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In Bacillus subtilis colonies, motile bacteria move collectively, spontaneously forming dynamic clusters. These bacterial clusters share similarities with other systems exhibiting polarized collective motion, such as bird flocks or fish schools. Here we study experimentally how velocity and orientation fluctuations within clusters are spatially correlated. For a range of cell density and cluster size, the correlation length is shown to be 30% of the spatial size of clusters, and the correlation functions collapse onto a master curve after rescaling the separation with correlation length. Our results demonstrate that correlations of velocity and orientation fluctuations are scale invariant in dynamic bacterial clusters.

Genetic algorithms (GA) have been widely applied to solve water resources system optimization. With the increase of the complexity and the larger problem scale of water resources system, GAs are most frequently faced with the problems of premature convergence, slow iterations to reach the global optimal solution and getting stuck at a local optimum. A novel chaos genetic algorithm (CGA) based on the chaos optimization algorithm (COA) and genetic algorithm (GA), which makes use of the ergodicity and internal randomness of chaos iterations, is presented to overcome premature local optimum and increase the convergence speed of genetic algorithm. CGA integrates powerful global searching capability of the GA with that of powerful local searching capability of the COA. Two measures are adopted in order to improve the performance of the GA. The first one is the adoption of chaos optimization of the initialization to improve species quality and to maintain the population diversity. The second is the utilization of annealing chaotic mutation operation to replace standard mutation operator in order to avoid the search being trapped in local optimum. The Rosenbrock function and Schaffer function, which are complex and global optimum functions and often used as benchmarks for contemporary optimization algorithms for GAs and Evolutionary computation, are first employed to examine the performance of the GA and CGA. The test results indicate that CGA can improve convergence speed and solution accuracy. Furthermore, the developed model is applied for the monthly operation of a hydropower reservoir with a series of monthly inflow of 38 years. The results show that the long term average annual energy based CGA is the best and its convergent speed not only is faster than dynamic programming largely, but also overpasses the standard GA. Thus, the proposed approach is feasible and effective in optimal operations of complex reservoir systems.

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Rainfall-runoff simulation and prediction in watersheds is one of the most important tasks in water resources management. In this research, an adaptive data analysis methodology, ensemble empirical mode decomposition (EEMD), is presented for decomposing annual rainfall series in a rainfall-runoff model based on a support vector machine (SVM). In addition, the particle swarm optimization (PSO) is used to determine free parameters of SVM. The study data from a large size catchment of the Yellow River in China are used to illustrate the performance of the proposed model. In order to measure the forecasting capability of the model, an ordinary least-squares (OLS) regression and a typical three-layer feed-forward artificial neural network (ANN) are employed as the benchmark model. The performance of the models was tested using the root mean squared error (RMSE), the average absolute relative error (AARE), the coefficient of correlation (R) and NashSutcliffe efficiency (NSE). The PSO-SVM-EEMD model improved ANN model forecasting (65.99%) and OLS regression (64.40%), and reduced RMSE (67.7%) and AARE (65.38%) values. Improvements of the forecasting results regarding the R and NSE are 8.43%, 18.89% and 182.7%, 164.2%, respectively.Consequently, the presented methodology in this research can enhance significantly rainfall-runoff forecasting at the studied station.

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Threonine 37 The position of Thr-37 and its interaction with f lavin allow for speculations about its role in enzyme activity. This residue was mutated to alanine and the mutant enzyme was studied and compared with the wild-type OYE1 to evaluate its mechanistic function. The mutation has different effects on the two separate half-reactions of the enzyme. The mutant enzyme has enhanced activity in the oxidative half-reaction but the reductive half-reaction is slowed down by more than one order of magnitude. The peaks of the absorption spectra for enzyme bound with phenolic compounds are shifted toward shorter wavelengths than those of wild-type OYE1, consistent with its lower redox potential. It is suggested that Thr-37 in the wild-type OYE1 increases the redox potential of the enzyme by stabilizing the negative charge of the reduced f lavin through hydrogen bonding with it.

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