A model of personality and health : coping styles as a moderator in the perfectionism-health connection

The purpose of this study was to examine a model of personality and health. Specifically, this thesis examined perfectionism as a predictor of health status and health behaviours, as moderated by coping styles. A community sample of 813 young adults completed the Multidimensional Perfectionism Scale, the Coping Strategy Indicator, and measures of health symptoms, health care utilization, and various health behaviours. Multiple regression analyses revealed a number of significant findings. First, perfectionism and coping styles contributed significant main effects in predicting health status and health behaviours, although coping styles were not shown to moderate the perfectionism-health relationship. The data showed that perfectionism did constitute a health risk, both in terms of health status and health behaviours. Finally, an unexpected finding was that perfectionism also included adaptive features related to health. Specifically, some dimensions of perfectionism were also associated with reports of better health status and involvement in some positive health behaviours.

Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.