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

Reimagining Senior Housing: Developing the Community-Integrated Shared Equity Model for Ontario Through Comparative Policy Analysis
(2026) Zoe el Helou; Dunn, Jim; Clinical Health Sciences (Health Research Methodology)
Canada is facing an imminent housing crisis, as one in four Canadians will reach 65 by the mid-2030s, and 75% of Ontario's seniors reside in car-dependent suburban areas. Current housing systems trap home equity, while fragmented governance and restrictive regulations prevent the integration of housing with care services. This thesis examines how integrated financing and policy mechanisms can enable older adults' transition from suburban homes to age-friendly housing while maintaining community connections and ensuring long-term affordability. This thesis applies comparative documentary policy analysis on 120 sources across four OECD countries: Denmark, the Netherlands, Germany, and Sweden. Through the policy triangle framework, the analysis examines policy context, content, processes, and actors shaping housing transformation from 1987 to 2024. Maximum variation sampling enabled identification of convergent principles while maintaining sensitivity to contextual factors, with analysis progressing through structured country-specific examination, cross-national patterns, and transferability to Ontario. Cross-national findings demonstrate hospitalization reductions of 15-28%, fall prevention improvements of 24-31%, and institutional care delays of 2.3-3.1 years alongside substantial social capital formation. Synthesizing these findings within Ontario's regulatory context, the thesis proposes the Community-Integrated Shared Equity (CISE) model, a theoretically grounded framework integrating social capital theory, social support theory and behavioural economics principles. The CISE model operates through three interdependent pillars addressing financial barriers, community preservation, and operational coordination while accounting for Canadian specificities. Policy recommendations span federal, provincial, and municipal jurisdictions, proposing National Housing Strategy reallocation, Planning Act amendments, and municipal zoning reforms to enable implementation pathways from pilot demonstrations to systemic transformation.
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.

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