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The use of wind energy has been growing worldwide continuously over the last years due to global efforts to combat climate change. The modern turbines are becoming ever more cost effective and are gaining features that aim to reduce further the impact on the environment, such as reducing noise or increasing the height. In this context, the current study investigates the persistence of wind in Fernando de Noronha archipelago, an important Brazilian ecological site, using hourly wind speed data at 100-m height. To this end, data from Era5 reanalysis were used, as they present high resolution and good performance in estimating meteorological data, and two well-known methods were applied to quantify wind persistence: duration curve and conditional probability. In addition, a novel method is proposed regarding persistence probability of periods of viable energy generation. The results obtained show that Fernando de Noronha archipelago presents rather high wind persistence on a monthly scale, with considerably long intervals of wind speed useful for wind power generation.

Wind turbines operate almost uninterruptedly, and their operation is often subject to harsh environments, as well as complex and dynamic loads. Fourier analysis, a standard diagnostic technique, presents some limitations regarding the use of non-stationary, non-periodic, noisy data, which is precisely the case with wind turbine data. Due to these limitations, unseen faults could progress and cause severe, and even catastrophic, failure in wind turbines. Information theory quantifiers, such as entropy, divergence, and, statistical complexity measure, are proposed to evaluate the health status of wind turbine components. In this work, this is done via the decomposition of the signal in time, frequency, and time-frequency domain, namely via Bandt and Pompe, power spectrum, and wavelet packet decomposition. Two different real data sets from operational wind turbines were characterized by the proposed methods. Results demonstrate that the proposed method can distinguish (cluster) well between the states of fault, but also presented some limitations, mainly related to the complexity of the signal from operational wind turbines. Based on these results, new methods, complementary to Fourier analysis, are proposed to be employed in wind turbine data, aiming to increase the capability of detecting faults in such a complex environment.

In this work, we compare three distinct schemes for detrended fluctuation analysis (DFA) of three-dimensional (3D) [Formula: see text] data sets: (i) an [Formula: see text] set of one-dimensional (1D) DFA runs for each vertical line of length [Formula: see text], (ii) a set of [Formula: see text] two-dimensional (2D) DFA runs for each of the [Formula: see text] planes along the vertical direction, and (iii) a single 3D DFA run. The objective of this work is two-fold: (i) to compare the performance of 1D, 2D, and 3D DFA methods in terms of compatibility as well as the richness of obtained results, and (ii) to demonstrate how these three complementary approaches shed new light on the complexity of the structure of the two real soil samples, through the analysis of grayscale high-resolution X-ray computed tomography scan images.

Degradation of soils due to land use change driven by economic factors represents a major concern in many parts of the world. Important questions regarding soil degradation demand further efforts to better understand the effect of land use change on soil functions. With the advent of 3d Computer Tomography techniques and computing power, new methods are becoming available to address these questions. In this work, we investigate how land use change affects soil structure by using information theory to quantify the complexity of soil 3d X-ray CT soil samples in northeastern Brazil. We implement the Fisher-Shannon method, borrowed from information theory, to quantify the complexity of 14 3d CT soil samples from native Atlantic forest sites, and15 samples from nearby sites converted to sugarcane plantation. The distinction found between the samples from the Atlantic forest and the sugarcane plantation is found to be quite pronounced. The discrimination results at the level of 89.6% accuracy were obtained in terms of Fisher information measure (FIM) alone, and 93% level accuracy was attained considering the complexity in the Fisher Shannon plane (FSP). Atlantic forest samples are found to be generally more complex than those from the sugar plantation. The approach introduced in the current work does not use arbitrary parameters, and it provides a rather precise quantitative FSP complexity measure, that may be seen as a quantifier of soil degradation level.

