Bond strength of adhesive systems with different solvents to dry and wet dentin

Aim: This study evaluates bond strength between dentin and composite using adhesives with different solvents to dry and wet dentin. Materials and methods: Ninety bovine incisors were used; the vestibular surfaces were worn by the exposure of an area with a diameter of 4 mm of dentin. The specimens were divided into 6 groups, according to the type of adhesive used and hydratation stals: Group SB-wet: Single Bond 2 in wet dentin, Group SBdry: Single Bond 2 in dry dentin, Group SL-wet: Solobond M in wet dentin, Group SL-dry: Solobond M in dentin dry. Group XPwet: XP Bond in wet dentin, Group XP-dry: XP Bond in dentin dry. They were cut to obtain specimens in the shape of stick with 1 × 1 mm and subjected to microtensile test in universal testing machine with a cross speed of 1mm/min. The data were analyzed with ANOVA and Tukey's tests (5%). Results: ANOVA showed significant differences for surface treatment and interaction, but no difference was found for adhesive factor. The Tukey's test showed that the samples with wet dentin shown higher values of bond strength. Conclusion: The adhesive did not influence in the bond strength. The groups with wet dentin showed higher values of bond strength than groups with dry dentin.

Automatic microstructural characterization and classification using artificial intelligence techniques on ultrasound signals

Secondary phases such as Laves and carbides are formed during the final solidification stages of nickel based superalloy coatings deposited during the gas tungsten arc welding cold wire process. However, when aged at high temperatures, other phases can precipitate in the microstructure, like the γ″ and δ phases. This work presents a new application and evaluation of artificial intelligent techniques to classify (the background echo and backscattered) ultrasound signals in order to characterize the microstructure of a Ni-based alloy thermally aged at 650 and 950 °C for 10, 100 and 200 h. The background echo and backscattered ultrasound signals were acquired using transducers with frequencies of 4 and 5 MHz. Thus with the use of features extraction techniques, i.e.; detrended fluctuation analysis and the Hurst method, the accuracy and speed in the classification of the secondary phases from ultrasound signals could be studied. The classifiers under study were the recent optimum-path forest (OPF) and the more traditional support vector machines and Bayesian. The experimental results revealed that the OPF classifier was the fastest and most reliable. In addition, the OPF classifier revealed to be a valid and adequate tool for microstructure characterization through ultrasound signals classification due to its speed, sensitivity, accuracy and reliability. © 2013 Elsevier B.V. All rights reserved.

Classification of masses in mammographic image using wavelet domain features and polynomial classifier

Breast cancer is the most common cancer among women. In CAD systems, several studies have investigated the use of wavelet transform as a multiresolution analysis tool for texture analysis and could be interpreted as inputs to a classifier. In classification, polynomial classifier has been used due to the advantages of providing only one model for optimal separation of classes and to consider this as the solution of the problem. In this paper, a system is proposed for texture analysis and classification of lesions in mammographic images. Multiresolution analysis features were extracted from the region of interest of a given image. These features were computed based on three different wavelet functions, Daubechies 8, Symlet 8 and bi-orthogonal 3.7. For classification, we used the polynomial classification algorithm to define the mammogram images as normal or abnormal. We also made a comparison with other artificial intelligence algorithms (Decision Tree, SVM, K-NN). A Receiver Operating Characteristics (ROC) curve is used to evaluate the performance of the proposed system. Our system is evaluated using 360 digitized mammograms from DDSM database and the result shows that the algorithm has an area under the ROC curve Az of 0.98 ± 0.03. The performance of the polynomial classifier has proved to be better in comparison to other classification algorithms. © 2013 Elsevier Ltd. All rights reserved.

ECG arrhythmia classification based on optimum-path forest

An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) signals, since the non-invasive nature and simplicity of the ECG exam. According to the application, ECG data analysis consists of steps such as preprocessing, segmentation, feature extraction and classification aiming to detect cardiac arrhythmias (i.e.; cardiac rhythm abnormalities). Aiming to made a fast and accurate cardiac arrhythmia signal classification process, we apply and analyze a recent and robust supervised graph-based pattern recognition technique, the optimum-path forest (OPF) classifier. To the best of our knowledge, it is the first time that OPF classifier is used to the ECG heartbeat signal classification task. We then compare the performance (in terms of training and testing time, accuracy, specificity, and sensitivity) of the OPF classifier to the ones of other three well-known expert system classifiers, i.e.; support vector machine (SVM), Bayesian and multilayer artificial neural network (MLP), using features extracted from six main approaches considered in literature for ECG arrhythmia analysis. In our experiments, we use the MIT-BIH Arrhythmia Database and the evaluation protocol recommended by The Association for the Advancement of Medical Instrumentation. A discussion on the obtained results shows that OPF classifier presents a robust performance, i.e.; there is no need for parameter setup, as well as a high accuracy at an extremely low computational cost. Moreover, in average, the OPF classifier yielded greater performance than the MLP and SVM classifiers in terms of classification time and accuracy, and to produce quite similar performance to the Bayesian classifier, showing to be a promising technique for ECG signal analysis. © 2012 Elsevier Ltd. All rights reserved.

