Controllability on impulsive systems with delays in both input and impulse and its applications to multi-agent networks.

This paper investigates the controllability of impulsive systems with input delay and impulse delay and its applications in multi-agent networks. We adopt the geometric and algebraic analytical tools to establish some easily verified controllability conditions for the considered system model. First, the analytic solution of the considered system is established on every impulsive interval by using ordinary differential equation theory. Then, according to the solution derived, some sufficient complete controllability criteria are developed to reveal the role of the Gramian matrices in different subintervals. By introducing a row matrix of different kinds of Gramian matrices, a complete controllability condition that is proved to be necessary and sufficient is further obtained. By using the relevant geometric matrix theory, the derived algebraic controllability condition is then converted to a geometric one. On other hand, we introduce a multi-agent network with delayed input and impulse and investigate its controllability. By resorting to graph theory and matrix theory, several factors affecting the controllability of the considered multi-agent networks are investigated, such as the topology structure, the inner coupling matrix, and the dynamics of each agent. Finally, two numerical examples are worked out to verify the derived controllability criteria.

Constructing Multiscroll Memristive Neural Network With Local Activity Memristor and Application in Image Encryption.

Memristor possesses synapse-like properties that can mimic excitation and inhibition between neurons. This article introduces the Sigmoid functions to the memristor and constructs a new memristive Hopfield neural network (HNN). Its most distinctive feature is the simple topology, which contains only unidirectional connections in neurons. The equilibrium points analysis reveals the mechanism of its multiscroll attractors generation. Homogeneous and heterogeneous coexisting attractors are observed with the variation of the network parameters. Note that the state equation of memristor can affect the number of coexisting attractors. A hardware implementation is designed for it, and the multiscroll attractors are captured in the oscilloscope. Finally, it is also applied to developing an image encryption algorithm with excellent performance.

Observer-Based Dynamic Event-Triggered Semiglobal Bipartite Consensus of Linear Multi-Agent Systems With Input Saturation.

Observer-based dynamic event-triggered semiglobal bipartite consensus (SGBC) is investigated for linear multi-agent systems (MASs) with input saturation under a competitive network. Based on the estimated relative information and low-gain feedback technology, distributed dynamic event-triggered control (DETC) protocols are proposed for solving the observer-based SGBC problems for MASs under a fixed topology and a jointly connected topology, respectively. It is turned out that the SGBC of MASs can be achieved under the proposed protocols. By using gauge transformation and the Lyapunov theory, the bipartite consensus conditions are obtained. Moreover, Zeno behaviors will be excluded. Finally, two simulation examples are presented to verify the theoretical results efficiently.

Neuroscience and Network Dynamics Toward Brain-Inspired Intelligence.

This article surveys the interdisciplinary research of neuroscience, network science, and dynamic systems, with emphasis on the emergence of brain-inspired intelligence. To replicate brain intelligence, a practical way is to reconstruct cortical networks with dynamic activities that nourish the brain functions, instead of using only artificial computing networks. The survey provides a complex network and spatiotemporal dynamics (abbr. network dynamics) perspective for understanding the brain and cortical networks and, furthermore, develops integrated approaches of neuroscience and network dynamics toward building brain-inspired intelligence with learning and resilience functions. Presented are fundamental concepts and principles of complex networks, neuroscience, and hybrid dynamic systems, as well as relevant studies about the brain and intelligence. Other promising research directions, such as brain science, data science, quantum information science, and machine behavior are also briefly discussed toward future applications.

Multiagent Meta-Reinforcement Learning for Adaptive Multipath Routing Optimization.

