Correlations and variability in electrical signals related to earthquake activity

Evaluating complex fluctuations in geoelectric time series is an important task not only for earthquake prediction but also for understanding complex processes related to earthquake preparation. Previous studies have reported alterations, such as the emergence of correlated dynamics in geoelectric potentials prior to an important earthquake (EQ). However, the presence of correlations and its relation with variability has not been widely explored. In this work we apply the detrended fluctuation analysis and the multiscale entropy methods to analyze the fluctuations of geoelectric time series monitored in two sites located in Mexico. We systematically calculate the correlation exponents and the sample entropy (S_E) of geoelectric time series. Important differences in the scaling exponents and entropy profiles for several time scales are observed. In particular, a complex behavior, characterized by a high entropy across several scales and crossover in the correlation exponents, is observed in the vicinity of an that occurred on Sept. 14, 1995. Moreover, we compare the changes in the entropy of the original data with their corresponding shuffled version to see whether correlations in the original data are related to variability.     

Bayesian wavelet-based analysis of functional magnetic resonance time series

Wavelet methods for image regularization offer a data-driven alternative to Gaussian smoothing in functional magnetic resonance (fMRI) analysis. Their impact has been limited by the difficulties in integrating regularization in the wavelet domain and inference in the image domain, precluding the probabilistic decision on which areas are activated by a task. Here we present an integrated framework for Bayesian estimation and regularization in wavelet space that allows the usual voxelwise hypothesis testing. This framework is flexible, being an adaptation to fMRI time series of a more general wavelet-based functional mixed-effect model. Through testing on a combination of simulated and real fMRI data, we show evidence of improved signal recovery, without compromising test accuracy in image space.     

Temperature-electric field hysteresis loop of multicaloric effects in PbZr0.8Ti0.2O3 thin films

The Multicaloric effect in the PbZr_0.8Ti_0.2O₃ thin films is investigated with the application of sine wave electric field, dc electric field and stress using a phase field method combined with the thermodynamic analysis. The simulation results show that the adiabatic temperature change-electric field curve presents a shape of butterfly in the presence of the sine wave electric field. In order to detect the effect of the sine wave electric field, the multicaloric effect and the domain structures under the direct electric field and the sine wave electric field are compared. It is found that the domain switching behaviors are quite different under the different applied electric fields. And the negative multicaloric effect in the PbZr_0.8Ti_0.2O₃ thin film is attribute to the domain switching under the external field.     

Enhancing electrical properties of high-Curie temperature piezoelectric ceramics BNT-PZT and their mechanism

Due to the urgent demands for high-Curie temperature (T_C/T_m) piezoelectric materials in geothermal exploration, aerospace and related fields, high-T_C/T_m ferroelectrics have attracted booming research attention. The high-T_m 0.25Bi(Ni_1/2Ti_1/2)O₃-0.75 Pb(Zr_1/2Ti_1/2)O₃ (0.25BNT-0.75PZT) ceramics were prepared by solid-state sintering method and via partial oxalate route, where the 0.25BNT-0.75PZT ceramics prepared via the partial oxalate route exhibit better electrical properties (T_m = 232 °C (10 kHz), ε_m = 10963, P_r = 26.14 μC/cm², E_c = 15.61 kV/cm, d₃₃ = 521 pC/N, d₃₃* = 553.3 pm/V, K_p = 47.1%, and Q_m = 21.6). The nano-scale domain configuration of the ceramics was revealed by piezoelectric force microscopy (PFM), and the relationship between the micro-structure and macro-electrical properties was analyzed. The ferroelectric phase transition mechanism was studied by temperature dependent Raman spectroscopy. The reduction of energy barrier of lattice distortion and polarization deflection is caused by nanometer-sized domain structure, low symmetric polar nano-regions and/or coexistence of multi-ferroelectric phases, contributing to the excellent electrical properties of the 0.25BNT-0.75PZT ceramics prepared via the partial oxalate route.     

