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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.

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.

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.

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.

Background: Emergency cervical cerclage (ECC) is of potential value in twin pregnancy, when the cervix is dilated to >1cm. McDonald and Shirodkar were two main techniques of transvaginal cerclage at present. As ECC at extremely high risk of spontaneous preterm birth (sPTB) especially for twins with cervical dilated ≥ 3cm and prolapsed membranes, so which technique has more advantages is still uncertain. Objectives: The aim of our study was to evaluate the effectiveness of ECC performed with combined McDonald-Shirodkar technique in twin pregnancies between 18–26 weeks with painless cervical dilation 1-6cm. Methods: A retrospective, cohort study matched with the degree of cervical dilation was conducted. The study group (case group) included twin pregnancies who underwent combined McDonald-Shirodkar approach with cervical dilation ≥1 cm between 18–26 weeks of gestation at four institutions, from December 2015 to December 2022. To minimize confounding factors, we elucidated the causality structure using a DAG (Figure 1) and performed 1:1 case-control Matching. A control group performed McDonald approach. The primary outcome was gestational age (GA) at delivery. The secondary outcomes were pregnancy latency, the rates of sPTB at Results: 84 twin pregnancies were managed with either combined McDonald-Shirodkar approach (case group: n=42) or McDonald approach (control group: n=42). Demographic characteristics were not significantly different in two groups(p>0.05). After adjusting for confounders which were represented by a directed acyclic graph (DAG, Figure 1), median GA at delivery was significantly higher (30.5 vs 27 weeks, Bate: 3.40, 95% confidence interval (CI): 2.13-4.67, pConclusions: ECC performed with the combined McDonald-Shirodkar procedure in twin pregnancies with cervical dilation 1-6 cm in mid-trimester pregnancy may reduce the rate of sPTB and improve perinatal and neonatal outcomes compared with McDonald procedure, especially for twins with cervical dilation of 3-6 cm and prolapsed membranes.

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.

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.

A systematic analysis of serum, placental tissue and urine from women with intrahepatic cholestasis of pregnancy was performed by untargeted metabolomics.

This brief focuses on the observer-based state, fault and disturbance reconstructions for Takagi-Sugeno (T-S) fuzzy-approximation-based nonlinear dynamics subject to an enlarged class of disturbances and abrupt actuator fault. By developing a brand-new fuzzy adaptive observer (FAO), unknown system state, fault and disturbance can be reconstructed simultaneously, where some classic assumptions imposed on the disturbance such as the first-order derivative being equal to zero or generation from an exogenous system are removed successfully in our work. Significantly, to reduce the conservatism, the stability criterion of error dynamic is inferred in terms of using fuzzy Lyapunov functions, and expressed in a linear matrix inequality (LMI) framework. At last, simulation study on a real plant is provided to illustrate the practicability of the given procedure.

This study focuses on the fixed-time event-triggered time-varying formation tracking issue for a class of nonlinear multi-agent systems with multi-dimensional dynamics, uncertain disturbances and non-zero control input of leader. Firstly, a distributed fixed-time event-triggered control scheme is proposed such that the time-varying formation tracking can be achieved with intermittent controller updates and intermittent communication. To reduce the chattering phenomenon, a novel control protocol with saturation function is designed. It should be noted that continuous triggering condition monitoring is needed in the triggering mechanism. To address above issue, a novel triggering mechanism is further proposed. Moreover, the fixed-time event-triggered time-varying formation containment with multiple leaders is considered. It is verified that the Zeno behavior can be excluded. Finally, two examples are presented to demonstrate the feasibility of the main theoretical findings.

This study investigates the distributed fault-tolerant output regulation for heterogeneous linear multi-agent systems in the presence of actuator faults. For the systems which are not the neighbors of exosystem, the distributed fixed-time observer is put forward to observe the state of exosystem. Note that it is dependent on the global information of network topology. To address this issue, the fully distributed adaptive fixed-time observer is further proposed. It can estimate not only the state of exosystem, but also the system matrix of exosystem. Based on the proposed observer, a novel fault-tolerant controller is developed to compensate for actuator faults. Moreover, it is proven that the proposed controller is effective to address the fault-tolerant output regulation problem by the Lyapunov stability theory. Finally, two illustrative examples are given to illustrate the feasibility of the main theoretical findings.

