A Numerical Study of the FENE-CR Model Applied to a Jet Flow Problem

The FENE-CR model is investigated through a numerical algorithm to simulate the time-dependent moving free surface flow produced by a jet impinging on a flat surface. The objective is to demonstrate that by increasing the extensibility parameter L, the numerical solutions converge to the solutions obtained with the Oldroyd-B model. The governing equations are solved by an established free surface flow solver based on the finite difference and marker-and-cell methods. Numerical predictions of the extensional viscosity obtained with several values of the parameter L are presented. The results show that if the extensibility parameter L is sufficiently large then the extensional viscosities obtained with the FENE-CR model approximate the corresponding Oldroyd-B viscosity. Moreover, the flow from a jet impinging on a flat surface is simulated with various values of the extensibility parameter L and the fluid flow visualizations display convergence to the Oldroyd-B jet flow results.

Validation of analytical methodology for quantification of cefazolin sodium pharmaceutical dosage form by high performance liquid chromatography to be applied for quality control in pharmaceutical industry

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

Artificial Immune Systems Applied to the Analysis of Structural Integrity of a Building

This paper presents the application of artificial immune systems for analysis of the structural integrity of a building. Inspired by a biological process, it uses the negative selection algorithm to perform the identification and characterization of structural failure. This paper presents the application of artificial immune systems for analysis of the structural integrity of a building. Inspired by a biological process, it uses the negative selection algorithm to perform the identification and characterization of structural failure. This methodology can assist professionals in the inspection of mechanical and civil structures, to identify and characterize flaws, in order to perform preventative maintenance to ensure the integrity of the structure and decision-making. In order to evaluate the methodology was made modeling a two-story building and several situations were simulated (base-line condition and improper conditions), yielding a database of signs, which were used as input data for the negative selection algorithm. The results obtained by the present method efficiency, robustness and accuracy.

Artificial Immune Systems with Negative Selection Applied to Health Monitoring of Aeronautical Structures

In this paper we present a system for aircraft structural health monitoring based on artificial immune systems with negative selection. Inspired by a biological process, the principle of discrimination proper/non-proper, identifies and characterizes the signs of structural failure. The main application of this method is to assist in the inspection of aircraft structures, to detect and characterize flaws and decision making in order to avoid disasters. We proposed a model of an aluminum beam to perform the tests of the method. The results obtained by this method are excellent, showing robustness and accuracy.

Non-destructive thermal wave method applied to study thermal properties of fast setting time endodontic cement

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Effect of Silver Nanoparticles in a Hydroxyapatite Coating applied by Atmospheric Plasma Spray

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

An innovative material based on natural rubber and leather tannery waste to be applied as antistatic flooring

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

A comparative evaluation of the CF:CS and CRS models in Pb-210 chronological studies applied to hydrographic basins in Brazil

The Constant Flux: Constant Sedimentation (CF:CS) and Constant Rate of Supply (CRS) of unsupported/excess Pb-210 models have been applied to a Pb-210 data set providing of eighteen sediments profiles sampled at four riverine systems occurring in Brazil, South America: Corumbatai River basin (S1=Site 1, Sao Paulo State), Atibaia River basin (S2=Site 2, Sao Paulo State), Ribeirao dos Bagres basin (S3=Site 3, Sao Paulo State) and Amazon River mouth. (S4=Site 4, Amapa State). These sites were chosen for a comparative evaluation of the performance of the CF:CS and CRS models due to their pronounced differences on the geographical location, geological context, soil composition, biodiversity, climate, rainfall, and water flow regime, among other variable aspects. However, all sediments cores exhibited a common denominator consisting on a database built from the use of the same techniques for acquiring the sediments major chemical composition (SiO2, Al2O3, Na2O, K2O, CaO, MgO, Fe2O3, MnO, P2O5, TiO2 and LOI-Loss on Ignition) and unsupported/excess 210Pb activity data. In terms of sedimentation rates, the performance of the CRS model was better than that of the CF:CS model as it yielded values more compatible with those expected from field evidences. Under the chronological point of view, the CRS model always provided ages within the permitted range of the Pb-210-method in the studied sites, whereas the CF:CS model predicted some values above 150 years. The SiO2 content decreased in accordance with the LOI increase in all cores analyzed and such inverse relationship was also tracked in the SiO2-LOI curves of historical trends. The SiO2-LOI concentration fluctuations in sites S1 and S3 also coincided with some Cu and Cr inputs in the drainage systems. (C) 2014 Elsevier Ltd. All rights reserved.

