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.

Extended Abstract Background: Honeybees have an important role in the pollination of plants. Apis florea Fabricius, 1787, has been described over the last two centuries.   A. florea is distributed in Vietnam, south-eastern China, India, Nepal, southern Thailand, Sri Lanka, Sudan, southern Iran, Pakistan, Saudi Arabia, and Oman. The climatic zones of A. florea change from tropical rainforest in the east to savannah in the west. Furthermore, westwards, the climatic zones change from tropical to subtropical steppe and semi-desert. Morphometric data were used in addition to analyses of molecular data to study the honeybees. Morphometric analyses are flexible tools employed by users in different areas of the world. The geometric morphometric method uses landmarks that can be located precisely on the vein junctions of wings. The geometric morphometric method has been used to compare the populations of A. florea. In addition, traditional or standard morphometric methods have been used to study the populations of A. florea in various areas of the world. The landmark configurations eliminate the effects of position, orientation, and size of shapes. Data on shapes are converted into Procrustes coordinates using the geometric morphometric method. Regression analysis between centroid size and Procrustes coordinates creates new data on the residuals, which can be applied in other analyses. No published research has utilized residual coordinates (residual data) to compare the populations of A. florea. Therefore, the current research aimed to compare the performance efficacy of residual and Procrustes coordinates in differentiation and relationships among the populations of A. florea in various areas of the world. Methods: Samples of the A. florea honeybee species were prepared from their distributed areas. The right forewings of honeybees were used to study the relationships among the various populations. Eighty samples were selected from each area. A tps file was built by importing the right forewing pictures into TpsUtil V. 1. 64 software. Then, the tps file was loaded into tpsDig V. 2. 18. Twenty landmarks were digitized in the vein junctions of the forewings. This was followed by the raw data obtained from landmarks, loaded into the MorphoJ software V. 1. 06d, and converted into Procrustes coordinates for use in future analyses, followed by analyzing the Procrustes coordinates. Mahalanobis distances and canonical variates were obtained using permutation tests. The regression between Procrustes data and centroid sizes was calculated, and an allometry test was performed afterward. The residual coordinates (residual data) were obtained after the removal of the size effect (size correction) from shape variables. The residual and Procrustes data were imported into PAST software v. 3. 19, and the populations of A. florea were compared using Canonical Variate Analysis (CVA). Moreover, clusters were drawn with the residual and Procrustes data using SAS v. 8 software. Results: Multivariate (MANOVA) and pair-wise analyses of residual and Procrustes data were tested for the populations of A. florea. The Procrustes and residual data of the populations showed statistically significant differences using MANOVA (p < 0. 001). Moreover, the tested pairwise comparisons indicated that all populations were significantly different in Procrustes and residual data (p < 0. 001). The landmark configurations of forewings were superimposed, and variations were obtained between the populations. The highest variation was found in the vein junction of R and Rs, landmark 19 (S2 = 0. 0000622). The lowest variation was observed in the vein junction of Cu and 1m-cu, landmark 8 (S2 = 0. 0000109). The populations of various areas were compared using Canonical Variate Analysis (CVA). In the CVA of Procrustes data (Procrustes coordinates), the first and second components included 75. 94% of all variation (CV1 = 28. 74% and CV2 = 47. 20%). In addition, in the CVA of residual data, the first and second components included 83. 06 % of all variation (CV1 = 31. 46 % and CV2 = 51. 60 %). The CVA results of Procrustes data showed that the Pakistan samples overlapped with the Iranian samples, except for the Kerman population. Sudan samples overlapped with the Iranian samples of Bushehr, Shiraz, and Sistan and Balochestan. Additionally, Oman samples showed partial overlapping with South India and Kerman (Iran). The CVA results of residual data showed that the Pakistan samples overlapped with the Iranian samples, except for the Kerman population. Sudan samples were differentiated from Iranian samples. In addition, the Oman samples showed partial overlapping with South India. Both Procrustes and residual data differentiated the populations of Thailand and Vietnam from the other populations. Cluster analysis was used to compare the populations of A. florea in various areas. The cluster derived from Procrustes data indicated that Sri Lanka was closer to the populations of Iran, except for the Kerman samples. Furthermore, the Sudan and Pakistan populations were categorized under one group. In addition, the cluster derived from the residual data indicated that the Pakistan population was closer to the populations of Iran, except for the Kerman samples. The Sri Lankan population indicated a closer relationship with India, and the Sudanese population was differentiated from the other populations.    Conclusions: The recent findings showed that residual data revealed greater efficacy than Procrustes data in differentiation and relationships between the populations of A. florea. The results of the derived cluster from residual data indicated closer relationships of A. florea populations from Pakistan and Sri Lanka with Iran and South India, respectively.

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.

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.

Economic freedom and lack of imposing limitations on output and distribution of goods & services have remarkable effects on economic growth as well as per capita income of countries. The present paper is based on the framework of Lakatos methodology and uses the Skipton model to deal with the impact of economic freedom on per capita of 87 countries (including low-medium and high income) during 1980-2003. It also concentrates on combinations of Carlson - Lundstrom and Weinhold models to investigate the effect of economic freedom on economic growth of the same countries for the period under consideration.Our findings support the positive impacts of economic freedom on both per capita and economic growth of all different samples of the countries. We have also shown that among the elements of economic freedom, judiciaries independence; property rights security; sound money; labour market laws; and credibility have the highest impacts on per capita incomes. In additions the paper concludes that judiciaries independence; property rights security; labour market laws; and credibility have the highest effects on economic growth.

ICT1 has broad effects on economy in micro and macro levels. Effects on kind, nature of jobs and labor market are the most important. Omission, creation, change of jobs and teleworking are the four ways through which ICT influences kinds of jobs. Also, ICT influences the structure of jobs, and changes the nature of jobs in society via reduction of average work hardship, making jobs more mental, but instable and more specialized.This paper studies the effect of ICT on amount of employment using a microeconomic pattern in a ftame work of assessing effective factors on employment appalling a logarithmic model. This model has been estimated with panel data approach for 47 countries (including 22 OECD members and 25 developing countries) over the years of 2000- 2003.The results indicate positive and significant effect of ICT on employment. Also the elasticity of employment in proportion to the costs of information and communication is 0.11, which indicates 1% increase in the costs of ICT, increases employment by 0.11%.

This study examines an organizational performance prediction model based on big data analysis and strategic intelligence. In today’s complex and changing world, organizations need to use modern techniques such as big data analysis and artificial intelligence to make strategic decisions and improve their performance. Big data, by providing deep insights and advanced analytics, allows organizations to make more accurate predictions about their performance. Also, strategic intelligence is proposed as a tool for analyzing information and making informed decisions in line with sustainable competitive advantage. The main purpose of this article is to design an organizational performance prediction model using these two strategic approaches. The research method used in this study is thematic analysis and interviews with 15 experts in the fields of management and information technology. The results of the research show that combining big data with strategic intelligence can help organizations predict and improve their performance and provide solutions for optimal management of strategic processes and decisions.