This study focuses on the fixed-time leader-following consensus problem for a class of multi-agent system with multi-dimensional nonlinear dynamics. First, the distributed static fixed-time control protocol is put forward to settle this issue. Different from the existing fixed-time control methods, the proposed control protocol is continuous such that the chattering phenomenon can be avoided. Note that the proposed static fixed-time controller is dependent on the global information of network topology. To address above issue, the fully distributed adaptive fixed-time controller is further proposed. Moreover, the results in fixed-time leader-following consensus are extended to containment consensus. And the case of directed graph is also considered. Finally, several examples are provided to illustrate the feasibility of the theoretical results.

This paper discusses the containment control (CC) problem of general linear multi-agent systems (MAS)s by means of two kinds of distributed event-triggered mechanisms. Two types of event-triggered control protocols, namely, the state feedback control law and the dynamic output feedback control protocol, are designed for each follower. Under the proposed control protocols and triggering mechanisms, the containment control problem can be solved by proving that the containment error converges to zero based on the assumption and algorithms. At last, we verify the rationality of the theoretical results. Through two numerical simulation, we can see that the trajectory of each follower converges to the convex hull formed by all leaders. In addition, in order to verify the advantages of the obtained results, we give a simulation example and compare the methods designed in this paper with the methods in other literature.

Opinion maximization in social networks is an optimization problem, which targets at determining some influential individuals (i.e., seed nodes), propagating the desired opinion to their neighbors, and eventually obtaining maximum opinion spread. Previous studies assume that influence power of one individual is mainly calculated by using some network structure properties and once the opinion of one individual is determined, its opinion usually keeps unchanged. However, in the real scenario, the influence power of one individual may be unknown and should be closely associated with the dynamic opinion formation process. In this paper, we propose a novel Influence Power-based Opinion Framework (IPOF) to solve the opinion maximization problem, which is composed of two phases: 1) influence power estimation, and 2) elimination of influence overlapping (EIO). Specifically, we design the exponential influence power and estimate the unknown parameter of influence power through maximum likelihood estimation due to its simplicity, practicability, and superior convergence in large samples. To generate the opinion series dynamically, the weighted voter model is proposed by leveraging influence power and intimate degree. Moreover, we also prove that the likelihood function is concave by using Hessian matrix. To determine the initial seed nodes and facilitate large opinion propagation, influence power-based EIO algorithm is proposed. Experimental results in six social networks demonstrate that the proposed approach outperforms the state-of-the-art benchmarks.

This study is concerned with observer-based fault estimation (FE) and fault-tolerant controller design for a class of discrete-time Takagi–Sugeno (T–S) fuzzy systems. There exist multiple time-varying state delays, sensor and actuator faults, local non-linear dynamics and exogenous disturbances in the systems. In comparison with the existing results, the approach suggested in this study is more flexible and feasible. By means of the n-step induction FE, a novel fuzzy adaptive descriptor observer is developed to obtain the n-step error functions. Then, an active dynamic output feedback fault-tolerant controller is designed to stabilise the closed-loop fuzzy system. Furthermore, a set of delay-dependent sufficient conditions are provided by the fuzzy Lyapunov function which utilises the form of linear matrix inequalities. The stability results from the observer and the controller in this study have less conservatism compared with the ones from the existence of observers and fault-tolerant controllers. At last, a simulation example is presented to demonstrate the advantages and effectiveness of the approach proposed in the study.

This study focuses on the distributed bipartite consensus tracking for linear multi-agent systems with input time delay based upon event-triggered transmission mechanism. Both cooperative interaction and antagonistic interaction between neighbor agents are considered. A novel distributed bipartite control technique with event-triggered mechanism is raised to address this consensus issue. Different from the existing methods, our control technique does not need continuous communication among agents, is capable of addressing the case of input delay, and is applicable for the signed communication topology. Moreover, to avoid continuous monitoring of one's own state, a self-triggered control strategy is further proposed. And when the system states cannot be measured, the observer-based bipartite control technique with event-triggered mechanism is thus put forward. Furthermore, the results in leader-following consensus are extended to containment control. It is proven that the proposed controllers fulfill the exclusion of Zeno behavior in two consensus problems. Finally, simulation experiments are used to test the practicability of the theoretical analysis.

