Artificial immune systems for classification of petroleum well drilling operations

This paper presents two approaches of Artificial Immune System for Pattern Recognition (CLONALG and Parallel AIRS2) to classify automatically the well drilling operation stages. The classification is carried out through the analysis of some mud-logging parameters. In order to validate the performance of AIS techniques, the results were compared with others classification methods: neural network, support vector machine and lazy learning.

Análise morfológica da corrosão por pites em aço inoxidável austenítico AISI 310S submetido à exposição em névoa salina

O artigo teve como objetivo estudar a evolução do crescimento, distribuição e classificação dos pites em aços inoxidáveis austeníticos AISI 310S no estado como recebida e tratado termicamente, submetidos a diferentes tempos de exposição em meio salino. A aplicabilidade deste trabalho baseou-se no desenvolvimento de uma técnica para caracterização morfológica da corrosão localizada, associado com os aspectos de descrição de formas, tamanho, parâmetros específicos e populacionais. A metodologia consistiu nas seguintes etapas: preparação dos corpos de prova, testes de corrosão via névoa salina em diferentes condições, análise microestrutural, análise dos perfis dos pites, processamento digital e análise de imagens, visando caracterizar a distribuição, morfologia e o tamanho dos pites. Os resultados obtidos no processamento digital e análise de imagens dos perfis foram submetidos á análise estatística, utilizando à mediana como parâmetro de avaliação na liga como recebido e tratada. A liga como recebido, exibe a seguinte morfologia: pites hemisféricos> região de transição A> região de transição B> irregulares> cônicos. A quantidade de pites na liga tratada a cada tempo de exposição é: região de transição B> hemisféricos> região de transição A> cônicos> irregulares.

Espectroscopia de impedância eletroquímica aplicada ao estudo das reações heterogêneas em ânodos dimensionalmente estáveis

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

Comparação entre análises para permanência no rebanho de vacas Nelore utilizando modelo linear e modelo de limiar

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

Comparação entre alguns critérios de seleção para crescimento na raça Nelore

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

A structure-activity relationship study of quinone compounds with trypanocidal activity

A set of 25 quinone compounds with anti-trypanocidal activity was studied by using the density functional theory (DFT) method in order to calculate atomic and molecular properties to be correlated with the biological activity. The chemometric methods principal component analysis (PCA), hierarchical cluster analysis (HCA), stepwise discriminant analysis (SDA), Kth nearest neighbor (KNN) and soft independent modeling of class analogy (SIMCA) were used to obtain possible relationships between the calculated descriptors and the biological activity studied and to predict the anti-trypanocidal activity of new quinone compounds from a prediction set. Four descriptors were responsible for the separation between the active and inactive compounds: T-5 (torsion angle), QTS1 (sum of absolute values of the atomic charges), VOLS2 (volume of the substituent at region B) and HOMO-1 (energy of the molecular orbital below HOMO). These descriptors give information on the kind of interaction that occurs between the compounds and the biological receptor. The prediction study was done with a set of three new compounds by using the PCA, HCA, SDA, KNN and SIMCA methods and two of them were predicted as active against the Trypanosoma cruzi. (c) 2005 Elsevier SAS. All rights reserved.

Optimum-Path Forest Classifier for Large Scale Biometric Applications

This paper addresses biometric identification using large databases, in particular, iris databases. In such applications, it is critical to have low response time, while maintaining an acceptable recognition rate. Thus, the trade-off between speed and accuracy must be evaluated for processing and recognition parts of an identification system. In this paper, a graph-based framework for pattern recognition, called Optimum-Path Forest (OPF), is utilized as a classifier in a pre-developed iris recognition system. The aim of this paper is to verify the effectiveness of OPF in the field of iris recognition, and its performance for various scale iris databases. The existing Gauss-Laguerre Wavelet based coding scheme is used for iris encoding. The performance of the OPF and two other - Hamming and Bayesian - classifiers, is compared using small, medium, and large-scale databases. Such a comparison shows that the OPF has faster response for large-scale databases, thus performing better than the more accurate, but slower, classifiers.

Thermal behaviour of corn starch granules under action of fungal alpha-amylase

Corn starch, partially hydrolyzed by fungal alpha-amylase was investigated by using thermal analysis, microscopy and X-ray diffraction. After enzymatic treatment lower degradation onset temperatures were observed. DSC analysis showed almost similar range of gelatinization temperature, however, the enthalpies of gelatinization increased for the partially hydrolyzed starch granules. According to the X-ray diffraction analysis, stronger cereal pattern peaks were recognized after enzymatic digestion. The results suggested that the hydrolysis was more pronounced in the amorphous part of the starch granules.

A Fast Large Scale Iris Database Classification with Optimum-Path Forest Technique: A Case Study

Majority of biometric researchers focus on the accuracy of matching using biometrics databases, including iris databases, while the scalability and speed issues have been neglected. In the applications such as identification in airports and borders, it is critical for the identification system to have low-time response. In this paper, a graph-based framework for pattern recognition, called Optimum-Path Forest (OPF), is utilized as a classifier in a pre-developed iris recognition system. The aim of this paper is to verify the effectiveness of OPF in the field of iris recognition, and its performance for various scale iris databases. This paper investigates several classifiers, which are widely used in iris recognition papers, and the response time along with accuracy. The existing Gauss-Laguerre Wavelet based iris coding scheme, which shows perfect discrimination with rotary Hamming distance classifier, is used for iris coding. The performance of classifiers is compared using small, medium, and large scale databases. Such comparison shows that OPF has faster response for large scale database, thus performing better than more accurate but slower Bayesian classifier.

