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Influence of the Coating on the Radiative and Conductive Heat Transfer of 22MnB5 Steel in Hot Stamping
No full textIn hot stamping of Al-Si coated 22MnB5 steel, the heat transfer coefficient (HTC) during quenching is critical for determining the microstructure and mechanical properties of the formed part. Additionally, the radiative properties elucidate how the surface transforms as the steel is heated before quenching. Knowledge of the surface transformations is paramount for understanding the damage caused by the molten Al-Si coating to ceramic rollers in a production environment. This work investigates the effect of the coating on the HTC during quenching and explores the link between radiative properties and surface state changes, including the melting of the Al-Si coating and oxide layer growth.
Experiments were performed using a hydraulic press fitted with cooled dies to study the impact of interfacial pressure, coating weight, and dwell time on the HTC. The HTC increased with interfacial pressure, before saturating between 6 and 10 MPa. Specimens with higher coating weights had lower HTCs, which was corroborated by a higher arithmetic roughness for specimens with higher coating weights. Furnace dwell time did not significantly affect the HTC or the roughness of the specimen.
Ex situ reflectance measurements of hot stamped specimens revealed minima and maxima between 200 and 1000 nm, due to thin film interference. Wave optics analysis on the reflectance spectra suggested that the oxide layer grew with dwell time. This was confirmed using high resolution – scanning electron microscopy, wherein the measured oxide layer thicknesses were within 50 nm of the estimated oxide layer thicknesses. Additional samples were heated in a muffle furnace for between three and sixty minutes. Wave optics analysis on the reflectance spectra suggested that the oxide layer grew parabolically, as per Wagner’s law. Microscopy measurements revealed that the interdiffusion layer grew linearly simultaneously with the oxide layer.
In situ specular reflectance measurements of specimens during heating were performed using a laser-driven light source. The specular reflectance peaked twice; the first peak was attributed to initial coating liquefaction, and the second peak was attributed to subsequent intermetallic reactions. In situ measurements performed on specimens coated with Thermoboost® and iron nitrate revealed a significantly lower specular reflectance peak and higher heating rates
Automated Segmentation of Head and Neck Cancer on Computed Tomography Imaging
No full textHead and neck cancer (HNC) is the sixth leading cause of cancer death worldwide. Diagnosis and treatment are aided by CT imaging, and tumour segmentation on CT is an important but time consuming part of treatment planning. In this thesis, we investigate different approaches for the segmentation of HNC on head and neck CT scans.
For a segmentation convolutional neural network (CNN), it is important to start with pretrained weights to achieve good performance. Most prior work uses weights obtained through classification pretraining tasks. We conjecture that a segmentation pretraining task may be better suited for medical image segmentation, as then the pretraining task is more closely related to the final task. We develop a novel self-supervised segmentation pretraining task which we then use to pretrain the model on unlabelled CT images prior to fine-tuning it on expert-labelled images.
We compare model performance after pretraining on this new task against existing pretraining methods, including out-of-domain pretraining using ImageNet and in-domain pretraining using the Jigsaw task. All in-domain pretraining, both using Jigsaw and the novel segmentation method created here, were performed on a composite pretraining dataset of over 618,000 CT images which was created by combining and preprocessing 8 separate
medical imaging datasets.
We find that optimal performance is obtained with ImageNet out-of-domain pretraining, and this performance rivals previously published work which used PET-CT combination images rather than CT alone. Our novel pretraining segmentation task improves performance over random starting weights but does not exceed the performance of ImageNet pretraining. We conjecture that this might be because the pretraining dataset for our task is much smaller than the pretraining dataset used for ImageNet
Examining the Association between Functional Social Support, Marital Status, and Memory in Middle-Aged and Older Adults in Canada: A Mixed Methods Study
No full textAlthough several studies have found that functional social support (FSS) promotes memory through stress-buffering mechanisms, the role of marital status in shaping this association remains underexplored. This thesis – a sequential explanatory mixed-methods study – is the first research to examine whether marital status modifies the association between FSS and memory in community-dwelling, middle-aged and older adults (45-85 years old).