In this paper, some significant efforts on fuzzy oblique decision tree (FODT) have been done to improve classification accuracy and decrease tree size. Firstly, to eliminate data redundancy and improve classification efficiency, a forward greedy fast feature selection algorithm based on neighborhood rough set (NRS_FS_FAST) is introduced. Then, a new fuzzy rule generation algorithm (FRGA) is proposed to generate fuzzy rules. These fuzzy rules are used to construct leaf nodes for each class in each layer of the FODT. Different from the traditional axis-parallel decision trees and oblique decision trees, the FODT takes dynamic mining fuzzy rules as decision functions. Moreover, the parameter δ, which can control the size of the tree, is optimized by genetic algorithm. Finally, a series of comparative experiments are carried out with five traditional decision trees (C4.5, Best First Tree (BFT), amulti-class alternating decision tree (LAD), Simple Cart (SC), Naive Bayes Tree (NBT)), and recently proposed decision trees (FRDT, HHCART, and FMMDT-HB) on UCI machine learning datasets. The experimental results demonstrate that the FODT exhibits better performance on classification accuracy and tree size than the chosen benchmarks.

In this paper, the distributed output consensus control of linear heterogeneous multi-agent systems (LH_MASs) based on event-triggered transmission strategy under directed topology is addressed. Under the assumption that the state of each node is unmeasurable, the state observer is first designed. Then, in order to decrease the information interaction among agents, the event-triggered consensus protocol (ETCP) based on the designed state observer and dynamic compensator is proposed. Here, a novel triggering function is designed such that each agent does not need to monitor the states of its neighbors for event detection. In addition, on account of state-dependent threshold and time-dependent threshold, the combined threshold is presented to take both advantages into account at the same time. By selecting an appropriate Lyapunov function and regulation parameters, the output consensus of LH_MASs can be achieved and the Zeno behavior can be excluded. To avoid continuous monitoring of each agent’s state, we further propose the self-triggered consensus protocol (STCP). Furthermore, the novel event-triggered consensus protocol for mitigating external disturbances (ED_ETCP) is raised. Finally, several numerical examples are provided to illustrate the validity of the theoretical results.

Opinion Maximization Problem (OMP) targets at selecting a subset of influential initial nodes and eventually generating the maximum opinion spread. The current OMP methods mainly pay attention to the improvement of efficient algorithms, which hardly obtain high efficiency and stable accuracy in large-scale social networks. In this paper, we study the OMP with the community-based approach. To be specific, we first formulate the OMP and construct the weight-based opinion model to estimate the dynamic change of opinion value. In particular, to generate the influential individuals, we propose a Community-based Approach for the OMP (CAOM), including: community detection, selection of candidate nodes and generation of seed nodes. Then, to reduce the computational complexity effectively and distribute seed nodes into the reasonable communities, the significant communities are devised. Based on the one-hop measure and the potential nodes of each community, the candidate nodes are selected. For each community, we acquire the influence score based on its neighbors within community and beyond community. Finally, we develop the two-hop measure and Elimination of Overlapping Influence (EOI) to determine seed nodes from candidate nodes. Experimental results in ten social networks demonstrate that CAOM can accelerate the opinion spread with smaller running time compared with the baselines.

In this paper, guaranteed-performance consensus (GPC) for descriptor nonlinear multi-agent systems (DNMASs) with a leader is studied. The interactions among followers are bidirectional for leader-following DNMASs. Firstly, one designs a novel distributed nonlinear consensus protocol based on state feedback to reach consensus for DNMASs. The performance function is constructed by state errors among agents, which is time integration of quadratic function. Secondly, not only are sufficient conditions presented for guaranteed-performance consensus to ensure the scalability of DNMASs based on the Riccati inequality, but also an upper bound of the cost function is derived. It is shown that the guaranteed-performance costs are dependent on initial states of agents. Moreover, the conclusions are extended to achieve the leaderless GPC. Finally, simulation examples are presented to demonstrate the effectiveness of theoretical results.

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.

