Restricted Theses and Dissertations
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Item Embargo Implementation of an In-Shelter Drug Poisoning Management Program in Calgary, AB: A Qualitative Study of Staff Perspectives and Experiences(2024-11-22) Aryal, Sarina; Tang, Karen; Campbell, David; Milaney, Katrina; Sawatzky, DavidBackground: Substance use has disproportionately impacted people with lived experience of homelessness (PWLEH) and the organizations that support them. Emergency housing shelters continue to experience a significant increase in drug poisoning events, while having limited medical training to appropriately respond to them. In-shelter drug poisoning management programs have emerged as potential solution to this issue, by having on-site medically trained healthcare professionals present to provide emergent medical care for these events. However, limited information exists on the implementation of these program in shelters. Objective: To explore the implementation of the newly implemented in-shelter drug poisoning management program, the Riverfront Dynamic Overdose Response Capacity (DORC) program, through the perspectives and experiences of staff involved in its development and delivery. Methods: A qualitative descriptive study using an ethnographic approach was used for this study. Participants were staff at the Calgary Drop-In Centre including dedicated program staff, program planners, and general shelter staff. Data collection involved interviews and field notes which were analyzed using a thematic analysis approach. Data were coded inductively by two independent coders. These codes were combined to develop themes which were reviewed and refined both individually and within the larger research group. Findings: Twenty staff members involved in the development and delivery of the program participated in 17 interviews and 6 observations. The Riverfront DORC program was perceived to be beneficial in responding to drug poisoning events and other emergent medical needs because of the onsite emergency medical team. However, participants perceived that the program led Riverfront DORC program clients to experience isolation from social or recreational opportunities and other supports found in other areas of the building. Lastly, participants commented on the challenges with the implementation of the program due to micro-and-macro factors such as limited communication between program planners and frontline staff, competing spatial and staffing needs, and conflicting policies around drug use in the building. Conclusion: The findings of this study describe the key aspects of implementation that affected the optimal delivery of in-shelter drug poisoning management programs. Shelters intending to implement similar programs must take into considerations the micro and macro factors that may act as a barrier for successful implementation.Item Embargo Molecular Targeting of High-Risk Pediatric Leukemia: Identification of Targets and Biological Implications for Therapy(2024-11-26) Sharma, Ritul; Narendran, Aru; Childs, Sarah; Neri, Paola; Riabowol, KarlAcute leukemias with KMT2A (mixed-lineage leukemia or MLL) rearrangements are associated with poor prognosis and high relapse rates, particularly in infant and pediatric populations. The outcomes for patients with KMT2A-rearranged (KMT2A-r) leukemia remain suboptimal, underscoring the urgent need for novel, targeted therapeutic approaches. This thesis focuses on disrupting key molecular interactions within the KMT2A fusion protein complex that drives leukemogenesis while characterizing novel preclinical models to facilitate targeted drug discovery. By utilizing established cell line models, cell-based assays and drug combination studies, this study demonstrated the therapeutic potential of inhibiting the interaction between KMT2A fusion protein and a transcriptional kinase, CDK9. In addition, the study demonstrates that targeting CDK9 can decrease venetoclax and steroid resistance in KMT2A-r leukemia. Furthermore, this thesis investigates the biological implications of treatment induced differentiation, with a focus on identifying therapeutic strategies to treat KMT2A-r leukemias. Our results demonstrate that menin inhibitor induced differentiation in KMT2A-r cells carries a distinct inflammatory profile with elevated secretion of HMGB1 and IL-8. These cytokines are associated with promoting migratory responses. In addition, our findings showed a synergistic interaction between menin inhibition and corticosteroids, which is the first line treatment for patients with differentiation syndrome. In parallel, the study evaluates the efficacy of the ETS inhibitor TK216, targeting SPI1 overexpression in pediatric AML and B-ALL leukemias, with the goal of uncovering its therapeutic potential in high-risk pediatric leukemia. The research presented herein offers a framework for developing feasible novel therapeutic strategies for KMT2A-r leukemias, building on a foundation of preclinical modeling and mechanistic insights that may inform future clinical interventions.Item Embargo INVESTIGATING HOW HOST-MICROBE INTERACTIONS INFLUENCE THE PROGRESSION OF TYPE 1 DIABETES(2024-11-25) Abbott, Spencer; McCoy, Kathleen; McDonald, Braedon; Reimer De Bruyn, RayleneThe global prevalence of allergies and autoimmune diseases, such as type 1 diabetes (T1D), has increased significantly over the past 50 years. Epidemiological evidence points to the gut microbiome as being one of the strongest drivers of increased disease incidence. Recent research has described associations between microbiome signatures and disease incidence through the induction of immune responses, however the underlying mechanisms by which the gut microbiome influences T1D progression remains largely unknown. Using gnotobiotic mouse models, this thesis aims to explore the role of host-microbe interactions in T1D development. One way in which the microbiome can modulate autoimmunity is through molecular mimicry. Previous work in the lab identified BacINT36-44, which will be referred to as Integrase, expressed by a commensal strain of Bacteroides as a bonafide molecular mimic to the