Data for the quantitative portion of the study were obtained from the Tracking Cohort of the Canadian Longitudinal Study on Aging (CLSA). Memory was regressed on FSS, adjusting for various sociodemographic, lifestyle, and health-related covariates, in a linear mixed effects model. The moderating effect of marital status was assessed by adding its interaction with FSS to the model. Results showed significant and positive adjusted associations for overall FSS (β: 0.07; 95% CI: 0.01, 0.13) and two FSS subtypes, namely positive interactions (β: 0.06; 95% CI: 0.01, 0.11) and affectionate support (β: 0.05; 95% CI: 0.00, 0.11), with memory. However, the interaction between marital status and FSS (overall and subtypes) was not statistically significant (likelihood ratio test p-value = 0.75), indicating that FSS did not have differing effects on memory according to marital status. Two sensitivity analyses – one stratifying the sample by baseline marital status and another comparing different missing data approaches – confirmed the initial findings.
For the qualitative portion, semi-structured interviews were conducted with a sample of 18 community-dwelling adults who resembled the CLSA population and reported different categories of marital status. Each interview was audio recorded, transcribed verbatim, and thematically analyzed with the goal of explaining and contextualizing the quantitative results. Three major themes emerged from the interviews to help explain why marital status, contrary to a priori expectations, did not impact the FSS-memory association: “learning to cope” – participants used other alternatives besides a spouse to deal with memory challenges; “context matters” – contextual factors such as marital quality may affect this association; and, “doing more harm than good” – some spouses may provide too much support, reducing participants’ autonomy to stimulate their own cognitive processes.
The thesis findings highlight the complexity of marital relationships and emphasize the need to consider factors such as satisfaction with one’s marital status and adequacy of support to provide a comprehensive understanding of how social and marital dynamics impact cognitive trajectories. This research suggests public health initiatives focused on enhancing social support could promote cognitive health in aging populations, regardless of marital status
Detecting Unchecked Exception-Related Behavioural Breaking Changes with UnCheckGuard
No full textThe ubiquitous use of third-party libraries in software development has enabled devel-
opers to quickly add new functionality to their client software. Unfortunately, library usage
also carries a cost in terms of software maintenance: library upgrades may include breaking
changes, in which client expectations about library behaviour are no longer met in new
library versions. Behavioural breaking changes can be particularly insidious, and in their
full generality, could require sophisticated program analysis techniques to (approximately)
detect.
In this work, we present our UnCheckGuard tool, which detects a class of behavioural
breaking changes—those related to exceptions thrown by Java libraries. UnCheckGuard
analyzes both sides of the library/client duet. On the library side, UnCheckGuard creates
a list of new exceptions that may be thrown by methods in a library’s public API, includ-
ing by its transitive callees. On the client side, UnCheckGuard identifies client methods
that call library methods with new exceptions. To reduce false positives, UnCheckGuard
additionally filters out new exceptions that cannot be triggered by particular clients, using
taint analysis. It therefore can be used by client developers as a tool to screen library
updates for relevant incompatibilities.
We have evaluated UnCheckGuard on 302 libraries and 352 library-client pairs drawn
from the DUETS collection and found 120 libraries with newly-added exceptions, as well
as 1708 callsites to library methods which, when upgraded to the latest version, may
introduce a behavioural breaking change in the client due to a newly added unchecked
exception. These findings highlight the practical value of UnCheckGuard in identifying
exception-related incompatibilities introduced by library upgrades
Barriers and Drivers to Sustainable Residential Home Construction in Ontario, Canada: Perspectives from the Construction Sector
No full textOver the past few decades, the province of Ontario in Canada has seen significant growth in its regions and townships, especially in suburban developments. These developments are within towns and more prominent cities, causing considerable suburban sprawl, in contrast to mid-rise and high-rise developments. The suburban neighbourhood has become more attractive for homebuyers because of the space, privacy, and relatively reduced living expenses while being close enough to major cities for convenient amenities and facilities. The construction industry has taken some initiative to develop more sustainable buildings; however, the focus is mostly on non-residential buildings or residential high-rise developments, but there is limited information on sustainable single-home residential construction. This study aimed to understand the barriers and drivers to sustainable residential construction in Ontario, including knowledge, awareness, familiarity and interest in sustainable residential construction by those who are involved in the sector. A survey was developed based on Rogers’ Theory of innovation adoption and included both closed (yes, no, and Likert scale) and open-ended questions. The survey was sent to Ontario companies that are involved in residential construction and included questions on sociodemographic characteristics and the length of time in the industry, as well as questions on knowledge and awareness of existing sustainable construction standards and interest in future sustainable applications within the industry. Findings show that construction industry professionals generally possess the necessary experience, knowledge, and interest in sustainable construction. They identified barriers such as costs of sustainable housing, lack of materials, and lack of homebuyer interest. Drivers included government financial incentives and local by-laws and material availability. Recommendations for promoting sustainable residential home construction include developing a database on sustainable materials, developing green standards at the local level, upgrade the Ontario Building code, and provide life cycle cost benefits of sustainable housing to homebuyers.