We investigated the influence of the construction of cascade dams and reservoirs on the predictability and complexity of the streamflow of the São Francisco River, Brazil, by using complexity entropy causality plane (CECP) in its standard and weighted form. We analyzed daily streamflow time series recorded in three fluviometric stations: São Francisco (upstream of cascade dams), Juazeiro (downstream of Sobradinho dam), and Pão de Açúcar station (downstream of Sobradinho and Xingó dams). By comparing the values of CECP information quantifiers (permutation entropy and statistical complexity) for the periods before and after the construction of Sobradinho (1979) and Xingó (1994) dams, we found that the reservoirs’ operations changed the temporal variability of streamflow series toward the less predictable regime as indicated by higher entropy (lower complexity) values. Weighted CECP provides some finer details in the predictability of streamflow due to the inclusion of amplitude information in the probability distribution of ordinal patterns. The time evolution of streamflow predictability was analyzed by applying CECP in 2 year sliding windows that revealed the influence of the Paulo Alfonso complex (located between Sobradinho and Xingó dams), construction of which started in the 1950s and was identified through the increased streamflow entropy in the downstream Pão de Açúcar station. The other streamflow alteration unrelated to the construction of the two largest dams was identified in the upstream unimpacted São Francisco station, as an increase in the entropy around 1960s, indicating that some natural factors could also play a role in the decreased predictability of streamflow dynamics.

In this work, we analyze the complexity of monthly rainfall temporal series recorded from 1962 to 2012, at 133 gauge stations in the state of Pernambuco, northeastern Brazil. To this end, we employ the modified multiscale entropy method (MMSE), which is well suited for short time series, to analyze the rainfall regularity across a wide range of temporal scales, from one month to one year. We identify the temporal scales that distinguish rainfall regularity in the inland semiarid Sertão region, the transitional inland Agreste region, and the coastal, tropical humid Zona da Mata region, by comparing the results for stations across the study area and performing statistical significance tests. Our work contributes to the establishment of multiscale methods based on information theory in climatological studies.

In this work, we analyze the regularity of monthly rainfall temporal series during the period 1953 to 2012, recorded at 133 gauging stations in the state of Pernambuco, northeastern Brazil. We use sample entropy method (SampEn), which is suitable for short and noisy data and recently attracted the attention of hydrologists as promising for rainfall studies. By comparing the SampEn values of the analyzed series, we find that for both the original and deseasonalized series entropy increases (regularity decreases) in the west–east direction from the inland Sertão region towards the coastal Zona da Mata. SampEn values for the semiarid Sertão region are significantly different from the humid coastal Zona da Mata and subhumid transition Agreste regions. By comparing two 30 year subperiods (1953–1982 and 1983–2012), we found that in the second period, the rainfall amount decreases in Sertão and Agreste, and increases in Zona de Mata, and that the Agreste and Zona da Mata regions become more similar in respect to the regularity of rainfall dynamics. In the second subperiod, the rainfall regime changes the most in Zona da Mata (both original and anomalies series show a significant difference in SampEn values). By analyzing time dependent SampEn, we identified several periods of increasing entropy, which are related to specific climatic phenomena such as subsequent El Niño and La Niña episodes. This work represents a contribution to establishing the use of information theory-based methods in climatological studies.

A novel heuristic approach is proposed here for time series data analysis, dubbed Generalized weighted permutation entropy, which amalgamates and generalizes beyond their original scope two well established data analysis methods: Permutation entropy and Weighted permutation entropy. The method introduces a scaling parameter to discern the disorder and complexity of ordinal patterns with small and large fluctuations. Using this scaling parameter, the complexity-entropy causality plane is generalized to the complexity-entropy-scale causality box. Simulations conducted on synthetic series generated by stochastic, chaotic, and random processes, as well as real world data, are shown to produce unique signatures in this three dimensional representation.

We study the direction of information flow between Ibovespa index and its constituent companies using the Transfer entropy method. We find stronger information transfer from individual stocks towards the composite index, than in the opposite direction. Our results differ from those found for developed market, where market index was identified as driving force, indicating that the role of index increases with the maturity of the market.

We investigate intrinsic correlations between Brazilian energy (ethanol) and food market (sugar) by applying recently introduced method Detrended partial cross correlation analysis (DPCCA) on returns and volatility series of sugar, ethanol and crude oil prices. The results show that intrinsic cross-correlations between ethanol and sugar are positive and increase with time scale for both returns and volatility, indicating that the diversion of sugar cane for biofuel production (due to the variations in ethanol prices), affects more sugar prices than the price movements in external energy market. Ethanol/oil volatility series and sugar/oil return series do not show correlations for small scales, while at larger scales intrinsic correlations become negative. The intrinsic correlations in oil/ethanol return series and oil/sugar volatility series show similar behavior as corresponding ethanol/sugar series, indicating the existence of strong correlations between sugar and ethanol price variations.

