Identification of critical nodes for cascade faults of grids based on electrical PageRank

In this paper,the electrical PageRank method is proposed to identify the critical nodes in a power grid considering cascading faults as well as directional weighting.This method can rapidly and accurately focus on the critical nodes in the power system.First,the proposed method simulates the scenario in a grid after a node is attacked by cascading faults.The load loss of the grid is calculated.Second,the electrical PageRank algorithm is proposed.The nodal importance of a grid is determined by considering cascading faults as well as directional weights.The electrical PageRank values of the system nodes are obtained based on the proposed electrical PageRank algorithm and ranked to identify the critical nodes in a grid.Finally,the effectiveness of the proposed method is verified using the IEEE39 node system.The proposed method is highly effective in preventing the occurrence of cascading faults in power systems.

An Optimized Framework for Surgical Team Selection

In the healthcare system,a surgical team is a unit of experienced personnel who provide medical care to surgical patients during surgery.Selecting a surgical team is challenging for a multispecialty hospital as the performance of its members affects the efficiency and reliability of the hospital’s patient care.The effectiveness of a surgical team depends not only on its individual members but also on the coordination among them.In this paper,we addressed the challenges of surgical team selection faced by a multispecialty hospital and proposed a decision-making framework for selecting the optimal list of surgical teams for a given patient.The proposed framework focused on improving the existing surgical history management system by arranging surgery-bound patients into optimal subgroups based on similar characteristics and selecting an optimal list of surgical teams for a new surgical patient based on the patient’s subgroups.For this end,two population-based meta-heuristic algorithms for clustering of mixed datasets and multi-objective optimization were proposed.The proposed algorithms were tested using different datasets and benchmark functions.Furthermore,the proposed framework was validated through a case study of a real postoperative surgical dataset obtained from the orthopedic surgery department of a multispecialty hospital in India.The results revealed that the proposed framework was efficient in arranging patients in optimal groups as well as selecting optimal surgical teams for a given patient.