Drought prediction is an important item in realm of hydrometeorology and hydrology, and selection of suitable meteorological Variables for drought prediction is a goal in recent studies. In this paper, suitable feature selection is investigated with application of Mutual Information (MI) on the predictor’ s time series and the well-known statistical machine learning methods, Support Vector Machine (SVM), is proposed to predict drought class based on Standardized Precipitation Index (SPI) in some seasonal scale scenario in the main watersheds of Tehran. In current study, ground weather temperature (at 300, 500, 700 and 850 mi bar) and geopotential height (at 300, 500, 700 and 850 mi bar) was applied in prediction models based on data from 1975 to 2005 in the main watershed of Tehran. Regarding to the amount of predictors, suitable feature selection is investigated with application of Mutual Information (MI) on the predictor’ s time series and target time series and the well-known statistical machine learning methods, support vector machine (SVM), is applied to predict SPI class. One of the important issue in this research is use of different Variables, for example regarding to selected data points, the effective regions on Tehran precipitation are southern, southwestern and northwestern of Iran in spring, northern and northwestern in autumn and northwestern and western in winter. SVM depicted accurate results in classification and prediction of SPI and it is suitable and applicable. The predicted SPI in winter and autumn are more accurate than the other scenarios.

ABSTRACTDuring the last century, ports of Persian Gulf become the most important storage, refining and transportation of crude oil, oil derivation, petrochemical products and also, business activities. Nowadays, due to functional diversity between ports, the impact of port activities on the development of urban spaces will be different, and this issue leads to structural differences of port cities. The purpose of this research is to identify the key Variables affecting the relations and spatial structure of port-city in Dubai as the most thriving and successful port-city in the Persian Gulf. This research is based on deductive reasoning and carried out by analysis of cross effects with secondary data (results of other prominent articles) and also primary data (questionnaire). sampling method is based on purposive sampling and Micmac software was used to analyze the data. Output of Micmac software shows the position of the instability of the system. Therefore, the position of each Variables in the conceptual model can be categorized to driver, linkage, dependent, and autonomous factors. The research findings indicate 4 Variables (like as “income, cost and investmnets in port-city”) can be introduced as a driver factor, 5 Variables (like as “advanced multimodal transportation in the port”) can be introduced as a linkage factor, 6 Variables (like as “quality of living environment and desirable city”) can be introduced as a dependent factor, 3 Variables (like as “environmental protection and sustainable development of the city-port”) can be introduced as an autonomous factor. The rapid development of Dubai can be explained by the purposeful distribution of revenues from trade and tourismExtended AbstractIntroductionDuring the last century, ports of Persian Gulf become the most important storage, refining and transportation of crude oil, oil derivation, petrochemical products and also, business activities. The global increase in oil demand since the 1950s led to the creation of discovery of new oil wells and large industrial areas. With the construction of new oil or gas facilities in the port areas, increasing oil revenues contributed to the countries of the Persian Gulf region and form a new geographical relationship between ports and coastal cities which had an impact on the spatial structure and relation of them. Nowadays, due to functional diversity between ports, the impact of port activities on the development of urban spaces will be different, and this issue leads to structural differences of port cities. In the 1950s, Containerization (container loading technology) was based in some of the world's ports and created a huge transformation in the shipping industry, which had a significant impact on the development of ports. Although the ports of the Persian Gulf have made a significant contribution to the economic growth and physical development of other ports in the world, but few researches have been done on the Variables and factors affecting the development of the spatial structure of the important ports of the Persian Gulf. This region, due to its unique geographical location, has always been considered as a most important corridor in the field of economic and transportation throughout history. Some of the most important energy and commercial ports in this region are: Dubai, Manama, Kuwait, Dammam, Doha, Bushehr, Bandar-Abbas and Assaluyeh. In this regard, the purpose of this research is to identify the key Variables affecting the relations and spatial structure of port-city in Dubai as the most thriving and successful port-city in the Persian Gulf. MethodologyThis research is based on deductive reasoning and carried out by analysis of cross effects with secondary data (results of other prominent articles) and also primary data (questionnaire). The method used in this research is quantitative and the sampling method is based on purposive sampling. the Variables affecting the spatial structure of port-city relations were identified in the form of a review of 31 prominent articles. 25 experts (who had comprehensive knowledge and information about the development process of the city-port of Dubai) participated to determine the effectiveness or influence of Variables. Finally, the output of data analysis was done by the Micmac software. Results and discussionOutput of Micmac software shows the position of the Variables in the diagram that it indicates their status in the system and their role in the dynamics and changes of the system. The method of distribution and dispersion of the Variables in the spatial structure of port-city relations indicates the instability of the system. Therefore, based on the output of the system, the position of each Variables in the conceptual model can be identified in five categories (driver, linkage, dependent, autonomous and regulatory Variables). According to the results, some Variables such as “the changes in the strategic positions” and “geopolitics of port-city”, “the modification of the management method” and “regional planning of the ports”, “the promotion of local governance in the relations between port-city” and “the way of national and international management of ports have been introduced as driver Variables. On the other hand, five Variables consist of “advanced multimodal transportation in the port” and “income, cost and investment in the port-city”, “the contrast and physical integration of the port - city space”, “the development of various industrial and production activities in the ports and their local hinterland” and “their qualities Financial and commercial policies of ports” have been introduced as linkage Variables. Findings of research also shows that some Variables consist of “quality of living environment and desirable city”, “quantity and quality of transit corridors (rail, road and air) to hinterland”, “construction and launching the new port (sea port or dry port)”, “terminal facilities and infrastructure and warehousing”, “conflict or convergence between the city and the port” and “increase in population growth rate and migration to the port - city” categorized as dependent Variables. Three Variables including “cultural and historical background of ports”, “use of cheaper energy, Less-polluted, clean and renewable energies in the city-port” and “environmental protection and sustainable development of the city-port” categorized as autonomous Variables. Finally, a variable with the title of “improvement in management and information technology and development in loading, unloading and storage of commodities” have been introduced as regulatory Variables. The findings of this research have a remarkable similarity with other researches carried out in the field of city-port relation and clearly emphasize the direct impact of the "investment in port-city" in the development of them. For example, Grossmann (2008) emphasized that city-port of Hamburg has become one of the largest ports in Northern Europe during the last few decades due to huge domestic and foreign investment. ConclusionThis research carried out in order to introduce and explain the key Variables affecting the development of the spatial structure of the port-city of Dubai. On this basis, 21 Variables have been extracted by systematic reviews of prominent articles. In the following, 25 experts were selected with the purposive sampling method. Among the 21 Variables extracted, 4 Variables (like as “geopolitics of port-city”) introduced as a driver factor, 5 Variables (like as “advanced multimodal transportation in the port”) introduced as a linkage factor, 6 Variables (like as “quality of living environment and desirable city”) introduced as a dependent factor, 3 Variables (like as “environmental protection and sustainable development of the city-port”) introduced as an autonomous factor. The rapid development of Dubai can be explained by the purposeful distribution of revenues from trade and tourism. A part of these revenues has been directed towards investment in important transportation infrastructures such as airports and ports of this city. These strategies have a wide impact on improving the economic growth of the city and the development of the port in order to adapt with the global trade and advanced technologies. FundingThere is no funding support. Authors’ ContributionAuthors contributed equally to the conceptualization and writing of the article. All of the authors approved the content of the manuscript and agreed on all aspects of the work declaration of competing interest none. Conflict of InterestAuthors declared no conflict of interest. Acknowledgments We are grateful to all the scientific consultants of this paper.

