Partioning of dietary energy of chickens fed maize or wheat-based diets with and without a commercial blend of phytogenic feed additives

The aim of the study was to investigate the effects of a standardized mixture of a commercial blend of phytogenic feed additives containing 5% carvacrol, 3% cinnamaldehyde, and 2% capsicum on utilization of dietary energy and performance in broiler chickens. Four experimental diets were offered to the birds from 7 to 21 d of age. These included 2 basal control diets based on either wheat or maize that contained 215 g CP/kg and 12.13 MJ/kg ME and another 2 diets using the basal control diets supplemented with the plant extracts combination at 100 mg/kg diet. Each diet was fed to 16 individually penned birds following randomization. Dietary plant extracts improved feed intake and weight gain (P < 0.05) and slightly (P < 0.1) improved feed efficiency of birds fed the maize-based diet. Supplementary plant extracts did not change dietary ME (P > 0.05) but improved (P < 0.05) dietary NE by reducing the heat increment (P < 0.05) per kilogram feed intake. Feeding phytogenics improved (P < 0.05) total carcass energy retention and the efficiency of dietary ME for carcass energy retention. The number of interactions between type of diet and supplementary phytogenic feed additive suggest that the chemical composition and the energy to protein ratio of the diet may influence the efficiency of phytogenics when fed to chickens. The experiment showed that although supplementary phytogenic additives did not affect dietary ME, they caused a significant improvement in the utilization of dietary energy for carcass energy retention but this did not always relate to growth performance.

Superpixel-based roughness measure for multispectral satellite image segmentation

The new generation of artificial satellites is providing a huge amount of Earth observation images whose exploitation can report invaluable benefits, both economical and environmental. However, only a small fraction of this data volume has been analyzed, mainly due to the large human resources needed for that task. In this sense, the development of unsupervised methodologies for the analysis of these images is a priority. In this work, a new unsupervised segmentation algorithm for satellite images is proposed. This algorithm is based on the rough-set theory, and it is inspired by a previous segmentation algorithm defined in the RGB color domain. The main contributions of the new algorithm are: (i) extending the original algorithm to four spectral bands; (ii) the concept of the superpixel is used in order to define the neighborhood similarity of a pixel adapted to the local characteristics of each image; (iii) and two new region merged strategies are proposed and evaluated in order to establish the final number of regions in the segmented image. The experimental results show that the proposed approach improves the results provided by the original method when both are applied to satellite images with different spectral and spatial resolutions.