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High stability at acidic environment is required for 1,3-1,4-β-glucanase to function in biofuel, brewing and animal feed industries. In this study, a mesophilic β-glucanase from Bacillus terquilensis CGX 5-1 was rationally engineered through sequence alignment and surface charge engineering to improve its acidic resistance ability. Nineteen singly-site variants were constructed and Q1E, I133L and V134A variants showed better acidic stability without the compromise of catalytic property and thermostability. Furthermore, four multi-site variants were constructed and one double-site variant Q1E/I133L with better stability at acidic environment and higher catalytic property was obtained. The fluorescence spectroscopy and structural analysis showed that more surface negative charge, decreased exposure degree of residue No.1, shifted side chain direction of residue No.133 and the lower total and folding free energy might be the reason for the improvement of acidic stability of Q1E/I133L variant. The obtained Q1E/I133L variant has potential applications in industries. • An effective method was adopted to engineer β-glucanase towards better acidic tolerance. • Q1E/I133L variant had better acidic resistance ability than that of wild-type. • Q1E/I133L variant showed favorable catalytic efficiency. • The structural stability of Q1E/I133L variant was improved after mutation. • More surface negative charge and lower folding free energy were critical. [ABSTRACT FROM AUTHOR]

Abstract: Mixed output-feedback controller with pole placement constraints against the internal uncertainty of moment-of-inertia variation and space environmental disturbances is proposed for mircosatellite attitude control. The multi-objective controller is designed based on linear model of attitude dynamics. The performance takes into account both robustness stability against moments-of-inertia uncertainty and the disturbance rejection aspect. performance takes into account the LQG aspect which avoids the undesirable wheels’ saturation effect. In addition, the closed-loop poles can be forced into some sector of the stable half-plane to obtain well-damped transient responses. The problem is then reduced to a convex optimization involving linear matrix inequalities (LMIs), so it can be efficiently solved. The simulation results demonstrate that the presented mixed control system is robust stable and optimal in the sense of norm, and has good steady-state and dynamic performances against the parameter uncertainties and various disturbances for the microsatellite attitude control system. [Copyright &y& Elsevier]

Abstract: This paper considers a class of online gradient learning methods for backpropagation (BP) neural networks with a single hidden layer. We assume that in each training cycle, each sample in the training set is supplied in a stochastic order to the network exactly once. It is interesting that these stochastic learning methods can be shown to be deterministically convergent. This paper presents some weak and strong convergence results for the learning methods, indicating that the gradient of the error function goes to zero and the weight sequence goes to a fixed point, respectively. The conditions on the activation function and the learning rate to guarantee the convergence are relaxed compared with the existing results. Our convergence results are valid for not only S–S type neural networks (both the output and hidden neurons are Sigmoid functions), but also for P–P, P–S and S–P type neural networks, where S and P represent Sigmoid and polynomial functions, respectively. [ABSTRACT FROM AUTHOR]