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RISS 인기검색어
- 검색키워드
- 저자 : Alaa M. A. Abdeen (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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Alaa El-Din Abdin,Mostafa A. M. Abdeen 대한기계학회 2005 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.19 No.8
Groundwater flow and behavior have to be investigated based on heterogeneous subsurface formation since the homogeneity assumption of this formation is not valid. Over the past twenty years, stochastic approach and Monte Carlo technique have been utilized very efficiently to understand the groundwater flow behavior. However, these technique require lots of computational and numerical efforts according to the various researchers' comments. Therefore, utilizing new techniques with much less computational efforts such as Artificial Neural Network (ANN) in the prediction of the stochastic behavior for the groundwater based on heterogeneous subsurface formation is highly appreciated. The current paper introduces the ANN technique to investigate and predict the stochastic behavior of a well draw down in a confined aquifer based on subsurface heterogeneous hydraulic conductivity. Several ANN models are developed in this research to prediet the unsteady two dimensional well draw down and its stochastic characteristics in a confined aquifer. The results of this study showed that ANN method with less computational efforts was very efficiently capable of simulating and predicting the stochastic behavior of the well draw down resulted from the continuous constant pumping in the middle of a confined aquifer with subsurface heterogeneous hydraulic conductivity.
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Abdin Alaa El-Din,Abdeen Mostafa A. M. The Korean Society of Mechanical Engineers 2005 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.19 No.8
Groundwater flow and behavior have to be investigated based on heterogeneous subsurface formation since the homogeneity assumption of this formation is not valid. Over the past twenty years, stochastic approach and Monte Carlo technique have been utilized very efficiently to understand the groundwater flow behavior. However, these techniques require lots of computational and numerical efforts according to the various researchers' comments. Therefore, utilizing new techniques with much less computational efforts such as Artificial Neural Network (ANN) in the prediction of the stochastic behavior for the groundwater based on heterogeneous subsurface formation is highly appreciated. The current paper introduces the ANN technique to investigate and predict the stochastic behavior of a well draw down in a confined aquifer based on subsurface heterogeneous hydraulic conductivity. Several ANN models are developed in this research to predict the unsteady two dimensional well draw down and its stochastic characteristics in a confined aquifer. The results of this study showed that ANN method with less computational efforts was very efficiently capable of simulating and predicting the stochastic behavior of the well draw down resulted from the continuous constant pumping in the middle of a confined aquifer with subsurface heterogeneous hydraulic conductivity.
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Shikimic acid recovers diarrhea and its complications in SD rats fed lactose diet to induce diarrhea
Khaled M. M. Koriem,Alaa M. A. Abdeen 한국실험동물학회 2023 Laboratory Animal Research Vol.39 No.4
Background: Diarrhea is the increase of excretion of human water content and an imbalance in the physiologic processes of the small and large intestine while shikimic acid is an important biochemical metabolite in plants. This study aims to study the anti-diarrheal activity of shikimic acid through restoring kidney function, antioxidant activity, inflammatory markers, sodium/potassium-ATPase activity, apoptosis genes, and histology of the kidney in SD rats fed lactose diet to induce diarrhea. Results: Thirty-six male SD rats (150 ± 10 g, 12 weeks old) were divided into 2 equal groups (18 rats/group) as follows: normal and diarrheal rats. Normal rats were divided into 3 equal groups of 6 rats each: the control, shikimic acid, and desmopressin drug groups. Diarrheal rats were also divided into 3 equal groups of 6 rats each: diarrheal, diarrheal rats + shikimic acid, and diarrheal rats + desmopressin drug groups. Shikimic acid restored serum urea and creatinine, urinary volume, kidney weight, sodium, potassium, and chloride balance in serum and urine. The acid returned the antioxidant (superoxide dismutase, glutathione peroxidase, catalase, malondialdehyde, NADPH oxidase activity, conjugated dienes, and oxidative index) activity and the inflammatory markers (tumor necrosis factor-α, interleukin-1β, interleukin-6, and interleukin-10) to values approaching the control values. Shikimic acid also restored the sodium/potassium-ATPase activity, the apoptosis genes p53 and bcl-2, and the histology of kidney tissue in diarrheal rats to be near the control group. Conclusions: Shikimic acid rescues diarrhea and its complications through restoring kidney function, serum and urinary electrolytes, antioxidant activity, inflammatory markers, sodium/potassium-ATPase activity, the apoptosis genes, and the histology of the kidney in diarrheal rats to approach the control one.
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