minimal manual effort. Results demonstrate that the generated microbenchmarks exhibit stability comparable to manually crafted ones and effectively detect real-world performance bugs. Second, the Performance Mutation Testing (PMT) framework is developed to systematically evaluate the robustness of microbenchmarks in detecting artificial performance bugs, achieving competitive mutation scores. Third, a clustering approach is proposed to optimize the execution of microbenchmarks by grouping functionally similar tests based on code coverage information. This strategy reduces execution time by 81.2% to 86.2% across three large-scale projects while preserving accuracy and reliability. Evaluated on three diverse open-source Java projects, the proposed solutions address stability, detection capabilities, and scalability challenges in performance testing workflows. The findings highlight the potential of ju2jmh and its associated methodologies to transform performance microbenchmarking practices, providing developers with practical tools to integrate reliable and efficient performance testing into modern software development pipelines. These advancements pave the way for future research into extending automated performance testing across different programming languages and development ecosystems.
Software Infrastructure for Isolation and Performance Monitoring in Virtualized Systems
(University of Waterloo, 2025-02-03) Rahman, Abdur
Modern multiprocessor System-on-Chip (SoC) architectures host a rich tapestry of heterogeneous components, enabling multiple workloads with differing requirements to run simultaneously on the same hardware platform. However, managing and isolating these concurrently running applications presents significant challenges. Traditional virtualization techniques, even with static partitioning hypervisors, could struggle to ensure robust isolation due to contention in shared system resources such as caches and memory bandwidth. To address this issue, this thesis investigates memory bandwidth contention among cores and explores isolation strategies by implementing MemGuard in the Bao Hypervisor on ARMv8-based systems. This implementation is complemented by cache coloring and DRAM bank partitioning techniques. The results, evaluated using the San Diego Vision Benchmark Suite, quantify the effectiveness of these mechanisms in reducing interference and provide insights into program behavior under varying isolation parameters. Beyond improving isolation, performance monitoring must extend beyond core-level observation to encompass system-wide interactions. To this end, this thesis develops a comprehensive software infrastructure for an Advanced Performance Monitoring Unit (APMU), designed for event-driven monitoring and dynamic runtime reconfiguration. By leveraging an LLVM-based toolchain to support custom instructions and integrating seamlessly with the hypervisor and guest OS layers, the APMU framework enables diverse applications while optimizing memory utilization and execution time. Collectively, the results and infrastructure presented in this work contribute to more predictable, secure, and efficient computing systems, advancing the state of the art in virtualization, performance isolation, and heterogeneous system analysis.
Large Language Models for Build System Maintenance: An Empirical Study of CodeGen’s Next-Line Prediction
(University of Waterloo, 2025-01-31) Akin-Taylor, Akinbowale
Build systems play a crucial role in software development and are responsible for compiling source code into executable programs. Despite their importance, build systems often receive limited attention because their impact is not directly visible to end users. This oversight can lead to inadequate maintenance, frequent build failures, and disruptions that require additional resources. Recognising and addressing the maintenance needs of build systems is essential to preventing costly disruptions and ensuring efficient software production. In this thesis, I explore whether applying a Large Language Model (LLM) can reduce the burden of maintaining build systems. I aim to determine whether the prior content in build specifications provides sufficient context for an LLM to generate subsequent lines accurately. I conduct an empirical study on CodeGen, a state-of-the-art Large Language Model (LLM), using a dataset of 13,343 Maven build files. The dataset consists of the Expert dataset from the Apache Software Foundation (ASF) for fine-tuning (9,426 build files) and the Generalised dataset from GitHub for testing (3,917 build files). I observe that (i) fine-tuning on a small portion of data (i.e., 11% of fine-tuning datasets) provides the largest improvement in performance by 13.93% (ii) When applied to the Generalised dataset, the fine-tuned model retains 83.86% of its performance, indicating that it is not overfitted. Upon further investigation, I classify build-code content into functional and metadata subgroups based on enclosing tags. The fine-tuned model performs substantially better in suggesting functional than metadata build-code. The findings highlight the potential of leveraging LLMs like CodeGen to relieve the maintenance challenges associated with build systems, particularly in functional content. My thesis highlights the limitations of large language models in suggesting the metadata components of build code. Future research should focus on developing approaches to enhance the accuracy and effectiveness of metadata generation.
Analyzing Access Control logic in the Android Automotive Framework
(University of Waterloo, 2025-01-30) Jumana, .
The Android Automotive Operating System (AAOS) is a specialized version of the Android OS designed specifically for in-vehicle hardware. Prominent car manufacturers, including Honda, General Motors (GM), Volvo, and Ford have already adopted it, with Porsche planning to follow soon. Despite its popularity, little has been done to evaluate the security of AAOS integration, particularly at the framework layer where access control vulnerabilities are likely to arise. To bridge the gap, we perform the first security evaluation of automotive APIs in AAOS. Our study is enabled by AutoAcRaptor, an automated tool that identifies automotive-specific entry points, generates their access control specifications, and analyzes them for potential security risks. AutoAcRaptor leverages static analysis and NLP to perform a three-staged analysis pipeline: 1) Convergence Analysis, 2) Similarity Analysis, and 3) Cross-Image Analysis. Our evaluation demonstrates that the tool is able to efficiently focus the security analysis on auto-specific functionality and pinpoint automotive APIs with likely anomalous access control.
