Nonfragile output-feedback control for switched singular positive systems with time-varying delay under the Round-Robin protocol.

This paper investigates a class of switched singular positive systems with time-varying delay, where the system parameters are uncertain. In order to decrease the consumption of the communication resources and avoid the phenomenon of data congestion, Round-Robin protocol is employed here. This is the first few attempts to introduce the Round-Robin protocol for such kind of positive systems. Moreover, the nonfragile controller is designed to suppress the undesired dynamical behaviors, which may occur if no control is implemented. By means of the matrix decomposition technique and the mode-dependent average dwell time method, sufficient conditions are established to ensure that the obtained closed-loop system has a prescribed l1 disturbance attenuation performance. Finally, three examples are presented to illustrate feasibility and effectiveness of the proposed control scheme.

Fault Detectability of Asynchronous Switched Boolean Networks: A Set Reachability Approach.

This article addresses the fault detectability problem of asynchronous switched Boolean networks, which is focused on whether occurrence of the faults would have an impact on the outputs of the considered network. By applying the semi-tensor product method, the asynchronous switching scheme of the considered system is converted into multiple switching signals. Based on them, an augmented system is established to transform the fault detectability issue of the original system into a set reachability problem. Moreover, some criteria for the fault detectability of the asynchronous switched Boolean control networks are also proposed. Additionally, two algorithms are given to design feasible control sequences after explicitly analyzing the considered augmented system. Finally, an example is provided to illustrate the significance of the obtained theoretical results.

Interaction of isoquinoline alkaloids with pyrimidine motif triplex DNA by mass spectrometry and spectroscopies reveals diverse mechanisms.

Isoquinoline alkaloids represent an important class of molecules due to their broad range of pharmacology and clinical utility. Prospective development and use of these alkaloids as effective anticancer agents have elicited great interest. In this study, in order to reveal structure-activity relationship, we present the characterization of bioactive isoquinoline alkaloid-DNA triplex interactions, with particular emphasis on the sequence selectivity and preference of binding to the two types of DNA triplexes, by electrospray ionization mass spectrometry (ESI-MS) and various spectroscopic techniques. The six alkaloids, including coptisine, columbamine, epiberberine, berberrubine, jateorhizine, and fangchinoline, were selected to explore their interactions with the TC and TTT triplex DNA structures. Berberrubine, fangchinoline, coptisine, columbamine, and epiberberine have preference for TC rich DNA sequences compared to TTT rich DNA triplex based on affinity values in MS. The experimental results from different fragmentation modes in tandem MS, subtractive and hyperchromic effects in UV absorption spectra, fluorescence quenching and enhancement in fluorescence spectra, and strong conformational changes in circular dichroism (CD) hinted that the interaction between isoquinoline alkaloid-TC/TTT DNA had diverse mechanisms including at least two different binding modes: the electrostatic binding and the intercalation binding. Interestingly, columbamine, berberrubine, and fangchinoline can stabilize TTT triplex as inferred from optical thermal melting profiles, while it was not the case in TC triplex. These results provide new insights into binding of isoquinoline alkaloids to pyrimidine motif triplex DNA.

Aperiodic Sampled-Data Control for Stabilization of Memristive Neural Networks With Actuator Saturation: A Dynamic Partitioning Method.

This article is concerned with the local stabilization of memristive neural networks subject to actuator saturation via aperiodic sampled-data control. A dynamic partitioning point is elegantly introduced, which is placed between the latest sampling instant and the present time to utilize more information of the inner sampling. To analyze the stability of the closed-loop system, a time-dependent two-side looped functional, which fully utilizes the state information on the entire sampling interval as well as at the dynamic partitioning point, is constructed. It relaxes the positive definiteness of traditional Lyapunov functional inside the sampling interval and therefore, provides the possibility to derive less conservative stability results. Besides, an auxiliary system is established to describe the dynamics at the partitioning point. On the basis of the constructed looped functional, the discrete-time Lyapunov theorem, and some estimation approaches, a linear matrix inequalities-based stability criterion is developed, and then, the sampled-data saturated controller is designed to ensure the local asymptotic stability of the closed-loop system. Thereafter, two optimization problems are developed to seek the desired feedback gain and to expand the estimation of the region of attraction or to enlarge the upper bound of the sampling interval. Eventually, a numerical example is given to demonstrate the effectiveness and the superiority of the proposed results.

