In this article, after defining a conceptional framework for defining and measuring shadow economy in Iran a close attention is also paid to a more precise definition of shadow economy itself. It is also tried to estimate it' s changing process and size during 1975-2007 based on the new definition.Direct and indirect approaches are also briefly discussed to estimate the shadow economy. Then, the strengths and weaknesses of each method are pointed out. So far, almost all of the researches carried out in Iran regarding estimation of shadow economy have mainly focused on structural equation modeling approach using LISREL. Here in this paper for the first, time both structural equation modeling software programs of Amos Graphics and LISREL are applied to estimate the shadow economy in Iran. A comparison of the process and output of both software packages is also done in this research. Finally, in addition to investigating the direct effects of the causal variables, the interactional effects of them on latent variable of the shadow economy are also analyzed.

Sector of countries economy related to shadow economy. Production in sector of the shadow economy associated with evading rules such as environmental laws. On this basis, activity of most economic units in the shadow economy have the negative externalities. In Program of the sustainable development, has been considered environmental quality. The growth of shadow economy is one of the factors that destroys environmental quality which depends to various factors such as corruption. In this paper, has been studied the relationship among corruption, shadow economy and environmental pollution using linear structural relationship pattern for OPEC like Iran countries over (2000-2012). The results from estimated models show that i) there is a positive relationship between corruption and the shadow economy; so that increasing corruption by one unite lead to rise the shadow economy by 0.73, ii) increasing illegal activities in sector of the shadow economy have the significant and positive effect on the environmental pollution indexes; so that, with growth of the shadow economy by one unite, are affected environmental pollution indexes include carbon dioxide emissions, consumption of fossil fuels and forest area by (0.91), (0.47) and (-0.53), respectively.

Introduction Urban floods have been exacerbated by climate change and urbanization, as well as restrictions on the drainage of urban infrastructure, and over the past decade have had many negative effects on cities around the world [4]. As a result, the demand for more resilience has not been successful in many cases [2]. Accordingly, the resilience of key urban buildings is one of the necessities of urban resilience [3]. In this regard, research on urban resilience in events such as floods was reviewed, some of the most important of which are mentioned below. In 2019, Wang and colleagues evaluated the resilience of the urban basin to floods, and the CADDIES model was used to simulate floods. Based on the results, vulnerable basins were identified and strategies were developed to increase the city's resilience to floods [4]. In 2019, Barajas et al. worked on an article on the resilience of urban buildings in the face of flood risk in the Mexican metropolitan area, and addressed the resilience of buildings in Mexico City during the floods of recent decades. Findings show that building resilience is a complex and sequential process that of course depends on social, economic and institutional conditions [1]. Research Methods In this research, in order to achieve the model of resilience of important buildings against floods, data analysis is performed in several stages, which include the following: 1- Identification of significant assets 2- Modeling river flow using HecRAS software 3- Adaptation of assets and modeling results from rivers in different return periods 4- Counting assets affected by floods 5- Modeling of building resilience components using structural equation modeling of LISREL software 6-Counting and ranking the components extracted from the model using AHP-TOPSIS combined method 7- Ranking of key buildings affected by floods using AHP-TOPSIS combined method Discussion and conclusion The asset layers of the city of Hamedan and the rivers of the city have been adapted in the GIS context and five buildings of the University of Technology, the Faculty of Art and Architecture, Payam-e Noor University, the Blood Transfusion Building and the Amiran Hotel have been identified as vulnerable centers of Hamedan. Conclusion Components (adaptability-flexibility, connection of failure-safe feedback, dependence on environmental ecosystems, diversity, learning-memory-prediction, performance, response speed, fragmentation redundancy, resourcefulness, and robustness) are effective variables on flood resilience of buildings. In testing the hypothesis using the structural equation model, the software output indicates the suitability of the fitted structural model to test the research hypotheses. Weighting indicators Resilience components Sub-components of resilience Weight Compatibility - Flexibility Change while maintaining or improving performance 0.049 Evolution 0.045 Adopt alternative strategies quickly 0.05 Timely response to changing circumstances 0.027 Open design and flexible structures 0.049 Connection - Feedback - Safety - Failure Shock absorption 0.007 Absorb the cumulative effects of challenges with a slow start 0.012 Avoid catastrophic failure if you exceed the threshold 0.007 Gradual failure instead of sudden 0.013 Failure without cascading effects (demino effect) 0.024 Parallel analysis of technology system - human 0.005 Identify locking effects and possible discrepancies with reduction 0.014 Identify synergies with other city policies, value added estimation 0.015 Dependence on local ecosystems Flood control 0.012 Bioclimatic design and management 0.006   Resilience components Sub-components of resilience Weight Variety Spatial diversity - key assets and tasks that are physically distributed and not all of them are affected by a specific event at any time 0.0146 Functional Diversity - Multiple methods of dealing with a particular need 0.021 Balance variation with potential cascading effects 0.013 Learning-Memory - Prediction Learn from past experiences and failures 0.003 Use information and experience to create fresh compatibility 0.003 Avoid repeating past mistakes 0.005 Collect, store, and share experiences 0.009 Construction based on long-term value and city history 0.007 Integrate resilience into long-term development scenarios 0.02 Function Performance capacity 0.056 System quality in a suitable and efficient way 0.013 Self-sufficiency - reducing external dependence 0.019 It performs better than other buildings 0.039 Response speed In taking casualties, including mortality and disease 0.007 Reorganize 0.015 Maintain performance and re-establish it 0.032 Restore structure 0.017 Establish public order 0.013 Prevent disruption in the future 0.005 Redundancy - fragmentation Systems replacement or systems agents 0.054 Buffer from external shocks or changes in demand 0.013 Replacing components with modular parts 0.026 Balance redundancy with potential cascading effects 0.077 plan Identify and predict problems 0.013 Prioritize 0.011 Mobilize resources of visualization, planning, collaboration and action 0.014 re-evaluation 0.006 Integrate resilience into work and management processes 0.052 Getting cooperation from citizens 0.03 Strength Surface resistance to stress 0.003 No degradation and loss of performance 0.015 Capacities that ensure adequate margins 0.006