Evaluating the impact of participation in school-based physical education lessons on adolescent health and wellbeing in Ontario: Findings from the COMPASS study
(University of Waterloo, 2025-01-30) Buchan, Marisa Claire
Physical activity rates among adolescents in Canada are critically low; only about one in every three grade 9 students are meeting the recommended 60 minutes per day of moderate to vigorous physical activity (MVPA). These high rates of physical inactivity among youth are alarming, as physical activity is essential for both physical and mental wellbeing, and it sets the foundation for healthy habits in adulthood. School-based physical activities including physical education (PE) classes, and intramural and varsity sport programs are ideally situated for the promotion of physical activity as they can reach a large number of youth and overcome many of the barriers associated with extracurricular activities. PE is designed to provide opportunity for youth of all ages to engage in physical activity that is structured into their weekly routine. However, in secondary school, a period that is critical for establishing healthy behaviour patterns for later in life, PE becomes non-mandatory for students in many provinces and territories across Canada, resulting in a missed opportunity to engage adolescents in regular physical activity. Ontario currently has the most lenient PE policy in Canada, with students only required to complete one secondary school-level PE course. To date, only four studies have examined the impact of PE programming in Canada on physical activity levels, only one of which included students from the province of Ontario. No published studies to date have explored the impact of PE participation on mental health outcomes among adolescents in Canada. The lack of evidence in this domain renders it challenging to determine the effectiveness of PE or make recommendations to enhance PE programs to maximize their impact on student health and wellbeing. This dissertation aimed to provide a deeper understanding of the patterns of physical activity behaviours and the impacts of participating in non-mandatory secondary school PE on physical activity and mental health outcomes among adolescents in Ontario. Specifically, Study 1 characterized longitudinal physical activity profiles of non-mandatory PE participation, adherence to physical activity guidelines, and sport participation throughout secondary school. Study 2 quantified the impact of participation in PE on physical activity levels, over time. Study 3 quantified the impact of PE participation on student mental health, over time. This dissertation utilized linked longitudinal data from students in Ontario who participated in four consecutive years of the COMPASS Study (Time 1: 2015-16; Time 2: 2016-17; Time 3: 2017-18; Time 4: 2018-19). The COMPASS Study is a school-based prospective cohort study (2012-2027) that collects demographic, behavioural, and mental health data from students annually across Canada. Study 1 utilized a repeated measures latent class analysis to identify longitudinal physical activity profiles of adolescents in Ontario. Studies 2 and 3 utilized linear mixed models to estimate the average effect of PE participation on (a) minutes of MVPA (Study 2) and (b) symptoms of anxiety, (c) symptoms of depression, and (d) psychological wellbeing (Study 3), over time. Models in Studies 2 and 3 were adjusted using doubly robust propensity score methodology to account for self-selection biases that may influence PE participation. Findings from Study 1 illustrated that there are distinct, clustered physical activity profiles among adolescents which vary by sex; three physical activity profiles were identified among both female and male students: Guidelines, PE & Sports, and Guidelines & Sports. A fourth profile was identified among male students only: Inactive. Study 2 demonstrated that participation in secondary school PE had a significant positive impact on MVPA levels over time, and effects were most pronounced for male students and during the semester of PE participation. Study 2 also illustrated that the benefits of PE remained present in the semester opposite to PE participation, suggesting that the benefits of PE extended beyond the MVPA accumulated during class-time. In Study 3, PE participation was not associated with symptoms of anxiety or depression, over time. Study 3 also found that male students enrolled (but not currently participating) in PE were found to have higher psychological wellbeing compared to those not enrolled in PE within the academic year. This dissertation fills an important gap with respect to our understanding of PE programming in Ontario secondary schools. Findings from this dissertation revealed that many students are choosing not to enroll in PE, with a particularly high-risk subgroup of male adolescents showing low participation across several physical activity behaviours during secondary school. Among male students who elect to participate, PE was found to positively impact time spent in MVPA and psychological wellbeing. These results highlight the potential of PE for improving the health and wellbeing of adolescents, although low participation rates limit these benefits being experienced at the population-level. Importantly, all three studies identified sex-based differences in the physical activity profiles and the impact of PE on health outcomes; female students were found to have lower PE participation rates and experienced reduced benefits compared to male students. These result underscore the importance of promoting inclusive environments in PE to ensure health benefits are experienced by all adolescents, regardless of sex and other key characteristics. Findings from this dissertation offer valuable insights for public health programming, particularly within the school context; decision-makers in Canada should explore ways to increase PE participation across secondary schools, paying particular attention to female students and those not participating in other forms of physical activity.
Toward Adaptive and User-Centered Intelligent Vehicles: AI Models with Granular Classifications for Risk Detection, Cognitive Workload, and User Preferences
(University of Waterloo, 2025-01-29) Lee, Hyowon
As artificial intelligence (AI) increasingly integrates into our transportation systems, intelligent vehicles have emerged as research topics. Many advancements aim to enhance both the safety and comfort of drivers and the reliability of intelligent vehicles. The main focus of my research is addressing and responding to the varying states and needs of drivers, which is essential for improving driver-vehicle interactions through user-centered design. To contribute to this evolving field, this thesis explores the use of physiological signals and eye-tracking data to decode user states, perceptions, and intentions. While existing studies mostly rely on binary classification models, these approaches are limited in capturing the full spectrum of user states and needs. Addressing this gap, my research focuses on developing AI-driven models with more granular classifications for cognitive workload, risk severity levels, and user preferences for self-driving behaviours. This thesis is structured into three core domains: collision risk detection, cognitive workload estimation, and perception of user preferences for self-driving behaviours. By integrating AI techniques with multi-modal physiological data, my studies develop ML (Machine Learning) models for the domains introduced above and achieve high performance of the ML models. Feature analytical techniques are employed to enhance model interpretability for a better understanding of features and to improve the model performance. These findings pave the way for a new paradigm of intelligent vehicles that are not only more adaptive but also more aligned with user needs and preferences. This research lays the groundwork for the future development of user-centered intelligent companion systems in vehicles, where adaptive, perceptive, and interactive vehicles can better meet the complex demands of their users.