islet autoantigen, IGRP206-214. Here, we further explored how Integrase expression by bacterial species in the gut influences antigen-specific immune responses in the T1D susceptible non-obese diabetic mouse model. Although microbial Integrase expression did not influence disease incidence in monocolonized mice, antigen-specific T cells were found to significantly modulate microbiome composition in specific-pathogen free mice. Additional analysis suggested that these changes were specific to Integrase expressing species. Integrase expression within the commensal gut microbiome also induced the expansion of antigen specific T cells both locally, within the gastrointestinal associated lymphoid tissue, and systemically, in the spleen. In addition, we sought to further investigate how the transfer of maternal antibodies influences T1D development in the offspring. It is widely accepted that the transfer of maternal antibodies during breast feeding shapes immune responses in the offspring. Thus, we explored how the transfer of maternal antibodies influences immune maturation in response to the microbiome. Together, this thesis reveals how the gut microbiome influences T1D, how molecular mimicry between gut microbes and self-antigens can shape antigen-specific immune responses, and the pathogenic role of maternal antibodies in non-obese diabetic mice.Item Open Access Statistical Inferences for Two-Component Semiparametric Location-Scale Mixture Models(2024-11-25) Zhang, Na; Ware, Anthony Frank; Lu, Xuewen; Aminghafari, Mina; Chen, Gamai; Ji, Yungqi; Qi, Yongchen; Ware, Anthony Frank; Lu, XuewenMixture models serve as a powerful statistical tool, particularly in capturing heterogeneous populations by representing them as a mixture of several distributions. These models are particularly useful in various fields, including genomics, economics, and social sciences, where data often arises from a combination of distinct subpopulations. Among the various mixture models, the two-component location-scale mixture model is of special interest due to its simplicity and flexibility in modeling diverse data structures. Traditional methods of parameter estimation in mixture models, such as Maximum Likelihood Estimation (MLE), are widely used due to their desirable asymptotic properties such as asymptotic efficiency. However, MLE can be highly sensitive to model misspecification and the presence of outliers, often leading to biased or inefficient estimates. Recognizing these limitations, this thesis explores the use of Minimum Hellinger Distance Estimation (MHDE), a robust alternative estimation which offers a balance between efficiency and robustness, meaning that while MHDE may not be as efficient as MLE in perfectly specified models (i.e., when the model exactly fits the data), it remains sufficiently efficient while being far more robust to data that does not perfectly align with the model assumptions. The choice of MHDE is motivated by its robustness in the face of outliers and its ability to provide more reliable estimates when the underlying distribution deviates from the assumed model. Focusing on semiparametric mixtures introduces additional flexibility by allowing for an unspecified distribution, which enables the model to capture complex data structures without imposing strict parametric assumptions. In these semiparametric models, the emphasis is placed on estimating the unknown parameter vector, while the form of the mixing distribution remains unspecified. This approach strikes a balance between parametric precision and nonparametric flexibility, making it particularly useful in situations where the true distribution is unknown or deviates from common parametric forms. This thesis mainly focuses on three primary objectives, which include minimum Hellinger distance estimation for both parametric and semiparametric location-scale mixture models, and estimation for location-scale mixture when data are right-censored. The first objective focuses on the estimation of the unknown parameters using minimum Hellinger distance, with a particular emphasis on the parametric location-scale mixtures. In this thesis, the Parametric Hellinger Distance Estimation (MHDEP) method is explored in depth. The Hellinger distance is defined in terms of the Hellinger integral, which was introduced by Ernst Hellinger in 1909. Chapter 2 delves into the methodology, theoretical asymptotic normality properties, simulation studies and real data analysis of MHDEP. This approach is chosen for its advanced robustness and competitive efficiency to be compared with the classical parametric likelihood-based estimations, especially in scenarios where traditional estimation methods may perform bad due to model misspecification or data irregularities. The second objective focuses on Semiparametric Hellinger Distance Estimation (SEMIMHDE) for mixture models with unknown component distributions. While the full derivation of identifiability conditions is ongoing, identifiability remains crucial for ensuring reliable parameter estimation. It ensures that the parameters of interest, such as mixing proportions, location, and scale parameters can be uniquely determined from the data. We first constructed the SEMIMHDE by deriving a custom base function for each component in the semiparametric mixture model. Subsequently, we adapted Hellinger Minimization for Mixtures (HMIX) algorithm which is originally designed for parametric mixtures using MHDE, to accommodate the semiparametric setting. This modified HMIX algorithm allows the estimation of unknown component distributions. To assess the performance of SEMIMHDE, we conducted a series of simulation studies, evaluating its efficiency, robustness, and sensitivity to model misspecification compared to other parametric estimation methods. Finally, we applied SEMIMHDE to the Old Faithful Geyser dataset, demonstrating its practical applicability and illustrating how it can handle real-world data. The third objective aims to refine and advance the current methodologies, specifically Kaplan-Meier-weighted MHDEP and SEMIMHDE for right-censored mixture is constructed, to provide a robust and