Gravitational waves : a 100-year tool applied to the dark energy problem

Recent observations from type Ia Supernovae and from cosmic microwave background (CMB) anisotropies have revealed that most of the matter of the Universe interacts in a repulsive manner, composing the so-called dark energy constituent of the Universe. Determining the properties of dark energy is one of the most important tasks of modern cosmology and this is the main motivation for this work. The analysis of cosmic gravitational waves (GW) represents, besides the CMB temperature and polarization anisotropies, an additional approach in the determination of parameters that may constrain the dark energy models and their consistence. In recent work, a generalized Chaplygin gas model was considered in a flat universe and the corresponding spectrum of gravitational waves was obtained. In the present work we have added a massless gas component to that model and the new spectrum has been compared to the previous one. The Chaplygin gas is also used to simulate a L-CDM model by means of a particular combination of parameters so that the Chaplygin gas and the L-CDM models can be easily distinguished in the theoretical scenarios here established. We find that the models are strongly degenerated in the range of frequencies studied. This degeneracy is in part expected since the models must converge to each other when some particular combinations of parameters are considered.

Crescimento, teores e acúmulo de nutrientes em plantas de milho fertirrigadas com água residuária do beneficiamento de frutos de café

A cultura do cafeeiro sempre ocupou lugar de destaque na economia do país, dada sua importância na área econômica e social, buscando cada vez mais, um mercado diferenciado, com novas tecnologias para a melhoria da qualidade da bebida. A atividade de lavagem e despolpa de frutos do cafeeiro, necessária para a redução do custo de secagem e a melhoria da qualidade de bebida, é geradora de grandes volumes de resíduos sólidos e líquidos, ricos em material orgânico e inorgânico. A água residuária da atividade de beneficiamento do café (ARC) é gerada anualmente em grande volume no Espírito Santo, e aliado aos nutrientes existentes nesse efluente indica a sua viabilidade de reaproveitamento na fertirrigação de culturas agrícolas. O objetivo do presente trabalho foi avaliar o efeito de diferentes doses de água residuária de café no crescimento, na absorção e interação entre nutrientes e no estado nutricional do milho. Para tanto, foi conduzido um experimento em casa de vegetação utilizando-se o delineamento experimental inteiramente casualizado, onde foram aplicadas 7 doses de ARC, com 3 repetições, em unidades experimentais constituídas por vasos com 2 dm³ de solo. As doses foram equivalentes a 0,00, 15,17, 30,35, 45,52, 60,70, 75,87 e 91,05 litros de ARC por m² de solo. Realizou-se a semeadura de cinco sementes de milho híbrido BR 206 por vaso e cinco dias após a germinação das plantas foi feito o desbaste, mantendo-se três plantas por vaso. Aos trinta dias após a germinação determinou-se o diâmetro do caule (DC), área foliar (AF), matéria seca da parte aérea (MSPA), matéria seca do sistema radicular (MSR), relação parte aérea/raiz (MSPA/MSR), razão massa radicular (matéria seca raiz/matéria seca total) e razão área foliar (área foliar/matéria seca total). Na parte aérea das plantas foi determinado os teores dos macronutrientes (N, P, K, Ca, Mg e S). Os dados foram submetidos a análise de variância e as variáveis em função das doses de ARC submetidas a análise de regressão. Para as variáveis dependentes foi calculado o coeficiente de correlação linear de Pearson. A ARC serviu como fonte de nutrientes para as plantas de milho, aumentou o rendimento da maioria das variáveis de crescimento e os teores de N, K e S. Porém diminuiu o teor de Ca, Mg e P da parte aérea das plantas além de indicar que altas doses promovem desbalanceamento na relação entre nutrientes.

