For sustainable management of the Caspian Forests of Iran, a most appropriate and fastest method of data collection and combination is essential. Combination of Satellite digital data with field data, using Geographic Information Systems, (GIS) could be applied to obtain accurate information for multiple decisions making which is influenced by different factors. In this study eight senses of Landsat 7ETM+ data accosted from Caspian Forests of Iran, June and July 2000, were used.Based on OIF index and statistical analysis of the ETM data, color composite 3,4 and 5 were selected for unsupervised classification. The ground observation information were collected from 3860 plots (30*30), using unsupervised map as a primary map.Overall, combining the ETM and the ground data throw the field information, using the supervised classification method, boundary of the Caspian Forests of Iran were achieved in six classes (Dense forest, Sparse forest, Rangeland, Farm land, Water and Bare soil).The classification accuracy assessment showed that the accuracy rates of the dense forest, sparse forast, rangeland, farmland, water and bara soil were 83, 78, 54, 76 and 98 percent, respectively.

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.

We compared land use maps of 1988 and 2004 of northern mountainous forests of Iran that have been extracted from landsat images in 15 years period and investigated on boundary changes and calculated deforestation areas as well. This information is essential for afforestation and forest extension in the deforestation areas of upper border of the mountainous forests. For this reason, we used landsat TM and ETM+ images in 1988 and 2004 for providing land use maps and also collected information from 2960 sample plot (90´90m) using systematic random sampling. Based on extracted maps, 121528 ha of northern mountainous forests of Iran has been deforested in 15 years period. The deforestation area were estimated 8101 ha per year.  It is 0.45 percent of the northern forests. The percentage of deforestation area in terms of provinces (Guilan, Mazandran and Golestan) was estimated 1182, 4647 and 2272 ha per year or annually 0.21%, 0.49% and 0.69%, respectively. The results showed that the maximum deforestation percentage occurred in the Golestan and the minimum in the Guilan province.

We first describe how to “fuzzify” the estimated binary columns to create a [0, 1]-valued column. We refer to this [0, 1] -valued column as the soft segmentation column of the noisy spectrogram column. Similarly to the collection of soft segmentation columns as the soft segmentation image, or simply as the soft segmentation. The band-dependent posterior probability that the hard segmentation column value of pixel is 1, given that bin and the binary values in the neighborhood configuration of the pixel are equal. Symbolically, each pixel of the soft segmentation column is set to the soft segmentation column value of the pixel in a row was set to zero.

Despite error resilient methods that are applied on video data in transmitter side, occurring error along video data transferring for communication channels is inevitable. Error concealment is a useful method for improving the quality of damaged videos on the receiver side. In this paper, a fast and hybrid boundary matching algorithm is presented for more accurate estimating of damaged motion vectors (MVs) from received video. According to the preference list of error concealment, the proposed algorithm performs the error concealment for each damaged macroblock (MB). In the presented method, the boundary distortion is calculated for each pixel from each candidate MB's boundary with use of proposed hybrid boundary matching criterion. Then, depending on the accuracy of each adjacent boundary from damaged MB, a special weight is given to them through match process. Finally, the list of error concealment preference is updated and the candidate MV with the lowest boundary distortion is selected as the MV of damaged MB. Experimental results show that the proposed algorithm increases the average of PSNR for different test sequences more than 1.8 dB in comparison with reference methods and without significant increasing in calculation time and with improving the quality of reconstructed videos.

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.

This paper presents a method for performance evaluation, ranking and clustering based on the double-frontier view to analyze the complex networks. The model allows us to open the structure of the “ black box” and can help to obtain important information about efficient and inefficient points of the system. In this paper, we consider a three-stage network, in respect to the additional desirable and undesirable inputs and outputs and utilize the cooperative approach to measure the efficiency of the overall system. Due to the fact that, a conclusion implying only one of these two, optimistic or pessimistic views is one-sided and incomplete, so, in this paper we used the double-frontier to analyze the network. Moreover, a heuristic technique was used to convert non-linear models into linear models. After obtaining the effective and inefficient points of the network, the DMUs are classified into several clusters by the k-means algorithm. Finally, in this article, in order to apply the proposed model a factory producing dairy products with a production area, warehouse premises and a delivery point are simulated. This factory has been regarded as a dynamic network with a time period of 24 intervals. The results of the ranking showed that, the time periods, (10) and (1) were the best and poorest respectively, in context to the efficiency within 24 phases of time.