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In this paper, the secure consensus control issue is investigated for a class of discrete time-varying stochastic multi-agent systems (MASs) subject to cyber-attacks. In order to give a comprehensive characterization of malicious threats against communication networks, a generic model is presented to take into account both random false data injection attacks (FDIAs) and replay attacks. The main objective of the problem under study is to design a control protocol via output feedback such that, despite the existence of mixed attacks, all the individual agents can be driven to reside within a desired ellipsoidal region in a pre-specified probability. Sufficient conditions are provided for the existence of the requested controller and the feedback gains are formulated in terms of the solution to certain matrix inequalities. Within the established framework, two optimization problems are considered with the aim to ensure the sub-optimal consensus performances from different perspectives. Finally, a simulation example is employed to illustrate the validity of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Perpetually improving mortality prediction in intensive care units (ICUs) via the implementation of eHealth evaluation approaches has become a major research hotspot in the field of medical data mining for the purpose of saving lives. Recently, researchers have attempted to achieve improved prediction accuracy by using only deep learning-based techniques. However, some problems remain. (1) Most of the existing methods utilize independent clinical features to predict mortality by eliminating the correlations between the latent features, but this technique may fail to comprehensively capture and evaluate the statuses of patients. (2) Several clinical features are needed to ensure strong prediction accuracy, but most methods only use static features that are not extendable. (3) An effective practical framework that unifies traditional ICU scoring systems and state-of-the-art deep learning methods to predict mortality is also lacking. (4) Moreover, the interpretability of existing deep learning-based methods needs to be further improved. Therefore, we propose a novel d ual-core m utual l earning f ramework (DMLF) between ICU scoring systems and clinical features for mortality prediction. In particular, we mutually utilize s equential o rgan f ailure a ssessment (SOFA) scores and clinical measurement features to learn a unified model for enhancing the accuracy and interpretability of our DMLF. Experiments conducted on five real-world disease datasets show that the DMLF achieves significantly better prediction accuracy and area under the receiver operating characteristic curve (AUROC) values than six baselines and four state-of-the-art methods. Moreover, clinicians utilize a familiarized SOFA system to conduct mortality prediction and achieve increased interpretability, which benefits the adoption of the proposed framework in real clinical scenarios. [ABSTRACT FROM AUTHOR]