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One of the most important dimensions of cooperative systems, which have existed in Iran since 1974, is cooperative rural production enterprises. In this study, the performance of rural production cooperatives in Iran is examined in relation to the social capital components of the members. This study's statistical population consisted of 16,870 members from 41 cooperatives. 217 samples of cooperative members and 98 samples of the cooperative's management and board of directors were selected using the Cochran formula, and questionnaires were filled out for each group. The results showed that from the viewpoint of the members of rural cooperatives, the most important factor in improving the performance of cooperatives was the use of effective extension training (model-extension sites). The results also showed that members' satisfaction and training members of cooperatives can play an important role in improving the performance of cooperatives. The results of this research, while emphasizing the role of education (especially extension activities), can help rural development planners in improving the performance of rural cooperatives in their regions. [Display omitted] • The study identified the impacts of social capitals on the performance of rural production cooperatives. • The most important factor in improving the performance was the use the extension trainings. • Members' satisfaction and training play an important role in improving the performance. • The level of education in production cooperatives has a direct relationship with trust and satisfaction. • Cooperatives do not perform well in the fields of sales, marketing and insurance. [ABSTRACT FROM AUTHOR]

Mud window is an inseparable part of well design. It is so crucial for having a successful drilling operation, especially in gas fields. Reliable and accurate design of mud weight is the key to having a safe wellbore. Many wellbore stability problems occur because of the inaccurate mud weight in both gas and oil reservoirs. Inaccurate and even sometimes wrong mud weight is caused by lack of adequate knowledge about the field geomechanics. Quantifying the geomechanical uncertainties allows taking the knowledge incompleteness into account. It can reduce the risks of unreliable mud weight design. The geomechanical parameters are only known at some sparse points of measurements. Therefore, it is necessary to estimate the parameters in the whole reservoir as if there is a decision to drill an infill well or conduct a well work-over or side-track an old well. Geostatistical algorithms are utilized to create 3D mechanical earth modeling (MEM) of the system. Several MEM realizations are created to span the range of geomechanical uncertainty adequately. The mud window is designed under uncertainty based on Mohr-Coulomb failure criteria. In this article, at first, 1D MEMs at well locations are built by the available data, and then ordinary Kriging is used to propagate 1D MEMs onto 3D MEM. Smooth plus rough – nonlinear problem (LMAP) and Rough plus smooth – nonlinear problem (RML approximate) are the methodologies applied to quantify the geomechanical uncertainties. In the end, the optimum mud window and the corresponding uncertainty ranges are identified based on the posterior probability distribution of geomechanical parameters in one of the gas reservoirs in the south of Iran. The resulting mud window is narrower compared to the mud window designed with prior information. Consequently, mud window design would be more reliable with less possibility of kick or any wellbore instability. • A novel methodology is developed to design a reliable safe mud window based on updated geometrical uncertainty distribution • The used approach allows to estimate the uncertainty ranges for geomechanical parameters and their dependent parameters. • 1D and 3D Mechanical Earth Modeling were built to obtain geomechanical parameters, using Ordinary Kriging methods [ABSTRACT FROM AUTHOR]

Abstract: A set of twelve simple sequence repeat markers was used to evaluate the genetic background of 738 pomegranate (Punica granatum L.) accessions, which belonged to the Iranian National Pomegranate Collection, from 23 provinces of Iran. Genetic diversity among the accessions was analysed using several variability parameters. Forty-three alleles were detected with a mean of 3.59 alleles per locus. The highest levels of polymorphism were found for the locus ABRII-MP26. The mean values of expected heterozygosity and polymorphic information content were 0.521 and 0.458, respectively. Neighbour-Net analysis based on uncorrected P distances and a model-based method separated the accessions into 8 main groups and several subgroups, with a tendency to cluster by geographical origin. From the simple sequence repeat analysis, it can be concluded that the genetic base of the Iranian National Pomegranate Collection is broad enough to ensure future progress in breeding programmes and these markers are effective tools for conducting genetic diversity studies in pomegranate and developing strategies for germplasm management. [Copyright &y& Elsevier]