Objective: This study was conducted with the aim of modeling the causes of students' tendency to drugs use by means of LLISREL software. Method: This study was carried out as a survey research wherein the number of 220 female students was selected as the participants through stratified random sampling method. The required data were collected through questionnaire.Results: The hypothesis testing results show that the female students' desire for drug use was the same based on education level, parental employment status, and academic discipline; and the observed difference was not significant. In addition, the results of the structural model indicate that religiosity, social capital, family disputes, and social alienation can explain nearly 31% of the variations of female students' desire for drug use. Religiosity and social capital had a negative impact but social alienation and family disputes had a positive effect on female students' desire for drug use.Conclusion: Family disputes and social alienation provide the grounds for the increased desire female students for drug use while social capital and religiosity prevent the increased desire of female students for drug use.

In order to study the genotype x environment interaction, replicated yield trials were performed using 19 barley genotypes in a randomized complete block design with four replications, at eight agricultural research stations of temperate regions for three years. Mean grain yield and two environmental variables, mean air temperature and mean soil pH were used for Redundancy Analysts. In this method, two variables RA1 and RA2 were constructed. Each one was the linear combination of environmental variables mentioned above. The genotypes 3 and 15 in relation to RA1 and the genotypes 1, 11, 12, 13, 14 and 19 in relation to RA2 had large percentage sum of squares per genotype (R2) These values for genotypes 3 and 15 were 87 and 81, and for next six cultivates mentioned above were 22, 20, 38, 29, 18 and 36, respectively. Thus in comparison with other genotypes, the interactions among these cultivars and the locations under study were determined mainly by air temperature and soil pH, and these genotypes were more sensitive to the temperature and pH. The R2 for RA1 and RA2 models were small for the genotypes 2, 4, 5, 7 and 16. Therefore, these cultivars were considered to be adapted and insensitive to the environmental factors. The genotypes 5 and 7 had the high grain yield, too. The replacement of residuals obtained from the regressions of local indices and the environmental variables in Redundancy Analysis model, and doing Partial Redundancy Analysis, showed that in addition to the air temperature and soil pH, the other environmental variables were more or less effective in forming the genotype by location interactions. Cultivars 5, 17 and 18 showed smaller interactions as compared to others. To construct the LISREL model, the means of air temperature, soil pH, date of maturity, date of heading and plant height as the environment and phonologic variables, and the mean grain yield and 1000 grain weight as the variables related to the genotype × environment interaction, were entered in the model. Results indicated the further importance of air temperature and grain yield variables. Also combination of LISREL and Redundancy Analysis models indicated that because of large coefficients of grain yield and air temperature in the LISREL model, study of genotype× environment interaction using these variables by Redundancy Analysis model was worthwhile.      

