Graduate Theses & Dissertations

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Relationship Between Precarious Employment, Behaviour Addictions and Substance Use Among Canadian Young Adults
This thesis utilized a unique data-set, the Quinte Longitudinal Survey, to explore relationships among precarious employment and a range of mental health problems in a representative sample of Ontario young adults. Study 1 focused on various behavioural addictions (such as problem gambling, video gaming, internet use, exercise, compulsive shopping, and sex) and precarious employment. The results showed that precariously employed men were preoccupied with gambling and sex while their female counterparts preferred shopping. Gambling and excessive shopping diminished over time while excessive sexual practices increased. Study 2 focused on the association between precarious employment and substance abuse (such as tobacco, alcohol, cannabis, hallucinogens, stimulants, and other substances). The results showed that men used cannabis more than women, and the non-precarious employed group abused alcohol more than individuals in the precarious group. This research has implications for both health care professionals and intervention program developers when working with young adults in precarious jobs. Author Keywords: Behaviour Addictions, Precarious Employment, Substance Abuse, Young Adults
Stability Properties of Disease Models under Economic Expectations
Comprehending the dynamics of infectious diseases is very important in formulating public health policies to tackling their prevalence. Mathematical epidemiology (ME) has played a very vital role in achieving the above. Nevertheless, classical mathematical epidemiological models do not explicitly model the behavioural responses of individuals in the presence of prevalence of these diseases. Economic epidemiology (EE) as a field has stepped in to fill this gap by integrating economic and mathematical concepts within one framework. This thesis investigated two issues in this area. The methods employed are the standard linear analysis of stability of dynamical systems and numerical simulation. Below are the investigations and the findings of this thesis: Firstly, an investigation into the stability properties of the equilibria of EE models is carried out. We investigated the stability properties of modified EE systems studied by Aadland et al. [6] by introducing a parametric quadratic utility function into the model, thus making it possible to model the maximum number of contacts made by rational individuals to be determined by a parameter. This parameter in particular influences the level of utility of rational individuals. We have shown that if rational individuals have a range of possible contacts to choose from, with the maximum of the number of contacts allowable for these individuals being dependent on a parameter, the variation in this parameter tends to affect the stability properties of the system. We also showed that under the assumption of permanent recovery for disease coupled with individuals observing or not observing their immunity, death and birth rates can affect the stability of the system. These parameters also have effect on the dynamics of the EE SIS system. Secondly, an EE model of syphilis infectivity among &ldquo men who have sex with men &rdquo (MSM) in detention centres is developed in an attempt at looking at the effect of behavioural responses on the disease dynamics among MSM. This was done by explicitly incorporating the interplay of the biology of the disease and the behaviour of the inmates. We investigated the stability properties of the system under rational expectations where we showed that: (1) Behavioural responses to the prevalence of the disease affect the stability of the system. Therefore, public health policies have the tendency of putting the system on indeterminate paths if rational MSM have complete knowledge of the laws governing the motion of the disease states as well as a complete understanding on how others behave in the system when faced with risk-benefit trade-offs. (2) The prevalence of the disease in the long run is influenced by incentives that drive the utility of the MSM inmates. (3) The interplay between the dynamics of the biology of the disease and the behavioural responses of rational MSM tends to put the system at equilibrium quickly as compared to its counterpart (that is when the system is solely dependent on the biology of the disease) when subjected to small perturbation. Author Keywords: economic and mathematical epidemiology models, explosive path, indeterminate-path stability, numerical solution, health gap, saddle-path stability, syphilis,
Disability-Mitigating Effects of Education on Post-Injury Employment Dynamics
Using data drawn from the Workplace Safety and Insurance Board’s (WSIB) Survey of Workers with Permanent Impairments, this thesis explores if and how the human capital associated with education mitigates the realized work-disabling effects of permanent physical injury. Using Cater’s (2000) model of post-injury adaptive behaviour and employment dynamics as the structural, theoretical, and interpretative framework, this thesis jointly studies, by injury type, the effects of education on both the post-injury probability of transitioning from non-employment into employment and the post-injury probability of remaining in employment once employed. The results generally show that, for a given injury type, other things being equal, higher levels of education are associated with higher probabilities of both obtaining and sustaining employment. Author Keywords: permanent impairment, permanent injury, post-injury employment
