MicroRNAs (miRNAs) are small non-coding RNAs orchestrating regulatory networks through sequence-specific target recognition. Understanding miRNA-disease correlations is crucial as high-throughput sequencing data growth outpaces experimental validation, necessitating computational approaches for association discovery. Existing frameworks model miRNA-disease interactions as uniform binary relationships, overlooking semantic diversity in different association mechanisms. We propose BKAMDA (MiRNA-Disease Associations prediction Based on Knowledge-Awareness), a novel knowledge-aware model for predicting miRNA-disease associations. Unlike existing methods learning only from miRNA-disease networks, BKAMDA leverages knowledge graphs to delineate distinct association types. By simulating informational propagation within knowledge graphs across diverse miRNA-disease relationships, the model investigates latent connections across various relationship types. Comparative analysis with competitive baselines using real-world experimentally validated datasets demonstrates excellent performance across multiple metrics. Three disease case studies further confirm model accuracy and effectiveness for precision medicine applications. Our knowledge-aware approach significantly advances miRNA-disease association prediction by capturing semantic diversity in biological interactions.

This study addresses the bipartite output consensus problem of heterogeneous uncertain multiagent systems (MASs) under hybrid event-triggered control mechanism. Initially, a fully distributed adaptive bipartite compensator is composed, which consists of time-varying coupling weights and hybrid event-triggered mechanism to estimate the state of the leader. The hybrid event-triggered mechanism includes the event-triggered mechanism for the leader and the edge-event triggered mechanism for all edges to reduce the information transmission among agents. Then, a novel distributed output feedback controller is put forward for uncertain system dynamics. With the aid of the proposed controller, the bipartite output consensus problem of heterogeneous uncertain MASs can be resolved. Furthermore, the above results can be extended to the case of switching topology. Lastly, the validity of the theoretical findings is confirmed through four simulation examples.

Insulators are critical components in power transmission systems. However, defects caused by environmental factors and aging can adversely affect power grid reliability and safety. Traditional inspection methods are often inefficient and error prone. Although deep learning techniques, particularly the You Only Look Once (YOLO) family of models, have advanced automated inspection, existing models struggle to detect small-scale defects. To address these limitations, we propose an enhanced insulator defect detection approach based on YOLOv11. Specifically, we introduce a novel module called MDELA (Multi-Dilation and Efficient Local Attention) into the neck of the YOLOv11s model. The MDELA module leverages depth-wise convolutions with multiple dilation rates to capture multi-scale defect features and incorporates an efficient local attention (ELA) mechanism to prioritize defect regions. The proposed YOLOv11s+MDELA model demonstrated superior performance on the Innopolis High Voltage Challenge test dataset, achieving precision, recall, and mAP0.5 scores of 100.0%, 94.3%, and 97.3%, respectively. These results significantly surpass the baseline YOLOv11s performance, which attained 94.3% precision, 80.8% recall, and 92.5% mAP0.5. Moreover, the improved accuracy was accomplished with only a marginal increase in computational complexity (9.5 million parameters and 22.2 GFLOPs compared to 9.4 million parameters and 21.3 GFLOPs for the baseline). Ablation studies further confirmed the individual positive contributions of both multi-dilation convolution and ELA components within the MDELA module. The source code is publicly available at https://github.com/tcyhx/mdela.

Since its inception, blockchain technology has been characterized by its core attributes of immutability, traceability, and decentralization, which are fundamental to ensuring data security. In the contemporary digital landscape, medical data has emerged as a critical asset, and the integration of blockchain into healthcare has facilitated a range of innovative solutions for secure and efficient data sharing. Beyond its role in data security, blockchain’s smart contracts have attracted significant research interest due to their potential to automate processes and enhance efficiency in medical research and healthcare operations. In this context, this survey provides a systematic and in-depth exploration of blockchain applications in healthcare, with a focus on: (1) analyzing the technical foundations of blockchain and its suitability for healthcare applications; (2) synthesizing the eight key domains where blockchain has demonstrated impact in the healthcare sector; and (3) critically examining the challenges that hinder blockchain adoption in healthcare while identifying future research directions. By presenting a comprehensive review of blockchains transformative potential in healthcare, this survey offers valuable insights for researchers and practitioners engaged in this evolving interdisciplinary field.

