Life after hockey : an examination of athletic career transition and the National Hockey League's career transition program

The existent body of athletic career retirement literature is scant in studies of career transition programs. In an effort to attend to this analytical gap, the present study set out to examine the transitions of National Hockey League (NHL; ice hockey) alumni, as well as the effect ~and effectiveness of their respective career transition program, the Life After Hockey program. Interviews with 17 NHL/program alumni revealed that quality of transition (to post-playing life) was affected by: the continuity between pre- and postretirement environments; athletic identity; physical/psychological health (particularly with respect to post-concussion syndrome); selective coping strategies (e.g., preretirement planning (e.g., financial planning, continued education), positive reinterpretation, alcohol/substance abuse); and social support. Also affecting quality of transition, and found to be highly effective (particularly in generating new occupational opportunities, assisting in the acquisition of new skills, and providing a system of continuous support), was the Life After Hockey program.

Automatic Inference of Graph Models for Complex Networks with Genetic Programming

Complex networks can arise naturally and spontaneously from all things that act as a part of a larger system. From the patterns of socialization between people to the way biological systems organize themselves, complex networks are ubiquitous, but are currently poorly understood. A number of algorithms, designed by humans, have been proposed to describe the organizational behaviour of real-world networks. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. The algorithms, called graph models, represent significant human effort. Deriving accurate graph models is non-trivial, time-intensive, challenging and may only yield useful results for very specific phenomena. An automated approach can greatly reduce the human effort required and if effective, provide a valuable tool for understanding the large decentralized systems of interrelated things around us. To the best of the author's knowledge this thesis proposes the first method for the automatic inference of graph models for complex networks with varied properties, with and without community structure. Furthermore, to the best of the author's knowledge it is the first application of genetic programming for the automatic inference of graph models. The system and methodology was tested against benchmark data, and was shown to be capable of reproducing close approximations to well-known algorithms designed by humans. Furthermore, when used to infer a model for real biological data the resulting model was more representative than models currently used in the literature.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.

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.

Fit For Action: A Comparative Case Study of the Implementation of an Adaptive Fitness and Conditioning Program for Moderate Functioning Teens and Transition Age Youth with ASD

The purpose of my research was to develop and refine pedagogic approaches, and establish fitness baselines to adapt fitness and conditioning programs for Moderate-functioning ASD individuals. I conducted a seven-week study with two teens and two trainers. The trainers implemented individualized fitness and conditioning programs that I developed. I conducted pre and post fitness baselines for each teen, a pre and post study interview with the trainers, and recorded semi-structured observations during each session. I used multi-level, within-case and across case analyses, working inductively and deductively. My findings indicated that fundamental movement concepts can be used to establish fitness baselines and develop individualized fitness programs. I tracked and evaluated progressions and improvements using conventional measurements applied to unconventional movements. This process contributed to understanding and making relevant modifications to activities as effective pedagogic strategies for my trainers. Further research should investigate fitness and conditioning programs with lower functioning ASD individuals.