The effectiveness of a health versus appearance message on pregnant women's intentions to exercise postpartum : the moderating role of self-monitoring

Despite the confimied health benefits of exercise during the postpartum period, many new mothers are not sufficiently active. The present research aimed to examine the effectiveness of 2 types of messages on intention to exercise after giving birth on 2 groups of pregnant women (low and high self-monitors) using the Theory of Planned Behavior as a theoretical basis. Participants were 2 1 8 pregnant women 1 8 years of age and older (Mean age = 27.9 years, SD = 5.47), and in their second or third trimester. Women completed a demographics questionnaire, a self-monitoring (SM) scale and the Godin Leisure Time Exercise Questionnaire for current and pre-pregnancy exercise levels. They then read one of two brochures, describing either the health or appearance benefits of exercise for postpartum women. Women's attitudes, social norms, perceived behavioral control, and intentions to exercise postpartum were then assessed to determine whether one type of message (health or appearance) was more effective for each group. A MANOVA found no significant effect (p>0.05) for message type, SM, or their interaction. Possible reasons include the fact that the two messages may have been too similar, reading any message about exercise may result in intentions to exercise, or lack of attention given to the brochure. Given the lack of research in this area, more studies are necessary to confirm the present results. Two additional exploratory analyses were conducted. Pearson correlations found higher levels of pre-pregnancy exercise and current exercise to be associated with more positive attitudes, more positive subjective norms, higher perceived behavioral control, and higher intention to exercise postpartum. A hierarchical regression was conducted to determine the predictive utility of attitudes, subjective norms, and perceived behavioral control on intention for each self-monitoring group. Results of the analysis demonstrated the three independent variables significantly predicted intention (p < .001) in both groups, accounting for 58-62% of the variance in intention. For low self-monitors, attitude was the strongest predictor of intention, followed by perceived behavioral control and subjective norm. For high self-monitors, perceived behavioral control was the strongest predictors, followed by attitudes and subjective norm. The present study has practical and real world implications by contributing to our understanding of what types of messages, in a brochure format, are most effective in changing pregnant women's attitudes, subjective norm, perceived behavioral control and intention to exercise postpartum and provides ftirther support for the use of the Theory of Planned Behavior with this population.

Business leadership in the classroom

This action research assesses a framework that assists business educators in promoting leadership within a classroom. It is designed to better prepare students to assume leadership and fill the "leadership gap" in business. Two classes of 2nd-year community college business students participated in running and managing their own business community as teams of sales professionals by developing and practicing their own individual leadership for 28 weeks during their sales courses. The intent was to assess the development of leadership resulting from the implementation of the "Business Leadership in the Classroom" framework. This framework balances leadership principles to simulate a business environment with the practical elements of a learning community under the facilitation of an experienced business educator. The action research approach was used to assess and adjust approaches to business leadership on a continuous basis throughout the research. Data were collected from 61 students based on journals, surveys, peer group reviews, and my (facilitator) reflective journal.The findings reveal that both individual and collective business leadership views and practical skills developed over time. A business leadership mind-set evolved that ranged from a general awareness of the importance of leadership, to a conscious and deliberate use of individual leadership. Areas important in building a progression of leadership included: leadership teams, membership roles, weekly leadership teams, peer feedback, and activity-based learning. Emerging themes included leadership, leadership style, teamwork, as well as influence and motivation. The research framework was effective in supporting the development of business leadership but required some adjustments. These included increased structure and feedback mechanisms. Interpretation of the findings demonstrates the importance of real-world practical education in the classroom. Results show how focusing on a single mind-set such as business leadership, can result in enormous individual growth and development. When business students are encouraged to act as real businesspeople, managing their own learning, the results are effective in preparing them for the business world. All participants expressed their leadership in different ways based on personality and individual strengths. There was an overwhelming and, in some cases, passionate interest in leadership. The use of action research with a range of data collection methods provides a way to measure and track individual student learning and to generate adjustments to the research framework design and learning approaches. The findings generate implications and recommendations to continue this research further. Key recommendations center around how to ensure leadership development is sustained, including improved approaches to heighten the real-world feel of the classroom. Specifically, the use of leadership goals and action plans for each individual participant and an active use of outside business resource people as contacts for participants is recommended.

