UWSpace

UWSpace is the University of Waterloo’s institutional repository for the free, secure, and long-term home of research produced by faculty, students, and staff.

Depositing Theses/Dissertations or Research to UWSpace

Are you a Graduate Student depositing your thesis to UWSpace? See our Thesis Deposit Help and UWSpace Thesis FAQ pages to learn more.

Are you a Faculty or Staff member depositing research to UWSpace? See our Waterloo Research Deposit Help and Self-Archiving pages to learn more.

Photo by Waterloo staff

Communities in UWSpace

Select a community to browse its collections.

Recent Submissions

  • Thumbnail Image
    Item type: Item ,
    Integration of a smart multidose blister package for medication intake: A mixed method ethnographic informed study of older adults with chronic diseases
    (Public Library of Science, 2022-01-21) Faisal, Sadaf; Ivo, Jessica; Tennant, Ryan; Prior, Kelsey-Ann; Grindrod, Kelly; McMillan, Colleen
    Smart adherence products are marketed to assist with medication management. However, little is known about their in-home integration by older adults. It is necessary to investigate the facilitators and barriers older adults face when integrating these products into their medication taking routines before effectiveness can be examined. The aim of this study was to (a) examine the integration of a smart multidose blister package and (b) understand medication intake behaviour of adults with chronic diseases using an integrated theoretical model comprised of the Technology Acceptance Model (TAM), Theory of Planned Behaviour (TPB) and Capacity, Opportunity, Motivation and Behaviour (COM-B) Model. An ethnographic-informed study was conducted with older adults using the smart multidose blister package to manage their medications for eight weeks. Data was collected quantitatively and qualitatively using in-home observations, photo-elicitation, field notes, semi-structured interviews, system usability scale (SUS) and net promoter scale (NPS). The interview guide was developed with constructs from the TAM, TPB and COM-B Model. Data were analyzed using the Qualitative Analysis Guide of Leuven (QUAGOL) framework to generate themes and sub-themes which were mapped back to TAM, TBP and COM-B Model. Ten older adults with an average age of 76 years, of which 80% were female, participated in the study. On average, participants reported five medical conditions, while the average number of medications was 11.1. The mean SUS was 75.50 and overall NPS score was 0. Qualitative analysis identified three themes; (1) factors influencing medication intake behaviour (2) facilitators to the product use and, (3) barriers to the product use. The smart blister package was found to be easy to use and acceptable by older adults. Clinicians should assess an older adult’s medication intake behavior as well as barriers and facilitators to product use prior to recommending an adherence product for managing medications.
  • Thumbnail Image
    Item type: Item ,
    DeLUCS: Deep learning for unsupervised clustering of DNA sequences
    (Public Library of Science, 2022-01-21) Arias, Pablo Millan; Alipour, Fatemeh; Hill, Kathleen A.; Kari, Lila
    We present a novel Deep Learning method for the Unsupervised Clustering of DNA Sequences (DeLUCS) that does not require sequence alignment, sequence homology, or (taxonomic) identifiers. DeLUCS uses Frequency Chaos Game Representations (FCGR) of primary DNA sequences, and generates “mimic” sequence FCGRs to self-learn data patterns (genomic signatures) through the optimization of multiple neural networks. A majority voting scheme is then used to determine the final cluster assignment for each sequence. The clusters learned by DeLUCS match true taxonomic groups for large and diverse datasets, with accuracies ranging from 77% to 100%: 2,500 complete vertebrate mitochondrial genomes, at taxonomic levels from sub-phylum to genera; 3,200 randomly selected 400 kbp-long bacterial genome segments, into clusters corresponding to bacterial families; three viral genome and gene datasets, averaging 1,300 sequences each, into clusters corresponding to virus subtypes. DeLUCS significantly outperforms two classic clustering methods (K-means++ and Gaussian Mixture Models) for unlabelled data, by as much as 47%. DeLUCS is highly effective, it is able to cluster datasets of unlabelled primary DNA sequences totalling over 1 billion bp of data, and it bypasses common limitations to classification resulting from the lack of sequence homology, variation in sequence length, and the absence or instability of sequence annotations and taxonomic identifiers. Thus, DeLUCS offers fast and accurate DNA sequence clustering for previously intractable datasets.
