Theft is among the most common crimes committed in all societies. The present study aims at exploring the stationary and non stationary theft rates in the cities of Zanjan. All those who committed theft during 2007 to 2008 in the cities of Zanjan (N=493) were the statistical sample. The required data of this field study were collected via questionnaire, interview, and observation. SPSS was used to analyze the collected data. The sample findings are as follows: 1.There is a significant relationship between "The theft technique and thieves' motivations".2.There are significant relationships between."The theft technique and criminal records", "The theft technique and drug abuse", "The theft technique and the theft type", "The theft technique and residential place", and "The theft technique and the place of birth of thieves".

The accurate estimation of the design Precipitation is one of the requirements for the construction of hydraulic structures, which is done by various methods of frequency analysis. Classical methods of fitting observational data use the assumption of constant parameters of distribution functions,while, many studies have been done on non-stationary data due to factors such as climate change. Therefore, this paper aims to use the functions of non-stationary parameters-if necessary-and compare them with the stationary assumption of the maximum daily precipitation data of the Atrak river basin. Mann-Kendall test and White test were used to check the non-stationary in the mean and variance of annual data. The Generalized extreme value distribution function was also fitted to the data time series. Among the 24 stations with long-term data, 5 stations with trends and 6 stations with variance non-stationary were detected. Evaluation criteria including Akaike (AIC), Bayesian (BIC), Root mean square error (RMSE), and Nash-Sutcliffe efficiency (NSE) coefficient was determined under stationary and non-stationary assumptions, for all stations. The results showed that in all stations with non-stationary, considering the mentioned conditions in the analytical calculations is a good choice. Also, the lower (5%), median (50%), and upper (95%) limit values with the return period of 100 years with both assumptions were determined and compared with the classical maximum likelihood method. The underestimation of the maximum likelihood method compared to the Bayesian method used by using Markov chain Monte Carlo (MCMC) in parameter estimation was observed. Also, Akaike criterion provided better results among the used evaluation criteria.

Climate changes and human activities are the two main factors affecting the hydrological cycle and stationery/ non-stationary of hydrological systems. The existing indicators for drought monitoring are based on the assumption of stability of atmospheric conditions and environmental factors, and the research results of the last two decades indicate that these indicators do not have the necessary validity in the current non-stationary environment. Therefore, in general, the aim of this article is to provide a comprehensive review of the latest drought indicators in non-stationary conditions and to express the challenges and opportunities of future research. In this regard, the indicators that have been developed for different types of droughts should be reviewed first, since the methodology of these indicators is mainly proposed for stationary weather conditions, then we will review some of these indicators that In a non-stationary state, it is developed in Iran and other regions of the world. Studying and reviewing tools and methods for calculating non-stationary in determining non-stationary drought indicators is one of the other factors that will be discussed in this article. A review of various research showed that indices such as NSPI, NSRI, NSPEI, NRDI, etc. were among the most important non-stationary indices that were used to quantify meteorological/hydrological droughts. From the methodological point of view, all the research used the generalized collective model for location, scale and shape (GAMLSS) to model the non-stationary behavior of indicators.

In this paper, the problem of diagonalization of the autocovariance matrices of stationary processes is discussed. Basically, there is no explicit form for the diagonal matrices. An analytic solution has been provided for stationary ARMA models based on Band matrices. The results have been compared with Dargahi-Noubary and Fuller in a numerical study. It has been demonstrated that the results are more accurate compared to Dargahi-Noubary and Fuller approaches.

In this paper, we study Projected non-stationary Simultaneous It-erative Reconstruction Techniques (P-SIRT). Based on algorithmic op-erators, convergence result are adjusted with Opial’ s Theorem. The advantages of P-SIRT are demonstrated on examples taken from to-mographic imaging.

The separation of unobserved sources from mixed observed data is a fundamental ‎signal processing problem. Most of the proposed techniques for solving this problem ‎rely on independence or at least uncorrelation assumption for source signals. This ‎paper introduces a technique for cases that source signals are correlated with each ‎other. The method uses Wold decomposition principle for extracting desired and ‎proper information from the predictable part of the observed data, and exploits ‎approaches based on second-order statistics to estimate the mixing matrix and source ‎signals. Simulation results are provided to illustrate the effectiveness of the method‏.‏

The Gaussian random field is commonly used to analyze spatial data. One of the important features of this random field is having essential properties of the normal distribution family, such as closure under linear transformations, marginalization and conditioning, which makes the marginal consistency condition of the Kolmogorov extension theorem. Similarly, the skew-Gaussian random field is used to model skewed spatial data. Although the skew-normal distribution has many of the properties of the normal distribution, in some definitions of the skew-Gaussian random field, the marginal consistency property is not satisfied. This paper introduces a stationery skew-Gaussian random field, and its marginal consistency property is investigated. Then, the spatial correlation model of this skew random field is analyzed using an empirical variogram. Also, the likelihood analysis of the introduced random field parameters is expressed with a simulation study, and at the end, a discussion and conclusion are presented.

Spatial aliasing is unavoidable in some seismic data acquisitions and has serious adverse impacts on the performance of multichannel data processing and interpretation. So far, many methods have been used for removing spatial aliasing, such as interpolation methods of seismic traces by various transforms like Radon transform. In this paper, a new method for complete removal of spatial aliasing using stationary wavelet transform is presented. Our method has several advantages over previous ones such as, fixing the number of traces, avoiding transfiguration of wavelet shape that constructs the traces, minimizing the phase shift in seismic events, and reducing the run time.