The regional order control system has been affected by the change in the structure and the change in the pattern of the international order.in the meantime, one of the campaigns to control the order of the Iranian network is the erosion of the unipolar structure of the global system and the emergence of new centers of power in the form of multiple configurations.based on this, the main question is raised, what effect does the multibody structure of regional orders have on the order control patterns of regional powers in Iran's regional network?In this article, it is argued that the multibody structure moves regional order control patterns from axial polarity to axial centers, based on this, order control in Iran's regional network in the dimensions of structures, dynamics, cycles, actors' behaviors and strategy of control has changed and the control of order in Iran’s regional network is set based on the cluster-adaptive strategy of multibody nested networks. Using the abduction method, in the first step, the present research examines the emergence of various elements of order in the form of structures, power cycles, dynamics, and control models, and provides a suitable theoretical framework for emerging realities in the form of complex networks. By using it, order in Iran’s regional network is explained. Finally, the cluster-adaptive control strategy based on the concept of focality and appropriate to the regional environment of Iran is explained as the inovation of the research.

In this study, weather conditions such as air humidity, temperature air, and wind speed were investigated in relation to wind turbine efficiency with the approach of an exergy study. In this study, the wind speed has been investigated in two different climatic regions of Iran with an approximate distance of 1200 km, in the names of Ardabil and Marvast. The amount of wind density of Ardabil is equal to 66 (kW/m2) and Marvast is equal to 123 (kW/m2). Power production using a 10 (Kw) wind turbine in the Ardabil region is 2.3 (MWh) and in the Marvast region is 3.2 (MWh) per year. The highest wind turbine exergy efficiency is 0.48 in the Ardabil region, and the highest exergy efficiency in the Marvast region is 0.18. The amount of reduction of CO2 gas production, using wind turbines in comparison to gas and diesel power plants in Ardabil, are 1.1 and 2.1 tons and in Marvast are 1.5 and 2.9 tons per year. This reduction in CO2 greenhouse gas per year is equal to using a forest region of 1000 (m2) to 3000 (m2). The use of wind turbines reduces the fuel consumption of diesel power plants in the Ardabil region for the amount of 797.4 liters and in the Marvast region for the amount of 1244 liters of diesel per year. According to this review, it can be concluded that in addition to wind speed, air humidity plays a significant role in the selection, installation, and commissioning of wind turbines in the region. According to this survey, it can be seen that in the Ardabil region, the wind speed of the wind turbine has a higher exergy efficiency than in the Marvast region, and it can be concluded that the wind turbine has performed better in the Ardabil region.

Flood is one of natural hazardous disasters that causes economic and life damages every year. Therefore, scientists have tried to assess the variability of this phenomenon. In this study, the ability of Artificial Neural Network (ANN) and Geographical Information System (GIS) in the estimation of flood discharges of 90 sub-basins of Hamedan Province (with concentration time less than 24 hours) was assessed using a data period of 16 years collected at the 17 hydrometric stations throughout the area. To this regard, area of the basins, elevation and mean slope, bindery of hydrologic groups of soil, weighted curve number as well as daily and 5 day precipitations which were occur before the corresponding floods used as the input variables and the flood discharge defined as the output. Considering the training, validation and testing sets the results showed that the best structure was a feed forward ANN with two hidden layers of 5 and 4 processing elements. In this model, the coefficient of determination, root mean squared error and mean absolute error were 0.87, 0.72 and 2.83, respectively. Finally, using the maximum daily and 5 day precipitations for a return period of 25 years and application of the model, the spatial distribution of the direct runoff was predicted. Therefore, flood mapping of the province was specified to determine the priorities of flood control between the regions. Finally, the results indicated that ANN method was an appropriate tool for flood discharge modeling, especially in the case of missing data or inadequate hydrometric stations.

Network-on-chip provides an efficient communication platform for Systems-on-chip. The static power consumption is an important issue in these networks. Switching the power supply on virtual channels during idle time is a common method for reducing the network power consumption. The traffic load at the network level and non-continuous idle period of virtual channel have caused the sources to be switched on and off continuously, which leads to increase in power consumption and other overheads. This will be more important, in partially connected 3D chip networks in which a limited number of vertical connections has been used. In this paper, a routing algorithm is proposed who employs an appropriate policy for packet distribution, and reduces the load distribution in the network and creates a continuous idle time for the resources, result in suitable power management in the network. In this routing scheme the network is divided to north and south region and some restriction applied in usage of elevators in each region and try to increase the utilization of the used resources as well as the ideal time of low traffic paths. The simulation results, derived by BookSim, show the proposed method improve the network power consumption by 18% to 30% comparing previous algorithms, and the network delay has been reduced by 32%.