This paper investigates the performance of the 12 entropy-based features for the monitoring and detection of bearing faults. These entropy measures were proposed both in time, frequency and time–frequency domain. Probability mass function (PMF) was extracted from the time waveforms using four different methods: (i) via power spectral density, (ii) via ordinal pattern distribution, (iii) via wavelet packet tree and iv) ensemble empirical mode decomposition. Three different entropy measures were used in the article: (i) Shannon entropy, (ii) Renyi entropy and (iii) Jensen–Renyi divergence. A new bearing produces a vibration time series characterised by random noise without prominent periodic content. As soon as a fault develops, impulses are produced, what excites structural resonances generating a train of impulse responses. As defect grows, it becomes a distributed fault, and then no sharp impulses are generated but rather an amplitude modulated random noise signal. The proposed methodology has been applied to detect bearing faults by the analysis of two real bearing datasets, from run-to-failure experiments. Three bearings that presented different defects in the test (inner race fault, rolling elements fault and outer race fault) were analysed to validate the performance of the entropy-based features. The modified Z-score has been implemented and used as an index to detect changes of the entropy features. The results clearly demonstrate that the proposed approach represents a valuable non-parametric tool for early detection of anomalies in bearings vibration signals.

The use of wind energy has been growing worldwide continuously over the last years due to global efforts to combat climate change. The modern turbines are becoming ever more cost effective, and are gaining features that aim to reduce further the impact on the environment, such as reducing noise or increasing the height. In this context, the current study investigates the persistence of wind in Fernando de Noronha archipelago, an important Brazilian ecological site, using hourly wind speed data at 100m height. To this end, data from Era5 reanalysis were used, as they present high resolution and good performance in estimating meteorological data, and two well-known methods were applied to quantify wind persistence: duration curve and conditional probability. In addition, a novel method is proposed regarding persistence probability of periods of viable energy generation. The results obtained show that Fernando de Noronha archipelago presents rather high wind persistence on a monthly scale, with considerably long intervals of wind speed useful for wind power generation.

The objective of this study was to develop a software based on image processing and computer vision techniques for monitoring the feeding/collective behavior of broilers (Cobb) and validate it based on the results obtained from the visual analysis of an expert. The visual analysis was performed based on the observation and quantification of behaviors in the interval of 10 min at each hour of the day, in the period of 24 hours, totaling 1728 frames/day, for males and females. The software was developed using the Hoshen-Kopelman algorithm, for labeling clusters, which would basically be the grouping of similar pixels. This software is written in the 1999 standard of the C language. After this stage of programming, the Hoshen-Kopelman algorithm identified the birds in the region of interest (feeder and drinker), with removal of the background color to obtain the descriptors, using the same range of visual analysis. Then, the software was validated through linear regression analysis, using the R platform. Based on the correlation analyses, it was found that the coefficient of determination (R2) ranged from 0.74 to 0.97 for all software validation events. The number of broilers in the regions of interest showed R2 above 0.70 for females and above 0.89 for males, which allowed the characterization of the ingestive behavior of broilers by computer vision. Some factors that caused interference in the accuracy of the software were decisive for the result, especially the arrangement of the cameras, uneven lighting, obstruction caused by the lighting system, pendular movement of the feeders and drinker, and shade generated by them. Despite the highlighted interferences, the results allowed us to infer that the software identified and quantified the feeding behavior of broilers in an appropriate manner.

We analyze trend and persistence in Standardized Precipitation Index (SPI) time series derived from monthly rainfall data at 133 gauging stations in Pernambuco state, Brazil, using a suite of complementary methods to address the spatially explicit tendencies, and persistence. SPI was calculated for 1-, 3-, 6-, and 12-month time scales from 1950 to 2012. We use Mann&ndash, Kendall test and Sen&rsquo, s slope to determine sign and magnitude of the trend, and detrended fluctuation analysis (DFA) method to quantify long-term correlations. For all time scales significant negative trends are obtained in the Sert&atilde, o (deep inland) region, while significant positive trends are found in the Agreste (intermediate inland), and Zona da Mata (coastal) regions. The values of DFA exponents show different scaling behavior for different time scales. For short-term conditions described by SPI-1 the DFA exponent is close to 0.5 indicating weak persistency and low predictability, while for medium-term conditions (SPI-3 and SPI-6) DFA exponents are greater than 0.5 and increase with time scale indicating stronger persistency and higher predictability. For SPI-12 that describes long-term precipitation patterns, the values of DFA exponents for inland regions are around 1, indicating strong persistency, while in the shoreline the value of the DFA exponent is between 1.0 and 1.5, indicating anti-persistent fractional Brownian motion. These results should be useful for agricultural planning and water resource management in the region.

