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The Effect of the Family’s Socioeconomic Factors on Nutrition of Elementary School Children

Health 2014年第19期6卷 2657-2667页

作者： Fereshteh Farzianpour Ali Reihani Seyyed Mostafa hosseini Halea Saderzadeh Yaganeh seyead shahab hosseini Department of Health Management and Economics School of Public Health Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Statistics School of Public Health Tehran University of Medical Sciences Tehran Iran Department of Nutrition School of Public Health Tehran University of Medical Sciences Tehran Iran Tehran University Tehran Iran

Introduction: Anthropometric indices are used for assessing the nutrition status of people and societies. The indices determine the growth of the child’s nutrition status, his socioeconomic status and his quality of life. This research aims to study the anthropometric indices of elementary school children in the Iranian city of Piranshahr using the Body Mass Index (BMI) and waist circumference in the first six months of 2011. Methods: In this descriptive-analytical cross-sectional study, 1803 students aged between 7 and 11 in Piranshahr were studied. Multi-stage cluster sampling was used. The research setting was an elementary school in Piranshahr. Demographic data were collected through interviews and record in questionnaires. A Secca stadiometer was used to measure the height of each student standing without shoes (accuracy of 0.1 centimeter). The weight was measured using a spring scale with an accuracy of 0.1 kilogram with the least possible clothes on. The weight was divided by the square of height (square meter) to calculate BMI. To determine overweight and obesity, BMI percentiles of Center for Disease Control (CDC) were used. In order to use appropriate tests, the normality and equality of variances were measured by Leven and K-S tests, respectively. Results: The study found that 231 children (12.8%) were at risk of overweight and 96 children (5.3%) were overweight. Conclusion: There was a meaningful difference between boys and girls in terms of nutrition status of BMI, father’s higher education level, shortness, abdominal obesity and family history of obesity, and father’s jobs

关键词： Anthropometric Socioeconomic Factors Nutrition Status Elementary School Children Piranshahr Iran

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Machine learning based prediction of flyrock distance in rock blasting:A safe and sustainable mining approach

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Green and Smart Mining Engineering 2024年第3期1卷 346-361页

作者： Blessing Olamide Taiwo Yewuhalashet Fissha shahab hosseini Mohammad Khishe Esma Kahraman Babatunde Adebayo Mohammed Sazid Patrick Adeniyi Adesida Oluwaseun Victor Famobuwa Joshua Oluwaseyi Faluyi Adams Abiodun Akinlabi Department of Mining Engineering Federal University of Technology AkureAkure 340001Nigeria HNF Global Resources Limited Akoko EdoIgarra 312101Nigeria Department of Geosciences Geotechnology and Materials Engineering for ResourcesGraduate School of International Resource SciencesAkita UniversityAkita 010-8502Japan Department of Mining Engineering Aksum UniversityAksum 7080TigrayEthiopia Faculty of Engineering Tarbiat Modares UniversityTehran 14115-111Iran Department of Electrical Engineering Imam Khomeini Naval Science University of NowshahrNowshahr 46517Iran Applied Science Research Center Applied Science Private UniversityAmman 11937Jordan Department of Mining Engineering Cukurova UniversityAdana 01250Turkey Department of Mining Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Mining West Virginia UniversityMorgantown 26506USA

Flyrock is a significant environmental and safety concern in mining and *** arises from various geological and blast design factors,posing risks to workers,machinery,and nearby *** study examined how these factors affect the rate and distance of flyrock projections caused by *** address this issue,advanced machine learning(ML)models were used to predict flyrock distances in the Akoko Edo dolomite *** models examined included bidirectional recurrent neural networks(BRNNs),support vector regression(SVR)with different kernels(SVR-S,SVR-RBF,SVR-L,SVR-P),long short-term memory(LSTM)networks,and random forest(RF)algorithms.A case study was conducted using 258 blasting data samples to develop these *** factors influencing flyrock were identified:blast hole burden distance,maximum instantaneous charge,and rock brittleness *** these factors,a flyrock possibility assessment chart was created to enhance the safety of small-scale mining *** model’s prediction accuracy was evaluated using correlation coefficients and four performance *** LSTM model stood out,achieving the highest coefficient of correlation(R2=0.99)for both training and testing *** indicates that the LSTM model accurately predicts blast-induced flyrock *** study also revealed that the Gaussian-RBF kernel SVR has high prediction accuracy when compared to other SVR variants(SVR-S,SVR-L,and SVR-P).In conclusion,the study compared various ML models for flyrock reduction and found that the LSTM model was the most effective in estimating blast-induced flyrock distances.

关键词： Blasting Flyrock Safety chart Mine production reliability Long short-term memory networks

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