Modeling and analysis of the effect of optimal virus control on the spread of HFMD.

A within-host and between-host hand, foot and mouth disease (HFMD) mathematical model is established and the affect of optimal control in its within-host part on HFMD transmission is studied. Through define two basic reproduction numbers, by using the fast-slow system analysis method of time scale, the global stabilities of the between-host (slow) system and within-host (fast) system are researched, respectively. An optimal control problem with drug-treatment control on coupled within-host and between-host HFMD model is formulated and analysed theoretically. Finally, the purposed optimal control measures are applied to the actual HFMD epidemic analysis in Zhejiang Province, China from April 1, 2021 to June 30, 2021. The numerical results show that the drug control strategies can reduce the virus load per capita and can effectively prevent large-scale outbreaks of HFMD.

Effects of impulse on prescribed-time synchronization of switching complex networks.

The specified convergence time, designated by the user, is highly attractive for many high-demand applications such as industrial robot control, missile guidance, and autonomous vehicles. For the application of neural networks in the field of secure communication and power systems, the importance of prescribed-time synchronization(PTs) and stable performance of the system is more prominent. This paper introduces a prescribed-time controller without the fractional power function and sign function, which can reach synchronization at a prescribed time and greatly reduce the chattering phenomenon of neural networks. Additionally, by constructing synchronizing/desynchronizing impulse sequences, the PTs of switching complex networks(SCN) is achieved with impulse effects, where the time sequences of switching and impulse occurrences in the networks are constrained by the average dwell time. This approach effectively reduces the impact of frequent mode switching on network synchronization, and the synchronization time can be flexibly adjusted within any physically allowable range to accommodate different application requirements. Finally, the effectiveness of the proposed control strategy is demonstrated by two examples.

H ∞ state estimation of quaternion-valued inertial neural networks: non-reduced order method.

This paper concentrates on the problem of H ∞ state estimation for quaternion-valued inertial neural networks (QVINNs) with nonidentical time-varying delay. Without reducing the original second order system into two first order systems, a non-reduced order method is developed to investigate the addressed QVINNs, which is different from the majority of existing references. By constructing a new Lyapunov functional with tuning parameters, some easily checked algebraic criteria are established to ascertain the asymptotic stability of error-state system with the desired H ∞ performance. Moreover, an effective algorithm is provided to design the estimator parameters. Finally, a numerical example is given out to illustrate the feasibility of the designed state estimator.

Fixed-time and prescribed-time synchronization of quaternion-valued neural networks: A control strategy involving Lyapunov functions.

A control strategy containing Lyapunov functions is proposed in this paper. Based on this strategy, the fixed-time synchronization of a time-delay quaternion-valued neural network (QVNN) is analyzed. This strategy is extended to the prescribed-time synchronization of the QVNN. Furthermore, an improved two-step switching control strategy is also proposed based on this flexible control strategy. Compared with some existing methods, the main method of this paper is a non-decomposition one, does not contain a sign function in the controller, and has better synchronization accuracy. Two numerical examples verify the above advantages.

Finite-time synchronization of quaternion-valued neural networks with delays: A switching control method without decomposition.

For a class of quaternion-valued neural networks (QVNNs) with discrete and distributed time delays, its finite-time synchronization (FTSYN) is addressed in this paper. Instead of decomposition, a direct analytical method named two-step analysis is proposed. That method can always be used to study FTSYN, under either 1-norm or 2-norm of quaternion. Compared with the decomposing method, the two-step method is also suitable for models that are not easily decomposed. Furthermore, a switching controller based on the two-step method is proposed. In addition, two criteria are given to realize the FTSYN of QVNNs. At last, three numerical examples illustrate the feasibility, effectiveness and practicability of our method.

Finite-Time and Fixed-Time Synchronization of Quaternion-Valued Neural Networks With/Without Mixed Delays: An Improved One-Norm Method.

In this article, the finite-time synchronization (FTSYN) of a class of quaternion-valued neural networks (QVNNs) with discrete and distributed time delays is studied. Furthermore, the FTSYN and fixed-time synchronization (FIXSYN) of the QVNNs without time delay are investigated. Different from the existing results, which used decomposition techniques, by introducing an improved one-norm, we use a direct analytical method to study the synchronization problems. Incidentally, several properties of one-norm of the quaternion are analyzed, and then, three effective controllers are proposed to synchronize the drive and response QVNNs within a finite time or fixed time. Moreover, efficient criteria are proposed to guarantee that the synchronization of QVNNs with or without mixed time delays can be realized within a finite and fixed time interval, respectively. In addition, the settling times are reckoned. Compared with the existing work, our advantages are mainly reflected in the simpler Lyapunov analytical process and more general activation function. Finally, the validity and practicability of the conclusions are illustrated via four numerical examples.

Remarkable Ionic Conductivity in a LZO-SDC Composite for Low-Temperature Solid Oxide Fuel Cells.