In this article, we investigate the routing problem of packet networks through multiagent reinforcement learning (RL), which is a very challenging topic in distributed and autonomous networked systems. In specific, the routing problem is modeled as a networked multiagent partially observable Markov decision process (MDP). Since the MDP of a network node is not only affected by its neighboring nodes' policies but also the network traffic demand, it becomes a multitask learning problem. Inspired by recent success of RL and metalearning, we propose two novel model-free multiagent RL algorithms, named multiagent proximal policy optimization (MAPPO) and multiagent metaproximal policy optimization (meta-MAPPO), to optimize the network performances under fixed and time-varying traffic demand, respectively. A practicable distributed implementation framework is designed based on the separability of exploration and exploitation in training MAPPO. Compared with the existing routing optimization policies, our simulation results demonstrate the excellent performances of the proposed algorithms.

Resilient Delayed Impulsive Control for Consensus of Multiagent Networks Subject to Malicious Agents.

Impulsive control is widely applied to achieve the consensus of multiagent networks (MANs). It is noticed that malicious agents may have adverse effects on the global behaviors, which, however, are not taken into account in the literature. In this study, a novel delayed impulsive control strategy based on sampled data is proposed to achieve the resilient consensus of MANs subject to malicious agents. It is worth pointing out that the proposed control strategy does not require any information on the number of malicious agents, which is usually required in the existing works on resilient consensus. Under appropriate control gains and sampling period, a necessary and sufficient graphic condition is derived to achieve the resilient consensus of the considered MAN. Finally, the effectiveness of the resilient delayed impulsive control is well demonstrated via simulation studies.

Prevalence and risk factors of severe postpartum hemorrhage: a retrospective cohort study.

BACKGROUND: Although maternal deaths are rare in developed regions, the morbidity associated with severe postpartum hemorrhage (SPPH) remains a major problem. To determine the prevalence and risk factors of SPPH, we analyzed data of women who gave birth in Guangzhou Medical Centre for Critical Pregnant Women, which received a large quantity of critically ill obstetric patients who were transferred from other hospitals in Southern China. METHODS: In this study, we conducted a retrospective case-control study to determine the prevalence and risk factors for SPPH among a cohort of women who gave birth after 28 weeks of gestation between January 2015 and August 2019. SPPH was defined as an estimated blood loss ≥1000 mL and total blood transfusion≥4 units. Logistic regression analysis was used to identify independent risk factors for SPPH. RESULTS: SPPH was observed in 532 mothers (1.56%) among the total population of 34,178 mothers. Placenta-related problems (55.83%) were the major identified causes of SPPH, while uterine atony without associated retention of placental tissues accounted for 38.91%. The risk factors for SPPH were maternal age < 18 years (adjusted OR [aOR] = 11.52, 95% CI: 1.51-87.62), previous cesarean section (aOR = 2.57, 95% CI: 1.90-3.47), history of postpartum hemorrhage (aOR = 4.94, 95% CI: 2.63-9.29), conception through in vitro fertilization (aOR = 1.78, 95% CI: 1.31-2.43), pre-delivery anemia (aOR = 2.37, 95% CI: 1.88-3.00), stillbirth (aOR = 2.61, 95% CI: 1.02-6.69), prolonged labor (aOR = 5.24, 95% CI: 3.10-8.86), placenta previa (aOR = 9.75, 95% CI: 7.45-12.75), placenta abruption (aOR = 3.85, 95% CI: 1.91-7.76), placenta accrete spectrum (aOR = 8.00, 95% CI: 6.20-10.33), and macrosomia (aOR = 2.30, 95% CI: 1.38-3.83). CONCLUSION: Maternal age < 18 years, previous cesarean section, history of PPH, conception through IVF, pre-delivery anemia, stillbirth, prolonged labor, placenta previa, placental abruption, PAS, and macrosomia were risk factors for SPPH. Extra vigilance during the antenatal and peripartum periods is needed to identify women who have risk factors and enable early intervention to prevent SPPH.

Consensus Tracking Control of Uncertain Multiagent Systems With Sampled Data and Time-Varying Delay.

