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.

Background: Independent Component Analysis (ICA) is the most common and standard technique used in Functional neuroscience data analysis. Objective: In this study, two of the significant Functional brain techniques are introduced as a model for neuroscience data analysis. Material and Methods: In this experimental and analytical study, Electroencephalography (EEG) signal and Functional Magnetic Resonance Imaging (fMRI) were analyzed and managed by the developed tool. The introduced package combines Independent Component Analysis (ICA) to recognize significant dimensions of the data in neuroscience. This study combines EEG and fMRI in the same package for analysis and comparison results. Results: The findings of this study indicated the performance of the ICA, which can be dealt with the presented easy-to-use and learn intuitive toolbox. The user can deal with EEG and fMRI data in the same module. Thus, all outputs were analyzed and compared at the same time,the users can then import the neuroFunctional datasets easily and select the desired portions of the Functional biosignal for further processing using the ICA method. Conclusion: A new toolbox and Functional graphical user interface, running in cross-platform MATLAB, was presented and applied to biomedical engineering research centers.

Todays gamma knife radiosurgery is used widely for treatment of very small brain tumors. In order to investigate accuracy of dosimetry and treatment planning calculations, using Monte Carlo simulation with dedicated code named as beamnrc including non-CT data and CT data options is necessary. The aim of this study is choosing the best options in order to have an accurate tools based on their advantages and disadvantages. In this study, gamma knife unit 4C along with standard water equivalent phantom and EBT3 films were used to obtain dose distributions. Monte Carlo simulation was done with non-CT data and CT data options of the code and their resulting dose were compared. Comparison the calculated and measured dose distributions at X, Y and Z axis showed gamma value below 1 which verified Monte Carlo simulations. Also comparing the dose distributions from both non-CT data and CT data with each other implies that there is no significant difference between two methods. Based on the obtained results using non-CT data and CT data results in the same dose distribution. So for simplicity, using non CT data for regular phantom shapes is preferred.

This paper introduces a Functional mixed effect random model to model spa-tial data. In this model, the spatial locations form the index set, while the contributing effects to the response variable are set as a linear mixture of fixed and random effects. These fixed and random effects are linear combinations of L2 functions and random elements, respectively. However, the corresponding linear factors depend on the spa-tial location variable. Therefore, we develop estimation procedures to estimate the fixed and random coeficients, using spatial Functional principal component analysis. Then, we perform prediction by adapting the Functional universal kriging method to our model.

With modern technology development, Functional data analysis (FDA) has received considerable recent attention in many scientific fields. The estimation of mean in FDA is of interest, because it is not only important by itself but it is a prelude to other issues such as dimension reduction and modeling of Functional data In this paper we construct a two-sample Behrens-Fisher problem when data are functions and obtain the asymptotic properties of the test statistic as data dimension increases with the sample size. The power of the proposed test is also investigated. The proposed test used for inference about the differences in the mean temperature functions of the Western and the South-western weather stations of Iran.