Graduate Theses & Dissertations

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Modelling Request Access Patterns for Information on the World Wide Web
In this thesis, we present a framework to model user object-level request patterns in the World Wide Web.This framework consists of three sub-models: one for file access, one for Web pages, and one for storage sites. Web Pages are modelled to be made up of different types and sizes of objects, which are characterized by way of categories. We developed a discrete event simulation to investigate the performance of systems that utilize our model.Using this simulation, we established parameters that produce a wide range of conditions that serve as a basis for generating a variety of user request patterns. We demonstrated that with our framework, we can affect the mean response time (our performance metric of choice) by varying the composition of Web pages using our categories. To further test our framework, it was applied to a Web caching system, for which our results showed improved mean response time and server load. Author Keywords: discrete event simulation (DES), Internet, performance modelling, Web caching, World Wide Web
Application of Data Science to Paramedic Data
Paramedic data has significant potential for research. Paramedics see many patients every year and collect a wide variety of crucial data at each encounter. This data is rarely used for good reason: it’s messy and hard to work with. But like theunderdog character in a classic movie, with a little bit of work and a lot of understanding, paramedic data has significant potential to change the world of medical research. Paramedics throughout the world are involved in research every day, but most of this research uses purpose-built data structures and never takes advantage of the existing data that paramedics create as part of their everyday work. Through a project-based approach grounded in developing a better understanding of the opioid crisis, this thesis will examine the quantity and structure of the existing paramedic data, the complexities of its current design, the steps necessary to access it, and the processes necessary to clean existing data to a point where it can be easily modelled. Once we have our dataset, we will explore the challenges of choosing key metrics by examining the effectiveness of metrics currently employed to monitor the opioid crisis and the influences public health programs and changing policies have had on these metrics. Next, we will explore the temporal distributions of opioid and other intoxicant use with an eye to providing data to support public health in their harm reduction efforts. And lastly, we will look at the effect of fixed- and floating-point temporal influences on intoxicant-related calls with an eye to how these temporal points can affect call volumes. By using this exploration of the opioid crisis, this thesis will show that with a more thorough understanding of what paramedic data is, what data points are available, and the processes needed to transform it, paramedic data has the potential to greatly expand the limits of health care data science into a more precise and more all-encompassing discipline. Author Keywords: Ambulance, Data Science, Opioid, Overdose, Paramedic, Pre-hospital
ADHD Symptomatology Across Adulthood
Objective: To improve on several methodological issues and research gaps regarding current literature investigating the stability of ADHD symptomatology across adulthood and relationships between the two core ADHD symptom dimensions (i.e., inattention and hyperactivity-impulsivity) and multiple life outcomes in adults. Method: A large sample of postsecondary students were initially assessed for ADHD symptomatology using the Conners’ Adult ADHD Rating Scale (CAARS). Six years later, academic success was assessed using students’ official academic records (e.g., final GPAs and degree completion status), and fifteen years later, participants were re-assessed using the CAARS and several measures of life success (e.g., relationship satisfaction, career satisfaction, and stress levels). Results: Inattention and hyperactivity-impulsivity symptoms showed strong stability across the 15-year period. Additionally, greater inattention symptoms during emerging adulthood and early middle adulthood were consistently associated with poorer life success (e.g., lower GPAs, poorer relationship and career satisfaction), particularly for men. Associations for hyperactivity-impulsivity symptoms were less consistent. Conclusion: ADHD symptomatology can be conceptualized as a stable, dimensional trait across adulthood, with robust associations with measures of life success. Author Keywords: academic success, ADHD, adults, job satisfaction, relationship satisfaction, stability
Assessing factors associated with wealth and health of Ontario workers after permanent work injury
I drew on Bourdieu’s theory of capital and theorized that different forms of economic, cultural and social capital which injured workers possessed and/or acquire over their disability trajectory may affect certain outcomes of permanent impairments. Using data from a cross-sectional survey of 494 Ontario workers with permanent impairments, I measured workers’ different indicators of capital in temporal order. Hierarchical regression analyses were used to test the unique association of workers’ individual characteristics, pre-injury capital, post-injury capital, and the outcomes of permanent impairments. The results show that factors related to individual characteristics, pre-injury and post-injury capital were associated with workers’ perceived health change, whereas pre-injury and post-injury capital were most relevant factors in explaining workers’ post-injury employment status and income recovery. When looking at the significance of individual predictors, post-injury variables were most relevant in understanding the outcomes of permanent impairment. The findings suggest that many workers faced economic and health disadvantages after permanent work injury. Author Keywords: Bourdieu, hierarchical regression, theory of capital, work-related disability, workers with permanent impairments
