A new perspective on the PPP hypothesis

This paper the stastistical properties of the real exchange rates of G-5 countries for the Bretton-Woods peiod, and draw implications on the purchasing power parity (PPP) hypothesis. In contrast to most previous studies that consider only unit root and stationary process to describe the real exchange tae, this paper also considers two in-between processes, the locally persistent process ans the fractionally integrated process, to complement past studies. Seeking to be consistent with tha ample evidence of near unit in the real exchange rate movements very well. This finding implies that: 1) the real exchange movement is more persistent than the stationary case but less persistent than the unit root case; 2) the real exchange rate is non-stationary but the PPP reversion occurs and the PPP holds in the long run; 3) the real exchange rate does not exhibit the secular dependence of the fractional integration; 4) the real exchange rate evolves over time in a way that there is persistence over a range of time, but the effect of shocks will eventually disappear over time horizon longer than order O (nd), that is, at finite time horizon; 5) shocks dissipation is fasters than predicted by the fractional integracion, and the total sum of the effects of a unit innovation is finite, implying that a full PPP reversion occurs at finite horizons. These results may explain why pasrt empirical estudies could not provide a clear- conclusion on the real exchange rate processes and the PPP hypothesis.

Dinâmica monetária eficiente sob encontros aleatórios: uma classe de métodos numéricos que exploram concavidade

A dificuldade em se caracterizar alocações ou equilíbrios não estacionários é uma das principais explicações para a utilização de conceitos e hipóteses que trivializam a dinâmica da economia. Tal dificuldade é especialmente crítica em Teoria Monetária, em que a dimensionalidade do problema é alta mesmo para modelos muito simples. Neste contexto, o presente trabalho relata a estratégia computacional de implementação do método recursivo proposto por Monteiro e Cavalcanti (2006), o qual permite calcular a sequência ótima (possivelmente não estacionária) de distribuições de moeda em uma extensão do modelo proposto por Kiyotaki e Wright (1989). Três aspectos deste cálculo são enfatizados: (i) a implementação computacional do problema do planejador envolve a escolha de variáveis contínuas e discretas que maximizem uma função não linear e satisfaçam restrições não lineares; (ii) a função objetivo deste problema não é côncava e as restrições não são convexas; e (iii) o conjunto de escolhas admissíveis não é conhecido a priori. O objetivo é documentar as dificuldades envolvidas, as soluções propostas e os métodos e recursos disponíveis para a implementação numérica da caracterização da dinâmica monetária eficiente sob a hipótese de encontros aleatórios.

Parallel Cyclostationarity-Exploiting Algorithm for Energy-Efficient Spectrum Sensing

The evolution of wireless communication systems leads to Dynamic Spectrum Allocation for Cognitive Radio, which requires reliable spectrum sensing techniques. Among the spectrum sensing methods proposed in the literature, those that exploit cyclostationary characteristics of radio signals are particularly suitable for communication environments with low signal-to-noise ratios, or with non-stationary noise. However, such methods have high computational complexity that directly raises the power consumption of devices which often have very stringent low-power requirements. We propose a strategy for cyclostationary spectrum sensing with reduced energy consumption. This strategy is based on the principle that p processors working at slower frequencies consume less power than a single processor for the same execution time. We devise a strict relation between the energy savings and common parallel system metrics. The results of simulations show that our strategy promises very significant savings in actual devices.

