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.

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

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USING REPRODUCIBLE PERFORMANCE VARIATION AND GROWTH AS THE BIOMARKERS TO PREDICT TOLERANCE TO COPPER IN JUVENILE RANIBOW TROUT (ONCORHYNCHUS MYKISS)
(2001-11) Sheryl Emma Edwards
This study investigated whether or not growth and reproducible performance variation (i.e: biomarkers) in juvenile rainbow trout could be used to predict Cu sensitivity in individuals. Therefore there were two main objectives. The first was to identify and describe reproducible performance variability in individual fish. This was accomplished by evaluating individual performance in a series oftests designed to challenge the resting physiology ofthe fish (ie: challenge test). With the exception of growth, the performance measures had to show both individual variability and reproducibility from trial to trial. The second objective involved examining the relationship(s) that the biomarkers had on an individual’s tolerance to copper. This was accomplished through the use of both univariate and multivariate analytical techniques to determine how each biomarker affected tolerance individually, as well as how each ofthe biomarkers interacted with one another to affect tolerance. In addressing these objectives, this study has produced four important results. The first is that the reproducible performance variation identified in each ofthe challenge tests is real, and the tests can therefore be used as biomarkers ofsensitivity. Second, the performance of an individual in any one challenge test is unrelated to its performance in the others. Third, individual performance in the challenge tests did not significantly predict growth. Fourth, sensitivity to copper can be predicted, although a significant prediction is contingent upon two factors. The first is that multiple biomarkers be measured in each individual, and multivariate analyses be conducted to examine how the biomarkers interact with one another to effect tolerance. This is in contrast to examining how each biomarker effects tolerance individually. Second, the appropriate ‘suite’ of biomarkers must be evaluated. Not all combinations of biomarkers will convey tolerance, so it is important to ensure that all possible combinations ofbiomarkers are evaluated for their effects on tolerance. Based on the results ofthis study, the following conclusions are apparent: (1) individual variation amplified through the use of challenge tests is a useful tool for predicting individual tolerance; (2) tolerance cannot be reliably predicted using individual biomarkers; (3) multivariate analysis is a valuable tool for improving both interpretation and analysis of data with multiple biomarkers; and (4) since individual tolerance can be predicted, mortality due to copper exposure cannot be completely random.
Modeling and Experimental Characterization of Core Loss of A Switched Reluctance Machine
(2026) Vithana Pathirannahalage, Sudesh Champika, Priyadarshana
Core loss significantly impacts the efficiency and thermal performance of Switched Reluctance Machines (SRMs), especially under high speeds. Conventional measurement methods are often inadequate for capturing losses in assembled stator cores, where flux paths are complex, and excitation waveforms are non-sinusoidal. This thesis addresses these limitations by developing and experimentally validating a practical core loss characterization method tailored for SRM stators. The study begins with a comprehensive review of core loss mechanisms and measurement challenges, highlighting the effects of manufacturing processes such as punching and welding on material properties. A core loss measurement method is introduced based on transformer induction theory, enabling time-domain reconstruction of magnetic field quantities from voltage and current waveforms. The method is validated using a ring core and SRM stator geometry with finite element analysis (FEA), replicating practical excitation conditions. An experimental setup is built with a fractional SRM stator core and custom magnetizing yokes. The experimental setup captures induced voltage and current waveforms from the stator. The proposed method is experimentally validated over a wide frequency range from 100 Hz to 10,000 Hz and under varying flux density levels, confirming the applicability for practical core loss characterization of SRM stators.
From Laboratory to Pocket: Development of Deep Learning Algorithms for Ionizing Radiation Detection Systems
(2026) Yanfeng Xie
This thesis focuses on the application and development of deep learning algorithms for ionizing radiation detection systems. The main content is organized into three studies (published or under review) that address distinct radiation detection challenges: (a) the unfolding of beta radiation fluence spectra in mixed radiation fields, accompanied by the design of dedicated detection hardware; (b) accelerating gamma-ray spectroscopic measurement and analysis of High-Purity Germanium (HPGe) detectors; and (c) detecting ionizing radiation using a modern smartphone with no hardware modifications. In these works, a variety of deep learning models are explored in depth, including one-dimensional convolutional neural networks, physics-informed neural net works, 3D–2D hybrid convolutional neural networks, and dual-branch multilayer perceptrons. The research provides innovative solutions to several key challenges in applying deep learning to radiation detection, such as the scarcity of high-quality datasets, the lack of interpretability of neural network models, and insufficient stability and generalization on independent external test sets. A unified and efficient deep learning development workflow for different detection systems is also proposed to guide future research and implementation. Beyond methodological innovations, this thesis achieves significant progress in three practical applications: (a) real-time beta fluence spectrum unfolding; (b) a three- to five-fold acceleration of HPGe gamma spectrometry measurement using deep learning, along with effective targeted feature learning in the Compton region of the spectrum; and (c) demonstration that a standard smartphone (with no hardware modifications or camera shielding) can rapidly detect dangerous radiation levels and estimate their dose within six seconds. These successes across diverse application domains clearly demonstrate the effectiveness of the general deep learning–based approach to radiation detection developed in this work.
PATIENT-PHYSICIAN GENDER MATCH AND THE PROMOTION OF PATIENT ENGAGEMENT STRATEGIES IN PRIMARY CARE
(McMaster University, 2025-04) Rojoub, Dalia; Acai, Anita; Psychology
Effective patient-physician collaboration enhances patient experience and improves treatment outcomes. Engagement strategies, such as shared decision making, can be implemented into healthcare settings to facilitate active involvement of patients in their care. However, patient gender, physician gender, and the interaction of the two may impact the effectiveness of these strategies. The aim of this study was to examine the effects of patient and physician gender on the perceived promotion of patient engagement strategies in primary care. An online questionnaire was administered to 708 McMaster University undergraduate students to assess their general engagement, shared decision making, and satisfaction with care. Participants were grouped based on their identified gender (man, woman, or gender minority) and their physician's identified gender (man or woman). A Kruskal-Wallis H test was then run to assess between-group differences. While overall engagement scores did not differ significantly, it was found that man patients with woman physicians scored significantly higher for shared decision-making and satisfaction as compared to their woman patient counterparts, regardless of physician gender. Additionally, the gender minority patients with woman physicians scored significantly lower than other groups for satisfaction with care. These findings challenge prior research suggesting that gender concordance improves engagement and highlight the need for more inclusive healthcare research and education that considers diverse gender identities.
Sulphur Isotope Effects in Chemical and Biological Processes
(1957-10) Ford, Richard; Thode, H. G.; Chemistry
The sulphur isotope effect in the oxidation of aqueous sodium sulphite with molecular oxygen has been measured under a variety of conditions. The observed effect is compared with theoretical calculations based on the reaction mechanism. The uncatalysed and enzyme catalyzed sulphur isotope exchange between sulphate and sulphite has been examined. The oxidation of sulphide to internally-stored sulphur and the subsequent oxidation of the sulphur to sulphate by Chromatium have been studied. Sulphur isotope fractionation in mixed cultures of Chromatium and Desulphovibrio desulphuricans has also been examined. The results are discussed in relation to the natural variations in sulphur isotope abundance.

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