Using a sample including 2642 observations of 2003-2016 annual data of firms listed in Tehran Security Exchange, this study investigates the implications of correlation between non-discretionary accruals and Partitioning Variables when testing the positive accounting theories hypotheses. In earnings management detection tests, it is common for researchers to use Variables which partition their sample into two groups, for which differences in motivation for income manipulation are predicted. Since earnings management stimuli are assumed to be correlated with Variables like firm performance, leverage or size, the use of these proxies are popular in empirical tests of positive accounting theories hypotheses. The correlation between non-discretionary accruals and the Partitioning variable implies that part of the variation of non-discretionary accruals is generated by the Partitioning variable. If accruals model does not control for the correlation, this part will be added to the discretionary accruals. In this study, we provide evidence that, when correlation between non-discretionary accruals and the above-mentioned Partitioning Variables remains uncontrolled, accrual-based models tend to generate measurement error in the estimate of discretionary accruals that significantly affects the sign and the magnitude of correlation between discretionary accruals and the Partitioning Variables. The findings of a Monte Carlo simulation also indicate that the Jones model relatively generates less type I error when it is adjusted to control for the relation between non-discretionary accruals and firm performance.

Graph Partitioning is a well-known problem in the literature. In this paper, path Partitioning of trees in which the given tree is partitioned into edge-disjoint paths is considered. A linear time algorithm is given for computing a path Partitioning of minimum height.

A thorough recognition of the nature and duties of the genes is based upon having adequate information about the proteins. However, the proteomic projects follow a slow trend, therefore, solving the protein-related problems has become as one of the most important challenges in bio-informatics. Consequently, the presence of tools which can enhance the structural recognition, classification, and interpretation of proteins will be advantageous. Statistical methods are among the tools to help solve bio-informatics problems. These methods may be used to help predict the third structures of proteins, study proteins collectively, as well as extract new interactions among the protein collections. One of the very efficient and useful methods in the collective study of protein subsets is the cluster analysis. In the present study, the recognized protein sequences related to esophagus, stomach, and colon cancers are analyzed through Partitioning, non-Partitioning, and fuzzy clustering methods. Needleman-Wunsch global alignment algorithm was used to determine pair-wise similarities. The evaluations have shown that the clusters obtained through using the AGNES method have produced more powerful structures, yet, it can be said that the PAM clustering method, compared to other ones, has produced the best results in predicting ability of the 3D structure of the unknown protein sequences.