Análise de DFA e de agrupamento do perfil de densidade de poços de petróleo

In recent years, the DFA introduced by Peng, was established as an important tool capable of detecting long-range autocorrelation in time series with non-stationary. This technique has been successfully applied to various areas such as: Econophysics, Biophysics, Medicine, Physics and Climatology. In this study, we used the DFA technique to obtain the Hurst exponent (H) of the profile of electric density profile (RHOB) of 53 wells resulting from the Field School of Namorados. In this work we want to know if we can or not use H to spatially characterize the spatial data field. Two cases arise: In the first a set of H reflects the local geology, with wells that are geographically closer showing similar H, and then one can use H in geostatistical procedures. In the second case each well has its proper H and the information of the well are uncorrelated, the profiles show only random fluctuations in H that do not show any spatial structure. Cluster analysis is a method widely used in carrying out statistical analysis. In this work we use the non-hierarchy method of k-means. In order to verify whether a set of data generated by the k-means method shows spatial patterns, we create the parameter Ω (index of neighborhood). High Ω shows more aggregated data, low Ω indicates dispersed or data without spatial correlation. With help of this index and the method of Monte Carlo. Using Ω index we verify that random cluster data shows a distribution of Ω that is lower than actual cluster Ω. Thus we conclude that the data of H obtained in 53 wells are grouped and can be used to characterize space patterns. The analysis of curves level confirmed the results of the k-means

Comparações entre os valores genéticos para características de crescimento de bovinos da raça Nelore preditos com modelos de dimensão finita ou infinita

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Bio-óleo e biogás da degradação termoquímica de lodo de esgoto doméstico em cilindro rotativo

The objective of this study was to produce biofuels (bio-oil and gas) from the thermal treatment of sewage sludge in rotating cylinder, aiming industrial applications. The biomass was characterized by immediate and instrumental analysis (elemental analysis, scanning electron microscopy - SEM, X-ray diffraction, infrared spectroscopy and ICP-OES). A kinetic study on non-stationary regime was done to calculate the activation energy by Thermal Gravimetric Analysis evaluating thermochemical and thermocatalytic process of sludge, the latter being in the presence of USY zeolite. As expected, the activation energy evaluated by the mathematical model "Model-free kinetics" applying techniques isoconversionais was lowest for the catalytic tests (57.9 to 108.9 kJ/mol in the range of biomass conversion of 40 to 80%). The pyrolytic plant at a laboratory scale reactor consists of a rotating cylinder whose length is 100 cm with capable of processing up to 1 kg biomass/h. In the process of pyrolysis thermochemical were studied following parameters: temperature of reaction (500 to 600 ° C), flow rate of carrier gas (50 to 200 mL/min), frequency of rotation of centrifugation for condensation of bio-oil (20 to 30 Hz) and flow of biomass (4 and 22 g/min). Products obtained during the process (pyrolytic liquid, coal and gas) were characterized by classical and instrumental analytical techniques. The maximum yield of liquid pyrolytic was approximately 10.5% obtained in the conditions of temperature of 500 °C, centrifugation speed of 20 Hz, an inert gas flow of 200 mL/min and feeding of biomass 22 g/min. The highest yield obtained for the gas phase was 23.3% for the temperature of 600 °C, flow rate of 200 mL/min inert, frequency of rotation of the column of vapor condensation 30 Hz and flow of biomass of 22 g/min. The non-oxygenated aliphatic hydrocarbons were found in greater proportion in the bio-oil (55%) followed by aliphatic oxygenated (27%). The bio-oil had the following characteristics: pH 6.81, density between 1.05 and 1.09 g/mL, viscosity between 2.5 and 3.1 cSt and highest heating value between 16.91 and 17.85 MJ/ kg. The main components in the gas phase were: H2, CO, CO2 and CH4. Hydrogen was the main constituent of the gas mixture, with a yield of about 46.2% for a temperature of 600 ° C. Among the hydrocarbons formed, methane was found in higher yield (16.6%) for the temperature 520 oC. The solid phase obtained showed a high ash content (70%) due to the abundant presence of metals in coal, in particular iron, which was also present in bio-oil with a rate of 0.068% in the test performed at a temperature of 500 oC.

Experience generalization for multi-agent reinforcement learning

On-line learning methods have been applied successfully in multi-agent systems to achieve coordination among agents. Learning in multi-agent systems implies in a non-stationary scenario perceived by the agents, since the behavior of other agents may change as they simultaneously learn how to improve their actions. Non-stationary scenarios can be modeled as Markov Games, which can be solved using the Minimax-Q algorithm a combination of Q-learning (a Reinforcement Learning (RL) algorithm which directly learns an optimal control policy) and the Minimax algorithm. However, finding optimal control policies using any RL algorithm (Q-learning and Minimax-Q included) can be very time consuming. Trying to improve the learning time of Q-learning, we considered the QS-algorithm. in which a single experience can update more than a single action value by using a spreading function. In this paper, we contribute a Minimax-QS algorithm which combines the Minimax-Q algorithm and the QS-algorithm. We conduct a series of empirical evaluation of the algorithm in a simplified simulator of the soccer domain. We show that even using a very simple domain-dependent spreading function, the performance of the learning algorithm can be improved.