Health Care Costs Associated with Minor Ailments and Cost Minimization Analysis of Pharmacists Prescribing for Minor Ailments in Ontario, Canada
(University of Waterloo, 2025-01-29) Koo, Vanessa
Background: Pharmacists in Ontario, Canada are now able to prescribe for certain minor ailments. Minor ailments are defined as health conditions that can be managed with minimal treatment and/or self-care strategies. Economic evaluations with costs and burden associated with minor ailment conditions using Ontario health administrative data are needed to accurately evaluate the economic impact of the pharmacist prescribing for minor ailment (PPMA) program. Objectives: The objectives of this research thesis are to: 1) assess the baseline characteristics, mean health care costs, and predictors of health care costs of minor ailment cases using a retrospective analysis of Ontario health administrative data for patients presenting with minor ailment conditions and 2) perform a cost-minimization analysis to determine the economic impact of a remunerated program for PPMA compared with usual care. Methodology: First, using Ontario health administrative data from ICES, baseline characteristics, mean health care costs, and predictors of health care costs were determined for patients diagnosed with the 16 studied minor ailments. Second, a decision-analytic model was implemented to perform the cost-minimization analysis for the minor ailments. Two prescribing strategies were considered in this analysis: PPMA and usual care. In the PPMA strategy, patients have the option of either seeking care from a community pharmacist or a physician. In the usual care model, all patients seek care from physicians. Probabilities and costs used in the model were derived from mostly Ontario health administrative data, literature, or expert opinion when there was insufficient literature. This analysis used a public payer perspective and outcomes were expressed in costs in 2019 Canadian dollars. Results: Analysis of Ontario health administrative data from ICES identified that the minor ailments with the highest number of unique patients billed were musculoskeletal sprains and strains (8,099,393; 24%), gastroesophageal reflux disease (5,822,495; 17%), dermatitis (5,649,829; 17%), urinary tract infection (3,356,887; 10%), and insect bites and urticaria (2,699,684; 8%). Health care costs varied by minor ailment and cost category, with older age, lower income quintiles, urban residency, and comorbidities as predictors of higher total health care costs. Cost-minimization analyses from a public payer perspective provided evidence that implementing a PPMA program for the studied minor ailments could yield cost savings for the Ontario government compared to the usual care model, with savings ranging from $19.05 to $77.38 per patient. One-way sensitivity analyses showed that results were most sensitive to the likelihood of patients receiving care from a pharmacist rather than a physician. In probabilistic sensitivity analyses, the PPMA model proved cost-saving in 100% of the simulations for all 16 minor ailments studied. Conclusion: The results of the thesis research identified the baseline characteristics, health care cost burden, and predictors of total health care costs for patients presenting with minor ailments using Ontario health administrative data. In addition, the cost-minimization analyses conducted from a public payer perspective provided evidence that implementing a PPMA program provided cost-savings for the Ontario government when compared to the usual care model for the studied minor ailments. The results of this research can continue to help shape implementation strategies of a PPMA program in Ontario, Canada.
Techno-Economic Assessment of Carbon Capture for Integrated Steel Mills in Canada
(University of Waterloo, 2025-01-29) Titcombe, Anne Adetola
Globally and within Canada, steel production accounts for 10% and 23% of total industrial CO2-eq emissions, respectively. This is primarily owed to the prevalence of the traditional blast furnace-based integrated steel mill, responsible for 73% of steel production globally. This thesis investigates the techno-economic feasibility of carbon capture methods within a Canadian integrated steel mill, focusing on reducing the direct emission intensity of hot rolled steel slabs till non-emitting steel production methods can be employed. The study emphasizes two post-combustion capture techniques: First, Monoethanolamine (MEA) absorption identified as the primary technology due to its maturity and cost efficiency. Second, hybrid methods combining vacuum pressure swing adsorption with low-temperature purification (VPSA-LTP) are explored for their commercial potential and lower thermal energy penalty relative to the chemical absorption base case. A systematic framework involving performance modelling using Aspen Plus and Aspen Adsorption, and cost assessment evaluates energy consumption, cost implications, and environmental benefits of both carbon capture methods. The opportunity for waste heat recovery for the steel production process was also evaluated. A surrogate-based optimization framework was developed and proven to be a tool for conducting a less-computationally intensive techno-economic assessment of batch separation processes. Key findings highlight that the lowest capture cost of $75 per tonne of CO2 captured ($86 per tonne of CO2 avoided) is achieved using a single-point of capture: the central power station, due to its volume and high CO2 composition. To achieve this minimum cost alongside its’ lowest achievable steel emission intensity, this carbon capture implementation includes MEA absorption with an oxy-combustion boiler and waste heat recovery from flared gas and flue gases to offset energy demand. In the case of natural gas supply constraints and overall reliance on electricity, using a hybrid VPSA-LTP process offers the lowest electricity consumption at a cost of $120 per tonne of CO2 avoided. Overall, carbon capture can be used to reduce the emission intensity to 0.76 tonnes of CO₂ per tonne of hot rolled steel slabs while increasing the production cost by 17% to $741 per tonne of steel. It is recommended that advanced solvents and sorbent be explored to further reduce the energy penalty and increase the productivity of their respective methods. There must also be evaluation of alternative decarbonization schemes for further emission reduction and the potential of heat integration with the existing power station to generate more steam in lieu of electricity. There must also be a multi-disciplinary assessment of the impact of policies on the viability of carbon capture as a decarbonization solution for the steel industry.