Modelagem da distribuição geográfica atual e futura de Guerlinguetus (Rodentia, Sciuridae) no Brasil

O gênero Guerlinguetus, pertencente à família Sciuridae, possui sete espécies existentes no Brasil. Essas espécies têm sido tratadas como sinônimos de Sciurus aestuans, mas foram distinguidas em um gênero próprio com base nas revisões taxonômicas mais abrangentes disponíveis até recentemente. Apesar disso, pouco há na literatura sobre a distribuição das espécies desse gênero no Brasil e nenhuma pesquisa visando o conhecimento de sua distribuição futura havia sido realizada. A modelagem de distribuição potencial de espécies tem se tornado um componente importante dos planos de conservação e uma grande quantidade de técnicas têm sido desenvolvidas com esta finalidade. Ela pode ser uma ferramenta importante para determinar o grau de ameaça principalmente em espécies ou grupos com poucas informações disponíveis sobre sua distribuição. Com isso, o objetivo do presente trabalho foi verificar a influência das possíveis mudanças climáticas na distribuição das espécies de Guerlinguetus no Brasil, através da modelagem potencial das distribuições atual e futura dessas espécies. Para isso foram utilizados dados climáticos e topográficos e o cenário pessimista de emissão de CO2 (A2) para o ano 2070, do Modelo de Circulação Geral CSIRO, com base no quarto relatório do Painel Intergovernamental de Mudanças Climáticas. Os modelos gerados apresentaram perda significativa de áreas consideradas ambientalmente adequadas, do modelo de distribuição atual para o modelo de distribuição futura para as espécies de Guerlinguetus, com exceção de G. aestuans. Apesar da expansão da região potencial de distribuição de G. aestuans para o ano de 2070, a média dos valores de adequabilidade ambiental diminuiu em relação ao modelo de distribuição atual. A localização dessas espécies de hábito arborícola em áreas geograficamente espalhadas dentro da Amazônia, Cerrado e Mata Atlântica é preocupante, devido a grande perda de área original desses biomas, tornando possível a hipótese de ameaça à sobrevivência das espécies devido a mudanças ambientais futuras.

Variabilidade espacial do estado nutricional do cafeeiro conilon utilizando índice DRIS

A combinação da agricultura de precisão e do Sistema Integrado de Recomendação Foliar (DRIS) possibilita monitorar espacialmente o balanço nutricional dos cafezais para fornecer recomendações de adubação mais equilibradas e mais ajustadas economicamente. O objetivo deste trabalho foi avaliar a variabilidade espacial do estado nutricional do cafeeiro conilon, utilizando o Índice de Balanço Nutricional (IBN) e sua relação com a produtividade. A produtividade das plantas em cada ponto amostral foi determinada e construiu-se o seu mapa considerando a variabilidade espacial; determinou-se o Índice de Equilíbrio Nutricional (IBN) das plantas em cada ponto amostral e construiu-se o seu mapa; e utilizou-se a análise de componentes principais (ACP) para estimar o IBN do cafeeiro por cokrigagem. Os dados do cafeeiro conilon foram coletados em fazenda experimental, no município de Cachoeiro de Itapemirim-ES. O IBN do cafeeiro e a sua produtividade foram analisados por meio de geoestatística, com base nos modelos e parâmetros dos semivariogramas, utilizando o método de interpolação krigagem ordinária para estimar valores para locais não amostrados. O índice de Balanço Nutricional da lavoura do cafeeiro conilon apresentou dependência espacial, porém não apresentou correlação linear e nem espacial com a produtividade. A lavoura em estudo se encontra em desequilíbrio nutricional, sendo que entre os macronutrientes, o Potássio foi o que apresentou maior desequilíbrio na área, entre os micronutrientes, o Zinco e o Ferro foram os que apresentaram menores concentrações nas folhas. A confecção dos mapas possibilitou a distinção de regiões com maior e menor desequilíbrio nutricional e produtividade, o que possibilita adotar o manejo de forma diferenciada e localizada. A análise multivariada baseada em componentes principais fornece componentes com alta correlação com as variáveis originais P, Ca, Zn , Cu, K e B. A cokrigagem utilizando as componentes principais permite estimar o IBN e a produtividade da área.

Measuring fund performance using multi-factor models: evidence for the Portuguese market

This paper examines the performance of Portuguese equity funds investing in the domestic and in the European Union market, using several unconditional and conditional multi-factor models. In terms of overall performance, we find that National funds are neutral performers, while European Union funds under-perform the market significantly. These results do not seem to be a consequence of management fees. Overall, our findings are supportive of the robustness of conditional multi-factor models. In fact, Portuguese equity funds seem to be relatively more exposed to smallcaps and more value-oriented. Also, they present strong evidence of time-varying betas and, in the case of the European Union funds, of time-varying alphas too. Finally, in terms of market timing, our tests suggest that mutual fund managers in our sample do not exhibit any market timing abilities. Nevertheless, we find some evidence of timevarying conditional market timing abilities but only at the individual fund level.