comprehensive toolkit to analyze finite location-scale mixture models when data are right-censored. Right-censoring is a common issue in many practical applications, such as survival analysis, where the complete data for some observations is not available. Chapter 4 focuses on applying MHDEP and SEMIMHDE to various censoring rate scenarios from low to high rate to evaluate their finite sample performance. Simulation results show that both methods maintain good finite-sample performance even at high levels of censoring, demonstrating their robustness and reliability under different degrees of right-censoring. This study is anticipated to provide more reliable and versatile solutions for complex statistical modeling challenges, broadening the applicability of these methods to a wider range of practical problems. Additionally, both estimation methods were applied to a real right-censored dataset to assess their performance in scenarios where complete observations are unavailable. In summary, this thesis makes significant contributions to the field of both parametric and semiparametric mixture models by addressing fundamental issues of efficiency, robustness and model misspecification using Minimum Hellinger distance estimations. The proposed MHDEP and SEMIMHDE not only advance the theoretical properties of these models but also provide practical tools for more accurate and reliable statistical analysis in various applied settings.Item Embargo Data-driven needle puncture detection for the delivery of urgent medical care in space(2024-11-21) L'Orsa, Rachael; Westwick, David T.; Sutherland, Garnette R.; Goldsmith, Peter B.; Kuchenbecker, Katherine J.; Sun, Qiao; Majewicz Fey, AnnNeedle thoracostomy (NT) is a surgical procedure that treats one of the most preventable causes of trauma-related death: dangerous accumulations of air between the chest wall and the lungs. However, needle-tip overshoot of the target space can result in the inadvertent puncture of critical structures like the heart. This type of complication is fatal without urgent surgical care, which is not available in resource-poor environments like space. Since NT is done blind, operators rely on tool sensations to identify when the needle has reached its target. Needle instrumentation could enable puncture notifications to help operators limit tool-tip overshoot, but such a solution requires reliable puncture detection from manual (i.e., variable-velocity) needle insertion data streams. Data-driven puncture-detection (DDPD) algorithms are appropriate for this application, but their performance has historically been unacceptably low for use in safety-critical applications. This work contributes towards the development of an intelligent device for manual NT assistance by proposing two novel DDPD algorithms. Three data sets are collected that provide needle forces and displacements acquired during insertions into ex vivo porcine tissue analogs for the human chest, and factors affecting DDPD algorithm performance are analyzed in these data. Puncture event features are examined for each sensor, and the suitability of both accelerometer measurements and diffuse reflectance measurements are evaluated within the context of NT. Finally, DDPD ensembles are proposed that yield a 5.1-fold improvement in precision as compared to the traditional force-only DDPD approach. These results lay a foundation for improving the urgent delivery of percutaneous procedures in space and other resource-poor settings.Item Embargo Essays on Corporate Strategic Adaptation(2024-11-22) Simoes, Sean Cyril Neil; Petricevic, Olga; Verbeke, Alain; Jones, Vernon; Kano, Liena; Moran, Pablo; Bu, MaoliangThe essays in this dissertation utilize transaction cost theory (TCT) and complementary frameworks, namely the resource-based view and dynamic capabilities to explore how firms adapt to their business environment. In Chapter 1, I challenge the prevailing notion that multinational enterprises (MNEs) must adapt to conditions in ‘Bottom of the Pyramid’ (BOP) markets by creating informal institutions. Rather, using the case of India’s digital and financial initiatives, I argue that MNEs should work with governments to develop formal institutions, and leverage these institutions to accurately assess the reliability of BOP customers. In Chapter 2, I explore how MNEs have adapted their international strategies in response to vulnerability created by macro-level decoupling between the U.S. and China. Utilizing elements of the Profiting from Innovation (PFI) framework, I find that the framework helps explain the MNE’s position in the China market. This position along with elements of the PFI framework in turn helps explain firm responses. The remaining chapters explore cases of how firms have adapted in different ways. In Chapter 3, I discuss how Volkswagen failed to adapt its products to the U.S. market which put in place stringent emissions standards, with bounded reliability coupled with a disconnect between headquarters and U.S. personnel, and a lack of controls, contributing to a sustainability crisis. In Chapter 4, I explore how MNE Cummins adapted its global diversity and inclusion strategy and mandated the use of English as a common language in its Indian subsidiaries and joint ventures, including on the shop floor, with benefits for individuals as well as the Indian entities. And in Chapter 5, I explore how Corning, another large MNE, built and leveraged its resources and capabilities to adapt to changing markets, by launching and growing new product families. Using a sample of 23 product families, I find two combinations of causal conditions that help explain this capability to reconfigure Corning’s business. In summary, this dissertation utilizes TCT thinking and associated frameworks to contribute to an enhanced understanding of strategic adaptation by large firms.Item Embargo Respiratory Patterns Recognition and Cough Detection Using Signals from Capacitive Touchpads in Smartphones Commonly Worn in Shirt Pockets(2024-11-14) Gupta, Vedant; Vyas, Rushi; Pandey, Richa; Ginde, GouriThe timely identification of respiratory distress, often indicated by coughs, has become