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Aims: E-health intervention can be a valuable way to deal with asthma and reduce its global burden of it. This research aimed to determine the type of e-health interventions in the interventional studies in asthma based on PubMed.
Information & Methods: The research was carried out from 2000 to 2018 using content analysis and scientometric techniques. To reach the research population, the keywords of two concepts of asthma and e-health were identified, and then the search formula was developed using “Mesh” and “Ti” tags. By examining the 452 articles, the research population was limited to 102 articles that had somehow used e-health technologies to intervene in asthma. The extracted data from the articles were: the publication year, type of e-health technology used in the intervention, thematic scope of the intervention, and the affiliated countries of the articles.
Findings: The highest number of articles has published in 2016. Seventeen categories of e-health technology were identified by analyzing the content of 102 articles. The findings indicate that web-based systems, text messaging, and mobile applications were at the forefront of the technologies used in the studies. The highest number of interventions occurred in “asthma control”, “medication adherence”, and “self-management”. The United States has the highest share among the affiliated countries of the articles.
Conclusion: Web-based solutions have been the most extensively employed technology. In most studies, the key aim of deploying e-health interventions has been to improve “asthma control”. The United States has contributed the most to the studies.
 

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In this paper the As-Is model of the organizational affairs process of the NIORDC (National Iranian Oil Refining and Distribution Company) was drawn by the use of BPMN modeling tool, and then its simulation model was modeled by using SIMPROCESS simulation software and relative parameters were defined. Necessary scheduling in defining simulation parameters were extracted and set according to available organizational documents and archives. Afterwards, justification and verification of simulation models were tested by using statistical tests. In the next stage, several reengineering scenarios were suggested and designed according to BPR rules, experts' viewpoints, brainstorming sessions, and the facts found during project implementation. These scenarios were modeled by using BPMN modeling tool, and then their simulation models were designed. Since these scenarios are about the future situation of the process, only some justification and verification steps of simulation models were considered. Finally, on the basis of defined indicators in the beginning of the project, values of them were calculated for each scenario and each one was analyzed on the basis of cost-benefit analysis. At last, these scenarios were ranked by using AHP method and their scores were counted in order to suggest to the management.

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Today, forecasting of economic and commercial variables as an important scientific field is developing, and forecasting of macroeconomic variables is of special importance for planners, policy makers and economic enterprises. The agricultural sector, as a producer of strategic products and provider of food for the growing population, has a great influence on economic, social and political decisions. Considering the importance of the agricultural sector in Iran as well as the existence of different and uncontrollable influential factors, the researchers who focus on agricultural sector’ growth, try to use methods of forecasting in order to get results close to reality, reduce the prediction errors, and design policies and plans to improve the place of this sector. In this paper, the mixed frequency data-sampling model (MIDAS) has been used to predict the growth of agricultural sector’ value added. Comparison of the model predictions with actual data indicates the predictive power of the model. This model has predicted the growth rate of agricultural sector's value added over the period 2017-2021 by 3.215%, 2.53%, 2.92%, 5.29%, and 5.99%, respectively.