In this article, the adaptive consensus tracking control is developed for uncertain multiagent systems with time-varying state delay in the case that leader's state is accessible at sampling instants. By proposing a distributed sampled observer with hybrid form, adaptive tracking controller with the complementary term is designed for first-order multiagent systems, and then is extended to high-order multiagent systems with the aid of dynamic surface control. Through the complementary term, the effects of parameter estimation error as well as dynamical terms with time-varying delays are eliminated and thus less conservative condition on time delays is required. It is proved that, under criteria in terms of linear matrix inequalities (LMIs), tracking error and estimation error exponentially converge to zero for first-order systems, and to a sufficiently small neighborhood of zero for high-order systems.

Hybrid Neural Adaptive Control for Practical Tracking of Markovian Switching Networks.

While neural adaptive control is widely used for dealing with continuous- or discrete-time dynamical systems, less is known about its mechanism and performance in hybrid dynamical systems. This article develops analytical tools to investigate the neural adaptive tracking control of the hybrid Markovian switching networks with heterogeneous nonlinear dynamics and randomly switched topologies. A gradient-descent adaptation law built on neural networks (NNs) is presented for efficient distributed adaptive control. It is shown that the proposed control scheme can guarantee a stable closed-loop error system for any positive control gain and tuning gain. The tracking error is demonstrated to be practically uniformly exponentially stable with a threshold in the mean-square sense. This study further reveals how the topological structure affects the NN function, by measuring the influence of the switched topologies on the learning performance.

Distributed Control of Nonlinear Multiagent Systems With Unknown and Nonidentical Control Directions via Event-Triggered Communication.

In this paper, the leader-following output consensus problem for a class of uncertain nonlinear multiagent systems with unknown control directions is investigated. Each agent system has nonidentical dynamics and is subject to external disturbances and uncertain parameters. The agents are connected through a directed and jointly connected switching network. A novel two-layer distributed hierarchical control scheme is proposed. In the upper layer, to save the communication resources and to handle the switching networks, an event-triggered communication scheme is proposed, and a Zeno-free event-triggered mechanism is designed for each agent to generate the asynchronous triggering time instants. Furthermore, to avoid the continuous monitoring of the system states, a Zeno-free self-triggering algorithm is proposed. In the lower layer, to handle the unknown control directions problem and to achieve the output tracking of the local references generated in the upper layer, the Nussbaum-type function-based technique is combined with internal model principle. With the proposed two-layer distributed hierarchical controller, the leader-following output consensus is achieved. The obtained result is further extended to the formation control problem. Finally, three numerical examples are provided to demonstrate the effectiveness of the proposed theoretical results.

Delayed Impulsive Control for Consensus of Multiagent Systems With Switching Communication Graphs.

Delayed impulsive controllers are proposed in this paper to enable the agents in a class of second-order multiagent systems (MASs) to achieve state consensus, based, respectively, on the relative full-state and partial-state sampled-data measurements among neighboring agents. It is a challenging task to analyze the consensus behaviors of the considered MASs as the dynamics of such MASs will be subjected to joint effects from delay-dependent impulses, aperiodic sampling, and switchings among different communication graphs. A novel analytical approach, based upon the discretization method, state augmentation, and linear state transformation, is developed to establish the sufficient consensus criteria on the range of the impulsive intervals and the control parameters. Remarkably, it is found that consensus in the closed-loop MASs can be always ensured by skillfully selecting the control parameters as long as the nonuniform delays and the impulsive intervals are bounded. A numerical example is finally performed to validate the effectiveness of the proposed delayed impulsive controllers.

A study on 85Kr measurement with an internal gas proportional counter.

A measurement method of 85Kr using an internal gas proportional counter (IGPC) is presented in this study. The operation conditions of the IGPC were determined and optimized, including the operating voltage, pressure, sample volume, interference from other gas components such as nitrogen or air, and mitigation of the memory effect. The IGPC was calibrated using certified standards, and the detection efficiency was approximately 58% for typical samples. A lower limit of detection of approximately 0.11 MBq/m3(Kr) was achieved after counting for 5 h with 1 mL pure Kr, corresponding to the atmospheric activity concentration of 0.18 Bq/m3 (air). It was shown that the IGPC could be used effectively for measuring 85Kr.

