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Graphene has exceptional electronic, mechanical, and thermal properties, and it is expected to have important applications in integrated circuits and other microelectronic fields. Its performances are greatly affected by surface adhesion force when it is used in a humid environment. In this paper, based on the law of variable water contact angle changing in the process of water vapor condensation, we established a cone-plane contact model, which is related to relative humidity and dwell time, to reveal the internal mechanism of the influence of relative humidity and dwell time on silica/graphene adhesion force. First, the silica/graphene adhesion force dependence of dwell time was measured by atomic force microscopy (AFM) at 45-85% RH. Then, the changing process of the meniscus between the AFM tip and the graphene surface was discussed, and the function of adhesion force with variables of dwell time and contact angle was established. Furthermore, the theoretical and experimental results were compared and analyzed. The results show that with the increase of relative humidity and dwell time, the capillary condensation increases, but the water contact angle of the cone material decreases. This causes the adhesion force to increase first and then decrease after it reaches a threshold value. Furthermore, the variable water contact angle of the graphene surface increases, but the adhesion force decreases gradually with the increase of surface water film. The theoretical results are in good agreement with the experimental results.

In this article, the quasi-consensus problem of the Markovian switching heterogeneous multi-agent system with cooperative–competitive interactions and parameter uncertainties is investigated. The switch of the agent’s dynamics is combined with Markov processes, and the topologies among agents switch between cooperation and competition. Based on graph theory and Lyapunov function theory, the sufficient conditions are given to ensure the quasi-consensus of the Markovian switching multi-agent system. Finally, a numerical simulation is given to illustrate the validity of the theoretical results.

In order to study the impact of continuous vaccination and voluntary isolation for the COVID-19, a susceptible-exposed-infected-recovered-quarantine-vaccines (SEIR-QV) model is proposed. A basic regeneration number R 0 is defined to determine the extinction or persistence of the disease. We numerically analyze the impact of key parameters based on actual parameters of COVID-19, such as the vaccination rate, population importation rate, and natural (or causal) mortality transmission rate on the dynamics of disease transmission. Then we obtain sensitivity indices of some parameters on R 0 by sensitivity analysis. Finally, the stability of the system and the effectiveness of the optimal control strategy are verified by numerical simulation.

Based on the SEIR model, which takes into account prevention and control measures, prevention and control awareness, and economic level and medical level indicators, this paper proposes an infectious disease model of “susceptible-exposed-infected-removed-asymptomatic-isolated” (short for SEIR-AQ) to assess and predict the development of the COVID-19 pandemic with different prevention and control strategies. The kinetic parameters of the SEIR-AQ model were obtained by fitting, and the parameters of the SEIR-AQ model were solved through the Euler method. Furthermore, the effects of different countries’ prevention and control strategies on the number of infections, the proportion of isolation, the number of deaths, and the number of recoveries were also simulated. The theoretical analysis showed that measures such as isolation for prevention and control and medical tracking isolation had a significant inhibitory effect on the development of the COVID-19 pandemic, among which stratified treatment and enhanced awareness played a key role in the rapid regression of the peak of COVID-19-infected patients. Conclusion of the Simulation. The SEIR-AQ model can be used to evaluate the development status of the COVID-19 epidemic and has some theoretical value for the prediction of COVID-19.

This paper investigates the problem of sliding mode control (SMC) for a class of discrete-time stochastic systems subject to hybrid cyber attacks. Both stochastic nonlinearities and norm-bounded parameter uncertainties are introduced into the system, which occur according to mutually independent Bernoulli-distributed white noise sequences. Moreover, hybrid cyber attacks consisting of denial-of-service attack and deception attack are considered in the transmission channel. By employing the Lyapunov stability theory, sufficient conditions are established for guaranteeing the stability of the state trajectory in the specified sliding surface. Meanwhile, an SMC law is given to ensure the reachability of the specified sliding surface. Finally, a simulation example is put forward to verify the effectiveness of the proposed theoretical results.

