History and goal: Today, the majority countries throughout the world have established the subject regarding winning patients’ satisfaction and promoting services quality on top of their activities. Amid this, six sigma is considered one of the modern tools to boost performance and promote products and services. This study aims to analyze the level of sigma implementation capabilities in Mazandaran- located academic hospital in 2011. \nMethods: This research has been done through a descriptive and cross- sectional method. Statistical community included 4 academic hospital using validation standard country- based hospitals (2011 Ratified health, treatment and medical training department) and has been done in 78 hospital wards. The required information was collected based on author- prepared questionnaire six sigma and with 5 six sigma DMAIC cycle phase and 38 questions and based on Likert spectrum. In data analysis, first Kolmogorov- Smirnov Test was used to test data normalization that after confirming data normality t-test, Binomial test and variance analysis were utilized.\nFindings: According to the results achieved in defining phase, implementation capability was 88% and in measurement phase, six sigma implementation capability was 13%. However, in analysis and promotion and control phase, there was no implementation capability in the mentioned hospitals.\nConclusion: Six sigma from DMAIC cycle perspective has poor influence and lacks proper implementation capability. Thus concerning the special importance of six sigma in health area, particularly treatment, academic officials, special attention and accurate planning by hospital administrators to utilize and implement is inevitable

A rarefied gas flow in a high speed rotating circular cylinder has been researched numerically using the Consistent Boltzmann Algorithm (CBA) scheme and the Direct Simulation Monte-Carlo (DSMC) method. The Argon rarefied gas is supposed to be contained in a rotating cylinder which rotates with angular velocity of 15000 rad/sec. The results of the flow simulation using the CBA scheme is compared with the results of the DSMC method, the analytical solution and the numerical results of Bird[1]. The simulation results using the CBA scheme showed better agreement than the DSMC method results and simulated by Bird[1] in the comparison with the analytical solution.

Wave propagation in the unbounded domains is one of the important engineering issues. To solve this problem, many efforts have been made. This paper elucidates the finite point method which is used to solve the problems of wave propagation in unbounded domains which is considered as one of the best methods in such cases. To afford the reliability of finite point method, wave propagation in unbounded domain against sinusoidal unit point stimulation is considered to be shown.

To investigate the effect of different concentrations of alfalfa and Bermuda grass extracts on callus induction and rye embryo regeneration, the regeneration and growth of rye seedlings were randomly carried out at the Islamic Azad University, Tabriz branch in 2012, using a factorial experiment based on completely randomized design in 3 replicates in both greenhouse and in-vitro conditions. In this study, immature rye embryos were used as explants for generating callus, and water extracts of alfalfa and Bermuda grass were added to the environment with a concentration of 5, 10, 15, 20, 25 and 30 percent. This extract was also used in the regeneration period. The results showed that callus induction from Rye and its components in 5% alfalfa extracts significantly increased compared to the control condition, but increasing alfalfa extract concentration caused a significant reduction in these traits. The use of 25 and 30% alfalfa extracts stopped callus induction and its components. The application of bermuda grass extract with a concentration of 30 %, reduced rye callus induction to 50 percent. Alfalfa extract concentrations of 20 and 25 percent, respectively caused a reduction of 35 and 75 percent in callus fresh weight in comparison with the control conditions. Applying 25 and 30 percent concentrations of bermuda grass extract led to a reduction of 68 and 80% respectively. Alfalfa extracts with 15, 20 and 25 percent concentrations caused reductions of 40, 63 and 69% in rooting. The 30% concentration of alfalfa extract stopped rooting in rye callus. Concentrations of 15, 20, 25 and 30% caused reductions of 31, 50, 55 and 81 percent in the shooting of rye callus, respectively. The results indicate that alfalfa extract had greater effect on rooting, and the bermuda grass extracts had greater effect on shooting.

In recent years, lung tumor diagnosis and the projection of tumor segmentation in 3D has gained significant momentous in the therapeutic field. Establishing the dissimilarity exists in the three dimensional volume representation of tumor cells affords more information, which can sharpen the treatment of a multiplicity of tumors. The volume reconstruction information is indispensable in the case of surgical operations. This process introduces a contour based segmentation algorithm to acquire the appropriate differentiation of pixel boundary that scrutinizes the exact difference between tumor and non tumor cells along the tumor boundary. With the aid of aforementioned formulation, extracted tumor part pixels are reconstructed for the entire 2D slices of the patient data set. Proposal work on 3D voxel reconstruction relies on encountering the isosurfaces. Originally, volume data are subjected to the smoothening process which computes the isosurface data from the smoothened volume data. The generated outcome of this process comprises the vertices and faces of the isosurfaces and directly flows to patch the data. Exploit the 3D reconstructed model to enumerate the voxel damaged by tumor. Proposal work associated with the percentage of damaged voxel along with accurate and reliable perception, simplifies the physician task in lung tumor diagnosis and assist the surgical procedure. Experimental evaluations across the wide range of images show the superiority of the proposed work with the classification accuracy rate of 99.33%.

This paper aims to reveal a comparative analysis of classifier performance of MR brain images, particularly for the brain tumor detection and classification. The detection of brain tumor stands in need of Magnetic Resonance Imaging (MRI). The moment invariant feature extraction has been evaluated to categorize the MRI Slices as Normal, Benign and Malignant by Neural Network Classifier. In our comparative study, we examine the precision rate of aforementioned classification with extracted features and the classification of brain images with selected features by association rule based neural network classifier. The results are then analyzed with Receiver Operating Characteristics (ROC) curve and compared to illustrate the method producing higher accuracy rate in tumor recognition. Factually, our analysis proves that the classifier works below feature extraction followed by rule pruning method affords better accuracy rate.