Icaritin, a metabolite of Icarrin, Alleviates non-alcoholic fatty liver disease via inhibition of lipogenesis and ER stress.

Non-alcoholic fatty liver disease (NAFLD) is one of the most serious global public health concerns. However, there are currently no effective drugs for treatment of this disease. Icariin (ICA), a small-molecule natural product extracted from Epimedium brevicornu Maxim, offers various pharmacological activities. In the present work, we wondered whether ICA can attenuate NAFLD in db/db mice treated with ICA for 8 weeks and how ICA exerts an influence on NAFLD. In db/db mice, ICA treatment had a robust effect on inhibition of lipogenesis associated with NAFLD amelioration by decreasing liver lipid deposition, together with ameliorating insulin sensitivity, glucose tolerance, and fasting serum glucose. Of note, ICA-treated rats showed a much higher concentration of icaritin (ICT) in plasma, a major metabolite of ICA, about 2000 times higher than that of ICA by liquid chromatography mass spectrometry (LC-MS). Interestingly, ICT, rather than ICA, can dramatically decrease hepatic lipogenesis-related markers in oleate acid/palmitate acid (OA/PA)-induced steatosis in primary hepatocytes (PH) and HepG2 cells, and hepatic lipid accumulation in db/db mice, demonstrating the inhibitory effect of ICT on lipogenesis. Mechanistically, we found that anti-lipogenic activities of ICT were related to reducing endoplasmic reticulum (ER) stress as evidenced by Western blot, qPCR, and other assays in thapsigargin (THP) induced-ER stress models. To our knowledge, this is the first report showing the unexpected and key role for ICT on the prevention of NAFLD in db/db mice through an ER stress mechanism.

Security Control of Multiagent Systems Under Denial-of-Service Attacks.

Security control aims to guarantee the consensus of multiagent systems (MASs) in the presence of the denial-to-service attacker. Most of the existing distributed controllers are invalid during the attack interval due to the paralyzed communication channels. In order to overcome this difficulty, a novel hybrid distributed control protocol is designed. Here, the controller uses the latest information saved in the buffers in the presence of malicious attacks, which will further enhance the security of MASs. Some sufficient conditions on the coupled strength and attack parameters are derived to achieve the leader-following consensus of MASs. Furthermore, we estimate the upper bounds of denial-of-service (DoS) frequency and DoS duration which the MASs can tolerate before losing consensus. Notice that we also reduce the computational complexity via the property of the Kronecker product. Besides, an observer-based model is proposed and the corresponding consensus criterion is established to reduce the effects of attackers on the controller. Finally, the efficiency of our theoretical results is illustrated by a numerical example.

Locally Minimum-Variance Filtering of 2-D Systems Over Sensor Networks With Measurement Degradations: A Distributed Recursive Algorithm.

This article tackles the recursive filtering problem for an array of 2-D systems over sensor networks with a given topology. Both the measurement degradations of the network outputs and the stochastic perturbations of network couplings are modeled to reflect engineering practice by introducing some random variables with given statistics. The goal of the addressed problem is to devise the distributed recursive filters capable of cooperatively estimating the true state in order to ensure locally minimal upper bound (UB) on the second-order moment of the filtering error (also viewed as the general error variance). For this purpose, the general error variance regarding the underlying target plant is first provided to facilitate the subsequent filter design, and then a certain UB on the error variance is constructed by exploiting the stochastic analysis and the induction approach. Furthermore, in view of the inherent sparsity of the sensor network, the gain parameters of the desired distributed filters are determined, and the proposed recursive filtering algorithm is shown to be scalable. Finally, an illustrative example is given to demonstrate the validity of the established filtering strategy.

