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.

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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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Now showing 1 - 5 of 5

Recent Submissions

Resolving Temporal Graph Functional Dependency Violations
(2026) Bhandari, Satyagni
Data is often riddled with errors and noise, from misspellings and missing values, that need to be dealt with to use the data effectively. This kind of preprocessing is very costly; an estimated cost to U.S. businesses of $3 trillion as reported in 2016 by Harvard Business Review. Temporal graphs are used to record changes in graph data, capturing both topological and attribute value constraints and relationships. As data values change, snapshots of temporal graphs may become inconsistent with respect to a set of graph quality constraints that impose topological and attribute value requirements. For example, monitoring patient drug dosages over time involves relationships between attributes such as a patient’s condition, treatment, symptoms and specific dosage values, while adhering to strict dosing requirements over specific time intervals. This thesis studies the problem of resolving violations to a specific class of graph data quality rules called Temporal Graph Functional Dependencies (TGFDs). The thesis introduces a new algorithm called TGFD-Correct, that processes and groups all violations sharing the same antecedent values (with respect to the TGFD), and selects a single (consequent) value for each group that balances two objectives. First, fixing the data (with respect to the TGFD), and second, minimizing changes to the distribution of consequent values from a prespecified objective. As each group is being handled independently, the repair step can be run in parallel across many processor cores, making it fast enough for millions of records. Our method had a 3-point improvement over the best performing baseline that we tested from the literature, whilst maintaining a runtime footprint 17 times smaller. Taken together, the findings show that careful, distribution-aware cleaning of temporal graphs is possible, and demonstrates a path toward more flexible tools that can keep pace with ever-growing, time-stamped data.
The Numerical Solution of Differential Equations by Third and Fourth Order Runge-Kutta Methods
(1964-10) Ebos, Frank
C. Runge originally suggested the numerical methods of solving differential equations which will be examined, and were subsequently improved on by, to mention a few, K.Heun, and W. Kutta. The entirety of these methods have, as a result, been referred to as the Runge-Kutta methods for the numerical solution of differential equations. The first section of the thesis consists of the derivation of third and fourth order Runge-Kutta methods and their respective truncation errors. Notation, definitions, and various concepts are introduced as needed in the various sections. The numerical solutions of differential equations using third order Runge-Kutta methods are then discussed in the second section. Various formulae and relationships are derived here for third order methods. In all numerical tables that follow, the results were obtained using a Bendix Model G-15 Digital Computer. In the third section, one considers fourth order Runge-Kutta methods for the numerical solution of ordinary differential equations. However, in addition to considerations of symmetry, reduction of operations and storage requirements, as examined in section two, one examines a Runge-Kutta method due to Blum which basically modifies a programming procedure. Finally in the last section, one investigates methods due to A. Ralston which minimize a bound on the truncation error derived in the first section. An appendix is also included containing various programs for the Bendix G-15D that have been needed throughout the sections.
Values and Preferences of Patients with Small Renal Masses: Integrating Evidence and Patient Perspectives
(2026) Kandi Keleh, Maryam
Small renal masses present patients and clinicians with preference-sensitive choices among partial nephrectomy, percutaneous thermal ablation, and active surveillance. This thesis set out to generate decision-ready evidence on both short-term burdens and longer-term oncologic outcomes for each option, and to explore how patients value the trade-offs involved. We conducted coordinated prognosis-focused systematic reviews and meta-analyses to estimate strategy-specific probabilities for peri-operative outcomes (major complications, blood loss, hospital stay, procedure time) and for five-year oncologic endpoints (overall mortality, cancer-specific mortality, local recurrence, metastasis). Methods emphasized applicability to decisions: QUIPS for risk of bias; GRADE adapted for prognosis (including a decision-threshold approach to imprecision); ICEMAN to guard against over-interpretation of subgroup signals; and improved conversions of medians/IQRs to means/SDs to avoid distorted weights in single-arm syntheses. We then built and piloted a bilingual decision aid and used structured interviews with a probability trade-off task to elicit values and risk thresholds. Across strategies, short-term burdens differed in ways patients care about, while five-year cancer-specific outcomes were generally favourable; estimates are presented as descriptive prognosis for each option rather than comparative effects, with certainty ratings that make remaining uncertainty explicit. The pilot demonstrated the feasibility and acceptability of eliciting preferences and showed meaningful variation in how patients balance avoiding procedures against small differences in oncologic risk. By integrating rigorous prognosis evidence with empirical preference elicitation, this thesis provides a transparent foundation for shared decision-making and supports conditional, values-sensitive recommendations in guidelines. It also offers practical methods, both statistical and procedural, for future decision-aid development in SRMs and similar preference-sensitive conditions.
Random Effect Models For Clustered, Overdispersed Time-to-Event Data, With Applications
(2026) Wang, Yan Qiao
Exploring Barriers Equity-Deserving Families Face in Accessing the CWELCC Child care System
(Prepared by the McMaster Research Shop for Today's Family, 2025-12) Daniel, Eden; Mahmood, Haniyyah; Roop Kaur, Mehar; Vithanage, Randil; Van Lange, Leiann
The Canada-Wide Early Learning and Child Care (CWELCC) system aims to reduce child care costs to an average of $10, though the average cost of child care in Ontario is currently $19 per day. The aim of this report is to understand the barriers that equity deserving families face in accessing CWELCC-subsidized child care in Ontario. We also wanted to identify strategies that can be implemented to advance equitable access. Today’s Family is an organization participating in CWELCC that provides licensed child care services across Hamilton, Halton, Haldimand, Norfolk, Oxford and Peel, supporting over 7,500 children annually. Despite the reduction in child care costs as a result of CWELCC, it is still relatively expensive, especially for low-income families. Today’s Family provides additional subsidies for households that cannot afford the current $22 per day fee, though barriers still exist in accessing equitable care. To answer our research question, we completed a literature review to examine barriers to entering licensed child care in Canada and promising practices that can support equitable access. Common themes across the literature were identified and integrated with information from Today’s Family’s administrative datasets, which include data on waitlists and active enrolments.

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