Talking about making policies, we expect a domain of outcomes including bureaucratic and sovereign decisions. The outcomes are indeed the result of mechanisms and instruments that must be utilized to implement a policy. Implementation of policies usually suffers from 2types of deficiencies: deficiencies caused by inefficient executives and those caused by insufficient infrastructures. what comes before these deficiencies is the lack of an optimum methodology for implementing the policies; because, the executive can claim not having a clear plan of action, and the problem of insufficient infrastructures usually cannot be solved in a short while. The present article, being centered on the discussion of performance and performance studies, aims to propose a Model for implementation cultural policies, by means of which the operational literature and ideas are developed with a cultural approach, of the objectives. Complete and accurate implementation of the plan of action is the only way of realizing the policies. On the other hand, improper implementation can abandon all hopes for solving the problem. Every instrument can be applied to a certain problem; and since the policy makers can have an extensive domain of action, knowing the proper instruments is the key to final realization of the policies.

Introduction: Triple vulnerability Model focuses on the interaction of three types of vulnerabilities in development and maintenance of emotional disorders. Despite the prominence of this Model, there is not enough research done to assess its experimental validity. The aim of this study was to evaluate vulnerability factors of generalized anxiety disorder based on Triple vulnerability Model.Materials and Methods: For this purpose, 320 students from Tabriz University (Iran) were selected by cluster sampling and responses to NEO Five-Factor Inventory, Anxiety Control Questionnaire-revised, Intolerance of Uncertainty Scale and Generalized Anxiety Disorders Questionnaire-IV. Analysis was performed using structural equation Modeling.Results: Evaluation of hypothetical Model using fit indices showed that the hypothetical Model fitted with the measurement Model. The results showed that neuroticism and extroversion have significant effect on generalized anxiety disorder by mediation of lack of perceived control and intolerance of uncertainty. In addition, hypothetical Model was a better fit in compare to the alternative Models.Conclusion: our result, along with the support of Triple vulnerability Model for generalized anxiety disorder, proposes a suitable framework for the etiology of this disorder.

Plant development can be defined as a programmed qualitative change in plant form, which leads plant to maturity, and researchers call it as phasic development or phenology. Recognizing the timing of occurring each development stage is necessary for managing system in order to yield increment. The timing of occurring development stages depend on climate, genotype specifications and sowing date then determination of these times in different regions is difficult and it is only possible through the using of crop simulation Models which can predict the timing of occurrence each development stage by integrating effective factors. The Model was constructed based on linear equation of plant temperature response. In order to Model evaluation two experiments were carried out in agricultural and natural resources research center of Khuzistan in 2003-2004 and 2004-2005 cropping years. Wheat development stages were determined based on Kirby and Appleyard’s scale by stereoscopic microscope and required GDD for each development stage as well. The constructed Model was calibrated and run for simulation. Comparison of simulated and observed data showed that the Model can strongly predict wheat development stages.

It has recently been proven that integrating statistical process control (SPC), maintenance policy (MP), and production could bring benefits for the entire production system. In the literature of integrated Triple-component Models, independent observations have generally been studied. The existence of correlated structures in practice put the traditional control charts in trouble. The mixed EWMA-CUSUM (MEC) chart has been developed as an effective tool of SPC for monitoring only the autoregressive (AR) processes. Nevertheless, it has not been extended for moving average (MA) and ARMA processes. Besides, MEC has been designed only based on statistical measures. However, in an imperfect production system, the decision variables of MEC together with the other components should be determined according to the resulting costs and satisfaction of some criteria. This paper proposes an integrated Triple-component Model by applying the MEC chart for monitoring various autocorrelated processes. Due to the complexity of the Model, a particle swarm optimization (PSO) algorithm is employed to reach optimal solutions. The applicability of the Model is investigated via an industrial example. The effects of Model parameters on the solutions are studied through a sensitivity analysis. Moreover, extensive comparisons and a real data set are provided for more investigations.

The performance a valve has been frequently estimated with numerical methods owing to limitations such as cost and place. In this study, for the Triple-offset butterfly valves, the different sizes in various disc-opening cases was numerically conducted using different turbulence Models of the two-equation turbulence Models of k–ε, k-ω, and Reynolds stress Model. The numerical calculations were validated against experimentally obtained valve flow test results. The numerical effect with the different turbulence Models were analyzed with respect to the disc-opening cases. From the numerical analysis, the Reynolds stress Model exhibits the most pronounced turbulence effects among the various turbulence Models showing higher value of Reynolds normal stress near the valve disc region. The sensitivity of the turbulence Model constants was examined using the 300 mm valve to observe the sensitivity of the turbulence Model parameters in the two-equation turbulence Models.