Consensus Problems Over Cooperation-Competition Random Switching Networks With Noisy Channels.

In this paper, distributed iterative algorithms for consensus problems are considered for multiagent networks. Each agent randomly contacts with other agents at each instant and receives corrupted information due to the noisy channel from its neighborhood. Neighbors of each agent are cooperative or competitive, i.e., the elements in the adjacent weight matrix may be positive or negative. In such a framework, asymptotic consensus and mean square consensus problems are investigated, based on random graph theory and stochastic stability theory. The control gains have been designed such that cooperation-competition random multiagent networks can reach almost sure consensus and mean square consensus. Simulation examples are finally given to illustrate the effectiveness of the obtained results.

Multistability of Delayed Hybrid Impulsive Neural Networks With Application to Associative Memories.

The important topic of multistability of continuous-and discrete-time neural network (NN) models has been investigated rather extensively. Concerning the design of associative memories, multistability of delayed hybrid NNs is studied in this paper with an emphasis on the impulse effects. Arising from the spiking phenomenon in biological networks, impulsive NNs provide an efficient model for synaptic interconnections among neurons. Using state-space decomposition, the coexistence of multiple equilibria of hybrid impulsive NNs is analyzed. Multistability criteria are then established regrading delayed hybrid impulsive neurodynamics, for which both the impulse effects on the convergence rate and the basins of attraction of the equilibria are discussed. Illustrative examples are given to verify the theoretical results and demonstrate an application to the design of associative memories. It is shown by an experimental example that delayed hybrid impulsive NNs have the advantages of high storage capacity and high fault tolerance when used for associative memories.

A correlation study of continuously monitored gamma dose rate and meteorological conditions.

In this paper, the correlations between the continuously monitored gamma dose rate (GDR) and meteorological parameters, including precipitation, air temperature, relative humidity, air pressure, wind direction, and wind speed, were analyzed by using one year of the hourly dataset from a monitoring system with ten stations. The correlation coefficients are varied by the range of each meteorological parameter. Precipitation would enhance the GDR up to 84%, which is highly related to precipitation intensity and ground type. Strong and positive correlation between the GDR and light precipitation was identified, while the correlation was reduced with increasing of precipitation. Air temperature could cause a fluctuation of the average GDR within the range 1.8-5.3 nGy h-1, and different correlation characteristics were indicated for low and high air temperature. The GDR was positively correlated with relative humidity, though relative humidity is inversely correlated with air temperature. Correlations between the GDR and air pressure were mainly negative. Diurnal variations between the GDR and the air temperature, relative humidity, and air pressure were also analyzed. The wind played an important role also in the fluctuation of the GDR with the GDR difference up to 2.00 nGy h-1 averaged from the sixteen wind-directions. Lower GDR can be found in the direction of prevailing wind because of the dilution effect of the radon progenies in the surface air. In this paper, some exploratory interpretation of physical influence mechanisms of meteorological parameters on the GDR was also presented, which suggests further work should be carried out to explore the variation and correlation principle.

Dynamic Analysis of Hybrid Impulsive Delayed Neural Networks With Uncertainties.

Neural networks (NNs) have emerged as a powerful illustrative diagram for the brain. Unveiling the mechanism of neural-dynamic evolution is one of the crucial steps toward understanding how the brain works and evolves. Inspired by the universal existence of impulses in many real systems, this paper formulates a type of hybrid NNs (HNNs) with impulses, time delays, and interval uncertainties, and studies its global dynamic evolution by a robust interval analysis. The HNNs incorporate both continuous-time implementation and impulsive jump in mutual activations, where time delays and interval uncertainties are represented simultaneously. By constructing a Banach contraction mapping, the existence and uniqueness of the equilibrium of the HNN model are proved and analyzed in detail. Based on nonsmooth Lyapunov functions and delayed impulsive differential equations, new criteria are derived for ensuring the global robust exponential stability of the HNNs. Convergence analysis together with illustrative examples show the effectiveness of the theoretical results.

