The Nutritional Knowledge, Eating Habits, and Body Image of Adolescent Females

This mixed methods investigation examined the nutritional knowledge and habits of adolescent girls in grades 9 through 12 at a secondary school in southern Ontario. Through questionnaires, interviews, and the use of teaching and curriculum documents, this study attempted to understand whether the current nutrition curriculum is influential in developing students' nutritional knowledge, healthy eating habits, and a favourable body image. Data collection occurred over a 2-month period, involving 90 female participants, and the data analysis program SPSS was used for analysis of the quantitative questionnaire data. Interview data were organized into categories, and analysis of any emerging themes occurred. Teaching and curriculum documents were examined to determine any overlap and develop an understanding of the participants' exposure and experience within nutrition within the classroom setting. The findings of this study suggest that the current nutrition education did have an impact on the participants' nutrition knowledge. However, the impact on their eating habits and body image was limited in the context it was measured and tested. The knowledge learned within the classroom may not always be applied outside of the classroom. This study suggests that improvement in the current nutrition curriculum may be needed to have a bigger impact on adolescent females. The findings from the study shine light on areas of improvements for educators as well as development of future curriculum. Changes may need to be made not only in the specific curriculum content and expectations but also the delivery of it by the classroom teacher.

An Exploration of Male Adolescents' Body Image Perceptions

There has been an increasing concern among researchers and the general population of our culture's increasing emphasis on "ideal" physical attractiveness-for both females and males. Despite this growing concern, research on body image has focused primarily on women and girls, with little research aimed specifically for males. Prior research (Grogan & Richards, 2002; Hargreaves & Tiggemann, 2006) stated that body image was a "feminine" or a "gay" issue, according to men and boys. The present study investigates this issue, particularly within the theoretical framework of multiple selves and gender theories. This exploratory case study involved semi-structured interviews with six male adolescents between the ages of 13 and 18 years. Researcher's fieldnotes were taken after the interviews. Content analysis of the interviews and fieldnotes revealed that for these six male adolescents, body image is not relevant to them, as they think about and discuss their issues of physical appearance with family and close peers. Traditional stereotypic notions of masculinity and what it means to be an adolescent male for the participants are discussed within the context of developmentally appropriate and gender-inclusive curriculum.

Genetic Programming for Non-Photorealistic Rendering

This thesis focuses on developing an evolutionary art system using genetic programming. The main goal is to produce new forms of evolutionary art that filter existing images into new non-photorealistic (NPR) styles, by obtaining images that look like traditional media such as watercolor or pencil, as well as brand new effects. The approach permits GP to generate creative forms of NPR results. The GP language is extended with different techniques and methods inspired from NPR research such as colour mixing expressions, image processing filters and painting algorithm. Colour mixing is a major new contribution, as it enables many familiar and innovative NPR effects to arise. Another major innovation is that many GP functions process the canvas (rendered image), while is dynamically changing. Automatic fitness scoring uses aesthetic evaluation models and statistical analysis, and multi-objective fitness evaluation is used. Results showed a variety of NPR effects, as well as new, creative possibilities.

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.

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

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.

The Effects of a Social-Evaluative Body Image Threat on Shame, Social Physique Anxiety, Body Dissatisfaction and Cortisol Responses in University Men

Men struggle with body image concerns particularly related to the desire to be muscular. In women, social-evaluative body image threats have been linked to increased shame and cortisol responses, consistent with social self-preservation theory (SSPT), but no research has investigated these responses in men. Men (n = 66) were randomly assigned to either a social-evaluative threat (SET) or non-social-evaluative threat (N-SET) condition. Participants provided saliva samples and completed body shame, body dissatisfaction and social physique anxiety measures prior to and following their condition, during which anthropometric and strength measures were assessed. Results indicated men in the SET condition had higher body shame, social physique anxiety, and body dissatisfaction and had higher levels of cortisol than men in the N-SET condition post-social-evaluative threat. These findings, consistent with SSPT, suggest that social-evaluative body image threats may lead to increased body shame and social physique anxiety, greater body dissatisfaction and higher cortisol levels.

