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RISS 인기검색어
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- 저자 : Ramin RIAHI (검색결과 2 건)
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Roya Riahi,Seyede Shahrbanoo Daniali,Ramin Heshmat,Mostafa Qorbani,Roya Kelishadi,Mohammad Esmaeil Motlagh 질병관리본부 2019 Osong Public Health and Research Persptectives Vol.10 No.5
Objectives: Misperception of weight status is a risk factor that affects psychological health. The aim of this study was to evaluate the association between weight misperception patterns and psychological distress among Iranian children and adolescents. Methods: This was a cross-sectional nationwide study where data was collected from 14,440 students, aged 7–18 years who participated in the national school-based surveillance program (CASPIAN-V). The students’ weight perception and psychological distress were assessed by validated questionnaires. Weight misperception was classified as misperception of being either underweight or overweight with respect to actual weight. Results: The rate of weight misperception in all study participants was 59.1%. In groups with a perception of being underweight or overweight, the risks of worthlessness, being worried, experiencing aggression, insomnia, or depression, were significantly higher than groups with an accurate weight perception (p < 0.05). The risk of anxiety in girls of normal weight who perceived themselves as underweight, decreased by 57% compared to girls with an accurate weight perception (OR: 0.43; 95% CI, 0.28-0.66). Conclusion: Weight misperception is highly prevalent among Iranian children and adolescents and is associated with their psychological health status. Appropriate education intervention needs to be developed to improve the children and adolescents’ perception of their body weight status.
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Nurul Haqimin MOHD SALLEH,Ramin RIAHI,Zaili YANG,Jin WANG 한국해운물류학회 2017 The Asian journal of shipping and Logistics Vol.33 No.2
One of the biggest concerns in liner operations is punctuality of containerships. Managing the time factor has become a crucial issue in today’s liner shipping operations. A statistic in 2015 showed that the overall punctuality for containerships only reached an on-time performance of 73%. However, vessel punctuality is affected by many factors such as the port and vessel conditions and knock-on effects of delays. As a result, this paper develops a model for analyzing and predicting the arrival punctuality of a liner vessel at ports of call under uncertain environments by using a hybrid decision-making technique, the Fuzzy Rule-Based Bayesian Network (FRBBN). In order to ensure the practicability of the model, two container vessels have been tested by using the proposed model. The results have shown that the differences between prediction values and real arrival times are only 4.2% and 6.6%, which can be considered as reasonable. This model is capable of helping liner shipping operators (LSOs) to predict the arrival punctuality of their vessel at a particular port of call.
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