Economic Feasibility in Commercial Egg Production in a Conventional and Cage-Free Systems with Different Stocking Densities

Adapting existing laying facilities to meet animal welfare certifications is not a simple task. It causes higher expenses to producers, who are often hesitant to accept the need for changes. Considerations of their financial situation make them insecure to make assertive decisions in this area, as they seek to maintain the economic efficiency of laying poultry. This study aims to analyze the economic viability of laying systems (conventional cages and Cage-Free) with different housing densities. The data source was a systematized literature review. Five articles were found containing reports on systems. Further data sources were the lineage handbook and a survey with companies specializing in poultry farming. An analysis of economic viability was performed in the multiple scenarios. Densities in each system were used to compose different scenarios: Conventional: 1,500 cm²/bird, 750 cm²/bird, and 398 cm²/bird; and Cage-Free:7 birds/m² and 13 birds/m². The scenarios were evaluated in terms of Net Present Value, Internal Rate of Return, Modified Internal Rate of Return, Discounted Payback Method, Profitability Index, Equivalent Uniform Annual Value, and Monte Carlo Simulation. The best scenario was the Conventional, with 398 cm²/bird, presenting a higher NPV. The Cage-Free system, with seven birds per square meter, had a lower NPV compared to conventional systems. All scenarios had satisfactory chances of success. The risks of negative or null financial return were low according to Monte Carlo simulations. Conventional and Cage-Free production are economically viable when using densities of 1,500 cm²/bird, 750 cm²/bird, or 398 cm²/bird (conventional), and 7 birds/m², or 13 birds/m² (Cage-Free).(AU)

X-Ray cardiac angiographic vessel segmentation based on pixel classification using machine learning and region growing.

This work proposes a pixel-classification approach for vessel segmentation in x-ray angiograms. The proposal uses textural features such as anisotropic diffusion, features based on the Hessian matrix, mathematical morphology and statistics. These features are extracted from the neighborhood of each pixel. The approach also uses the ELEMENT methodology, which consists of creating a pixel-classification controlled by region-growing where the result of the classification affects further classifications of pixels. The Random Forests classifier is used to predict whether the pixel belongs to the vessel structure. The approach achieved the best accuracy in the literature (95.48%) outperforming unsupervised state-of-the-art approaches.