Detecção, classificação e localização de faltas de curto-circuito em sistemas de distribuição de energia elétrica usando sistemas inteligentes

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

Ubiquitous Computing: Any Ethical Implications?

In this article, the authors investigate, from an interdisciplinary perspective, possible ethical implications of the presence of ubiquitous computing systems in human perception/action. The term ubiquitous computing is used to characterize information-processing capacity from computers that are available everywhere and all the time, integrated into everyday objects and activities. The contrast in approach to aspects of ubiquitous computing between traditional considerations of ethical issues and the Ecological Philosophy view concerning its possible consequences in the context of perception/action are the underlying themes of this paper. The focus is on an analysis of how the generalized dissemination of microprocessors in embedded systems, commanded by a ubiquitous computing system, can affect the behaviour of people considered as embodied embedded agents.

Development of a CAD system for automatic classification of microcalcifications based on FPGA

This paper presents a Computer Aided Diagnosis (CAD) system that automatically classifies microcalcifications detected on digital mammograms into one of the five types proposed by Michele Le Gal, a classification scheme that allows radiologists to determine whether a breast tumor is malignant or not without the need for surgeries. The developed system uses a combination of wavelets and Artificial Neural Networks (ANN) and is executed on an Altera DE2-115 Development Kit, a kit containing a Field-Programmable Gate Array (FPGA) that allows the system to be smaller, cheaper and more energy efficient. Results have shown that the system was able to correctly classify 96.67% of test samples, which can be used as a second opinion by radiologists in breast cancer early diagnosis. (C) 2013 The Authors. Published by Elsevier B.V.

Pruning methods to MLP neural networks considering proportional apparent error rate for classification problems with unbalanced data

This article deals with classification problems involving unequal probabilities in each class and discusses metrics to systems that use multilayer perceptrons neural networks (MLP) for the task of classifying new patterns. In addition we propose three new pruning methods that were compared to other seven existing methods in the literature for MLP networks. All pruning algorithms presented in this paper have been modified by the authors to do pruning of neurons, in order to produce fully connected MLP networks but being small in its intermediary layer. Experiments were carried out involving the E. coli unbalanced classification problem and ten pruning methods. The proposed methods had obtained good results, actually, better results than another pruning methods previously defined at the MLP neural network area. (C) 2014 Elsevier Ltd. All rights reserved.

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.

Toward Satellite-Based Land Cover Classification Through Optimum-Path Forest

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

Classification of Eucalyptus urograndis hybrids under different water availability based on biometric traits

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Archival Classification and Knowledge Organization: Theoretical Possibilities for the Archival Field

The main goal of this study is to outline a possible relation between archival classification and knowledge organization theory. In this sense, we seek to contribute to the conceptual classification in Archival Science, since there is a lack of systematization about archival classification; not just classification, but even the study of historical and conceptual aspects of the discipline. In the context of knowledge organization there is a considerable amount of research on how to build classification schemes and indexing systems that can help contribute to and expand archival classification theory. In order to comprehend this vast field of theories and methodologies we construct a parallel comparing the classification concepts in both areas and analyzing these concepts.

Influence of the rotary and/or oscillatory reciprocating systems in the morphological changes of narrow and curved molar root canals anatomy

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Cellulite: poor correlation between instrumental methods and photograph evaluation for severity classification

SynopsisBackgroundCellulite refers to skin relief alterations in womens thighs and buttocks, causing dissatisfaction and search for treatment. Its physiopathology is complex and not completely understood. Many therapeutic options have been reported with no scientific evidence about benefits. The majority of the studies are not controlled nor randomized; most efficacy endpoints are subjective, like not well-standardized photographs and investigator opinion. Objective measures could improve severity assessment. Our purpose was to correlate non-invasive instrumental measures and standardized clinical evaluation.MethodsTwenty six women presenting cellulite on buttocks, aged from 25 to 41, were evaluated by: body mass index; standardized photography analysis (10-point severity and 5-point photonumeric scales) by five dermatologists; cutometry and high-frequency ultrasonography (dermal density and dermis/hypodermis interface length). Quality of life impact was assessed. Correlations between clinical and instrumental parameters were performed.ResultsGood agreement among dermatologists and main investigator perceptions was detected. Positive correlations: body mass index and clinical scores; ultrasonographic measures. Negative correlation: cutometry and clinical scores. Quality of life score was correlated to dermal collagen density.ConclusionCellulite caused impact in quality of life. Poor correlation between objective measures and clinical evaluation was detected. Cellulite severity assessment is a challenge, and objective parameters should be optimized for clinical trials.

Bonding ability of paste-paste glass ionomer systems to tooth structure: in vitro studies

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)