Background:Healthy nutrition is very important considering the weight status especially in children. The aim of this study was to assess the relationship between junk foods intake and weight in 6-7-years old children.Materials and Methods:This cross-sectional study was carried out in Shahin Shahr and Meymeh, Iran, in 2009. Anthropometrics measures were done and 24-hour food recall used for dietary information and analyzed with food processor 2 and then compared with dietary reference intakes 2008 (DRI).Findings:61.1 percent of the subjects were residing in dormitories and 12.7 percent were marred. Prevalence of overweight or obesity and abdominal obesity was 6.9 percent and 46.1 percent respectively. Mean (±SD) systolic blood pressure was 105.2 ± 15.6 mm/Hg and diastolic was 62.2 ± 10.4 mm/Hg. Totally, 3.9 percent of the subjects had hypertension. The analysis of food intake indicate that (B12, folate, magnesium, potassium, calcium) with level below the recommended ones, and (vitamin C, E, pantothenic acid, B1, B3, phosphate, zinc) with up levels the recommended ones, and energy intake, macronutrient, vitamin A, pyridoxine, iron, selenium were in general appropriate.Conclusion:These results indicated appropriate level of macronutrients intake and unbalance mainly existed in micronutrients. It is recommended to increase intake important food groups such as dairy, vegetable, fruit that include good source of micronutrients, and also it is suggested that need for strategies can improve competence in the area of nutrition.
Background: Pigging operation is one of the maintenance activities that is used to check pipeline functionality in operational conditions using a PIG device and high pressure of liquid/gas, which is potentially hazardous. Objectives: The present study aimed at customizing SPAR-H methodology for the pigging operation using Bayesian networks (BNs). It also aimed at identifying and analyzing human errors in pigging operation in a gas transmission company. Methods: The current article was composed of two main steps. In the first step, the SPAR-H BN model was developed using expertelicited prior probabilities of pigging operation applied to Bayesian network. In this step, CPTs of PSF nodes are constructed using prior probabilities, which are achieved from expert opinion. The CPT of error node is developed using coding process of SPAR-H formula in a simulation node. In the second step, a descriptive study was carried out to estimate the probability of human errors in pigging operation in a gas transmission plant in Iran. First, hierarchical task analysis (HTA) was conducted by walking through the pigging operation and interviews with workers. Next, the SPAR-H BN model was utilized for estimation of human error probability. Results: The developed model was tested on the pigging operation subtasks. In the considered case study, the mean probability of human error was estimated as 0.184. The highest probability of human error was related to "opening the kicker valve for enhancing pressure" subtask. Conclusions: The BNs were helpful to adapt the SPAR-H methodology to the pigging operation using dedicated prior probabilities. Beside that, the probabilities of human error can be updated taking into account the more realistic operational and environmental conditions.
Investigations of technological systems accidents reveal that technical, human, organizational, as well as environmental factors influence the occurrence of accidents. Despite these facts, most traditional risk assessment techniques focus on technical aspects of systems and have some limitations of incorporating efficient links between risk models and human and organizational factors. This paper presents a method for risk analysis of technological systems. Application of the presented framework makes it possible to analyze the influence of technical, human, organizational, and environmental risk factors on system safety. It encompasses system lifecycle from design to operational phase to give a comprehensive picture of system risks. The developed framework comprises the following main steps: (1) development of a conceptual risk analysis framework, (2) identifying risk influencing factors in different levels of technical, human, organizational, and environmental factors providing the possibility of analyzing interactions in a multi‐level system, (3) modeling system risk using dynamic Bayesian network (DBN), (4) assignment of probabilities and risk quantification in node probability tables (NPTs) based on industry records and experts extracted knowledge, (5) implementation of the model for wind turbines risk analysis combining use of V‐model, risk factors, and DBN in order to analyze the risk, and (6) analyzing different scenarios and the interactions in different levels. Finally, the various steps of the framework, the research objective fulfillment, and case study results are presented and discussed.
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