Tree search algorithms are vital for the search methods in structured data. Such algorithms deal with nodes which can be taken from a data structure. One famous Tree data structure is split Tree. In this paper, to compute the split Tree in polar coordinates, a method has been introduced. Assuming that the algorithm inputs (in form of points) have been distributed in the form of a circle or part of a circle, polar split Tree can be used. For instance, we can use these types of Trees to transmit radio and telecommunication waves from host stations to the receivers and to search the receivers. Since we are dealing with data points that are approximately circular distributed, it is suggested to use polar coordinates. Furthermore, there are several researches by search algorithms for the central anchor which leads to the assignment of a virtual polar coordinate system. In this paper, the structure of Cartesian split Tree will be explained and the polar split Tree will be implemented. Then, by doing nearest neighbor search experiments, we will compare the polar split Tree and polar quad Tree in terms of searching time and amount of distance to the closest neighbor and in the end, better results will be achieved.

Let S be a set of imprecise points that is represented by axis-aligned pairwise disjoint squares in the plane. A precise instance of S is a set of points, one from each region of S. In this paper, we study the optimal minimum spanning Tree (OptMST) problem on S. The OptMST problem looks for the precise instance of S such that the weight of the MST in this instance, maximize (Max-MST) or minimize (Min-MST) between all precise instances of S under L1-metric. We present a ( 3 7)-approximation algorithm for Max-MST. This is an improvement on the best-known approximation factor of 1=3. If S satis es k-separability property (the distance between any pair of squares are at least k: amax where amax is the maximum length of the squares), the factor parameterizes to 2k+3 2k+7. We propose a new lower bound for Min-MST problem on S under L1-metric where S contains unit squares and provide an approximation algorithm with (1 + 2p2) asymptotic factor.

In order to evaluate the possible correlation between the Tree density and the human population density, the forested area in Nav Asalem district located in Guilan Province was selected. The descriptors of Tree number and basal area per hectare as well as the stand density index were used to determine the Tree density, which was conducted from a 2014 forest inventory including 62 cluster (558 plots) systematically scattered over 30 % of the forest area. In addition, to determine the density of the human population, circular buffers at intervals of 1 to 7 km from the center of each cluster was considered and population density of each layer was calculated using buffering functions.Statistical results showed that the average basal area, average number of Trees and the average stand density index was 23.16 m2/ha, 243 per ha and 178.25 respectively and also different human population density in each buffer. Using Pearson correlation test indicated a significant negative correlation between the stand density index and basal area (DBH>15 cm) with human population density. There was no significant relationship between the number of Trees per hectare and the human population density except at 7 km. This findings support studies regarding the disturbance has strong correlative with the number of residents per unit area at up to 7 km from clusters and greater control on anthropogenic interventions should be the main priority of sustainable forestry in Hyrcanian forests of northern Iran.Due to the existence of an effective relationship between the components of the Tree density and human population in the forest, policy-makers and planners of natural resources could benefit management patterns appropriate to above components to achieve sustainable management.

In this note we investigate totally transitive maps on Trees. We use some mild conditions and improve the lower bound of the topological entropy of transitive Tree maps. Also we show that the totally transitive maps coincide with the mixing maps on Trees.

Let G be a simple undirected graph and let Delta(G) be a simplicial complex whose faces correspond to the independent sets of G. A graph G is called shellable if Delta(G) is a shellable simplicial complex. We prove that the complement of a d-Tree is a pure shellable graph. This generalizes a recent result of Ferrarello who used a theorem due to R. Froberg to prove that the complement of a d-Tree is a Cohen-Macaulay graph. 

With the beginning of the 19th century, England entered into a transitional period. Sociologists believe that in each era of transformation, the ruling class tries to establish its own values, but some resistance to these new values is inevitable. Thomas Hardy's novels in general, and Under the Greenwood Tree specifically, are no exception. Based on these notions, this paper tries to interpret Under the Greenwood Tree. Much of the criticism of Hardy's work insists on the point that the created characters in his work along with their new ideas emphasize Hardy's attempts in standing against the ideological discourses of the middle-class. Under the light of Althusser's theory of Ideological State Apparatuses (ISAs) and Antonio Gramsci's notion of hegemony, Hardy can be considered as a true subject of his society, one who tries to strengthen the pillars of his society through depicting characters who have to be in the mainstream of the current ideological discourses of his age. Therefore, dealing with various aspects of this novel, this study tries to see how Louis Althusser's notions can help us to understand Hardy himself, his characters as well as the era in which he lived and wrote.

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.

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.