DEA is a nonparametric method for calculating the relative efficiency of a DMU that yields to a reference Target for an inefficient DMU. However, it is very hard for inefficient DMUs to be efficient by benchmarking a Target DMU which has different inputs. Finding appropriate benchmarks based on the similarity of inputs makes it easier for an inefficient DMU to try to be like its Target DMUs. But it is rare to discover a Target DMU, which is both the most efficient and similar in inputs, in real situation. Therefore, it is necessary to find the most similar and closest real DMU in terms of inputs on the strong efficiency frontier, which has the highest possible output. In this paper, a combination of the Enhanced Russell model and the additive model is proposed as a new model to improve the efficiency of the inefficient DMUs. The proposed model is applied on a dataset of a large Canadian Bank branches. The Target introduced by the proposed method is more practical Target for the evaluated unit. The inefficient unit can improve its efficiency more easily by this benchmark.

Statement of Problem: Dental unit Water Systems (DUWS) have been demonstrated to be contaminated with biofilms. This biofilm could be a site for growth and colonization of pathologic microorganisms. Purpose: The aim of this research was to measure the Total Cultivable Flora (TCF) in DUWS of Khorasgan dental school and the effect of stabilized chlorine dioxide on its reduction.Materials and Method: In this research, 60 samples of water from high speed turbines, air-water syringes, cavitrons and wash basins were collected. The samples were cultured in agar nutrition media and incubated for 5 days. After the incubation period, colonial counting was done by HPC method. In the next stage, stabilized chlorine dioxide was added in a container for each unit and the samples were taken from air-water syringes, cavitrons and turbine outlets .As for the first stage, after 5 days of culture and incubation, the samples were observed under microscope for counting the bacterial colonies. Kruskal-Walis and Mann-Whitney tests were used for statistical analysis.Results: A high level of bacterial contamination (more than 200 CFU) was observed in high speed turbines, air-water syringes, cavitrons and wash basins. The highest colonial count was observed from cavitron and the lowest from washbasin outlets but the difference was not statistically significant .After using chlorine dioxide, the amount of bacterial colonies was significantly reduced (p <0.05). Conclusion: The amount of bacterial contamination in DUWS of Khorasgan dental school was higher than the accepted level .These bacteria were mainly opportunistic .The addition of an anti-bacterial agent in the DUWS will significantly reduce the bacterial count to an acceptable level.

Basic models of Data Envelopment Analysis are intrinsically preference-free, in the sense that they consider all inputs and outputs and also all decision making units of the same importance. Although this property is beneficial in many ways, it has some drawbacks simultaneously, as the decision makers’ preferences are not taken into account in the process of evaluating units. To overcome this drawback many researchers have developed several techniques for incorporating the preferences into the evaluation model. One of the underlying approaches is value efficiency analysis, which evaluates decision making units by comparing them with the most preferred unit. The most preferred unit is a unit which satisfies the decision maker most. On the other hand, the issue of benchmarking is an important aspect in data envelopment analysis, as it enables the analyst to choose a Target for each inefficient unit. The Target unit for each unit is located on the efficient frontier and determines the path of improvement for that inefficient unit. In this paper, the issue of Target setting based on the concept of value efficiency is investigated. We aim to develop a Target setting model which is able to determine Target units that are not only efficient but also value efficient, as well. Moreover, the Targets are determined based on closest distance from the evaluated unit. Some properties of the model are also discussed. Finally, we perform the proposed model on a real data set of 42 Spanish Universities.

Several microRNAs (miRNAs) are involved in the differentiation of neurons and neurogenesis in vertebrates’ brain. However, there is scant knowledge of miRNAs and their Target genes in fish. Microarray is recognized as a standard method for a general study of genes that are under miRNAs’ control, although the high cost of this method has limited its application. With advances in bioinformatics algorithms and computer simulation, mRNA Targets for miRNAs can be predicted. Therefore, this study tries to determine genes and the relevant miRNAs by investigating the bioinformatics of genes and miRNAs involved in regulating active genes in differentiating neurons and neurogenesis in the brain of zebrafish with the help of Target Scan and DIANA tools. The results of bioinformatics investigations indicate that expression of neurod6b, neurod2, neurod4, olfm1a and olfm1b, gle1, sox11a and sox11b, c adm1b, notch2 and notch1b genes is more likely to be influenced by dremiRNAs during neurogenesis. In conclusion, the product of these genes can be used as a new appropriate candidate in experimental studies for understanding neurogenesis in the brain of zebrafish.