This study investigated the potential of methods from information science to detect hydrological alterations caused by human activity. In particular, the influence of the construction of a ...

As queimadas são processos complexos que podem ter causas naturais ou serem resultado da interferência humana. O efeito do fogo em um ecossistema varia de acordo com sua sensibilidade, mas a recorrência do fogo pode afetar o equilibrio ambiental e a saúde humana. Buscando reduzir os efeitos negativos das queimadas, faz-se necessário monitorar sua ocorrência e compreender sua dinâmica. No Brasil, o monitoramento é realizado via satélite, com que se detectam os focos de calor. Esse processo é realizado pelo Instituto Nacional de Pesquisas Espaciais (INPE), que disponibiliza os dados utilizados neste trabalho. Com o objetivo de estudar a variabilidade temporal das queimadas nos biomas Amazônia, Cerrado, Caatinga e Mata Atlântica, utiliza-se neste trabalho o método Grafo de Visibilidade Horizontal, com que se transformam as séries temporais de focos de calor desses biomas em redes complexas. Delas, extraem-se as medidas topológicas com as quais é possível avaliar se a dinâmica que governa cada rede gerada representa um processo estocástico ou caótico. Tais medidas são: o Coeficiente λ da distribuição de grau do nó, o Coeficiente de Agrupamento e o Comprimento Médio do Caminho. Os resultados obtidos mostraram que as propriedades topológicas das redes variam de acordo com o número de focos de calor e com a relação (n° de focos de calor)/(área do bioma). A dinâmica de queimadas apresentou características de um processo estocástico correlacionado para os biomas Amazônia, Cerrado e Mata Atlântica, e um processo caótico para o bioma Caatinga.

This paper is concerned with the persistence of a single species population in a habitat represented by a finite rooted tree with a lethal boundary and whose interior is a Cayley tree. This Cayley tree is a habitat outside which the organisms cannot live. The population is assumed to be governed by an irreducible cooperative system of differential equations that admits zero as an equilibrium solution. The system is determined by five parameters that describes the structure of the tree representing the habitat and the demographic aspects of the population such as migration and reproduction. It is shown that the population is persistent if, and only if, the zero solution is unstable. The stability of the zero solution is determined by the eigenvalue of the Jacobian of the system at zero with largest real part. After obtaining analytically such eigenvalue in terms of the parameters of the system, two criteria for evaluation of persistence are derived. One of them is the minimum patch size, which, here, is defined as the minimum number of levels that makes the tree a habitat capable of sustaining the population.

We study the daily discharge data of Brazos River in Texas, USA, from 1900 to 2017, in terms of concepts drawn from the non-extensive statistics recently introduced by Tsallis. We find that the Brazos River discharge indeed follows non-extensive statistics regarding equilibrium, relaxation and sensitivity. Besides being the first such finding of a full-fledged q -triplet in hydrological data with possible future impact on water resources management, the fact that all three Tsallis q -triplet values are remarkably close to those of the logistic map at the onset of chaos opens up new questions towards a deeper understanding of the Brazos River dynamics, that may prove relevant for hydrological research in a more general sense.

We study auto-correlations and cross-correlations of IBovespa index and its constituent companies. We use Detrended Fluctuation Analysis (DFA) to quantify auto-correlations and Detrended Cross-Correlation Analysis (DCCA) to quantify cross-correlations in absolute returns of daily closing prices of IBovespa and the individual companies. We find persistent long-term correlations and cross-correlations which are weaker than those found for USA market. Our results indicate that market indices of developing markets exhibit weaker coupling with its constituents than for mature developed markets.

