Disordered Eating Risk and Body Image Dissatisfaction of Division I Male and Female Cheerleaders

The sport of cheerleading is growing both in the high school and college setting, however there is little research on cheerleaders specifically, both sideline and competitive. It is clear that while this sport does not benefit from being affiliated with the NCAA, the athletes are still at large risk for disordered eating and eating disorders, and are in need of more accurate screening and prevention methods. With the lack of cheerleading studies in general, there is an even larger scarcity of studies that focus on males in cheerleading. The current study aims to fill the gap in the research regarding disordered eating risk in both male and female Division I cheerleaders by analyzing the perceived level of body satisfaction. Therefore, the purpose of this study is to gain more awareness on the body perceptions of collegiate cheerleaders, and investigate if male cheerleaders suffer from similar levels of disordered eating and body image issues as compared to their female counterparts

An Examination of Sport Consumers\u27 Twitter Usage

In the sport industry, many stakeholders, including sport organizations, players, coaches, sports reporters, and fans, utilize Twitter. Twitter has become a practical marketing tool, in part, although Twitter users have not been studied in terms of sociodemographics, team identification, media consumption, team related Twitter consumption, or game consumption of their favorite team. Exploring the demographics and consumptive behavior of Twitter users can be valuable for sport organizations to create marketing plans and make managerial decisions. The purpose of this study was to determine the makeup of sport consumers on Twitter for market segmentation purposes and examine their sport media consumption levels, sport-related Twitter usage, team identification level, and team consumption. Differences between Generation X and Y consumers were also determined. An online survey was administered to Twitter users (N = 219). Descriptive statistics, chi-square analyses and MANOVAs revealed characteristics about the users

An Analysis of Collegiate Athletic Department Social Media Practices, Strategies, and Challenges

Similar to professional sport organizations, intercollegiate athletic programs frequently use social media to reach consumers. However, athletic departments face unique challenges, such as simultaneously managing multiple teams’ social accounts and strategies, while monitoring and advising the socialactivity of student-athletes and coaches. The tactics used to interact with consumers and challenges of using social media have yet to be studied from an athletic department point of view. The purpose of this study was to explore intercollegiate athletic departments’ social media usage patterns, strategies, and challenges. Seven college athletic departments were studied via personal interviews with staff members. The results suggested that while schools are primarily utilizing two forms of social media (Facebook and Twitter), they lack a clear communication strategy for use. They typically used Facebook and Twitter differently to interact with consumers, but regardless of medium, they highlighted the value of consistency through controlling the message, account names, hashtags, and direct communication. Their biggest concerns were staying abreast of the changing landscape of social media and staffing to meet these needs. The importance of being in the digital space is critical for sport marketers, yet the athletic departments interviewed for this study failed to incorporate their social media as part of a greater communication, branding, or marketing plan

Numerical Solution of Optimization Test-Cases by Genetic Algorithms

In this report, we present the numerical solution of four optimization problems by Genetic Algorithms (GAs). The test-cases involve two single-object- ive and two multi-objective optimization problems. In all four cases, the analytical functions to be optimized present a large number of local optima and the GA is demonstrated to be the most adequate optimizer. These four test-cases are part of the database developed within the INGENET European thematic network

Quels sont les patients atteints d'un cancer du sein dont la décision de prise en charge thérapeutique bénéficie de l'utilisation d'un système d'aide à la décision ? Un exemple utilisant la fouille de données et OncoDoc2

Session 2 : Utilisateurs et usagesNational audienceOncoDoc2 est un système d'aide à la décision (SAD) s'appuyant sur des recommandations de pratique clinique (RPC) pour la prise en charge des cancers du sein. Il a été utilisé comme intervention dans un essai randomisé contrôlé dont l'objectif principal était d'évaluer son impact sur la conformité des décisions des réunions de concertation pluridisciplinaire aux RPC. Nous avons utilisé un algorithme de fouille de données pour découvrir les régularités des profils patients, ou " motifs émergents " (ME), associées à la conformité et à la non-conformité des décisions selon que le système OncoDoc2 était ou non utilisé, afin d'évaluer quels profils patients pouvaient bénéficier de l'utilisation du système. Les ME associés à la non conformité des décisions prises sans le système sont associées à la conformité quand le système est utilisé sauf dans certaines situations cliniques pour lesquelles la force de la recommandation est faible