Examining and describing professional development and continuing education in the personal training and fitness industry : sites and issues of disconnect

The following phenomenologically oriented study examines and describes the relevance and effectiveness of professional development and continuing education programs for real-world situations of personal trainers. The participants were personal trainers, facility managers, and persons involved in the accreditation process. Data collection took place in 3 phases. The first phase consisted of the participants completing the PUMP Questionnaire, followed by focus groups with personal trainers and interviews with managers. The study's 3 data sets required reduction via a content analysis by question, content analysis by existential categories, and further thematic analysis using the lived relation existential dimension. The discussion contains the salient sites and issues of disconnect between clients, personal trainers, and facility managers and how they might affect the personal training experience. The intergenerational disconnect emphasized between Boomers as clients and Millennials as personal trainers requires further exploration and dialogue and underscores the need for different approaches to content and delivery of professional development and continuing education experiences for personal trainers and managers of fitness facilities.

Using genetic algorithms for the single allocation hub location problem

Hub location problem is an NP-hard problem that frequently arises in the design of transportation and distribution systems, postal delivery networks, and airline passenger flow. This work focuses on the Single Allocation Hub Location Problem (SAHLP). Genetic Algorithms (GAs) for the capacitated and uncapacitated variants of the SAHLP based on new chromosome representations and crossover operators are explored. The GAs is tested on two well-known sets of real-world problems with up to 200 nodes. The obtained results are very promising. For most of the test problems the GA obtains improved or best-known solutions and the computational time remains low. The proposed GAs can easily be extended to other variants of location problems arising in network design planning in transportation systems.

Automatic Structure Generation using Genetic Programming and Fractal Geometry

Three dimensional model design is a well-known and studied field, with numerous real-world applications. However, the manual construction of these models can often be time-consuming to the average user, despite the advantages o ffered through computational advances. This thesis presents an approach to the design of 3D structures using evolutionary computation and L-systems, which involves the automated production of such designs using a strict set of fitness functions. These functions focus on the geometric properties of the models produced, as well as their quantifiable aesthetic value - a topic which has not been widely investigated with respect to 3D models. New extensions to existing aesthetic measures are discussed and implemented in the presented system in order to produce designs which are visually pleasing. The system itself facilitates the construction of models requiring minimal user initialization and no user-based feedback throughout the evolutionary cycle. The genetic programming evolved models are shown to satisfy multiple criteria, conveying a relationship between their assigned aesthetic value and their perceived aesthetic value. Exploration into the applicability and e ffectiveness of a multi-objective approach to the problem is also presented, with a focus on both performance and visual results. Although subjective, these results o er insight into future applications and study in the fi eld of computational aesthetics and automated structure design.

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.

Heuristics for the Critical Node Detection Problem in Large Complex Networks

Complex networks have recently attracted a significant amount of research attention due to their ability to model real world phenomena. One important problem often encountered is to limit diffusive processes spread over the network, for example mitigating pandemic disease or computer virus spread. A number of problem formulations have been proposed that aim to solve such problems based on desired network characteristics, such as maintaining the largest network component after node removal. The recently formulated critical node detection problem aims to remove a small subset of vertices from the network such that the residual network has minimum pairwise connectivity. Unfortunately, the problem is NP-hard and also the number of constraints is cubic in number of vertices, making very large scale problems impossible to solve with traditional mathematical programming techniques. Even many approximation algorithm strategies such as dynamic programming, evolutionary algorithms, etc. all are unusable for networks that contain thousands to millions of vertices. A computationally efficient and simple approach is required in such circumstances, but none currently exist. In this thesis, such an algorithm is proposed. The methodology is based on a depth-first search traversal of the network, and a specially designed ranking function that considers information local to each vertex. Due to the variety of network structures, a number of characteristics must be taken into consideration and combined into a single rank that measures the utility of removing each vertex. Since removing a vertex in sequential fashion impacts the network structure, an efficient post-processing algorithm is also proposed to quickly re-rank vertices. Experiments on a range of common complex network models with varying number of vertices are considered, in addition to real world networks. The proposed algorithm, DFSH, is shown to be highly competitive and often outperforms existing strategies such as Google PageRank for minimizing pairwise connectivity.