In this paper we propose three distinct points for improving scale sensitive DFA based multiple linear regression inference: the scale dependent standardized Regression Coefficients β ˆ s t d D F A s as a measure of dependent variables relative importance, scale dependent effect size f 2 ( s ) , and Intersection-Union hypothesis test, which can handle the composite hypothesis of no cross-correlation, and of having no advantage over the standard OLS linear regression model. We applied this framework on a model for acoustic impedance of well log data, as a function of neutron effective porosity, shale volume, and resistivity. We find that the neutron effective porosity is more important for modeling acoustic impedance than resistivity and, that both are more important than the shale volume, for all scales. Results from Intersection-Union test suggest the rejection of compound null hypothesis for neutron effective porosity and resistivity for scales between 40 and 500 ft, suggesting a robust model for acoustic impedance based on neutron effective porosity and resistivity on these scales. This approach represents a novel framework for scale sensitive regression models, and we believe that it can be useful for time series studies in many diverse areas.

Nas últimas décadas o comércio de frutas no Brasil cresceu expressivamente atendendo ao mercado interno e externo. Dentre as principais frutas produzidas e comercializadas, destaca-se a manga, a fruta mais exportada pelo Brasil. Neste trabalho foram analisadas as séries temporais de retornos e de volatilidade de preços semanais de duas variedades de manga, Palmer e Tommy Atkins, produzidas no Vale do São Francisco, a região com a maior produção de manga no país. Foram utilizados os métodos Detrended Fluctuation Analysis (DFA) e Detrended Cross-Correlation Analysis (DCCA) para calcular expoentes de escala de autocorrelações e correlações cruzadas entre as séries analisadas. Os resultados mostraram que as séries de volatilidade apresentam persistência mais forte do que as séries de retornos que apresentaram dois regimes de invariância de escala com correlações antipersistentes nas escalas maiores. As correlações cruzadas entre as séries de retornos também apresentaram dois regimes de escala com expoentes semelhantes as séries de retornos da variedade Tommy. Os valores do coeficiente de correlação obtidos pelo método Detrended Cross Correlation Coefficient mostraram que para ambas, retornos e volatilidade, as correlações entre as séries são positivas, aumentam com escala temporal e são mais fortes para as séries de retornos.

Using joint permutation entropy we address the issue of minimizing the cost of monitoring, while minimizing redundancy of the information content, of daily streamflow data recorded during the period 1989–2016 at twelve gauging stations on Brazos River, Texas, USA. While the conventional entropy measures take into account only the probability of occurrence of a given set of events, permutation entropy also takes into account local ordering of the sequential values, thus enriching the analysis. We find that the best cost efficiency is achieved by performing weekly measurements, in comparison with which daily measurements exhibit information redundancy, and monthly measurements imply information loss. We also find that the cumulative information redundancy of the twelve considered stations is over 10% for the observed period, and that the number of monitoring stations can be reduced by half bringing the cumulative redundancy level to less than 1%.

We present the results of applying detrended fluctuation analysis (DFA) to study correlations in mountain stream temperatures over a 9-year period for impacted and unimpacted conditions. Permutation entropy and complexity are also computed for stream temperature time series at various locations characterized by varying degrees of human impact, and a graphical representation of the “complexity-entropy causality plane” is used to examine the degree of disturbance caused by human activities. Urban development such as canopy removal and urban infrastructure can lead to a higher degree of anti-persistent correlations indicated by lower values of DFA exponents. This effect is more pronounced downstream due to cumulative urbanization effects with downstream distance. Seasonal variations also influence stream temperature dynamics: less anti-persistent behavior is observed in winter months, and stronger anti-persistent correlations during summer. The position in the complexity-entropy plane, reflecting distribution of short term temperature patterns, is also affected, where most sites experience a shift towards higher entropy values after restoration, and only for one site entropy is reduced. Our results indicate the important role urbanization plays on stream evolution greatly influenced by human activities. This information may provide valuable insights for watershed management and water resources management.

Artificial neural networks (ANNs) are widely used in applications with complex decision boundaries. A large number of activation functions have been proposed in the literature to achieve better representations of the observed data. However, only a few works employ Tsallis statistics, which has successfully been applied to various other fields. This paper presents a random neural network (RNN) with $q$ -Gaussian activation functions [ $q$ -generalized RNN (QRNN)] based on Tsallis statistics. The proposed method employs an additional parameter $q$ (called the entropic index) which reflects the degree of nonextensivity. This approach has the flexibility to model complex decision boundaries of different shapes by varying the entropic index. We conduct numerical experiments to analyze the efficiency of QRNN compared with RNNs and several other classical methods. Statistical tests (Wilcoxon and Friedman) are used to validate our results and show that the QRNN performs significantly better than RNNs with different activation functions. In addition, we find that QRNN outperforms many of the compared classical methods, with the exception of support vector machines, in which case it still exhibits a substantial advantage in terms of implementation simplicity and speed.

