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.

Sudden change in quantum and classical correlations and the Unruh effect

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

Particle Competition and Cooperation in Networks for Semi-Supervised Learning

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

A new method to control particle size and particle size distribution of SnO2 nanoparticles for gas sensor applications

The controlled growth of SnO2 nanoparticles for gas sensor applications is reported by these authors. Nb2O5 additive is used to control nucleation and growth of the SnO2 (see Figure), which is synthesized by the polymeric precursor method. Preliminary gas sensing measurements are performed and it is demonstrated that the response time of the Nb2O5-doped SnO2 is faster than that of the undoped material.

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.

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.

Resting heart rate: Its correlations and potential for screening metabolic dysfunctions in adolescents

Background: In pediatric populations, the use of resting heart rate as a health index remains unclear, mainly in epidemiological settings. The aims of this study were to analyze the impact of resting heart rate on screening dyslipidemia and high blood glucose and also to identify its significance in pediatric populations.Methods: The sample was composed of 971 randomly selected adolescents aged 11 to 17 years (410 boys and 561 girls). Resting heart rate was measured with oscillometric devices using two types of cuffs according to the arm circumference. Biochemical parameters triglycerides, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol and glucose were measured. Body fatness, sleep, smoking, alcohol consumption and cardiorespiratory fitness were analyzed.Results: Resting heart rate was positively related to higher sleep quality (β = 0.005, p = 0.039) and negatively related to cardiorespiratory fitness (β = -0.207, p = 0.001). The receiver operating characteristic curve indicated significant potential for resting heart rate in the screening of adolescents at increased values of fasting glucose (area under curve = 0.611 ± 0.039 [0.534 - 0.688]) and triglycerides (area under curve = 0.618 ± 0.044 [0.531 - 0.705]).Conclusion: High resting heart rate constitutes a significant and independent risk related to dyslipidemia and high blood glucose in pediatric populations. Sleep and cardiorespiratory fitness are two important determinants of the resting heart rate. © 2013 Fernandes et al.; licensee BioMed Central Ltd.

The impact of trade liberalization and fluctuations of commodity prices on government finances: the case of Saint Lucia

Includes bibliography

Measurements of t(t)over-bar Spin Correlations and Top-Quark Polarization Using Dilepton Final States in pp Collisions at root s=7 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Particle-vortex and Maxwell duality in the AdS(4) x CP3/ABJM correspondence

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Photoluminescence activity of Ba1-xCaxTiO3: dependence on particle size and morphology

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

Moderating effect of ammonia on particle growth and stability of quasi-monodisperse silver nanoparticles synthesized by the Turkevich method

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

Particle competition and cooperation for semi-supervised learning with label noise

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

Particle Competition and Cooperation in Networks for Semi-Supervised Learning

Semi-supervised learning is one of the important topics in machine learning, concerning with pattern classification where only a small subset of data is labeled. In this paper, a new network-based (or graph-based) semi-supervised classification model is proposed. It employs a combined random-greedy walk of particles, with competition and cooperation mechanisms, to propagate class labels to the whole network. Due to the competition mechanism, the proposed model has a local label spreading fashion, i.e., each particle only visits a portion of nodes potentially belonging to it, while it is not allowed to visit those nodes definitely occupied by particles of other classes. In this way, a "divide-and-conquer" effect is naturally embedded in the model. As a result, the proposed model can achieve a good classification rate while exhibiting low computational complexity order in comparison to other network-based semi-supervised algorithms. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method.