  • Thumbnail Image
    Item type: Item ,
    Use of open-text responses to recode categorical survey data on postpartum contraception use among women in the United States: A mixed-methods inquiry of Pregnancy Risk Assessment Monitoring System data
    (Public Library of Science, 2022-01-05) Richards, Nicole K.; Morley, Christopher P.; Wojtowycz, Martha A.; Bevec, Erin; Levandowski, Brooke A.
    Background Postpartum contraception prevents unintended pregnancies and short interpregnancy intervals. The Pregnancy Risk Assessment Monitoring System (PRAMS) collects population-based data on postpartum contraception nonuse and reasons for not using postpartum contraception. In addition to quantitative questions, PRAMS collects open-text responses that are typically left unused by secondary quantitative analyses. However, abundant preexisting open-text data can serve as a resource for improving quantitative measurement accuracy and qualitatively uncovering unexpected responses. We used PRAMS survey questions to explore unprompted reasons for not using postpartum contraception and offer insight into the validity of categorical responses. Methods and findings We used 31,208 categorical 2012 PRAMS survey responses from postpartum women in the US to calculate original prevalences of postpartum contraception use and nonuse and reasons for contraception nonuse. A content analysis of open-text responses systematically recoded data to mitigate survey bias and ensure consistency, resulting in adjusted prevalence calculations and identification of other nonuse themes. Recoded contraception nonuse slightly differed from original reports (21.5% versus 19.4%). Both calculations showed that many respondents reporting nonuse may be at a low risk for pregnancy due to factors like tubal ligation or abstinence. Most frequent nonuse reasons were not wanting to use birth control (27.1%) and side effect concerns (25.0%). Other open-text responses showed common themes of infertility, and breastfeeding as contraception. Comparing quantitative and qualitative responses revealed contradicting information, suggesting respondent misinterpretation and confusion surrounding the term “pregnancy prevention.” Though this analysis may be limited by manual coding error and researcher biases, we avoided coding exhaustion via 1-hour coding periods and validated reliability through intercoder kappa scores. Conclusions In this study, we observed that respondents reporting contraception nonuse often described other methods of pregnancy prevention and contraception barriers that were not included in categorical response options. Open-text responses shed light on a more comprehensive list of pregnancy prevention methods and nonuse options. Our findings contribute to survey questions that can lead to more accurate depiction of postpartum contraceptive behavior. Additionally, future use of these qualitative methods may be used to improve other health behavior survey development and resulting data.
  • Thumbnail Image
    Item type: Item ,
    The rule of social connection on the experience of COVID-19 related post-traumatic growth and stress
    (Public Library of Science, 2021-12-15) Matos, Marcela; McEwan, Kirsten; Kanovsky, Martin; Halamova, Julia; Steindl, Stanley R.; Ferreira, Nuno; Linharelhos, Mariana; Rijo, Daniel; Asano, Kenichi; Vilas, Sara P.; Marquez, Margarita G.; Gregorio, Sonia; Brito-Pons, Gonzalo; Lucena-Santos, Paola; Oliveira, Margareth da Silva; de Souza, Erika Leonardo; Llobenes, Lorena; Gumiy, Natali; Costa, Maria Ileana; Habib, Noor; Hakem, Reham; Khrad, Hussain; Alzahrani, Ahmad; Cheli, Simone; Petrocchi, Nicola; Thoulouli, Elli; Issari, Philia; Simos, Gregoris; Lunding-Gregersen, Vibeke; Elklit, Ask; Kolts, Russell; Kelly, Allison C.; Bortolon, Catherine; Delamillieure, Pascal; Paucsik, Marine; Wahl, Julia E.; Zieba, Mariusz; Zatorski, Mateusz; Komendzinski, Tomasz; Zhang, Shuge; Basran, Jaskaran; Kagialis, Antonios; Kirby, James; Gilbert, Paul