In this paper, the leader-following control of heterogeneous discrete-time multi-agent systems (HD_MASs) in the presence of system uncertainties under directed topology is addressed. It aims to achieve reference tracking, disturbance rejection and robust control while the references and disturbances are generated by an autonomous exosystem. In practice, these agents are often different types of devices, thus they have different internal dynamics. Moreover, it is difficult to measure all states of each aircraft due to high cost or technical limitation. In this case, a novel leader-following output consensus problem is formulated and solved in this paper. Firstly, an appropriate linear transformation is proposed to divide the state information of each agent into measurable and unmeasurable parts. Then the reduced-order observer is designed only for unmeasurable parts. Based on the designed observer, the distributed feedback controller is proposed such that the outputs of all followers reach the same trajectory with the leader. In light of the internal model principle and discrete-time algebraic Riccati equation, the robust leader-following consensus of HD_MASs is achieved. Furthermore, this paper extends the results to continuous-time multi-agent systems. Finally, several simulation experiments are presented to verify the effectiveness of the theoretical results.

This paper develops an integral reinforcement learning (IRL)-based adaptive control method for the multi-player non-zero-sum (NZS) games of the nonlinear continuous-time systems with partially unknown dynamics, in the context of event-triggered mechanism. With the principle of IRL method, the requirement for the system drift dynamics is relaxed in the controller design. Moreover, different from the conventional iteration computation methods, the algorithm developed in this work is implemented in an online adaptive fashion, which provides a new way to combine the IRL algorithm and the event-triggered control framework in solving the NZS game issues. In the event-based algorithm, a state-dependent triggering condition is presented, which not only guarantees the closed-loop system stability, but also reduces the computation and communication loads of the controlled plant. By means of Lyapunov theorem, the uniform ultimate boundedness (UUB) properties of the system states and the critic weight estimation errors have been proved. Finally, two numerical examples are utilized to demonstrate the efficacy of the proposed method.

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 $\pi _{ij}$ and system matrix $A_{i}$ 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. Note to Practitioners —In the practical engineering systems, many useful signals and interference vary randomly. Therefore, the tracking control of stochastic systems and dynamics, such as the Markovion, Ito’s, Wiener, and Martingale processes, plays an important role in the modern industry. As a matter of fact, it is always desired to reduce the requirement of exact information and transition probability in the homogeneous Markovian process, which is very difficult to obtain accurate measurements. One way is integrating the adaptive reinforcement learning (RL) technique into the Markovian systems to learn this implicit information. However, a major restriction of the RL technique is that the control policy should be related to the finite performance index, which generally invalidates the optimal tracking solutions. In order to tackle this difficulty, by designing a novel parallel scheme via integral RL (IRL) technique, the solution of the coupled algebraic Riccati equation is solved, and the transition probability can be completely unknown during the learning process.

In this paper, the finite-time time-varying output formation-tracking (FT-TV-OFT) problem of heterogeneous linear multi-agent systems (HL-MASs) is investigated. First, we propose an adaptive finite-time observer for each follower, aiming to estimate not only the leader’s state but also the leader’s system matrix. Compared with conventional observers, the dependence of the observer parameters on any global information is completely removed. Moreover, exact estimate can be achieved in a finite time. Then, an adaptive algorithm is used to calculate the regulation equations, such that control gains can be obtained. Next, an adaptive distributed controller is constructed to solve the FT-TV-OFT problem. Moreover, we also extend above results to the case of the directed communication topology. Finally, examples are given to demonstrate the effectiveness of the results.

This paper proposes a novel classification technology—fuzzy rule-based oblique decision tree (FRODT). The neighborhood rough sets-based FAST feature selection (NRS_FS_FAST) is first introduced to reduce attributes. In the axiomatic fuzzy set theory framework, the fuzzy rule extraction algorithm is then proposed to dynamically extract fuzzy rules. And these rules are regarded as the decision function during the tree construction. The FRODT is developed by expanding the unique non-leaf node in each layer of the tree, which results in a new tree structure with linguistic interpretation. Moreover, the genetic algorithm is implemented on $$\sigma $$ to obtain the balanced results between classification accuracy and tree size. A series of comparative experiments are carried out with five classical classification algorithms (C4.5, BFT, LAD, SC and NBT), and recently proposed decision tree HHCART on 20 UCI data sets. Experiment results show that the FRODT exhibits better classification performance on accuracy and tree size than those of the rival algorithms.

