Aspectos teórico-numéricos dos métodos SPH e MPS

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Multi-objective evolutionary particle swarm optimization in the assessment of the impact of distributed generation

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

The effects of alternative forced-molting methods on the performance and egg quality of commercial layers

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

Development and application of methods for determination of residual monomer in dental acrylic resins using high performance liquid chromatography

Two high-performance liquid chromatographic methods for determination of residual monomer in dental acrylic resins are described. Monomers were detected by their UV absorbance at 230 nm, on a Nucleosil((R)) C-18 (5 mu m particle size, 100 angstrom pore size, 15 x 0.46 cm i.d.) column. The separation was performed using acetonitrile-water (55:45 v/v) containing 0.01% triethylamine (TEA) for methyl methacrylate and butyl methacrylate, and acetonitrile-water (60:40 v/v) containing 0.01% TEA for isobutyl methacrylate and 1,6-hexanediol dimethacrylate as mobile phases, at a flow rate of 0.8 mL/min. Good linear relationships were obtained in the concentration range 5.0-80.0 mu g/mL for methyl methacrylate, 10.0-160.0 mu g/mL for butyl methacrylate, 50.0-500.0 mu g/mL for isobutyl methacrylate and 2.5-180.0 mu g/mL for 1,6-hexanediol dimethacrylate. Adequate assay for intra- and inter-day precision and accuracy was observed during the validation process. An extraction procedure to remove residual monomer from the acrylic resins was also established. Residual monomer was obtained from broken specimens of acrylic disks using methanol as extraction solvent for 2 h in an ice-bath. The developed methods and the extraction procedure were applied to dental acrylic resins, tested with or without post-polymerization treatments, and proved to be accurate and precise for the determination of residual monomer content of the materials evaluated. Copyright (c) 2005 John Wiley & Sons, Ltd.

EFFECT of DIFFERENT AIRBORNE-PARTICLE ABRASION/BONDING AGENT COMBINATIONS on THE BOND STRENGTH of A RESIN CEMENT TO A BASE METAL ALLOY

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

Effect of Airborne-Particle Abrasion and Mechanico-Thermal Cycling on the Flexural Strength of Glass Ceramic Fused to Gold or Cobalt-Chromium Alloy

Purpose: To evaluate the effect of airborne-particle abrasion and mechanico-thermal cycling on the flexural strength of a ceramic fused to cobalt-chromium alloy or gold alloy.Materials and Methods: Metallic bars (n = 120) were made (25 mm x 3 mm x 0.5 mm): 60 with gold alloy and 60 with Co-Cr. At the central area of the bars (8 mm x 3 mm), a layer of opaque ceramic and then two layers of glass ceramic (Vita VM13, Vita Zahnfabrick) were fired onto it (thickness: 1 mm). Ten specimens from each alloy group were randomly allocated to a surface treatment [(tungsten bur or air-particle abrasion (APA) with Al(2)O(3) at 10 mm or 20 mm away)] and mechanico-thermal cycling (no cycling or mechanically loaded 20,000 cycles; 10 N distilled water at 37 degrees C and then thermocycled 3000 cycles; 5 degrees C to 55 degrees C, dwell time 30 seconds) combination. Those specimens that did not undergo mechanico-thermal cyclingwere stored inwater (37 degrees C) for 24 hours. Bond strength was measured using a three-point bend test, according to ISO 9693. After the flexural strength test, failure types were noted. The data were analyzed using three factor-ANOVA and Tukey's test (alpha = 0.05).Results: There were no significant differences between the flexural bond strength of gold and Co-Cr groups (42.64 +/- 8.25 and 43.39 +/- 10.89 MPa, respectively). APA 10 and 20 mm away surface treatment (45.86 +/- 9.31 and 46.38 +/- 8.89 MPa, respectively) had similar mean flexural strength values, and both had significantly higher bond strength than tungsten bur treatment (36.81 +/- 7.60 MPa). Mechanico-thermal cycling decreased the mean flexural strength values significantly for all six alloy-surface treatment combinations tested when compared to the control groups. The failure type was adhesive in the metal/ceramic interface for specimens surface treated only with the tungsten bur, and mixed for specimens surface treated with APA 10 and 20 mm.Conclusions: Considering the levels adopted in this study, the alloy did not affect the bond strength; APA with Al(2)O(3) at 10 and 20 mm improved the flexural bond strength between ceramics and alloys used, and the mechanico-thermal cycling of metal-ceramic specimens resulted in a decrease of bond strength.