DFA e análise de agrupamento aplicadas a perfis de porosidade neutrônico em poços de petróleo

Peng was the first to work with the Technical DFA (Detrended Fluctuation Analysis), a tool capable of detecting auto-long-range correlation in time series with non-stationary. In this study, the technique of DFA is used to obtain the Hurst exponent (H) profile of the electric neutron porosity of the 52 oil wells in Namorado Field, located in the Campos Basin -Brazil. The purpose is to know if the Hurst exponent can be used to characterize spatial distribution of wells. Thus, we verify that the wells that have close values of H are spatially close together. In this work we used the method of hierarchical clustering and non-hierarchical clustering method (the k-mean method). Then compare the two methods to see which of the two provides the best result. From this, was the parameter � (index neighborhood) which checks whether a data set generated by the k- average method, or at random, so in fact spatial patterns. High values of � indicate that the data are aggregated, while low values of � indicate that the data are scattered (no spatial correlation). Using the Monte Carlo method showed that combined data show a random distribution of � below the empirical value. So the empirical evidence of H obtained from 52 wells are grouped geographically. By passing the data of standard curves with the results obtained by the k-mean, confirming that it is effective to correlate well in spatial distribution

Mean-field equations for cigar- and disc-shaped Bose and Fermi superfluids

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

Fortran programs for the time-dependent Gross-Pitaevskii equation in a fully anisotropic trap

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

BCS-BEC crossover in a trapped Fermi super-fluid using a density-functional equation

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

C programs for solving the time-dependent Gross-Pitaevskii equation in a fully anisotropic trap

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

Estimativa da probabilidade do evento extremo de precipitação de janeiro de 2000 no Vale do Paraíba, baseada na distribuição generalizada de pareto

Um evento extremo de precipitação ocorreu na primeira semana do ano 2000, de 1º a 5 de janeiro, no Vale do Paraíba, parte leste do Estado de São Paulo, Brasil, causando enorme impacto socioeconômico, com mortes e destruição. Este trabalho estudou este evento em 10 estações meteorológicas selecionadas que foram consideradas como aquelas tendo dados mais homogêneos do Que outras estações na região. O modelo de distribuição generalizada de Pareto (DGP) para valores extremos de precipitação de 5 dias foi desenvolvido, individualmente para cada uma dessas estações. Na modelagem da DGP, foi adotada abordagem não-estacionaria considerando o ciclo anual e tendência de longo prazo como co-variaveis. Uma conclusão desta investigação é que as quantidades de precipitação acumulada durante os 5 dias do evento estudado podem ser classificadas como extremamente raras para a região, com probabilidade de ocorrência menor do que 1% para maioria das estações, e menor do que 0,1% em três estações.

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.

Spectral analysis and low-frequency multipath mitigation for kinematic applications

Low-frequency multipath is still one of the major challenges for high precision GPS relative positioning. In kinematic applications, mainly, due to geometry changes, the low-frequency multipath is difficult to be removed or modeled. Spectral analysis has a powerful technique to analyze this kind of non-stationary signals: the wavelet transform. However, some processes and specific ways of processing are necessary to work together in order to detect and efficiently mitigate low-frequency multipath. In this paper, these processes are discussed. Some experiments were carried out in a kinematic mode with a controlled and known vehicle movement. The data were collected in the presence of a reflector surface placed close to the vehicle to cause, mainly, low-frequency multipath. From theanalyses realized, the results in terms of double difference residuals and statistical tests showed that the proposed methodology is very efficient to detect and mitigate low-frequency multipath effects. © 2008 IEEE.