Multivariate analysis of fresh-cut carambola slices stored under different temperatures

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Efeito do período de jejum na fase final de crição de frangos de corte machos sobre rendimento e composição da carcaça

The objective of this work was to evaluate the effect of fasting period in the last growing phase on carcass yield and composition of male broilers. Two thousand one-day old male chicks were distributed in five randomized blocks according to a 4x2 factorial (four feeding programs (P): ad libitum or one of three fasting schedules: 8-12, 12-16 and 8-16; and two strains (S): Ross or Hubbard-Peterson, Fifty birds were used per replicate. Birds were raised under identical feed and management conditions until day 42. The fasting schedules were applied from day 43 to day 56. At day 56, five birds per replicate were randomly sampled, weighed, slaughtered, eviscerated, dry-cooled, cut and deboned. No effects of P or SxP interaction were observed for carcass characteristics. birds, which showed higher weights and yields of head plus neck, feet, leg bones and wings. The ad libitum birds showed higher crude protein in thigh meat than those submitted to the 8-12 h fast. A SxP interaction was observed for meat ash content. The R broilers showed higher ash content in breast and thigh meat than the H birds in the 8-12 h fast treatment. on the other hand, the R broilers submitted to the 8-12 h fast showed higher ash contents in breast and thigh meat than birds from the same strain in the other feeding programs. Fasting in the last phase of rearing did not alter the yield of whole carcass, carcass cuts and abdominal fat, but morning fast influenced carcass chemical composition.

Qualidade da cana-de-açúcar (Saccharum spp) submetida à aplicação de maturadores químicos em final de safra

Pós-graduação em Agronomia (Produção Vegetal) - FCAV

Image segmentation through combined methods: watershed transform, unsupervised distance learning and normalized cut

Research on image processing has shown that combining segmentation methods may lead to a solid approach to extract semantic information from different sort of images. Within this context, the Normalized Cut (NCut) is usually used as a final partitioning tool for graphs modeled in some chosen method. This work explores the Watershed Transform as a modeling tool, using different criteria of the hierarchical Watershed to convert an image into an adjacency graph. The Watershed is combined with an unsupervised distance learning step that redistributes the graph weights and redefines the Similarity matrix, before the final segmentation step using NCut. Adopting the Berkeley Segmentation Data Set and Benchmark as a background, our goal is to compare the results obtained for this method with previous work to validate its performance.