A Modular Event-Triggered Containment Control Scheme for Nonlinear Heterogeneous Multiagent Systems With Unknown Leaders.

This article addresses the containment control problem in multiagent systems with nonlinear heterogeneous followers and multiple unknown leaders whose dynamics are exclusively known to their neighbors. The primary goal is to ensure the convergence of each follower to the dynamic convex hull spanned by the leaders under the constraints of limited communication resources. To achieve this, this article introduces a modular event-triggered containment control scheme with three modules. The first module, Module I-signal generator, is designed for each follower to generate a reference signal asymptotically entering the dynamic convex hull without relying on follower dynamics. The second module, Module II-event-triggered mechanism, is tailored to save communication resources effectively by determining when to broadcast information based on perturbed system stability and input-to-state stability theories. The third module, Module III-tracking controller, treats each follower as an independent agent and is crafted to track the reference signal generated by Module I using an output regulation approach. It is established that the system achieves containment control without Zeno behavior under the influence of these modules, and the theoretical results are validated through simulation examples, demonstrating the practical validity of the proposed approach.

Lactobacillus plantarum ST-III culture supernatant protects against acute alcohol-induced liver and intestinal injury.

The beneficial effects of probiotics have been studied in inflammatory bowel disease, nonalcoholic steatohepatitis, and alcoholic liver disease (ALD). Probiotic supplements are safer and more effective; however, their potential mechanisms are unclear. An objective of the current study was to examine the effects of extracellular products of Lactobacillus plantarum on acute alcoholic liver injury. Mice on a standard chow diet were supplemented with Lactobacillus plantarum ST-III culture supernatant (LP-cs) for two weeks and administered alcohol at 6 g/kg body weight by gavage. Alcohol-induced liver injury was assessed by measuring plasma alanine aminotransferase activity levels and triglyceride content determined liver steatosis. Intestinal damage and tight junctions were assessed using histochemical staining. LP-cs significantly inhibited alcohol-induced fat accumulation, inflammation, and apoptosis by inhibiting oxidative stress and endoplasmic reticulum stress. LP-cs significantly inhibited alcohol-induced intestinal injury and endotoxemia. These findings suggest that LP-cs alleviates acute alcohol-induced liver damage by inhibiting oxidative stress and endoplasmic reticulum stress via one mechanism and suppressing alcohol-induced increased intestinal permeability and endotoxemia via another mechanism. LP-cs supplements are a novel strategy for ALD prevention and treatment.

A New Grey Wolf Optimizer Tuned Extended Generalized Predictive Control for Distillation Process.

The distillation process plays an essential role in the petrochemical industry. However, the high-purity distillation column has complicated dynamic characteristics such as strong coupling and large time delay. To control the distillation column accurately, we proposed an extended generalized predictive control (EGPC) method inspired by the principles of extended state observer and proportional-integral-type generalized predictive control method; the proposed EGPC can adaptively compensate the system for the effects of coupling and model mismatch online and performs well in controlling time-delay systems. The strong coupling of the distillation column needs fast control, and the large time delay requires soft control. To balance the requirement for fast and soft control at the same time, a grey wolf optimizer with reverse learning and adaptive leaders number strategies (RAGWO) was proposed to tune the parameters of EGPC, and these strategies enable RAGWO to have a better initial population and improve its exploitation and exploration ability. The benchmark test results indicate that the RAGWO outperforms the existing optimizers for most of the selected benchmark functions. Extensive simulations show that the proposed method in terms of fluctuation and response time is superior to other methods for controlling the distillation process.

Lactobacillus plantarum-Derived Postbiotics Ameliorate Acute Alcohol-Induced Liver Injury by Protecting Cells from Oxidative Damage, Improving Lipid Metabolism, and Regulating Intestinal Microbiota.

Here, the aim was to evaluate the protective effect of Lactobacillus plantarum-derived postbiotics, i.e., LP-cs, on acute alcoholic liver injury (ALI). After preincubation with LP-cs, HL7702 human hepatocytes were treated with alcohol, and then the cell survival rate was measured. C57BL/6 male mice were presupplemented with or without LP-cs and LP-cs-loaded calcium alginate hydrogel (LP-cs-Gel) for 3 weeks and given 50% alcohol gavage to establish the mouse model of ALI, LP-cs presupplementation, and LP-cs-Gel presupplementation. The histomorphology of the liver and intestines; the levels of serum AST, ALT, lipid, and SOD activity; liver transcriptomics; and the metagenome of intestinal microbiota were detected in all mouse models. In vitro, LP-cs significantly increased the survival rate of alcohol-treated cells. In vivo, presupplementation with LP-cs and LP-cs-Gel restored the levels of serum AST, ALT, and SOD activity, as well as TC and TG, after acute alcohol intake. In the LP-cs-presupplemented mice, the genes involved in fatty acid metabolic processes were upregulated and the genes involved in steroid biosynthesis were downregulated significantly as compared with the ALI mice. LP-cs significantly increased the abundance of intestinal microbiota, especially Akkermansia muciniphila. In conclusion, LP-cs ameliorates ALI by protecting hepatocytes against oxidative damage, thereby, improving lipid metabolism and regulating the intestinal microbiota. The effect of LP-cs-Gel is similar to that of LP-cs.