Keywords: sustainable homes, neighbourhood developments, third-party sustainability certifiers, residential construction industry, Ontari
Understanding the relationship between gait and cognition in mild cognitive impairment subtypes and probable REM sleep behaviour disorder
No full textIntroduction: Mild cognitive impairment (MCI) represents a transition state between normal cognition and dementia. Individuals with amnestic MCI (aMCI) are more likely to develop Alzheimer’s disease (AD), whereas individuals with non-amnestic MCI (naMCI) are more likely to progress to non-AD dementias. Individuals with multi-domain (MD-) MCI are more likely to develop dementia than individuals with single-domain (SD-) MCI. Previous research has demonstrated that individuals with MCI have slower gait compared to cognitively unimpaired (CU) older adults, but limited research has characterized gait differences across MCI subtypes over time in these groups. There is also growing evidence that specific gait characteristics are selectively associated with specific cognitive domains, but few studies have investigated these associations longitudinally in MCI. REM sleep behavior disorder (RBD) is a prodromal biomarker of α-synucleinopathies, particularly Parkinson’s disease (PD) and dementia with Lewy bodies (DLB). Individuals with RBD are more likely to develop MCI than healthy controls, and the presence of MCI in RBD is associated with greater risk for early phenoconversion. Recently, research has highlighted that individuals with isolated RBD exhibit subtle changes in gait. Despite this, no studies to date have explored how the presence of RBD influences gait characteristics in people with MCI. Therefore, this study aimed to a) characterize baseline and longitudinal gait characteristics across MCI subtypes, b) explore how the presence of RBD impacts gait characteristics in MCI, and c) assess whether baseline gait characteristics can predict future decline in specific cognitive domains in MCI.
Methods: This study involved secondary data analysis of data from the Mayo Clinic Study of Aging. 382 individuals with MCI (180 SD-aMCI, 48 SD-naMCI, 134 MD-aMCI, 20 MD-naMCI), and 382 age-, sex- and education-matched CU individuals were included. Mean follow-up duration for the entire sample was 16.8 months. Participants completed gait assessment using an instrumented gait walkway. Informants completed the Mayo Sleep Questionnaire, which was used to determine probable RBD (pRBD) status. Cognition was measured using domain-specific z-scores for attention, memory, language, visuospatial function, as well as global cognition. Linear mixed effects models were used to compare gait outcomes over time between groups and by pRBD status. Principal component analysis and linear mixed effects models were used to derive gait components and assess if they predict change in cognitive domains from baseline to follow-up visits.
Results: All individuals with MCI walked slower, with shorter steps, longer step time, and increased double support (%) compared to CU individuals. Variability of step length, stride velocity and swing time were increased in MD-aMCI compared to CU individuals. Over time, stride velocity and step length decreased, and step time increased in MD-aMCI. The presence of pRBD was associated with decreased stride velocity and step length, and increased stride width and double support (%), particularly in MD-naMCI. Swing time increased over time in people with MD-naMCI and pRBD. Principal component analysis identified three gait factors: pace and stability, timing and rhythm, and variability. The pace and stability factor was negatively associated with global cognition and attention. Surprisingly, the rhythm and timing factor was positively associated with memory. Variability was negatively associated with global cognition and visuospatial function, but this association weakened at follow-up.
