Uso de redes neurais artificiais na identificação de vocalização de suínos

Animal welfare has been an important research topic in animal production mainly in its ways of assessment. Vocalization is found to be an interesting tool for evaluating welfare as it provides data in a non-invasive way as well as it allows easy automation of process. The present research had as objective the implementation of an algorithm based on artificial neural network that had the potential of identifying vocalization related to welfare pattern indicatives. The research was done in two parts, the first was the development of the algorithm, and the second its validation with data from the field. Previous records allowed the development of the algorithm from behaviors observed in sows housed in farrowing cages. Matlab® software was used for implementing the network. It was selected a retropropagation gradient algorithm for training the network with the following stop criteria: maximum of 5,000 interactions or error quadratic addition smaller than 0.1. Validation was done with sows and piglets housed in commercial farm. Among the usual behaviors the ones that deserved enhancement were: the feed dispute at farrowing and the eventual risk of involuntary aggression between the piglets or between those and the sow. The algorithm was able to identify through the noise intensity the inherent risk situation of piglets welfare reduction.

Insegurança alimentar em domicílios brasileiros com crianças menores de cinco anos

This article analyzes food insecurity and hunger in Brazilian families with children under five years of age. This was a nationally representative cross-sectional study using data from the National Demographic and Health Survey on Women and Children (PNDS-2006), in which the outcome variable was moderate to severe food insecurity, measured by the Brazilian Food Insecurity Scale (EBIA). Prevalence estimates and prevalence ratios were generated with 95% confidence intervals. The results showed a high prevalence of moderate to severe food insecurity, concentrated in the North and Northeast regions (30.7%), in economic classes D and E (34%), and in beneficiaries of conditional cash transfer programs (36.5%). Multivariate analysis showed that the socioeconomic relative risks (beneficiaries of conditional cash transfers), regional relative risks (North and Northeast regions), and economic relative risks (classes D and E) were 1.8, 2.0 and 2.4, respectively. Aggregation of the three risks showed 48% of families with moderate to severe food insecurity, meaning that adults and children were going hungry during the three months preceding the survey.

Avaliação postural em escolares das redes estadual e particular de ensino de primeiro grau

Universidade Estadual de Campinas . Faculdade de Educação Física