Affine Toda systems coupled to matter fields

We investigate higher grading integrable generalizations of the affine Toda systems, where the flat connections defining the models take values in eigensubspaces of an integral gradation of an affine Kac-Moody algebra, with grades varying from l to -l (l > 1). The corresponding target space possesses nontrivial vacua and soliton configurations, which can be interpreted as particles of the theory, on the same footing as those associated to fundamental fields. The models can also be formulated by a hamiltonian reduction procedure from the so-called two-loop WZNW models. We construct the general solution and show the classes corresponding to the solitons. Some of the particles and solitons become massive when the conformal symmetry is spontaneously broken by a mechanism with an intriguing topological character and leading to a very simple mass formula. The massive fields associated to nonzero grade generators obey field equations of the Dirac type and may be regarded as matter fields. A special class of models is remarkable. These theories possess a U(1 ) Noether current, which, after a special gauge fixing of the conformal symmetry, is proportional to a topological current. This leads to the confinement of the matter field inside the solitons, which can be regarded as a one-dimensional bag model for QCD. These models are also relevant to the study of electron self-localization in (quasi-)one-dimensional electron-phonon systems.

Interaction of the nonionic surfactant C12E8 with high molar mass poly(ethylene oxide) studied by dynamic light scattering and fluorescence quenching methods

Dynamic light scattering has been used to investigate ternary aqueous solutions of n-dodecyl octaoxyethylene glycol monoetber (C12E8) with high molar mass poly(ethylene oxide) (PEO). The measurements were made at 20 °C, always below the cloud point temperature (Tc) of the mixed solutions. The relaxation time distributions are bimodal at higher PEO and surfactant concentrations, owing to the preacute of free surfactant micelles, which coexist with the slower component, representing the polymer coil/micellar cluster comptex. As the surfactant concentration is increased, the apparent hydrodynamic radius (RH) of the coil becomes progressively larger. It is suggested that the complex structure consists of clusters of micelles sited within the polymer coil, as previously concluded for the PEO-C12E8-water system. However. C12E8 interacts less strongly than C12E8 with PEO; at low concentrations of surfactant the complex does not contribute significantly to the total scattered intensity. The perturbation of the PEO coil radius with C12E8 is also smaller than that in the C12E8 system. The addition of PEO strongly decreases the clouding temperature of the system, as previously observed for C12E8/PEO mixtures in solution Addition of PEO up to 0.2% to C12E8 (10 wt %) solutions doss not alter the aggregation number (Nagg) of the micelles probably because the surfactant monomers are equally partitioned as bound and unbound micelles. The critical micelle concentration (cmc), obtained from the I1/I3 ratio (a measure of the dependence of the vibronic band intensities on the pyrene probe environment), does not change when PEO is added, suggesting that for neutral polymer/surfactant systems the trends in Nagg and the cmc do not unambiguously reflect the strength of interaction.

An approach based on neural networks for identification of fault sections in radial distribution systems

The main objective involved with this paper consists of presenting the results obtained from the application of artificial neural networks and statistical tools in the automatic identification and classification process of faults in electric power distribution systems. The developed techniques to treat the proposed problem have used, in an integrated way, several approaches that can contribute to the successful detection process of faults, aiming that it is carried out in a reliable and safe way. The compilations of the results obtained from practical experiments accomplished in a pilot radial distribution feeder have demonstrated that the developed techniques provide accurate results, identifying and classifying efficiently the several occurrences of faults observed in the feeder.

Fault identification in distribution lines using intelligent systems and statistical methods

The main objective involved with this paper consists of presenting the results obtained from the application of artificial neural networks and statistical tools in the automatic identification and classification process of faults in electric power distribution systems. The developed techniques to treat the proposed problem have used, in an integrated way, several approaches that can contribute to the successful detection process of faults, aiming that it is carried out in a reliable and safe way. The compilations of the results obtained from practical experiments accomplished in a pilot distribution feeder have demonstrated that the developed techniques provide accurate results, identifying and classifying efficiently the several occurrences of faults observed in the feeder. © 2006 IEEE.

Malware distributed collection and pre-classification system using honeypot technology

Malware has become a major threat in the last years due to the ease of spread through the Internet. Malware detection has become difficult with the use of compression, polymorphic methods and techniques to detect and disable security software. Those and other obfuscation techniques pose a problem for detection and classification schemes that analyze malware behavior. In this paper we propose a distributed architecture to improve malware collection using different honeypot technologies to increase the variety of malware collected. We also present a daemon tool developed to grab malware distributed through spam and a pre-classification technique that uses antivirus technology to separate malware in generic classes. © 2009 SPIE.

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.

Halo nuclei in a three-body model and universal characteristics of low-energy few-body systems

Within general characteristics of low-energy few-body systems, we revise some well-known correlations found in nuclear physics, and the properties of low-mass halo nuclei in a three-body neutron-neutron-core model. In this context, near the critical conditions for the occurrence of an Efimov state, we report some results obtained for the neutron- 19C elastic scattering. © 2010 American Institute of Physics.