Conclusions: Gait analysis may be a helpful tool to distinguish MCI from normal aging. Gait variability and slowing of gait may be specific markers of multi-domain impairment. Gait is impacted by comorbid pRBD in MCI, particularly in MD-naMCI. These findings provide further support for the notion of selective associations between specific gait and cognitive domains in people with MCI. Importantly, further investigation with longer follow-up duration, larger sample sizes, and integration of neuroimaging and fluid biomarkers is needed to better understand the role of gait in predicting future progression to dementia in individuals at risk
Design and Assessment of Membrane-supported Ammonia Cracking for Hydrogen Refuelling Stations
No full textAs Canada aims to reduce greenhouse gas emissions, there is a growing shift toward cleaner energy and fuel sources. Hydrogen has emerged as a promising alternative fuel source due to its high gravimetric energy density and ability to power fuel cell electric vehicles without producing direct carbon dioxide emissions. However, there are currently challenges in storing and transporting large amounts of hydrogen. Ammonia is gaining attention as a hydrogen carrier because it can be stored under moderate pressure or refrigeration and leverages existing infrastructure. Once delivered, hydrogen can be extracted from ammonia through on-site decomposition and purification. Although this pathway shows promise, its competitiveness depends on the system's energy requirements, operating costs and emissions. Furthermore, most existing ammonia decomposition and hydrogen refuelling models are proprietary, limiting accessibility for researchers and small-scale developers. This thesis addresses this gap by developing an open-source process model for a palladium membrane supported ammonia decomposition process at hydrogen refuelling stations. The process delivers 500 kg of hydrogen gas at 350 bar per day, and its cost and emissions performance were compared to other hydrogen production pathways. A Python-based model was created using Cantera, a chemical kinetic and thermodynamic library, to simulate the isothermal Pd membrane reactor. In the reactor, ammonia decomposes to nitrogen and hydrogen, while hydrogen is separated using the membrane. This eliminates the need for additional hydrogen purification steps. The base case achieved 99.92% conversion and 95.9% hydrogen recovery. To preheat the ammonia feedstock to the membrane reactor, the unconverted ammonia and unrecovered hydrogen were mixed with some ammonia feedstock and combusted with air. The combustion generates NOx emissions, which were reduced by 85% using a selective catalytic reduction unit, bringing NOx emissions well below provincial limits. While the system has no direct carbon dioxide emissions, indirect emissions from electricity consumption, ammonia feed and transportation for the process were estimated at 4.86 kg CO₂e/kg H₂, with an electricity requirement of 9.77 kWh/kg H₂. An economic analysis shows a capital expenditure of approximately 1.6 million for the base case. The levelized cost of hydrogen (LCOH) at 350 bar was estimated at $10.38 kg/H2. A sensitivity analysis was also conducted to evaluate the impact of temperature, pressure and membrane permeance on conversion, hydrogen recovery, NOx emissions, and LCOH. The impact of capital and operating expenditure on LCOH was also analyzed, with the price of ammonia being the main contributor to changes in LCOH. These results from a detailed study of the ammonia to hydrogen pathways contribute to a better understanding of clean hydrogen technologies for transportation applications and also provide key insights for future deployment in clean fuel strategies across Ontario and beyond
Investigating the Impacts of Policy Stringency on the Public Perceptions of Pandemic Policies, Sentiment Analysis of Tweets During the Second and Third COVID-19 Pandemic Waves in Ontario
No full textBackground: Public sentiment plays a critical role in shaping compliance and trust during public health crises. The COVID-19 pandemic was the first example of an infodemic, and the significant amount of social media data from that time period can be useful for understanding public responses and experiences throughout the pandemic. This study explores emotional responses to COVID-19 policy interventions in Ontario, Canada, using Twitter data to assess how public sentiments varied across lockdowns, mask policies, and vaccine mandates. The research aims to understand how policy stringency and a regional context influenced public attitudes throughout the pandemic.
