The aim of the study was to investigate the effect of stretching before intense exercise \non hematologic parameters and cell injury. The subjects of the present study were 12 \nadolescent members of the Kurdistan Province futsal team (age: 14.83 ± 0.38 years, \nheight: 173.92 ± 5.90 cm, weight: 63.50 ± 7.94 kg, peak power: 459.42 ± 95.94 \nwatts). The RAST test was used to estimate the anaerobic power and to create the \ntraining load. Indexes of blood including white and red blood cells, hemoglobin, \nhematocrit, platelet and the levels of CK and LDH enzyme activities were measured. \nThe results of the pre and the post tests showed that among hematological parameters \nand cell damage indexes, the red blood cell and hemoglobin in both groups and the \nlevel of LDH in the training group were not significantly changed (p>0.05). Other \nparameters in the pre-test and post-test measures were statistically changed (p<0.05). \nBased on the results of the present study, it seems that stretching before intense \ntraining decreases the cell damage and side effects of exercise training on the \nhematological factors

The purpose of the present study was to investigate the relationship between flexibility, speed and agility measures of successful wrestlers. Twenty two senior Greco-Roman wrestlers (age: 23.27±2.00 years) who were medalists in the International championships level were selected as subjects of this study. The body weight, height, flexibility (sit-and-reach, trunk-and-neck extension and shoulder-and-wrist elevation tests), agility (4×9 m shuttle run) and speed (40-yd sprint) were measured. The major results (mean ± SD) are as follow: body weight (kg): 82.55±19.47, height (cm): 176±9.05, sit-and-reach (cm): 39.59±5.61, trunk-and-neck extension (cm/cm): 0.60±0.07, shoulder-and-wrist elevation (cm/cm): 0.51±0.15, agility (s): 8.42±0.56, speed (s): 4.90±0.24. In present study no significant correlation were found between values of speed and agility (p>0.05).

This paper studies the convergence of Godard blind equalization which based on least mean square(LMS) algorithm. It focuses on studying the effect of changing the step-size of LMS algorithm on the convergence of Godard algorithm. Simulation results show that the increase in step-size has negative impact on the convergence.

The purpose of this research is to investigate and prioritize increasing accelerating global competition, inefficient manufacturing and service organizations, the emergence of new technologies, globalization and extreme attention of customers and clients to quality showing that organizations in the past years are held constant and uniform so using European quality management model for this purpose is one of the method of the survival. The main goal of this research is to prioritize improvement projects based on the European Quality Model in Ministry of Finance contacts. Statistic society included contracts specialists and experts in order to accomplish this, first the project was identified by experts and Multi Criteria Decision Making Techniques for prioritizing them specifically fuzzy TOPSIS approach (fuzzy-TOPSIS) was used, Application of fuzzy variables are more realistic because of nearing to conditions and Finally, the project database and project rules and regulations are in the first place and qualified suppliers to identify key features were the latter and Finally, the application was submitted.

As the number of users proverbial with the Internet is growing, the traffic on the servers are also rising and the server gets overloaded. To distribute the server load over the several available computing resources in a web server cluster, a load balancing approach has been used which enhances the use of resources, achieves high throughput, minimizes the response time and also reduces the overload of each resource. The time needed to perform all tasks on the resources is minimized to fulfill users’ expectations of minimum response time. So, the load of each web server has divided among the resources according to their processing time and type of request contents. In this paper, an efficient dynamic load balancing technique has been analyzed and implemented based on the sorted contents with the priority of weights and serving time of web contents. The proposed algorithm has been achieved high throughput and minimized the average response time and average waiting time.

This paper focuses machine learning techniques Naïve Bayesian Classifier method for the imputation of missing values in large datasets. Single value imputation produce biased results, whereas multiple imputation generates right value to replace. The study was conducted to implement several algorithms in unsupervised techniques like Mean, Median, Standard Deviation, and Regression and supervised machine learning techniques like Naïve Bayesian classifier provides different methods to calculate Class prior probability, Posterior probability, Predictor probability and Maximum Likelihood. Both methods are trying to find hidden structure in unlabeled data. The performance of this method has been compared by using Correlation statistics analysis which produces the imputed values are positively related or negatively related or not related with each other. To evaluate the performance, the standard machine learning repository dataset has been used. We experimentally shows that our approach significantly outperforms some standard machine learning methods for handling missing values in classification tasks.