To study the effect of phosphorus （P） deficiency on leaf photosynthesis and carbohydrates partitioning and to determine whether the characteristics of leaf photosynthesis and carbohydrates partitioning are related t...To study the effect of phosphorus （P） deficiency on leaf photosynthesis and carbohydrates partitioning and to determine whether the characteristics of leaf photosynthesis and carbohydrates partitioning are related to low P tolerance in rice plants, a hydroponic culture experiment supplied with either sufficient P （10 mg/L） or deficient P （0.5 mg/L） was conducted by using two rice genotypes different in their responses to low P stress. Results showed that the plant growth of Zhenongda 454 （low P tolerant genotype） was less affected by P deficiency compared with Sanyang＇ai （low P sensitive genotype）. Under P-deficient conditions, photosynthetic rates of Zhenongda 454 and Sanyang＇ai were decreased by 16% and 35%, respectively, and Zhenongda 454 showed higher photosynthetic rate than Sanyang＇ai. Phosphorus deficiency decreased the stomatal conductance for both genotypes, but had no significant influence on leaf internal CO2 concentration （Ci）, suggesting that the decrease in leaf photosynthetic rate of rice plants induced by P deficiency was not due to stomatal limitation. Phosphorus deficiency increased the concentration of soluble carbohydrates and sucrose in shoots and roots for both genotypes, and also markedly increased the allocation of soluble carbohydrates and sucrose to roots. Under deficient P supply, Zhenongda 454 had higher root/shoot soluble carbohydrates content ratio and root/shoot sucrose content ratio than Sanyang＇ai. In addition, phosphorus deficiency increased the concentration of starch in roots for both genotypes, whereas had no effect on the content of starch in shoots or roots. Compared to genotype Sanyang＇ai, the better tolerance to low-P stress of Zhenongda 454 can be explained by the fact that Zhenongda 454 maintains a higher photosynthetic rate and a greater ability to allocate carbohydrates to the roots under P deficiency.展开更多
Objective During present investigation the data of a laboratory-scale anoxic sulfide oxidizing （ASO） reactor were used in a neural network system to predict its performance. Methods Five uncorrelated components of t...Objective During present investigation the data of a laboratory-scale anoxic sulfide oxidizing （ASO） reactor were used in a neural network system to predict its performance. Methods Five uncorrelated components of the influent wastewater were used as the artificial neural network model input to predict the output of the effluent using back-propagation and general regression algorithms. The best prediction performance is achieved when the data are preprocessed using principal components analysis （PCA） before they are fed to a back propagated neural network. Results Within the range of experimental conditions tested, it was concluded that the ANN model gave predictable results for nitrite removal from wastewater through ASO process. The model did not predict the formation of sulfate to an acceptable manner. Conclusion Apart from experimentation, ANN model can help to simulate the results of such experiments in finding the best optimal choice for ASO based denitrification. Together with wastewater collection and the use of improved treatment systems and new technologies, better control of wastewater treatment plant （WTP） can lead to more effective maneuvers by its operators and, as a consequence, better effluent quality.展开更多
文摘To study the effect of phosphorus （P） deficiency on leaf photosynthesis and carbohydrates partitioning and to determine whether the characteristics of leaf photosynthesis and carbohydrates partitioning are related to low P tolerance in rice plants, a hydroponic culture experiment supplied with either sufficient P （10 mg/L） or deficient P （0.5 mg/L） was conducted by using two rice genotypes different in their responses to low P stress. Results showed that the plant growth of Zhenongda 454 （low P tolerant genotype） was less affected by P deficiency compared with Sanyang＇ai （low P sensitive genotype）. Under P-deficient conditions, photosynthetic rates of Zhenongda 454 and Sanyang＇ai were decreased by 16% and 35%, respectively, and Zhenongda 454 showed higher photosynthetic rate than Sanyang＇ai. Phosphorus deficiency decreased the stomatal conductance for both genotypes, but had no significant influence on leaf internal CO2 concentration （Ci）, suggesting that the decrease in leaf photosynthetic rate of rice plants induced by P deficiency was not due to stomatal limitation. Phosphorus deficiency increased the concentration of soluble carbohydrates and sucrose in shoots and roots for both genotypes, and also markedly increased the allocation of soluble carbohydrates and sucrose to roots. Under deficient P supply, Zhenongda 454 had higher root/shoot soluble carbohydrates content ratio and root/shoot sucrose content ratio than Sanyang＇ai. In addition, phosphorus deficiency increased the concentration of starch in roots for both genotypes, whereas had no effect on the content of starch in shoots or roots. Compared to genotype Sanyang＇ai, the better tolerance to low-P stress of Zhenongda 454 can be explained by the fact that Zhenongda 454 maintains a higher photosynthetic rate and a greater ability to allocate carbohydrates to the roots under P deficiency.
文摘Objective During present investigation the data of a laboratory-scale anoxic sulfide oxidizing （ASO） reactor were used in a neural network system to predict its performance. Methods Five uncorrelated components of the influent wastewater were used as the artificial neural network model input to predict the output of the effluent using back-propagation and general regression algorithms. The best prediction performance is achieved when the data are preprocessed using principal components analysis （PCA） before they are fed to a back propagated neural network. Results Within the range of experimental conditions tested, it was concluded that the ANN model gave predictable results for nitrite removal from wastewater through ASO process. The model did not predict the formation of sulfate to an acceptable manner. Conclusion Apart from experimentation, ANN model can help to simulate the results of such experiments in finding the best optimal choice for ASO based denitrification. Together with wastewater collection and the use of improved treatment systems and new technologies, better control of wastewater treatment plant （WTP） can lead to more effective maneuvers by its operators and, as a consequence, better effluent quality.
