In this paper, we answer two questions of Yoshioka [12]. We prove that every wγ, strongly quasi Nagata T2–space is metrizable; every regular c–semistratifiable quasi–γ, β–space is a Moore space.
This paper is focused on performing a new method for solving linear and nonlinear higher-order boundary value problems (HBVPs).This direct numerical method based on spectral method.The trial function of this method is the Monic Chebyshev polynomials (MCPs).This method was relying on derivative of MCPs which explicit in the series expansion.The advantage of this method is solved HBVPs without transforming it to a system of lower-order ordinary differential equations (ODEs).This method supported by examples of HBVPs in wide application.The mentioned examples showed that the proposed method is efficient and accurate.
The subject of routing in computer networks has got a considerable place in recent years. The reason of that is the determining role of routing in the efficiency of these networks. Service quality and security are considered as the most important challenges of routing due to the lack of reliable methods. Routers use routing algorithms for finding the best path to destination. When we speak about the best rout, we consider parameters such as the number of hops, changing time and the cost of communicational costs of sending packets. Using smart factors locally and nationally, in this thesis it has been tried to improve routing operations. The ant colony algorithm is a multifactor solution for optimization issues which has some models based on collective intelligence of ants and has been applicable by converting to an efficient technology in computer networks. Although the ant is a simple creature, but a colony of ants can do useful tasks such as finding the shortest route toward food and sharing this information with other ants through leaving a chemical material named Pheromone. This algorithm includes three stages. The first phase is clustering network nods to smaller colonies, this phase is accomplished using learning automata in accordance with network need; for example placing the nods which will have more interaction in a cluster in future. Second phase is finding the routes of network by ants; and the third phase is sending network traffic to destination through the found routs by ants.