Large vision-language models (VLMs) have shown significant performance boost in various application domains. However, adopting them to deal with several sequentially encountered tasks has been challenging because finetuning a VLM on a task normally leads to reducing its generalization power and the capacity of learning new tasks as well as causing catastrophic forgetting on previously learned tasks. Enabling using VLMs in multimodal continual learning (CL) settings can help to address such scenarios. To improve generalization capacity and prevent catastrophic forgetting, we propose a novel prompt-based CL method for VLMs, namely Cluster-based Modality Fusion Prompt (CluMo). We design a novel Key-Key-Prompt pair, where each prompt is associated with a visual prompt key and a textual prompt key. We adopt a two-stage training strategy. During the first stage, the single-modal keys are trained via K-means clustering algorithm to help select the best semantically matched prompt. During the second stage, the prompt keys are frozen, the selected prompt is attached to the input for training the VLM in the CL scenario. Experiments on two benchmarks demonstrate that our method achieves SOTA performance. The code is publicly available here.

Sentiment propagation plays a crucial role in the continuous emergence of social public opinion and network group events. By analyzing the maximum Influence of sentiment propagation, we can gain a better understanding of how network group events arise and evolve. Influence maximization (IM) is a critical fundamental issue in the field of informatics, whose purpose is to identify the collection of individuals and maximize the specific information's influence in real-world social networks, and the sentiments expressed by nodes with the greatest influence can significantly impact the emotions of the entire group. The IM issue has been established to be an NP-hard (nondeterministic polynomial) challenge. Although some methods based on the greedy framework can achieve ideal results, they bring unacceptable computational overhead, while the performance of other methods is unsatisfactory. In this article, we explicate the IM problem and design a local influence evaluation function as the objective function of the IM to estimate the influence spread in the cascade diffusion models. We redefine particle parameters, update rules for IM problems, and introduce learning automata to realize multiple search modes. Then, we propose a multisearch particle Swarm optimization algorithm (MSPSO) to optimize the objective function. This algorithm incorporates a heuristic-based initialization strategy and a local search scheme to expedite MSPSO convergence. Experimental results on five real-world social network datasets consistently demonstrate MSPSO's superior efficiency and performance compared with baseline algorithms.

Telemedicine Monitoring (TM) integrates mobile communication technology and Internet of Things (IoT) technology for health monitoring and data management. Amidst the escalating demand for telemedicine, traditional cloud computing struggles to guarantee real-time performance and data privacy. To address these challenges, we systematically survey the application of fog and edge computing technologies in TM systems. We focus on the following key aspects: (1) We delve into the theoretical foundations of fog and edge computing, underscoring their salient advantages including low latency, location awareness, high mobility, and more. (2) We elaborate on the architecture of a TM system hinged on fog and edge computing. (3) We outline key challenges facing fog/edge computing-based TM systems, including bandwidth limitations, low latency, data security, privacy, heterogeneity, and reliability. (4) We discuss the need for future advancements in the realms of security defense capability, system adaptability, and convergence of scheduling algorithms to refine the construction of the TM system and stimulate the development of telemedicine.

This article focuses on the finite-time fault estimation observer and controller design for switched random models subject to multiple time-varying delays. It is assumed that there exist actuator faults, model nonlinearities, external disturbances, and sensor faults in these systems. First, an observer is proposed to obtain the estimations of the system states, disturbances, as well as sensor and actuator faults. Compared with the existing results, the suggested observer estimates these sizes and shapes of states, exogenous disturbances, actuator, and sensor faults more accurately. This kind of unknown nonlinear dynamic can be approached by this generalized fuzzy hyperbolic model. Delay-dependent sufficient conditions of robust mean-square finite-time boundedness are obtained for the error system via a piecewise Lyapunov function. Observer matrices are obtained and finite-time fault estimation is realized. Second, we establish a novel controller based on fault estimation information. In addition, the piecewise function is developed to acquire delay-dependent adequate conditions of finite-time control. Controller matrices are obtained and finite-time fault-tolerant control can be achieved. At last, this validity of the method shown in this work is demonstrated via a practical simulation example.

The rumors in the healthcare system have the attributes of fast spread and severe social influence. Even worse, it may cause the collapse of medical services and the death of many patients. To prevent its serious impact on society, the target of rumor suppression for the healthcare system is to restrain the spread of rumors (negative opinions) and maximize the spread of antirumors (positive opinions). Therefore, in this article, for the first time, we propose comprehensive learning-based particle swarm optimization with opinion maximization (OM) to address the rumors suppression problem in the healthcare system. We define the rumor suppression problem in the healthcare system based on OM and devise two opinion propagation models. Then, we propose a directed acyclic graph-based objective function to evaluate the opinion propagation and solve this problem using comprehensive learning particle swarm optimization. Experimental results show that our proposed scheme achieves better results for positive opinion propagation in the scenario of rumor suppression in the healthcare system than the baseline algorithms.

