Structure Elucidation and Absolute Stereochemistry of Isomeric Monoterpene Chromane Esters

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

Hamiltonian reduction and the construction of q-deformed extensions of the Virasoro algebra

In this paper we employ the construction of the Dirac bracket for the remaining current of sl(2) q deformed Kac-Moody algebra when constraints similar to those connecting the sl(2)-Wess-Zumino-Witten model and the Liouville theory are imposed to show that it satisfies the q-Virasoro algebra proposed by Frenkel and Reshetikhin The crucial assumption considered in our calculation is the existence of a classical Poisson bracket algebra induced in a consistent manner by the correspondence principle, mapping the quantum generators into commuting objects of classical nature preserving their algebra.

Harmonics filtering and detection of disturbances using wavelets

Traditional mathematical tools, like Fourier Analysis, have proven to be efficient when analyzing steady-state distortions; however, the growing utilization of electronically controlled loads and the generation of a new dynamics in industrial environments signals have suggested the need of a powerful tool to perform the analysis of non-stationary distortions, overcoming limitations of frequency techniques. Wavelet Theory provides a new approach to harmonic analysis, focusing the decomposition of a signal into non-sinusoidal components, which are translated and scaled in time, generating a time-frequency basis. The correct choice of the waveshape to be used in decomposition is very important and discussed in this work. A brief theoretical introduction on Wavelet Transform is presented and some cases (practical and simulated) are discussed. Distortions commonly found in industrial environments, such as the current waveform of a Switched-Mode Power Supply and the input phase voltage waveform of motor fed by inverter are analyzed using Wavelet Theory. Applications such as extracting the fundamental frequency of a non-sinusoidal current signal, or using the ability of compact representation to detect non-repetitive disturbances are presented.

Invariance for Impulsive Control Systems

In this article, we provide invariance conditions for control systems whose dynamics are given by measure driven differential inclusions. The solution concept plays a critical role in the extension of the conventional conditions for the impulsive control context. A couple of examples illustrating the specific features of impulsive control systems are included.

The no-central chi-square chart with two-stage samplings

Throughout this article, it is assumed that the no-central chi-square chart with two stage samplings (TSS Chisquare chart) is employed to monitor a process where the observations from the quality characteristic of interest X are independent and identically normally distributed with mean μ and variance σ2. The process is considered to start with the mean and the variance on target (μ = μ0; σ2 = σ0 2), but at some random time in the future an assignable cause shifts the mean from μ0 to μ1 = μ0 ± δσ0, δ >0 and/or increases the variance from σ0 2 to σ1 2 = γ2σ0 2, γ > 1. Before the assignable cause occurrence, the process is considered to be in a state of statistical control (defined by the in-control state). Similar to the Shewhart charts, samples of size n 0+ 1 are taken from the process at regular time intervals. The samplings are performed in two stages. At the first stage, the first item of the i-th sample is inspected. If its X value, say Xil, is close to the target value (|Xil-μ0|< w0σ 0, w0>0), then the sampling is interrupted. Otherwise, at the second stage, the remaining n0 items are inspected and the following statistic is computed. Wt = Σj=2n 0+1(Xij - μ0 + ξiσ 0)2 i = 1,2 Let d be a positive constant then ξ, =d if Xil > 0 ; otherwise ξi =-d. A signal is given at sample i if |Xil-μ0| > w0σ 0 and W1 > knia:tl, where kChi is the factor used in determining the upper control limit for the non-central chi-square chart. If devices such as go and no-go gauges can be considered, then measurements are not required except when the sampling goes to the second stage. Let P be the probability of deciding that the process is in control and P 1, i=1,2, be the probability of deciding that the process is in control at stage / of the sampling procedure. Thus P = P1 + P 2 - P1P2, P1 = Pr[μ0 - w0σ0 ≤ X ≤ μ0+ w 0σ0] P2=Pr[W ≤ kChi σ0 2], (3) During the in-control period, W / σ0 2 is distributed as a non-central chi-square distribution with n0 degrees of freedom and a non-centrality parameter λ0 = n0d2, i.e. W / σ0 2 - xn0 22 (λ0) During the out-of-control period, W / σ1 2 is distributed as a non-central chi-square distribution with n0 degrees of freedom and a non-centrality parameter λ1 = n0(δ + ξ)2 / γ2 The effectiveness of a control chart in detecting a process change can be measured by the average run length (ARL), which is the speed with which a control chart detects process shifts. The ARL for the proposed chart is easily determined because in this case, the number of samples before a signal is a geometrically distributed random variable with parameter 1-P, that is, ARL = I /(1-P). It is shown that the performance of the proposed chart is better than the joint X̄ and R charts, Furthermore, if the TSS Chi-square chart is used for monitoring diameters, volumes, weights, etc., then appropriate devices, such as go-no-go gauges can be used to decide if the sampling should go to the second stage or not. When the process is stable, and the joint X̄ and R charts are in use, the monitoring becomes monotonous because rarely an X̄ or R value fall outside the control limits. The natural consequence is the user to pay less and less attention to the steps required to obtain the X̄ and R value. In some cases, this lack of attention can result in serious mistakes. The TSS Chi-square chart has the advantage that most of the samplings are interrupted, consequently, most of the time the user will be working with attributes. Our experience shows that the inspection of one item by attribute is much less monotonous than measuring four or five items at each sampling.