An Investigation of the Role of Attention in the Cross-Race Effect: An Ecological Approach

The current set of studies was conducted to examine the cross-race effect (CRE), a phenomenon commonly found in the face perception literature. The CRE is evident when participants display better own-race face recognition accuracy than other-race recognition accuracy (e.g. Ackerman et al., 2006). Typically the cross-race effect is attributed to perceptual expertise, (i.e., other-race faces are processed less holistically; Michel, Rossion, Han, Chung & Caldara, 2006), and the social cognitive model (i.e., other-race faces are processed at the categorical level by virtue of being an out-group member; Hugenberg, Young, Bernstein, & Sacco, 2010). These effects may be mediated by differential attention. I investigated whether other-race faces are disregarded and, consequently, not remembered as accurately as own-race (in-group) faces. In Experiment 1, I examined how the magnitude of the CRE differed when participants learned individual faces sequentially versus when they learned multiple faces simultaneously in arrays comprising faces and objects. I also examined how the CRE differed when participants recognized individual faces presented sequentially versus in arrays of eight faces. Participants’ recognition accuracy was better for own-race faces than other-race faces regardless of familiarization method. However, the difference between own- and other-race accuracy was larger when faces were familiarized sequentially in comparison to familiarization with arrays. Participants’ response patterns during testing differed depending on the combination of familiarization and testing method. Participants had more false alarms for other-race faces than own-race faces if they learned faces sequentially (regardless of testing strategy); if participants learned faces in arrays, they had more false alarms for other-race faces than own-races faces if ii i they were tested with sequentially presented faces. These results are consistent with the perceptual expertise model in that participants were better able to use the full two seconds in the sequential task for own-race faces, but not for other-race faces. The purpose of Experiment 2 was to examine participants’ attentional allocation in complex scenes. Participants were shown scenes comprising people in real places, but the head stimuli used in Experiment 1 were superimposed onto the bodies in each scene. Using a Tobii eyetracker, participants’ looking time for both own- and other-race faces was evaluated to determine whether participants looked longer at own-race faces and whether individual differences in looking time correlated with individual differences in recognition accuracy. The results of this experiment demonstrated that although own-race faces were preferentially attended to in comparison to other-race faces, individual differences in looking time biases towards own-race faces did not correlate with individual differences in own-race recognition advantages. These results are also consistent with perceptual expertise, as it seems that the role of attentional biases towards own-race faces is independent of the cognitive processing that occurs for own-race faces. All together, these results have implications for face perception tasks that are performed in the lab, how accurate people may be when remembering faces in the real world, and the accuracy and patterns of errors in eyewitness testimony.

A Comparative Analysis of Community-Based Service Providers for "Extreme" Case ASD: Exploring Activity Program Options

The purpose of my research was to examine how community-based organizations in the Niagara region provide programs for children with Autism Spectrum Disorder (ASD), who are considered to represent “extreme” or “severe” cases. A qualitative, comparative case study was conducted that focused on three organizations who provide summer recreation and activity programs, in order to examine the issues these organizations face when determining program structure and staff training; and to understand what the threshold for physical activity is in this type of setting, and how the unique needs surrounding these “severe” cases are met while attending the program. Purposeful sampling was employed to select a supervisor and senior staff member from each organization to discuss the training process, program development and implementation, and the resources and strategies used within their organization’s community-based program. A confirming comparative analysis was comparative analysis of a parents survey with six mothers whose children are considered “severe” indicated that camp staffs’ expectations are unrealistic where as the parents and supervisors have more realistic expectations within the “real world” of camp. There is no definition of “severe” or “extreme” and therefore severity is dependent upon the context.

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.