Models for the Reverse Engineering of Java/Swing Applications

Abstract. Interest in design and development of graphical user interface (GUIs) is growing in the last few years. However, correctness of GUI's code is essential to the correct execution of the overall software. Models can help in the evaluation of interactive applications by allowing designers to concentrate on its more important aspects. This paper describes our approach to reverse engineering abstract GUI models directly from the Java/Swing code.

Component identification through program slicing

This paper reports on the development of specific slicing techniques for functional programs and their use for the identification of possible coherent components from monolithic code. An associated tool is also introduced. This piece of research is part of a broader project on program understanding and re-engineering of legacy code supported by formal methods

Color Models and Illumination Changes

Color model representation allows characterizing in a quantitative manner, any defined color spectrum of visible light, i.e. with a wavelength between 400nm and 700nm. To accomplish that, each model, or color space, is associated with a function that allows mapping the spectral power distribution of the visible electromagnetic radiation, in a space defined by a set of discrete values that quantify the color components composing the model. Some color spaces are sensitive to changes in lighting conditions. Others assure the preservation of certain chromatic features, remaining immune to these changes. Therefore, it becomes necessary to identify the strengths and weaknesses of each model in order to justify the adoption of color spaces in image processing and analysis techniques. This chapter will address the topic of digital imaging, main standards and formats. Next we will set the mathematical model of the image acquisition sensor response, which enables assessment of the various color spaces, with the aim of determining their invariance to illumination changes.

Extracting and verifying coordination models from source code

Current software development relies increasingly on non-trivial coordination logic for com- bining autonomous services often running on di erent platforms. As a rule, however, in typical non-trivial software systems, such a coordination layer is strongly weaved within the application at source code level. Therefore, its precise identi cation becomes a major methodological (and technical) problem which cannot be overestimated along any program understanding or refactoring process. Open access to source code, as granted in OSS certi cation, provides an opportunity for the devel- opment of methods and technologies to extract, from source code, the relevant coordination information. This paper is a step in this direction, combining a number of program analysis techniques to automatically recover coordination information from legacy code. Such information is then expressed as a model in Orc, a general purpose orchestration language

SHACC: a functional prototyper for a component calculus

Over the last decade component-based software development arose as a promising paradigm to deal with the ever increasing complexity in software design, evolution and reuse. SHACC is a prototyping tool for component-based systems in which components are modelled coinductively as generalized Mealy machines. The prototype is built as a HASKELL library endowed with a graphical user interface developed in Swing

On the specification of a component repository

The lack of a commonly accepted de nition of a software component, the proliferation of competing `standards' and component frameworks, is here to stay, raising the fundamental question in component-based development of how to cope in practice with heterogeneity. This paper reports on the design of a Component Repository aimed to give at least a partial answer to the above question. The repository was fully speci ed in Vdm and a working prototype is currently being used in an industrial environment

Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

IDEA MANAGEMENT SYSTEM - AN ESSENTIAL COMPONENT IN THE CONTINUOUS IMPROVEMENT OF QUALITY AND INNOVATION

It is known the power of ideas is tremendous. But there are employees in many companies who have good ideas but not put them into practice. On the other hand, there are many others who have good ideas and are encouraged to contribute their ideas for innovation in the company. This study attempts to identify factors that contribute to success in managing ideas and consequent business innovation. The method used was the case study applied to two companies. During the investigation, factors considered essential for the success of an idea management program were identified, of which we highlight, among others, evidences the results, involvement of the top management, establishment of goals and objectives; recognition; dissemination of good results. Companies with these implemented systems, capture the best ideas from their collaborators and apply them internally. This study intends to contribute to business innovation in enterprises through creation and idea management, mainly through collecting the best ideas of their own employees. The results of this study can be used to help improving deployed suggestions systems, as well as, all managers who wish to implement suggestions systems/ideas management systems.