Introduction: To assess efficiency as a relative concept, the difference between real and expected values must be determined by comparing the productivity of economic units of industry and the efficiency in potential production conditions. As economic units, hospitals need to utilize efficiency Analysis in order to make optimal use of their available resources. Therefore, the present study compared the efficiency of different hospitals of Shahid Beheshti University of Medical Sciences.Methods: Since this applied analytical research referred to the documents and performance statistics of hospitals and therfore used real Data, evaluating the validity and reliability had no relevance. In order to measure the relative efficiency of health care in 16 hospitals associated with Shahid Beheshti University of Medical Sciences and also to determine efficient and inefficient hospitals during 2005-2007, CCR (Charnes-Cooper-Rhodes) input oriented multiplier model of Data Envelopment Analysis (DEA) was used for Data Analysis. In addition, AP (Anderson-Peterson) model was used to rank efficient hospitals. LINDO software was utilized to solve the model.Results: Based on our results, 43.75%, 31.25% and 37.5% of hospitals were efficient respectively in 2005, 2006 and 2007.Conclusion: The results suggested that the efficiency of inefficient hospitals could be improved through the potential savings in resources (62.44% in infrastructure, 43.88% in the number of active beds, 37.15% in the number of physicians and 38.58% in the number of paramedics).

The neoclassical growth model is tested by use of panel Data procedure in this research. In the econometric test, simoultanously time series and cross detection will be compared on the basis of panel Data method through which their observed points increase and consequently the estimation efficiency will be increased. The examination of neoclassical growth theory has been done with reference to external & internal factors of 52 selected countries from 1960 to 2000. The independent variable of model has been selected on the basis of the result of previous research which explains the result in three separate models: developed countries, developing countries, and whole countries. These factors are such as: Gross National Products with lag of period, work force age, growth rate, education level, the change of capital accumulation and economic trade volum. The consequences of this research is that: neoclassical growth model can explain the major part of economic growth of the countries with use of internal variables. Also with the use of panel procedure of fixed effect, we can see the fundamental differences and structure of the growth process for different countries; and show how the economic, and social conditions affect on the growth.

Todays gamma knife radiosurgery is used widely for treatment of very small brain tumors. In order to investigate accuracy of dosimetry and treatment planning calculations, using Monte Carlo simulation with dedicated code named as beamnrc including non-CT Data and CT Data options is necessary. The aim of this study is choosing the best options in order to have an accurate tools based on their advantages and disadvantages. In this study, gamma knife unit 4C along with standard water equivalent phantom and EBT3 films were used to obtain dose distributions. Monte Carlo simulation was done with non-CT Data and CT Data options of the code and their resulting dose were compared. Comparison the calculated and measured dose distributions at X, Y and Z axis showed gamma value below 1 which verified Monte Carlo simulations. Also comparing the dose distributions from both non-CT Data and CT Data with each other implies that there is no significant difference between two methods. Based on the obtained results using non-CT Data and CT Data results in the same dose distribution. So for simplicity, using non CT Data for regular phantom shapes is preferred.

Missing Data is a chronic disease in applications of Data envelopment Analysis. Very often, important input or output variables are not completely specified and/or the decision-making units do not report all the required statistics. Therefore, the missing values in the inputs and outputs cannot be studied using the original Data envelopment Analysis models. This paper introduces methods for finding missing Data when the existing Data is certain. In this article, after explaining the essential concepts of missing values, we describe some methods of missing value imputation that reduce the complexity of Data Analysis. There are several methods for imputing missing Data, including various methods of simple imputation and multiple imputation. This paper is the first systematic attempt to utilize Data containing missing values using statistical approaches in the DEA. In particular, we examine what happens if we keep empty entries in the Data set and assign a certain numeric value to them. To show how the proposed methods work, they will be used to evaluate a set of secondary public schools in Greece in some of which there are missing input or output values.

In this research, a new fuzzy streaming Data classification method is presented. Fuzzy Data classification is one of the most challenging areas in Data Analysis due to high-volume and sophisticated computation engaged in Data Analysis techniques. However, in some other areas such as mathematical programming, great achievements are obtained in modeling of systems measured by fuzzy Data. So, in this research classification based on Data envelopment Analysis (DEA) is selected to handle fuzzy Data classification. On the other hand, the proposed method can be applied for dealing with streaming Data by using an updating schema. This feature makes the proposed method suitable for non-stationary environment in which Data pattern varies over the time. Experiments using simulated Data demonstrate the capability of the proposed method in dealing with uncertain and non-stationary environment.