The historical center cities were exposed to change, which included its social and economical structures, Which led to sweeping changes in land use, causing a change in the result, in urban fabric and the physical structure and therefore to see visual. We have attracted these centers as a result of pressure from urban development and contemporary reflection of the agents of change which have been hit during the late period a development residential newly not sympathize mostly with the content of the historical and at the expense of removing large parts of the urban fabric and replace it with patterns of structural and styles of architecture has not been connected with reality. Which led to the loss of some historical and architec

The article aims to study the crisis of political change  from three Phases . The first focuses on the crises of political legitimacy and democratic postponement, as fundamental issues in analyzing the phenomenon of power struggle through the dialectic between the concept of historical legitimacy and institutional fragility from the beginning of statehood in 1962 to the stage of multi-partyism and the cessation of the electoral process in the 1990s. While the second focuses on the question of the monopoly of power in the post-terrorism and national reconciliation according to considerations  Political, social and security measures to prolong the life of the regime and avoid the demands of political change brought about

The evolution in the field of Artificial Intelligent (AI) with its training algorithms make AI very important in different aspect of the life. The prediction problem of behavior of dynamical control system is one of the most important issue that the AI can be employed to solve it. In this paper, a Convolutional Multi-Spike Neural Network (CMSNN) is proposed as smart system to predict the response of nonlinear dynamical systems. The proposed structure mixed the advantages of Convolutional Neural Network (CNN) with Multi -Spike Neural Network (MSNN) to generate the smart structure. The CMSNN has the capability of training weights based on a proposed training algorithm. The simulation results demonstrated that the proposed

Magnetic levitation (Maglev) systems are employed in a wide range of applications and are therefore of significant practical importance, which has led to growing research interest. This paper presents the design of a terminal synergetic control (TSC) and feedback linearization-based proportional-integral-derivative plus second-order derivative (FL-PIDD2) controller for the Maglev system. For developing the control law of both controllers, the mathematical model of the Maglev system is converted into a canonical system where the expression of the nonlinearity is displayed in the last differential dynamic equation of the system. The determination of the TSC and FL-PIDD2 gains for achieving the desired dynamic response is carried out using the