Graduate Theses & Dissertations

Pages

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,
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
An Investigation of the Impact of Big Data on Bioinformatics Software
As the generation of genetic data accelerates, Big Data has an increasing impact on the way bioinformatics software is used. The experiments become larger and more complex than originally envisioned by software designers. One way to deal with this problem is to use parallel computing. Using the program Structure as a case study, we investigate ways in which to counteract the challenges created by the growing datasets. We propose an OpenMP and an OpenMP-MPI hybrid parallelization of the MCMC steps, and analyse the performance in various scenarios. The results indicate that the parallelizations produce significant speedups over the serial version in all scenarios tested. This allows for using the available hardware more efficiently, by adapting the program to the parallel architecture. This is important because not only does it reduce the time required to perform existing analyses, but it also opens the door to new analyses, which were previously impractical. Author Keywords: Big Data, HPC, MCMC, parallelization, speedup, Structure
THE PROPENSITY TOWARD EXTREMIST MIND-SET AS PREDICTED BY PERSONALITY, MOTIVATION, AND SELF-CONSTRUAL
ABSTRACT The Propensity Toward Extremist Mind-Set as Predicted by Personality, Motivation, and Self-Construal Nick Fauset Multivariate regression analyses were used to determine the effects of Personality (Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness), Motivation (External, Amotivation, Intrinsic, and Identified), and Self-Construal (Independent and Interdependent) on three domains of Extremist Mind-Set (Proviolence, Vile World, and Divine Power). Participants consisted of first year undergraduate students (209 females, 76 males) enrolled in Introductory Psychology (N=279) and/or Introductory Economics (N=7), whom participated for course credit. The Motivation measure was problematic for students to complete and this variable was dropped from the model due to missing data. Decreases in Neuroticism, Openness, Agreeableeness, and Interdependent were significantly correlated with increases in Proviolence. Decreases in Agreeableness were correlated with increases in Vile World. Decreases in Openness, and increases in Agreeableness and Interdependent were significantly correlated with increases in Divine Power. These observations provide an interesting perspective on the types of Canadian undergraduate students who are more likely to score highly on measures of Extremism. Keywords: Militant Extremist Mental Mind-Set, Extremism, Personality, Five Factor Model, Motivation, Intrinsic, Extrinsic, Self-Construal, Independent, Interdependent Author Keywords: Extremism, Militant Extremist Mental Mind-Set, Motivation, Personality, Self-Construal
An Emprirical Investigation into the Relationship Between Education and Health
Health literature has long noted a positive correlation between health and levels of education. Two competing theories have been advanced to explain this phenomenon: (1) education "causes" health by allowing individuals to process complex information and act on it; and, (2) education and health are merely correlated through some third underlying characteristic. Determining which of these two theories is correct is of importance to public policy. But that task is empirically difficult because, from the standard, static perspective, the theories are observationally equivalent. We exploit a way in which the two theories have different implications regarding the sort of behaviour we should observe over time. We use smoking as a measure of health behaviour and find that smoking rates between "high" and "low" educated individuals expand when information is hard to process, and then contract as it becomes more easily processable. This approach is then repeated using physical activity as a measure of health-related behaviour to address limitations of the smoking model. Our novel approach to estimating the differences in the behavioural responses to changes in the processability of health-related information, across education groups, provides strong evidence in support of the view that education and health are causally linked. Author Keywords: applied statistics, education, health economics, public health, public policy, smoking
An Application of the Sinc-Collocation Method in Oceanography
In this thesis, we explore the application of the Sinc-Collocation method to an oceanography model. The model of interest describes a wind-driven current with depth-dependent eddy viscosity and is formulated in two different systems; a complex-velocity system and a real-value coupled system. In general, the Sinc-based methods excel over other traditional numerical methods due to their exponentially decaying errors, rapid convergence and handling problems in the presence of singularities at end-points. In addition, the Sinc-Collocation approach that we utilize exploits first derivative interpolation, whose integration is less sensitive to numerical errors. We present several model problems to demonstrate the accuracy, and stability of the method. We compare the approximate solutions determined by the Sinc-Collocation technique with exact solutions and also with those obtained by the Sinc-Galerkin approach in earlier studies. Our findings indicate that the method we utilized outperforms those used in past studies. Author Keywords: Boundary Value Problems, Eddy Viscosity, Oceanography, Sinc Numerical Methods, Wind-Driven Currents
