The antimicrobial susceptibility of MRSA in this area exhibited multidrug resistance, and clonal complexes CC398 and CC188, which have been reported among human MRSA isolates, do occur in Xinjiang dairy cows. This study provides a foundation for further MRSA monitoring.

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An approach for parameter estimation of proportional-integral-derivative (PID) control system using a new nonlinear programming (NLP) algorithm was proposed. SQP/IIPM algorithm is a sequential quadratic programming (SQP) based algorithm that derives its search directions by solving quadratic programming (QP) subproblems via an infeasible interior point method (IIPM) and evaluates step length adaptively via a simple line search and/or a quadratic search algorithm depending on the termination of the IIPM solver. The task of tuning PI/PID parameters for the first-and second-order systems was modeled as constrained NLP problem. SQP/IIPM algorithm was applied to determining the optimum parameters for the PI/PID control systems. To assess the performance of the proposed method, a Matlab simulation of PID controller tuning was conducted to compare the proposed SQP/IIPM algorithm with the gain and phase margin (GPM) method and Ziegler-Nichols (ZN) method. The results reveal that, for both step and impulse response tests, the PI/PID controller using SQP/IIPM optimization algorithm consistently reduce rise time, settling-time and remarkably lower overshoot compared to GPM and ZN methods, and the proposed method improves the robustness and effectiveness of numerical optimization of PID control systems.

Neural networks are generally exposed to a dynamic environment where the training patterns or the input attributes (features) will likely be introduced into the current domain incrementally. This paper considers the situation where a new set of input attributes must be considered and added into the existing neural network. The conventional method is to discard the existing network and redesign one from scratch. This approach wastes the old knowledge and the previous effort. In order to reduce computational time, improve generalization accuracy, and enhance intelligence of the learned models, we present ILIA

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