Cadmium can be found in wastewater in high concentrations and can cause dangerous effect on aqueous environment and human health. At present, many methods have been used to heavy metals removal. Among this clean up techniques, adsorption techniques by using low-cost adsorbent are more considerable. In this study, batch adsorption experiments were conducted for removal of cadmium ions from aqueous solutions by using saxaul Tree ash as a low-cost adsorbent. The batch experiments were conducted in various conditions as follows: pH (3 to 11), adsorbent dose (0.5 to 6 g/L) g, contact time (15 to 210 min), and initial cadmium concentration (5-50 mg/L). Based on the results, pH=5 with an adsorbent dose of 4 g/L with a 75 min contact time were found to be the optimum for adsorption of cadmium on saxaul Tree ash. Also, the results showed that adsorption efficiency is decreased with increase in initial concentrations of cadmium. The isotherm experiments showed that Freundlich model satisfactorily represents the proportional fitting. Therefore, base on the results of this study, saxaul Tree ash can be used for efficient removal of cadmium ions from aqueous solution.

The main goal of this project is to preserve genetic resources of forests by vegetative propagation of adult Trees. Fagus orientalis Lipsky, which its micropropagation, investigated in this project, is one of the most important Trees in the forests of Iran. Buds were taken from 3 different genotypes grown in the north forests of Iran. Surface sterilization of the explants was very difficult. Elimination of first three layers of the bud scales was necessary. Intact bud scales caused severe contamination even with very strong sterilizing treatments. Using %0.1 (w/v) solutions of mercuric chloride for 3 minutes was the most effective way for surface sterilization of the explants. The rate of shoot and bud development was very low, and this was the same on WPM and DKW. Culturing established buds horizontally, cold treatment or high auxin concentration pretreatment activated axillary buds. Using 0.2mg/l 2ip (6-dimethylamino purin) together with 0.5 mg/l BA (benzylamino purin) improved shoot proliferation. There were no significant differences between the three genotypes. None of the micropropagated shoots were rooted.

Since ancient times, "Tree" has been the focus of attention as a symbol, in works of art. In Mesopotamia and later in Iran, this symbol was known as a holy Tree or Tree of life as early as 3500 BC, and was delineated in different shapes.After the advent of Islam, this symbol prevailed in works of art; however, no longer as a mythological holy Tree but rather standing for heavenly blessings and the paradise. This symbol has been used in different branches of art, including rug-making. In the art of carpet patterning, the Tree has prefigured a great variety of designs, notably the plant Mihrabi (prayer niche).No doubt, the predominance of the Tree image in a large group of prayer rugs, is not accidental and must have an objective more than conveying mere beauty. One interpretation suggests an epitome of paradise. Given the Islamic restrictions imposed on the portrayal of human beings, a Tree depicted in a prayer niche may well stand for a man in prayer.

The purpose of recent study is recognition of the most important financial ratios by which can evaluate company's performance. Therefore the total of accepted companies in stock exchange in Tehran which was active in 1390-1393 are considered as statistical universe of the research through which 102 companies organize the mass of statistical sample based on systematic elimination sampling method. The from the view point of exploratory and functional purpose, the research method is descriptive and interconnection including post – eventual researches. Analysis of data is accomplished by factorial analysis, structural equations modeling and two algorithms by using of CHAID, C&RT software, SPSS, SMARTTPLS, CLEMENTIN decision Tree. after explanatory, factorial analysis, the results of research show that the number of 24 ratios from the total of considered 28 financial ratios is effective in evaluation of company's performance which these ratios are classified in seven categories in terms of weight of each of them from total variance by using of main factor analysis PCA. in next stage, for studying the type of relations and the amount of variant interconnection, confirmatory factorial analysis is performed in structural equations modeling and the main model presented. Finally the result and drawing decision Tree indicate that decision Tree algorithms are presented the best prediction with the highest accuracy and among the sum of ratios, activity ratio has the most effect on performance evaluation.

The application Layer Multicast (ALM) is an alternative and deployable approach to IP multicasting. Topology awareness link stress and delay stretch considerably, therefore it is a very important metric for ALM. This work describes a novel, highly stable and low overhead ALM approach using a binning technique to cluster nearby receivers, referred to as Bincast. Bincast uses a constant number of landmarks to cluster nearby nodes. Then, it constructs a k-ary Tree between cluster members. The most stable node is selected as the head of each cluster. Cluster heads are connected to the source through a higher level Tree. Detailed performance evaluation revealed that Bincast has a lower delay stretch than similar methods with approximately the same stress. Besides, it is more stable due to the selection of stable nodes as cluster heads.

