Aplicações da teoria de nós no estudo da estrutura de proteínas

Pós-graduação em Ciência e Tecnologia de Materiais - FC

Aplicação de métodos computacionais no estudo das fases intermetálicas ordenadas pt-sn

Fuel cells are a very promising solution to the problems of power generation and emission of pollutant to the environment, excellent to be used in stationary application and mobile application too. The high cost of production of these devices, mainly due to the use of noble metals as anode, is a major obstacle to massive production and deployment of this technology, however the use of intermetallic phases of platinum combined with other metals less noble has been evaluated as electrodes in order to minimize production costs and still being able to significantly improve the catalytic performance of the anode. The study of intermetallic phases, exclusively done by experimental techniques is not complete and demand that other methods need to be applied to a deeper understanding of the behavior geometric properties and the electronic structure of the material, to this end the use of computer simulation methods, which have proved appropriate for a broader understanding of the geometric and electronic properties of the materials involved, so far not so well understood.. The use of computational methods provides answers to explain the behavior of the materials and allows assessing whether the intermetallic may be a good electrode. In this research project was used the Quantum-ESPRESSO package, based on the DFT theory, which provides the self-consistent field calculations with great precision, calculations of the periodic systems interatomic force, and other post-processing calculations that points to a knowledge of the geometric and electronic properties of materials, which may be related to other properties of them, even the electrocatalytic. The electronic structure is determined from the optimized geometric structure of materials by analyzing the density of states (DOS) projected onto atomic orbital, which determines the influence of the electrocatalytic properties of the material... (Complete abstract click electronic access below)

Modelagem e Simulação Computacional das propriedades estruturais, eletrônicas e constantes elásticas do ZnO

The electronic, structural properties and elastic constants of the wurtzite phase of zinc oxide, ZnO, was investigated using computer simulation at Density Functional Theory level, with B3LYP hybrid functional and Hartree-Fock methodology. The electronic properties as well the band energy was investigated through the analysis of the band structures and density of states (DOS), and the mechanical properties was studied through the calculus of the elastic constants C11, C33, C44, C12 e C13. The results are in good agreement with experimental data found in the literature and in accordance with results obtained by another theoretical methodology

A radial basis function network (RBFN) for function approximation

A radial basis function network (RBFN) circuit for function approximation is presented. Simulation and experimental results show that the network has good approximation capabilities. The RBFN was a squared hyperbolic secant with three adjustable parameters amplitude, width and center. To test the network a sinusoidal and sine function,vas approximated.

A simulation tool for transient stability analysis on vector computers

This paper deals with transient stability analysis based on time domain simulation on vector processing. This approach requires the solution of a set of differential equations in conjunction of another set of algebraic equations. The solution of the algebraic equations has presented a scalar as sequential set of tasks, and the solution of these equations, on vector computers, has required much more investigations to speedup the simulations. Therefore, the main objective of this paper has been to present methods to solve the algebraic equations using vector processing. The results, using a GRAY computer, have shown that on-line transient stability assessment is feasible.

Estudo de materiais supercondutores em forma de SQUID com uma constrição usando métodos de paralelização computacional

Pós-graduação em Ciência e Tecnologia de Materiais - FC

Aplicação de um jogo digital e análise de conceitos da teoria cinética dos gases

Pós-graduação em Educação para a Ciência - FC

Effect of target bias on magnetic field enhanced plasma immersion ion implantation

Recent studies have demonstrated that sheath dynamics in plasma immersion ion implantation (PIII) is significantly affected by an external magnetic field, especially in the case when the magnetic field is parallel to the workpiece surface or intersects it at small angles. In this work we report the results from two-dimensional, particle-in-cell (PIC) computer simulations of magnetic field enhanced plasma immersion implantation system at different bias voltages. The simulations begin with initial low-density nitrogen plasma, which extends with uniform density through a grounded cylindrical chamber. Negative bias voltage is applied to a cylindrical target located on the axis of the vacuum chamber. An axial magnetic field is created by a solenoid installed inside the target holder. A set of simulations at a fixed magnetic field of 0.0025 T at the target surface is performed. Secondary electron emission from the target subjected to ion bombardment is also included. It is found that the plasma density around the cylindrical target increases because of intense background gas ionization by the electrons drifting in the crossed E x B fields. Suppression of the sheath expansion and increase of the implantation current density in front of the high-density plasma region are observed. The effect of target bias on the sheath dynamics and implantation current of the magnetic field enhanced PIII is discussed. (C) 2007 Elsevier B.V. All rights reserved.

Effect of electron magnetic trapping in a plasma immersion ion implantation system

In this work we describe a two-dimensional computer simulation of magnetic field enhanced plasma immersion implantation system. Negative bias voltage of 10.0 kV is applied to a cylindrical target located on the axis of a grounded vacuum chamber filled with uniform nitrogen plasma. A pair of external coils creates a static magnetic field with main vector component along the axial direction. Thus, a system of crossed ExB field is generated inside the vessel forcing plasma electrons to rotate in azimuthal direction. In addition, the axial variation of the magnetic field intensity produces magnetic mirror effect that enables axial particle confinement. It is found that high-density plasma regions are formed around the target due to intense background gas ionization by the trapped electrons. Effect of the magnetic field on the sheath dynamics and the implantation current density of the PIII system is investigated. By changing the magnetic field axial profile (varying coils separation) an enhancement of about 30% of the retained dose can be achieved. The results of the simulation show that the magnetic mirror configuration brings additional benefits to the PIII process, permitting more precise control of the implanted dose.

The Impact on Geographic Location Accuracy due to Different Satellite Orbit Ephemerides

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

ZERO SOUND-VELOCITY IN PI, RHO-MESON GASES

Sharp transitions are perhaps absent in QCD, so that one looks for physical quantities which may reflect the phase change. One such quantity is the sound velocity which was shown in lattice theory to become zero at the transition point for pure glue. We show that even in a simple bag model the sound velocity goes to zero at temperature T = T(v) not-equal 0 and that the numerical value of this T(v) depends on the nature of the meson. The average thermal energy of mesons goes linearly with T near T(v), with much smaller slope for the pion. The T(v) - s can be connected with the Boltzmann temperatures obtained from transverse momentum spectrum of these mesons in heavy-ion collision at mid-rapidity. It would be interesting to check the presence of different T(v) - s in present day finite T lattice theory.

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.

Static converter: didactic support software

Teaching a course of special electric loads in a continuing education program to power engineers is a difficult task because they are not familiarized with switching topology circuits. Normally, in a typical program, many hours are dedicated to explain the thyristors switching sequence and to draw the converter currents and terminal voltages waveforms for different operative conditions. This work presents teaching support software in order to optimize the time spent in this task and, mainly to benefit the assimilation of the proposed subjects, studying the static converter under different non-ideal operative conditions.

Neural networks training using the constructivism paradigms

The Backpropagation Algorithm (BA) is the standard method for training multilayer Artificial Neural Networks (ANN), although it converges very slowly and can stop in a local minimum. We present a new method for neural network training using the BA inspired on constructivism, an alphabetization method proposed by Emilia Ferreiro based on Piaget philosophy. Simulation results show that the proposed configuration usually obtains a lower final mean square error, when compared with the standard BA and with the BA with momentum factor.

Another prefiltering algorithm for adaptive IIR filtering

An algorithm for adaptive IIR filtering that uses prefiltering structure in direct form is presented. This structure has an estimation error that is a linear function of the coefficients. This property greatly simplifies the derivation of gradient-based algorithms. Computer simulations show that the proposed structure improves convergence speed.