A statistical analysis of loss factor to determine the energy losses

Given that the total amount of losses in a distribution system is known, with a reliable methodology for the technical loss calculation, the non-technical losses can be obtained by subtraction. A usual method of calculation technical losses in the electric utilities uses two important factors: load factor and the loss factor. The load factor is usually obtained with energy and demand measurements, whereas, to compute the loss factor it is necessary the learning of demand and energy loss, which are not, in general, prone of direct measurements. In this work, a statistical analysis of this relationship using the curves of a sampling of consumers in a specific company is presented. These curves will be summarized in different bands of coefficient k. Then, it will be possible determine where each group of consumer has its major concentration of points. ©2008 IEEE.

Bifundamental fuzzy 2-Sphere and fuzzy killing spinors

We review our construction of a bifundamental version of the fuzzy 2-sphere and its relation to fuzzy Killing spinors, first obtained in the context of the ABJM membrane model. This is shown to be completely equivalent to the usual (adjoint) fuzzy sphere. We discuss the mathematical details of the bifundamental fuzzy sphere and its field theory expansion in a model-independent way. We also examine how this new formulation affects the twisting of the fields, when comparing the field theory on the fuzzy sphere background with the compactification of the 'deconstructed' (higher dimensional) field theory.

Fuzzy community structure detection by particle competition and cooperation

Identification and classification of overlapping nodes in networks are important topics in data mining. In this paper, a network-based (graph-based) semi-supervised learning method is proposed. It is based on competition and cooperation among walking particles in a network to uncover overlapping nodes by generating continuous-valued outputs (soft labels), corresponding to the levels of membership from the nodes to each of the communities. Moreover, the proposed method can be applied to detect overlapping data items in a data set of general form, such as a vector-based data set, once it is transformed to a network. Usually, label propagation involves risks of error amplification. In order to avoid this problem, the proposed method offers a mechanism to identify outliers among the labeled data items, and consequently prevents error propagation from such outliers. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method. © 2012 Springer-Verlag.

Utilização de um algoritmo de caminho mínimo no processo de recolhimento do palhiço da cana-de-açúcar

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

A reescritura de Katherine Mansfield por Érico Veríssimo: análise descritiva da tradução para o português da obra Bliss and other stories

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Teoria dos grafos e suas aplicações

Pós-graduação em Matemática Universitária - IGCE

Posso ajudar? Cuidador formal e relação de ajuda: a percepção do idoso acerca do apoio oferecido por um serviço socioassistencial de um município do interior de São Paulo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

O processo metaficcional em Osman Lins e Maria Gabriela Llansol: recriando a dimensão textual

Pós-graduação em Letras - IBILCE

Construção e análise da rede integrada de interações entre genes humanos envolvida com a regulação da trnsição G1/S do ciclo celular plea adesão à matriz extracelular

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Analisando a determinação sexual de vertebrados com base em redes de interação entre proteínas

Pós-graduação em Ciências Biológicas (Genética) - IBB