This paper investigates the bipartite finite-time output consensus problem of heterogeneous linear multi-agent systems (HL-MASs) described by a directed signed graph. First, by combining the event-triggered mechanism and finite-time control, we propose a bipartite finite-time event-triggered observer (bipartite FT-ET observer) to estimate the leader’s state. Triggering condition is presented by introducing a dynamic variable. Compared with conventional observer to solve the output consensus problem, exact estimation of the leader’s state can be achieved by only intermitted communication among agents. Moreover, Zeno behavior can be avoided by choosing parameters appropriately. Then, we construct a finite-time controller based on bipartite FT-ET observer, such that the bipartite output consensus can be achieved in a finite time. Finally, an example is provided to illustrate the effectiveness of the 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.

Influence Maximization is an important problem in social networks, and its main goal is to select some most influential initial nodes (i.e., seed nodes) to obtain the maximal influence spread. The existing studies primarily concentrate on the corresponding methods for influence maximization, including greedy algorithms, heuristic algorithms and their extensions to determine the most influential nodes. However, there is little work to ensure efficiency and accuracy of the proposed schemes at the same time. In this paper, a Two-stage Iterative Framework for the Influence Maximization in social networks, (i.e., TIFIM) is proposed. In order to exclude less influential nodes and decrease the computation complexity of TIFIM, in the first stage, an iterative framework in descending order is proposed to select the candidate nodes. In particular, based on the results of the last iteration and the two-hop measure, the First-Last Allocating Strategy (FLAS) is presented to compute the spread benefit of each node. We prove that TIFIM converges to a stable order within the finite iterations. In the second stage, we define the apical dominance to calculate the overlapping phenomenon of spread benefit among nodes and further propose Removal of the Apical Dominance (RAD) to determine seed nodes from the candidate nodes. Moreover, we also prove that the influence spread of TIFIM according to RAD converges to a specific value within finite computations. Finally, simulation results show that the proposed scheme has superior influence spread and running time than other existing ones.

In this paper, a novel online iterative scheme, based on fuzzy adaptive dynamic programming, is proposed for distributed optimal leader-following consensus of heterogeneous nonlinear multi-agent systems under directed communication graph. This scheme combines game theory, adaptive dynamic programming together with generalized fuzzy hyperbolic model (GFHM). Firstly, based on precompensation technique, an appropriate model transformation is proposed to convert the error system into augmented error system, and an exquisite performance index function is defined for this system. Secondly, on the basis of Hamilton–Jacobi–Bellman (HJB) equation, the optimal consensus control is designed and a novel policy iteration (PI) algorithm is put forward to learn the solutions of the HJB equation online. Here, the proposed PI algorithm is implemented on account of GFHMs. Compared with dual-network model including critic network and action network, the proposed scheme only requires critic network. Thirdly, the augmented consensus error of each agent and the weight estimation error of each GFHM are proved to be uniformly ultimately bounded, and the stability of our method has been verified. Finally, some numerical examples and application examples are conducted to demonstrate the effectiveness of the theoretical results.

In this paper, an online neural network (NN) approach for solving H∞ optimal control problem is proposed for unknown affine nonlinear discrete-time systems with input quantization over finite-horizon. Different from value and policy iteration of traditional approximation dynamic programming (ADP) technology which always requires adequate number of iterations and more than one iteration loops to guarantee stability of the controlled systems and convergence of system states and control laws, an online NN-based finite-horizon H∞ constrained-input optimal control method is presented using actor-critic-disturbance structure which can be applied as time goes forward. The terminal cost function has been considered, though the value of system state converges to zero in the regulation problem over finite-horizon. Additionally, an input quantization has been implemented to eliminate the quantization error by using the dynamic quantizer in the control process. Moreover, an NN identification strategy is presented to obviate the dependance of the system input dynamics. The stability analysis of the proposed control algorithm is provided by using Lyapunov stability theorem. Finally, a simulation example is given to verify the feasibility and effectiveness of designed control algorithm.

