Applied Modeling and Quantitative Methods

Video games have always been intrinsically linked with the technology available for the progress of the medium. With improvements in technology correlating directly to improvements in video games, this has recently not been the case. One recent technology video games have not fully leveraged is Cloud technology. This Thesis investigates a potential solution for video games to leverage Cloud technology. The methodology compares the relative performance of a Local, Cloud and a proposed Hybrid Model of video games. We find when comparing the results of the relative performance of the Local, Cloud and Hybrid Models that there is potential in a Hybrid technology for increased performance in Cloud gaming as well as increasing stability in overall game play.

Author Keywords: cloud, cloud gaming, streaming, video game

Predicting vehicle speed at critical road segments, such as pedestrian crossings during left-turn maneuvers at signalized intersections, is essential for improving traffic safety and supporting autonomous driving systems. This thesis presents a novel two-stage hybrid deep learning framework enhanced with reinforcement learning to forecast vehicle left-turn speed at pedestrian crossings.

Using a multivariate time series dataset of vehicle speed and acceleration, the final three seconds of data are intentionally removed to simulate real-world decision-making prior to reaching pedestrian crossings. In stage one, a Convolutional Neural Network (CNN) imputes the removed values. Stage two uses the imputed data to forecast speed, combining Temporal Convolutional Networks (TCNs) and Long Short-Term Memory (LSTM) networks as feature extractors, followed by a Random Forest Regressor (RFR) for robust speed predictions.

Reinforcement learning is employed to dynamically adjusts the TCN's dilation rate, improving temporal pattern capture. Experimental results show the proposed framework outperforms standalone, hybrid, and state-of-the-art models.

Author Keywords: Data Imputation, Dynamic Dilation, Left-Turn Maneuver, Reinforcement Learning, Temporal Convolutional Network, Time series Forecasting

Climate change is a global challenge with profound environmental, health, andsocio-economic implications. Canada's diverse geography offers a unique lens to study localized climate trends. This thesis models and clusters climate variables, focusing on temperature trends, using Bayesian hierarchical models and clustering techniques to uncover regional patterns and health impacts. Three decades of hourly temperature data from the Meteorological Service of Canada were split into 18 annual parts to capture seasonal variations. Metrics like mean, minimum, and extreme temperatures were analyzed. Bayesian models revealed regional variability, with examples of British Columbia and the Northern regions exhibiting notable trends. Clustering identified regional dependencies and linked temperature trends with morbidity and mortality risks from air pollutants (PM2.5, O3). Summer risks stemmed from O3, while winter risks were PM2.5 driven. Findings highlight the need for region-specific strategies, offering actionable insights for policy makers addressing climate-health linkages.

Author Keywords: Bayesian models, Climate change, Clustering, Temperature Trends, Time Series

Accurately forecasting financial distress in companies is crucial in the turbulent economic conditions of our time. This study highlights the potential benefits of incorporating qualitative data into financial distress prediction models. The study assessed the relative effectiveness of traditional distress prediction models against integrated models, determined which variables significantly impacted the predictive performance and ascertained the consistency of the models across Africa.The study employed three distinct classification techniques to evaluate the performance of both models: logistic regression, decision trees, and random forests, to ensure that the best-performing technique was identified. The study found that incorporating governance factors into the model did not positively impact the model's performance, affirming that traditional distress prediction models are relatively effective. The study also found that Current Ratio, ROA, ROE, DCE, and Asset Turnover significantly impacted the predictive performance of the models. Finally, it identified regional discrepancies in the performance of the analyzed models.

Author Keywords: Decision Tree, Financial Distress, Integrated Models, Logistic Regression, Random Forest, Traditional Models

This thesis presents an approach for automated grading of UML Use Case diagrams. Many software engineering courses require students to learn how to model the behavioural features of a problem domain or an object-oriented design in the form of a use case diagram. Because assessing UML assignments is a time-consuming and labor-intensive operation, there is a need for an automated grading strategy that may help instructors by speeding up the grading process while also maintaining uniformity and fairness in large classrooms. The effectiveness of this automated grading approach was assessed by applying it to two real-world assignments. We demonstrate how the result is similar to manual grading, which was less than 7% on average; and when we applied some strategies, such as configuring settings and using multiple solutions, the average differences were even lower. Also, the grading methods and the tool are proposed and empirically validated.

Author Keywords: Automated Grading, Compare Models, Use Case

The Multidimensional Inventory of Personal Intelligence (MIPI) was designed to measure three related dimensions of the personal intelligence (PI) construct: emotional intelligence (EI), social intelligence (SI), and motivational intelligence (MI). The MIPI has psychometric properties and a theoretical structure that improves on the shortcomings of existing trait EI measures. The aim of the first study was to create and validate a shortened form (MIPI- Short) that maintains the same factorial structure of the original MIPI. The purpose of the second study was to validate the new scale with measures of conceptually similar constructs (e.g., emotional intelligence, Alexithymia) with various measurement methodologies (self-report, observer-report, and performance-based). Results from Study 1 found that the MIPI-Short had good factorial structure in two independent samples, as well as adequate internal reliability, and good incremental validity. The results of Study 2 demonstrated that the MIPI-Short had good construct validity as it generally related as expected with measures of EI and Alexithymia. The findings of both studies provide evidence for the validity of the MIPI-Short as a brief measure of Personal Intelligence. Directions for further research are emphasized, as the validation process is on-going for any assessment tool.

Author Keywords: Emotional Intelligence, Personal Intelligence, Socio-Emotional Competencies

This thesis is focused on studying the population dynamics of a predator-prey system in a patchy environment, taking anti-predation responses into consideration. Firstly, we conduct mathematical analysis on the equilibrium solutions of the system. Using techniques from calculus we show that particular steady state solutions exist when the parameters of the system meet certain criteria. We then show that a further set of conditions leads to the local stability of these solutions. The second step is to extend the existing mathematical analysis by way of numerical simulations. We use octave to confirm the previous results, as well as to show that more complicated dynamics can exist, such as stable oscillations. We consider more complex and meaningful functions for nonlinear dispersal between patches and nonlinear predation, and show that the proposed model exhibits behaviours we expect to see in a population model.

Author Keywords: Anti-predation response, Asymptotic stability, Dispersal, Patch model, Population dynamics, Predator-prey

Video games have always been intrinsically linked with the technology available for the progress of the medium. With improvements in technology correlating directly to improvements in video games, this has recently not been the case. One recent technology video games have not fully leveraged is Cloud technology. This Thesis investigates a potential solution for video games to leverage Cloud technology. The methodology compares the relative performance of a Local, Cloud and a proposed Hybrid Model of video games. We find when comparing the results of the relative performance of the Local, Cloud and Hybrid Models that there is potential in a Hybrid technology for increased performance in Cloud gaming as well as increasing stability in overall game play.

Author Keywords: cloud, cloud gaming, streaming, video game

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

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