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Measuring the relative efficiency is one of the most important issues among hospitals in today's economy. These days, we hear that cost reduction is a necessity for survival of business owners and one primary to reduce the expenditures is to increase relative efficiency. The proposed study of this paper first uses output oriented data envelopment analysis (DEA) to measure the relative efficiencies of nine hospitals. The proposed model uses four types of employee namely specialists, physicians, technicians and other staffs as input parameters. The model also uses the number of surgeries, hospitalized and radiography as the outputs of the proposed model. Since the implementation of DEA leads us to have more than one single efficient unit, we implement supper efficiency technique to measure the relative efficiency of efficient units.

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Nowadays, the advancement of microgrids promises numerous economic and environmental advantages of renewable energies to nations and societies. The presence of decentralized energy units, however, makes serious technical challenges; for instance, criteria and procedure of fault recognition and diagnosis in this condition is entirely changing. This article, therefore, proposed a novel accurate and fast technique based on Artificial Neural Networks (ANN) for earth fault detection. A sample distributed power system considered for the proposed technique and different earth faults applied to this system consist of one phase, two phases and three phases faults. Also, any alteration of current and voltage signals of all phases is investigated at the fault occurrence moment. Analysis of simulation results demonstrates how the proposed technique could make faster responses and improve the reliability of the distributed power system by more accurate fault recognition in comparison with the other traditional methods such as the Wavelet Transformation technique. The proposed technique is likely to enhance the growth of renewable energy sources usage by decreasing operational risk factors and fault recognition delays.

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In this study, an efficient approach is suggested to prioritize the natural gas storage in existing underground reservoirs. The paper uses expert knowledge and integrates the Geospatial Information System (GIS) with spatial multi criteria decision making .The proposed method is based on TOPSIS as well as Hierarchical Additive Weighting Method. In order to consider different options, the most important possible criteria have been considered in this study, including: reservoir characteristics, distance from the center of gravity taking, distance from production centers, distance from the country's road network, the temperature around the reservoir areas, environmental characteristics of the reservoir, consumption gas in areas around the reservoir, population density, and the number of important industrial centers covered by each of the reservoirs. To evaluate the results of research, results of implemented methods were compared with each others and according to evaluation obtained from several experts; the final prioritization of different reservoirs due to their performances has been done. The results indicate that TOPSIS is more precise than Hierarchical Additive Weighting Method, and conform to experts opinions about prioritization of natural gas storage in underground reservoirs. .