Introduction: In the last 10 years The incidence of diabetes has doubled worldwide with annual increasing rate of about 6%. More than 2 million people in Iran are now affected by this disease.The present research deals with the relation between the observed complications of type 2 diabetic patients and some related features like Blood Glucose Level, Blood Pressure, Age, and Family History. The main purpose was to predict the patients’ complications based on the observed signs.Methods: The research data were gathered from 856 patient records related to the 2009’s cases in the Diabetes Center of Golestan province. A new model based on the standard methodology CRISP was developed. In the modeling section, two well-known data mining techniques called C5.0 decision Tree and Neural Network were used. Celementine 12.0 software was implemented For data analysis.Results: The results of data mining showed that the variables of high blood pressure, age, and family history had the most impact on the observed complications. Based on the created decision Tree, some rules have been extracted which can be used as a pattern to predict the probability of occurring these complications in the patients. The accuracy of the C5.0 model on the data was shown to be 89.74% and on the Artificial Neural Network was 51.28%.Conclusion: As the highest accuracy was shown to be achieved using C5.0 algorithm, according to the created rules, it can be predicted which complication (s) any diabetic patient with new specified features may probably suffer from.

Introduction: Today, one of the most common diseases and causes of death in the world is heart diseases. Data mining techniques are very useful to create predictive models for identifying people at risk and decreasing the disease complications. In this study, using C4.5 decision Tree method, the prevention and diagnosis of this disease are discussed.Methods: This was an applied descriptive study. UCI standard data and Cleveland data collection were used. The database contains 297 records. Analysis was performed through Weka software and using CRISP3 methodology. The C4.5 decision Tree model, using input variables and determining the target variable, was created.Results: According to the applied model, it was found that high levels of cholesterol, sex, age, high maximum heart rate, scan thallium higher than 3 and abnormal ECG have the greatest impact on the risk of coronary heart disease. Furthermore, by using the created decision Tree, some rules were extracted that can be used as a model to predict the risk of coronary heart disease. The accuracy of the model created by using decision Tree was over 80 percent.Conclusion: According to our calculations, the rate of categorization was 72.6% and the accuracy of C4.5 algorithm was 80.2% that in comparison with the results of studies in the field of data mining of heart diseases, the obtained accuracy for the suggested algorithm is acceptable.

Aqueous extracts of Ailanthus altissima Swingle leaflets, rachises and roots reduced radicle growth of garden cress. The growth inhibitory activities of aqueous root extracts were significantly higher than that of the leaflets and rachises aqueous extracts. Methanol extracts of leaflets were more toxic than the aqueous extracts, whereas the chloroform extracts exhibited very low inhibitory activities. The aqueous, methanol and chloroform extracts of leaflets at 50 g 1-1  inhibited growth by 63.1, 90.6 and 9.2%, respectively. In contrast, the chloroform extracts of rachises significantly reduced the radicle length in garden cress. At 50 g 1-1, the aqueous, methanol and chloroform extracts of rachises inhibited growth by 61.6, 90.2 and 84.3%, respectively. The Ailanthus leaflets and rachises had the highest seasonal activity in spring and early autumn such that phytotoxin synthesis apparently follows optimum temperatures for growth. Postemergence applications of aqueous extracts of leaflets on garden cress, pigweed, barnyard grass and maize exhibited broad-spectrum herbicidal activities by reducing growth to 58-71 % of the control. Aqueous extracts of leaflets and rachises significantly reduced peroxidase activity by up to 85% and respiration (O2 uptakes) in root pieces by up to 66% in all plant species tested. Ailanthus tissues aqueous extracts also decreased mitosis in onion root tips. In the presence of 50 g 1-1 of root extract, 90% reduction in mitosis was observed. These biochemical and physiological changes are probably among the various cellular targets for Ailanthus allelochemicals responsible for growth inhibition in higher plants.

Introduction: The prevalence of Crimean-Congo fever, a common disease between human and animal, shows an increasing rate by coming summer season. Detection of this disease by the use of necessary tests, lasts at least about one week. There are several data mining and machine learning techniques to create predictive models for identifying at risk people. In this study, C4.5 decision Tree method has been used due to its simplicity and efficiency.Methods: In this applied descriptive study, data related to suspected cases of Crimean-Congo fever were used. These data have been collected from health centers of Iran in a four-year period since 2014 and contained 965 records with 29 features. First, by using the quadratic programming feature selection method, the variables which were effective on the model were selected and then, the C4.5 decision Tree model was created through using input variables and determining the target variable. Data analysis was performed through Matlab software.Results: According to the applied model, it was found that fever, bleeding, sudden onset of symptoms, increased liver enzyms, increased total Bilirubin, decreased Hemoglobin, Hematuria, Leukocytosis, Proteinuria and Leukopenia have the greatest impact in the diagnosis of this disease.Conclusion: According to the obtained results, the sensitivity of the proposed model is 95% and its specificity is 50%. Therefore, this model showed acceptable efficiency in diagnosing this disease in comparison with other studies done in medical data mining field.

In this paper, the issue of a plane Tree in the village of Zar-Abad, located in the Alamoot of Qazveen (Iran), known as "the bleeding plane" is examined, from an anthropological perspective. The author regards the issue as a "totally social phenomenon", and tries to look at it from different angles, exploiting various techniques. It is found that the plane's secretion is not blood, but is a type of resin that, unlike others, dries out,. No link was also established between the secretion and the day of 'Ashoora, as it can be noticed in other times during the year.There is, however, no deception, because what people conceive is the result of their attitude towards the phenomenon, and what they feel counts more than the reality perse.