In previous studies we first concentrated on utilizing crisp simulation to produce discrete event fuzzy systems simulations. Then we extended this research to the simulation of continuous fuzzy systems models. In this paper we continue our study of continuous fuzzy systems using crisp continuous simulation. Consider a crisp continuous system whose evolution depends on differential equations. Such a system contains a number of parameters that must be estimated. Usually point estimates are computed and used in the model. However these point estimates typically have uncertainty associated with them. We propose to incorporate uncertainty by using fuzzy numbers as estimates of these unknown parameters. Fuzzy parameters convert the crisp system into a fuzzy system. Trajectories describing the behavior of the system become fuzzy curves. We will employ crisp continuous simulation to estimate these fuzzy trajectories. Three examples are discussed.

Spatial datasets may contain extreme values and exhibit heavy tails. So, the Gaussianity assumption for the corresponding random field is not reasonable. A sub-Gaussian α-stable (SGαS) random field may be more suitable as a model for heavy-tailed spatial data. This paper focuses on geostatistical data and presents an algorithm for Simulating SGαS random fields.

Virtualization is a technique to make the cloud computations secure. This technique can reduce the costs and increase the reliability of systems by means of sharing the physical resources between several VMs (Virtual Machines). However, virtualized environments are vulnerable to some security issues such as lack of performance isolation. These days, using the virtualization as a technique is growing slowly in large organizations. Despite these advantages, virtualizations have created several security challenges. So, the main concern of virtualization service providers and their customers is how to detect and encounter these security challenges. In this research, the resource freeing attack is selected among all available virtualization attacks because of its simplicity and its drastic effects. Resource freeing attack causes an unfair resource distribution among VMs. We use Cloudsim as a powerful cloud simulation toolkit to implement resource freeing attack. After Simulating the attack, the CPU's efficiency, response time diagrams, and the obtained bandwidth are measured. The simulation results show the significant changes in VM behavior after occurring the attack in the virtualized environment. These changes help cloud providers to detect resource freeing attack in the cloud.

Aerosol impact on people health, social and economic activities, land and water ecosystems and meteorological parameter. The GOCART scheme was evaluated for Simulating PM10 in this study. GOCART was run into WRF model as a host model. Reanalysis data from FNL for every 6 hour was used for initial conditions. One domain and two nests were used to cover region from West Africa till East Asia, Iran and Khuzestan Province. Primary results shown that the model overestimate surface moisture and the results was weak for Simulating PM10, so we modified surface moisture using welting point of soil texture in desert region for summer. In addition, erodibility index was defined using surface moisture and threshold wind velocity and coefficient of this index modified using Tir and Day PM10 data at 1387. Results of modified model were compared with observed data in environmental station in Ahwaz for one week from 25, 3, 1388 till 31, 3, 1388. Statistical analyses shown that, GOCART has a good capability for Simulating PM10.