Genetic and Electrophysiological Correlates of Self-Regulation in Adolescence

Self-regulation is considered a powerful predictor of behavioral and mental health outcomes during adolescence and emerging adulthood. In this dissertation I address some electrophysiological and genetic correlates of this important skill set in a series of four studies. Across all studies event-related potentials (ERPs) were recorded as participants responded to tones presented in attended and unattended channels in an auditory selective attention task. In Study 1, examining these ERPs in relation to parental reports on the Behavior Rating Inventory of Executive Function (BRIEF) revealed that an early frontal positivity (EFP) elicited by to-be-ignored/unattended tones was larger in those with poorer self-regulation. As is traditionally found, N1 amplitudes were more negative for the to-be-attended rather than unattended tones. Additionally, N1 latencies to unattended tones correlated with parent-ratings on the BRIEF, where shorter latencies predicted better self-regulation. In Study 2 I tested a model of the associations between selfregulation scores and allelic variations in monoamine neurotransmitter genes, and their concurrent links to ERP markers of attentional control. Allelic variations in dopaminerelated genes predicted both my ERP markers and self-regulatory variables, and played a moderating role in the association between the two. In Study 3 I examined whether training in Integra Mindfulness Martial Arts, an intervention program which trains elements of self-regulation, would lead to improvement in ERP markers of attentional control and parent-report BRIEF scores in a group of adolescents with self-regulatory difficulties. I found that those in the treatment group amplified their processing of attended relative to unattended stimuli over time, and reduced their levels of problematic behaviour whereas those in the waitlist control group showed little to no change on both of these metrics. In Study 4 I examined potential associations between self-regulation and attentional control in a group of emerging adults. Both event-related spectral perturbations (ERSPs) and intertrial coherence (ITC) in the alpha and theta range predicted individual differences in self-regulation. Across the four studies I was able to conclude that real-world self-regulation is indeed associated with the neural markers of attentional control. Targeted interventions focusing on attentional control may improve self-regulation in those experiencing difficulties in this regard.

Characterizing Dynamic Optimization Benchmarks for the Comparison of Multi-Modal Tracking Algorithms

Population-based metaheuristics, such as particle swarm optimization (PSO), have been employed to solve many real-world optimization problems. Although it is of- ten sufficient to find a single solution to these problems, there does exist those cases where identifying multiple, diverse solutions can be beneficial or even required. Some of these problems are further complicated by a change in their objective function over time. This type of optimization is referred to as dynamic, multi-modal optimization. Algorithms which exploit multiple optima in a search space are identified as niching algorithms. Although numerous dynamic, niching algorithms have been developed, their performance is often measured solely on their ability to find a single, global optimum. Furthermore, the comparisons often use synthetic benchmarks whose landscape characteristics are generally limited and unknown. This thesis provides a landscape analysis of the dynamic benchmark functions commonly developed for multi-modal optimization. The benchmark analysis results reveal that the mechanisms responsible for dynamism in the current dynamic bench- marks do not significantly affect landscape features, thus suggesting a lack of representation for problems whose landscape features vary over time. This analysis is used in a comparison of current niching algorithms to identify the effects that specific landscape features have on niching performance. Two performance metrics are proposed to measure both the scalability and accuracy of the niching algorithms. The algorithm comparison results demonstrate the algorithms best suited for a variety of dynamic environments. This comparison also examines each of the algorithms in terms of their niching behaviours and analyzing the range and trade-off between scalability and accuracy when tuning the algorithms respective parameters. These results contribute to the understanding of current niching techniques as well as the problem features that ultimately dictate their success.