Agro-Ecological Zoning (AEZ) of Southern Ontario and the Projected Shifts Caused by Climate Change in the Long-term Future
This thesis proposes an agro-ecological zoning (AEZ) methodology of southern Ontario for the characterization and mapping of agro-ecological zones during the historical term (1981-2010), and their shifts into the long-term (2041-2070) projected climate period. Agro-ecological zones are homogenous areas with a unique combination of climate, soil, and landscape features that are important for crop growth. Future climate variables were derived from Earth System Models (EMSs) using a high emission climate forcing scenario from the Intergovernmental Panel on Climate Change 5th Assessment Report. The spatiotemporal shifts in agro-ecological zones with projected climate change are analyzed using the changes to the length of growing period (LGP) and crop heat units (CHU), and their manifestation in agro-climatic zones (ACZ). There are significant increases to the LGP and CHU into the long-term future. Two historical ACZs exist in the long-term future, and have decreased in area and shifted northward from their historical locations. Author Keywords: Agro-climatic Zones, Agro-ecological Zones, Agro-ecological Zoning, Climate Change, Crop Heat Units, Length of Growing Period
Self-Organizing Maps and Galaxy Evolution
Artificial Neural Networks (ANN) have been applied to many areas of research. These techniques use a series of object attributes and can be trained to recognize different classes of objects. The Self-Organizing Map (SOM) is an unsupervised machine learning technique which has been shown to be successful in the mapping of high-dimensional data into a 2D representation referred to as a map. These maps are easier to interpret and aid in the classification of data. In this work, the existing algorithms for the SOM have been extended to generate 3D maps. The higher dimensionality of the map provides for more information to be made available to the interpretation of classifications. The effectiveness of the implementation was verified using three separate standard datasets. Results from these investigations supported the expectation that a 3D SOM would result in a more effective classifier. The 3D SOM algorithm was then applied to an analysis of galaxy morphology classifications. It is postulated that the morphology of a galaxy relates directly to how it will evolve over time. In this work, the Spectral Energy Distribution (SED) will be used as a source for galaxy attributes. The SED data was extracted from the NASA Extragalactic Database (NED). The data was grouped into sample sets of matching frequencies and the 3D SOM application was applied as a morphological classifier. It was shown that the SOMs created were effective as an unsupervised machine learning technique to classify galaxies based solely on their SED. Morphological predictions for a number of galaxies were shown to be in agreement with classifications obtained from new observations in NED. Author Keywords: Galaxy Morphology, Multi-wavelength, parallel, Self-Organizing Maps
Cloud Versus Bare Metal
A comparison of two high performance computing clusters running on AWS and Sharcnet was done to determine which scenarios yield the best performance. Algorithm complexity ranged from O (n) to O (n3). Data sizes ranged from 195 KB to 2 GB. The Sharcnet hardware consisted of Intel E5-2683 and Intel E7-4850 processors with memory sizes ranging from 256 GB to 3072 GB. On AWS, C4.8xlarge instances were used, which run on Intel Xeon E5-2666 processors with 60 GB per instance. AWS was able to launch jobs immediately regardless of job size. The only limiting factors on AWS were algorithm complexity and memory usage, suggesting a memory bottleneck. Sharcnet had the best performance but could be hampered by the job scheduler. In conclusion, Sharcnet is best used when the algorithm is complex and has high memory usage. AWS is best used when immediate processing is required. Author Keywords: AWS, cloud, HPC, parallelism, Sharcnet
Utilizing Class-Specific Thresholds Discovered by Outlier Detection
We investigated if the performance of selected supervised machine-learning techniques could be improved by combining univariate outlier-detection techniques and machine-learning methods. We developed a framework to discover class-specific thresholds in class probability estimates using univariate outlier detection and proposed two novel techniques to utilize these class-specific thresholds. These proposed techniques were applied to various data sets and the results were evaluated. Our experimental results suggest that some of our techniques may improve recall in the base learner. Additional results suggest that one technique may produce higher accuracy and precision than AdaBoost.M1, while another may produce higher recall. Finally, our results suggest that we can achieve higher accuracy, precision, or recall when AdaBoost.M1 fails to produce higher metric values than the base learner. Author Keywords: AdaBoost, Boosting, Classification, Class-Specific Thresholds, Machine Learning, Outliers
SPAF-network with Saturating Pretraining Neurons
