The capital budgeting process is based on the technique of reducing future cash flows of the net present value which implies a process of discounting by using the discount rate. Usually, in capital budgeting process the discount rate is presented through the cost of capital. The determination of the cost of capital primarily depends on the capital structure, but since the process of capital budgeting implies a long time period, it also implies uncertainty and vagueness. Subjective perception, thinking, judgment and decision making, including a large number of predicted vague data is often expressed solely in linguistic variables by the management and this is the main characteristic of the capital budgeting process, especially in the determination of the cost of the capital through a long time period. The main intention of this paper is to present the use of fuzzy logic in the process of determining the cost of capital and providing an alternative approach in the appraisal of the cost of capital in the presence of fuzziness. The integration and implementation of linguistic variables i.e. qualitative information in the determination of the cost of capital using fuzzy numbers in the capital budgeting process will also be discussed. Through the formulation of a fuzzy system and the use of fuzzy numbers we will propose a process and methodology* for the use of fuzzy numbers in the process of the cost of capital determination. We examine the presented methods and suggest new ideas that could improve further research and implementation of fuzzy logic in the capital budgeting process.

Due to resource limitations in wireless sensor networks, prolonging the network lifetime has been of a great interest. An efficient routing technique is known as hierarchical routing based on clustering, in which finding the optimum cluster heads and number of them has been a challenge. In this paper, a two-level fuzzy logic is utilized to evaluate the qualification of sensors to become a cluster head. In the first level (Local Level), the qualified nodes are selected based on their energy and number of neighbors of them. Then, in the second level (Global Level), nodes’ overall cooperation is considered in the whole network with three fuzzy parameters. These parameters are centrality, proximity to base station and distance between cluster heads. Simulation results in five metrics show that the proposed approach consumes less energy and prolongs the network life time about 54 % compared with other algorithms.