Exact coexistence and locally asymptotic stability of multiple equilibria for fractional-order delayed Hopfield neural networks with Gaussian activation function.

This paper explores the multistability issue for fractional-order Hopfield neural networks with Gaussian activation function and multiple time delays. First, several sufficient criteria are presented for ensuring the exact coexistence of 3n equilibria, based on the geometric characteristics of Gaussian function, the fixed point theorem and the contraction mapping principle. Then, different from the existing methods used in the multistability analysis of fractional-order neural networks without time delays, it is shown that 2n of 3n total equilibria are locally asymptotically stable, by applying the theory of fractional-order linear delayed system and constructing suitable Lyapunov function. The obtained results improve and extend some existing multistability works for classical integer-order neural networks and fractional-order neural networks without time delays. Finally, an illustrative example with comprehensive computer simulations is given to demonstrate the theoretical results.

H∞ estimation for stochastic semi-Markovian switching CVNNs with missing measurements and mode-dependent delays.

This article is devoted to the H∞ estimation problem for stochastic semi-Markovian switching complex-valued neural networks subject to incomplete measurement outputs, where the time-varying delay also depends on another semi-Markov process. A sequence of random variables with known statistical property is introduced to depict the missing measurement phenomenon. Based on the generalized Itoˆ's formula in complex form concerning with the semi-Markovian systems, complex-valued reciprocal convex inequality as well as intensive stochastic analysis method, some mode-dependent sufficient conditions are presented guaranteeing the estimation error system to be exponentially mean-square stable with a prespecified H∞ disturbance attenuation level. In addition, the mode-dependent estimator gain matrices are appropriately designed according to the feasible solutions of certain complex matrix inequalities. In the end, one numerical example is provided to illustrate effectiveness of the theoretical results.

Impacts of preferences on the emergence of cooperation.

Behavior decision making, where individuals can efficiently express their preferences for all options, has a great impact on cooperation. Hereby, we institute a minimal model in well-mixed populations where whether and how to sanction defectors are decided by cooperators via different decision-making mechanisms. The results illustrate that whether cooperation can outbreak depends on the cooperators' preferences for sanction and complying with the electoral outcome. We highlight the role of individuals' preferences in the emergence of cooperation and show that there exists an intermediate degree of the cooperators' preference for sanction at which the cooperators' preference for complying with the electoral outcome has a negligible impact on cooperation. We point out whether conformity facilitates the emergence of cooperation depends on the cooperators' preference for sanction. We find, compared with individual decision making, whether collective decision making is more conducive to promoting cooperation crucially depends on cooperators' preferences as well as the consensus required for employing sanction.

Evolution of cooperation under punishment.

Punishment has been considered as an effective mechanism for promoting and sustaining cooperation. In most existing models, punishment always comes as a third strategy alongside cooperation and defection, and it is commonly assumed to be executed based on individual decision rules rather than collective decision rules. Differently from previous works, we employ a democratic procedure by which cooperators cast votes independently and simultaneously for whether to impose punishment on defectors, and we establish a relationship between the cooperators' willingness to punish defectors (WTPD) and whether the punishment is inflicted on defectors. The results illustrate that the population can evolve to full cooperation under consensual punishment. It is noteworthy that, compared with autonomous punishment, whether consensual punishment is more in favor of cooperation crucially depends on the minimum number of votes required for punishment execution as well as the cooperators' WTPD. Our findings highlight the importance of collective decision making in the evolution of cooperation and may provide a mathematical framework for explaining the prevalence of democracy in modern societies.

Synchronization of coupled neural networks under mixed impulsive effects: A novel delay inequality approach.

