Physiological and Biochemical Response of Artificial Wetland Plant under Electric Field

By measuring wetland plants chlorophyll content,malondialdehyde(MDA) content and superoxide dismutase(SOD) enzyme activity,the changes of wetland plant physiological characeristics under different power strength were studied,and the mechanism of electric field on plant physiological characteristics was analyzed to provide a theoretical basis for the pollutant removal ability strengthening of artificial wetland under electricfield.The results showed that compared with the control plants,low-intensity-voltage(1 V and 3 V) had no significant effect on the normal physiological and biochemical indexes of the plants,and the growth trend was better than the control group;with the voltage increasing,plant chlorophyll content,MDA content and SOD activity were greatly affected,indicating that plants were under strong oxidative stress,and the growth was damaged.Therefore,a suitable electric field could enhance the sewage treatment effect of constructed wetland.

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.