important for public health readiness and response to pandemics like COVID-19, SARS, and Influenza. Traditional methods of monitoring respiratory health, including hospitalization rates, doctor reports, and wearable sensors, have limitations in real-time reporting, extra costs, etc. With smartphones used by 66 out of every 100 persons, they are useful tools in various public health initiatives. Our project studies the use of capacitive touchpad sensors present in smartphones for monitoring respiratory patterns and distress. Specifically, this study examines how different touchpad scan patterns, orientations, and electrode spacings affect respiratory monitoring by detecting capacitive fluctuations. Our measurements with a commercial 5 by 6 element capacitive touchpad sensor array (0.8 cm pitch) worn on the chest or pocket registered fluctuations over the baseline due to cough-related chest surface movements. Furthermore, when the touchpad is placed on the chest or pocket, this method can also detect breathing rate by registering changes in capacitance over the baseline. Through this approach, we were able to measure very low capacitance values (0–100 pF), which are typically challenging to detect with conventional sensors. We also explored how varying electrode spacing impacts the fringing fields in the capacitive touchpad, as different configurations alter the depth and sensitivity of the capacitive field. This investigation allowed us to assess whether specific spacing setups could capture respiratory patterns deeper within body tissue, providing a non-invasive approach to respiratory health monitoring. This pioneering prototype demonstrates the potential for capacitive sensing to offer real-time, accessible respiratory monitoring using widely available smartphone technology.Item Embargo Distributed Deep Learning Methods for Medical Imaging Analysis(2024-10-29) Souza De Andrade, Raissa Cristina; Forkert, Nils Daniel; Wilms, Matthias; Pike, G. Bruce; Barker, KenRecent advancements in deep learning have equipped healthcare professionals with valuable tools to support clinical decision-making and reduce workloads. However, many medical centers lack sufficient datasets to train deep learning models, especially for rare diseases or centers in remote or underserved areas. Although collecting and curating datasets from multiple centers into a centralized repository is commonly employed to solve this problem, this approach is often infeasible due to its costs and privacy regulations that prohibit data sharing. Consequently, many centers and populations will not benefit from cutting-edge artificial intelligence. The distributed deep learning framework proposed in this work addresses these challenges by training accurate models while patient data remains securely stored within its center. Thus, privacy concerns are addressed while collaborative multi-center training is facilitated. A key innovation of this work is the development and evaluation of the travelling model, a method well-suited for scenarios where individual centers have very limited data availability. The travelling model is evaluated across various scenarios, including extreme cases where centers contribute only a single medical image, and is applied to critical medical imaging tasks such as brain age prediction, disease classification, and tumour segmentation. In general, the travelling model effectively increases the overall dataset quantity and diversity without compromising patient data privacy. However, solutions for the inherent acquisition shift biases caused by variations in equipment and protocols across centers and decentralized data quality control are needed to leverage its full potential. Therefore, this work also developed and integrated two novel methods into the travelling model approach, a data harmonization for reducing acquisition shift biases and automated decentralized data quality control. The results of this work demonstrate that the travelling model framework achieved performances comparable to models trained on a centralized repository across all evaluated tasks. Moreover, it performed better than the commonly used federated learning in cases where centers contributed fewer than five datasets. Additionally, the proposed data harmonization method reduced scanner variability by 23%, improving disease classification accuracy by 4%. Finally, the automated decentralized quality control method effectively identified incorrect and low-quality datasets, enabling more robust and reliable model performance.Item Embargo Bicycling Injuries in Children: The Role of the Built Environment(2024-10-28) Aucoin, Janet; Hagel, Brent; Nettel-Aguirre, Alberto; Winters, Meghan; McCormack, GavinBackground: Bicycling has many health benefits for children but can result in injuries, some severe. Additionally, children’s perceptions of injury risk decrease participation. The built environment is associated with the risk of bicycling injury in adults, yet less is known about risks for children who have different bicycling behaviours and locations than adults. This dissertation improves understanding of child bicyclist injury risk factors and perceptions in Canada, with an emphasis on the role of the built environment. Methods: We recruited 333 injured child bicyclists (ages 5-17) who presented to pediatric emergency departments in Vancouver, Calgary, and Toronto from May 2018 - October 2021. Using data from participant interviews and in-person site audits of injury/control locations from the injured child’s route, Chapter 3 used a case-crossover study design to examine associations between built environment characteristics and child bicyclist injuries. Chapter 4 used data from the case-crossover study to explore injury circumstances and examine risk factors associated with severe child bicyclist injuries. In 2021, 40 participants also completed a qualitative interview to explore injured child bicyclists’ perceptions of risk factors and safety (Chapter 5). Results: The results in Chapter 3 provided evidence that unpaved off road locations, presence of debris, poor surface quality, surface grade, and construction were risk factors