Semiclassical particle spectrum of double sine-Gordon model

We present new theoretical results on the spectrum of the quantum field theory of the double sine-Gordon model. This non-integrable model displays different varieties of kink excitations and bound states thereof. Their mass can be obtained by using a semiclassical expression of the matrix elements of the local fields. In certain regions of the coupling-constants space the semiclassical method provides a picture which is complementary to the one of the form factor perturbation theory, since the two techniques give information about the mass of different types of excitations. In other regions the two methods are comparable, since they describe the same kind of particles. Furthermore, the semiclassical picture is particularly suited to describe the phenomenon of false vacuum decay, and it also accounts in a natural way the presence of resonance states and the occurrence of a phase transition. (C) 2004 Elsevier B.V. All rights reserved.

Particle Competition and Cooperation in Networks for Semi-Supervised Learning

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

Statistical methods applied to the analysis of blasting vibrations in a diabase quarry in the city of Campinas, (SP) Brazil

Statistical methods of multiple regression analysis, trend surface analysis and principal components analysis were applied to seismographic data recorded during production blasting at a diabase quarry in the urban area of Campinas (SP), Brazil. The purpose of these analyses was to determine the influence of the following variables: distance (D), charge weight per delay (W), and scaled distance (SD) associated with properties of the rock body (orientation, frequency and angle of geological discontinuities; depth of bedrock and thickness of the soil overburden) in the variation of the peak particle velocity (PPV). This approach yielded variables with larger influences (loads) on the variation of ground vibration, as well as behavior and space tendency of this variation. The results showed a better relationship between PPV and D, with D being the most important factor in the attenuation of the ground vibrations. The geological joints and the depth to bedrock have a larger influence than the explosive charges in the variation of the vibration levels, but frequencies appear to be more influenced by the amount of soil overburden.

Particle Competition and Cooperation in Networks for Semi-Supervised Learning with Concept Drift

Concept drift is a problem of increasing importance in machine learning and data mining. Data sets under analysis are no longer only static databases, but also data streams in which concepts and data distributions may not be stable over time. However, most learning algorithms produced so far are based on the assumption that data comes from a fixed distribution, so they are not suitable to handle concept drifts. Moreover, some concept drifts applications requires fast response, which means an algorithm must always be (re) trained with the latest available data. But the process of labeling data is usually expensive and/or time consuming when compared to unlabeled data acquisition, thus 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 also based on the assumption that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenge in machine learning. Recently, a particle competition and cooperation approach was used to realize graph-based semi-supervised learning from static data. In this paper, we extend that approach to handle data streams and concept drift. The result is a passive algorithm using a single classifier, which naturally adapts to concept changes, without any explicit drift detection mechanism. Its built-in mechanisms provide a natural way of learning from new data, gradually forgetting older knowledge as older labeled data items became less influent on the classification of newer data items. Some computer simulation are presented, showing the effectiveness of the proposed method.

