Do dividends signal more earnings? a theoretical analysis

The signaling models have contributed to the literature of corporate finance by the formalization of "the informational content of dividends hypothesis". However, these models are under criticism of empirical works, as weak evidences were found supporting one of the main predictions: the positive relation between changes in dividends and changes in earnings. We claim that the failure to verify this prediction does not invalidate the signaling approach. The mo deIs developed up to now assume or derive utility functions with the single-crossing property. We show that signaling is possible in the absence of this property and, in this case, changes in dividend and changes in earnings can be positively or negatively related.

Food waste in lower-middle income households: a qualitative analysis of antecedents and a typology of food wasters

Esta dissertação doutoral, com base em dados empíricos coletados com 50 mães distribuídas no Brasil (n = 30) e nos EUA (n = 20), tem como objetivo fornecer uma melhor compreensão do desperdício de alimento no contexto da baixa renda. A tese é composta por três artigos, que combinados, cumprem os objetivos de identificar os antecedentes do desperdício de alimento e delinear uma tipologia dos desperdiçadores de alimento. Adicionalmente, contextualiza o desperdício global e um capítulo propõe uma agenda futura para estudos sobre desperdício de alimento no âmbito do consumidor. O desperdício de alimento nas famílias, enquanto tema de pesquisa, oferece a oportunidade para o trabalho acadêmico em marketing cumprir os critérios de relevância social, gerencial e para políticas públicas. No primeiro estudo, descrevem-se os fatores do chamado "paradoxo do desperdício de alimento", a identificação e análise do desperdício de alimento em famílias com restrições orçamentárias, enquanto apresentam-se o itinerário do consumo de alimentos e os antecedentes do desperdício. Este primeiro artigo, elaborado com dados coletados em famílias brasileiras, ilustra também o papel das normas culturais, tais como o preparo abundante de alimento para mostrar hospitalidade ou como forma de não ser percebido como pobre, no aumento do desperdício. No segundo artigo, uma grounded-theory (teoria fundamentada nos dados) destaca o papel do afeto e da abundância no desperdício de alimento familiar. Para enriquecer as contribuições teóricas, este segundo estudo apresenta um framework com seis dimensões do desperdício de alimento (1. Afeto; 2. Abundância; 3. Multiplicidade de escolhas; 4. Conveniência; 5. Procrastinação; 6. Rotina sem planejamento). Baseado em dados empíricos coletados em famílias americanas, este estudo proporciona novas explicações, a exemplo de como o estoque abundante de comfort foods - uma forma de impulsionar tanto emoções positivas para si quanto mostrar afeto para crianças – pode gerar mais desperdício de alimentos. Em síntese, o segundo artigo identifica uma consequência negativa do afeto e da abundância de alimentos no contexto familiar, e apresenta um framework teoricamente relevante. Finalmente, o terceiro artigo, a partir do conjunto de dados dos estudos anteriores e de nova coleta com dez famílias, propõe uma tipologia comportamental do desperdício de alimento, uma contribuição original aos estudos de comportamento do consumidor. A identificação de cinco tipos de desperdiçadores de alimentos - (1) Mães carinhosas; (2) Cozinheiras abundantes; (3) Desperdiçadoras de sobras; (4) Procrastinadoras; (5) Mães versáteis - contribui para a teoria, enquanto implicações potenciais para educadores nutricionais e agentes públicos são exploradas a partir dos resultados. Como uma forma de explicar as características de cada um dos cinco tipos identificados, compara-se aspectos das amostras brasileira e norte-americana, que apresentam similaridades no comportamento de desperdício de alimento. Os níveis de desperdício percebidos por país também são comparados. Em suma, os achados dos três artigos podem contribuir para maximizar os resultados de campanhas de conscientização voltadas à mitigação do desperdício de alimento, e apresentam ideias para varejistas interessados em iniciativas de sustentabilidade. Mais abrangentemente, os resultados apresentados também podem ser aplicados para incrementar programas de combate à fome e projetos de educação nutricional realizados pelo setor público ou ONGs.

Image processing algorithm for the identification of Martian dust devil tracks in MOC and HiRISE images

This paper presents a method for automatic identification of dust devils tracks in MOC NA and HiRISE images of Mars. The method is based on Mathematical Morphology and is able to successfully process those images despite their difference in spatial resolution or size of the scene. A dataset of 200 images from the surface of Mars representative of the diversity of those track features was considered for developing, testing and evaluating our method, confronting the outputs with reference images made manually. Analysis showed a mean accuracy of about 92%. We also give some examples on how to use the results to get information about dust devils, namelly mean width, main direction of movement and coverage per scene. (c) 2012 Elsevier Ltd. All rights reserved.