    Background Historically social connection has been an important way through which humans have coped with large-scale threatening events. In the context of the COVID-19 pandemic, lockdowns have deprived people of major sources of social support and coping, with others representing threats. Hence, a major stressor during the pandemic has been a sense of social disconnection and loneliness. This study explores how people’s experience of compassion and feeling socially safe and connected, in contrast to feeling socially disconnected, lonely and fearful of compassion, effects the impact of perceived threat of COVID-19 on post-traumatic growth and post-traumatic stress. Methods Adult participants from the general population (N = 4057) across 21 countries worldwide, completed self-report measures of social connection (compassion for self, from others, for others; social safeness), social disconnection (fears of compassion for self, from others, for others; loneliness), perceived threat of COVID-19, post-traumatic growth and traumatic stress. Results Perceived threat of COVID-19 predicted increased post-traumatic growth and traumatic stress. Social connection (compassion and social safeness) predicted higher post-traumatic growth and traumatic stress, whereas social disconnection (fears of compassion and loneliness) predicted increased traumatic symptoms only. Social connection heightened the impact of perceived threat of COVID-19 on post-traumatic growth, while social disconnection weakened this impact. Social disconnection magnified the impact of the perceived threat of COVID-19 on traumatic stress. These effects were consistent across all countries. Conclusions Social connection is key to how people adapt and cope with the worldwide COVID-19 crisis and may facilitate post-traumatic growth in the context of the threat experienced during the pandemic. In contrast, social disconnection increases vulnerability to develop post-traumatic stress in this threatening context. Public health and Government organizations could implement interventions to foster compassion and feelings of social safeness and reduce experiences of social disconnection, thus promoting growth, resilience and mental wellbeing during and following the pandemic.
  • Thumbnail Image
    Item type: Item ,
    Overcoming Critical Challenges Hindering Resistance Spot Welding of Dissimilar Advanced High Strength Steel Joints: LME Mitigation and Weld Class Prediction
    (University of Waterloo, 2026-05-04) Nooranfar, Melika
    Reducing carbon dioxide emissions from the transportation sector has driven demand for lighter vehicles. Advanced high-strength steels (AHSS) enable the use of thinner gauges without compromising crashworthiness due to ability to absorb high fracture energy. Because these materials are exposed while in-service AHSS are typically zinc-coated for corrosion protection. However, excellent mechanical strength is insufficient for these materials to be used for automotive application, they must also be capable of being welded into the automotive structure. Resistance spot welding remains the dominant joining method in automotive body-in-white production, yet two challenges affect weld quality in dissimilar stack-ups: liquid metal embrittlement (LME) cracking and the absence of reliable offline quality prediction. Most existing studies have focused on similar stack-ups, leaving dissimilar joints inadequately addressed. This research examines both challenges using dissimilar configurations representative of industrial practice. The first part investigates LME mitigation in two-sheet joints of zinc-coated 3G-980 AHSS and interstitial-free steel. A short high-current pre-pulse (16 kA, 20 ms) reduced the crack index from 0.56 to 0.14, a 75% reduction. Cross-sectional analysis revealed that the pre-pulse shifted the nugget toward the IF sheet, increasing the distance between the susceptible 3G-980 surface and the fusion boundary. This geometric shift reduced the overlap between liquid zinc and tensile stresses, suppressing crack formation. Contrary to welding made in similar material joint configurations where high-current pre-pulses intensified cracking, the same approach effectively mitigates LME in dissimilar configurations. The second part develops a machine learning framework for weld quality classification in three-sheet dissimilar AHSS stack-ups. Each weld was classified as acceptable (Ok), No weld, or Expulsion based on online assessments. Random Forest and XGBoost classifiers were trained on a 137-sample dataset, with XGBoost achieving 89.3% accuracy and superior performance near class boundaries. The trained models enabled identification of no weld regions and provided a basis for adaptive parameter selection. Both LME severity and weld class are critical indicators of joint integrity yet have rarely been addressed together for dissimilar coated AHSS. This thesis provides an experimentally grounded vii framework linking welding parameters to quality outcomes, offering practical pathways for process optimization in automotive resistance spot welding.