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.

Link prediction is an important to check link between nodes in social networks. The modeling of social networks leads to emergence of signed, directed and weighted social networks. The relationships of users in social networks are characterized by subjective, asymmetric and ambiguous aspects related to this domain, then both terms of trust and distrust are challenging. To solve the problem of sparsity in networks and overcome ambiguity in relationships, a trust-distrust method based on fuzzy computational is proposed to calculate strength of links. The purpose of proposed link prediction is to solve problem of sparsity in signed social networks by combining descriptive features of users with the direct influence of top nodes and the indirect influence of common nodes on rating prediction. Trust is determined by a Mamdani fuzzy system based on mirroring of similarity fuzzy features, overall trust and overall distrust. The evaluation of the proposed method was done with the accuracy measure on datasets of Epinions and Slashdot. The accuracy of proposed method in Epinions and Slashdot datasets is 0.991 and 0.998, respectively. The obtained results show that proposed method works well for problem of data sparsity in signed social networks and show the effectiveness of proposed model.

The spatial prediction of an unknown quantity at a specific site is one of the most important topics in the fuzzy spatial analysis. Under the assumption that covariance is known, optimal prediction and mean square error of predictor are determinable by using fuzzy kriging methods. When the parameters of the parametric covariance function are unknown, their estimates are usually replaced in optimal prediction as real values. But, determination of this predictor and its mean square error are usually difficult. Therefore, to solve this problem, in this paper the Bayesian approach is used to extend the fuzzy kriging to a new method, namely Bayesian fuzzy kriging. Then, in an applied example, its accuracy is compared with other spatial predictors.

Background & Aim: Subarachnoid hemorrhage (SAH) resulting from ruptured intracranial aneurysm (IA) is a still major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach can be used as an ef ficient predictor.Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients were assessed by ten significant variables; including World Federation of Neurological Surgeons scale (WFNS), rebleeding before operation, age, severespasm, External Ventricular Drainage (EVD), ischemia, modified Fisher scale (mFisher), infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy system predicts modified Rankine scale (mRs) based on table look up scheme which converts conscious and subconscious knowledge of the expert into fuzzy IF-THEN rules.Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making. It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology. Thus, this research can pioneer new studies in neurosur gery area.

This study aims to develop an Adaptive Network-based Fuzzy Inference System technique (ANFIS) and using the parameters of a complex mathematical model in the RO membrane performances. The ANFIS was constructed by using a subtractive clustering method to generate initial fuzzy inference systems. The model trained by 70% of the data set and then its validity is examined by remained 30% data set. The result indicated that this method could predict the performance of the RO membrane faster and more accurately than previous numerical techniques. The squared correlation coefficient between real data and predicted data of this technique was 0. 9973 for separation factor, 0. 9916 for NP and 0. 9975 NT, which are better in comparison with numerical methods, and previous Artificial Neural network used by the author to model these membranes. It was observed that the squash factor, reject ratio, and accept ratio has no significant effect on ANFIS performance. Results showed that for all cases better performances achieved when this parameter has a value of more than 0. 5, as 0. 86 for separation factor, 0. 91 for net pre flux, and 0. 83 for total flux. This technique just takes a few seconds to model RO membrane performance which is very faster than other numerical methods. So, this technique could be a powerful method to predict RO membranes.