Testing the application of novel technology for monitoring grape vine health and berry maturity using transmitted visible and near infrared light
(University of Waterloo, 2025-01-29) Riddoch, Bronwyn
Climate change is impacting wine-growing regions globally, with varying effects on vineyards. While some regions may benefit from warmer temperatures, others may face detrimental consequences, especially with the predicted increase in extreme weather events or less than optimal conditions. Precision viticulture uses remote and proximal sensing technologies to monitor these changes and adapt vineyards by providing insights into vine health and grape maturity. This information can be used to determine when intervention is needed in vineyards to maintain grape quality. However, existing precision viticulture methods are limited, such as the inability to provide continuous, real-time data and the utilization of reflected light, which can lead to inaccurate measurements. Current research has not yet investigated the potential of using transmitted light for monitoring vine health and grape maturity, a method that could provide more accurate insights. This thesis seeks to fill this gap by evaluating the feasibility of applying a novel system, TreeTalker-Wine© (TTW), to continuously monitor grapevine health and maturity through transmitted light in commercial vineyards. To test the application of TTWs for monitoring vine health, the sensors were deployed under the canopy of Cabernet Franc in two commercial vineyards in Niagara, Ontario, Canada. Spectral data collected by the TTWs was used to calculate the daily Normalized Difference Vegetation Index (NDVIT) based on transmitted light. The resulting NDVIT values were consistent with expected ranges and aligned with viticultural management practices and weather events. To assess the potential of TTWs for monitoring grape maturity, partial least squares (PLS) models were developed for Cabernet Franc, Chardonnay, and Riesling varieties using spectral data from the grape clusters, along with air temperature and Total Soluble Solids (TSS) content. Grape clusters were collected bi-weekly from a third vineyard in Niagara, Ontario, Canada, starting at the pea-size stage and continuing through veraison. After veraison, sampling frequency increased to weekly until harvest. After each collection day, the fruit was transported to a laboratory with a plant growth chamber designed to replicate the vineyard’s environmental conditions. Grape clusters were suspended over the TTWs in the plant growth chamber to collect spectral signatures of the fruit before the entire cluster was juiced to determine TSS content. The results of the PLS models suggest that TTWs are able to determine TSS content from the spectral signatures of the grape clusters, however unique models are required for each grape variety. These findings indicate that TTWs offer a promising approach to precision viticulture. Future research is needed to assess a broader range of grape varieties to better understand the relationship between NDVIT and vine health, as well as to refine the TSS prediction models. This will enable further exploration of the potential of transmitted light in monitoring grapevine health and maturity, supporting more accurate and timely viticulture practices in changing climate.
Evaluating the Potential Environmental and Human Toxicity of Solvents Proposed for use in Post-Combustion Carbon Capture
(University of Waterloo, 2025-01-28) Ghiasi, Fatima
Carbon dioxide emitted by industrial activities is a growing concern due to the effects on global climate. For this reason, firms are being urged to lower their carbon footprint. Post combustion carbon capture is being explored as a method for the power and materials industries to decarbonize. The most mature technique of carbon capture is amine absorption. Different amines are being explored to potentially be used within post-combustion carbon capture units. Many biological molecules are amines, and amines that resemble them can disrupt biological processes, harming organisms. In addition, if an amine is soluble within lipids, it can persist within the food chain and cause long term toxic effects that are not immediately visible. 151 solvents were compared based on four properties: volatility, lipophilicity, mutagenicity, and neuroactivity. Machine learning models were trained to predict these values. Due to their hydrophilicity, amino acids were determined to have the lowest potential of causing environmental toxicity.
CADC++: Extending CADC with a Paired Weather Domain Adaptation Dataset for 3D Object Detection in Autonomous Driving
(University of Waterloo, 2025-01-28) Tang, Mei Qi
Lidar sensors enable precise 3D object detection for autonomous driving under clear weather but face significant challenges in snowy conditions due to signal attenuation and backscattering. While prior studies have explored the effects of snowfall on lidar returns, its impact on 3D object detection performance remains underexplored. Conducting such an evaluation objectively requires a dataset with abundant labelled data from both weather conditions and ideally captured in the same driving environment. Current driving datasets with lidar data either do not provide enough labelled data in both snowy and clear weather conditions, or rely on simulation methods to generate data for the weather domain with insufficient data. Simulations, nevertheless, often lack realism, introducing an additional domain shift that impedes accurate evaluations. This thesis presents our work in creating CADC++, a paired weather domain adaptation dataset that extends the existing snowy dataset, CADC, with clear weather data. Our CADC++ clear weather data have been recorded on the same roads and around the same days as CADC. We pair each CADC sequence with a clear weather one as closely as possible, both spatially and temporally. Our curated CADC++ achieves similar object distributions as CADC, enabling minimal domain shift in environmental factors beyond the presence of snow. Additionally, we propose track-based auto-labelling methods to overcome a limited labelling budget. Our approach, evaluated on the Waymo Open Dataset, achieves a balanced performance across stationary and dynamic objects and still surpasses a standard 3D object detector when using as low as 0.5% of human-annotated ground-truth labels.
Exploring the Wellbeing and Food Security of Ethical Vegans through the Human and More-than-Human World
(University of Waterloo, 2025-01-27) Russell, Julia
Background: Health and environmental data reveal significant challenges faced by populations around the word in relationship to food. Complex choices and constraints shape people’s dietary patterns. While there is often debate about which eating patterns people should follow, for vegans ethical motivations are paramount. Vegan diets can be healthy diets, but there remains a gap in the literature surrounding veganism and overall wellbeing, including food security and connection to nature. Objectives: Taking into consideration both human and more-than-human factors this research explored how vegans live their lives embedded within the complex circumstances that shape their wellbeing. This exploration was informed by the application of a holistic model of health (the Revised Mandala of Health) and the use of an ecofeminist lens. Through a series of three studies and one methodological reflection, this research addressed the following objectives: (1) To investigate how ethical veganism influences a person’s experience and conceptualization of food security; (2) To explore the success and challenges faced by ethical vegans, how they may resist these challenges, and the implications for