Recently, appreciable ionic conduction has been frequently observed in multifunctional semiconductors, pointing out an unconventional way to develop electrolytes for solid oxide fuel cells (SOFCs). Among them, ZnO and Li-doped ZnO (LZO) have shown great potential. In this study, to further improve the electrolyte capability of LZO, a typical ionic conductor Sm0.2Ce0.8O1.9 (SDC) is introduced to form semiconductor-ionic composites with LZO. The designed LZO-SDC composites with various mass ratios are successfully demonstrated in SOFCs at low operating temperatures, exhibiting a peak power density of 713 mW cm-2 and high open circuit voltages (OCVs) of 1.04 V at 550 °C by the best-performing sample 5LZO-5SDC, which is superior to that of simplex LZO electrolyte SOFC. Our electrochemical and electrical analysis reveals that the composite samples have attained enhanced ionic conduction as compared to pure LZO and SDC, reaching a remarkable ionic conductivity of 0.16 S cm-1 at 550 °C, and shows hybrid H+/O2- conducting capability with predominant H+ conduction. Further investigation in terms of interface inspection manifests that oxygen vacancies are enriched at the hetero-interface between LZO and SDC, which gives rise to the high ionic conductivity of 5LZO-5SDC. Our study thus suggests the tremendous potentials of semiconductor ionic materials and indicates an effective way to develop fast ionic transport in electrolytes for low-temperature SOFCs.

A Bulk-Heterostructure Nanocomposite Electrolyte of Ce0.8Sm0.2O2-δ-SrTiO3 for Low-Temperature Solid Oxide Fuel Cells.

Since colossal ionic conductivity was detected in the planar heterostructures consisting of fluorite and perovskite, heterostructures have drawn great research interest as potential electrolytes for solid oxide fuel cells (SOFCs). However, so far, the practical uses of such promising material have failed to materialize in SOFCs due to the short circuit risk caused by SrTiO3. In this study, a series of fluorite/perovskite heterostructures made of Sm-doped CeO2 and SrTiO3 (SDC-STO) are developed in a new bulk-heterostructure form and evaluated as electrolytes. The prepared cells exhibit a peak power density of 892 mW cm-2 along with open circuit voltage of 1.1 V at 550 °C for the optimal composition of 4SDC-6STO. Further electrical studies reveal a high ionic conductivity of 0.05-0.14 S cm-1 at 450-550 °C, which shows remarkable enhancement compared to that of simplex SDC. Via AC impedance analysis, it has been shown that the small grain-boundary and electrode polarization resistances play the major roles in resulting in the superior performance. Furthermore, a Schottky junction effect is proposed by considering the work functions and electronic affinities to interpret the avoidance of short circuit in the SDC-STO cell. Our findings thus indicate a new insight to design electrolytes for low-temperature SOFCs.

H∞ synchronization of delayed neural networks via event-triggered dynamic output control.

This paper investigates H∞ exponential synchronization (ES) of neural networks (NNs) with delay by designing an event-triggered dynamic output feedback controller (ETDOFC). The ETDOFC is flexible in practice since it is applicable to both full order and reduced order dynamic output techniques. Moreover, the event generator reduces the computational burden for the zero-order-hold (ZOH) operator and does not induce sampling delay as many existing event generators do. To obtain less conservative results, the delay-partitioning method is utilized in the Lyapunov-Krasovskii functional (LKF). Synchronization criteria formulated by linear matrix inequalities (LMIs) are established. A simple algorithm is provided to design the control gains of the ETDOFC, which overcomes the difficulty induced by different dimensions of the system parameters. One numerical example is provided to demonstrate the merits of the theoretical analysis.

Novel bifurcation results for a delayed fractional-order quaternion-valued neural network.

This paper reports the innovative results on the stability and bifurcation for a delayed fractional-order quaternion-valued neural network(FOQVNN). Delay-stimulated bifurcation criteria of the developed FOQVNN are attained. Then, the bifurcation diagrams are perfectly exhibited to authenticate the veracity of the bifurcation results. Besides, the stability zone is more larger of the addressed FOQVNN in comparison with its counterpart if other parameters are intercalated. It further witnesses that the amplitudes of bifurcation oscillation get bigger with the augmentation of time delay. It discloses that the bifurcation phenomena engender earlier as the order incrementally magnifies. The exactness and merits of the achieved analytic results are eventually substantiated by a simulation example.

Global dissipativity of memristor-based neutral type inertial neural networks.

The problem of global dissipativity for memristor-based inertial networks with time-varying delay of neutral type is investigated in this paper. Based on a proper variable substitution, the inertial system is transformed into a conventional system. Some sufficient criteria are established to ascertain the global dissipativity for the aforementioned inertial neural networks by employing analytical techniques and Lyapunov method. Meanwhile, the globally exponentially attractive sets and positive invariant sets are also presented here. Finally, numerical examples and simulations are given out to corroborate the effectiveness of obtained results.

Global dissipativity analysis for delayed quaternion-valued neural networks.

The problem of global dissipativity analysis for quaternion-valued neural networks (QVNNs) with time-varying delays is firstly investigated in this paper. The QVNN is studied as a single entirety without any decomposition. Several algebraic conditions ensuring the global dissipativity and globally exponential dissipativity for QVNNs are derived by employing Lyapunov theory and some analytic techniques. Furthermore, the positive invariant sets, globally attractive sets and globally exponentially attractive sets are figured out as well. Finally, the effectiveness is notarized by deducing two simulation examples.

Matrix measure based dissipativity analysis for inertial delayed uncertain neural networks.

The present paper is devoted to investigating the global dissipativity for inertial neural networks with time-varying delays and parameter uncertainties. By virtue of a suitable substitution, the original system is transformed to the first order differential system. By means of matrix measure, generalized Halanay inequality, and matrix-norm inequality, several sufficient criteria for the global dissipativity of the addressed neural networks are proposed. Meanwhile, the specific estimations of positive invariant sets and globally attractive sets are obtained. Finally, two examples are provided to validate our theoretical results.