Path analysis and expected response in indirect selection for grain yield in soybean

This study aimed to determine the best auxiliary trait for indirect selection of soybean grain yield, through path analysis and in avoidance of the adverse effects of multicollinearity and expected response. Seventy-nine F5 soybean genotypes from the cross FT-Cometa x Bossier were used. The populations were distributed on the field was the families inserted with replicated controls. Primary and secondary traits of grain yield were evaluated in four phenotypically superior plants per family. The traits number of pods, height and number of nodes were considered as the most important, showing the best combination of direct effect and genotypic correlation. The number of pods achieved the highest expected gain through the estimation method based on the selection differential. On the other hand, plant height, by the method based on selection intensity, was not a good indicator of the most productive plants.

Automatic segmentation of the secondary austenite-phase island precipitates in a superduplex stainless steel weld metal

Duplex and superduplex stainless steels are class of materials of a high importance for engineering purposes, since they have good mechanical properties combination and also are very resistant to corrosion. It is known as well that the chemical composition of such steels is very important to maintain some desired properties. In the past years, some works have reported that γ 2 precipitation improves the toughness of such steels, and its quantification may reveals some important information about steel quality. Thus, we propose in this work the automatic segmentation of γ 2 precipitation using two pattern recognition techniques: Optimum-Path Forest (OPF) and a Bayesian classifier. To the best of our knowledge, this if the first time that machine learning techniques are applied into this area. The experimental results showed that both techniques achieved similar and good recognition rates. © 2012 Taylor & Francis Group.

Correlation and path analysis as an indirect selection criterion for sunflower achene productivity

The study was conducted in two different locations in South Brazil, in tillage in the 2009/2010 season on eight sunflower hybrids, aiming to determine the path correlations and coefficients between primary and secondary characters on the main variable of achene productivity. The correlations were similar between environments. The characters of the head diameter and mass of a thousand achenes had a significant influence on sunflower productivity. Based on the magnitude of the direct and indirect effects, we highlighted all primary components on the main variable, beside the good determination coefficient and low residual effect. The secondary component, the number of achenes, despite the significant direct effect on productivity, was indirectly influenced by the primary components, making it an undesirable character for selection.

Ultrasonic sensor signals and optimum path forest classifier for the microstructural characterization of thermally-aged inconel 625 alloy

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)