As mobile data traffic increases, and the number of services provided by the mobile NETWORK increases, service load flows as well, which requires changing in the principles, MODELs, and strategies for media transmission streams serving to guarantee the given nature of giving a wide scope of services in Flexible and cost-effective. Right now, the fundamental question remains what number of NETWORK slices will be cost effective for slice managing and giving the required functionality. So, the aim is to improve the efficiency of mobile NETWORK by forming an ideal slice in a multi-service communication NETWORK. In this paper, we propose a MODEL to demonstrate NETWORK resource allocation system that forms devoted NETWORK slices to serve particular types of services independently on shared infrastructure. This MODEL solves the problem of creating a strategy to form multi-service core mobile communication NETWORK slices, which allow the providing of a wide scope of services with certain quality indicators according to the effective dynamic configuration of the system. A resource management system MODEL is created, to provide a method that considers costs related with excessive resource allocation, and also reduces the number of NETWORK recalculations, allowing for a reasonable proportion of management costs and Qualities of Service.

Capacity of lines and stations is one of the most influential and important factors in NETWORK planning and operation. Various methods and methodologies exist for calculating capacity. Accurately and appropriately determining this concept in railway NETWORK lines is a very complex task. This becomes even more important when considering the number of NETWORK blocks and their different characteristics and properties. In recent years, the Scott method has been used to calculate line capacity. This research aims to identify the various methods of calculating capacity in NETWORKs. Furthermore, while comparing these methods, an attempt has been made to introduce a suitable method for use in the country. To this end, by considering the characteristics of the country's railway NETWORK and calculating the capacity of 8 selected routes, an attempt has been made to identify a method suitable for the country's railway NETWORK. The International Union of Railways (UIC) has presented methods for calculating NETWORK capacity under codes 405 and 406. Based on the results of this research, the method presented in the UIC406 guideline, which is the latest and most up-to-date method of this union, has a good fit with the country's NETWORK conditions and can be used in the country's railway NETWORK. One of the outputs of this study is a software for calculating the capacity of the country's rail NETWORK based on UIC code 406. The outputs of this software include train capacity (train set of each block), tonnage capacity in two modes of dominant tonnage and maximum tonnage (nominal tonnage), NETWORK bottlenecks based on the performance-to-capacity ratio, and sensitivity analysis of various solutions to increase NETWORK capacity.

Plant development can be defined as a programmed qualitative change in plant form, which leads plant to maturity, and researchers call it as phasic development or phenology. Recognizing the timing of occurring each development stage is necessary for managing system in order to yield increment. The timing of occurring development stages depend on climate, genotype specifications and sowing date then determination of these times in different regions is difficult and it is only possible through the using of crop simulation MODELs which can predict the timing of occurrence each development stage by integrating effective factors. The MODEL was constructed based on linear equation of plant temperature response. In order to MODEL evaluation two experiments were carried out in agricultural and natural resources research center of Khuzistan in 2003-2004 and 2004-2005 cropping years. Wheat development stages were determined based on Kirby and Appleyard’s scale by stereoscopic microscope and required GDD for each development stage as well. The constructed MODEL was calibrated and run for simulation. Comparison of simulated and observed data showed that the MODEL can strongly predict wheat development stages.