A State Space Approach to Decentralized Fault SE-Coprognosability of Partially Observed Discrete Event Systems.

The problem of fault prognosis in the context of discrete event systems (DESs) is a crucial subject to study the security and maintenance of cyber-physical systems. In this article, the decentralized fault prognosis of partially observed DESs is analyzed with a universal state-estimate-based protocol. It follows (M,K) as the performance bound of any expected decentralized prognosers, where any fault can be predicted K steps before its occurrence and the fault is guaranteed to occur within M steps once a corresponding fault alarm is issued. To determine whether expected decentralized prognosers exist, the notion of state-estimate-coprognosability (SE-coprognosability) under the case of one fault type is proposed. Compared with existing other kinds of coprognosability, SE-coprognosability is a more generalized concept. Meanwhile, combining the formal method and algebraic state space approach, a novel state estimation algorithm is presented and based on which, the verification of SE-coprognosability is also solved.

Distributed Adaptive Fault-Tolerant Control for Multiagent Systems via Virtual-Actuator-Based Reconfiguration.

This article aims to develop a virtual-actuator-based control scheme for the consensus tracking problem of multiagent systems (MASs) against actuator faults and mismatched disturbances. The proposed scheme has a double-layer structure. In the cyber layer, the nominal controller is designed with neighboring information for the fault-free case. While in the physical layer, the fault compensator, working as the virtual actuator, is applied to reconfigure faulty plants adaptively. This design enjoys the advantages that the nominal controller needs no adjustment and all its properties can be preserved after failure. Moreover, the proposed control scheme is distinguished by the following features: 1) the commonly imposed rank condition on outage faults is removed; 2) the norm bound of the leader's input is allowed to be unknown even though the topologies are switching and directed; and 3) there is no need to use the estimates of faults in the virtual actuator design, which means the negative impacts caused by the inaccurate fault estimation can be avoided. Finally, a numerical example is given to validate the effectiveness of the theoretical results.

Intracellular calcium homeostasis and its dysregulation underlying epileptic seizures.

Biological activities require a delicate balance between excitatory and inhibitory signals in the brain. Disruption of this balance could lead to neurological disorders, such as epilepsydue to a relative enhancement of excitatory signals. In general, cytosolic calcium plays a key role in the transmission of excitatory signals mainly by promoting the release of synaptic vesicles containing neurotransmitters. A series of molecular components responsible for maintaining intracellular calcium homeostasis, including voltage-gated calcium (CaV) channels, the endoplasmic reticulum (ER) calcium sensor stromal interaction molecule (STIM), the PM calcium channel Orai, ER-resident inositol trisphosphate receptors (IP3Rs) and ryanodine receptors (RyRs), sarco-endoplasmic reticulum calcium ATPase (SERCA), and transmembrane and coiled-coil domains 1 (TMCO1), have been demonstrated to be involved in calcium dysregulation that underlies epileptic seizures. More importantly, epileptic phenotypes were confirmed in several molecular components by transgenic animal models, including CACNA1A, CACNA1E, CACNA1G, CACNA2D1, ORAI1 and IP3R1. Calcium-binding proteins (CaBPs), such as calmodulin, parvalbumin, calretinin, and calbindin, provide an additional layer of defense by acting as calcium reservoirs to buffer rapid increases in cytosolic calcium concentrations and participate in cellular functions by regulating the activities of ion channels or acting as calcium-modulated sensors, and a series of lines of evidence support their implication with epileptic activities. Overall, stroke represents the most common environmental cause of acquired epilepsy in older adults, and preventing calcium disruption due to reperfusion injury might be an effective way to treat acute symptomatic seizures and decrease the risk for acquired poststroke epilepsy.

Semi-Global Bipartite Fault-Tolerant Containment Control for Heterogeneous Multiagent Systems With Antagonistic Communication Networks and Input Saturation.

