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- 저자 : Mahmoud Miri (검색결과 3 건)
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Mahmoud Hashemzaei,Sahar Fanoudi,Mohadeseh Najari,Mansoureh Fotouhi,Maryam Belaran,Najmeh Sadat Alipour,Zahra Dadrezaei,Fatemeh Miri,Kaveh Tabrizian 한국식품영양과학회 2021 Preventive Nutrition and Food Science Vol.26 No.1
Quercetin and resveratrol are found in a variety of fruits and vegetables and have several biological and pharmacological properties. In this study, the effects of quercetin [50 mg/kg, intraperitoneal (i.p.)] and resveratrol (50 mg/kg, i.p.) on zinc chloride (ZnCl₂; 75 mg/kg/d, 2 weeks oral gavage) and sodium metavanadate (SMV; 22.5 mg/kg/d, 2 weeks oral gavage) induced passive avoidance memory retention were investigated in step-through passive avoidance tasks. ZnCl₂ was dissolved in saline and SMV was dissolved in drinking water. Mice received ZnCl₂ or SMV orally for two weeks and were administered quercetin or resveratrol by i.p. injection on day 14, days 12 and 14, or days 10, 12, and 14. At the end of treatment, animals were trained for one day in a step-through passive avoidance task, then alterations in avoidance memory retention were evaluated after 24, 48, 96, and 168 h. Oral consumption of ZnCl₂ and SMV decreased latency time compared with control groups. Both quercetin and resveratrol (50 mg/kg, i.p.) prevented ZnCl₂- and SMV-induced avoidance memory retention impairments and did not significantly alter muscle strength, as demonstrated in rotarod tasks. No significant differences were observed between mice who received single, double, or triple doses of quercetin or resveratrol. The results suggest that quercetin and resveratrol may have preventive effects on ZnCl₂- and SMV-induced memory impairment in male mice.
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Structural reliability assessment using an enhanced adaptive Kriging method
Jafar Vahedi,Mohammad Reza Ghasemi,Mahmoud Miri 국제구조공학회 2018 Structural Engineering and Mechanics, An Int'l Jou Vol.66 No.6
Reliability assessment of complex structures using simulation methods is time-consuming. Thus, surrogate models are usually employed to reduce computational cost. AK-MCS is a surrogate-based Active learning method combining Kriging and Monte-Carlo Simulation for structural reliability analysis. This paper proposes three modifications of the AK-MCS method to reduce the number of calls to the performance function. The first modification is related to the definition of an initial Design of Experiments (DoE). In the original AK-MCS method, an initial DoE is created by a random selection of samples among the Monte Carlo population. Therefore, samples in the failure region have fewer chances to be selected, because a small number of samples are usually located in the failure region compared to the safe region. The proposed method in this paper is based on a uniform selection of samples in the predefined domain, so more samples may be selected from the failure region. Another important parameter in the AK-MCS method is the size of the initial DoE. The algorithm may not predict the exact limit state surface with an insufficient number of initial samples. Thus, the second modification of the AK-MCS method is proposed to overcome this problem. The third modification is relevant to the type of regression trend in the AK-MCS method. The original AK-MCS method uses an ordinary Kriging model, so the regression part of Kriging model is an unknown constant value. In this paper, the effect of regression trend in the AK-MCS method is investigated for a benchmark problem, and it is shown that the appropriate choice of regression type could reduce the number of calls to the performance function. A stepwise approach is also presented to select a suitable trend of the Kriging model. The numerical results show the effectiveness of the proposed modifications.
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Structural reliability assessment using an enhanced adaptive Kriging method
Vahedi, Jafar,Ghasemi, Mohammad Reza,Miri, Mahmoud Techno-Press 2018 Structural Engineering and Mechanics, An Int'l Jou Vol.66 No.6
Reliability assessment of complex structures using simulation methods is time-consuming. Thus, surrogate models are usually employed to reduce computational cost. AK-MCS is a surrogate-based Active learning method combining Kriging and Monte-Carlo Simulation for structural reliability analysis. This paper proposes three modifications of the AK-MCS method to reduce the number of calls to the performance function. The first modification is related to the definition of an initial Design of Experiments (DoE). In the original AK-MCS method, an initial DoE is created by a random selection of samples among the Monte Carlo population. Therefore, samples in the failure region have fewer chances to be selected, because a small number of samples are usually located in the failure region compared to the safe region. The proposed method in this paper is based on a uniform selection of samples in the predefined domain, so more samples may be selected from the failure region. Another important parameter in the AK-MCS method is the size of the initial DoE. The algorithm may not predict the exact limit state surface with an insufficient number of initial samples. Thus, the second modification of the AK-MCS method is proposed to overcome this problem. The third modification is relevant to the type of regression trend in the AK-MCS method. The original AK-MCS method uses an ordinary Kriging model, so the regression part of Kriging model is an unknown constant value. In this paper, the effect of regression trend in the AK-MCS method is investigated for a benchmark problem, and it is shown that the appropriate choice of regression type could reduce the number of calls to the performance function. A stepwise approach is also presented to select a suitable trend of the Kriging model. The numerical results show the effectiveness of the proposed modifications.
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