for child bicyclist injuries. In Chapter 4, motor vehicle involvement and intersections were associated with higher odds of severe injury in child bicyclists. In Chapter 5, child bicyclists shared that some of the factors that contribute to perceptions of safety included sharing spaces with motor vehicles, road design, debris, surface quality, and surface grade. Conclusions: This study adds to evidence suggesting built environment supports are important for increasing bicycling safety for Canadian children. There is a critical need to provide child bicyclists with safe spaces where they are physically separated from motor vehicles and to ensure that routes used by child bicyclists are properly maintained.Item Embargo Predicting Hemorrhage in ICU Patients with Deep Learning Techniques(2024-10-21) Ghias, Meghdad; Sun, Qiao; McBeth, Paul B.; Thekinen, Joseph; Moshirpour, MohammadHemorrhage is a leading cause of trauma-related mortality, making early prediction of blood transfusion needs critical for timely intervention. This thesis investigates the application of data-driven modeling techniques to predict hemorrhage in intensive care unit (ICU) patients, aiming to improve patient outcomes through early-stage detection. We developed a Long Short-Term Memory (LSTM)-based architecture to model sequential patient data, capturing the dynamic evolution of vital signs and interventions to predict hemorrhage within a 5-hour window. By using irregularly sampled time series data, Our model achieved an Area Under the Curve (AUC) of 0.99, surpassing existing literature, where AUC values typically range from 0.70 to 0.95. We also compared the performance of our LSTM model with a time series Transformer, finding that LSTM outperformed the Transformer architecture in this study. A key contribution of this research is the comparative analysis of imputation methods, evaluating their impact on data distribution and prediction performance. While imputation techniques significantly altered data distribution, their effect on prediction performance was minimal. Additionally, Shapley values were employed to interpret the model, revealing feature contributions that aligned with surgeons’ understanding of hemorrhage mechanisms, further validating the model. To test external validity, we applied the ICU-trained model to prehospital datasets collected locally in collaboration with Shock Trauma Air Rescue Service (STARS). Although differences in data distribution posed challenges to maintaining high performance outside ICU settings, this research highlights the potential of sequential modeling in hemorrhage prediction and paves the way for future improvements in prehospital care.Item Embargo A Case of One: An Autobiographical Design Approach to Explore a Personal Informatics Preparation Stage Procedure(2024-09-20) Zhang, Xinchi; Schroeder, Meadow; Ringland, Kathryn; Zhao, Richard; Wang, MeaThis thesis exploration was started as a personal design endeavor to have a system that can support realistic task arrangement during my graduate school. This exploration landed to the often-overlooked area — the preparation stage in the Stage-Based Model (SBM) — in the personal informatics (PI) field. Personal informatics supports people to gain self-understanding through reflection on their relevant personal data. The preparation stage, which can involve many decision-making processes such as understanding the motivation of collecting personal information, deciding the information to collect, and choosing the appropriate tools, is where prior PI research focused significantly less on. This thesis aims to narrow this gap by introducing a procedure and an accompanying artifact, Qubio. I took an autobiographical design approach. Autobiographical design offers many advantages such as close use to allow rapid iteration whenever needed (fast tinkering). Then, combining with reflection, diligent documentation (46+ hrs recordings, 262 reflection entries), and long-term usage (47 months), I established a personal reflective procedure to determine what data I might track. The procedure includes 1) externalization of obligations and interests, 2) mapping (for goal choices), and 3) task arrangement, which is supported by the token-based artifact, Qubio. This exploration bridges the preparation stage of the Stage-Based Model in PI and the Integrated Model of Goal-Focused Coaching (Integrated Model) in psychology. I conclude this thesis by discussing research opportunities in connection to the Integrated Model for the preparation stage in PI and suggesting collaboration between PI and personal information visualization to support visualization agency in PI practices. I further suggest revisiting established PI models to potentially integrate the field’s expanded understanding of PI related activities. Finally, I reflect on how an autobiographical design approach produced a personalized procedure and artifact.Item Embargo An Intersectional Approach to Addressing Discrimination Among Pregnant Individuals in a Contemporary Population: A Cross-Sectional Study(2024-09-25) Dharamsi, Shazia; Metcalfe, Amy; Ahmed, Sofia; Ruzycki, ShannonBackground: Pregnant individuals may be vulnerable to discrimination due to existing social stigma and cultural norms surrounding pregnancy and motherhood, which can be exacerbated due to gender, race, or other aspects of their identity; however, it is unclear how intersecting identities overlap and interact to shape peoples’ everyday experiences. The purpose of this study was to compare the prevalence and experiences of discrimination among pregnant individuals with diverse intersectional identities. Methods: This secondary analysis used baseline data from 1,605 participants enrolled in a longitudinal pregnancy cohort study, the P3 Cohort Study. Latent class analysis was applied to identify different subgroups within the population based on combinations of their social positions and identities (i.e., race, gender expression, emotional health, physical health, income, disability, chronic illness, and age). Perceived discrimination was assessed via the Everyday Discrimination Scale (EDS), and linear regression