This article focuses on the bipartite output tracking control for heterogeneous linear multiagent systems under the asynchronous edge-based event-triggered transmission mechanism. First, the distributed bipartite edge-based event-triggered compensator is established to estimate the state of the exosystem. The estimated state of the compensator is the same as the state of the exosystem in modulus and opposite in sign because of the existence of antagonistic communications. To be independent of the topology information, the adaptive compensator with an edge-based event-triggered mechanism is then established. And the observer is proposed to recover the unmeasurable system states. Then, the distributed control scheme based on the compensator and the observer is designed to address the bipartite output tracking problem. Moreover, the results in the signed fixed graph are extended to signed switching graphs. The Zeno behavior of each edge is ruled out. Finally, two numerical examples, one application example and one comparison example, are given to demonstrate the feasibility of the main theoretical findings.

The finite-time output consensus (FTOC) issue of heterogeneous fractional-order multiagent systems (HFO-MASs) is investigated in this article. First, a new principle of finite-time convergence for absolutely continuous functions is developed if a fractional derivative inequality is satisfied. Next, in order to remove the assumption that the leader's system matrix is known to all agents in previous studies, a distributed adaptive finite-time observer is designed, which can estimate not only the leader's state but also the leader's system matrix. Then, a novel finite-time event-triggered compensator with intermittent communication is constructed to estimate the leader's state by introducing a dynamic threshold for a novel triggering function. In this case, the high frequency triggering is restrained and the triggering number is significantly reduced. The Zeno behavior does not exist by choosing parameters appropriately. In addition, two finite-time control strategies are constructed based on the above distributed observer and event-triggered compensator, respectively, to achieve output consensus in finite time. The feasibility of the proposed method is ensured by the comprehensive theoretical demonstration of the finite-time consensus stability and the analysis of the Zeno behavior. Finally, the examples are given to demonstrate the conclusion.

This article considers the time-varying formation (TVF) tracking issue of heterogeneous multiagent systems (HMASs) with the dynamic event-triggered control. The HMASs contain heterogeneous multiple leaders, all of which have the input signals to generate flexible reference, and only the output information can be measured. All leaders do not have access to the same followers, that is, the well-informed follower assumption is removed in this article. In this setting, the adaptive multileader state compensator is designed for each follower to estimate the integrated state information of all leaders, which can equip with two kinds of dynamic event-triggered mechanisms, that is, node-based event-triggered mechanism and edge-based event-triggered mechanism, to save communication bandwidth. Then, the TVF controllers are built by some estimation values to regulate the followers to achieve and maintain the geometric shape while tracking the reference which is the convex combination of outputs of leaders. The event-triggered compensator and TVF controller constitute the control protocol of HMASs, which are independent of global information with the fully distributed manner. The stability analysis and numerical simulations are given to verify the presented control protocol.

The time-varying formation (TVF) tracking problem is studied for linear multiagent systems (MASs), where followers reach a preset TVF when tracking the leader's state. Followers are divided into the informed ones, which directly receive the leader's information, and uninformed ones. To alleviate communication requirements, trigger mechanisms are designed for the leader and all edges. Note that the designed trigger mechanisms enable the leader to send information intermittently and each follower to transmit information asynchronously when the corresponding trigger mechanism is satisfied. To address the TVF tracking problem, the node-event (for the leader) and (dynamic) edge-event triggered adaptive control strategy is proposed, which is fully distributed and has no relation to the system network's scale. Moreover, the MASs do not exhibit the Zeno behavior. Finally, a practice example is introduced to effectively illustrate the theoretical results.

This study investigates the adaptive bipartite event-triggered time-varying output formation tracking for heterogeneous linear multi-agent systems (MASs) under signed directed communication topology. Both cooperative communication and antagonistic communication among agents are considered. The fully distributed bipartite compensator based on the novel composite event-triggered transmission mechanism is first put forward to estimate the state of the leader. Compared with the existing methods, our compensator can save communication resources using event-triggered transmission mechanism; is independent of the global information of the network graph; and is applicable for the signed directed graph. With the developed compensator, the distributed control protocol is designed to achieve the time-varying output formation tracking. Moreover, the case that the networked systems subject to external disturbances is also considered. To estimate the state of leader with disturbance, the fully distributed bipartite compensator based on an innovative composite event-triggered mechanism is presented. And the novel distributed control protocol is proposed to address the output formation tracking issue for linear MASs with heterogeneous dynamics and external disturbances. It is shown that the Zeno-behavior can be excluded in both transmission mechanisms. Finally, the effectiveness of the developed control methods is illustrated through three simulation examples.