Sport and social media research: A review

The emergence of social media has profoundly impacted the delivery and consumption of sport. In the current review we analysed the existing body of knowledge of social media in the field of sport management from a service-dominant logic perspective, with an emphasis on relationship marketing. We reviewed 70 journal articles published in English-language sport management journals, which investigated new media technologies facilitating interactivity and co-creation that allow for the development and sharing of user-generated content among and between brands and individuals (i.e., social media). Three categories of social media research were identified: strategic, operational, and user-focussed. The findings of the review demonstrate that social media research in sport management aligns with service-dominant logic and illustrates the role of social media in cultivating relationships among and between brands and individuals. Interaction and engagement play a crucial role in cultivating these relationships. Discussion of each category, opportunities for future research as well as suggestions for theoretical approaches, research design and context are advanced

Recovering and Characterizing Image Features Using An Efficient Model Based Approach

Edges, corners and vertices are strong and useful features in computer vision. This paper deals with the development of an efficient model based approach in order to detect and characterize precisely these important features. The key of our approach is first to propose some efficient models associated to each of these features and second to efficiently extract and characterize these features directly from the image. The models associated to each feature include a large number of intrinsic parameters (Grey level intensities, location, orientation of the line segments... ) but also an important parameter which is associated to the blurring effect due to the acquisition system. The important problem of the initialization phase in the minimization process is also considered and an original and efficient solution is proposed. In order to test and compare the reliability, the robustness and the efficiency of the different proposed approaches, a large number of experiments involving noisy..

A Model Based Method for Characterization and Location of Curved Image Features

This report deals with the development of a parametric model based method to locate and characterize precisely important curved features such as ellipses and B-splines based curves. The method uses all the grey level information of the pixels contained within a window around the feature of interest and produces the complete parametric model that best approximates in a mean-square sense the observed grey level image intensities within the working area. Different solutions have been developed to reduce the computational time required by such approaches and a large number of experiments involving real images have been carried out in order to test and compare the reliability, the robustness and the efficiency of the different proposed approaches

Why Teams Rebrand: Uncovering the Motives and Process of Team Rebranding Initiatives

While rebranding is very common in professional sport, little research has been devoted to this topic. In particular, no studies have examined the reasons why teams decide to make changes to their names, logos, and/or colors, and the process they go through while doing so. Through content analysis of public statements from teams and interviews with team executives, this study found five common reasons for rebranding, as well as recommendations from team executives for how to handle this process. Based on the results, practical implications are provided for teams who may be deciding to engage in some aspect of rebranding. Subscribe to JAS

Classification of Fixed Point Network Dynamics from Multiple Node Timeseries Data

Fixed point networks are dynamic networks encoding stimuli via distinct output patterns. Although, such networks are common in neural systems, their structures are typically unknown or poorly characterized. It is thereby valuable to use a supervised approach for resolving how a network encodes inputs of interest and the superposition of those inputs from sampled multiple node time series. In this paper, we show that accomplishing such a task involves finding a low-dimensional state space from supervised noisy recordings. We demonstrate that while standard methods for dimension reduction are unable to provide optimal separation of fixed points and transient trajectories approaching them, the combination of dimension reduction with selection (clustering) and optimization can successfully provide such functionality. Specifically, we propose two methods: Exclusive Threshold Reduction (ETR) and Optimal Exclusive Threshold Reduction (OETR) for finding a basis for the classification state space. We show that the classification space—constructed through the combination of dimension reduction and optimal separation—can directly facilitate recognition of stimuli, and classify complex inputs (mixtures) into similarity classes. We test our methodology on a benchmark data-set recorded from the olfactory system. We also use the benchmark to compare our results with the state-of-the-art. The comparison shows that our methods are capable to construct classification spaces and perform recognition at a significantly better rate than previously proposed approaches