Confirmatory factor analysis of different versions of the Body Shape Questionnaire applied to Brazilian university students

This study aimed at evaluating the validity, reliability, and factorial invariance of the complete (34-item) and shortened (8-item and 16-item) versions of the Body Shape Questionnaire (BSQ) when applied to Brazilian university students. A total of 739 female students with a mean age of 20.44 (standard deviation = 2.45) years participated. Confirmatory factor analysis was conducted to verify the degree to which the one-factor structure satisfies the proposal for the BSQ's expected structure. Two items of the 34-item version were excluded because they had factor weights (lambda)< 40. All models had adequate convergent validity (average variance extracted =.43-.58; composite reliability=.85-.97) and internal consistency (alpha =.85-.97). The 8-item B version was considered the best shortened BSQ version (Akaike information criterion = 84.07, Bayes information criterion = 157.75, Browne-Cudeck criterion= 84.46), with strong invariance for independent samples (Delta chi(2)lambda(7)= 5.06, Delta chi(2)Cov(8)= 5.11, Delta chi(2)Res(16) = 19.30). (C) 2014 Elsevier Ltd. All rights reserved.

Nonlinear formulation based on FEM, Mazars damage criterion and Fick's law applied to failure assessment of reinforced concrete structures subjected to chloride ingress and reinforcements corrosion

Structural durability is an important design criterion, which must be assessed for every type of structure. In this regard, especial attention must be addressed to the durability of reinforced concrete (RC) structures. When RC structures are located in aggressive environments, its durability is strongly reduced by physical/chemical/mechanical processes that trigger the corrosion of reinforcements. Among these processes, the diffusion of chlorides is recognized as one of major responsible of corrosion phenomenon start. To accurate modelling the corrosion of reinforcements and to assess the durability of RC structures, a mechanical model that accounts realistically for both concrete and steel mechanical behaviour must be considered. In this context, this study presents a numerical nonlinear formulation based on the finite element method applied to structural analysis of RC structures subjected to chloride penetration and reinforcements corrosion. The physical nonlinearity of concrete is described by Mazars damage model whereas for reinforcements elastoplastic criteria are adopted. The steel loss along time due to corrosion is modelled using an empirical approach presented in literature and the chloride concentration growth along structural cover is represented by Fick's law. The proposed model is applied to analysis of bended structures. The results obtained by the proposed numerical approach are compared to responses available in literature in order to illustrate the evolution of structural resistant load after corrosion start. (C) 2014 Elsevier Ltd. All rights reserved.

A smooth evolutionary structural optimization procedure applied to plane stress problem

Topological optimization problems based on stress criteria are solved using two techniques in this paper. The first technique is the conventional Evolutionary Structural Optimization (ESO), which is known as hard kill, because the material is discretely removed; that is, the elements under low stress that are being inefficiently utilized have their constitutive matrix has suddenly reduced. The second technique, proposed in a previous paper, is a variant of the ESO procedure and is called Smooth ESO (SESO), which is based on the philosophy that if an element is not really necessary for the structure, its contribution to the structural stiffness will gradually diminish until it no longer influences the structure; its removal is thus performed smoothly. This procedure is known as "soft-kill"; that is, not all of the elements removed from the structure using the ESO criterion are discarded. Thus, the elements returned to the structure must provide a good conditioning system that will be resolved in the next iteration, and they are considered important to the optimization process. To evaluate elasticity problems numerically, finite element analysis is applied, but instead of using conventional quadrilateral finite elements, a plane-stress triangular finite element was implemented with high-order modes for solving complex geometric problems. A number of typical examples demonstrate that the proposed approach is effective for solving problems of bi-dimensional elasticity. (C) 2014 Elsevier Ltd. All rights reserved.

The development of genomics applied to dairy breeding

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data

Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.

Optimum-Path Forest Applied for Breast Masses Classification

In Computer-Aided Diagnosis-based schemes in mammography analysis each module is interconnected, which directly affects the system operation as a whole. The identification of mammograms with and without masses is highly needed to reduce the false positive rates regarding the automatic selection of regions of interest for further image segmentation. This study aims to evaluate the performance of three techniques in classifying regions of interest as containing masses or without masses (without clinical findings), as well as the main contribution of this work is to introduce the Optimum-Path Forest (OPF) classifier in this context, which has never been done so far. Thus, we have compared OPF against with two sorts of neural networks in a private dataset composed by 120 images: Radial Basis Function and Multilayer Perceptron (MLP). Texture features have been used for such purpose, and the experiments have demonstrated that MLP networks have been slightly better than OPF, but the former is much faster, which can be a suitable tool for real-time recognition systems.