The neoclassical growth model is tested by use of panel DATA procedure in this research. In the econometric test, simoultanously time series and cross detection will be compared on the basis of panel DATA method through which their observed points increase and consequently the estimation efficiency will be increased. The examination of neoclassical growth theory has been done with reference to external & internal factors of 52 selected countries from 1960 to 2000. The independent variable of model has been selected on the basis of the result of previous research which explains the result in three separate models: developed countries, developing countries, and whole countries. These factors are such as: Gross National Products with lag of period, work force age, growth rate, education level, the change of capital accumulation and economic trade volum. The consequences of this research is that: neoclassical growth model can explain the major part of economic growth of the countries with use of internal variables. Also with the use of panel procedure of fixed effect, we can see the fundamental differences and structure of the growth process for different countries; and show how the economic, and social conditions affect on the growth.

Climate change in the past decade had serious impacts on food security and livelihoods in different parts of the world. EOF is one of the important methods for studying rainfall patterns in an area. The main objectives of the paper were to determine the best weight coefficients and frequency functions, determine homogeneous regions, model seasonal time series, and find the Severity-Area-Frequency (SAF), Duration-Area-Frequency (DAF), and Meteorological Drought Curves in three basins of Karun, Dez and Karkheh. In order to achieve the above objectives, 69 (Ministry of Energy’ s) weather stations were selected in the research basins. After qualitative control, test and reconstruction of DATA, frequency functions and four weight factors (EOF) were obtained. Then, with the homogenization of the rainfall zone, the 291 cells in the ArcGIS9. 3 environments were examined by ordinary kriging interpolation method. The results of the interpolation for first to forth weight functions (EOF) showed that the spherical semi covariogram with second degree of polonomial trend and unisotropy, the Gaussian no-trend with isotropy, the Gaussian with second degree of trend, and the spherical with second degree of trend and no-isotropy have the best distribution respectively. The homogenization of the region showed that the minimum average monthly precipitation in southern parts of Khuzestan Province and some eastern parts of Charmahalo-Bakhtiari Province was 14. 5-24. 3 mm and maximum average monthly precipitation in northern parts of Khuzestan Province and south of Lorestan Province was 65. 3-80. 4 mm. Using the ARMA Stochastic Modeling Method with the SAMS program for each cell for 4 time series of frequency functions (PC), 12. 000 rainfall DATA were obtained and then analysis of droughts was conducted. The results showed that the Gamma and Log-Normal statistical distribution for the return periods of 5, 10, 50, 100 and 200 years were the best fit for the series of relative shortages and drought persistence, respectively.

The quality of seismic DATA varies tremendously, from areas where excellent reflections (or refractions) are obtained to areas in which the most modern equipment, complex field techniques, and sophisticated DATA processing do not yield usable DATA. Between these extremes, lie most areas in which useful results can be obtained. Seismic records are generally affected by various types of noise, such as ground rolls, multiples, random noise, and reflection and reflected refraction from near surface structures. Random noise resultiing from random oscillation during DATA acquisition is one of the most important and harmful noises that exists in seismic DATA over all times and frequencies. Many efforts have been made to remove this type of noise from seismic DATA. The predictive filter is an ordinary method commonly used for random noise attenuation from seismic DATA. This filter can be used in various domains, such as the f-x domain (Haris and White, 1997) and the discrete Cosine domain (Lu and Liu, 2007). Jones and Levy (1987) removed events which were not coherent trace-to-trace events by means of the Karhunen-Loeve transform.The EMPIRICAL mode decomposition (EMD) method is an algorithm for the analysis of multicomponent signals that breaks them down into a number of amplitude and frequency modulated zero-mean signals, termed intrinsic mode functions (IMFs). An IMF must fulfill two requirements: (1) the number of extrema and the number of zero crossings are either equal or differ at most by one; (2) at any point, the mean value of the envelope defined by the local maxima and the envelope defined by the local minima is zero.In contrast to conventional decomposition methods such as wavelets, which perform the analysis by projecting the signal under consideration onto a number of predefined basis vectors, EMD expresses the signal as an expansion of basic functions that are signal-dependent and estimated via an iterative procedure called sifting. Apart from the specific applications of EMD, a more generalized task in which EMD can prove useful is signal denoising (Kopsinis and McLaughlin, 2009). When EMD is used for denoising, the problem is to identify properly which IMFs contain noise characteristics. Certain modes will consist mainly of noise, whereas other modes will contain both signal and noise characteristics. In the case of white Gaussian noise, the noise-only energy of the modes decreases logarithmically. The first mode, carrying the highest amount of noise energy, will consist mainly of noise, and the effect of noise should gradually weaken with higher modes.In this paper, a new signal denoising method based on the EMPIRICAL mode decomposition framework is used to suppress random noises in seismic DATA. A Noisy signal is decomposed into oscillatory components (IMFs). The EMPIRICAL mode decomposition denoising method involves filtering each intrinsic mode function and reconstructing s the estimated signal using the processed intrinsic mode functions. The direct application of wavelet-like thresholding to the decomposition modes is, in principle, wrong and can have catastrophic consequences regarding the continuity of the reconstructed signal. This arises as a result of the special attributes of IMFs; namely, they resemble an AM/FM modulated sinusoid with zero mean. Consequently, we used the interval thresholding method instead of direct theresholding method to denoise seismic signal.the efficiency of the proposed method was tested on both synthetic and real seismic DATA. In every case, results show that the denoising algorithm can suppress random noise significantly.

In Bayesian prediction of a Gaussian space-time model, unknown parameters are considered as random variables with known prior distributions and, then the posterior and Bayesian predictive distributions are approximated with discrtization method. Since prior distributions are often unknown, Bayesian predictive distribution would not be known. As an alternative an EMPIRICAL Bayes approach is used to estimate the prior distributions. Replacing these estimates in the Bayesian predictive distribution, an EMPIRICAL Bayes space-time predictor and prediction variance are determined. Then an environmental example is used to illustrate the application of the proposed method. Finally the accuracy of the EMPIRICAL Bayes space-time predictor is considered with cross validation criterion.

Service quality is often conceptualized as the comparison between service expectations and the actual performance perceptions. It enhances customer satisfaction, decreases customer defection, and promotes customer loyalty. Substantial literature has examined the concept of service quality, its dimensions, and measurement methods. We introduce the perceived service quality index (PSQI) as a single measure for evaluating the multiple-item service quality construct based on the SERVQUAL model. A slack-based measure (SBM) of efficiency with constant inputs is used to calculate the PSQI. In addition, a nonlinear programming model based on the SBM is proposed to delineate an improvement guideline and improve service quality. An EMPIRICAL study is conducted to assess the applicability of the method proposed in this study. A large number of studies have used DEA as a benchmarking tool to measure service quality. These models do not propose a coherent performance evaluation construct and consequently fail to deliver improvement guidelines for improving service quality. The DEA models proposed in this study are designed to evaluate and improve service quality within a comprehensive framework and without any dependency on external DATA.