The knowledge related to the methodology of the FIELD trial study as a type of intervention studies, yet for many of our researchers is not fully understood. The aim of the current study was a better understanding of conducting this type of research. FIELD trial studies are done on healthy individuals and aim to prevent. These types of studies such as clinical trials are performed on both individual and collective levels. One type of these studies is Community Intervention Trial which is usually done on a large scale population. FIELD trial study should be carried out in stages, such as the formulation of hypotheses, selection of the population (reference population, study population, and sampling), measuring the baseline variables (before conducting preventive intervention), random allocation of subjects to intervention and control groups, doing interventions and measuring outcome. The methodology of FIELD trial studies is very similar to clinical trials. The difference is that FIELD trials are conducted on healthy individuals and aim to prevent and also the sample size required to this type of study is relatively more, and these studies are usually time consuming and costly.

Objective: People who have higher social health can more successfully deal with the challenges and ups and downs of playing key social roles and participate more in collective activities and prevent social deviations and anomalies. In this regard, foresight and the development of a forward-looking strategy model by the media can play an important role in promoting the social health of their audience (individuals).Method: This research has been done qualitatively and quantitatively. In the qualitative phase, content analysis and Q methods were used, and in the quantitative phase, factor analysis was used to analyze the Q method data.Results: The content analysis of the media in this study showed that the most media production in the FIELD of social deviations with 576 cases and the lowest media production in the FIELD of violence with 237 cases. The FIELD of social quality with 485 and the FIELD of social issues with 312 cases are in the second and third ranks. A small questionnaire was made and based on it, the desired model was designed using PLS software. Conclusion: This model showed that the FUNCTION of media is effective in promoting social health of society and can have the greatest impact on improving quality of life, reducing violence, reducing social deviations and social issues.

Purpose: Clustering and co-word analysis is a method to reveal relationships and links and illustrate the intellectual structure of a scientific FIELD. This research tries to study the intellectual structure of articles in the FIELD of futures studies in Iran by using the technique of co-word analysis. Method: The current research is a descriptive-analytical development with a scientometric approach. The statistical population is 921 articles retrieved records in the FIELD of futures studies. Findings: The findings showed that articles in the FIELD of futures studies in Iran are often associated with positive growth, and in terms of frequency, the keywords scenario, Islamic Republic, and foresight are the most frequent in futures studies. The findings related to the hierarchical clustering led to the formation of 8 clusters in this FIELD, namely "ICT visions", "geographers who love the future", "knowledge development", " Futuristic higher education", "Future of Religion", "Regional Relations", "Strategic Foresight" and "Heavy Weight of Method". Conclusion: According to the findings of the current research and the high frequency of the keyword scenario, as well as the density and relationships of this keyword with other keywords, it can be concluded that the scenario is the dominant approach in futures studies. Also, according to the resulting clusters, it was observed that these researches have a high variety, but addressing the future in many areas is still neglected.

A characteristic of Data Envelopment Analysis (DEA) is to allow individual decision making units (DMUs) to select the factor weights which are the most advantageous for them in calculating their efficiency scores. This flexibility in selecting the weights, on the other hand, deters the comparison among DMUs on a common base. For dealing with this difficulty and assessing all the DMUs on the same scale, this paper proposes using a multiple objective linear programming (MOLP) approach based on scalarizing FUNCTION for generating common set of weights under the DEA framework. This is an advantageous of the proposed approach against general approaches in the literature which are based on multiple objective nonlinear programming.

In this article a spatial model is presented for extreme values with marginal generalized extreme value (GEV) distribution. The spatial model would be able to capture the multi-scale spatial dependencies. The small scale dependencies in this model is modeled by means of copula FUNCTION and then in a hierarchical manner a random FIELD is related to location parameters of marginal GEV distributions in order to account for large scale dependencies. Bayesian inference of presented model is accomplished by offered Markov chain Monte Carlo (MCMC) design, which consisted of Gibbs sampler, random walk Metropolis-Hastings and adaptive independence sampler algorithms. In proposed MCMC design the vector of location parameters is updated simultaneously based on devised multivariate proposal distribution. Also, we attain Bayesian spatial prediction by approximation of the predictive distribution. Finally, the estimation of model parameters and possibilities for capturing and separation of multi-scale spatial dependencies are investigated in a simulation example and analysis of wind speed extremes.