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.

Synthesis and characterization of lead zirconate titanate (PZT) obtained by two chemical methods

Lead zirconate titanate (PZT) was synthesized at the ratio of Zr/Ti=52/48 using two synthesis methods: the polymeric precursor method (PPM) and the microwave-assisted hydrothermal method (MAHM). The synthesized materials were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), particle size distribution by sedimentation, hysteresis measurements and photoluminescence (PL). The results showed that PZT powders are composed of tetragonal and rhombohedral phases. Different particle sizes and morphologies were obtained depending upon the synthesis method. From the hysteresis loop verified that PZT powders synthesized by the PPM have a typical loop of ferroelectric material and are more influenced by spatial charges while particles synthesized by the MAHM have a hysteresis loop similar to paraelectric material and are less influenced by spatial charges. Both samples showed PL behavior in the green region (525 nm) whereas the sample synthesized by the PPM showed higher intensity in spectra. © 2013 Elsevier Ltd and Techna Group S.r.l.

Desenvolvimento de software utilizando a técnica sph (smoothed particle hydrodynamics) na geração de ondas de submersão

Pós-graduação em Engenharia Elétrica - FEIS

Influence of air-particle deposition protocols on the surface topography and adhesion of resin cement to zirconia

Objectives: This study evaluated the influence of air-particle abrasion protocols on the surface roughness (SR) of zirconia and the shear bond strength (SBS) of dual-polymerized resin cement to this ceramic. Materials and methods. Sintered zirconia blocks (n = 115) (Lava, 3M ESPE) were embedded in acrylic resin and polished. The specimens were divided according to the 'particle type' (Al: 110 mu m Al2O3; Si: 110 mu m SiO2) and 'pressure' factors (2.5 or 3.5 bar) (n = 3 per group): (a) Control (no air-abrasion); (b) Al2.5; (c) Si2.5; (d) Al3.5; (e) Si3.5. SR (Ra) was measured 3-times from each specimen after 20 s of air-abrasion (distance: 10 mm) using a digital optical profilometer. Surface topography was evaluated under SEM analyses. For the SBS test, 'particle type', 'pressure' and 'thermocycling' (TC) factors were considered (n = 10; n = 10 per group): Control (no air-abrasion); Al2.5; Si2.5; Al3.5; Si3.5; Control(TC); Al2.5(TC); Si2.5(TC); Al3.5(TC); Si3.5(TC). After silane application, resin cement (Panavia F2.0) was bonded and polymerized. Specimens were thermocycled (6.000 cycles, 5-55 degrees C) and subjected to SBS (1 mm/min). Data were analyzed using ANOVA, Tukey's and Dunnett tests (5%). Results. 'Particle' (p = 0.0001) and 'pressure' (p = 0.0001) factors significantly affected the SR. All protocols significantly increased the SR (Al2.5: 0.45 +/- 0.02; Si2.5: 0.39 +/- 0.01; Al3.5: 0.80 +/- 0.01; Si3.5: 0.64 +/- 0.01 mu m) compared to the control group (0.16 +/- 0.01 mu m). For SBS, only 'particle' factor significantly affected the results (p = 0.015). The SiO2 groups presented significantly higher SBS results than Al2O3 (Al2.5: 4.78 +/- 1.86; Si2.5: 7.17 +/- 2.62; Al3.5: 4.97 +/- 3.74; Si3.5: 9.14 +/- 4.09 MPa) and the control group (3.67 +/- 3.0 MPa). All TC specimens presented spontaneous debondings. SEM analysis showed that Al2O3 created damage in zirconia in the form of grooves, different from those observed with SiO2 groups. Conclusions. Air-abrasion with 110 mu m Al2O3 resulted in higher roughness, but air-abrasion protocols with SiO2 promoted better adhesion.

EVALUATION OF ROUGHNESS, WETTABILITY, AND MORPHOLOGY OF AN YTTRIA-STABILIZED TETRAGONAL ZIRCONIA POLYCRYSTAL CERAMIC AFTER DIFFERENT AIRBORNE-PARTICLE ABRASION PROTOCOL

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.

Effect of particle size on the flexural strength and phase transformation of an airborne-particle abraded yttria-stabilized tetragonal zirconia polycrystal ceramic.

Statement of problem Because airborne-particle abrasion is an efficient method of improving the bond at the zirconia-cement interface, understanding its effect on the strength of yttria-stabilized tetragonal zirconia polycrystal is important. Purpose The purpose of this study was to evaluate the effect of the particle size used for airborne-particle abrasion on the flexural strength and phase transformation of a commercially available yttria-stabilized tetragonal zirconia polycrystal ceramic. Material and Methods For both flexural strength (20.0 × 4.0 × 1.2 mm) (n=14) and phase transformation (14.0-mm diameter × 1.3-mm thickness) (n=4), the zirconia specimens were made from Lava, and their surfaces were treated in the following ways: as-sintered (control); with 50-μm aluminum oxide (Al2O3) particles; with 120-μm Al2O3 particles; with 250-μm Al2O3 particles; with 30-μm silica-modified Al2O3 particles (Cojet Sand); with 120-μm Al2O3 particles, followed by 110-μm silica-modified Al2O3 particles (Rocatec Plus); and with Rocatec Plus. The phase transformation (%) was assessed by x-ray diffraction analysis. The 3-point flexural strength test was conducted in artificial saliva at 37°C in a mechanical testing machine. The data were analyzed by 1-way ANOVA and the Tukey honestly significant difference post hoc test (α=.05). Results Except for the Cojet Sand group, which exhibited statistically similar flexural strength to that of the as-sintered group and for the group abraded with 250-μm Al2O3 particles, which presented the lowest strength, airborne-particle abrasion with the other particle sizes provided the highest values, with no significant difference among them. The as-sintered specimens presented no monoclinic phase. The groups abraded with smaller particles (30 μm and 50 μm) and those treated with the larger ones (110 μm and/or 120 μm particles and 250 μm) exhibited percentages of monoclinic phase that varied from 4% to 5% and from 8.7% to 10%. Conclusions Except for abrasion with Cojet Sand, depending on the particle size, zirconia exhibited an increase or a decrease in its flexural strength. Airborne-particle abrasion promoted phase transformation (tetragonal to monoclinic), and the percentage of monoclinic phase varied according to the particle size.