Semi-global bipartite fault-tolerant containment control framework on antagonistic communication networks is proposed in this article for heterogeneous multiagent systems (MASs) under the influence of input saturation and actuator faults. An observer is constructed to estimate the leaders' states on signed digraph, where the communication networks are antagonistic. A fully distributed virtual control approach is developed to acquire the containment trajectory. Based on the observer, a semi-global containment control method is developed to compensate for the detrimental impacts of both input saturation and actuator faults. Besides, the dynamics and state-space dimensions of the agents can be different. The proposed framework overcomes two drawbacks of the conventional containment control: 1) the containment trajectory is obtained under general antagonistic communication networks, which is more general in engineering applications and 2) both actuator faults and input saturation are solved for heterogeneous agents, which relaxes the limitation of homogeneous dynamics. Finally, a simulation example is conducted to test and verify the feasibility of the proposed method framework.

Composite Learning Adaptive Tracking Control for Full-State Constrained Multiagent Systems Without Using the Feasibility Condition.

This article proposes a distributed consensus tracking controller for a class of nonlinear multiagent systems under a directed graph, in which all agents are subject to time-varying asymmetric full-state constraints, internal uncertainties, and external disturbances. The feasibility condition generally required in the existing constrained control is removed by using the proposed nonlinear mapping function (NMF)-based state reconstruction technology, and the Lipschitz condition usually needed in the consensus tracking is also canceled based on the adaptive command-filtered backstepping framework. The composite learning of the neural network-based function approximator (NN-FAP) and the finite-time smooth disturbance observer (DOB) provides a novel scheme for handling internal and external uncertainties simultaneously. One advantage of this scheme is that the use of online historical data of the closed-loop system strengthens the excitation of NN's learning. Another advantage is that the DOB with NN-FAP embedding realizes that the finite-time observation for external disturbance in the case of the system dynamics is unknown. A complete controller design, sufficient stability analysis, and numerical simulation are provided.

Bearing fault diagnosis based on Gramian angular field and DenseNet.

Rolling bearings are the core components of mechanical and electrical systems. A practical fault diagnosis scheme is the key to ensure operational safety. There are excessive characteristic parameters with remarkable randomness and severe signal coupling in the rolling bearing operation, which makes the fault diagnosis to be challenging. To deal with this problem, the Gramian angular field (GAF) and DenseNet are combined to perform feature extraction and fault diagnosis. The GAF can convert 1-dimensional time series into an image, which can guarantee the completeness of feature information without temporal dependence. The GAF images are then trained by using the DenseNet to generate a data set network. In this process, the transfer learning (TL), which can solve the problem of insufficient samples, is integrated to the DenseNet to enhance its extensibility. The comparative simulations are carried out to illustrate the effectiveness of the proposed method.

A Networked Method for Multi-Evidence-Based Information Fusion.

Dempster-Shafer evidence theory is an effective way to solve multi-sensor data fusion problems. After developing many improved combination rules, Dempster-Shafer evidence theory can also yield excellent results when fusing highly conflicting evidence. However, these approaches still have deficiencies if the conflicting evidence is due to sensor malfunction. This work presents a combination method by integrating information interaction graph and Dempster-Shafer evidence theory; thus, the multiple evidence fusion process is expressed as a network. In particular, the credibility of each piece of evidence is obtained by measuring the distance between the evidence first. After that, the credibility of the evidence is evaluated, keeping the unreliable evidence out of the information interaction network. With the fusion of connected evidence, the accuracy of the fusion result is improved. Finally, application results show that the presented method is effective.

DNA Methylation Signature of Epileptic Encephalopathy-Related Pathogenic Genes Encoding Ion Channels in Temporal Lobe Epilepsy.

Epilepsy is characterized by highly abnormal synchronous discharge of brain neurons, and ion channels are fundamental in the generation and modulation of neural excitability. Considering that abnormal methylation can either activate or repress genes, this study was designed to explore the DNA methylation signature of pathogenic genes encoding ion channels in temporal lobe epilepsy (TLE). In total, 38 TLE patients and 38 healthy controls were enrolled in the study, and genomic DNA and total protein of the lymphocytes were extracted from peripheral blood samples to assess methylation and protein levels. The DNA methylation levels of all 12 genes examined were significantly lower in the TLE group than in the control group. After false-positive correction, 83.3% (10/12) of these genes, namely, gamma-aminobutyric acid type A receptor subunit beta1 (GABRB1), gamma-aminobutyric acid type A receptor subunit beta2 (GABRB2), gamma-aminobutyric acid type A receptor subunit beta1 (GABRB3), glutamate ionotropic receptor NMDA type subunit 1 (GRIN1), glutamate ionotropic receptor NMDA type subunit 2A (GRIN2A), glutamate ionotropic receptor NMDA type subunit 2B (GRIN2B), hyperpolarization activated cyclic nucleotide gated potassium channel 1 (HCN1), potassium voltage-gated channel subfamily A member 2 (KCNA2), potassium voltage-gated channel subfamily B member 1 (KCNB1), and potassium sodium-activated channel subfamily T member 1 (KCNT1), were still differentially expressed. Among these ion channels, HCN1 and KCNA2 were selected to evaluate the effects of DNA methylation, and the levels of these proteins were inversely upregulated in the TLE group compared to the control group. As the genes identified as having differential methylation levels are involved in both excitatory and inhibitory ion channels, this study observed by binary logistic regression that hypermethylated GARAB1 was an independent risk factor for TLE, indicating that the overwhelming effect of ion channels on TLE is probably inhibitory from the perspective of DNA methylation. All these findings support the involvement of DNA methylation in TLE pathologies, but the mechanisms need to be further investigated.