This article considers the distributed bipartite adaptive event-triggered fault-tolerant consensus tracking issue for linear multiagent systems in the presence of actuator faults based on the output feedback control protocol. Both time-varying additive and multiplicative actuator faults are taken into account in the meantime. And the upper/lower bounds of actuator faults are not required to be known. First, the state observer is designed to settle the occurrence of unmeasurable system states. Two kinds of event-triggered mechanisms are then developed to schedule the interagent communication and controller updates. Next, with the developed event-triggered mechanisms, a novel observer-based bipartite adaptive control strategy is proposed such that the fault-tolerant control problem can be addressed. Compared with some related works on this topic, our control scheme can achieve the intermittent communication and intermittent controller updates, and the more general actuator faults and network topology are considered. It is proved that the exclusion of Zeno behavior can be realized. Finally, three illustrative examples are given to demonstrate the feasibility of the main theoretical findings.

Considering that there are many systems with limited network bandwidth in practice, this article studies the leader-following consensus problem for a class of nonlinear multiagent systems (MASs). The purpose of this article is to reduce unnecessary information transmission between any pair of adjacent agents including the leader in the MASs through intermittent communication. The novel event-triggered and asynchronous edge-event triggered mechanisms are designed for the leader and all edges, respectively. The static and dynamic consensus protocols under these mechanisms are proposed to address the leader-following consensus problem for MASs with Lipschitz dynamics, and the systems will not exhibit Zeno behavior under these two control schemes. Note that the dynamic consensus protocol does not rely on any global values of MASs, it is a fully distributed way. Finally, a practice simulation example is introduced to illustrate the theoretical results obtained.

This study investigates the bipartite fixed-time time-varying output formation-containment tracking issue for heterogeneous linear multiagent systems with multiple leaders. Both cooperative communication and antagonistic communication between neighbor agents are taken into account. First, the bipartite fixed-time compensator is put forward to estimate the convex hull of leaders' states. Different from the existing techniques, the proposed compensator has the following three highlights: 1) it is continuous without involving the sign function, and thus, the chattering phenomenon can be avoided; 2) its estimation can be achieved within a fixed time; and 3) the communication between neighbors can not only be cooperative but also be antagonistic. Note that the proposed compensator is dependent on the global information of network topology. To deal with this issue, the fully distributed adaptive bipartite fixed-time compensator is further proposed. It can estimate not only the convex hull of leaders' states but also the leaders' system matrices. Based on the proposed compensators, the distributed controllers are then developed such that the bipartite time-varying output formation-containment tracking can be achieved within a fixed time. Finally, two examples are given to illustrate the feasibility of the main theoretical findings.

The consensus problem of general linear multiagent systems (MASs) is studied under switching topologies by using observer-based event-triggered control method in this article. On the basis of the output information of agents, two kinds of novel event-triggered adaptive control schemes are designed to achieve the leaderless and leader-follower consensus problems, which do not need to utilize the global information of the communication networks. Finally, two simulation examples are introduced to show that the consensus error converges to zero and Zeno behavior is eliminated in MASs. Compared with the existing output feedback control research, one of the significant advantages of our methods is that the controller protocols and triggering mechanisms do not rely on any global information, are independent of the network scale, and are fully distributed ways.

In this study, the state formation control issue of general linear networked multi-agent systems (MASs) is considered. Combining self-triggered strategy with general event-triggered strategy, a novel fully distributed asynchronous mixed self- and event-triggered control strategy is proposed, which can make the MASs achieve the prescribed state formation structure. The control strategy proposed in this study does not depend on any global network information. Therefore, no matter how large scale of the network is, the control strategy is feasible. Meanwhile, the control strategy uses the sampled state information at triggering instants instead of the real-time state information, which efficiently eliminates continuous communication among agents. Different from the existing studies, the event-triggered detector starts to work if and only if the next self-triggering instant comes in the control strategy proposed in this article. Thus, the control strategy can save more communication resources between sensor and event-triggered detector within the same inter-event interval compared with the previous event-triggered strategies. In addition, the control strategy designs a exponential decay term in the triggering function to rule out the unexpected Zeno behavior and reduce the triggering number. Finally, the numerical simulation result of multi-robot formation is given, which demonstrates the feasibility and performance of the proposed control strategy.

