Verifying the association between body image dissatisfaction and goals to the physical activity practice. The sample consisted of 299 goers of fitness center, of both genders, with ages between 16 and 50 years old. Information about gender, age, educational level, socioeconomic level and goals for the physical activity practice were collected using a previously tests questionnaire. Regarding the goals, the participants should identify if the motivation for the practice was related to aspects health, aesthetic or social relationships aspects, choosing yes or no. The body image was determined using the set of silhouettes and to verifying the body image dissatisfaction we considered the difference between the current silhouette and the ideal silhouette pointed out by the individuals. Numerical variables were expressed as mean ± standard deviation and categorical variables as absolute and relative frequency. Pearson's chi-square test was used to verify the associations among goals to the practice, gender, and age group. Analysis of variance was used to verify the associations between the goals to the practice and the body image dissatisfaction. There was a low statistically significant correlation between body mass index and body image dissatisfaction (r s 0.29 -p < 0.001). The proportion of women (69%) who practiced physical activity with aesthetics goals was higher than that of men (31%). In addition, 92% of the sample showed body image dissatisfaction. Regarding the goals to the physical activity practice, men aimed more muscular body while the women wished more lean body. Men and women have different perceptions regarding body image.
os pedidos de execução de aplicações na arquitetura cloud e no paradigma fog são geralmente heterogéneos e o escalonamento nessas arquiteturas é um problema de otimização com múltiplas restrições. Neste artigo, fizemos um levantamento sobre os trabalhos relacionados com o escalonamento na arquitetura cloud e no paradigma fog, identificamos as suas limitações, explorarmos perspetivas de melhorias e propomos um modelo de escalonamento sensíveis ao contexto para o paradigma fog. A solução proposta utiliza a normalização Min-Max, para resolver a heterogeneidade e normalizar os diferentes parâmetros de contexto. A prioridade dos pedidos é definida através da aplicação da técnica de análise de Regressão Linear Múltipla e o escalonamento é feito utilizando a técnica de Otimização de Programação Não Linear Multiobjetivo. Os resultados obtidos a partir de simulações no kit de ferramentas iFogSim, demonstram que a nossa proposta apresenta um melhor desempenho em comparação com as propostas não sensíveis ao contexto.
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