In the quest for a best approach to estimate the spatial distribution of monthly precipitation for the state of Pernambuco, Brazil, this work tests different versions (choices of parameters...

The purpose of this paper was to choose an appropriate information dissimilarity measure for hierarchical clustering of daily streamflow discharge data, from twelve gauging stations on the Brazos River in Texas (USA), for the period 1989&ndash, 2016. For that purpose, we selected and compared the average-linkage clustering hierarchical algorithm based on the compression-based dissimilarity measure (NCD), permutation distribution dissimilarity measure (PDDM), and Kolmogorov distance (KD). The algorithm was also compared with K-means clustering based on Kolmogorov complexity (KC), the highest value of Kolmogorov complexity spectrum (KCM), and the largest Lyapunov exponent (LLE). Using a dissimilarity matrix based on NCD, PDDM, and KD for daily streamflow, the agglomerative average-linkage hierarchical algorithm was applied. The key findings of this study are that: (i) The KD clustering algorithm is the most suitable among others, (ii) ANOVA analysis shows that there exist highly significant differences between mean values of four clusters, confirming that the choice of the number of clusters was suitably done, and (iii) from the clustering we found that the predictability of streamflow data of the Brazos River given by the Lyapunov time (LT), corrected for randomness by Kolmogorov time (KT) in days, lies in the interval from two to five days.

A evolução do mercado agrícola brasileiro alterou o processo de produção, exportação e consumo de commodities alimentares. Com isso, novos estudos acerca da relação entre o mercado de alimentos e outros mercados foram desenvolvidos, buscando explicar a ligação entre os preços de commodities agrícolas e não agrícolas. Visando contribuir para esse estudo, foram investigadas as correlações de longo prazo entre os preços de commodities agrícolas brasileiras, utilizando técnicas de Econofísica. Analisaram-se então as séries diárias de preços e retorno de preços da carne de frango, soja e milho, registrados entre 02/08/2004 e 16/06/2017 pelo Centro de Estudos Avançados em Economia Aplicada / Escola Superior de Agricultura Luiz de Queiroz / Universidade de São Paulo - CEPEA/ESALQ/USP. As correlações entre as séries temporais foram investigadas utilizando os métodos Detrended Fluctuation Analysis (DFA) e Detrended Cross Correlation Analysis (DCCA), para calcular o Detrended Cross Correlation Coefficient (DCCA Coefficient), que serve para quantificar correlações de longo prazo entre séries temporais não estacionárias. Os resultados obtidos apontam para a ausência de correlações nas escalas de até 30 dias e, para escalas maiores, acusam correlações mais fortes entre os preços de frango e milho que entre os preços de frango e soja. Após a crise alimentar de 2008, entretanto, as correlações entre as séries diárias de retorno de preços do frango e do milho diminuíram, enquanto que, entre as de frango e soja, aumentaram nas escalas menores e diminuíram nas escalas maiores.

High frequency data from the city of Petrolina, Northeast Brazil, are used to find the wind speed projection series at different angles, which are then analyzed by using several statistical tools: detrended fluctuation analysis (DFA), multifractal detrended fluctuation analysis (MFDFA) and Fisher–Shannon analysis (FSA). It is found that α , the DFA scaling exponent, varies between 0.6 (indicating weak persistence) and unity (strong persistence), at different time frames and at different projection angles. At lower projection angles wind speed is characterized by higher heterogeneity, lower disorder, higher organization and relative dominance of small fluctuations. The statistical analysis of wind speed with varying the direction could contribute to enrich the knowledge of the dynamics of wind speed.

In this work we analyze predictability and complexity of monthly rainfall temporal series recorded from 1950 to 2012, at 133 gauging stations in Pernambuco state, northeastern Brazil. To this end we use the complexity entropy causality plane (CECP) and Fisher Shannon plane (FS) formed by information quantifiers permutation entropy, permutation statistical complexity, and Fisher information measure, which serve to evaluate randomness and structural organization of the underlying process. By comparing the locations of analyzed stations in CECP and FS and performing statistical significance test, we distinguish rainfall regime in the deep inland, drier Sertao region, from the intermediate inland Agreste, and coastal, tropical humid Zona da Mata regions. We also perform time dependent CECP and FS analysis and for Sertao region identify the periods of higher entropy (lower complexity and Fisher information measure) which are related to El Nino episodes and historical droughts in northeastern Brazil. Our work represents a contribution to establishing the use of information theory based methods in climatological studies.

