Measurement of the weak mixing angle with the Drell-Yan process in proton-proton collisions at the LHC

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Measurement of the rapidity and transverse momentum distributions of Z bosons in pp collisions at root(s)=7 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Search for quark compositeness in dijet angular distributions from pp collisions at root s=7 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Power-law distributions for the citation index of scientific publications and scientists

The number of citations of a scientific publication or of an individual scientist has become an important factor of quality assessment in science. We report a study of the statistical distribution of the citation index of both scientific publications and scientists. We give numerical evidence that Tsallis (power law) statistics explains the entire distribution over eight orders of magnitude (10-4 to 10(4)). Also, we draw Zipf plots in order to analyze the statistical distribution of the citation index of Brazilian and international physicists and chemists. The relatively small group of Brazilian scientists seems more adequate to explain the dynamics of the citation index. In this case, we find that the distribution of the citation index can also be explained by a gradually truncated power law with similar parameters. We finally discuss possible mechanisms behind the citation index of scientists and scientific publications.

Geochemical characterization of heavy metal contaminated area using multivariate factorial kriging

This paper describes a geostatistical method, known as factorial kriging analysis, which is well suited for analyzing multivariate spatial information. The method involves multivariate variogram modeling, principal component analysis, and cokriging. It uses several separate correlation structures, each corresponding to a specific spatial scale, and yields a set of regionalized factors summarizing the main features of the data for each spatial scale. This method is applied to an area of high manganese-ore mining activity in Amapa State, North Brazil. Two scales of spatial variation (0.33 and 2.0 km) are identified and interpreted. The results indicate that, for the short-range structure, manganese, arsenic, iron, and cadmium are associated with human activities due to the mining work, while for the long-range structure, the high aluminum, selenium, copper, and lead concentrations, seem to be related to the natural environment. At each scale, the correlation structure is analyzed, and regionalized factors are estimated by cokriging and then mapped.

Size distributions of fine silicate and other particles in Masaya's volcanic plume

Direct-sampling and remote-sensing measurements were made at the crater rim of Masaya volcano (Nicaragua) to sample the aerosol plume emanating from the active vent. We report the first measurements of the size distribution of fine silicate particles (d <10 mu m) in Masaya's plume, by automated scanning electron microscopy (QEMSCAN) analysis of a particle filter. The particle size distribution was approximately lognormal with modal d similar to 1.15 mu m. The majority of these particles were found to be spherical. These particles are interpreted to be droplets of quenched magma produced by a spattering process. Compositional analyses confirm earlier reports that the fine silicate particles show a range of compositions between that of the degassing magma and nearly pure silica and that the extent of compositional variability decreases with increasing particle size. These results indicate that fine silicate particles are altered owing to reactions with acidic droplets in the plume. The emission flux of fine silicate particles was estimated as similar to 10(11) s(-1), equivalent to similar to 55 kg d(-1). Sun photometry, aerosol spectrometry, and thermal precipitation were used to determine the overall particle size distribution of the plume (0.01 < d(mu m) < 10). Sun photometry and aerosol spectrometry measurements indicate the presence of a large number of particles (assumed to be aqueous) with d similar to 1 mu m. Aerosol spectrometry measurements further show an increase in particle size as the nighttime approached. The emission flux of particles from Masaya was estimated as similar to 10(17) s(-1), equivalent to similar to 5.5 Mg d(-1) where d < 4 mu m.

Composition-resolved size distributions of volcanic aerosols in the Mt. Etna plumes

Particle size distributions for soluble and insoluble species in Mt. Etna's summit plumes were measured across an extended size range (10 nm < d < 100 mu m) using a combination of techniques. Automated scanning electron microscopy (QEMSCAN) was used to chemically analyze many thousands of insoluble particles (collected on pumped filters) allowing the relationships between particle size, shape, and composition to be investigated. The size distribution of fine silicate particles (d < 10 mu m) was found to be lognormal, consistent with formation by bursting of gas bubbles at the surface of the magma. The compositions of fine silicate particles were found to vary between magmatic and nearly pure silica; this is consistent with depletion of metal ions by reactions in the acidic environment of the gas plume and vent. Measurements of the size, shape and composition of fine silicate particles may potentially offer insights into preemission, synemission, and postemission processes. The mass flux of fine silicate particles from Mt. Etna released during noneruptive volcanic degassing in 2004 and 2005 was estimated to be similar to 7000 kg d(-1). Analysis of particles in the range 0.1 < d/mu m < 100 by ion chromatography shows that there are persistent differences in the size distributions of sulfate aerosols between the two main summit plumes. Analysis of particles in the range 0.01 mu m < d < 0.1 mu m by scanning transmission electron microscopy (STEM) shows that there are significant levels of nanoparticles in the Mt. Etna plumes although their compositions remain uncertain.