wellbeing; and (3) To explore ethical vegans’ experiences with the more-than-human world, and the implications for their wellbeing and coping strategies. Methods: The first study (Chapter 2) used focus groups to learn about vegans’ experiences and ideas related to food security at the individual, household and community levels. Four focus groups were held, of which three were comprised of vegans who in the year prior had experienced food security, with the final focus group being reserved for vegans who had experienced food insecurity. The focus groups’ data were analysed using Tracy’s phronetic iterative analysis approach. The second study (Chapter 3) used semi-structured interviews to collect data on veganism and wellbeing from 26 participants. The data from these semi-structured interviews were analysed using reflexive thematic analysis (RTA). The third study (Chapter 4) had six participants who took part in both a 2-day, group, body-map storytelling (BMST) workshop and then a follow-up individual, semi-structured interview. Data collected over the course of the body-map storytelling workshop included each participant’s presentation of their body-map to the group, as well as a written ‘testimonio’, two end of day reflective exercises, and one focus group. All data from this third study were analysed together using RTA. Finally, a fourth component was generated for this dissertation (Chapter 5). This methodological reflection was written based on the lead researcher’s impressions of the BMST workshop and comments about this workshop that were shared by the participants during data collection for the third study. Results: The results of Study 1 indicated that responsibility was a prominent feeling experienced by the participants both in relationship to their veganism and their experiences of food (in)security. The participants believed they should be informed about the food system, know how to shop frugally, and have cooking skills. Feelings of personal responsibility for food security may have been amplified by the perception of the absence of a vegan-friendly social safety net. The participants noted vegan foods could be nutritious, convenient, and inexpensive but not generally all these things at once. Therefore, compromising on at least one feature of their food was often needed. Participants in both the food secure and food insecure groups explained how at times they experienced difficulty accessing vegan-friendly foods. The results of Study 2 demonstrated that veganism is regarded as a positive way of living that though challenging at times, especially early on in one’s experience and in relation to social relationships, was of overall net benefit to the vegan participants. This benefit contributed to enhanced wellbeing. The areas of wellbeing the participants identified most often as being influenced by veganism were the mental and emotional realms. The participants’ identities influenced their experiences of veganism. In navigating life as vegans the participants eschewed the belief in a ‘perfect’ veganism, which may have contributed to the longevity of their veganism. The results of Study 3 showed the participants were ecologically embedded. Being ecologically embedded meant that when the participants perceived harm to the more-than-human world they subsequently felt a negative effect on their own wellbeing, including as solastalgia. However, being ecologically embedded also meant that participants experienced enhanced wellbeing through their connection to the more-than-human world. In the methodological reflection, the researchers argue that while BMST requires significant participant involvement it can be a rewarding approach for both participants and researchers, and a safe method of data collection if careful attention is paid to the social location of potential participants and the sensitivity of the research topic. The researchers found different data collection approaches were more and less effective during the BMST workshop and they offer practical considerations for the design and undertaking of future BMST workshops. Conclusions: Through the completion of three studies and one methodological reflection this research found that veganism was a positive force that enhanced wellbeing in the lives of the participants. As veganism is outside of the norm in Western cultures, being vegan did lead to challenges for the participants, but the challenges were not with veganism itself, rather the challenges lay in the social ramifications and accessibility of foods suitable for vegans. Participants felt the benefits of veganism outweighed the challenges. Considering the results via their alignment with the Revised Mandala of Health suggests that veganism can have health promoting properties for vegans and the more-than-human world. As plant-based diets are increasingly considered a way to address issues of health and sustainability, this study adds important understandings about the personal value and maintenance of veganism.
Deep Learning Models of Cellular Decision-Making Using Single-Cell Genomic Data
(University of Waterloo, 2025-01-27) Sadria, Mehrshad
Cellular decision-making, essential to regenerative medicine, disease research, and developmental biology, relies on complex molecular mechanisms that guide cells in responding to stimuli and committing to specific fates. This thesis introduces several deep learning methods to analyze single-cell RNA sequencing data, uncover regulatory programs driving these processes, and predict the outcomes of gene perturbations. By applying representation learning and generative models, meaningful structures within high-dimensional data are identified, enabling tasks such as mapping cellular trajectories, reconstructing regulatory networks, and generating realistic synthetic data. Furthermore, integrating deep learning with dynamical systems theory enables the prediction of cellular decision timing and the identification of key regulatory genes involved in these processes. These methods enhance our understanding of gene activity dynamics, improve predictions of cellular behavior, and offer new avenues for progress in regenerative medicine, developmental biology, and disease research.
A Survival-Driven Machine Learning Framework for Donor-Recipient Matching in Liver Transplantation: Predictive Ranking and Optimal Donor Profiling
(University of Waterloo, 2025-01-27) Wang, Yingke
Liver transplantation is a life-saving treatment for patients with end-stage liver disease. However, donor organ scarcity and patient heterogeneity make finding the optimal donor-recipient matching a persistent challenge. Existing models and clinical scores are shown to be ineffective for large national datasets such as the United Network for Organ Sharing (UNOS). In this study, I present a comprehensive machine-learning-based approach to predict posttransplant survival probabilities at discrete clinical important time points and to derive a ranking score for donor-recipient compatibility. Furthermore, I developed a recipient-specific "optimal donor profile," enabling clinicians to quickly compare waiting-list patients to their ideal standard, streamlining allocation decisions. Empirical results demonstrate that my score’s discriminative performance outperforms traditional methods while maintaining clinical interpretability. I further validate that the top compatibility list generated by our proposed scoring method is non-trivial, demonstrating statistically significant differences from the list produced by the traditional approach. By integrating these advances into a cohesive framework, our approach supports more nuanced donor-recipient matching and facilitates practical decision-making in real-world clinical settings.