Automatic Preparation of ETD Material from the Internet Archive for the DSpace Repository Platform

A big challenge associated with getting an institutional repository off the ground is getting content into it. This article will look at how to use digitization services at the Internet Archive alongside software utilities that the author developed to automate the harvesting of scanned dissertations and associated Dublin Core XML files to create an ETD Portal using the DSpace platform. The end result is a metadata-rich, full-text collection of theses that can be constructed for little out of pocket cost.

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.

Photograph - Julia Canby French (smaller image)

Black and white, 16 ½ cm x 11 ½ cm, of Julia Canby French (this is a larger versions of the photo listed above).

Photograph - Julia Canby Cleveland (larger image)

Black and white photograph, 16 ½ cm. x 11 ½ cm., of Julia Amelia Canby Cleveland (this is a larger version of the photo listed above).

Niagara Falls (Image from Eastern District of Pennsylvania Clerks Office)

The caption below reads "Entered according to Act of Congress in the year 1854 by F. Langenheim in the Clerks office of the district Court for the Eastern district of Pennsylvania".

Blueprint (inverted image) of the plan of the City of Toronto

Blueprint (inverted image) of the plan of the City of Toronto (85 cm. x 140 cm.), 1857.

Annotation XML et interrogation de corpus pour l’étude de la conceptualisation métaphorique

Département de linguistique et de traduction

Segmentation d’images intravasculaires ultrasonores

L'imagerie intravasculaire ultrasonore (IVUS) est une technologie médicale par cathéter qui produit des images de coupe des vaisseaux sanguins. Elle permet de quantifier et d'étudier la morphologie de plaques d'athérosclérose en plus de visualiser la structure des vaisseaux sanguins (lumière, intima, plaque, média et adventice) en trois dimensions. Depuis quelques années, cette méthode d'imagerie est devenue un outil de choix en recherche aussi bien qu'en clinique pour l'étude de la maladie athérosclérotique. L'imagerie IVUS est par contre affectée par des artéfacts associés aux caractéristiques des capteurs ultrasonores, par la présence de cônes d'ombre causés par les calcifications ou des artères collatérales, par des plaques dont le rendu est hétérogène ou par le chatoiement ultrasonore (speckle) sanguin. L'analyse automatisée de séquences IVUS de grande taille représente donc un défi important. Une méthode de segmentation en trois dimensions (3D) basée sur l'algorithme du fast-marching à interfaces multiples est présentée. La segmentation utilise des attributs des régions et contours des images IVUS. En effet, une nouvelle fonction de vitesse de propagation des interfaces combinant les fonctions de densité de probabilité des tons de gris des composants de la paroi vasculaire et le gradient des intensités est proposée. La segmentation est grandement automatisée puisque la lumière du vaisseau est détectée de façon entièrement automatique. Dans une procédure d'initialisation originale, un minimum d'interactions est nécessaire lorsque les contours initiaux de la paroi externe du vaisseau calculés automatiquement sont proposés à l'utilisateur pour acceptation ou correction sur un nombre limité d'images de coupe longitudinale. La segmentation a été validée à l'aide de séquences IVUS in vivo provenant d'artères fémorales provenant de différents sous-groupes d'acquisitions, c'est-à-dire pré-angioplastie par ballon, post-intervention et à un examen de contrôle 1 an suivant l'intervention. Les résultats ont été comparés avec des contours étalons tracés manuellement par différents experts en analyse d'images IVUS. Les contours de la lumière et de la paroi externe du vaisseau détectés selon la méthode du fast-marching sont en accord avec les tracés manuels des experts puisque les mesures d'aire sont similaires et les différences point-à-point entre les contours sont faibles. De plus, la segmentation par fast-marching 3D s'est effectuée en un temps grandement réduit comparativement à l'analyse manuelle. Il s'agit de la première étude rapportée dans la littérature qui évalue la performance de la segmentation sur différents types d'acquisition IVUS. En conclusion, la segmentation par fast-marching combinant les informations des distributions de tons de gris et du gradient des intensités des images est précise et efficace pour l'analyse de séquences IVUS de grandes tailles. Un outil de segmentation robuste pourrait devenir largement répandu pour la tâche ardue et fastidieuse qu'est l'analyse de ce type d'images.