Especially, network consistency projection is added to obtain network projection scores from the microbe space and the disease space. Ultimately, label propagation is utilized to reliably predict microbes related to diseases. NCPLP achieves better performance in various evaluation indicators and discovers a greater number of potential associations between microbes and diseases. Also, case studies further confirm the reliable prediction performance of NCPLP. To conclude, our algorithm NCPLP has the ability to discover these underlying microbe-disease associations and can provide help for biological study.In recent years, both distributed scheduling problem and energy-efficient scheduling have attracted much attention. As the integration of these two problems, the distributed energy-efficient scheduling problem is of great realistic significance. To the best of our knowledge, the distributed energy-efficient parallel machines scheduling problem (DEPMSP) has not been studied yet. This article aims to solve DEPMSP by integrating factory assignment and machine assignment into an extended machine assignment to handle the coupled relations of subproblems. A knowledge-based two-population optimization (KTPO) algorithm is proposed to minimize total energy consumption and total tardiness simultaneously. Five properties are derived by analyzing the characteristics of DEPMSP. The population is initialized by using two heuristics based on problem-specific knowledge and a random heuristic. The nondominated sorting genetic algorithm-II and differential evolution perform cooperatively on the population in parallel. Moreover, two knowledge-based local search operators are proposed to enhance the exploitation. Extensive simulation experiments are conducted by comparing KTPO with four algorithms from the literature. The comparative results and statistical analysis demonstrate the effectiveness and advantages of KTPO in solving DEPMSP.This work addresses the problem of aperiodically sampled control for the networked Takagi-Sugeno (T-S) fuzzy systems, where the aperiodically sampled input is generated by a periodic sampler and an event-triggered mechanism (ETM). The purpose of ETM is used to reduce the computational and communication burdens. For guaranteeing controller robustness, the practical stability of T-S fuzzy systems is considered by using the Lyapunov method and linear matrix inequality (LMI) technique. SM-164 purchase As one of the most powerful inequalities for deriving stability criteria using LMIs, Jensen's inequality has recently been improved by various authors for the stability analysis of delayed systems. However, these results are conservative to obtain lower bounds for integrals with an exponential term. Inspired by this, improved integral inequalities are derived in this work, and they are applied to obtain practical stability criteria for aperiodically sampled control. Finally, a numerical example on flight control of a helicopter is given to illustrate the effectiveness of the obtained practical stability criteria. Furthermore, the effectiveness of the improved Jensen inequalities on the exponential stability criteria is illustrated by numerical comparisons.This article focuses on event-triggered consensus control for multiagent systems subject to sensor faults or noises. First, a descriptor state observer with a low-pass filtering characteristic being developed for each agent using output information. The convergence regions of estimation errors can be reduced by a nonsingular suppression matrix. Leader-follower event-triggered consensus protocols with continuous-time communication are designed for multiagent systems based on the estimated states. By virtue of the Jordan form of the Laplacian matrix, the stability conditions are derived by using the Lyapunov analysis. Then, new self-triggered consensus protocols are designed for the multiagent systems to remove the requirement of the continuous monitoring triggering condition and continuous communication simultaneously. The triggering interval is proved greater than 0, and the Zeno behavior is excluded for all agents. Finally, numerical simulations are conducted to demonstrate the effectiveness of the proposed design.Beyond generating long and topic-coherent paragraphs in traditional captioning tasks, the medical image report composition task poses more task-oriented challenges by requiring both the highly accurate medical term diagnosis and multiple heterogeneous forms of information, including impression and findings. Current methods often generate the most common sentences due to dataset bias for the individual case, regardless of whether the sentences properly capture key entities and relationships. Such limitations severely hinder their applicability and generalization capability in medical report composition, where the most critical sentences lie in the descriptions of abnormal diseases that are relatively rare. Moreover, some medical terms appearing in one report are often entangled with each other and co-occurred, for example, symptoms associated with a specific disease. To enforce the semantic consistency of medical terms to be incorporated into the final reports and encourage the sentence generation for rare abnormal descriptions, we propose a novel framework that unifies template retrieval and sentence generation to handle both common and rare abnormality while ensuring the semantic coherency among the detected medical terms. Specifically, our approach exploits hybrid-knowledge co-reasoning 1) explicit relationships among all abnormal medical terms to induce the visual attention learning and topic representation encoding for better topic-oriented symptoms descriptions and 2) adaptive generation mode that changes between the template retrieval and sentence generation according to a contextual topic encoder. The experimental results on two medical report benchmarks demonstrate the superiority of the proposed framework in terms of both human and metrics evaluation.Compared to other system modeling techniques, the fuzzy discrete event systems (FDESs) methodology has the unique capability of modeling a class of event-driven systems as fuzzy automata with ambiguous state and event-invoked state transition. In two recent papers, we developed algorithms for online-supervised learning of the fuzzy automaton's event transition matrix using fuzzy states before and after the occurrence of fuzzy events. The post-event state was assumed to be readily available while the pre-event state was either directly available or estimatable through learning. This article is focused on algorithm development for learning the transition matrix in a different setting--when the pre-event state is available but the post-event state is not. We suppose the post-event state is described by a fuzzy set that is linked to a (physical) variable whose value is available. Stochastic-gradient-descent-based algorithms are developed that can learn the transition matrix plus the parameters of the fuzzy sets when the fuzzy sets are of the Gaussian type.