Methods: Data from the Twitter API was collected and analysed. Tweets posted between December 1st, 2020 and June 30th, 2021 from app users in Ontario were compiled and sorted using zero-shot classification into subgroups for relevant categories – vaccine mandates, mask policies, and lockdown measures. These tweets were then analyzed using sentiment analysis and the COVID-19 Stringency Index to identify trends in the sentiments expressed on Twitter over time, considering changes in the strictness of various pandemic measures.
Results: Initial temporal analysis identified key events that led to notable sentiment changes, such as sentiment spikes following the announcement of the CERB program in March, followed by lockdown sentiments declining sharply in April after the use of the “provincial emergency brake”. Lockdown sentiments remained consistently neutral to negative, with no significant threshold effects or time-based recovery. Vaccine mandates showed a positive shift in sentiment beyond identified stringency breakpoints, with further improvement over time. Mask mandates initially received positive sentiments but declined sharply after passing a stringency threshold. Ottawa Twitter users expressed more positive and emotionally reactive sentiment than Toronto users, particularly in response to lockdown and vaccine policies.
Conclusion: Social media data offers valuable insight into public sentiment dynamics during health emergencies. Findings suggest that policy framing and perceived intrusiveness significantly influence emotional responses, with vaccine mandates benefiting from proactive messaging and civic framing. Regional differences underscore the importance of localized communication strategies. These results can inform future public health interventions by emphasizing the role of timing, tone, and threshold sensitivity in shaping public trust and engagement
"The Pandemic has Aged Me": The Impact of Blocked Goals on Subjective Age
No full textMany individuals report feeling aged by the COVID-19 pandemic, a sentiment widely expressed across social media and journalism. This study investigates what it means to feel aged by a life experience, integrating theories of subjective aging and adult development. I developed a novel self-report measure to assess perceptions of being aged by the pandemic and examined its relation to blocked personal goals. Results from the first wave of a longitudinal study (n=234) supported the validity of this measure, showing that participants who experienced greater goal disruption during the pandemic were more likely to report feeling aged. Furthermore, participants who felt aged, particularly those classified as languishing rather than flourishing, reported lower life satisfaction. These findings suggest that feeling aged by hardship may indicate not just stress but a more lasting shift, with significant implications for subjective aging and overall well-being
Investigating AI-Enabled Space Debris Characterization and Adversarial Resilience
No full textThe accumulation of space debris in Earth’s orbit has emerged as a major concern for the safety and sustainability of space operations. As more satellites are launched and
breakup events occur, the density of debris continues to grow, increasing the likelihood of collisions with active spacecraft. To maintain reliable space operations, there is a pressing need for methods that can accurately identify and characterize debris, enabling improved tracking, collision avoidance, and long-term management of the orbital environment.
This thesis investigates the application of machine learning and deep learning models to classify and characterize space debris based on unique synthetic light curve data. Beyond the generation of the light curve dataset, three characterization experiments are presented in this work: (1) attitude classification using Extreme Gradient Boosting (XGBoost) and Wavelet Scattering Transform (WST) features, (2) shape classification using a Long Short-Term Memory (LSTM) network, Fully Convolutional Network (FCN), and a hybrid Long Short-Term Memory – Fully Convolutional Network (LSTM-FCN) model, and (3) multitask learning for simultaneous shape and material classification using the hybrid model. A fourth component of the thesis evaluates the LSTM-FCN model’s robustness against adversarial attacks generated using gradient-based methods. This adversarial study leveraged real publicly available debris and satellite light curve data from the Mini-MegaTORTORA
(MMT) database.
Results showed that the WST data augmentation method significantly improved classification performance for XGBoost by capturing multiscale frequency features. The LSTMFCN model outperformed both standalone LSTM and FCN models in shape classification tasks, while the multi-task architecture further enhanced performance by leveraging intertask dependencies. The adversarial study revealed that FCN-based surrogate models can produce highly effective attacks against the LSTM-FCN. It was also shown that when combating FCN-based attacks, common filtering-based defenses such as moving-average and wavelet filters are generally insufficient.
This work concludes that integrating Artificial Intelligence (AI) into Space Domain Awareness (SDA) is crucial for managing the growing challenge of space debris, but also emphasizes the need to defend such systems from tampering and perturbation. Model reliability is foundational to the future of autonomous space operations and the protection of critical space-based services that support life on Earth