This paper considers the synthesis of stabilizing controllers for nonlinear control-affine systems under multiple state constraints. A new control Lyapunov-barrier function approach is introduced for solving the considered problem. Assuming a classical control Lyapunov function, two possible methods for constructing new control Lyapunov-barrier functions are discussed. Sufficient conditions for the existence of new control Lyapunov-barrier functions are derived. With modifying the Sontag's formula, an explicit stateconstrained stabilizing feedback law is presented. Finally, two numerical examples are provided to illustrate the obtained theoretical results.

Purpose This paper presents our team’s results to establish an AIoT smart cage culture management system. Methods According to the built system, the farmed field information is transmitted to the data platform of Ocean Cloud, and all collected data and analysis results can be applied to the cage culture field after the bigdata analysis. Results This management system successfully integrates AI and IoT technologies and is applied in cage culture. Using underwater biological analysis images and AI feeding as examples, this paper explains how the system integrates AI and IoT into a feasible framework that can constantly acquire information about the health status of fish, survival rate of fish, as well as the feed residuals. Conclusion The results of our research enable the aquaculture operators or owners to efficiently reduce the feed residual, monitor the growth of fish, and increase fish survival rate, thereby increasing the feed conversion rate.

This paper addresses the event-triggered stabilization problem for nonlinear control-affine systems under state constraints. First, a strong control Lyapunov barrier function (CLBF) method is applied for constructing continuous state-constrained stabilizing controllers. Additionally, sufficient conditions for the existence of strong CLBFs are derived. With the obtained state-constrained feedback laws, a new eventtriggered policy is proposed for reducing the number of communication events without the input-to-state stability (ISS) assumption. It is proved that the Zeno behavior is excluded under this event-triggered policy, that is, the inter-execution times are lower bounded away from zero. Finally, two examples are included to illustrate the theoretical results. INDEX TERMS Control Lyapunov functions, Control Lyapunov barrier functions, event-triggered, State constraint, State feedback, Nonlinear systems.

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The prediction of protein subcellular localization (PSL) has been an important field of research. Many prediction systems nowadays have been developed that support the need. Most of these systems however focus on the development of new methods to describe a protein, which in turn can increase the prediction performance. In this paper,we propose a novel prediction system, an evolutionary algorithm based support vector machine for the prediction of PSL (ESVM-PSL) which aims to increase the prediction performance by optimizing SVMs. We apply ESVM-PSL to a set of proteins with jackknife validation. The prediction accuracy of SVMs is effectively increased from 48.9% to 67.5%. Our proposed method is also competitive with the previous systems in terms of prediction accuracy.