Multisynchronization of Coupled Heterogeneous Genetic Oscillator Networks via Partial Impulsive Control.

This paper focuses on the collective dynamics of multisynchronization among heterogeneous genetic oscillators under a partial impulsive control strategy. The coupled nonidentical genetic oscillators are modeled by differential equations with uncertainties. The definition of multisynchronization is proposed to describe some more general synchronization behaviors in the real. Considering that each genetic oscillator consists of a large number of biochemical molecules, we design a more manageable impulsive strategy for dynamic networks to achieve multisynchronization. Not all the molecules but only a small fraction of them in each genetic oscillator are controlled at each impulsive instant. Theoretical analysis of multisynchronization is carried out by the control theory approach, and a sufficient condition of partial impulsive controller for multisynchronization with given error bounds is established. At last, numerical simulations are exploited to demonstrate the effectiveness of our results.

Cluster Synchronization of Coupled Genetic Regulatory Networks With Delays via Aperiodically Adaptive Intermittent Control.

In this paper, we study the cluster synchronization of coupled genetic regulatory networks with time-varying delays via aperiodically adaptive intermittent control on some nodes. The network is intermittently coupled and the intra-cluster coupling strength is adaptively adjusted. The graph of the coupling topology of each cluster is only required to have a directed spanning tree. Two cases of delays are considered. In the first case, by using the switching Lyapunov-based function and Razumikhin-type technique, the cluster synchronization criterion is presented which indicates that the cluster synchronization is realized via the aperiodically adaptive intermittent control. The second case is investigated by using the switching Lyapunov functional. Both the cluster synchronization criteria are established by the Linear Matrix Inequalities (LMIs), the lower bound of the aperiodical time span, and the maximum uncontrolled ratio. It is shown that the results are applicable to both the situations that the upper bound of the delay is larger and smaller than the lower bound of the aperiodical coupling and control width. Numerical simulations are carried out to illustrate the theoretical results.

Multi-formation control of nonlinear leader-following multi-agent systems.

This paper deals with the multi-formation control problem for nonlinear leader-following multi-agent systems. Both the fixed topology case and the switching topology case are considered. The neighbor-based multi-formation control protocols are proposed under the assumption that for one subgroup, the total information received from other subgroups is zero. Then, based on the Lyapunov stability theory combined with the algebraic graph theory, sufficient conditions are established to ensure that the leader-following multi-agent systems with nonlinear dynamics can reach and maintain the desired multi-formation control. Finally, simulation examples are provided to illustrate the effectiveness of the theoretical results.

Multistability and Bifurcation Analysis of Inhibitory Coupled Cyclic Genetic Regulatory Networks With Delays.

Many biological systems have the conspicuous property to present more than one stable state and diverse rhythmic behaviors. A closed relationship between these complex dynamic behaviors and cyclic genetic structures has been witnessed by pioneering works. In this paper, a typical structure of inhibitory coupled cyclic genetic networks is introduced to further enlighten this mechanism of stability and biological rhythms of living cells. The coupled networks consist of two identical cyclic genetic subnetworks, which inhibit each other directly. Each subnetwork can be regarded as a genetic unit at the cellular level. Multiple time delays, including both internal and coupling delays, are considered. The existence of positive equilibriums for this kind of coupled systems is proved, and the stability for each equilibrium is analyzed without or with delays. It is shown that the coupled networks with positive cyclic genetic units have an ability to show multistability, while the coupled networks with negative units may present a series of Hopf bifurcations with the variation of time delays. Several numerical simulations are made to prove our results.