Predictor-Corrector Primal-Dual Interior Point Method for Solving Economic Dispatch Problems: A Postoptimization Analysis

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

About the Use of the HdHr Algorithm Group in Integrating the Movement Equation with Nonlinear Terms

This work summarizes the HdHr group of Hermitian integration algorithms for dynamic structural analysis applications. It proposes a procedure for their use when nonlinear terms are present in the equilibrium equation. The simple pendulum problem is solved as a first example and the numerical results are discussed. Directions to be pursued in future research are also mentioned. Copyright (C) 2009 H.M. Bottura and A. C. Rigitano.

Transient stability analysis of electric energy systems via a fuzzy ART-ARTMAP neural network

This work presents a methodology to analyze transient stability (first oscillation) of electric energy systems, using a neural network based on ART architecture (adaptive resonance theory), named fuzzy ART-ARTMAP neural network for real time applications. The security margin is used as a stability analysis criterion, considering three-phase short circuit faults with a transmission line outage. The neural network operation consists of two fundamental phases: the training and the analysis. The training phase needs a great quantity of processing for the realization, while the analysis phase is effectuated almost without computation effort. This is, therefore the principal purpose to use neural networks for solving complex problems that need fast solutions, as the applications in real time. The ART neural networks have as primordial characteristics the plasticity and the stability, which are essential qualities to the training execution and to an efficient analysis. The fuzzy ART-ARTMAP neural network is proposed seeking a superior performance, in terms of precision and speed, when compared to conventional ARTMAP, and much more when compared to the neural networks that use the training by backpropagation algorithm, which is a benchmark in neural network area. (c) 2005 Elsevier B.V. All rights reserved.

Sensitivity analysis by neural networks applied to power systems transient stability

This work presents a procedure for transient stability analysis and preventive control of electric power systems, which is formulated by a multilayer feedforward neural network. The neural network training is realized by using the back-propagation algorithm with fuzzy controller and adaptation of the inclination and translation parameters of the nonlinear function. These procedures provide a faster convergence and more precise results, if compared to the traditional back-propagation algorithm. The adaptation of the training rate is effectuated by using the information of the global error and global error variation. After finishing the training, the neural network is capable of estimating the security margin and the sensitivity analysis. Considering this information, it is possible to develop a method for the realization of the security correction (preventive control) for levels considered appropriate to the system, based on generation reallocation and load shedding. An application for a multimachine power system is presented to illustrate the proposed methodology. (c) 2006 Elsevier B.V. All rights reserved.

Identification of lubricant contamination by biodiesel using vibration analysis and neural network

Purpose - The purpose of this paper is to provide information on lubricant contamination by biodiesel using vibration and neural network.Design/methodology/approach - The possible contamination of lubricants is verified by analyzing the vibration and neural network of a bench test under determinated conditions.Findings - Results have shown that classical signal analysis methods could not reveal any correlation between the signal and the presence of contamination, or contamination grade. on other hand, the use of probabilistic neural network (PNN) was very successful in the identification and classification of contamination and its grade.Research limitations/implications - This study was done for some specific kinds of biodiesel. Other types of biodiesel could be analyzed.Practical implications Contamination information is presented in the vibration signal, even if it is not evident by classical vibration analysis. In addition, the use of PNN gives a relatively simple and easy-to-use detection tool with good confidence. The training process is fast, and allows implementation of an adaptive training algorithm.Originality/value - This research could be extended to an internal combustion engine in order to verify a possible contamination by biodiesel.

Analysis of biological tissues in infant chest for the development of an equivalent radiographic phantom

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

Feature selection through gravitational search algorithm

In this paper we deal with the problem of feature selection by introducing a new approach based on Gravitational Search Algorithm (GSA). The proposed algorithm combines the optimization behavior of GSA together with the speed of Optimum-Path Forest (OPF) classifier in order to provide a fast and accurate framework for feature selection. Experiments on datasets obtained from a wide range of applications, such as vowel recognition, image classification and fraud detection in power distribution systems are conducted in order to asses the robustness of the proposed technique against Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and a Particle Swarm Optimization (PSO)-based algorithm for feature selection.

An algorithm for arranging response surface designs in small blocks

We consider the problem of blocking response surface designs when the block sizes are prespecified to control variation efficiently and the treatment set is chosen independently of the block structure. We show how the loss of information due to blocking is related to scores defined by Mead and present an interchange algorithm based on scores to improve a given blocked design. Examples illustrating the performance of the algorithm are given and some comparisons with other designs are made. (C) 2000 Elsevier B.V. B.V. All rights reserved.

Real orthogonal polynomials in frequency analysis

We study the use of para-orthogonal polynomials in solving the frequency analysis problem. Through a transformation of Delsarte and Genin, we present an approach for the frequency analysis by using the zeros and Christoffel numbers of polynomials orthogonal on the real line. This leads to a simple and fast algorithm for the estimation of frequencies. We also provide a new method, faster than the Levinson algorithm, for the determination of the reflection coefficients of the corresponding real Szego polynomials from the given moments.

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.

Image analysis for assessing broiler breeder behavior response to thermal environment

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