Cities are complex ecosystems that are affected by social, economic, environmental and cultural factors to achieve sustainable urban development. On the other hand, custom, traditions, norms, values are formed in the context of social relations and self-wise during generations, which leads to some sort of uncontrolled social monitoring and control of the person's socialization behavior, which is considered as "social sustainability" from a special perspective. In this regard, the present study aims to measure social sustainability in Ardabil city. The type of applied research and its method is descriptive and survey, information gathering tool is a questionnaire. The statistical population is all residents of Ardebil. The sample size is based on the Cochran formula of 384 people who were selected by cluster sampling method and finally selected by simple random method. LISREL software is used to analyze data and information. The findings of the research show that among the indicators of social sustainability in (T) model, participation indices, spatial belonging, environmental factors had the highest level of significance and spatial justice indicators and the least significant indicator of vitality they show. Also, the results of the structural equation model show that according to the modeling indices, it can be argued that the proposed model of researchers has a good fit for goodness. And a good fit between the model presented with the structured model with experimental data. In other words, research indicators have the necessary qualifications for use as a final model of research.

The present study was conducted in order to investigate the behavior of shrimp buyers and evaluate their preferences with a statistical population of 600 people in Tehran's Besat market. In this research, shrimp buyers were asked three categories of questions. The questions of the first category were related to demographic characteristics and the questions of the second category were related to the amount and reasons for purchasing shrimp, as well as evaluating the preferences of buyers in indicators such as size, types of supply models, and species of shrimp (Friedman test). The third group of questions were prepared based on the Likert five-option scale and in order to investigate the factors affecting the attitude of the statistical community to the purchase of shrimp by using conceptual model design and structural equation modeling method (LISREL software). The results of this research showed that buyers who purchase 3 or 4 times a year and selected each time 1 to 1.5 kg of shrimp, had the highest frequency (54.33 and 56.34%, respectively). 64.56 percent of the buyers cited the nutritional value and good taste (together) as the main reason for buying and consuming shrimp. In the study of buyer’s preferences, it was found that large size shrimp (26 to 35 pieces per kilogram), bulk shrimp (compared to packaged and combined shrimp), marine shrimps (compared to cultured and combined shrimp) and the size of 500 to 750 grams of shrimp package have more fans. The results of the implementation of the research conceptual model showed that all hypotheses were confirmed; in such a way that variables of size, types of supply and shrimp species were significantly effective on buyer’s attitudes with effect coefficients of 0.19, 0.33 and 0.47, respectively…

Introduction and Purpose: Urban resilience is the ability of an urban system in a spatial and temporal scale to maintain or a prompt return to the desirable performances of the past against disturbances (natural hazards) aiming at being stable through changes. A city will be called resilient, when it is simultaneously capable of providing a balance between ecosystem and human functions, being flexible, having the power to be adaptive to uncertainty and unexpected events, as well as maintaining and investing on the existing potential opportunities. Reducing the resilience in the cities has increased the vulnerability of the city system; As a result the small disruptions and shocks might lead to the crisis. Therefore, via considering the importance of the subject, the research seeks to promote a theoretical knowledge in the area of urban environments resilience. Method: In this study, descriptive and analytical research method has been used to identify and compile effective elements on urban resilience and from tools such as observation, questionnaire and statistical analysis tool of structural equations LISREL. Findings: The results obtained from the LISREL software indicate that the institutional and administrative, economic, social, as well as physical and infrastructural indices with the respective direct and positive coefficients of 0. 96, 0. 64, 0. 24, and 0. 13 are among the most important indicators in urban resilience. Conclusion: The institutional-administrative index, which is the most influential component between resiliency elements, has a logical and meaningful correlation in relation to other components and the most correlation between the institutional-administrative indexes and the economic index is 0. 62 that As a key factor, has a lot of influence on the other three indices and by strengthening this index, it is possible to improve and enhance other effective indicators on urban resilience in order to finally improve the overall level of urban resilience in the region.