Capital Ratios and Liquidity Creation
Using quarterly data from the six largest Canadian banks, we investigate the relationship between regulatory capital ratio and on-balance sheet liquidity created in the Canadian economy by “Big Six”. We find a significant positive relationship between Tier 1 capital ratio and on-balance sheet liquidity creation for Canadian big six banks, implying that large banks in Canada favor risks and rely on capital to fund illiquid assets. In contrast, for smaller banks, the relationship is significantly negative. Our results are robust to dynamic panel regression using 2-Step GMM, two exogenous shocks - COVID-19 crisis and the Global Financial Crisis (2007-2009), mergers & acquisitions activities in the banking industry, and core deposits financing. The COVID-19 pandemic and core deposits adversely impact the Tier 1 capital ratio’s relationship with on-balance-sheet liquidity creation, while the global financial crisis (2007-2009) effect on the association is insignificant. Author Keywords: Big Six, COVID -19, Deposits, Liquidity Creation, Tier 1 Capital Ratio,
Time Series Algorithms in Machine Learning - A Graph Approach to Multivariate Forecasting
Forecasting future values of time series has long been a field with many and varied applications, from climate and weather forecasting to stock prediction and economic planning to the control of industrial processes. Many of these problems involve not only a single time series but many simultaneous series which may influence each other. This thesis provides methods based on machine learning of handling such problems. We first consider single time series with both single and multiple features. We review the algorithms and unique challenges involved in applying machine learning to time series. Many machine learning algorithms when used for regression are designed to produce a single output value for each timestamp of interest with no measure of confidence; however, evaluating the uncertainty of the predictions is an important component for practical forecasting. We therefore discuss methods of constructing uncertainty estimates in the form of prediction intervals for each prediction. Stability over long time horizons is also a concern for these algorithms as recursion is a common method used to generate predictions over long time intervals. To address this, we present methods of maintaining stability in the forecast even over large time horizons. These methods are applied to an electricity forecasting problem where we demonstrate the effectiveness for support vector machines, neural networks and gradient boosted trees. We next consider spatiotemporal problems, which consist of multiple interlinked time series, each of which may contain multiple features. We represent these problems using graphs, allowing us to learn relationships using graph neural networks. Existing methods of doing this generally make use of separate time and spatial (graph) layers, or simply replace operations in temporal layers with graph operations. We show that these approaches have difficulty learning relationships that contain time lags of several time steps. To address this, we propose a new layer inspired by the long-short term memory (LSTM) recurrent neural network which adds a distinct memory state dedicated to learning graph relationships while keeping the original memory state. This allows the model to consider temporally distant events at other nodes without affecting its ability to model long-term relationships at a single node. We show that this model is capable of learning the long-term patterns that existing models struggle with. We then apply this model to a number of real-world bike-share and traffic datasets where we observe improved performance when compared to other models with similar numbers of parameters. Author Keywords: forecasting, graph neural network, LSTM, machine learning, neural network, time series
Assessing the Cost of Reproduction between Male and Female Sex Functions in Hermaphroditic Plants
The cost of reproduction refers to the use of resources for the production of offspring that decreases the availability of resources for future reproductive events and other biological processes. Models of sex-allocation provide insights into optimal patterns of resource investment in male and female sex functions and have been extended to include other components of the life history, enabling assessment of the costs of reproduction. These models have shown that, in general, costs of reproduction through female function should usually exceed costs through male function. However, those previous models only considered allocations from a single pool of shared resources. Recent studies have indicated that the type of resource currency can differ for female and male sex functions, and that this might affect costs of reproduction via effects on other components of the life history. Using multiple invasibility analysis, this study examined resource allocation to male and female sex functions, while simultaneously considering allocations to survival and growth. Allocation patterns were modelled using both shared and separate resource pools. Under shared resources, allocation patterns to male and female sex function followed the results of earlier models. When resource pools were separate, however, allocations to male function often exceeded allocations to female function, even if fitness gains increased less strongly with investment in male function than with investment in female function. These results demonstrate that the costs of reproduction are affected by (1) the types of resources needed for reproduction via female or male function and (2) via trade-offs with other components of the life history. Future studies of the costs of reproduction should examine whether allocations to reproduction via female versus male function usually entail the use of different types of resources. Author Keywords: Cost of Reproduction, Gain Curve, Life History, Resource Allocation Patterns, Resource Currencies