Players and Avatars: The Connections between Player Personality, Avatar Personality, and Behavior in Video Games

The increasing variety and complexity of video games allows players to choose how to behave and represent themselves within these virtual environments. The focus of this dissertation was to examine the connections between the personality traits (specifically, HEXACO traits and psychopathic traits) of video game players and player-created and controlled game-characters (i.e., avatars), and the link between traits and behavior in video games. In Study 1 (n = 198), the connections between player personality traits and behavior in a Massively Multiplayer Online Roleplaying Game (World of Warcraft) were examined. Six behavior components were found (i.e., Player-versus-Player, Social Player-versus-Environment, Working, Helping, Immersion, and Core Content), and each was related to relevant personality traits. For example, Player-versus-Player behaviors were negatively related to Honesty-Humility and positively related to psychopathic traits, and Immersion behaviors (i.e., exploring, role-playing) were positively related to Openness to Experience. In Study 2 (n = 219), the connections between player personality traits and in-game behavior in video games were examined in university students. Four behavior components were found (i.e., Aggressing, Winning, Creating, and Helping), and each was related to at least one personality trait. For example, Aggressing was negatively related to Honesty-Humility and positively related to psychopathic traits. In Study 3 (n = 90), the connections between player personality traits and avatar personality traits were examined in World of Warcraft. Positive player-avatar correlations were observed for all personality traits except Extraversion. Significant mean differences between players and avatars were observed for all traits except Conscientiousness; avatars had higher mean scores on Extraversion and psychopathic traits, but lower mean scores on the remaining traits. In Study 4, the connections between player personality traits, avatar traits, and observed behaviors in a life-simulation video game (The Sims 3) were examined in university students (n = 93). Participants created two avatars and used these avatars to play The Sims 3. Results showed that the selection of certain avatar traits was related to relevant player personality traits (e.g., participants who chose the Friendly avatar trait were higher in Honesty-Humility, Emotionality, and Agreeableness, and lower in psychopathic traits). Selection of certain character-interaction behaviors was related to relevant player personality traits (e.g., participants with higher levels of psychopathic traits used more Mean and fewer Friendly interactions). Together, the results of the four studies suggest that individuals generally behave and represent themselves in video games in ways that are consistent with their real-world tendencies.

An Abstract Algebraic Theory of L-Fuzzy Relations for Relational Databases

Classical relational databases lack proper ways to manage certain real-world situations including imprecise or uncertain data. Fuzzy databases overcome this limitation by allowing each entry in the table to be a fuzzy set where each element of the corresponding domain is assigned a membership degree from the real interval [0…1]. But this fuzzy mechanism becomes inappropriate in modelling scenarios where data might be incomparable. Therefore, we become interested in further generalization of fuzzy database into L-fuzzy database. In such a database, the characteristic function for a fuzzy set maps to an arbitrary complete Brouwerian lattice L. From the query language perspectives, the language of fuzzy database, FSQL extends the regular Structured Query Language (SQL) by adding fuzzy specific constructions. In addition to that, L-fuzzy query language LFSQL introduces appropriate linguistic operations to define and manipulate inexact data in an L-fuzzy database. This research mainly focuses on defining the semantics of LFSQL. However, it requires an abstract algebraic theory which can be used to prove all the properties of, and operations on, L-fuzzy relations. In our study, we show that the theory of arrow categories forms a suitable framework for that. Therefore, we define the semantics of LFSQL in the abstract notion of an arrow category. In addition, we implement the operations of L-fuzzy relations in Haskell and develop a parser that translates algebraic expressions into our implementation.

A Scalability Study and New Algorithms for Large-Scale Many-Objective Optimization

Many real-world optimization problems contain multiple (often conflicting) goals to be optimized concurrently, commonly referred to as multi-objective problems (MOPs). Over the past few decades, a plethora of multi-objective algorithms have been proposed, often tested on MOPs possessing two or three objectives. Unfortunately, when tasked with solving MOPs with four or more objectives, referred to as many-objective problems (MaOPs), a large majority of optimizers experience significant performance degradation. The downfall of these optimizers is that simultaneously maintaining a well-spread set of solutions along with appropriate selection pressure to converge becomes difficult as the number of objectives increase. This difficulty is further compounded for large-scale MaOPs, i.e., MaOPs possessing large amounts of decision variables. In this thesis, we explore the challenges of many-objective optimization and propose three new promising algorithms designed to efficiently solve MaOPs. Experimental results demonstrate the proposed optimizers to perform very well, often outperforming state-of-the-art many-objective algorithms.