This paper studies the consensus of leader-following multiagent systems with nonlinear dynamics. Consider that control protocols for the consensus work under an intermittent framework due to inevitable factors; meanwhile, the event-triggered mechanism is introduced, so as to reduce the update frequency of the control protocols. In particular, threshold parameters in the event-triggered conditions are supposed to be dynamically changed, and the minimum event-triggered intervals are set to be positive in advance. Furthermore, according to whether event-triggered conditions depend on combined measurements or a single measurement, two forms of event-triggered schemes are designed; then, the corresponding distributed control protocols are demonstrated. Based on the graph theory and Lyapunov function method, sufficient conditions and a concrete algorithm for the consensus of multiagent systems are presented. It is shown that the intermittent dynamic event-triggered protocols given in this paper can effectively reduce the update frequency of control and exclude Zeno behavior. Finally, detailed numerical examples are supplied to illustrate the proposed results.

This paper addresses the bipartite consensus over cooperation-competition networks affected by denial-of-service (DoS) attacks. Consider that a network consists of multiple interactive agents, and the relationship between neighboring agents is cooperative or competitive. Meanwhile, information transmission among the agents is unavailable during the intervals of attacks. In order to save communication resources and exclude the Zeno behavior, an event-triggered scheme depending on the sampled-data information from neighboring agents is proposed, and efficient defense strategies in response to the attacks are put forward. Suppose that the frequency and duration of DoS attacks meet certain requirements, then according to the signed graph theory, the LaSalle’s invariance principle, and the convergence of monotone sequences, the results of bipartite consensus via the event-triggered protocol are provided, which are mainly related to the communication topology of the network, the sampling period, and the threshold parameters in the event-triggered scheme. It is shown that the bipartite consensus is realized even though the DoS attacks take place frequently. Furthermore, this paper discusses the bipartite consensus in the presence of DoS attacks with a random unsuccessful rate. Finally, numerical simulations illustrate the theoretical results.

State-of-charge (SOC) plays an important role in the battery management system, and the accuracy of its estimation directly affects the efficiency and life of the lithium battery. In this paper, a bidirectional gate recurrent unit neural network based on the attention mechanism is proposed for SOC estimation. The nesterov adaptive momentum optimized algorithm is developed to update weight matrices of the neural network. This method has several advantages over the traditional methods and structures: (1) the proposed structure can well catch the dynamics of the SOC when compared with the traditional neural network structures; (2) the proposed algorithm has faster convergence rates than the momentum gradient descent algorithm. The simulation examples show the effectiveness of the proposed algorithm and structure.

Optical glass-microprism arrays are generally embossed at high temperatures, so an online cooling process is needed to remove thermal stress, but this make the cycle long and its equipment expensive. Therefore, the hot-embossing of a glass-microprism array at a low strain rate with reasonable embossing parameters was studied, aiming at reducing thermal stress and realizing its rapid microforming without online cooling process. First, the flow-field, strain-rate, and deformation behavior of glass microforming were simulated. Then, the low-cost microforming control device was designed, and the silicon carbide (SiC) die-core microgroove array was microground by the grinding-wheel microtip. Lastly, the effect of the process parameters on forming rate was studied. Results showed that the appropriate embossing parameters led to a low strain rate, then, the trapezoidal glass-microprism array could be formed without an online cooling process. The standard deviation of the theoretical and experimental forming rates was only 7%, and forming rate increased with increasing embossing temperature, embossing force, and holding duration, but cracks and adhesion occurred at a high embossing temperature and embossing force. The highest experimental forming rate reached 66.56% with embossing temperature of 630 &deg, C, embossing force of 0.335 N, and holding duration of 12 min.

This study proposes two improved gradient descent parameter estimation algorithms for rational state-space models with time-delay. These two algorithms, based on intelligent search method and momentum method, can simultaneously estimate the time-delay and parameters without the matrix eigenvalue calculation in each iteration. Compared with the traditional gradient descent algorithm, the improved algorithms come with two advantages: having quicker convergence rates and less computational efforts, particularly meaningful for those large-scale systems. A simulated example is selected to illustrate the efficiency of the proposed algorithms.