In this paper, the synchronization problems of an array of coupled neural networks with mixed impulses are considered. Here mixed impulses contain desynchronizing delay-free impulses, synchronizing delay-free impulses, desynchronizing delayed impulses and synchronizing delayed impulses. A novel concept named average delayed impulsive gain is proposed to quantify the effects of mixed impulses. Besides, we establish a delayed impulsive differential inequality which extends famous Halanay inequality, and apply it to study the synchronization problems of delayed neural networks with mixed impulses. It is interesting to notice that both delay-free impulses and delayed impulses can contribute to the synchronization of coupled neural networks. Meanwhile, we also discuss the synchronization of neural networks only with delay-dependent impulses. Some sufficient conditions are derived to ensure the exponential synchronization of delayed neural networks. Finally, some numerical examples are provided to illustrate the validity and superiority of the obtained results.

Local Synchronization of Interconnected Boolean Networks With Stochastic Disturbances.

This paper is concerned with the local synchronization problem for the interconnected Boolean networks (BNs) without and with stochastic disturbances. For the case without stochastic disturbances, first, the limit set and the transient period of the interconnected BNs are discussed by resorting to the properties of the reachable set for the global initial states set. Second, in terms of logical submatrices of a certain Boolean vector, a compact algebraic expression is presented for the limit set of the given initial states set. Based on it, several necessary and sufficient conditions are derived assuring the local synchronization of the interconnected BNs. Subsequently, an efficient algorithm is developed to calculate the largest domain of attraction. As for the interconnected BNs with stochastic disturbances, first, mutually independent two-valued random logical variables are introduced to describe the stochastic disturbances. Then, the corresponding local synchronization criteria are also established, and the algorithm to calculate the largest domain of attraction is designed. Finally, numerical examples are employed to illustrate the effectiveness of the obtained results/ algorithms.

Event-Triggered Recursive Filtering for Shift-Varying Linear Repetitive Processes.

This paper addresses the recursive filtering problem for shift-varying linear repetitive processes (LRPs) with limited network resources. To reduce the resource occupancy, a novel event-triggered strategy is proposed where the concern is to broadcast those necessary measurements to update the innovation information only when certain events appear. The primary goal of this paper is to design a recursive filter rendering that, under the event-triggered communication mechanism, an upper bound (UB) on the filtering error variance is ensured and then optimized by properly determining the filter gains. As a distinct kind of 2-D systems, the LRPs are cast into a general Fornasini-Marchesini model by using the lifting technique. A new definition of the triggering-shift sequence is introduced and an event-triggered rule is then constructed for the transformed system. With the aid of mathematical induction, the filtering error variance is guaranteed to have a UB which is subsequently optimized with appropriate filter parameters via solving two series of Riccati-like difference equations. Theoretical analysis further reveals the monotonicity of the filtering performance with regard to the event-triggering threshold. Finally, an illustrative simulation is given to show the feasibility of the designed filtering scheme.

Synchronization of impulsive coupled complex-valued neural networks with delay: The matrix measure method.

In this paper, the exponential synchronization of the impulsive coupled delayed complex-valued neural networks (CVNNs) is studied. Without constructing the Lyapunov function, a novel approach based on the matrix measure and extended Halanay inequality is presented and some sufficient criteria for exponential synchronization of the addressed CVNNs are derived. In this paper, the restriction of the real and imaginary parts of activation functions which are supposed to depend only on the real and imaginary parts of the variables, respectively, is removed. Furthermore, by employing the average impulsive interval method, the requirement on the upper bound of the impulsive intervals is removed for impulsive signal transmission. Finally, numerical examples are provided to demonstrate the effectiveness of the theoretical results obtained, even for large-scale CVNNs with impulsive coupling.

Resilient State Estimation for 2-D Time-Varying Systems With Redundant Channels: A Variance-Constrained Approach.

This paper investigates the state estimation problem for a class of 2-D time-varying systems with error variance constraints, where the implemented estimator gain is subject to stochastic perturbations. Redundant channels are utilized as a protocol to strengthen the transmission reliability and the channels' packet dropout rates are described by mutually uncorrelated Bernoulli distributions. The objective of the addressed problem is to design a resilient estimator such that an upper bound on the estimation error variance is first guaranteed and then minimized at each time step, where the considered gain perturbations are characterized by their statistical properties. By employing the induction method and the variance-constrained approach, an upper bound on the estimation error variance is first constructed by means of the solutions to two Riccati-like difference equations and, subsequently, a locally minimal upper bound is achieved by appropriately designing the gain parameter. Then, an effective algorithm is proposed for designing the desired estimator, which is in a recursive form suitable for online applications. Finally, a numerical simulation is provided to demonstrate the usefulness of the proposed estimation scheme.