Academic Efficiency
Universities produce a significant and increasing share of basic research that is later commercialized by firms. We argue that the university's prominence as a producer of basic research is the result of a differential efficiency in research production that cannot be replicated by firms or individual agents - teaching. By using research accomplishments to signal knowledge and attract tuition-paying students, universities are uniquely positioned to undertake certain types of research projects. However, in a market for innovation without patent rights, a significant and increasing number of basic research projects, that are social welfare improving, cannot be initiated by firms or universities. The extension of patent rights to university-generated research elegantly redresses this issue and leaves us to ponder important questions about the future of our innovation-driven economies. Author Keywords: Innovation, Intellectual Property Rights, Research, Science Technology and Innovation Policy
Characteristics of Models for Representation of Mathematical Structure in Typesetting Applications and the Cognition of Digitally Transcribing Mathematics
The digital typesetting of mathematics can present many challenges to users, especially those of novice to intermediate experience levels. Through a series of experiments, we show that two models used to represent mathematical structure in these typesetting applications, the 1-dimensional structure based model and the 2-dimensional freeform model, cause interference with users' working memory during the process of transcribing mathematical content. This is a notable finding as a connection between working memory and mathematical performance has been established in the literature. Furthermore, we find that elements of these models allow them to handle various types of mathematical notation with different degrees of success. Notably, the 2-dimensional freeform model allows users to insert and manipulate exponents with increased efficiency and reduced cognitive load and working memory interference while the 1-dimensional structure based model allows for handling of the fraction structure with greater efficiency and decreased cognitive load. Author Keywords: mathematical cognition, mathematical software, user experience, working memory
Development of a Cross-Platform Solution for Calculating Certified Emission Reduction Credits in Forestry Projects under the Kyoto Protocol of the UNFCCC
This thesis presents an exploration of the requirements for and development of a software tool to calculate Certified Emission Reduction (CERs) credits for afforestation and reforestation projects conducted under the Clean Development Mechanism (CDM). We examine the relevant methodologies and tools to determine what is required to create a software package that can support a wide variety of projects involving a large variety of data and computations. During the requirements gathering, it was determined that the software package developed would need to support the ability to enter and edit equations at runtime. To create the software we used Java for the programming language, an H2 database to store our data, and an XML file to store our configuration settings. Through these choices, we can build a cross-platform software solution for the purpose outlined above. The end result is a versatile software tool through which users can create and customize projects to meet their unique needs as well as utilize the features provided to streamline the management of their CDM projects. Author Keywords: Carbon Emissions, Climate Change, Forests, Java, UNFCCC, XML
Educational Data Mining and Modelling on Trent University Students’ Academic Performance
Higher education is important. It enhances both individual and social welfare by improving productivity, life satisfaction, and health outcomes, and by reducing rates of crime. Universities play a critical role in providing that education. Because academic institutions face resource constraints, it is thus important that they deploy resources in support of student success in the most efficient ways possible. To inform that efficient deployment, this research analyzes institutional data reflecting undergraduate student performance to identify predictors of student success measured by GPA, rates of credit accumulation, and graduation rates. Using methods of cluster analysis and machine learning, the analysis yields predictions for the probabilities of individual success. Author Keywords: Educational data mining, Students’ academic performance modelling
Sinc-Collocation Difference Methods for Solving the Gross-Pitaevskii Equation
The time-dependent Gross-Pitaevskii Equation, describing the movement of parti- cles in quantum mechanics, may not be solved analytically due to its inherent non- linearity. Hence numerical methods are of importance to approximate the solution. This study develops a discrete scheme in time and space to simulate the solution defined in a finite domain by using the Crank-Nicolson difference method and Sinc Collocation Methods (SCM), respectively. In theory and practice, the time discretiz- ing system decays errors in the second-order of accuracy, and SCMs are decaying errors exponentially. A new SCM with a unique boundary treatment is proposed and compared with the original SCM and other similar numerical techniques in time costs and numerical errors. As a result, the new SCM decays errors faster than the original one. Also, to attain the same accuracy, the new SCM interpolates fewer nodes than the original SCM, which saves computational costs. The new SCM is capable of approximating partial differential equations under different boundary con- ditions, which can be extensively applied in fitting theory. Author Keywords: Crank-Nicolson difference method, Gross-Pitaevskii Equation, Sinc-Collocation methods

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Graduate student theses produced at Trent University. You may also browse theses and dissertations by year, program, or author.

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