Engineering cell-penetrating peptide mediated protein-bound nanoparticles for delivering siRNA and chemotherapeutics
(University of Waterloo, 2025-01-27) Wang, Jun
Proteins serve as the “workers” of biochemistry, orchestrating nearly all biological functions. Functional endogenous proteins are often related to the pharmacokinetics and pharmacodynamics of drugs and nanomedicines, particularly in processes such as drug absorption, biodistribution, and metabolism. That means the innate interactions between proteins and drugs/nanoparticles exist, but the discovery and application of these interactions are underappreciated so far. By imitating the protein binding behaviors and interactions, some proteins may hold significant promise in drug and nanoparticle delivery due to their biocompatibility and functionalities. This thesis presents a methodology for engineering biomimetic protein coronas to camouflage cationic peptide/siRNA (P/si) nanocomplexes by utilizing proteins derived from the innate P/si protein corona (P/si-PC), which was also applied to the peptide-based lipid nanoparticles (pLNP). By leveraging these protein corona species, an efficient method for producing protein-bound chemotherapeutic nanoparticles in aqueous phases using microfluidic technology was developed. For cationic nanoparticles, the spontaneous nanoparticle-protein corona formation and aggregation in biofluids can trigger unexpected biological reactions. This thesis presents a biomimetic strategy for camouflaging the P/si with single or dual proteins, which exploits the unique properties of endogenous proteins and stabilizes the cationic P/si for safe and targeted delivery. An in-depth study of P/si-PC formation and protein binding was conducted. The results provided insights into the biochemical and toxicological properties of cationic nanocomplexes and the rationales for engineering biomimetic protein camouflages. Based on this, the human serum albumin (HSA) and apolipoprotein AI (Apo-AI) ranked within the top 20 abundant protein species of P/si-PC were selected to construct biomimetic HSA-dressed P/si (P/si@HSA) and dual protein (HSA and Apo-AI)-dressed P/si (P/si@HSA_AI), given that the dual-protein camouflage plays complementary roles in efficient delivery. A branched cationic cell-penetrating peptide (CPP, b-HKR) was tailored for siRNA delivery, and their nanocomplexes including the cationic P/si and biomimetic protein-dressed P/si were produced by a precise microfluidic technology. The biomimetic anionic protein camouflage greatly enhanced P/si biostability and biocompatibility, which offers a reliable strategy for overcoming the limitation of applying cationic nanoparticles in biofluids and systemic delivery. Currently, commercially applied lipid nanoparticles (LNPs) for RNA delivery, such as in siRNA and mRNA vaccines, utilize similar lipid compositions and ratios, raising the risk of unintentional patent infringement. This research attempted to engineer a novel peptide-based LNP formulation stabilized and functionalized by artificial protein corona that constitutes HSA and lipoprotein (Apo-AI; apolipoprotein E, Apo-E). The cationic peptide (b-HKR) enabled efficient siRNA condensation and reversible protein binding. Combining b-HKR and the artificial protein corona offers an alternative to the commonly used ionizable lipids, PEG-lipids, and excipients (such as sucrose), providing both pH-responsive functionality and storage stability. The in vitro results showed that the dual protein (HSA and Apo-AI) functionalized pLNP (pLNP@HSA_AI) is optimal for enhanced stability and RNAi efficacy. In contrast, single protein-functionalized pLNPs encountered a dilemma: pLNP@HSA improved stability but showed almost no RNAi efficacy, while the pLNP@AI exhibited remarkable RNAi efficacy but aggregated upon the addition of Apo-AI. The dual protein (HSA and Apo-E) functionalized pLNP (pLNP@HSA_E) also showed promise in addressing this dilemma, although the use of Apo-E is less cost-effective than Apo-AI due to its limited availability. The use of endogenous proteins, particularly albumin, for the targeted delivery of chemotherapeutics has proven practical. However, how to effectively produce the protein-bound chemotherapeutics nanoparticles in a complete aqueous phase (without the use of organic solvents) is worth pursuing to eliminate the solvent-related safety risks. In this research, the protein-bound Dox (Dox) nanoparticles were successfully produced through a one-step microfluidic mixing process in aqueous phases, in which the nanoparticle formation was instantaneously mediated by a self-assembled nano-peptide (np). The np-mediated HSA-bound Dox (D-np-HSA) and dual proteins (HSA; Apo-AI)-bound Dox (D-np-HSA-AI) nanoparticles exhibited efficient drug encapsulation and pH-triggered drug releases. In vitro cellular studies showed that the nanoparticles (D-np-HSA and D-np-HSA-AI) exhibited superior efficacy in killing tumor cells (A549 and MCF7) while being less toxic to normal cells (NIH3T3) compared to free Dox. Notably, D-np-HSA-AI was less prone to induce drug resistance, and cell lines that developed resistance to free Dox remained sensitive to D-np-HSA-AI. Besides, the results revealed that drug resistance development of A549 is associated with cellular phenotypic (size, morphology, and dividing speed) changes. Cellular (cytoplasmic and nuclear) proteomics was conducted by comparing the protein species, abundances, and relation networks of normal, Dox-induced, and nanoparticle (D-np4-HSA-AI) induced A549 cells, which aimed to provide potential protein biomarkers associated with drug resistance and druggable protein/gene targets for overcoming the drug resistance.
Investigating Technology Implementation in a Canadian Community Hospital
(University of Waterloo, 2025-01-27) Allana, Sana
The integration of technology into healthcare has witnessed significant advancements. However, the widespread adoption of such technologies may not be uniformly positive. While highest levels of adoption are typically found in densely populated urban areas, community healthcare facilities face challenges due to insufficient resources, like infrastructure, funding, and specialized staff, exacerbated by their remote locations. This is cause for concern as community hospitals account for 90% of all hospitals in Canada. This reveals a major opportunity to improve technology adoption and implementation at community hospitals, to aid their existing challenges, increase equity in healthcare, and improve generalizability of healthcare technologies. This research aims to uncover the perceptions, expectations, cultural nuances, and barriers to technology adoption at a community-level hospital in Ontario, Canada. The study began with a contextual inquiry approach, incorporating semi-structured interviews and surveys. Data was collected from nine clinical and managerial staff members whose workflows were impacted by three pilot technology projects. The interviews aimed to explore staff expectations and experiences with how these pilot projects impacted their workflows, patient care, and the overall technology implementation process. The survey included demographic questions and items based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, designed to predict