Multiple Mittag-Leffler stability of fractional-order competitive neural networks with Gaussian activation functions.

In this paper, we explore the coexistence and dynamical behaviors of multiple equilibrium points for fractional-order competitive neural networks with Gaussian activation functions. By virtue of the geometrical properties of activation functions, the fixed point theorem and the theory of fractional-order differential equation, some sufficient conditions are established to guarantee that such n-neuron neural networks have exactly 3k equilibrium points with 0≤k≤n, among which 2k equilibrium points are locally Mittag-Leffler stable. The obtained results cover both multistability and mono-stability of fractional-order neural networks and integer-order neural networks. Two illustrative examples with their computer simulations are presented to verify the theoretical analysis.

Enhanced Treatment Properties of Iron Oxide Amended Sands Coupled with Polyelectrolyte to Humic Acid.

To improve the removal efficiency of micropollutant humic acid at low temperature, microflocculation filtration experiments were conducted with homemade iron oxide amended sands (IOAS), cationic polyelectrolyte (CPE) and polyaluminium chloride (PACl). Fractal properties of flocs structures and IOAS surface and their effects on the removal efficiency of humic acid were investigated. Results showed that IOAS had a porous surface with fractal dimensions (D) (D = 1.744) and a strong adsorption capacity for humic acid. The flocs produced by PACl were small in size, loosely packed with a higher D, and therefore settled slowly. By contrast, the formed flocs by adding CPE with only 1~5% of PACl had strong and open structures with a high effective density, rapid settling velocity and a lower D. Compared with PACl flocs, the larger size CPE flocs and PACl+CPE flocs deposited and packed up on IOAS, creating smaller pores and resulting in a lower D and higher filtration capacity.

Synchronization for the Realization-Dependent Probabilistic Boolean Networks.

This paper investigates the synchronization problem for the realization-dependent probabilistic Boolean networks (PBNs) coupled unidirectionally in the drive-response configuration. The realization of the response PBN is assumed to be uniquely determined by the realization signal generated by the drive PBN at each discrete time instant. First, the drive-response PBNs are expressed in their algebraic forms based on the semitensor product method, and then, a necessary and sufficient condition is presented for the synchronization of the PBNs. Second, by resorting to a newly defined matrix operator, the reachable set from any initial state is expressed by a column vector. Consequently, an easily computable algebraic criterion is derived assuring the synchronization of the drive-response PBNs. Finally, three illustrative examples are employed to demonstrate the applicability and usefulness of the developed theoretical results.

A Variance-Constrained Approach to Recursive Filtering for Nonlinear 2-D Systems With Measurement Degradations.

This paper is concerned with the recursive filtering problem for a class of nonlinear 2-D time-varying systems with degraded measurements over a finite horizon. The phenomenon of measurement degradation occurs in a random way depicted by stochastic variables satisfying certain probabilities distributions. The nonlinearities under consideration are dealt with through the Taylor expansion, where the high-order terms of the linearization errors are characterized by norm-bounded parameter uncertainties. The objective of the addressed problem is to design a filter which guarantees an upper bound of the estimation error variance and subsequently minimizes such a bound with the desired gain parameters. By means of mathematical induction, an upper bound is first derived for the estimation error variance by constructing two sets of Riccati-like difference equations, and then the obtained bound is minimized by properly selecting the filter parameter at each time step. Both the minimal upper bound and the desired filter parameter are suitable for recursive online computation. Furthermore, the effect of the stochastic measurement degradation on the filtering performance is discussed. Finally, a simulation example is presented to demonstrate the effectiveness of the designed filter.