Measurements of single top quark production cross sections and vertical bar V-tb vertical bar in p(p)over-bar collisions at root s=1.96 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Measurement of the inclusive jet cross section in p(p)over-bar collisions at root s=1.96 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Model indedpendent search for new phenomena in p(p)over-bar collisions at root s=1.96 TeV

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

Measurement of the WZ and ZZ production cross sections using leptonic final states in 8.6 fb(-1) of p(p)over-bar collisions

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Measurement of the t(t)over-bar production cross section in pp collisions at root s=7 TeV in dilepton final states containing a tau

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Measurement of B(B)over-bar angular correlations based on secondary vertex reconstruction at root s=7 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Search for light resonances decaying into pairs of muons as a signal of new physics

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Measurement of the inclusive W and Z production cross sections in pp collisions at root s = 7 TeV with the CMS experiment

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Search for a Higgs boson in the decay channel H -> ZZ((*)) -> q(q)over-barl(-)l(+) in pp collisions at root s=7 TeV

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

Search for anomalous t(t)over-bar production in the highly-boosted all-hadronic final state

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Thermal performance of multipass parallel and counter-cross-flow heat exchangers

A thorough study of the thermal performance of multipass parallel cross-flow and counter-cross-flow heat exchangers has been carried out by applying a new numerical procedure. According to this procedure, the heat exchanger is discretized into small elements following the tube-side fluid circuits. Each element is itself a one-pass mixed-unmixed cross-flow heat exchanger. Simulated results have been validated through comparisons to results from analytical solutions for one- to four-pass, parallel cross-flow and counter-cross-flow arrangements. Very accurate results have been obtained over wide ranges of NTU (number of transfer units) and C* (heat capacity rate ratio) values. New effectiveness data for the aforementioned configurations and a higher number of tube passes is presented along with data for a complex flow configuration proposed elsewhere. The proposed procedure constitutes a useful research tool both for theoretical and experimental studies of cross-flow heat exchangers thermal performance.

A new approach for thermal performance calculation of cross-flow heat exchangers

A new numerical methodology for thermal performance calculation in cross-flow heat exchangers is developed. Effectiveness-number of transfer units (epsilon-NTU) data for several standard and complex flow arrangements are obtained using this methodology. The results are validated through comparison with analytical solutions for one-pass cross-flow heat exchangers with one to four rows and with approximate series solution for an unmixed-unmixed heat exchanger, obtaining in all cases very small errors. New effectiveness data for some complex configurations are provided. (c) 2005 Elsevier Ltd. All rights reserved.

Phytophthora parasitica transcriptome, a new concept in the understanding of the citrus gummosis

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

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.