was performed to compare the frequency, chronicity, and overall number of discriminatory encounters between subgroups. Results: Utilizing latent class analysis, the following three subgroups emerged: (1) Mostly Privileged and Healthy; (2) Somewhat Privileged with Chronic Conditions; (3) Somewhat Privileged, Predominantly Racialized. Class 2 (βfrequency = 0.8, p = 0.042; βchronicity = 45.2, p < 0.001; βsituations = 0.4, p = 0.040) and Class 3 (βfrequency = 0.2, p < 0.001; βchronicity = 31.5, p < 0.001; βsituations = 0.5, p < 0.001) were both positively associated with the frequency, chronicity, and total number of discrimination situations relative to Class 1. The primary reported reasons underlying discrimination experiences varied across latent classes, reflecting the intersecting identities that defined each cluster.Item Embargo Tuning the Catalytic Performance of Nitrogen- and Iron-Nitrogen-Doped Mesoporous Carbons for CO2 Reduction(2024-09-20) Li, Jialang; Birss, Viola; Heyne, Belinda; Roesler, Roland; Thangadurai, Venkataraman; Kibria, Md; Aicheng, ChenThis thesis systematically explores the catalytic performance of nitrogen-doped and iron-nitrogen co-doped mesoporous carbon materials for electrochemical CO2 reduction (CO2RR). It particularly examines the effects of nitrogen doping speciation, structural disorder, various pore sizes, and single iron atom incorporation on catalytic performance. First, this research compares the CO2RR performance of N-doped mesoporous carbon materials in both powder (CIC) and self-supported film (NCS). These two materials were prepared using the same process except that the CICs are powder and the NCS is tape-casted into a film. Physical characterization revealed that the N-doped carbon powders possess a more disordered carbon wall structure and more micropores than the sheets, while X-ray photoelectron spectroscopy indicates a higher content of pyridinic nitrogen in the powder (53% of the total nitrogen content). The CO2RR experiments showed that the N-doped CIC-12 exhibits the highest FECO of 97%, higher than the 50% FECO shown by the N-NCS-12, attributed to the higher pyridinic N content and more structural disorder providing more active sites. Then the effect of varying pore sizes in N-doped CIC powders was explored, comparing with another method of templating carbon synthesized via aniline pyrolysis and having 22 nm pores (AD-22), containing 6 at% graphitic nitrogen and exhibiting lower structural disorder due to the absence of NH3 etching. N-doped CIC-12, with the highest structural disorder and pyridinic nitrogen, achieved 97% FECO at -0.45 V vs. RHE, while CIC-85 and CIC-22 gave lower FECO values. AD-22, containing only graphitic nitrogen, showed no CO2RR activity, confirming the inactivity of graphitic N towards CO2RR. This part of the work resulted in a first-time structure-property-performance relationship for CO2RR at N-doped carbons. Finally, the medium-performing N-CIC-85 powder was used to introduce single iron atoms to further enhance the CO2RR performance. The Fe-NCIC-85 catalysts, synthesized at 900 °C, achieved an excellent FECO of 97% at -0.45 V vs. RHE and maintained this high selectivity for over 100 hours without any signs of loss of stability beyond that. This enhancement was attributed to single-atom iron stabilization by the nitrogen doped onto the CIC powder surface during preparation through NH3 exposure.Item Embargo Artistic Expression and Personal Identity: The Figure of the Huntress in Virgil’s Aeneid and Ovid’s Metamorphoses(2024-09-19) Corvino, Jesse Ryan; Toohey, Peter; Cebrian, Reyes Bertolin; Hughes, Lisa; Konshuh, Courtnay; Mackie, ChristopherThe huntress who endeavours to remain unwed is at variance with the Roman girls and young women who almost universally become wives (and mothers). Why, then, is this seemingly unconventional literary figure of such interest to the early Imperial Latin poets Virgil and Ovid? In order to ascertain this, this thesis attempts to answer the following questions pertaining to the “how” and “why” of the huntress’ depiction in Virgil’s Aeneid and Ovid’s Metamorphoses: did the poets adhere to any sort of paradigm for the huntress (the “how”); and why did they include the huntress in their poems (the “why”). My methodological approach to answer these questions comprises lexical analyses of the passages containing these characters and the categorization of the data on the huntress’ characterization drawn from them according to the five characteristics that I propose comprise the huntress. The “how” of Virgil’s huntress consists of a paradigm, while Ovid does not have one. With respect to the question of “why”, the huntress is a means for Virgil to express the idea of the hunt in the Aeneid and permits a high level of creative expression that other female characters do not. Ovid’s purpose for including her in the Metamorphoses is to explore certain ideas relating to rape and the “erotic hunt” and the opposition of specific concepts pertaining to sexual/romantic relationships. Since the literary huntress is a diametric character to actual Roman girls and young women, the larger question arises about whether these poets’ ideas about the former reflect anything about their attitudes towards the latter. It is the case for Virgil that they do, and, building on Ramsby’s arguments about Roman women and autonomy, I argue that he uses the huntress to express his observations regarding women in his society. Ovid, on the other hand, seems to use the huntress to convey the theme of victimization with respect to personal identity which involves the huntress being forced to give up her identity due to the interference from a god or goddess, a theme which I suggest may have personally resonated with him.Item Embargo A Scalable Fabrication Methodology for the Manufacturing of Cardiovascular Perfusion and Drainage Cannulae(2024-09-20) Feddema, Joshua; Dalton, Colin; Sundararaj, Uttandaraman; Badv, Maryam; Kim, KeekyoungCannula manufacturing techniques are held as trade secrets by their respective companies, including large biomedical companies such as Medtronic Cardiopulmonary, Getinge, and Edwards [1]. While exact details are not available, polyvinyl chloride (PVC) plastisol dip molding is a known commercial method used to fabricate cannula. Through exploratory experimentation and the investigation of different fabrication techniques, this project aimed to refine a basic dip molding process into the controlled precision fabrication of cannulae. Crude tube-shaped structures can be produced with basic dip molding; however, significant research and development was required to refine the process to a clinically relevant level. For medical applications, cannulae require embedded coils, good surface finish, uniform and reproduceable geometries, acceptable tip and connector joints, and clinically relevant markings. This project investigated precision dip molding and subsequent cannula assembly to meet these requirements. The result of this work was a viable 18 step fabrication methodology that takes 56 minutes to produce cardiovascular cannula shafts specifically for placement into the aorta. These prototypes were considered clinically acceptable by an ICU physician, supporting the credibility of the research. Additional prototyping of cannula for the drainage of venous blood was conducted, showing the versatility of the fabrication methodology. Flow testing on the cannula prototypes showed excellent lumen reinforcement with a <2% flow reduction when bent according to ISO 18193 standards. Simulation based tools were built to aid in the future development of cannula designs. Beyond manufacturing, this research presents the underlying physics believed to govern fabrication with PVC plastisol, an atypical plasticizer polymer material. With this understanding, appropriate plastisol handling, and fabrication troubleshooting are achievable.Item Embargo Optimizing Pipeline Leak Detection: Leveraging Attention-based 1DCNN-BiLSTM for Enhanced Accuracy and Minimal False Alarms(2024-09-20) Khazali, Sahar; Moshirpour, Mohammad; Far, Behrouz; Drew, Steve; Kawash, JalalPipelines are an essential infrastructure for the transportation of fluids and gases in many industries. Leaks in pipelines present significant environmental and economic concerns, making accurate and timely leak detection crucial. Recent advances in deep learning, particularly sequential models, have shown promising capabilities in anomaly detection for time series data. However, the challenge remains to detect leaks accurately while minimizing false alarms. This paper presents a novel approach combining the CB-AttentionNet model, which integrates a 1D convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and multi-head attention mechanisms to capture local and long time series dependencies. Additionally, we introduce a probabilistic search framework using Monte Carlo methods to optimize window sizes dynamically, addressing the limitations of fixed window sizes in handling variable-length sequential data. Experimental results demonstrate that our method performs better in terms of accuracy and reducing false positives across various simulations with industrial pipeline data. Optimized window sizes, particularly between 45 and 60 seconds, offer an effective balance between reducing misclassified leaks and maintaining high training accuracy. Furthermore, our analysis of resource usage and evaluation time shows that the model’s performance is efficient and manageable within typical operational constraints.Item Embargo Computational Improvements for PT Flash Calculations: A New Look at the Initiation and Underemphasized Aspects of Calculation Method Modifications(2024-09-20) Shirazi Manesh, Amir Ahmad; Clarke, Matthew; Maini, Brij; Ponnurangam, Sathish; Yarranton, Harvey; Mehta, Sudarshan; Zendehboudi, SohrabPhase equilibrium calculations at constant pressure and constant temperature, PT flash calculations, are at the heart of thermodynamic modeling and simulations. Reliability and efficiency of simulations are heavily dependent on these calculations. Accordingly, there has been many attempts to improve these calculations using the available mathematical tools and the knowledge amassed in this regard is considerable. However, in relation to the characteristics of the problem, it seems that there are still capacities in the mathematical methods and these capacities should be used to attain better results. This study is an attempt to employ the connection between the special structure of the flash problem and the author’s understanding of mathematical methods to improve efficiency of the calculations while preserving reliability of the results. It was found a consideration should be made in design of the unidirectional search algorithms or in application of the line search methods to the problem and effect of making this consideration on flash results was discussed. It was shown that line search methods with a specific search direction can be applied to improve the efficiency of stability test calculations in hard cases. The efficiency of the line search methods with different types of factorization was evaluated and similarity of their abilities was found. With respect to initial guesses required to conduct stability analysis in PT flash calculations, as a global optimization problem, it was shown that considerably smaller sets of guesses, compared to the sets suggested in the literature, can provide the same level of results reliability. Application of these sets to the calculations can lead to a significant computational burden reduction. Two approaches which are customarily used in the stability analysis were evaluated and it was shown that searching for the lowest negative minimum of modified tangent plane distance function can cause considerably extra computational cost for the optimization-based calculations. Using the results, an approach to initialization of the calculations with a previously recommended stability test and prioritization of the guesses in the reduced size sets was suggested. A case of failure of successive substitution method in phase split calculations was reported and a combined line search-trust region method to remedy was suggested. Patterns of stability test and phase split iterations convergence behaviour