Polypropylene/graphite intercalation compound (PP/GIC) composites are prepared via melt mixing at three different temperatures (180, 200, and 220 °C). The dispersion of GICs in the composites is clearly improved due to the increased interlamellar spacing caused by in situ expansion of GICs at higher temperatures, which facilitates the intercalation of PP molecular chains into the GIC galleries. As a result, the PP/GIC composite with 10 wt% GICs prepared at 220 °C (PG220) presents a dielectric constant of about 1.3 × 108 at 103 Hz, which is about six orders higher than that of the composite prepared at 180 °C (PG180). Moreover, the thermal conductivity of the PG220 sample (0.63 Wm−1K−1) is 61.5% higher than that of the PG180 sample. The well-dispersed GICs accelerates the crystallization of PP by increasing the nucleation point and enhances the thermal stability of the composites. The PG220 sample shows a Young’s modulus that is about 21.2% higher than that of the PG180 samples. The results provide an efficient approach for fabricating polymer/GIC composites without complex exfoliation and dispersion processes.

In this paper, the problem of finite-time stabilisation is firstly studied for the Takagi–Sugeno T–S fuzzy model system with channel fading and parameter uncertainty. Two theorems are given for the cases with different types of uncertainty. The sufficient conditions in the form of the linear matrix inequalities are derived such that the stabilisation of the closed-loop system is guaranteed. At last, some illustrative examples are employed to demonstrate the efficiency of the results.

In this paper, the finite-time boundedness problem of a multi-layer switched system subject to average dwell time switching signal, Markov jump and time-varying polytopic uncertainties is investigated. The sufficient conditions guaranteed the conclusion are shown in the form of linear matrix inequalities to ease the formulation, and the switched Lyapunov-Krasovskii approach is used in detail proofs. At last, an illustrative example is employed to demonstrate the efficiency of the main result.

This paper considers the issue of cluster consensus for multiple agents in fixed and undirected networks. Agents in a network are supposed to split into several clusters, and a fraction of the agents in each cluster are pinned by virtual leaders. According to the Lyapunov stability theory and graph theory, some appropriate event-triggered protocols are developed for consensus of the agents belonging to the same cluster, which can greatly reduce both the number of communication updates and that of control actuation updates. Finally, a numerical example is shown to demonstrate the effectiveness of the proposed theoretical results.

In this paper, the state estimation problem is investigated for a class of fractional-order neural networks. The state of neurons can be estimated by some available output measurements. Some sufficient conditions are derived by checking the Mittag-Leffler stability of the error system. A numerical example is given to illustrate the effectiveness of the proposed method.

This paper proposes distributed bipartite and multipartite tracking consensus for linear multiagent systems (MASs) with arbitrarily nonzero projective parameters in networks, which includes traditional consensus, bipartite consensus and group consensus as its special items. Based on the projective similarity transformation and Riccati inequality, novel types of protocols are designed to achieve bipartite and multipartite consensus exponentially without analysing signed graph as in most current literature on bipartite problem. For obtaining multipartite consensus involving less global information, the distributed protocols with adaptive tuning of the coupling strength are further designed. The theoretical results are illustrated through two numerical simulation examples when linear systems are equilibrium point and periodic states.

This paper investigates the issue of mean square consensus for multiple agents affected by noises over directed networks. The centralized and decentralized event-triggered protocols are adopted for driving the agents to converge to the average value of their initial states eventually. Each agent in the network is supposed to update the state according to the information from the neighbors, while the control actuation updates are decided by the proposed event-triggered scheme. The corresponding conditions for guaranteeing the consensus are derived based on the graph theory, the Lyapunov functional approach, and the stochastic theory. Additionally, the consensus for agents in networks with switching topologies is also analyzed. Some numerical examples are presented to demonstrate the effectiveness of the theoretical results.

This paper is concerned with the issue of mean square cluster synchronization of non-identical nodes connected by a directed network. Suppose that the nodes possess nonlinear dynamics and split into several clusters, then an event-triggered control scheme is proposed for synchronization based on the information from stochastic sampling. Meanwhile, an equilibrium is considered to be the synchronization state or the virtual leader for each cluster, which can apply pinning control to the following nodes. Assume that a spanning tree exists in the subgraph consisting of the nodes belonging to the same cluster and the corresponding virtual leader, and the instants for updating controllers are determined by the given event-triggered strategy, then some sufficient conditions for cluster synchronization are presented according to the Lyapunov stability theory and linear matrix inequality technique. Finally, a specific numerical example is shown to demonstrate the effectiveness of the theoretical results.