In this article, the leader-following exponential consensus problem of fractional-order descriptor multiagent systems (FOD-MASs) with event-triggered control (ETC) protocol is investigated, which includes integer-order descriptor multiagent systems as the special case. Two classes of control schemes and the corresponding event-triggered conditions are presented, respectively. First, a distributed state feedback ETC protocol is developed to reach the leader-following exponential consensus. The leader-following exponential consensus is achieved in the sense of the Mittag-Leffler stability of fractional-order systems. Second, when full-state measurements are not available, a novel observer-type output feedback ETC strategy with some desirable characteristics is provided. For two distributed ETC protocols, consensus conditions are derived and convergence rate of the system can be adjusted. Also, the integral inequality is applied to get the fact that Zeno behavior is excluded, which verifies the feasibility of ETC schemes. Finally, the effectiveness of conclusions is demonstrated by the examples.

This article investigates the bipartite consensus problem for linear multiagent systems by the event-triggered output feedback control scheme. Both cooperative interaction and antagonistic interaction between neighbor agents are considered. Assuming that the system states are not available for measurement, the state observer is thus proposed to settle this scenario. Then, a novel observer-based bipartite control scheme is developed on the basis of two event-triggering mechanisms. One is designed for the communication between neighbors and another is for controller updates. Different from the existing methods, the proposed control strategy does not need continuous updates, avoids continuous communication between neighbors, and is applicable for the signed communication topology. Moreover, we extend the results from the bipartite leaderless consensus to the bipartite leader-following consensus and the bipartite containment consensus. It is proven that the proposed controllers fulfill the exclusion of Zeno behavior in three consensus problems. Finally, three examples are provided to illustrate the feasibility of the theoretical results.

This article is concerned with the optimal tracking control problem of the coupled Markov jump system (CMJS) by using the reinforcement learning (RL) technique. Based on the conventional optimal tracking architecture, an offline tracking iteration algorithm is first designed to solve the coupled algebraic Riccati equation that can hardly be solved by mathematical methods directly. To overcome the crucial requirements and existing shortcomings in the offline tracking method, a novel integral RL (IRL) tracking algorithm is first proposed for CMJS, which develops a transition-probability-free optimal tracking control scheme with a reconstructed augmented system and discounted cost function. Both the requirements of transition probability πij and system matrix Ai are avoided via the designed IRL algorithm. The stability and convergence of the novel schemes are proved by the Lyapunov theory, and the tracking objective is achieved as desired. Finally, we apply the designed algorithms in a fourth-order Markov jump control problem and the stochastic mass, spring, and damper system to track continuous sinusoidal waveforms, and the simulation results are provided to show the effectiveness and applicability.

This article addresses the adaptive bipartite event-triggered output consensus issue for heterogeneous linear multiagent systems. We consider both cooperative interaction and antagonistic interaction between neighbor agents in both fixed and switching topologies. An adaptive bipartite compensator consisting of time-varying coupling weights and dynamic event-triggered mechanism is first proposed to estimate the leader's state in a fully distributed manner. Different from the existing methods, the proposed compensator has three advantages: 1) it does not depend on any global information of the network graph; 2) it avoids the continuous communication between neighbor agents; and 3) it is applicable for the signed communication topology. Assume that the system states are unmeasurable, and we thus design the state observer. Based on the devised compensator and observer, the distributed control law is developed such that the bipartite event-triggered output consensus problem can be achieved. Moreover, we extend the results in fixed topology to switching topology, which is more challenging in that state estimation is updated in two cases: 1) the interaction graph is switched or 2) the event-triggered mechanism is satisfied. It is proven that no agent exhibits Zeno behavior in both fixed and switching interaction topologies. Finally, two examples are provided to illustrate the feasibility of the theoretical results.

In this paper, a fuzzy reinforcement learning (RL)-based tracking control algorithm is first proposed for partially unknown systems with actuator faults. Based on Takagi-Sugeno fuzzy model, a novel fuzzy-augmented tracking dynamic is developed and the overall fuzzy control policy with corresponding performance index is designed, where four kinds of actuator faults, including actuator loss of effectiveness and bias fault, are considered. Combining the RL technique and fuzzy-augmented model, the new fuzzy integral RL-based fault-tolerant control algorithm is designed, and it runs in real time for the system with actuator faults. The dynamic matrices can be partially unknown and the online algorithm requires less information transmissions or computational load along with the learning process. Under the overall fuzzy fault-tolerant policy, the tracking objective is achieved and the stability is proven by Lyapunov theory. Finally, the applications in the single-link robot arm system and the complex pitch-rate control problem of F-16 fighter aircraft demonstrate the effectiveness of the proposed method.