Genetic parameters for production traits of dairy Gyr (Bos indicus) x Holstein cattle estimated with a random regression model

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

Modeling of multiple cracks in reinforced concrete members using solid finite elements with high aspect ratio

This paper presents a new technique to model interfaces by means of degenerated solid finite elements, i.e., elements with a very high aspect ratio, with the smallest dimension corresponding to the thickness of the interfaces. It is shown that, as the aspect ratio increases, the element strains also increase, approaching the kinematics of the strong discontinuity. A tensile damage constitutive relation between strains and stresses is proposed to describe the nonlinear behavior of the interfaces associated with crack opening. To represent crack propagation, couples of triangular interface elements are introduced in between all regular (bulk) elements of the original mesh. With this technique the analyses can be performed integrally in the context of the continuum mechanics and complex crack patterns involving multiple cracks can be simulated without the need of tracking algorithms. Numerical tests are performed to show the applicability of the proposed technique, studding also aspects related to mesh objectivity.

Random regression models using different functions to model milk flow in dairy cows

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

Non-destructive linear model for leaf area estimation in Vernonia ferruginea Less

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

Optimal clearance of Sporothrix schenckii requires an intact Th17 response in a mouse model of systemic infection

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

Precise determination of the mass of the Higgs boson and tests of compatibility of its couplings with the standard model predictions using proton collisions at 7 and 8 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Incorporating priorities for waiting customers in the hypercube queuing model with application to an emergency medical service system in Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Down-regulation of protease activated receptor 2, interleukin-17 and other pro-inflammatory genes by subantimicrobial doxycycline dose in a rat periodontitis model

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Clinical evaluation of multiple-surface ART restorations: three-year follow-up

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

Desempenho de bovinos cruzados terminados em confinamento e qualidade dos músculos longissimus e semitendinosus in natura e maturados

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

The use of artificial neural networks in analysing the nutritional ecology of Chrysomya megacephala (F.) (Diptera: Calliphoridae), compared with a statistical model

Artificial neural networks (ANNs) have been widely applied to the resolution of complex biological problems. An important feature of neural models is that their implementation is not precluded by the theoretical distribution shape of the data used. Frequently, the performance of ANNs over linear or non-linear regression-based statistical methods is deemed to be significantly superior if suitable sample sizes are provided, especially in multidimensional and non-linear processes. The current work was aimed at utilising three well-known neural network methods in order to evaluate whether these models would be able to provide more accurate outcomes in relation to a conventional regression method in pupal weight predictions of Chrysomya megacephala, a species of blowfly (Diptera: Calliphoridae), using larval density (i.e. the initial number of larvae), amount of available food and pupal size as input data. It was possible to notice that the neural networks yielded more accurate performances in comparison with the statistical model (multiple regression). Assessing the three types of networks utilised (Multi-layer Perceptron, Radial Basis Function and Generalised Regression Neural Network), no considerable differences between these models were detected. The superiority of these neural models over a classical statistical method represents an important fact, because more accurate models may clarify several intricate aspects concerning the nutritional ecology of blowflies.