ADAPT
This thesis focuses on the design of a modelling framework consisting of loose-coupling of a sequence of spatial and process models and procedures necessary to predict future flood events for the years 2030 and 2050 in Tabasco Mexico. Temperature and precipitation data from the Hadley Centers Coupled Model (HadCM3), for those future years were downscaled using the Statistical Downscaling Model (SDSM4.2.9). These data were then used along with a variety of digital spatial data and models (current land use, soil characteristics, surface elevation and rivers) to parameterize the Soil Water Assessment Tool (SWAT) model and predict flows. Flow data were then input into the Hydrological Engineering Centers-River Analysis System (HEC-RAS) model. This model mapped the areas that are expected to be flooded based on the predicted flow values. Results from this modelling sequence generate images of flood extents, which are then ported to an online tool (ADAPT) for display. The results of this thesis indicate that with current prediction of climate change the city of Villahermosa, Tabasco, Mexico, and the surrounding area will experience a substantial amount of flooding. Therefore there is a need for adaptation planning to begin immediately. Author Keywords: Adaptation Planning, Climate Change, Extreme Weather Events, Flood Planning, Simulation Modelling
An Investigation of Load Balancing in a Distributed Web Caching System
With the exponential growth of the Internet, performance is an issue as bandwidth is often limited. A scalable solution to reduce the amount of bandwidth required is Web caching. Web caching (especially at the proxy-level) has been shown to be quite successful at addressing this issue. However as the number and needs of the clients grow, it becomes infeasible and inefficient to have just a single Web cache. To address this concern, the Web caching system can be set up in a distributed manner, allowing multiple machines to work together to meet the needs of the clients. Furthermore, it is also possible that further efficiency could be achieved by balancing the workload across all the Web caches in the system. This thesis investigates the benefits of load balancing in a distributed Web caching environment in order to improve the response times and help reduce bandwidth. Author Keywords: adaptive load sharing, Distributed systems, Load Balancing, Simulation, Web Caching
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
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:
Smote and Performance Measures for Machine Learning Applied to Real-Time Bidding
In the context of Real-Time Bidding (RTB) the machine learning problems of imbalanced classes and model selection are investigated. Synthetic Minority Oversampling Technique (SMOTE) is commonly used to combat imbalanced classes but a shortcoming is identified. Use of a distance threshold is identified as a solution and testing in a live RTB environment shows significant improvement. For model selection, the statistical measure Critical Success Index (CSI) is modified to add emphasis on recall. This new measure (CSI-R) is empirically compared with other measures such as accuracy, lift, efficiency, true skill score, Heidke's skill score and Gilbert's skill score. In all cases CSI-R is shown to provide better application to the RTB industry. Author Keywords: imbalanced classes, machine learning, online advertising, performance measures, real-time bidding, SMOTE
Effect of Listing a Stock on the S&P 500 Index on the Stock’s Volatility
This paper investigates the effect of listing a stock on the S&P 500 Index on the stock’s volatility, using various econometrics models: GARCH and EGARCH. The study mainly addresses three issues; firstly, it analyzes stock volatility in two sub-periods, secondly, it determines whether the announcement can account for the fluctuations in the price of the stock, and finally, it investigates the change in the stock’s variance. After isolating the effects of external and industry shock by using the returns on the S&P 500 Index as a proxy, the author finds evidence of structural change in the volatility of stocks after that stock is added to the index. Additionally, the existence of a dominant symmetric effect, which captures the response of volatility to news, indicate that following the onset of including the stock on the index, information flowing into the market increased. However, the rate at which old news is captured in price falls. The empirical evidence also suggests that on average a stocks variance falls and that the announcement to list a stock on the index has little effect on the stock’s price. Author Keywords: EGARCH, GARCH, S&P 500 Index, Symmetric Effect, Volatility

Pages

Search Our Digital Collections

Query

Enabled Filters

  • (-) ≠ Farell
  • (-) ≠ Bell
  • (-) ≠ Cultural Studies
  • (-) = Applied Modeling and Quantitative Methods

Filter Results

Date

2009 - 2029
(decades)
Specify date range: Show
Format: 2019/12/13

Author Last Name

Show more

Last Name (Other)

Show more