Analytical solution of velocity distribution for flow through submerged large deflection flexible vegetation

An analytical solution for predicting the vertical distribution of streamwise mean velocity in an open channel flow with submerged flexible vegetation is proposed when large bending occurs. The flow regime is separated into two horizontal layers： a vegetation layer and a free water layer. In the vegetation layer, a mechanical analysis for the flexible vegetation is conducted, and an approximately linear relationship between the drag force of bending vegetation and the streamwise mean flow velocity is observed in the case of large deflection, which differes significantly from the case of rigid upright vegetation. Based on the theoretical analysis, a linear streamwise drag force-mean flow velocity expression in the momentum equation is derived, and an analytical solution is obtained. For the free water layer, a new expression is presented, replacing the traditional logarithmic velocity distribution, to obtain a zero velocity gradient at the water surface. Finally, the analytical predictions are compared with published experimental data, and the good agreement demonstrates that this model is effective for the open channel flow through the large deflection flexible vegetation.

Using position specific scoring matrix and auto covariance to predict protein subnuclear localization

The knowledge of subnuclear localization in eukaryotic cells is indispensable for under-standing the biological function of nucleus, genome regulation and drug discovery. In this study, a new feature representation was pro-posed by combining position specific scoring matrix (PSSM) and auto covariance (AC). The AC variables describe the neighboring effect between two amino acids, so that they incorpo-rate the sequence-order information;PSSM de-scribes the information of biological evolution of proteins. Based on this new descriptor, a support vector machine (SVM) classifier was built to predict subnuclear localization. To evaluate the power of our predictor, the benchmark dataset that contains 714 proteins localized in nine subnuclear compartments was utilized. The total jackknife cross validation ac-curacy of our method is 76.5%, that is higher than those of the Nuc-PLoc (67.4%), the OET- KNN (55.6%), AAC based SVM (48.9%) and ProtLoc (36.6%). The prediction software used in this article and the details of the SVM parameters are freely available at http://chemlab.scu.edu.cn/ predict_SubNL/index.htm and the dataset used in our study is from Shen and Chou’s work by downloading at http://chou.med.harvard.edu/ bioinf/Nuc-PLoc/Data.htm.

Mean flow and turbulence structure of open channel flow with suspended vegetation

In combination with a channel bed,suspended vegetationin an open channel can alter flow structure and generate vertically asymmetric flow.This study investigated the mean flow and turbulence structure of an open channel with suspended vegetation through theoretical analysis and laboratory experiments.Three patterns of bionic leaves with different roughness were adopted to imitate suspended vegetation,and three-dimensional velocity field was measured by using an acoustic Doppler velocimeter.The measured data showed that the vertical profile of streamwise velocity obeys a two-power law and that the maximum velocity at the middle depth is close to the smooth boundary(i.e.,the channel bed in the experiment)under the combined action of vegetation cover and channel bed.Shear stress is linearly distributed along the vertical axis,and the vertical profile of turbulence intensity obeys an exponential law.Then,a two-power law expression was adopted to predict the vertical profile of streamwise velocity.Theoretical models for the vertical distribution of shear stress and turbulence intensity were also established.The predicted results validated by measurements showed that the different magnitudes of vegetation cover and channel bed boundary roughness exert an obvious impact on flow structure.