About MacSphere

MacSphere is McMaster University's Institutional Repository (IR). The purpose of an IR is to bring together all of a University's research under one umbrella, with an aim to preserve and provide access to that research. The research and scholarly output included in MacSphere has been selected and deposited by the individual university departments and centres on campus.

To contribute to McMaster's Institutional Repository, please sign on to MacSphere with your MAC ID.

If you have any questions, please contact the MacSphere Support Team.

Students wishing to deposit their PhD or Masters thesis, please follow the instructions outlined by the School of Graduate Studies.

Communities in MacSphere

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Recent Submissions

Challenges in the Practical Application of Data-Driven Fault Detection and Diagnosis
(2026) Wheat, Daphne Lesley; Mohrenschildt, Martin von; Habibi, Saeid; Computing and Software
Machine health and condition monitoring have become a billion-dollar industry, an area where fault detection and diagnosis is no longer just a subject of academic research, but are now increasingly embedded into commercial tools and products. This thesis addresses several practical challenges in the implementation of machine learning data-driven fault detection and diagnosis systems, from hardware design to testing methodology. This research introduces novel methods in the areas of vibration based ball bearing damage detection and optimal classification accuracy estimation. It also reveals how individual ball bearing parts contain their own unique signatures and recommendations on proper testing procedures to mitigate the impact of this effect. Lastly, it covers how advances in micro-electromechanical technology may be leveraged in order to reduce the cost of hardware while maintaining high sampling rates.
THE EFFECTS OF CS-UCS INTERVAL ON TRACE avoidance CONDITIONING IN THE RaT
(1964-05) Margaret Richards; A.H, Black; Psychology
The experiment in this thesis was designed to test the effects of different CS-UCS intervals on trace avoidance conditioning in the rat. Intervals of 5, 10, 30 and 60 seconds produced no differential effect on the acquisition of avoidance. Examination of the data yielded evidence that two opposing functions could account for the results. These were believed to interact in such a manner that avoidances in the short intervals were controlled by the conditioned fear of the CS, whereas in the longer intervals, they were a joint function of high response rates and the increased opportunity for avoidances to occur. Furthermore, it appeared that these high response rates were conditioned.
Using Machine Learning to Predict Hip Fracture Risk from Dual-Energy X-Ray Absorptiometry Images and Health Factors
(2026) Kramer, Taylor
Osteoporosis is a highly prevalent skeletal disease that greatly increases the risk of fragility fractures and affects millions of older adults worldwide. Hip fractures are especially dangerous, often resulting in long-term disability, increased need for institutional care, and increased healthcare costs. Current methods for diagnosing osteoporosis and subsequent fracture risk, such as bone mineral density (BMD) T-score from dual-energy x-ray absorptiometry (DXA) imaging and the Fracture Risk Assessment Tool (FRAX) have limitations in their ability to accurately predict fracture risk. Developments in machine learning and image processing tools have shown promise for improved fracture risk prediction. This work builds on these advances by integrating DXA imaging data with longitudinal clinical information to develop and evaluate predictive models aimed at improving fracture risk prediction. To evaluate the independent predictive ability of DXA images, a simple feed-forward neural network trained on DXA images reduced using principal component analysis (PCA) was developed. This model demonstrated that images alone contain predictive information for fracture risk; however, performance outcomes improved when clinical risk factors (CRFs) were incorporated in a multimodal feed-forward neural network approach. SHapley Additive exPlanations analysis revealed that imaging features contributed most strongly to the model’s predictions while age and body mass index (BMI) had comparatively smaller contributions. Both models achieved performance metrics within the range of values typically reported for FRAX-based assessments. Next, deep learning and transfer learning models were developed to assess how model performance was impacted by the addition of spatial pattern recognition within the images. Deep learning achieved moderate predictive ability, with performance similar to FRAX, while transfer learning models struggled to learn meaningful patterns from the available data. The purpose of this work was to improve fracture risk prediction by integrating DXA imaging data with clinical health information. The results of this work can inform the development of clinically relevant fracture risk prediction tools that support early intervention for at-risk individuals.
Adjoint-Based Inverse Design of Nanophotonic Structures for Imaging and Sensing Applications
(2026) Arfin, Rishad; Bakr, Mohamed; Electrical and Computer Engineering
This thesis proposes a systematic and efficient approach to design and optimize different classes of nanophotonic devices for emerging imaging and sensing applications. A computational inverse design approach is used to explore and discover efficient nanophotonic designs in the vast design space, achieving optimal performance. Adjoint sensitivity is highlighted and utilized in the design strategy to accelerate the development and optimization of these devices. The design methodology is demonstrated across several target applications, including complementary metal-oxide-semiconductor (CMOS) microlenses, multispectral metasurface routers for imaging, and metasurface optical sensors for gas and biosensing. The overall results presented in this thesis suggest that inverse design approaches by leveraging adjoint sensitivity provide an efficient way to develop and optimize compact nanostructures, achieving target functionalities for next-generation imaging and sensing applications.
Midwifery Services during the COVID-19 pandemic
(2026) Ku Carbonell, Susana Elsa
The COVID-19 pandemic placed unprecedented pressure on health systems worldwide, disrupting essential care services. Sexual and reproductive health services were among the most affected, yet the specific challenges faced by midwifery services remain underexplored. This dissertation examines how the pandemic affected midwifery care in Lima Metropolitana, Peru, and the Greater Toronto Area, Canada, situating these experiences within broader social, institutional, and historical contexts. Using a qualitative multiple case study design, the study employed trauma-informed, intersectional, and postcolonial feminist perspectives to interpret the relational, ethical, and structural dimensions of midwifery practice during the crisis. Findings indicate that midwives were vulnerable to fear as a manifestation of trauma, that resilience often took the form of resistance, and that the capacity to sustain care depended on access to structural supports, professional autonomy, and organizational flexibility. Core values and the philosophy of midwifery, such as relational continuity, autonomy, and respect, were both tested and upheld, while the systemic positioning of midwifery within each health system shaped these responses. The study highlights that midwifery is not only a service, but a relational and ethical practice embedded in historical and structural realities. This research contributes to knowledge by explaining how midwifery’s adaptive capacity during crises is shaped by broader structural, institutional, and social factors rather than by individual effort alone. It underscores the importance of understanding and addressing the systemic conditions that enable midwives to provide equitable, compassionate, and resilient care. By situating midwifery within the interplay of trauma, ethics, and power, this dissertation advances conceptual understanding of health service provision in crises and informs strategies to strengthen midwifery services in diverse sociopolitical contexts.

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