The parameters estimated from traditional A/Ci curve analysis are dependent upon some underlying assumptions that substomatal CO2 concentration (Ci) equals the chloroplast CO2 concentration (Cc) and the Ci value at which the A/Ci curve switches between Rubisco- and electron transport-limited portions of the curve (Ci-t) is set to a constant. However, the assumptions reduced the accuracy of parameter estimation significantly without taking the influence of Ci-t value and mesophyll conductance (gm) on parameters into account. Based on the analysis of Larix gmelinii's A/Ci curves, it showed the Ci-t value varied significantly, ranging from 24 Pa to 72 Pa and averaging 38 Pa. t-test demonstrated there were significant differences in parameters respectively estimated from A/Ci and A/Cc curve analysis (p<0.01). Compared with the maximum ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) carboxylation rate (Vcmax), the maximum electron transport rate (Jmax) and Jmax/Vcmax estimated from A/Cc curve analysis which considers the effects of gm limit and simultaneously fits parameters with the whole A/Cc curve, mean Vcmax estimated from A/Ci curve analysis (Vcmax-Ci) was underestimated by 37.49%; mean Jmax estimated from A/Ci curve analysis (Jmax-Ci) was overestimated by 17.8% and (Jmax-Ci)/(Vcmax-Ci) was overestimated by 24.2%. However, there was a significant linear relationship between Vcmax estimated from A/Ci curve analysis and Vcmax estimated from A/Cc curve analysis, so was it Jmax (p<0.05). and W. Zeng ... [et al.].
This paper presents a hybrid method to predict tunnel surrounding rock displacement, which is one of the most important factors for quality control and safety during tunnel construction. The hybrid method comprises two phases, one is support vector machine (SVM)-based model for predicting the tunnel surrounding rock displacement, and the other is GA-based model for optimizing the parameters in the SVM. The proposed model is evaluated with the data of tunnel surrounding rock displacement on the tunnel of Wuhan-Guangzhou railway in China. The results show that genetic algorithm (GA) has a good convergence and relative stable performance. The comparison results also show that the hybrid method can generally provide a better performance than artificial neural network (ANN) and finite element method (FEM) for tunnel surrounding rock displacement prediction.
Although plant performance under elevated CO2 (EC) and drought has been extensively studied, little is known about the leaf traits and photosynthetic performance of Stipa bungeana under EC and a water deficiency gradient. In order to investigate the effects of EC, watering, and their combination, S. bungeana seedlings were exposed to two CO2 regimes (ambient, CA: 390 ppm; elevated, EC: 550 ppm) and five levels of watering (-30%, -15%, control, +15%, +30%) from 1 June to 31 August in 2011, where the control water level was 240 mm. Gas exchange and leaf traits were measured after 90-d treatments. Gas-exchange characteristics, measured at the growth CA, indicated that EC significantly decreased the net photosynthetic rate (PN), water-use efficiency, nitrogen concentration based on mass, chlorophyll and malondialdehyde (MDA) content, while increased stomatal conductance (gs), intercellular CO2 concentration (Ci), dark respiration, photorespiration, carbon concentration based on mass, C/N ratio, and leaf water potential. Compared to the effect of EC, watering showed an opposite trend only in case of PN. The combination of both factors showed little influence on these physiological indicators, except for gs, Ci, and MDA content. Photosynthetic acclimation to EC was attributed to the N limitation, C sink/source imbalance, and the decline of photosynthetic activity. The watering regulated photosynthesis through both stomatal and nonstomatal mechanisms. Our study also revealed that the effects of EC on photosynthesis were larger than those on respiration and did not compensate for the adverse effects of drought, suggesting that a future warm and dry climate might be unfavorable to S. bungeana. However, the depression of the growth of S. bungeana caused by EC was time-dependent at a smaller temporal scale., H. Wang, G. S. Zhou, Y. L. Jiang, Y. H. Shi, Z. Z. Xu., and Obsahuje bibliografii