Desempenho de soja associado à posição do fertilizante e tamanho de sementes

The seed size used for seeding has caused doubts among soybean producers. The study aimed to determine whether there may be differences between seed size with respect to depth of fertilizer deposition. The field experiment was conducted at the Experimental Area UTFPR Campus Pato Branco, using a precision seeder for direct seeding. The design was a randomized blocks, with five repetitions. The treatments were composed by the combination of two seed sizes (large seed with 6,5 mm and 5,5 mm with small seed) and two fertilizer deposition depths in relation to the seed (fertilizer near the seed with about 3 cm away and fertilizer distant from the seeds with about 10 cm). Data were subjected to analysis of variance. When the test value F was significant at 5% probability was applied to the Duncan test for comparison of means. The shallower depth of fertilizer deposition provided larger number of pods per plant and increased number of grains per plant. Already the largest depth of fertilizer deposition provided greater plant height at 30 days after sowing and R2 stage, greater ground area mobilized, higher plant population in all periods, greater depth of deposition of seeds and a higher rate of emergency speed.

Aplicação da metodologia numérica “fast bounds crack” para uma estimativa eficiente da evolução do tamanho de trinca

A significant part of the life of a mechanical component occurs, the crack propagation stage in fatigue. Currently, it is had several mathematical models to describe the crack growth behavior. These models are classified into two categories in terms of stress range amplitude: constant and variable. In general, these propagation models are formulated as an initial value problem, and from this, the evolution curve of the crack is obtained by applying a numerical method. This dissertation presented the application of the methodology "Fast Bounds Crack" for the establishment of upper and lower bounds functions for model evolution of crack size. The performance of this methodology was evaluated by the relative deviation and computational times, in relation to approximate numerical solutions obtained by the Runge-Kutta method of 4th explicit order (RK4). Has been reached a maximum relative deviation of 5.92% and the computational time was, for examples solved, 130,000 times more higher than achieved by the method RK4. Was performed yet an Engineering application in order to obtain an approximate numerical solution, from the arithmetic mean of the upper and lower bounds obtained in the methodology applied in this work, when you don’t know the law of evolution. The maximum relative error found in this application was 2.08% which proves the efficiency of the methodology "Fast Bounds Crack".

Detecção de região saliente em imagens usando dissimilaridade de cor e amostragem por pixeis aleatórios

The purpose of this work is to demonstrate and to assess a simple algorithm for automatic estimation of the most salient region in an image, that have possible application in computer vision. The algorithm uses the connection between color dissimilarities in the image and the image’s most salient region. The algorithm also avoids using image priors. Pixel dissimilarity is an informal function of the distance of a specific pixel’s color to other pixels’ colors in an image. We examine the relation between pixel color dissimilarity and salient region detection on the MSRA1K image dataset. We propose a simple algorithm for salient region detection through random pixel color dissimilarity. We define dissimilarity by accumulating the distance between each pixel and a sample of n other random pixels, in the CIELAB color space. An important result is that random dissimilarity between each pixel and just another pixel (n = 1) is enough to create adequate saliency maps when combined with median filter, with competitive average performance if compared with other related methods in the saliency detection research field. The assessment was performed by means of precision-recall curves. This idea is inspired on the human attention mechanism that is able to choose few specific regions to focus on, a biological system that the computer vision community aims to emulate. We also review some of the history on this topic of selective attention.