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The characteristics of a scaffold that is the basic component of tissue engineering are considerably influenced by the fiber diameter of the fibrous scaffolds. Since the significant effect of the fiber diameter on the scaffold properties, many researchers have focused on estimating the fiber diameter based on the electrospinning parameters. With similar motivation, in this paper, a new and simple closed-form expression, which can help researchers in fabricating the electrospun poly (ԑ-caprolactone) (PCL) scaffold with desired fiber diameter, is presented. In order to construct the expression proposed, an experimental study has been performed to obtain the data set, in which 25 experimental data including average fiber diameter (AFD) values dependent on different combinations of parameters such as voltage, solution concentration, tip to collector (TTC) distance, and flow rate. Then, an expression has been constructed that is used to estimate the AFD of the electrospun PCL, and the coefficients of the expression were determined by using the artificial bee colony (ABC) algorithm. In order to validate the estimation ability of the expression, the metrics such as mean absolute error (MAE) and mean absolute percentage error (MAPE) have been used, and the optimization and test errors were respectively obtained as 3.30% and 1.27% in terms of MAPE. In addition, the results obtained were compared with those reported in the literature. Results show that our new expression can be successfully used to estimate the AFD of electrospun PCL prior to the electrospinning process. Thus, the number of test repetitions could be reduced by using the expression proposed, and time, cost, and labor could be saved in this way. This study contributes to the literature because there have been only a limited number of studies that focus on estimating the AFD of PCL nanofiber despite many studies about various polymers. [ABSTRACT FROM AUTHOR]

Microstrip antennas (MAs) have been widely used in the wireless communication systems since they have some superiorities over other microwave antennas. Compact microstrip antennas (CMAs) are obtained by modifying the conductor parts of MAs. CMAs have some advantages over the conventional MAs such as smaller size for the same resonant frequency. The models of artificial neural network (ANN) and/or adaptive neuro-fuzzy inference system (ANFIS) have been used for computing the resonant frequency of rectangular ring shaped CMA (RRCMA), C shaped CMA (CCMA), E shaped CMA (ECMA), H shaped CMA (HCMA) and L shaped CMA (LCMA) in the literature. In this survey study, the results obtained by the ANN and ANFIS models used for the resonant frequency computation of these slot loaded CMAs were compared with those of the other methods in the literature and it is seen that these methods are more successful. According to these results, it is seen that ANN and ANFIS networks which are the artificial intelligence techniques can be successfully used for computing the resonant frequency of slot loaded CMA. Moreover, when the ANN and ANFIS studies were investigated, it appears that they are faster and more effective than the other methods. [ABSTRACT FROM AUTHOR]

In this paper a simple and efficient heuristic search method based on the artificial bee colony (ABC) algorithm is presented and used for the optimal power ow (OPF) problem in power systems with static VAR compensator (SVC) devices. The total generation cost of a power system with SVC devices (which improve the voltage stability at load buses) is optimally minimized with the use of ABC. The ABC, which is based on the foraging behavior of honey bees searching for the best food source, is a recently proposed optimization algorithm. The performance of the presented ABC algorithm was tested and verified on the IEEE 11-bus and IEEE 30-bus power systems by comparing it with several other optimization methods. Furthermore, ABC is used not only for optimizing the total generation cost and active power loss, but also for improving the voltage stability of the 22-bus power system in Turkey. Our results illustrate that ABC can successfully be used to solve nonlinear problems related to power systems. [ABSTRACT FROM AUTHOR]

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