This paper investigates the projective cluster synchronization of fractional-order coupled-delay complex network via adaptive pinning control. The pinning control method is employed in designing controllers for guaranteeing projective cluster synchronization. Based on the Lyapunov stability theory and well-known Barbalat’s lemma, some new sufficient conditions are derived to guarantee the projective cluster synchronization of the fractional-order complex network. A numerical simulation is given to verify the effectiveness of the theoretical results.

This study investigates the issue of mean square consensus for multiple agents connected by a directed network. The graph of the network is supposed to have a spanning tree and each agent is taken as a Markovian jumping system. By utilising the event-triggered strategy, some sufficient conditions for consensus are presented whether the transition rates for the Markov chain being completely known or not. Furthermore, the event-triggered function designed in this study is dependent on the stochastic sampled-data from neighbouring agents. Theoretical results are provided according to the graph theory, Lyapunov functional and linear matrix inequality approach. Finally, a numerical example is given to demonstrate the effectiveness of the theoretical analysis.

This paper has investigated the existence, uniqueness and the global asymptotic stability of equilibrium point for delayed fractional-order neural networks with impulsive effects. A lemma has been given based on Riemann-Liouville operator, which plays an important role in the stability analysis. Some sufficient conditions are derived to ensure the existence, uniqueness and the asymptotic stability of the fractional-order neural networks. An illustrative example is given to show the effectiveness of the obtained results by using a new numerical method of fractional-order differential equations.

For multi-agent systems, significantly adding to the complexity of dynamical behaviors are intrinsic nonlinearity and stochastic noises due to environmental uncertainties. This paper deals with the mean square bounded consensus problem of multi-agent systems with intrinsic nonlinear dynamics, where each agent is affected by stochastic noises. Considering the impact of the former behaviors of nonlinear agents, we put forward a novel type of integral distributed delay protocol which is formed as a weighted sum of historical information exchange over all time interval [ t - ? , t ] . Compared with the previous work, the protocol expression of this paper is more general and includes many traditional protocols as its special cases. Based on a Lyapunov-based approach, together with results from matrix theory and algebraic graph theory, sufficient conditions are derived for the mean square bounded consensus. Simulation examples with nonlinear even chaotic agents illustrate the theoretical results. HighlightsA novel type of integral distributed delay protocol is proposed to control multi-agent systems.Sufficient conditions for the mean square bounded consensus are established.Numerical examples of complex nonlinear systems verify the effectiveness of the new protocol.

For the multi-agent community, one of the key challenges is the agents operating in open environments, in which stochastic noises affect the dynamics of agents. This paper deals with the leader-following consensus problem for a class of linear multi-agent systems model with randomly occurring nonlinearities and uncertainties, and stochastic disturbances. The communication topology is assumed to be undirected and fixed. The stochastic Brownian motions are used to describe the source of extrinsic disturbances. The randomly occurring nonlinearities are introduced to describe the nonlinear intrinsic dynamics occurring in a probabilistic way. The randomly occurring uncertainties are adopted to reflect more realistic dynamical behaviors of the agent systems that are caused by noisy environment. Sufficient conditions are derived to make all follower agents asymptotically reach the state of leader in the mean square sense. Simulation results illustrate the theoretical results.

This paper is concerned with the issue of mean square cluster synchronisation in complex networks, which consist of non-identical nodes with randomly occurring non-linearities. In order to guarantee synchronisation, distributed controllers depending on the information from the neighbours in the same cluster are applied to each node, meanwhile, the control gains are supposed to be updated according to the given laws. Based on the Lyapunov stability theory, the sufficient synchronisation conditions are derived and proved theoretically. Finally, a numerical example is presented to demonstrate the effectiveness of the results.

This paper is concerned with the consensus of second-order multi-agent systems with a leader and switching topologies. A delayed impulsive consensus protocol is proposed, which only depends on the agent's own information and its neighbour's partial information. Based on a Lyapunov function approach, sufficient conditions for the convergence to consensus are established, which provides the appropriate impulsive delay interval on the admissible switching topology. Finally, numerical simulations are provided to illustrate the theoretical results.