A cell functional minimization scheme for parabolic problem

We are interested in a robust and accurate finite volume scheme for 2-D parabolic problems derived from the cell functional minimization approach. The scheme has a local stencil, is locally conservative, treats discontinuity rigorously and leads to a symmetric positive definite linear system. Since the scheme has both cell centered unknowns and cell edge unknowns, the computational cost is an issue and a parallel algorithm is then suggested based on nonoverlapping domain decomposition approach. The interface condition is of the Dirichlet–Robin type and has a parameter λ. By choosing this parameter properly, the convergence of the iteration process could be sped up. Numerical results for linear and nonlinear problems demonstrate the good performance of the cell functional minimization scheme and its parallel version on distorted meshes.     

The risks and returns of stock investment in a financial market

The risks and returns of stock investment are discussed via numerically simulating the mean escape time and the probability density function of stock price returns in the modified Heston model with time delay. Through analyzing the effects of delay time and initial position on the risks and returns of stock investment, the results indicate that: (i) There is an optimal delay time matching minimal risks of stock investment, maximal average stock price returns and strongest stability of stock price returns for strong elasticity of demand of stocks (EDS), but the opposite results for weak EDS; (ii) The increment of initial position recedes the risks of stock investment, strengthens the average stock price returns and enhances stability of stock price returns. Finally, the probability density function of stock price returns and the probability density function of volatility and the correlation function of stock price returns are compared with other literatures. In addition, good agreements are found between them.     

Time series analysis of ozone data in Isfahan

Time series analysis used to investigate the stratospheric ozone formation and decomposition processes. Different time series methods are applied to detect the reason for extreme high ozone concentrations for each season. Data was convert into seasonal component and frequency domain, the latter has been evaluated by using the Fast Fourier Transform (FFT), spectral analysis. The power density spectrum estimated from the ozone data showed peaks at cycle duration of 22, 20, 36, 186, 365 and 40 days.According to seasonal component analysis most fluctuation was in 1999 and 2000, but the least fluctuation was in 2003. The best correlation between ozone and sun radiation was found in 2000. Other variables which are not available cause to this fluctuation in the 1999 and 2001. The trend of ozone is increasing in 1999 and is decreasing in other years.     

On reachable set bounding for discrete-time switched nonlinear positive systems with mixed time-varying delays and disturbance

This paper addresses the reachable set bounding for discrete-time switched nonlinear positive systems with mixed time-varying delays and disturbance, which contains switched linear positive systems as a special case. By resorting to a new method that does not involve the common Lyapunov–Krasovskii functional one, explicit criteria to ensure any state trajectory of the system converges exponentially into a prescribed sphere are obtained under average dwell time switching. The results can then be extended to more general time-varying systems. Finally, two numerical examples are used to demonstrate the effectiveness of the obtained results.     

Rules extraction in short memory time series using genetic algorithms

Data mining is performed using genetic algorithm on artificially generated time series data with short memory. The extraction of rules from a training set and the subsequent testing of these rules provide a basis for the predictions on the test set. The artificial time series are generated using the inverse whitening transformation, and the correlation function has an exponential form with given time constant indicative of short memory. A vector quantization technique is employed to classify the daily rate of return of this artificial time series into four categories. A simple genetic algorithm based on a fixed format of rules is introduced to do the forecasting. Comparing to the benchmark tests with random walk and random guess, genetic algorithms yield substantially better prediction rates, between 50% to 60%. This is an improvement compared with the 47% for random walk prediction and 25% for random guessing method. Received 29 August 2000     

A study of multi-beam DICANNE system

I.IntroductionTheprob1cmofsuppressingstrongpointinterferencesourceisavcryimportantthesisinsonardesign.Fortheana1ogsonar,thesolutionofthisprob1emisquitedifficu1t.Untilthedevelopmentofdigitalsonar,thereissomerescarchreportsaboutsuppressingstrongpointin-terferencesource.Theear1ierworkin1969,dedicatcdbyV.C.Anderson,istheconccptofDICANNE(DigitalInterfcrcnceCance1lingAdaptiveNu1lNetworkEquipment)['l.Thisisadoublecompensatormulti-bcamsystcm.Oneisusedtoformintcrfercncebeamandanothcrisusedtofor…