Introduction: Understanding hydrological phenomena is essential for the optimal use of water resources. Surface runoff is an important part of the hydrological cycle. Accurate runoff estimation can make a significant role in water engineering and the proper utilization of resources for the various uses of agriculture, drinking, hydropower and the environment. Therefore, the use and development of accurate and reliable methods to model the runoff of the catchments are essential. One of the new methods of runoff calculations is cellular automata. Cellular automata is a fundamental method for Simulating complex systems.Methodology: In cellular automata, the lattice space is divided into a number of cells and creates a cellular space (Fig2). A set of cells adjacent to the central cell is called a neighborhood (Fig1). In the runoff production process, the cell state is the water level, which is the sum of the cell height and water depth. The height of the cell is determined from the digital elevation model and the determination of water depth is controlled by the effective precipitation at the present time step and the balance between inlet and outlet flow at the last time step. The transition rules in the cellular automata model determine the behavior of cells at different time steps and define the future state of the cell. The first transition rule determines which neighboring cell can get water from the central cell at each time step (Fig3). The second transition rule is used to calculate the amount of flow to neighboring cells, in which the Manning equation is used. The first and second transition rule applies to all cells at each time step and as a result, the output flow from each central cell to its neighbors is determined. In the general view, each central cell is a neighbor of other cells, as a result, a third rule must be used for calculating the total flow for each cell. The evaluation of the cellular automata model is performed using the statistical indicators of correlation coefficient and root mean square error and Nash-Sutcliffe efficiency coefficient.Results and Discussion: First, the runoff is simulated on a uniform rectangular surface and the results of the cellular automata model are compared with the results of the Akan analytical solution. In order to evaluate the efficiency and accuracy of the cellular automata, the statistical parameters of the models were calculated. The results showed that the cellular automata model has high accuracy and efficiency (Fig 5). Then runoff in the Con catchment is simulated. This catchment is located in the northwest of Spain. (Fig 6). The results showed that the cellular automata model has been able to simulate runoff well in the catchment surface (Fig 7). At the outlet, the discharge is calculated based on the cellular automata and compared with the observed discharge. The results of the cellular automata model are shown with three different time steps (Fig 8). So far, various mathematical models for rainfall-runoff estimation have been proposed. In integrated models, the whole catchment is considered as a unit. These models have a simple structure and appropriate computation time, but are accompanied by many assumptions and the spatial distribution of variables is not considered. Therefore, integrated models are not suitable for large catchments. In semi-distributed models, the catchment is divided into a number of sub-catchments. In these models, important features of the catchment are shown, but for each sub-basin, moderate data is considered and the exact spatial distribution of data is not considered. In distribution models, spatial distribution data is considered, but the time required for computation and modeling is high. Therefore, it seems necessary to develop methods that have a simple structure and high accuracy at the same time. Due to the accuracy of the results and the ability to access the required information anywhere in the catchment, the cellular automata model can be used to predict runoff.Conclusion: The results showed that the cellular automata model has a high accuracy compared to the Akan analytical solution. Also, in Simulating the runoff of the con catchment, the runoff network at the catchment surface was well simulated. Comparing the computational discharge results from the cellular automata model and observational data, the values of the correlation coefficient, mean the square root of error and Nash-Sutcliffe coefficient were 0.99, 0.11 and 0.97. As the result, due to the accuracy of the results and the ease of implementation, the cellular automation model can be used to predict runoff in catchment without data and reliable results can be achieved.

Identifying the roots of a worm and reconstructing its spread path are among essential concerns in digital forensics. This knowledge assist the prosecutor in understanding how the attack happened in the network and how security protections were breached. Evaluating methods proposed for this purpose is problematic due to the lack of suitable datasets containing both worm traffic and normal traffic. In this paper, we investigate various approaches of generating such datasets and propose a technique to generate suitable datasets for these evaluations. ReaSE is a tool for creating realistic simulation environments, which considers three aspects, i. e., topology generation, normal traffic generation, and attack traffic generation. We modify ReaSE to make it suitable for generating these datasets. We also generate various datasets for Code Red I, Code Red II, SQL Slammer and modified version of them in different scenarios and make them accessible to the public.

In this article the possibility to use Eulerian approach in the conventional ISPH method in simulation of internal fluid flows is studied. The use of Eulerian approach makes it possible to use non-uniform particle distributions to increase the resolution in the sensitive parts of the domain, different boundary conditions can be employed more freely and particle penetration in the solid walls and tensile instability no longer require elaborate procedures. The governing equations are solved in an Eulerian framework containing both the temporal and local derivatives which make the momentum equations non-linear. Some special treatment andsmaller time steps are required to remedy this non-linearity of the problem. In this study, projection method is used to enforce incompressibility with the evaluation of an intermediate velocity and then this velocity is projected on the divergence-free space. This method is applied to the internal fluid flows in a shear-driven cavity, Couette flow, a flow inside a duct with variable area and flow around a circular cylinder within a constant area duct. The results are compared with the results of Lagrangian ISPH and WCSPH methods as well as finite volume and Lattice Boltzmann grid based schemes. The results of the studied scheme have the same accuracy for velocity field and have better accuracy in pressure distribution than ISPH and WCSPH methods. Non-uniform particle distributions are also studied to check the applicability of this method and Good agreement is also observed between uniform and non-uniform particle distributions.