In this paper, a novel risk-based, two-objective (technical and economical) optimal reactive power dispatch method in a wind-integrated power system is proposed which is more consistent with operational criteria.  The technical objective includes the minimization of the new voltage instability risk index. The economical objective includes cost minimization of reactive power generation and active power loss. The proposed voltage instability risk employs a hybrid possibilistic (Delphi-Fuzzy)-probabilistic approach that takes into consideration the operator’s experience, the wind speed and demand forecast uncertainties when quantifying the risk index. The decision variables are the reactive power resources of the system. To solve the problem, the modified multi-objective particle swarm optimization algorithm with sine and cosine acceleration coefficients is utilized. The method is implemented on the modified IEEE 30-bus system. The proposed method is compared with those in the previously published literature, and the results confirm that the proposed risk index is better at estimating the voltage instability risk of the system, especially in cases with severe impact and low probability. In addition, according to the simulation results compared to typical security-based planning, the proposed risk-based planning may increase the security and economy of the system due to better utilization of system resources.

Many dynamic processes are characterized by parametric or structural uncertainties due to internal and externaldisturbances. Existing deterministic models could not handle the uncertainties inherent in these processes. A valuablealternative to control these processes is the use of a type-3 Fuzzy system. Since type-3 Fuzzy systems use threedimensionalmembership functions, they have more capacity to model uncertainties. This paper introduces the designof a type-3 Fuzzy logic system (FLS) for the control of dynamic plants. Utilizing type-3 Fuzzy logic, the architectureof the type-3 Fuzzy control system (T3FCS) is proposed. The knowledge base of the controller is constructed and itsdesign stages are presented. The inference mechanism of type-3 FLS is developed using α slices and interval type-3membership functions. The proposed type-3 FLS is utilized for controlling nonlinear dynamic plants. The modelingof the proposed T3FCS is performed and transient response characteristic is derived using different stepwise excitationsignals. A comparison of the designed system with the type-1 FLS-based system is provided. The obtained simulationresult demonstrates the efficiency of using the proposed type-3 FLS in the control of dynamic systems characterized byuncertainties.

Gestational diabetes mellitus (GDM) is a kind of diabetes that requires persistent medical care in patient self management education to prevent acute complications. One of the common and main problems in diagnosis of the diabetes is the weakness in its initial stages of the illness. This paper intends to propose an expert system in order to diagnose the risk of GDM by using FIS model. The knowledge base was based on the opinion of experts and also by using ANFIS model in order to extract rules that based on real data. The data were collected from the patient's file in a medical health care center that contains effective risk factors in giving rise to GDM. The effective factors consist of fast blood suger, weight and age in the initial months of pregnancy and the output is the risk of GDM in pregnant women . Based on the results , the level of minimum square error in the Fuzzy expert system was calculated as 0.227. For diagnosis of illnesses such as diabetes, in lack of accesses to experts ,one can rely on intelligent softwares such as expert systems, wich can assist the speedy and better diagnosis.

In recent years, using Fuzzy sets theory in modeling of complex and uncertain hydrological phenomena has attracted research workers. For this reason, in this research for river flow forecasting, we have used models of FIS and ANFIS which are based on Fuzzy logic. Data of daily flow discharges were provided from Lighvanchay watershed for 6 years. For considering the randomness of data, return points test was used. Then correlogram of data was employed to determine the input optimum models and finally 5 models of discharge forecasting designed based on previous days' discharge. The results showed that ANFIS was more precise and less disperse (RMSE=0.0234) with compare to FIS (RMSE=0.1982). The ANFIS was also more precise in peak discharges simulation than FIS.

In this study, we employ Fuzzy Sumudu transform to find the solution for system of linear Fuzzy differential equations where the system possesses Fuzzy constant coeffcients instead of crisp. For this purpose, Fuzzy Sumudu transform has been revisited and a brief comparison with Fuzzy Laplace transform is provided alongside, particularly on the scale preserving property. For the sake of comparison, we introduce to the literature a time scaling theorem for Fuzzy Laplace transform. Next, the system with Fuzzy constant coeffcients is interpreted under the strongly generalized differentiability. From here, new procedures for solving the systems are proposed. A numerical example is then carried out for solving a system adapted from Fuzzy radioactive decay model. Conclusion is drawn in the last section and some potential research directions are given.

Fuzzy Mathematics theory has been studied extensively in this study. Most early interest in Fuzzy set theory pertained to representing uncertainty in human cognitive processes. Fuzziness can be found in many areas of daily life, such as engineering, business, medical and related health sciences, and the natural sciences. It is particularly frequent, however, in all areas in which human judgment, evaluation and decisions are important. However, in many real-life cases, the decision data of human judgments with preferences are often vague, so the traditional ways of using crisp values are inadequate. Due to a lack of information, the future state of the real system might not be known completely. Fuzzy mathematical modelling represents a real-world problem in a mathematical form to find solutions to the problem that correspond closely to how humans perceive it. Also, Fuzzy modelling increases the validity of ill-structured systems by offering a deeper understanding of the complexities of decision parameters. In this paper, we analyze the problem of the transgender using FCMs and Combined Fuzzy Cognitive Maps (CFCM). This paper is divided into five sections. Section one introduces the basic introduction of transgender and the previous research of Fuzzy Cognitive Maps (FCM) and Combined FCM. Section two introduces FCM and combined FCM and the basic operations. Section three shows the basic definitions of FCM and Combined CFCM. The fourth section provides methods to analyze transgender problems using combined FCM. The final section gives the conclusions and some suggestions based on our study.