Recursive neural networks are one of the adoptive models that suited for processing graphs. Originally recursive neural networks are proposed to processing DAOGs in which the children of any given node are ordered. This assumption is unnecessary constraints. In this paper the weight sharing method that can relax the constraint on the ordering of children in graph processing is introduced. Also a new model of recursive neural networks to process DAG-LE is introduced. Both methods guarantee the independence of the network output with respect to the permutations of the arcs leaving from each node. In continuation an object recognition based on the last recursive model is presented. This method dose not include any priori knowledge on the object model and it is invariant under image translation and rotation.

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.

A microarray is a small analytical device that allows genomic analysis with speed and precision. Through this technology the expression of thousands of genes can be simultaneously explored. Fundamental question about the microarray Data is how to choose the genetic markers. Detection of genetic markers, shall lead to early detection, treatment, and accurately prognosis; therefore, appropriate treatment and reduction of cost. Using a classical approach for microarray Data processing and statistical analysis, we are able to detect only two genes as differentially regulated in samples under investigation. Recently, in an effort to boost the biologically meaningful signal, game theory researchers, have been able to devise a method on gene expression Data, Thus we were able to identify a group of genes associated with a disorder. This method, which is called comparative analysis of Shapley value, by combining the concept of Shapley value of cooperative games and statistical bootstrap lead to selection of genes with powerful influence in occurrence of a disorder. In this paper, this approach will be developed and in a case study of this modeling the results of applying this approach on gene expression Data from autistic individuals, showed that coalitional games significantly increase the power to identify candidates.

In this paper, an alternative approach in operational modal analysis is presented, utilizing image processing technique and transmissibility functions. Imaging sensors do not impose additional mass on the structure due to their non-contact nature, while transmissibility functions, independent of excitation type, can directly extract mode shapes. The innovation of this research lies in combining these two techniques to record dynamic responses and identify modal properties. To capture the temporal response history from video signals, the block-matching method with sub-pixel accuracy was employed. Validation was conducted by recording the response of the tip of a cantilevered steel beam subjected to impact excitation, using a high-speed camera and a laser vibrometer, simultaneously. The RMSE plots in the time domain and the PSD in the frequency domain indicate high accuracy of this method. Using this approach, the displacement time histories of various points on the structure were extracted from the video signals, and the modal properties, including natural frequencies, damping ratios, and mode shapes, were identified using the transmissibility matrix method. The results obtained from the proposed method were compared with the stochastic subspace identification (SSI) method and analytical solutions. The findings reveal the accuracy of the modal identification approach introduced in this article. The highest relative error in estimating the natural frequencies of the first and second modes, compared to the values from the laser method, are 0.19% and 0.13%, respectively, and in comparison to the analytical values, they are 0.34% and 1.5%, respectively.