Water-based incidents are natural phenomena that have posed Iran to serious crises. Despite efforts to settle water crises and improve the conditions, we still witness a growing trend of water issues, especially in the agricultural sector in Iran. A necessary step to face water-based incidents and alleviate their implications is to have a deep understanding of resilience dimensions to enhance the tolerance level of the farmers. The present study evaluated farmers’ resilience level in the face of water-based crises in Harsin County. The research, which adopted a descriptive survey methodology, was conducted in 2020. The statistical population was composed of the heads of rural households in four rural districts of the county, amounting to about 6000 people. The sample size was determined to be 361 farmers based on Morgan’s table. The measurement instrument was a questionnaire whose validity was confirmed by a panel of experts and professionals and its reliability was confirmed for all sections as their Cronbach’s alpha was estimated at >0.8. SPSS was used for data analysis and Expert Choice was used for pairwise comparisons. Based on the results of the pairwise comparisons, infrastructural resilience to drought has the highest level of importance in the concept of resilience. According to the results of the study, the resilience level was evaluated to be relatively low in all studied rural districts, and the difference in the level of resilience among the studied villages is significant. Also, farmers with irrigated farming have a higher level of resilience than farmers with rainfed farming.

This study investigated the role of of Intelectual CAPITAL on labor PRODUCTIVITY among the staff the University of Urmia. Statistical population of the study included of all staff using a sample-stratified random sampling-relative-214 based on the Cochran formula was selected as the sample. Data were gathered through two standard questionnaires, CAPITAL and labor PRODUCTIVITY. Data collection, the data analyzed based on research hypothesis with using of descriptive statistics and inferential statistics such as mean, standard deviation, t test, Pearson's correlation and step by step regression analysis. Results showed between Intelectual CAPITAL and its elements with labor PRODUCTIVITY are positive and significant correlation. Structural CAPITAL, relational CAPITAL and cognitive CAPITAL have Predictive power of labor PRODUCTIVITY positive and significant. In conclusion it can be stated that social CAPITAL is effective on labor PRODUCTIVITY.

Purpose: The purpose of this study is to develop a human CAPITAL PRODUCTIVITY model in revolutionary institutions that is classified in terms of practicaldevelopmental purpose and hybrid or hybrid approach. Design/ Methodology/ Approach: The statistical population of the study is in the qualitative part of organizational experts in the field of human resources and in the quantitative part it includes managers and experts in the field of human resources (33 persons). Due to the limited statistical population and accessibility, the census method was used. The qualitative part of the interview tool and the quantitative part of the questionnaire tool for data collection and the method of structural-partial least squares equations were used to test the hypotheses and fit the model. Research Findings: Due to the composite reliability and Cronbach's alpha coefficients for all constructs (greater than 0. 7) and mean variance (greater than 0. 5), reliability and validity of the research instrument were acceptable. According to the results, the factor loadings of all the reagents in the 14 components of the research model were greater than 0. 4 and at the significant level of 5% error, which confirmed the factor loadings of the research measurement model. According to the values of the share index, the common average and the average redundancy of the model for all the variables show that the qualitative evaluation of the model is at the desired level and the model is confirmed. Limitations & Consequences: Impossibility of interview with the employees of all revolutionary institutions with the purpose of more richness of the results, is the main limitation of this research. Practical Consequences: The research model, by calculating all possible dimensions affecting PRODUCTIVITY and using the path-structural modeling technique, while identifying the factors, examines the indirect effects of the dimensions and components of the designed model. This model has a systemic approach to the PRODUCTIVITY of human CAPITAL. In this way, in addition to focusing on systemic factors, it examines the pervasive environment (extra-organizational environment) and environmental influences. Innovation or value of the Article: This study presents the model of human CAPITAL PRODUCTIVITY in revolutionary institutions and so far no such article has dealt with the PRODUCTIVITY of human CAPITAL in the institution, which is why it is innovative.

to purchase new machinery and equipment and replacing worn-out machines have great importance. Regarding the CAPITAL constraint in the mining sector, not using it indicates the weakness of management and the level of actual production is less than the potential level. Accordingly, the present study intends to investigate the factors affecting the average PRODUCTIVITY of CAPITAL in the mining sector of Iran over the period 1983-2017. CAPITAL in this sector is in the form of equipment and machinery as well as financial CAPITAL. Based on the results, the average PRODUCTIVITY of CAPITAL has been accompanied by many changes, and its lowest amount is observed in the years of the imposed war. Estimation of the econometric model showed that the inverse per capita CAPITAL, mineral price index, energy efficiency, and private sector share in ownership have a significant effect on CAPITAL PRODUCTIVITY at the level of 5%. Therefore, an increase in energy efficiency and  a decrease in per capita CAPITAL increase average CAPITAL PRODUCTIVITY. On the other hand, increasing the price of minerals increases CAPITAL PRODUCTIVITY.