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.

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 work presents an algorithm to construct a 3D magnetic susceptibility property from magnetic geophysical Data. Physical model discretization has substantial impact on accurate inverse modeling of the sought sources in potential field geophysics, where structural meshing suffers from edge preserving of complex-shaped geological sources. In potential field geophysics, a finite-Element (FE) methodology is usually employed to discretize the desired physical model domain through an unstructured mesh. The forward operator is calculated through a Gauss-Legendre quadrature technique rather than an analytic equation. To stabilize mathematical procedure of inverse modeling and cope with the intrinsic non-uniqueness arising from magnetometry Data modeling, regularization is often implemented by utilizing a norm-based Tikhonov cost function. A so-called fast technique, “Lanczos Bidiagonalization (LB) algorithm”, can be utilized to solve the central system of equations derived from optimizing the function, where it decreases the execution time of the inverse problem by replacing the forward matrix with a lower dimension one. In addition, to obtain best regularization parameter, a weighted generalized cross-validation (WGCV) curve is plotted, that makes a balance between misfit norm and model norm introduced in the cost function. In order to tackle the normal propensity of physical structures to focus at the shallow depth, an expression of depth weighting is used. This procedure is applied to a synthetic scenario presenting a complex-shaped geometry along with a real set of magnetic Data in central part of Iran. So the capability of the proposed algorithm for inversion indicates the accuracy of the inversion algorithm. Additionally, the modeling results pertaining to a field case study are in good agreement with the drilling Data.

Objective: CREB1 is an important downstream protein for many signaling pathways. By designing efficient siRNAs against CREB1, it may be possible to assess the role of molecules involved in signaling pathways in different cell types. In this research the efficiency of CREB1 knockdown by two different siRNAs in K562 cells has been studied. Materials and Methods: siRNAs have been designed according to the criteria suggested by Reynolds et al. K562 cells were transfected by siRNA using Lipofectamine 2000. The efficiency of CREB knockdown has been assessed by quantitative relative Real-time PCR. Results: Our results have shown that only one of the siRNAs has a high level of inhibitory effect on CREB1 gene expression. The expression of CREB1 by this siRNA was knocked-down by 87% in K562 cells. Conclusion: In this research, although two siRNAs were designed according to the Reynolds et al. criteria, only one showed an inhibitory effect. Reasons other than the aforementioned criteria may be involved in effectiveness of siRNAs.

In view of the numerous publications, there is clear interest in the application of the Finite Element Method (FEM) to compute the regional gravity anomaly involving only 8 nodes on the periphery of a rectangular map. Geophysical Data processing methods, such as trend analysis, analytical continuation and filtering can reveal the general structural properties of a region. Although, there were improvements in distinguishing the regional and residual gravity anomalies, no progress has been made in trend analysis, filtering or analytical continuation methods. With improvements in scientific computing, high degree trend analysis and filtering methods have been used more efficiently for 2-D Data. On the other hand, computed regional anomalies still contain residual anomaly effects. For this reason, the finite Element method (FEM) might be used to eliminate the residual anomaly effects on the regional anomalies. A brief description of the theory of FEM is presented for the sake of completeness.The gravity map in real space is superimposed by a weighted sum of discrete gravity values at eight stations coinciding with the eight nodes of a second-order iso-parametric Element to compute the regional anomalies on the finite Elements in the FEM application. For each node coordinate and field variable, the Element can be defined by the same shape functions. Due to this feature, it could be named an isoparametric Element. The observed gravity values for each node are obtained from the map. The non-dimensional reference plane, which is related to the real plane, was described by using the shape function to perform an easy computation. Eight observed gravity values on quadratic isoparametric Elements, which were superimposed onto the gravity map, are needed to compute regional anomolies. Other observed gravity Data are unnecessary.Gravity anomalies always include the total effects (combination of the structures which have different densities and depths) of the study area and beyond. Moreover, the well-known non-uniqueness of potential field modeling may lead to very different interpretation results. FEM is here applied in order to determine the regional anomalies. The method has advantages over the traditional methods. For instance, only a few observation points on a Bouguer gravity map are needed to compute the regional gravity anomaly. The FEM can be applied for any size of gravity map. In addition, it will be shown that the computed regional anomaly contains minimal residual anomaly effects. The FEM, which has been used in potential field interpretation for decades, allows complex problems to be solved easily and accurately. The first step of FEM is to identify the Elements and then to identify the boundary of the solution space. In this step, the solution space is divided into Elements. After determination of the geometrical structure of the solution space, the most suitable Elements should be selected for this geometrical structure. The agreement between the geometry and the Elements is quite important for the convergence to the best possible solution. FEM has been successfully applied to different modeling problems in the scientific world including in geophysics for decades.In this study, it was used to distinguish the regional and residual anomalies by using the necessary shape functions. These shape functions play quite an important role in changing the reference plane. After the method was applied, it was seen that it produced better results than those produced by the existing traditional methods. In addition to that, fewer gravity values were needed. For instance, only eight nodes with the shape functions were used to compute the regional anomaly.

