Parker, James

This study focuses on the world dynamics of a cholera model that includes delayed bacterial shedding and water disinfection. From the method of the next generation matrix, a basic reproduction number is found that sets a threshold of disease persistence. It is shown that the disease disappears if $R_0<1$, which means that the disease-free equilibrium is globally asymptotically stable. The system is not destabilized by the delay, which leads to periodic oscillations. The numerical simulations validate the theoretical analysis, which illustrates the importance of delay and disinfection in cholera prevention and control.

Author Keywords: Basic reproduction number, Cholera, Delay differential equations, Disinfection, Lyapunov function, Stability analysis

Recent developments in Artificial Intelligence (AI), particularly Large Language Models (LLMs), have provided powerful tools for Natural Language Processing (NLP) tasks like sentiment analysis. However, their fine-tuning and deployment present challenges, specifically in terms of computational efficiency and high training costs. To address these challenges, this work applies optimization techniques such as Quantized Low-Rank Adaptation (QLoRA) for parameter-efficient fine-tuning, followed by Generalized Post-Training Quantization (GPTQ) on the Llama 3.1 LLM. To evaluate these optimizations, we apply the model to a practical task: hate speech detection, using a curated dataset comprising of X (formerly Twitter) posts. Overall, the optimized model achieved a 67% reduction in size along with significant improvements in classification accuracy and inference speed compared to the base model.

Author Keywords: Generalized Post-Training Quantization, Large Language Models, Low-Rank Adaptation, Parameter-Efficient Fine-Tuning, Quantized Low-Rank Adaptation

This thesis develops an Long Short-Term Memory (LSTM) neural network model to assess the relationship between ambient air pollutant exposure and public health risks, accommodating both linear and nonlinear associations with distributed lags.The research makes three key contributions. First, Maximal Information Coefficient (MIC) methods identify the most relevant air pollutants and their associations with health outcomes. Second, an LSTM model extracts temporally dependent features from exposure series to estimate health impacts. Finally, the model's potential in air pollution epidemiology is explored using Local Interpretable Model-Agnostic Explanations (LIME) to interpret the exposure-health response relationship.

Author Keywords: air pollution epidemiology, Deep Learning, explainable AI, Long Short-Term Memory, Maximal Information Coefficient, public health assessmen

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

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

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

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

Previous research has suggested that childhood sensory sensitivity may predict adult obsessive compulsive (OC) behaviours. To date, however, research has not addressed how the separate dimensions – harm avoidance and incompleteness - may influence this relationship or why it exists. The current study used a retrospective design to test a) if sensory sensitivity in childhood predicts trait incompleteness in adulthood, as well as b) if emotion regulation variables mediate this relationship. Questionnaires pertaining to OC dimensions and childhood anxieties were completed independently by 172 undergraduate participants and their primary childhood caregiver. Results showed a linear relationship between sensory sensitivity in childhood and incompleteness in adults. Emotion regulation variables failed to mediate this relationship, although a trend for mediation was present. Additionally, exploratory analysis found perfectionism in childhood to be a predictor of trait incompleteness but not harm avoidance, whereas physical anxieties predicted harm avoidance and not incompleteness. Results are discussed in the context of clinical and theoretical implications.

Author Keywords: Distress Tolerance, Harm Avoidance, Incompleteness, Obsessive Compulsive Disorder, Sensory Sensitivity, Symmetry

Trait Emotional Intelligence (TEI) plays an important role in the health and wellness of children and adolescents. Not surprisingly, the literature on TEI and youth has expanded dramatically. Although the quality of this work continues to be uneven due to the continued proliferation of TEI-related measures with questionable psychometric features. One over-looked TEI measure in the field is the short form developed for the Emotional Quotient Inventory Youth Version (EQ-i:YV-S). The core goal of Study 1 was to examine the overall reliability and validity of the EQ-i:YV-S. The aim of Study 2 was to evaluate the utility of the EQ-i:YV-S as a measure of the effectiveness of a new school-based social and emotional learning program for elementary school students. Results from Study 1 demonstrated that the EQ-i:YV-S had good internal reliability, 6-month test-retest reliability, and convergent validity. Study 2 found that Total EI and most key EI-related dimensions had significant improvement from pretest to post test on the EQ-i:YV-S. These findings have important implications for TEI measurement in youth and the effectiveness of school-based psychoeducational programming for TEI, with the EQ-i:YV-S as a viable option for research in this area.

Author Keywords: emotional intelligence, psychoeducational programming, social-emotional competencies