• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1439-1442
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• 2010

Time-lag recurrent neural networks model (Time-Lag RNNM) is used to estimate daily pan evaporation (PE) using limited climatic variables such as max temperature ($T_{max}$), min temperature ($T_{min}$), mean wind speed ($W_{mean}$) and mean relative humidity ($RH_{mean}$). And, for the performances of Time-Lag RNNM, it is composed of training and test performances, respectively. The training and test performances are carried out using daily time series data, respectively. From this research, we evaluate the impact of Time-Lag RNNM for the modeling of the nonlinear time series data. We should, thus, construct the credible data of the daily PE using Time-Lag RNNM, and can suggest the methodology for the irrigation and drainage networks system. Furthermore, this research represents that the strong nonlinear relationship such as pan evaporation modeling can be generalized using Time-Lag RNNM.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1645-1648
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• 2010

The goal of this research is to apply the neural networks model for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks model consists of recurrent neural networks model (RNNM). The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks model, it is composed of training and test performances, respectively. The training and test performances consist of the historic, the generated, and the mixed data, respectively. From this research, we evaluate the impact of RNNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• Asian-Australasian Journal of Animal Sciences
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• v.24 no.9
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• pp.1274-1281
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• 2011

Dietary protein restriction affects lipid metabolism in rats. This study was performed to determine the effect of a low protein diet on hepatic lipid metabolism and insulin sensitivity in growing male rats. Growing rats were fed either a control 20% protein diet or an 8% low protein diet. Feeding a low protein diet for four weeks from 8 weeks of age induced a fatty liver. Expression of acetyl-CoA carboxylase, a key lipogenic enzyme, was increased in rats fed a low protein diet. Feeding a low protein diet decreased very low density lipoprotein (VLDL) secretion without statistical significance. Feeding a low protein diet down-regulated protein expression of microsomal triglyceride transfer protein, an important enzyme of VLDL secretion. Feeding a low protein diet increased serum adiponectin levels. We performed glucose tolerance test (GTT) and insulin tolerance test (ITT). Both GTT and ITT were increased in protein-restricted growing rats. Our results demonstrate that dietary protein restriction increases insulin sensitivity and that this could be due to low-protein diet-mediated metabolic adaptation. In addition, increased adiponectin levels may influences insulin sensitivity. In conclusion, dietary protein restriction induces a fatty liver. Both increased lipogenesis and decreased VLDL secretion has contributed to this metabolic changes. In addition, insulin resistance was not associated with fatty liver induced by protein restriction.