Nowadays, the fundamental role of having a purpose for life in physical and mental health has been confirmed. According to victor frankl, presence of a purpose in life gives life a meaning and increases resilience against pains and traumas. The importance of the purpose in life construct reveals the need for a reliable and valid tool to measure it. Crumbaugh and Maholick's purpose in life questionnaire is the first and one of the most applied tools for the assessment of life's purposefulness. The aim of this research is to determine the factor STRUCTURE of purpose in life questionnaire. The questionnaire was administered on 206 students who were selected through random stratified sampling at Ferdowsi University of Mashhad. Exploratory factor analysis showed that there are two factors "comprehension" and "purpose" and this finding were confirmed by confirmatory factor analysis. Altogether results of this research showed factor validity of the purpose in life questionnaire with a two factor pattern

due to providing social security needs, COMMUNITY policing approach has been applied by IRI Police in recent years. To settle this approach the present research aims at determining obligations and necessary characteristics in organizational STRUCTURE and leadership style within Mckinsey 7S model. The present research was applied type but taking into consideration nature and method was descriptive – survey type. For COMMUNITY policing settlement, it was tried by analyzing related documents and resources to suggest some principal amendments needed in current organizational STRUCTURE and leadership style of IRI police. Result of these analyses indicated that without applying some amendments in current IRI Police organizational STRUCTURE and leadership style in a way both become more effective and appropriate with obligations, the COMMUNITY policing approach settlement will not be successful. Based on Mitzberg structural framework, current IRI Police organizational STRUCTURE is sectional STRUCTURE and for settling COMMUNITY policing approach, this STRUCTURE needs to reform in areas such as cooperation, coordination, more delegation through field officers, more training and flexibility. Also high and middle rank managers should select their leadership styles by focus on cooperation, reliability and authority conferring.

Many real-world networks, including biological networks, internet, information and social networks can be modeled by a complex network consisting of a large number of elements connected to each other. One of the important issues in complex networks is the evaluation of node importance because of its wide usage and great theoretical significance, such as in information diffusion, control of disease spreading, viral marketing and rumor dynamics. A fundamental issue is to identify a set of most influential individuals who would maximize the influence spread of the network. In this paper, we propose a novel algorithm for identifying influential nodes in complex networks with COMMUNITY STRUCTURE without having to determine the number of seed nodes based on genetic algorithm. The proposed algorithm can identify influential nodes with three methods at each stage (degree centrality, random and structural hole) in each COMMUNITY and measure the spread of influence again at each stage. This process continues until the end of the genetic algorithm, and at the last stage, the most influential nodes are identified with maximum diffusion in each COMMUNITY. Our COMMUNITY-based influencers detection approach enables us to find more influential nodes than those suggested by page-rank and other centrality measures. Furthermore, the proposed algorithm does not require determining the number of k initial active nodes.

The ecological balance of microbial COMMUNITY (MC) is very important as an index of soil ecosystem health due to the pivotal and vital roles of MC. Microbial culture methods are suitable for this purpose because the majority of soil microorganisms unable to grow on synthetic media. DNA- based molecular analyses just provide microbial diversity data. Then a rapid method for the assessment of soil microbial COMMUNITY STRUCTURE is using phospholipid fatty acid (PLFA) patterns (because they are degraded rapidly after cells death, by the action of phosphatases). Moreover, PLFA provide broad information dealing with microbial diversity, biomass and their nutritional-physiological status. Certain and specific PLFAs, viz.trans/cis ratio (monounsaturated fatty acids (16: 1w7t) to 16: 1w7c), cy/pre ratio(cyclopropyl (cy17: 0+cy19: 0) fatty acids to precursor (16: 1w7+18: 1w7) fatty acids, S/M ratio (saturated to monosaturated fatty acids), G-/G+ratio (Gram negative bacteria to Gram positive bacteria fatty acids), F/B ratio (saprophytic fungi to bacterial fatty acids) were able as indicators of physiological or nutritional status in different environmental conditions, such as heavy metals polluted soils, changes in pH, depth changes drought, salinity, different agricultural managements and flooding are used for evaluation of soil microbial communities. The method of PLFA analysis in soil and determining microbial diversity, biomass and their physiological status are addressed here.

Assemblage STRUCTURE of benthic macroinvertebrates studied in Lasem river (Amol, Mazandaran Prov.) during 12 months, that they collected with surber sampler (40×40 cm, Mesh 100 µm). 33 families and 32 genera identified from benthic macroinvertebrates that aquatic insect larvae (Ephemeroptera, Diptera, Trichoptera and Plecoptera) had most abundance and diversity. The maximum amd minimum abundance was 2411.9 No/M2 in station 2 and 725.5 No/M2 in station 1 respectively. Benthic macroinvertebrates Frequency data was summarized to COMMUNITY STRUCTURE metrics including Total Richness, EPT Richness, EPT%, EPT/Chir and PCD. Hilsenhoff and BMWP/ASPT family level biotic index were also determined for each studied station. Minimum and maximum HFBI were 3.55 for station 1 and 4.25 for station 2 respectively. Maximum and minimum BMWP/ASPT index were 6.67 for station 3 and 5.72 for station 5 respectively. In end, stations was assayed quality condition. They classified in 3 groups that station 2 was  bad quality and station 3 was good quality condition.