Changes in retirement age and fertility: their effects on economic dependency, per capita income and transfer pension systems

Includes bibliography

PATH dependency e a transformação agrária do bioma amazônico: o sentido econômico das capoeiras para o desenvolvimento sustentável

As capoeiras - áreas alteradas por ação antrópica que se encontram em estágios de regeneração espontânea de cobertura florestal - são componentes da paisagem rural de grande significado na Amazônia. No último Censo Agropecuário, as áreas de capoeira perfaziam 4,5 milhões de hectares em toda a Região Norte. Uma literatura crescentemente importante considera tais áreas proxy de economias rurais decadentes e insustentáveis, sobre as quais se ergue uma pecuária de corte eficiente e sustentável. Este artigo procura estabelecer os diferentes tipos de capoeira que se constatam na economia rural da Amazônia, associando-as às diferentes formas de produção, cujos sistemas se expressam dinamicamente como trajetórias tecnológicas concorrentes. A partir daí a) demonstra que parte dessas áreas resulta de mudanças positivas nos sistemas produtivos que produzem capoeiras com grande capacidade de regeneração – estando associada, portanto, a inovações relevantes para o desenvolvimento da Região numa perspectiva que incorpora critérios de sustentabilidade ambiental; b) demonstra que os tipos de capoeira que indicam degradação, pela baixa capacidade de regeneração, se associam à pecuária de corte, a qual na Região tem apresentado dificuldades estruturais de modernização técnica e c) indica que o ambiente institucional, favorecendo os sistemas que produzem capoeira degradada em detrimento daqueles que produzem capoeiras de rápida recomposição, podem aprisionar (levar a um lock-in) a economia agrária da região nas piores soluções, tanto econômica, quanto social e ecologicamente.

Extractive summarization using complex networks and syntactic dependency

The realization that statistical physics methods can be applied to analyze written texts represented as complex networks has led to several developments in natural language processing, including automatic summarization and evaluation of machine translation. Most importantly, so far only a few metrics of complex networks have been used and therefore there is ample opportunity to enhance the statistics-based methods as new measures of network topology and dynamics are created. In this paper, we employ for the first time the metrics betweenness, vulnerability and diversity to analyze written texts in Brazilian Portuguese. Using strategies based on diversity metrics, a better performance in automatic summarization is achieved in comparison to previous work employing complex networks. With an optimized method the Rouge score (an automatic evaluation method used in summarization) was 0.5089, which is the best value ever achieved for an extractive summarizer with statistical methods based on complex networks for Brazilian Portuguese. Furthermore, the diversity metric can detect keywords with high precision, which is why we believe it is suitable to produce good summaries. It is also shown that incorporating linguistic knowledge through a syntactic parser does enhance the performance of the automatic summarizers, as expected, but the increase in the Rouge score is only minor. These results reinforce the suitability of complex network methods for improving automatic summarizers in particular, and treating text in general. (C) 2011 Elsevier B.V. All rights reserved.

Incorporating label dependency into the binary relevance framework for multi-label classification

In multi-label classification, examples can be associated with multiple labels simultaneously. The task of learning from multi-label data can be addressed by methods that transform the multi-label classification problem into several single-label classification problems. The binary relevance approach is one of these methods, where the multi-label learning task is decomposed into several independent binary classification problems, one for each label in the set of labels, and the final labels for each example are determined by aggregating the predictions from all binary classifiers. However, this approach fails to consider any dependency among the labels. Aiming to accurately predict label combinations, in this paper we propose a simple approach that enables the binary classifiers to discover existing label dependency by themselves. An experimental study using decision trees, a kernel method as well as Naive Bayes as base-learning techniques shows the potential of the proposed approach to improve the multi-label classification performance.

Temperature dependency of human muscle velocity recovery cycles

Velocity recovery cycles (VRCs) of human muscle action potentials have been proposed as a new technique for studying muscle membrane function. This study was undertaken to determine the temperature dependency of VRC parameters.

From EEG dependency multichannel matching pursuit to sparse topographic EEG decomposition

In this work, we present a multichannel EEG decomposition model based on an adaptive topographic time-frequency approximation technique. It is an extension of the Matching Pursuit algorithm and called dependency multichannel matching pursuit (DMMP). It takes the physiologically explainable and statistically observable topographic dependencies between the channels into account, namely the spatial smoothness of neighboring electrodes that is implied by the electric leadfield. DMMP decomposes a multichannel signal as a weighted sum of atoms from a given dictionary where the single channels are represented from exactly the same subset of a complete dictionary. The decomposition is illustrated on topographical EEG data during different physiological conditions using a complete Gabor dictionary. Further the extension of the single-channel time-frequency distribution to a multichannel time-frequency distribution is given. This can be used for the visualization of the decomposition structure of multichannel EEG. A clustering procedure applied to the topographies, the vectors of the corresponding contribution of an atom to the signal in each channel produced by DMMP, leads to an extremely sparse topographic decomposition of the EEG.

Age dependency of intrarenal resistance index (RI) in healthy adults and patients with fatty liver disease

BACKGROUND: Our aim was to investigate the influence of age and gender on intrarenal resistance index (RI) measurements in 78 healthy subjects (46 males, 32 females; group 1) and 35 subjects (group 2) with fatty liver disease (28 males and 7 females). SUBJECTS AND METHODS: First, each subject underwent a conventional abdominal ultrasound examination. Then, intrarenal RI values were determined from three distinct interlobar and cortical arteries respectively on both kidneys. The correlation of intrarenal RI with age and gender as a variable was statistically evaluated by linear regression. RESULTS: In group 1, the variables gender, kidney region and comparison of right versus left kidney had no significant effect on intrarenal RI (p>0.05). The variable age, on the other hand, showed a significant positive correlation on all four defined measuring points (p<0.01) with linear correlation coefficients of r = 0.26 (left kidney, central) to r = 0.37 (right kidney, cortical). Therefore normal RI values at ages 25, 45, 65 years could be defined as 0.59, 0.61 and 0.63, respectively. Age dependency can thus be expressed as a function with the formula y = 0.565 + 0.001.x. Patients with fatty liver disease showed age dependency on renal RI (p<0.01) as well. CONCLUSION: In accordance with other studies, the influence of age on intrarenal RI measurement is significant in healthy subjects. Intrarenal RI values from subjects with a fatty liver disease showed age dependency as well. Therefore it is necessary to consider the age of the examined person to interpret RI values correctly.