Equitable distribution of services at the regional scale and achieving a balanced spatial structure in the region are among the most important goals of sustainable regional planning, especially in developing countries such as Iran. In this regard, one of the most important strategies and policies in regional spatial planning is the decentralization of development. The purpose of this study is to identify the most important urban areas of Fars province in order to determine the priorities of development and decentralization of the unipolar development of the province. To this end, the regional network modelling based on graph theory and the concepts of social network analysis are used to conduct the spatial analysis of Fars province. The data used in this study includes the Fars province road network extracted from the OSM open source system, as well as the spatial information of the urban and rural areas of Fars province extracted from the website of the Statistics Center of Iran. Regarding research methods, Pandas Library and NetworkX Library were used in the Python programming platform to form the network graph and analyze the centrality indicators, while ARC GIS software was for final processing and visualization of data and information. According to thenetwork centrality indices and the location of the province cities, notwithstanding Shiraz as the main hub of development in the province, Zarghan, Sadra, Kavar, Lepui, Khane Zenian and Noorabad are the main development priorities. In addition, the Shiraz-Marvdasht and Shiraz-Khaneh Zenian roads were determined to be the most important transportation paths and communication corridors. The results and findings of this study showed that network-based modeling and the use of graph-based analytics (in particular, social network analysis techniques) can be useful and effective as new analytical methods in regional studies and planning.

Regional planning as an effort to eliminate the imbalance in geographical space can be done and the product of it is a balance of activity and population. In this paper firstly has been review the primate city and metropolitan area network and has been investigated the effects of planning before and after revolution on the organize the population. And its role in shaping the city networks in Iran has been analyzed. This article method is describes–analytical. That firstly based on documentary studies has been gathered the most important resources associated with the network of urban and regional planning in Iran. Then, based on obtained information has been data analyzed. And results are presented in the form of data analysis. The research results indicate a profound impact of regional planning in balance and harmony of urban network and population in Iran.

The objective of this paper is to develop an artificial neural network (ANN) model which can be used to predict temperature rise due to climate change in regional scale. In the present work data recorded over years 1985-2008 have been used at training and testing steps for ANN model. The multilayer perceptron (MLP) network architecture is used for this purpose. Three applied optimization methods are back propagation (BP) (in both input selection and weight optimization), genetic algorithm (GA) (in both input selection and weight optimization) and combined GA-particle swarm optimization (PSO) (input selection by GA and weight optimization by PSO). In this framework, natural and anthropogenic parameters which affect the incoming solar radiation are considered in order to predict the climate change induced temperature rise in regional scale. Inputs of ANN model are mean temperature, dew point temperature, relative humidity, wind speed, solar radiation, cloudiness, rainfall, station-level pressure (QFE) and greenhouse gases. For predicting monthly mean temperature, input data include one month, six months, 12 months and 24 months before recorded data. In this work, nine stations namely Tehran, Mashhad, Ramsar, Orumiyeh, Sanandaj, Yazd, Ahwaz, Bandar Abbas and Chabahar in nine different climatic region of Iran are chosen to determine the temperature rise over Iran. Results show that the averaged minimum square errors (MSE) are 0.0196, 0.0224 and 0.0228 for ANN-BP, ANN-GA and ANN-GA-PSO methods, respectively. The ANN model associated with BP optimization method predict annual mean temperature rise as 0.44, 0.49, 0.20, 0.12, 0.17, 0.46, 0.41, 0.06 and 0.01oC after 10 years for mentioned stations, respectively. These values show the average temperature rise of 0.26oC after 10 years (the base year is 2008) for Iran.

The evolution of regional and global systems is one controversial subject in international relations literature. Traditional and linear imagine to forming of global and regional systems will throw distance between current theories and new realities. Theory of complexity and chaos by attention to these realities is a new turning point in explaining evolution of regional networks. The complexity theory have been slowly making their way into international relations (IR), and will extend our understanding of the dynamics of international politics. In this respect the article wants to suggest a new perspective on evolution of systemic orders. Author argues that fundamental forces and pressures cause the enternce of states to multi body system and then evolution dynamic and emergent features of evolving system are resulted by relational inovations which make new constructions and emergent order patterns. This article argues for a relational ontology belives that complex interactions among the elements of an assemblage can produce emergent regional construction and order and in continue these emergent effects themselves reshape the system’s elements.