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 purpose of our study was to evaluate pregnancy outcomes of women with antiphospholipid antibodies (aPL) positivity and assess risk factors associated with adverse pregnancy outcomes. Pregnant women with aPL positivity were enrolled prospectively in China from January 2017 to March 2020. Treatment of low-dose aspirin and low molecular weight heparin were given. Pregnancy outcomes and coagulation function were recorded and compared with normal pregnancies. Multivariable logistic regression was performed to identify risk factors associated to intrauterine growth restriction (IUGR). 270 pregnant women, including 44 diagnosed as Antiphospholipid syndrome (APS), 91 as non-criteria APS (NCAPS) and 135 normal cases as control, were enrolled in the study. The live birth rate in aPL carriers and APS group was 97% and 95.5%, respectively. Adverse pregnancy outcomes did not show significant difference between aPL carriers and normal pregnancies, and between APS and NCAPS, except for IUGR. The incidence of IUGR was significantly higher in aPL carriers than normal pregnancies, and in APS patients than NCAPS (P < 0.05). After controlling for age, in vitro fertilization (IVF), pregnancy losses related to APS and treatment, anticardiolipin (aCL) positivity was the only variable significantly associated with IUGR, with an adjusted odds ratio of 4.601 (95% CI, 1.205-17.573). Better pregnant outcomes of aPL positive women, include APS and NCAPS, were achieved in our study with treatment based on low-dose aspirin (LDA) plus low molecular weight heparin (LMWH). The incidence of IUGR was still higher in them, and aCL positivity was the only one risk factor associated with IUGR.

Background Crown gall disease, caused by the pathogenic bacterium Agrobacterium tumefaciens, is responsible for extensive economic losses in orchards. Cherry rootstock ‘CDR-1’ (Prunus mahaleb) shows high resistance but the mechanism remains unclear. Here, we examined the morphology of pathogen-infected root neck surface, determined the activity of 10 defense-related enzymes and the content of salicylic acid (SA) and jasmonic acid (JA), and also applied transcriptome analysis, transient expression and transgenic verification to explore the crown gall resistance genes in ‘CDR-1’ plants. Results In our study, peroxidase increased in the first 10 days, while phenylalanine ammonialyase and lipoxygenase increased in the first 15 days post-infection. Four key enzymes in the AsA-GSH cycle also responded, to a certain extent; although JA content increased significantly after the treatment, the SA content did not. In a follow-up transcriptome analysis, the differentially expressed genes Pm4CL2, PmCYP450, PmHCT1, PmHCT2, and PmCAD were up-regulated. Based on the above results, we focused on the lignin biosynthetic pathway, and further measured lignin content, and found it increased significantly. The Pm4CL2 gene was used to conduct transient expression and transgenic experiments to verify its function in crown gall disease resistance. It showed the relative expression of the treatment group was almost 14-fold that of the control group at 12 h post-treatment. After the infection treatment, clear signs of resistance were found in the transgenic lines; this indicated that under the higher expression level and earlier activation of Pm4CL2, plant resistance was enhanced. Conclusions The crown gall resistance of ‘CDR-1’ is likely related to the lignin biosynthetic pathway, in which Pm4CL2 functions crucially during the plant defense response to the pathogen A. tumefaciens. The results thus offer novel insights into the defense responses and resistance mechanism of cherry rootstock ‘CDR-1’ against crown gall disease.

Agrobacterium tumefaciens causes crown gall disease in plants by transferring a portion of the tumor-inducing plasmid, transfer DNA, into the plant genome. To examine the physiological changes induced by A. tumefaciens in cherry rootstock ‘Gisela 6’, we determined the activity of defense-related enzymes, including superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), polyphenol oxidase (PPO), phenylalanine ammonialyase (PAL), lipoxygenase (LOX), ascorbate peroxidase (APX), monodehydroascorbate reductase (MDHAR), dehydroascorbate reductase (DHAR), and glutathione reductase (GR), and the content of phytohormones, salicylic acid (SA) and jasmonic acid (JA), in cherry plants before and after infection. Furthermore, we assayed the expression of genes encoding these enzymes and SA and JA biosynthesis genes using quantitative real-time PCR, and examined the morphology of the infected tissue surface. Infection with A. tumefaciens increased the activity of POD, SOD, PPO, APX, MDHAR, and GR, and upregulated the expression of the corresponding genes. It also elevated the JA content of cherry plants. No significant difference was noted in CAT and PAL activity between the infected and control groups. In the treatment group, a slight increase in LOX activity was observed at 15 days post-infection (dpi), whereas DHAR activity declined by almost 50% at 10 dpi. The total SA content showed a general upward trend in infected plants but did not show a clear difference compared with the control. Overall, our data suggest that Agrobacterium infection did not elicit a hypersensitive response in ‘Gisela 6’ but altered the expression level of genes involved in defense responses and phytohormone biosynthesis.