In this paper, we investigate the efficiency of gold market through time evolution of multifractal spectrum parameters (position of maximum, width and asymmetry), by applying multifractal detrended fluctuation analysis in sliding windows of 4, 10 and 20 years. We find that geopolitical and economic events can decrease market efficiency by inducing changes in gold price dynamics toward increased persistency and stronger multifractality with dominance of large fluctuations. For all analyzed geopolitical events and for all windows sizes, we found stronger multifractality of underlying stochastic process, indicated by the increase in the spectrum width [Formula: see text].

A produção, exportação e consumo doméstico de carne de frango no Brasil crescem constantemente, apresentando para esse produto variações de preço que comprometem o orçamento da população. O preço dessa commodity depende, principalmente, do custo da ração, que inclui o milho e a soja como fonte de energia e proteína, respectivamente. Estudar a dinâmica de preço desse produto requer, portanto, uma investigação também acerca dos preços dos insumos milho e soja. Com o objetivo de contribuir para o desenvolvimento e validação de modelos teóricos e computacionais para previsão dos preços de soja, milho e carne de frango, analisaram-se aqui seus preços diários (em moeda brasileira), registrados entre 03/01/2011 e 04/12/2019, obtidos do CEPEA/ESALQ/USP. A partir das séries temporais dos dados de preços originais, foram também criadas as séries de retorno e de volatilidade. Foi utilizado o método Grafo de Visibilidade Horizontal (HVG), implementado em linguagem C, para mapear as séries temporais em redes complexas e, então, calcular os índices topológicos de interesse. Os resultados mostraram que as redes dos preços da soja e do milho são menos integradas que a da carne de frango. Além disso, as séries originais dos preços da soja e do milho, bem como a série de retorno dos preços do frango e a de volatilidade dos preços de milho, são geradas por processos estocásticos correlacionados; a série de retorno dos preços de soja apresenta comportamento com tendência ao de processos não correlacionados; e as demais séries são governadas por processos caóticos.

Ginzburg-Landau theory is one of the most powerful phenomenological theories in physics, with particular predictive value in superconductivity. The formalism solves coupled nonlinear differential equations for both the electronic and magnetic responsiveness of a given superconductor to external electromagnetic excitations. With order parameter varying on the short scale of the coherence length, and the magnetic field being long-range, the numerical handling of 3D simulations becomes extremely challenging and time-consuming for realistic samples. Here we show precisely how one can employ graphics-processing units (GPUs) for this type of calculations, and obtain physics answers of interest in a reasonable time-frame - with speedup of over 100x compared to best available CPU implementations of the theory on a 2563grid. (C) 2016 Elsevier Inc. All rights reserved.

In this paper we investigated the influence of the construction of Sobradinho dam on daily streamflow of Sao Francisco river, Brazil, using permutation entropy method. We analyzed a long daily streamflow time series recorded during the period 1929-2010 encompassing the construction of Sobradinho dam between 1973 and 1979. We found that the original and deseasonalized streamflow time series are characterized by clear different complexity and entropy patterns before the construction of the dam; while, after it, their degree of randomness and complexity are nearly identical. Furthermore, investigating the oscillatory behavior of the entropy and complexity time variation, the periodicity of 3.67 years was identified, identical to one of the main periodicities revealed in the Multivariate ENSO Index (MEI). Such finding confirms the close relationship between streamflow dynamics and ENSO phenomenon. After the construction of the dam, the time variation of entropy and complexity changes almost abruptly toward stochastic regime characterized by higher entropy and lower complexity. Although the dam operations could be considered responsible for such abrupt dynamical change in the streamflow, we cannot exclude the presence of a co-induced ENSO effect; in fact, the analysis of MEI shows a strikingly similar and concomitant change in the long-term trend, identified by using the singular spectrum analysis. (C) 2016 Elsevier B.V. All rights reserved.

