Comparison of plant species diversity with different plant communities in deciduous forests

Species diversity is one of the most important indices used for evaluating the sustainability of forest communities. This study aims to characterize the forest communities and to identify and compare the plant species diversity in the study area. For this purpose, 152 relev‚s were sampled by a randomized-systematic method, using the Braun-Blanquet scale. Classification of the vegetation was conducted by the twinspan algorithm. Four communities, including Querco-Carpinetum betulii, Carpineto-Fagetum Oriental, Rusco-Fagetum Oriental and Fagetum Oriental were recognized. Species richness, Shannon, and Simpson indices were applied to quantify diversity of the different communities. Turkey test was used to investigate the differences in the species richness, diversity and evenness indices among the different communities. The results illustrate that Querco-Carpinetum betulii and Carpineto-Fagetum Oriental communities are significantly more diverse than Rusco-Fagetum Oriental and Fagetum Oriental communities. The spatial structure of the releves becomes more 'homogenous' and the dominance structure changes: the proportion of beech-forest species is gradually increasing. At the same time, the number of species per unit area decreases constantly, reaching eventually the value comparable to that recorded for hornbeam forest. Generally, species diversity is inversely correlated with the dominance of shade tolerant climax species

Modeling of a permeate flux of cross-flow membrane filtration of colloidal suspensions: a wavelet network approach

Although traditional artificial neural networks have been an attractive topic in modeling membrane filtration, lower efficiency by trial-and-error constructing and random initializing methods often accompanies neural networks. To improve traditional neural networks, the present research used the wavelet network, a special feedforward neural network with a single hidden layer supported by the wavelet theory. Prediction performance and efficiency of the proposed network were examined with a published experimental dataset of cross-flow membrane filtration. The dataset was divided into two parts: 62 samples for training data and 329 samples for testing data. Various combinations of transmembrane pressure, filtration time, ionic strength and zeta potential were used as inputs of the wavelet network so as to predict the permeate flux. Through the orthogonal least square alogorithm, an initial network with 12 hidden neurons was obtained which offered a normalized square root of mean square of 0.103 for the training data. The initial network led to a wavelet network model after training procedures with fast convergence within 30 epochs. Futher the wavelet network model accurately depicted the positive effects of either transmembrane pressure or zeta potential on permeate flux. The wavelet network also offered accurate predictions for the testing data, 96.4% of which deviated the measured data within the +/- 10% relative error range. Moreover, comparisons indicated the wavelet network model produced better predictability than the back-forward backpropagation neural network and the multiple regression models. Thus the wavelet network approach could be employed successfully in modeling dynamic permeate flux in cross-flow membrane filtration