factors influencing technology acceptance. The pilot technologies included a discharge planning tool, a portable X-ray scanner, and a digital pathology tool. A thematic analysis of the qualitative data was conducted, followed by affinity mapping to identify overarching themes. The Functional Resonance Analysis Method (FRAM) was also used to understand and model the impact of integrating the pilot technologies into preexisting, variable workflows. Finally, survey results were analyzed using frequency distributions to identify trends and triangulate findings. Overall, most staff reported a high level of technology use in both their work and daily lives. They also acknowledged that technology breakdowns at the workplace were inevitable, often resulting in time-consuming, manual workarounds. As well, for all pilot projects, staff felt overburdened by the additional workload required to manage the pilots alongside their regular duties. However, despite these challenges, all staff expressed an appreciation for innovation and a strong willingness to try new tools to improve their work. The discharge planning and X-ray scanner tools did not integrate well into existing workflows or provide additional value. Both tools performed inconsistently and failed to meet expectations for streamlining processes, leading to reluctance and distrust among staff. Additionally, change management planning was insufficient for both tools, with staff experiencing abrupt workflow changes, limited training, and a lack of clarity on project timelines or statuses. As a result, neither tool was requested for purchase following pilot testing. Conversely, staff decided to purchase the digital pathology tool, despite the disruptions to existing workflows, as the perceived benefits to both staff and patient care outweighed these challenges. Staff were excited about the tool’s potential and engaged in close collaboration with the manufacturer and project team. Furthermore, change management was carefully planned, with a phased implementation approach. The pilot was also driven by strong advocacy from a pathologist, which ensured alignment with clinical needs. Based on these findings, several recommendations were uncovered to improve the technology implementation process. First, the challenges with change management highlight the need for better resource allocation. This includes providing sufficient time for introducing new tools, clearly explaining the reasons for their selection, offering personalized training that covers tool usage, troubleshooting, and its impact on existing processes, and ensuring staff have the necessary bandwidth to manage change without disrupting daily operations. Second, communication channels should be improved. Startup companies should collaborate closely with the hospital during the development and testing phases to better understand staff needs and workflows, while also providing tailored support throughout the implementation process. Additionally, communication with hospital leadership must be strengthened to secure strong support, allocate resources effectively, and incorporate feedback on the challenges staff encounter, fostering a more collaborative environment that is better equipped to drive innovation. Finally, it is crucial to define and share specific success metrics for pilot projects. These metrics will help staff assess the technology's impact, make informed decisions about its use, evaluate the implementation process, identify lessons learned, and pinpoint areas for improvement, all of which can refine future technology adoption strategies. Overall, technology implementation and adoption are influenced by a variety of factors, which are further compounded by the high workload, staffing shortages, and unpredictable environments commonly found in community hospitals. By addressing these recommendations, health organizations can enhance the adoption and effectiveness of new technologies, ultimately improving staff workflows and patient care.
Assessing the prevalence and youth-directed marketing power of outdoor food and beverage advertisements around schools in six cities across Canada.
(University of Waterloo, 2025-01-24) Morielli, Amanda
Recent policy initiatives in Canada propose to restrict the commercial advertising of foods containing sugars, sodium, or saturated fat to youth on digital and broadcast media. While there is abundant research on youth’s exposure to food and beverage advertising on digital and broadcast media, there is limited research exploring youth’s exposure to outdoor food and beverage advertisements (e.g., freestanding billboards, restaurant exteriors, bus shelters). To address this research gap and inform policy decisions, Manuscript 1 of this thesis describes the prevalence, content, and youth-directed marketing power of outdoor food and beverage advertisements near schools. Manuscript 2 of this thesis explores the association between outdoor F&B advertisement prevalence, food outlet density, degree of urbanization, neighbourhood deprivation, and ethnocultural composition near schools to understand how the built environment and neighbourhood characteristics influence outdoor advertising environments. For this research, data on outdoor advertisements and food outlets within 1000 m of elementary and secondary schools in six cities across Canada (Vancouver, BC; Calgary, AB; Winnipeg, MB; Ottawa, ON; Quebec City, QC; and Halifax, NS) was analyzed, along with Statistics Canada data on deprivation and ethnocultural composition (from the Canadian Index of Multiple Deprivation). Descriptive statistics, chi-square tests, and negative binomial regression models were used to analyze the data. Most (64.5%) outdoor F&B advertisements near schools promote “unhealthy” food and beverage products. The most common marketing techniques used to target youth were youth product/convenience (39.4%), sense of urgency/limited time offer/seasonal (18.4%), and price promotion/discount (13.1%). School areas with high food outlet counts contained 7.429 times more advertisements than those with low counts (CI: 4.805 – 11.486, p < 0.05). The mean count of outdoor advertisements on food outlet exteriors (M = 23.22, SD = 35.52) was 10.6 times higher than the mean count of freestanding outdoor advertisements (M = 2.18, SD = 3.94), revealing that most outdoor F&B advertisements around schools are located on food outlets. Measures for deprivation and ethnocultural composition were not found to have notable patterns of significance with outdoor advertisement, except for residential instability. School areas with a high degree of residential instability contained 1.707 times more advertisements than the school areas with a low degree of residential instability (CI:1.029 - 2.832, p < 0.05). These findings suggest outdoor F&B advertisements near schools primarily promote unhealthy food choices and advertisement prevalence is influenced by features of the built environment, such as food outlet density. Future research should explore the impact of planning and public health policy interventions to reduce outdoor food and beverage advertising to youth. Opportunities for these professions (as well as other relevant disciplines) to collaborate to create healthier food environments for youth should also be identified.
