Bacterial Communities in the Sediments of Dianchi Lake, a Partitioned Eutrophic Waterbody in China

Bacteria play an important role in the decomposition and cycling of a variety of compounds in freshwater aquatic environments, particularly nutrient-rich eutrophic lakes. A unique Chinese eutrophic lake - Dianchi - was selected for study because it has two separate and distinct basins, Caohai with higher organic carbon levels and Waihai with lower organic carbon levels. Sediment bacterial communities were studied in the two basins using samples collected in each season from June 2010 to March 2011. Barcoded pyrosequencing based on the 16 S rRNA gene found that certain common phyla, Proteobacteria, Bacteroidetes, Firmicutes and Chloroflexi, were dominant in the sediments from both basins. However, from the class to genus level, the dominant bacterial groups found in the sediments were distinct between the two basins. Correlation analysis revealed that, among the environmental parameters examined, total organic carbon (TOC) accounted for the greatest proportion of variability in bacterial community. Interestingly, study results suggest that increasing allochthonous organic carbon could enhance bacterial diversity and biomass in the sediment. In addition, analysis of function genes (amoA and nosZ) demonstrated that ammonia-oxidizing bacteria (AOB) were dominant in sediments, with 99% belonging to Nitrosomonas. Denitrifying bacteria were comparatively diverse and were associated with some cultivatable bacteria

An Equivalent Model of Wind Farm Based on Multivariate Multi-Scale Entropy and Multi-View Clustering

Wind farm (WF) equivalence is an effective method to achieve accurate and efficient simulation of large-scale WF. Existing equivalent models are generally suitable for one certain or very few scenarios, and have difficulty reflecting the multiple aspects of dynamic processes of WF. Aiming at these problems, this paper proposes an equivalent model of WF based on multivariate multi-scale entropy (MMSE) and multi-view clustering. Firstly, the influence of the factors on the dynamic process of the wind turbine (WT) is discussed, including control mode, wind speed and its wake effect, resistance of crowbar resistor and so on. The relationship between these factors and the dynamic equivalence of WF is analyzed. Secondly, an overview of MMSE is given, and the applicability of MMSE on WF equivalence is analyzed. On this basis, this paper proposes the extraction process of a WT clustering indicator using MMSE. Then, the multi-view fuzzy C means (MV-FCM) algorithm is used for the clustering of WTs, and the equivalent model of WF is obtained after calculating the equivalent parameters. Finally, the IEEE14 power system including WF is simulated. The results show that the equivalent model could be applied to dynamic process simulation in various fault scenarios of power systems, and the error is small when the cluster number is 4. Compared with the detailed model, the simulation time of the WF equivalent model proposed in this paper is shortened by 86%, and the simulation accuracy is improved by about 44% compared with the comparative model

Effects of pH value and substrate concentration on hydrogen production from the anaerobic fermentation of glucose

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Simultaneous production of hydrogen and multi-walled carbon nanotubes by ethanol decomposition over Ni/Al2O3 catalysts

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Relative abundances of dominant phylogenetic groups in sediments derived from Caohai and Waihai sampling sites.

<p>Phylogenetic groups accounting for <1% of all classified sequences are summarized as “other” in the figure.</p

Community analysis using principal coordinate analysis (PCoA) of weighted UniFrac distance matrix.

<p>Community analysis using principal coordinate analysis (PCoA) of weighted UniFrac distance matrix.</p

Principal components analysis (PCA) and Redundancy analysis (RDA) with of bacterial communities as affected by sediment properties, based on the relative abundance of dominant bacterial phyla and proteobacterial classes.

<p>Total-N represents NH₃-N+NO₂⁻-N+NO₃⁻-N. Abbreviations in figure: Temp, temperature; <i>Actinobac.</i>, <i>Actinobacteria</i>; <i>Alphapro.</i>, <i>Alphaproteobacteria</i>; <i>Betapro.</i>, <i>Betaproteobacteria</i>; <i>Deltapro.</i>, <i>Deltaproteobacteria</i>; <i>Gammapro</i>.,<i>Gammaproteobacteria</i>; <i>Acidobac.</i>, <i>Acidobacteria</i>; Unclassified bac., Unclassified bacteria; Unclassified <i>Pro.</i>, Unclassified <i>Proteobacteri</i>a. <i>Epsilonpro</i>., <i>Epsilonproteobacteri</i>a.</p