Carbon Nuclear Magnetic Resonance Spectroscopic Profiles coupled to Partial Least-Squares Multivariate Regression for Prediction of Several Physicochemical Parameters of Brazilian Commercial Gasoline

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Multivariate calibrations on H-1 NMR profiles for prediction of physicochemical parameters of Brazilian commercial gasoline

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

GC Fingerprints Coupled to Pattern-Recognition Multivariate SIMCA Chemometric Analysis for Brazilian Gasoline Quality Studies

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Screening Brazilian commercial gasoline quality by hydrogen nuclear magnetic resonance spectroscopic fingerprintings and pattern-recognition multivariate chemometric analysis

The identification of gasoline adulteration by organic solvents is not an easy task, because compounds that constitute the solvents are already in gasoline composition. In this work, the combination of Hydrogen Nuclear Magnetic Resonance ((1)H NMR) spectroscopic fingerprintings with pattern-recognition multivariate Soft Independent Modeling of Class Analogy (SIMCA) chemometric analysis provides an original and alternative approach to screening Brazilian commercial gasoline quality in a Monitoring Program for Quality Control of Automotive Fuels. SIMCA was performed on spectroscopic fingerprints to classify the quality of representative commercial gasoline samples selected by Hierarchical Cluster Analysis (HCA) and collected over a 6-month period from different gas stations in the São Paulo state, Brazil. Following optimized the (1)H NMR-SIMCA algorithm, it was possible to correctly classify 92.0% of commercial gasoline samples, which is considered acceptable. The chemometric method is recommended for routine applications in Quality-Control Monitoring Programs, since its measurements are fast and can be easily automated. Also, police laboratories could employ this method for rapid screening analysis to discourage adulteration practices. (C) 2010 Elsevier B.V. All rights reserved.

Multivariate calibrations in gas chromatographic profiles for prediction of several physicochemical parameters of Brazilian commercial gasoline

Several Brazilian commercial gasoline physicochemical parameters, such as relative density, distillation curve (temperatures related to 10%, 50% and 90% of distilled volume, final boiling point and residue), octane numbers (motor and research octane number and anti-knock index), hydrocarbon compositions (olefins, aromatics and saturates) and anhydrous ethanol and benzene content was predicted from chromatographic profiles obtained by flame ionization detection (GC-FID) and using partial least square regression (PLS). GC-FID is a technique intensively used for fuel quality control due to its convenience, speed, accuracy and simplicity and its profiles are much easier to interpret and understand than results produced by other techniques. Another advantage is that it permits association with multivariate methods of analysis, such as PLS. The chromatogram profiles were recorded and used to deploy PLS models for each property. The standard error of prediction (SEP) has been the main parameter considered to select the "best model". Most of GC-FID-PLS results, when compared to those obtained by the Brazilian Government Petroleum, Natural Gas and Biofuels Agency - ANP Regulation 309 specification methods, were very good. In general, all PLS models developed in these work provide unbiased predictions with lows standard error of prediction and percentage average relative error (below 11.5 and 5.0, respectively). (C) 2007 Elsevier B.V. All rights reserved.

Screening Brazilian Commercial Gasoline Quality by ASTM D6733 GC and Pattern-Recognition Multivariate SIMCA Chemometric Analysis

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Multivariate analysis of stable isotope data in the traceability process for birds

A análise isotópica tem se mostrado uma ferramenta de suma importância ao processo de rastreabilidade, no entanto, existem divergências nas análises estatísticas dos resultados, uma vez que os dados são dependentes e advindos de vários elementos químicos tais como Carbono, Hidrogênio, Oxigênio, Nitrogênio e Enxofre (CHON'S). Com o intuito de estabelecer a análise propícia para os dados de rastreabilidade em aves pela técnica de isótopos estáveis e avaliar a necessidade da análise conjunta das variáveis, foram usados dados de carbono-13 e de nitrogênio-15 de ovos (albúmen + gema) de poedeiras e músculo peitoral de frangos de corte, os quais foram submetidos à análise estatística univariada (Anova e complementada pelo teste de Tukey) e multivariada (Manova e Discriminante). Os dados foram analisados no software Minitab 16, e os resultados, consolidados na teoria, confirmam a necessidade de análise multivariada, mostrando também que a análise discriminante esclarece as dúvidas apresentadas nos resultados de outros métodos de análise comparados nesta pesquisa.

Monitoring the mean vector and the covariance ­matrix of multivariate processes with sample means and sample ranges

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