Methods to estimate the number of surface nucleation sites on glass particles

Based on the Johnson-Mehl-Avrami-Kolmogorov (JMAK) theory, we propose two new models to describe the crystallisation kinetics of glass particles and use them to determine the density of nucleation sites, N(s), on glass powders. We tested these models with sintered compacts of diopside glass particles using sinter-crystallisation treatments at 825 degrees C (T(g)similar to 727 degrees C), that covered from null to almost 100% crystallised volume time fraction. We measured and compared the evolution of the crystallised volume fractions by optical microscopy and x-ray diffraction. Then we fit our expressions to experimental data using Ns and R (the average particle radius) as adjustable parameters. For comparison, we also fit to our data existing expressions that describe the crystallised volume fraction in glass powders. We demonstrate that all the methods allow one to estimate N(s) with reasonable accuracy. For our ground and water washed diopside glass powder, N(s) is between 10(10)-10(11) sites.m(-2). The reasonable agreement between experimental and adjusted R confirms the consistency of all five models tested. However, one of our equations does not require taking into account the change of crystallisation mode from 3-dimensional to 1-dimensional, and this is advantageous.

Polishing methods of an alumina-reinforced feldspar ceramic

The purpose of this study was to test the hypothesis that mechanical polishing methods of ceramic surfaces allow similar superficial roughness to that of glazed surfaces. Twenty-five Vitadur Alpha ceramic discs (5 mm x 2 mm) were prepared according to the manufacturer's specifications. All specimens were glazed and randomly assigned to 5 groups (n=5), according to finishing and polishing protocols: G1: glazed (control); G2: diamond bur finishing; G3: G2 + silicon rubber tip polishing; G4: G3 + felt disc/diamond polishing paste; G5: G3 + felt disc impregnated with fine-particle diamond paste. Next, surface roughness means (Ra - μm) were calculated. Qualitative analysis was made by scanning electron microscopy. Surface roughness data were submitted to ANOVA and Tukey's test at 5% significance level. G1 and G4 were statistically similar (p>0.05). G2 presented the highest roughness means (p<0.05) followed by groups G3, G5, G4 and G1 in a decreasing order. The hypothesis was partially confirmed as only the mechanical polishing (G4) produced similar superficial roughness to that of surface glazing, although finishing and polishing are technically critical procedures.

Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm

This paper describes an investigation of the hybrid PSO/ACO algorithm to classify automatically the well drilling operation stages. The method feasibility is demonstrated by its application to real mud-logging dataset. The results are compared with bio-inspired methods, and rule induction and decision tree algorithms for data mining. © 2009 Springer Berlin Heidelberg.

Particle competition and cooperation to prevent error propagation from mislabeled data in semi-supervised learning

Semi-supervised learning is applied to classification problems where only a small portion of the data items is labeled. In these cases, the reliability of the labels is a crucial factor, because mislabeled items may propagate wrong labels to a large portion or even the entire data set. This paper aims to address this problem by presenting a graph-based (network-based) semi-supervised learning method, specifically designed to handle data sets with mislabeled samples. The method uses teams of walking particles, with competitive and cooperative behavior, for label propagation in the network constructed from the input data set. The proposed model is nature-inspired and it incorporates some features to make it robust to a considerable amount of mislabeled data items. Computer simulations show the performance of the method in the presence of different percentage of mislabeled data, in networks of different sizes and average node degree. Importantly, these simulations reveals the existence of the critical points of the mislabeled subset size, below which the network is free of wrong label contamination, but above which the mislabeled samples start to propagate their labels to the rest of the network. Moreover, numerical comparisons have been made among the proposed method and other representative graph-based semi-supervised learning methods using both artificial and real-world data sets. Interestingly, the proposed method has increasing better performance than the others as the percentage of mislabeled samples is getting larger. © 2012 IEEE.

Fuzzy community structure detection by particle competition and cooperation

Identification and classification of overlapping nodes in networks are important topics in data mining. In this paper, a network-based (graph-based) semi-supervised learning method is proposed. It is based on competition and cooperation among walking particles in a network to uncover overlapping nodes by generating continuous-valued outputs (soft labels), corresponding to the levels of membership from the nodes to each of the communities. Moreover, the proposed method can be applied to detect overlapping data items in a data set of general form, such as a vector-based data set, once it is transformed to a network. Usually, label propagation involves risks of error amplification. In order to avoid this problem, the proposed method offers a mechanism to identify outliers among the labeled data items, and consequently prevents error propagation from such outliers. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method. © 2012 Springer-Verlag.