“AnnoTools”: Extending AnnoTree and AnnoView for Database-Wide Genome Annotation, Visualization, and Comparison
(University of Waterloo, 2025-01-24) Tan, Huagang
Genomic analysis has revolutionized our understanding of the biology and evolutionary history of bacterial and archaeal microorganisms, leading to numerous applications in biotechnology, medicine, and environmental sciences. One of the fundamental aspects of genomic analysis is protein functional annotation, which involves assigning biological functions to protein-coding sequences identified within genomes. These annotations are widely used to support analyses, such as examining gene or function distributions across the tree of life and comparing gene neighborhoods across taxa. By combining these analyses, researchers can comprehensively explore gene functions and the mechanisms of given genes or gene clusters. In this thesis, I will introduce a pipeline that supports genomic analysis. The project consists of three parts: data annotation, visualization, and the language model. The first part of the pipeline is the generation of protein function annotations. Raw protein sequence data is downloaded from the Genome Taxonomy Database (GTDB) and submitted to two tools: Kofamscan and DIAMOND. Kofamscan assigns KEGG ORTHOLOGY IDs to each input sequence, while DIAMOND assigns Uniref IDs, which are then mapped to InterPro IDs. Combining these IDs provides comprehensive and reliable annotations. The data is filtered for quality and stored on a remote server as an annotation database for further analysis. The second part of the pipeline involves updating two user-friendly, web-based visualization tools, AnnoTree and AnnoView, which utilize the annotation database. AnnoTree displays the distribution and taxonomy of different protein annotations across GTDB using a tree of life representation, offering insights into biological and evolutionary patterns through species phylogenies and supporting genome-wide co-occurrence analysis. AnnoView focuses on comparing and exploring gene neighborhoods, identifying functionally related genes clustered together in genomes as "gene clusters," thus emphasizing window-based co-occurrence analysis. The new annotation database not only provides more comprehensive and accurate annotations, enhancing the databases that both visualization tools rely on, but also extends their functionalities for fast data retrieval and new features. The last part of the pipeline involves the application of the Word2Vec language model, which treats genome contigs as sentences in natural language and trains the model using the annotation database. After training, the updated model can encode each annotation from a specific protein family into high-dimensional vectors with continuous number, allowing researchers to explore annotations that share similar genomic contexts. This allows protein functions prediction based on this comparative gene neighborhood analysis. Finally, I will use one protein domain in the Type VI Secretion System (T6SS) as a case study. T6SS is a cell envelope-spanning machine that translocates toxic effector proteins into eukaryotic and prokaryotic cells. Besides the conserved essential core components, there are various effector and accessory proteins in the system. Some proteins are annotated as Domains of Unknown Function (DUF) and are poorly explored. In this case, I will focus on PF20598 (DUF6795), which shares a similar genomic context with one of the T6SS proteins. Using the visualization tools AnnoTree and AnnoView, I will demonstrate that this DUF is part of the T6SS cluster, supporting the hypothesis that it may function as an adaptor protein in T6SS. In summary, the AnnoTools pipeline integrates all components to enhance comparative genomic analysis with a large-scale annotation database. The user-friendly web-based tools enable researchers to visualize data both genome-wide and at a window-based scale. The ultimate goal of this thesis is to provide researchers with a comprehensive and easy-to-use method for predicting functions of genes or gene clusters of interest.
Data-Based Modeling of Electrochemical Energy and Thermal Systems: Fuel Cell and Lithium-Ion Battery
(University of Waterloo, 2025-01-24) Legala, Adithya
As a solution to combat climate change and environmental pollution, electrochemical energy systems such as Proton Exchange Membrane Fuel Cell (PEMFC) and Lithium-Ion Battery (LIB) are being developed as the replacement for fossil fuel-powered combustion engines, especially for ground transportation and aviation applications. These electrochemical energy systems must be able to operate independently and in conjunction with each other by complementing their advantages and limitations, such as efficiency, range, thermal behavior, aging, and operating environment. This interoperability requires accurate real-time computational models to control, diagnose, and adapt according to field requirements. A typical electrochemical energy system model needs to incorporate effects related to reactant concentrations, system overpotentials, thermodynamics, porous media mechanics, membrane dynamics, gas diffusion, electrode degradation, electrolyte status, ion transport, and chemical kinetics across various operating conditions, all of which result in complex interactions affecting the accuracy and reliability of the system. Today, both PEMFC and LIB use complex computational physics-based fluid dynamics models in the product development phase, which requires enormous computational power and long lead times for iterative prototype improvements. On the other hand, both PEMFC and LIB rely on simple lookup tables and semi-empirical equations as plant models that require intensive calibration activity to determine the mode of control and diagnosis for automotive applications. However, considering the present-day automotive propulsion systems, which operate in widely varied applications and geographic locations and have short product development cycles, these approaches are not able to comprehend the complexities, hindering the ability of these systems to operate at their full potential and leading to catastrophic failures (e.g., Thermal runaway). Data-based modeling techniques are one of the potential solutions, which is quite in contrast with other empirical or physics-based models where the entire input-output relations of the model are established primarily based on the data. Data-based models use aspects of statistics, probability, and network architecture, avoiding the complexities of physics-based models and intensive calibration, providing better accuracy in most cases, primarily where the complex mechanisms can’t be modeled using specific governing equations, and fast, efficient computation with much less computational resource requirement. This thesis focuses on data acquisition (identifying and collecting the relevant data) and data-based model development by incorporating machine learning algorithms and regressors to predict the system's performance, thermal behavior, aging, and faults in real-time (on-board diagnostics). Data for these models is acquired through two approaches: experimentation by utilizing Fuel Cell and Green Energy Lab facilities such as the Automated Battery Test Station (ABTS), G20 fuel cell automated test station, and by partnering with the relevant industry. In the second approach, data is generated by simulation of physics-based models (CFD, Semi-empirical, equivalent circuit models) that are experimentally validated in the literature and developed within the research groups of UWaterloo. Development of a data-based model includes the identification of feature vectors (inputs), prediction attributes (outputs), state estimates (internal parameters), non-linearity of the systems, correlation factors of various system entities, and application of machine learning techniques such as feed-forward artificial neural network, support vector machine classifier - regressor, along with their respective adaptations and calibration processes. The primary objectives of this study are to develop data-based models for three main application areas: (i) Prediction of PEMFC performance, internal states of the membrane, cell voltage degradation, and system outputs. (ii) Prediction of LIB heat release rate during discharge and thermal dynamics of an open system during an exothermic reaction. (iii) Prediction of fuel cell battery hybrid electric vehicle’s system dynamics and thermal behavior. During this study, various data-based models were developed to tackle the problems encountered in fuel cell-battery hybrid systems, such as predicting the fuel cell performance, fuel cell voltage degradation, PEMFC membrane dynamics, lithium-ion battery thermal dynamics, thermal behavior during exothermic reactions and dynamics of fuel-cell battery hybrid system. The results presented in this study proved the data-based model’s applicability in surrogate modeling, real-time system monitoring, controls, and diagnostics of electrochemical energy systems both at the component level and system level. Additionally, the results implicate that the data-based model can serve as a complement and alternative to the traditional computational fluid dynamics models as well as complex physics-based and empirical models to predict thermal gradients and system internal states during multifaceted reactions.

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