Durante a última década, houve vários períodos consecutivos de aumento e queda dos preços das commodities. A formação de preços nos mercados agrícolas é o resultado de muitos fatores, como preços do petróleo, taxas de câmbio, demanda de biocombustíveis, especulação nos mercados futuros de commodities, políticas agressivas de estocagem dos países, restrições comerciais e crescimento econômico. A diversidade desses fatores, bem como a ocorrência de eventos sócio-políticos extremos, produzem um mercado com evolução complexa de preços. Este trabalho utiliza o método de entropia multiescala para analisar a evolução dos movimentos dos preços de commodities agrícolas brasileiras em diferentes escalas temporais durante o período de março de 2006 a março de 2016. Foi descoberto que a entropia das séries de volatilidade e retorno diminui à medida que a escala temporal aumenta, indicando flutuações mais regulares dos preços e a perda da diversidade de padrões nas tendências de longo prazo. Ao aplicar a entropia em maiores escalas em janelas móveis, descobrimos que durante a crise a entropia das flutuações de preços diminui, indicando maior regularidade e consequentemente menor eficiência no mercado de commodities agrícolas. O efeito é mais pronunciado para séries de volatilidade e para maiores escalas temporais.

A precipitação no Nordeste do Brasil (NE) é caracterizada por apresentar alta variabilidade temporal e espacial. No entanto, a disponibilidade de dados de precipitação ainda é limitada nesta região, por vários motivos. O Acesso a dados de precipitação fornece informações úteis para melhor entender a distribuição da chuva e apoiar gerenciamento do ecossistema. Neste artigo, apresentamos o programa Guarda Chuvas, o qual viabiliza o acesso e visualização de dados passados de precipitação pluviométrica do estado de Pernambuco, NE, Brasil, por meio de uma interface de fácil uso. O método de interpolação, trend surface analysis, foi utilizado para estimar a precipitação mensal () para o estado de Pernambuco em uma grade com resolução de 0,01 grau, totalizando 81.544 séries mensais de precipitação distribuídas espacialmente para estado de Pernambuco. O programa foi implementado em linguagem C, com uma interface gráfica construída utilizando interface de programação de aplicativos para o Windows. As séries históricas fornecidas pelo programa podem ser usadas como insumo para modelos de simulação e apoiar estudos direcionados ao desenvolvimento de políticas agrícolas, hídricas, ambientais e socioeconômica para o estado de Pernambuco. Além do interesse regional nas informações fornecidas pelo programa, a abordagem atual deve ser considerada útil para aplicações em outras partes do Brasil e do mundo.

We analyze predictability of selected Brazilian agricultural commodity prices (cotton, sugar, coffee, soybean and cattle) during the period 1997–2018 which encompasses the periods of agricultural commodities prices spikes between 2007 and 2011. We use information theory based methods entropy-complexity plane (HC) and entropy –Fisher information plane (HF), where Shannon entropy, statistical complexity and Fisher information measure are calculated for empirical Brandt-Pompe probability distribution of ordinal patterns. We find that coffee market shows lowest predictability (highest efficiency), while pork market shows highest predictability (lowest efficiency). By analyzing temporal evolution of efficiency index derived from Shannon entropy and Fisher information measure we observe that coffee and soybeans price series exhibit high and stable informational efficiency for all analyzed period, the efficiency of sugar market shows steady increase, while the efficiency of cattle and cotton market first decreases (until the crisis) and then increases in the post crisis period.

In order to address the overall properties of the Brazilian agricultural commodity market and the intricate effects of political and economic instabilities, in this work, we provide a comprehensive study of the multifractal properties of the Brazilian commodities using multifractal detrended fluctuation analysis (MFDFA). We focus on the daily price of 12 Brazilian agricultural commodities over the last two decades, and four commodities (sugar, soybean, coffee and cattle) are also studied in terms of time-dependent MFDFA to address the effects of particular political and economic instability events. All commodities exhibit multifractal properties, which are then used to evaluate market efficiency. We find that all commodities except coffee show lower market efficiency for prices in Brazilian Reals (BRL) than in US Dollars (USD) reflecting the presence of constrains in domestic agricultural market, such as minimum price policy. From time-dependent MFDFA, we find that after the 2007/2008 food crisis, the market efficiency increases, as indicated by the changes in price dynamics towards lower persistency and weaker multifractality, with the dominance of small fluctuations.