Rectangle array is widely used in resistivity and induced polarization (IP) studies. The purpose of this array is to restrict the wide areas especially in the exploration of sulfide minerals. On the contrary to the wide application of this array, less attention has been paid to the results of modelling and true estimates. The interpretations are normally qualitative.A 3D resistivity and IP model was developed for the geoelectric surveys with a rectangle array. We used the COMSOL environment to solve the DC-resistivity and Maxwell’s equations by the finite Element method. Codes were programmed in Matlab language.A common geometry of the model space was used for both resistivity and IP modelling. In the rectangle array, two current electrodes were located in a large distance and different potentials were measured on the profiles parallel to the current electrodes. Our model was formed by a homogeneous half space (a large block with dimensions 800×800×500m3, with a resistivity of 400 ohm.m). Two current electrodes with a 200-m distance were located on the surface. Non-polarizing electrodes were located in a 5-m distance. The two measuring electrodes were moved on the profiles (parallel to the current electrode direction). Nine parallel profiles were located symmetrically on each side of the current electrode direction. Each profile had a 40-m length. The distance between the profiles was 5 m. The electrode configuration could be changed in the model. IP and resistivity anomalies could be created from different blocked locations in the subsurface (into the half space). The blocks near the potential profiles had small dimensions. The block sizes increased as the depth increased. We calculated the geometrical factor for the half-space. Apparent resistivity for each dual potential electrode was calculated from different potentials measured during the code execution and its geometry factors.We compared the results from different anomalies by sensitivity Dpa/Dpi, where Dpa is the difference between the apparent resistivity of the anomaly and the homogeneous half-space (400 ohm.m) and Dpi is the difference between the resistivity value of the half-space and the anomaly in block number i.Frequency domain IP was calculated directly from Maxwell's equations. Block scheme of the model done in the modelling space resistivity were used here. There was a resistivity value for each subsurface block in the resistivity model while there were a resistivity and a dielectric value for each block in an IP model. Resistivity and dielectric values of each block are functions of the frequency. We used the Cole-Cole model in order to calculate the resistivity and dielectric values in each frequency. Four intrinsic Cole-Cole parameters (DC-resistivity, chargeability, time constant and frequency relaxation) were considered for each block. During the frequency changes, these parameters were constant. Finally, apparent resistivity and percentage frequency effect (PFE) maps were calculated in a frequency range of 0.1 to 12000 Hz.In this research, we studied the effect of size, depth and overburden thickness of the subsurface anomalies. The geoelectrical effects of vertical and horizontal anomalies were investigated. The impact of the potential electrode separation was also verified. The results showed that the qualitative interpretation using the apparent resistivity and appearent percentage frequency effect (PFE) maps was correct when anomaly had remarkable dimensions, a small depth and a high conductivity. The apparent-resistivity map reflected the effect of conductive and polarisable anomalies better than the PFE map.

Changes of up to 80°C has been reported for oral cavity temperature. This could well effecti on the nature of restorations for example failure of bonding of adhesive restorations. It is advocated that using opaque layer in porcelane to restorations could reduce this problem. This experimental study was designed to evaluate the effect mentioned using finitElement analysis method.Results showed that cooling has a more destructive effect than warming process restorations with the presence of opaque having a finitElement analysis effect on restorations.

Face recognition from digital images is used for surveillance and authentication in cities, organizations, and personal devices. Internet of Things (IoT)-powered face recognition systems use multiple sensors and one or more servers to process Data. All sensor Data from initial methods was sent to the central server for processing, raising concerns about sensitive Data disclosure. The main concern was that all Data from all sectors that could contain confidential information was placed in a central server. Federated learning can solve this problem by using several local model training servers for each region and a central aggregation server to form a global model in IoT networks. This article presents a novel approach to optimize Data transfer and convergence time in federated learning for a face recognition task using Non-dominated Sorting Genetic Algorithm II (NSGA II). The aim of the study is to balance the trade-off between training time and model accuracy in a federated learning environment. The results demonstrate the effectiveness of the proposed approach in reducing Data transfer and convergence time, leading to improved performance in face recognition accuracy. This research provides insights for researchers and practitioners to enhance the efficiency of federated learning in real-world applications.