New trends in musical genre classification using optimum-path forest

Musical genre classification has been paramount in the last years, mainly in large multimedia datasets, in which new songs and genres can be added at every moment by anyone. In this context, we have seen the growing of musical recommendation systems, which can improve the benefits for several applications, such as social networks and collective musical libraries. In this work, we have introduced a recent machine learning technique named Optimum-Path Forest (OPF) for musical genre classification, which has been demonstrated to be similar to the state-of-the-art pattern recognition techniques, but much faster for some applications. Experiments in two public datasets were conducted against Support Vector Machines and a Bayesian classifier to show the validity of our work. In addition, we have executed an experiment using very recent hybrid feature selection techniques based on OPF to speed up feature extraction process. © 2011 International Society for Music Information Retrieval.

Fault location in underground systems through optimum-path forest

In this paper we propose an accurate method for fault location in underground distribution systems by means of an Optimum-Path Forest (OPF) classifier. We applied the Time Domains Reflectometry method for signal acquisition, which was further analyzed by OPF and several other well known pattern recognition techniques. The results indicated that OPF and Support Vector Machines outperformed Artificial Neural Networks classifier. However, OPF has been much more efficient than all classifiers for training, and the second one faster for classification. © 2011 IEEE.

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.

Dissection of the role of pili and type 2 and 3 secretion systems in adherence and biofilm formation of an atypical enteropathogenic escherichia coli strain

Atypical enteropathogenic Escherichia coli (aEPEC) strains are diarrheal pathogens that lack bundle-forming pilus production but possess the virulence-associated locus of enterocyte effacement. aEPEC strain 1551-2 produces localized adherence (LA) on HeLa cells; however, its isogenic intimin (eae) mutant produces a diffuse-adherence (DA) pattern. In this study, we aimed to identify the DA-associated adhesin of the 1551-2 eae mutant. Electron microscopy of 1551-2 identified rigid rod-like pili composed of an 18-kDa protein, which was identified as the major pilin subunit of type 1 pilus (T1P) by mass spectrometry analysis. Deletion of fimA in 1551-2 affected biofilm formation but had no effect on adherence properties. Analysis of secreted proteins in supernatants of this strain identified a 150-kDa protein corresponding to SslE, a type 2 secreted protein that was recently reported to be involved in biofilm formation of rabbit and human EPEC strains. However, neither adherence nor biofilm formation was affected in a 1551-2 sslE mutant. We then investigated the role of the EspA filament associated with the type 3 secretion system (T3SS) in DA by generating a double eae espA mutant. This strain was no longer adherent, strongly suggesting that the T3SS translocon is the DA adhesin. In agreement with these results, specific anti-EspA antibodies blocked adherence of the 1551-2 eae mutant. Our data support a role for intimin in LA, for the T3SS translocon in DA, and for T1P in biofilm formation, all of which may act in concert to facilitate host intestinal colonization by aEPEC strains. ©2013, American Society for Microbiology.

Digital soil mapping: evaluation of sampling systems for soil surveys and refinement of soil maps at lower cost using legacy data

In soil surveys, several sampling systems can be used to define the most representative sites for sample collection and description of soil profiles. In recent years, the conditioned Latin hypercube sampling system has gained prominence for soil surveys. In Brazil, most of the soil maps are at small scales and in paper format, which hinders their refinement. The objectives of this work include: (i) to compare two sampling systems by conditioned Latin hypercube to map soil classes and soil properties; (II) to retrieve information from a detailed scale soil map of a pilot watershed for its refinement, comparing two data mining tools, and validation of the new soil map; and (III) to create and validate a soil map of a much larger and similar area from the extrapolation of information extracted from the existing soil map. Two sampling systems were created by conditioned Latin hypercube and by the cost-constrained conditioned Latin hypercube. At each prospection place, soil classification and measurement of the A horizon thickness were performed. Maps were generated and validated for each sampling system, comparing the efficiency of these methods. The conditioned Latin hypercube captured greater variability of soils and properties than the cost-constrained conditioned Latin hypercube, despite the former provided greater difficulty in field work. The conditioned Latin hypercube can capture greater soil variability and the cost-constrained conditioned Latin hypercube presents great potential for use in soil surveys, especially in areas of difficult access. From an existing detailed scale soil map of a pilot watershed, topographical information for each soil class was extracted from a Digital Elevation Model and its derivatives, by two data mining tools. Maps were generated using each tool. The more accurate of these tools was used for extrapolation of soil information for a much larger and similar area and the generated map was validated. It was possible to retrieve the existing soil map information and apply it on a larger area containing similar soil forming factors, at much low financial cost. The KnowledgeMiner tool for data mining, and ArcSIE, used to create the soil map, presented better results and enabled the use of existing soil map to extract soil information and its application in similar larger areas at reduced costs, which is especially important in development countries with limited financial resources for such activities, such as Brazil.