Epidemic Propagation With Positive and Negative Preventive Information in Multiplex Networks.

We propose a novel epidemic model based on two-layered multiplex networks to explore the influence of positive and negative preventive information on epidemic propagation. In the model, one layer represents a social network with positive and negative preventive information spreading competitively, while the other one denotes the physical contact network with epidemic propagation. The individuals who are aware of positive prevention will take more effective measures to avoid being infected than those who are aware of negative prevention. Taking the microscopic Markov chain (MMC) approach, we analytically derive the expression of the epidemic threshold for the proposed epidemic model, which indicates that the diffusion of positive and negative prevention information, as well as the topology of the physical contact network have a significant impact on the epidemic threshold. By comparing the results obtained with MMC and those with the Monte Carlo (MC) simulations, it is found that they are in good agreement, but MMC can well describe the dynamics of the proposed model. Meanwhile, through extensive simulations, we demonstrate the impact of positive and negative preventive information on the epidemic threshold, as well as the prevalence of infectious diseases. We also find that the epidemic prevalence and the epidemic outbreaks can be suppressed by the diffusion of positive preventive information and be promoted by the diffusion of negative preventive information.

Fully distributed containment control for second-order multi-agent systems with communication delay.

In this paper, fully distributed containment control problems of multi-agent systems with double-integrator dynamics are investigated under directed topologies. For the cases with and without communication delay, two new fully distributed control protocols are designed, which do not need any global information of the communication topology graph. Necessary and sufficient conditions are obtained to ensure the solvability of the considered containment control problems. Particularly, for the case with communication delay, the critical value of the maximum allowable time delay of containment control is found. Finally, numerical simulations are presented to demonstrate the effectiveness of the theoretical results.

Containment Control for General Second-Order Multiagent Systems With Switched Dynamics.

This paper investigates the distributed containment control problem for a class of general second-order multiagent systems with switched dynamics, which is composed of a continuous-time (CT) subsystem and a discrete-time (DT) subsystem. For this switched multiagent system under fixed directed topology, a distributed containment control protocol is proposed for each follower based on the relative local measurements of neighboring followers and leaders. Some necessary and sufficient conditions are derived under the condition that the network topology contains a directed spanning forest, and these conditions ensure that the general second-order containment control problem can be solved under arbitrary CT-DT switching. If the general second-order system is reduced to the double integrator system, some simpler containment conditions are presented. Furthermore, the similar results are also obtained under switching directed topology. Finally, some simulation examples are presented to show the efficiency of the theoretical results.

A novel complex network link prediction framework via combining mutual information with local naive Bayes.

As an important research direction of complex networks and data mining, link prediction has attracted more and more scholars' attention. In the early research, the common neighbor is regarded as a key factor affecting the formation of links, and the prediction accuracy is improved by distinguishing the contribution of each common neighbor more accurately. However, there is a drawback that the interactions between common neighbors are ignored. Actually, it is not just the interactions between common neighbors, but all the interactions between neighbor sets contribute to the formation of links. Therefore, the core of this work is how to better quantify and balance the contributions caused by common neighbors and the interactions between neighbor sets, so as to improve the accuracy of prediction. Specifically, local naive Bayes and mutual information are utilized to quantify the influence of the two aspects, and an adjustable parameter is introduced to distinguish the two contributions in this paper. Subsequently, the mutual information-based local naive Bayes algorithm is proposed. Simulation experiments are conducted on 5 datasets belonging to different fields, and 9 indexes are utilized for comparison. Numerical simulation results verify the effectiveness of the proposed algorithm for improving link prediction performance.

Measuring the complexity of directed graphs: A polynomial-based approach.