Transcriptome architecture across tissues in the pig

Background: Artificial selection has resulted in animal breeds with extreme phenotypes. As an organism is made up of many different tissues and organs, each with its own genetic programme, it is pertinent to ask: How relevant is tissue in terms of total transcriptome variability? Which are the genes most distinctly expressed between tissues? Does breed or sex equally affect the transcriptome across tissues?Results: In order to gain insight on these issues, we conducted microarray expression profiling of 16 different tissues from four animals of two extreme pig breeds, Large White and Iberian, two males and two females. Mixed model analysis and neighbor - joining trees showed that tissues with similar developmental origin clustered closer than those with different embryonic origins. Often a sound biological interpretation was possible for overrepresented gene ontology categories within differentially expressed genes between groups of tissues. For instance, an excess of nervous system or muscle development genes were found among tissues of ectoderm or mesoderm origins, respectively. Tissue accounted for similar to 11 times more variability than sex or breed. Nevertheless, we were able to confidently identify genes with differential expression across tissues between breeds (33 genes) and between sexes (19 genes). The genes primarily affected by sex were overall different than those affected by breed or tissue. Interaction with tissue can be important for differentially expressed genes between breeds but not so much for genes whose expression differ between sexes.Conclusion: Embryonic development leaves an enduring footprint on the transcriptome. The interaction in gene x tissue for differentially expressed genes between breeds suggests that animal breeding has targeted differentially each tissue's transcriptome.

An environment for knowledge discovery in biology

This paper describes a data mining environment for knowledge discovery in bioinformatics applications. The system has a generic kernel that implements the mining functions to be applied to input primary databases, with a warehouse architecture, of biomedical information. Both supervised and unsupervised classification can be implemented within the kernel and applied to data extracted from the primary database, with the results being suitably stored in a complex object database for knowledge discovery. The kernel also includes a specific high-performance library that allows designing and applying the mining functions in parallel machines. The experimental results obtained by the application of the kernel functions are reported. © 2003 Elsevier Ltd. All rights reserved.

Cross-cultural differences for three visual memory tasks in Brazilian children

Norms for three visual memory tasks, including Corsi's block tapping test and the BEM 144 complex figures and visual recognition, were developed for neuropsychological assessment in Brazilian children. The tasks were measured in 127 children ages 7 to 10 years from rural and urban areas of the States of São Paulo and Minas Gerais. Analysis indicated age-related but not sex-related differences. A cross-cultural effect was observed in relation to copying and recall of Complex pictures. Different performances between rural and urban children were noted. © Perceptual and Motor Skills 2005.

Automated extraction of road network from medium-and high-resolution images

This paper presents an automatic methodology for road network extraction from medium-and high-resolution aerial images. It is based on two steps. In the first step, the road seeds (i.e., road segments) are extracted using a set of four road objects and another set of connection rules among road objects. Each road object is a local representation of an approximately straight road fragment and its construction is based on a combination of polygons describing all relevant image edges, according to some rules embodying road knowledge. Each road seed is composed by a sequence of connected road objects in which each sequence of this type can be geometrically structured as a chain of contiguous quadrilaterals. In the second step, two strategies for road completion are applied in order to generate the complete road network. The first strategy is based on two basic perceptual grouping rules, i.e., proximity and collinearity rules, which allow the sequential reconstruction of gaps between every pair of disconnected road segments. This strategy does not allow the reconstruction of road crossings, but it allows the extraction of road centerlines from the contiguous quadrilaterals representing connected road segments. The second strategy for road completion aims at reconstructing road crossings. Firstly, the road centerlines are used to find reference points for road crossings, which are their approximate positions. Then these points are used to extract polygons representing the contours of road crossings. This paper presents the proposed methodology and experimental results. © Pleiades Publishing, Inc. 2006.

Orientação semi-automática de imagens CBERS usando rodovias como controle de campo

In this paper is proposed a methodology for semiautomatic CBERS image orientation using roads as ground control. It is based on an iterative strategy involving three steps. In the first step, an operator identifies on the image the ground control roads and supplies along them a few seed points, which could be sparsely and coarsely distributed. These seed points are used by the dynamic programming algorithm for extracting the ground control roads from the image. In the second step, it is established the correspondences between points describing the ground control roads and the corresponding ones extracted from the image. In the last step, the corresponding points are used to orient the CBERS image by using the DLT (Direct Linear Transformation). The two last steps are iterated until the convergence of the orientation process is verified. Experimental results showed that the proposed methodology was efficient with several test images. In all cases the orientation process converged. Moreover, the estimated orientation parameters allowed the registration of check roads with pixel accuracy or better.

Vulnerabilidade à erosão do solo da Região do Alto Rio Pardo, Pardinho, SP

This study aimed at the elaboration of a database with information and a map of erosion vulnerability for ecological zoning for the upper Pardo River, Botucatu, SP, by using the Geographical Information System - SPRING. The map of erosion vulnerability was made from spectrally homogeneous regions, producing a grid of zone averages, which was then subdivided, resulting in a vulnerability map to erosion. The results allowed us to conclude that digital imaging produced valuable information for mapping of soil use and database formation. The GIS - SPRING was efficient at identifying soil and vulnerability erosion classes and 95% of the basin presents a moderately stable vulnerability degree, through the presence of medium young soils in gently waring reliefs and covered by 49.27% of pasture and 29.88% crops.