In this work, various aspects of neural networks, pre-trained with denoising autoencoders (DAE) are explored. To saturate neurons more quickly for feature learning in DAE, an activation function that offers higher gradients is introduced. Moreover, the introduction of sparsity functions applied to the hidden layer representations is studied. More importantly, a technique that swaps the activation functions of fully trained DAE to logistic functions is studied, networks trained using this technique are reffered to as SPAF-networks. For evaluation, the popular MNIST dataset as well as all \(3\) sub-datasets of the Chars74k dataset are used for classification purposes. The SPAF-network is also analyzed for the features it learns with a logistic, ReLU and a custom activation function. Lastly future roadmap is proposed for enhancements to the SPAF-network. Author Keywords: Artificial Neural Network, AutoEncoder, Machine Learning, Neural Networks, SPAF network, Unsupervised Learning
Support Vector Machines for Automated Galaxy Classification
Support Vector Machines (SVMs) are a deterministic, supervised machine learning algorithm that have been successfully applied to many areas of research. They are heavily grounded in mathematical theory and are effective at processing high-dimensional data. This thesis models a variety of galaxy classification tasks using SVMs and data from the Galaxy Zoo 2 project. SVM parameters were tuned in parallel using resources from Compute Canada, and a total of four experiments were completed to determine if invariance training and ensembles can be utilized to improve classification performance. It was found that SVMs performed well at many of the galaxy classification tasks examined, and the additional techniques explored did not provide a considerable improvement. Author Keywords: Compute Canada, Kernel, SDSS, SHARCNET, Support Vector Machine, SVM
Psychometric Properties of a Scale Developed from a Three-Factor Model of Social Competency
While existing models of emotional intelligence (EI) generally recognize the importance of social competencies (SC), there is a tendency in the literature to narrow the focus to competencies that pertain to the self. Given the experiential and perceptual differences between self- vs. other-oriented emotional abilities, this is an important limitation of existing EI models and assessment tools. This thesis explores the psychometric properties of a multidimensional model for SC. Chapter 1 describes the evolution of work on SCs in modern psychology and describes the multidimensional model of SC under review. Chapter 2 replicates this model across a variety of samples and explores the model’s construct validity via basic personality and EI constructs. Chapter 3 further explores the predictive validity of the SC measure within a group of project managers and several success and wellness variables. Chapter 4 examines potential applications for the model and suggestions for further research. Author Keywords: emotional intelligence, project management, social competency, work readiness
Predicting the Pursuit of Post-Secondary Education
Trait Emotional Intelligence (EI) includes competencies and dispositions related to identifying, understanding, using and managing emotions. Higher trait EI has been implicated in post-secondary success, and better career-related decision-making. However, there is no evidence for whether it predicts the pursuit of post-secondary education (PSE) in emerging adulthood. This study investigated the role of trait EI in PSE pursuit using a large, nationally-representative sample of Canadian young adults who participated in the National Longitudinal Survey for Children and Youth (NLSCY). Participants in this dataset reported on their PSE status at three biennial waves (age 20-21, 22-23, and 24-25), and completed a four-factor self-report scale for trait EI (Emotional Quotient Inventory: Mini) at ages 20-21 and 24-25. Higher trait EI subscale scores were significantly associated with greater likelihood of PSE participation both concurrently, and at 2- and 4-year follow-ups. Overall, these associations were larger for men than women. Trait EI scores also showed moderate levels of temporal stability over four years, including full configural and at least partial metric invariance between time points. This suggests that the measure stays conceptually consistent over the four years of emerging adulthood, and that trait EI is a relatively malleable attribute, susceptible to change with interventions during this age period. Author Keywords: Emerging Adulthood, Longitudinal, Post-Secondary Pursuit, Trait Emotional Intelligence
Modelling Submerged Coastal Environments
Built upon remote sensing and GIS littoral zone characterization methodologies of the past decade, a series of loosely coupled models aimed to test, compare and synthesize multi-beam SONAR (MBES), Airborne LiDAR Bathymetry (ALB), and satellite based optical data sets in the Gulf of St. Lawrence, Canada, eco-region. Bathymetry and relative intensity metrics for the MBES and ALB data sets were run through a quantitative and qualitative comparison, which included outputs from the Benthic Terrain Modeller (BTM) tool. Substrate classification based on relative intensities of respective data sets and textural indices generated using grey level co-occurrence matrices (GLCM) were investigated. A spatial modelling framework built in ArcGISTM for the derivation of bathymetric data sets from optical satellite imagery was also tested for proof of concept and validation. Where possible, efficiencies and semi-automation for repeatable testing was achieved using ArcGISTM ModelBuilder. The findings from this study could assist future decision makers in the field of coastal management and hydrographic studies. Keywords: Seafloor terrain characterization, Benthic Terrain Modeller (BTM), Multi-beam SONAR, Airborne LiDAR Bathymetry, Satellite Derived Bathymetry, ArcGISTM ModelBuilder, Textural analysis, Substrate classification Author Keywords:

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