In this paper, we define novel graph measures for directed networks. The measures are based on graph polynomials utilizing the out- and in-degrees of directed graphs. Based on these polynomial, we define another polynomial and use their positive zeros as graph measures. The measures have meaningful properties that we investigate based on analytical and numerical results. As the computational complexity to compute the measures is polynomial, our approach is efficient and can be applied to large networks. We emphasize that our approach clearly complements the literature in this field as, to the best of our knowledge, existing complexity measures for directed graphs have never been applied on a large scale.

Antimicrobial susceptibility, virulence determinants profiles and molecular characteristics of Staphylococcus epidermidis isolates in Wenzhou, eastern China.

BACKGROUND: Staphylococcus epidermidis has emerged as an often encountered pathogen responsible for hospital-acquired infections. The aim of present study is to investigate the microbiological characteristic of S. epidermidis isolates isolated from sterile specimens and skin in a Chinese tertiary hospital. METHODS: A total of 223 non-duplicate S. epidermidis were collected from various sterile specimens of inpatients among 10 years in Wenzhou, China. 106 S. epidermidis obtained from the skin (urethral orifices) of healthy volunteers. All isolates were tested for antimicrobial susceptibility. PCR was used to detect the virulence- and resistance-associated genes and 7 housekeeping genes to determine the sequence types (STs) of selected isolates. RESULTS: The resistance rates to antimicrobials tested except linezolid and vancomycin and the prevalence of methicillin-resistant S. epidermidis (MRSE) of S. epidermidis clinical isolates were significantly higher than those among colonized isolates (P < 0.05). The positive rates of virulence-associated genes including aap, sesI, ACME-arcA, IS256, bhp, altE, aae and gehD for S. epidermidis clinical isolates were significantly higher than those for colonized isolate (P < 0.05). A total of 60 STs including 28 from clinical isolates and 32 from colonized isolates were identified by MLST. A novel, rarely encountered clone, ST466, was found to be the second prevalent clone among clinical isolates. The great majority of the S. epidermidis isolates tested (73.86%) belonged to clone complex 2 (CC2). Compared with ST2, ST130, ST20 and ST59 clones, ST466 clone had the highest resistance rate to tetracycline (50.00%), the second highest prevalence of ACME-arcA (65.00%), bhp (30.00%) and qacA/B (65.00%), very low prevalence of carriage of icaA (0.00%) and biofilm formation (0.00%), the lack of sesI and high prevalence of aap, altE and aae (> 90%), which was similar to the characteristics of ST59 clone with one locus difference from ST466. ST466 clone competence with Staphylococcus aureus was relatively stronger, relative to ST2, ST20, ST130 and ST59 clones. CONCLUSION: Taken together, a high-level of genetic diversity was found between clinical and colonized S. epidermidis isolates. A novel ST466 clone with distinct and similar characteristics relative to other prevalent clones, emerging as a prevalent clone in China, should be of major concern.

Analyses of integrated aircraft cabin contaminant monitoring network based on Kalman consensus filter.

The modern civil aircrafts use air ventilation pressurized cabins subject to the limited space. In order to monitor multiple contaminants and overcome the hypersensitivity of the single sensor, the paper constructs an output correction integrated sensor configuration using sensors with different measurement theories after comparing to other two different configurations. This proposed configuration works as a node in the contaminant distributed wireless sensor monitoring network. The corresponding measurement error models of integrated sensors are also proposed by using the Kalman consensus filter to estimate states and conduct data fusion in order to regulate the single sensor measurement results. The paper develops the sufficient proof of the Kalman consensus filter stability when considering the system and the observation noises and compares the mean estimation and the mean consensus errors between Kalman consensus filter and local Kalman filter. The numerical example analyses show the effectiveness of the algorithm.

Punishment in the form of shared cost promotes altruism in the cooperative dilemma games.

One phenomenon or social institution often observed in multi-agent interactions is the altruistic punishment, i.e. the punishment of unfair behavior by others at a personal cost. Inspired by the works focusing on punishment and the intricate mechanism behind it, we theoretically study the strategy evolution in the framework of two-strategy game models with the punishment on defectors, moreover, the cost of punishing will be evenly shared among the cooperators. Theoretical computations suggest that larger punishment on defectors or smaller punishment cost incurred by cooperators will enhance the fixation of altruistic cooperation in the population. Through the replicate dynamics, the group size of the randomly selected individuals from the sufficiently large population will notably affect the strategy evolution in populations nested within a dilemma. By theoretical modeling the concept of shared cost for punishment from one point of view, our findings underscore the importance of punishment with shared cost as a factor in real-life decisions in an evolutionary game context.