with respect to the location of successive substitution method basin of attraction in stability test calculations were reported. A modification for line search methods to improve the efficiency of stability test calculations in difficult to converge specifications was introduced.Item Embargo Template-Directed Design of Robust Phosphonate Metal-Organic Frameworks for Carbon Capture(2024-09-20) Gabert, Evan Darryl; Shimizu, George Kisa Hayashi; Roesler, Roland; Lu, Qingye Gemma; McCoy, Sean Thomas; Ward, Micheal D.Metal-organic frameworks are versatile and tunable materials. Phosphonate MOFs offer greater stability, but structural control is challenging. This thesis utilizes hydrogen-bonded metal-organic framework (HMOF) intermediates to control phosphonate MOF properties. HMOFs are porous hydrogen-bonded solids composed of secondary sphere interactions between phosphonate linkers and hexaaquachromium(III) ions. HMOFs are metastable and well ordered. Thus, they serve as an isolatable intermediate that is easily characterized. Upon heating, aqua ligands are irreversibly replaced by phosphonates. While the resulting MOFs are often poorly ordered, they retain properties of their prior HMOF structure. This work shows the flexibility of HMOF hydrogen bonds allow guest molecules to substantially change framework structure. If the guest is retained during dehydration, it will continue to shape pore structure and result in a unique MOF. The resulting MOFs are rigid and retain the guest-imprinted pore structure after guest removal. This “HMOF to MOF” method allows for the controlled synthesis of robust phosphonate MOFs. This proof-of-concept study develops the HMOF to MOF method towards the creation of solid sorbents for carbon capture. A novel highly flexible HMOF, H-CALF-55, was dehydrated in the presence of numerous guests. This resulted in formation of unique MOF structures with tunable isotherm shape and gas capacity (0.5 to 1.2 mmol/g). The MOFs retained their gas capacity after one week of exposure to boiling water or 6 M HCl. The unconventional use of CO2 guests resulted in 1.5 times higher CO2 capacity than guest-free synthesis. Lastly, the use of template mixtures to form MOFs imprinted by guest-guest interaction was explored. H-CALF-55 dehydrated around a combination of CO2 and water had increased CO2 capacity relative to the single-guest MOFs. Amine modified HMOFs were dehydrated around trace water. The resulting MOFs exhibited 3-fold higher CO2 capacities at 40 mbar when wetted prior to analysis with 5.2 mass percent water. No such effect was observed in MOFs prepared from bulk water. This suggests that the template used can have great impact on MOF properties, including tuning material performance in the presence of water. A critical metric for carbon capture materials.Item Embargo Novel Compression Strategies for Dynamic NeRF Plane Embeddings: Quantization, Pruning, and Spatiotemporal Decoupling(2024-09-18) Mohammed, Elsayed; Abou-Zeid, Hatem; Drew, Steve; Ghaderi, MajidDynamic neural radiance fields (NeRF) have recently been introduced to extend NeRF’s capabilities to small videos and time-changing immersive experiences. Dynamic NeRF models the temporal changes in a 3D scene in addition to the 3D scene structure and appearance. To accomplish this, the size of these models is typically very large, even for short immersive experiences. This thesis investigates compression strategies for dynamic NeRF to enhance memory and communication efficiency while maintaining rendering quality for future immersive applications such as virtual and augmented reality. Focusing on the hybrid KPlanes representation, we first analyze the sparsity and redundancy of embeddings and then propose three novel techniques for compression and optimization. Our key contributions include quantization approaches that significantly reduce memory requirements while maintaining visual fidelity, and pruning strategies that eliminate less significant embeddings. We also introduce a combined pruning and quantization method that achieves substantial model size reductions. Additionally, we propose a concept of decoupling spatiotemporal embeddings to reduce their number and enhance scalability for longer dynamic NeRF representations. The findings highlight the potential for dynamic NeRFs to meet the demands of next-generation communication technologies and facilitate seamless immersive experiences, paving the way for their broader application in real-world scenarios.Item Embargo Investigating the Impact of Code Comment Inconsistency on Bug Introducing Using an LLM Model(2024-09-18) Radmanesh, Shiva; Mohsirpour, Mohammad; Belostotski, Leonid; Barcomb, Ann; Drew, SteveCode comments are essential for clarifying code functionality, improving readability, and facilitating collaboration among developers. They serve as a guide to help both current and future developers understand the logic and purpose behind specific code segments. However, as software evolves, code changes frequently, and comments may not always be updated to reflect these changes. Despite their importance, comments often become outdated, leading to inconsistencies with the corresponding code. This can mislead developers and potentially introduce bugs. This thesis investigates the impact of code-comment inconsistency on bug introduction using large language models, specifically GPT-3.5. I first compare the performance of the GPT-3.5 model with other state-of-the-art models in detecting these inconsistencies, demonstrating the superiority of GPT-3.5 in this domain. Additionally, I analyze the temporal evolution of code-comment inconsistencies and their effect on bug proneness over various timeframes using the GPT-3.5 model and odds ratio analysis. Our findings reveal that inconsistent changes are around 1.5 times more likely to lead to a bug-introducing commit than consistent changes, highlighting the necessity of maintaining consistent and up-to-date comments in software development. This thesis provides new insights into the relationship between code-comment inconsistency and software quality, offering a comprehensive analysis of its impact over time.