Projective Synchronization of Discrete-Time Variable-Order Fractional Neural Networks With Time-Varying Delays.

This article is committed to studying projective synchronization and complete synchronization (CS) issues for one kind of discrete-time variable-order fractional neural networks (DVFNNs) with time-varying delays. First, two new variable-order fractional (VF) inequalities are built by relying on nabla Laplace transform and some properties of Mittag-Leffler function, which are extensions of constant-order fractional (CF) inequalities. Moreover, the VF Halanay inequality in discrete-time sense is strictly proved. Subsequently, some sufficient projective synchronization and CS criteria are derived by virtue of VF inequalities and hybrid controllers. Finally, we exploit numerical simulation examples to verify the validity of the derived results, and a practical application of the obtained results in image encryption is also discussed.

Fixed-Time Aperiodic Intermittent Control for Quasi-Bipartite Synchronization of Competitive Neural Networks.

This paper concerns a class of coupled competitive neural networks, subject to disturbance and discontinuous activation functions. To realize the fixed-time quasi-bipartite synchronization, an aperiodic intermittent controller is initially designed. Subsequently, by combining the fixed-time stability theory and nonsmooth analysis, several criteria are established to ensure the bipartite synchronization in fixed time. Moreover, synchronization error bounds and settling time estimates are provided. Finally, numerical simulations are presented to verify the main results.

Prescribed-time cluster practical consensus for nonlinear multi-agent systems based on event-triggered mechanism.

This paper investigates the prescribed-time event-triggered cluster practical consensus problem for a class of nonlinear multi-agent systems with external disturbances. To begin, to reach the prescribed-time cluster practical consensus, a new time-varying function is introduced and a novel distributed continuous algorithm is designed. Based on the Lyapunov stability theory and inequality techniques, some sufficient conditions are given, ensuring the prescribed-time cluster practical consensus. Moreover, to avoid different clusters' final states overlapping, a virtual leader is considered for each cluster. In this case, an event-triggered distributed protocol is further established and some related conditions are given for achieving prescribed-time cluster practical consensus. Additionally, it is proven that the Zeno behavior can be avioded by choosing parameters appropriately. Finally, some numerical examples are presented to show the effectiveness of the theoretical results.

Distributed consensus of discrete time-varying linear multi-agent systems with event-triggered intermittent control.

The consensus problem of discrete time-varying linear multi-agent systems (MASs) is studied in this paper. First, an event-triggered intermittent control (ETIC) protocol is designed, aided by a class of auxiliary functions. Under this protocol, some sufficient conditions for all agents to achieve consensus are established by constructing an error dynamical system and applying the Lyapunov function. Second, in order to further reduce the communication burden, an improved event triggered intermittent control (I-ETIC) strategy is presented, along with corresponding convergence analysis. Notably, the difference between the two control protocols lies in the fact that the former protocol only determines when to control or not based on the trigger conditions, while the latter, building upon this, designs new event trigger conditions for the update of the controller during the control stage. Finally, two numerical simulation examples are provided to demonstrate the effectiveness of the theoretical results.

Phylogeography and demographic history of macaques, fascicularis species group, in East Asia: Inferred from multiple genomic markers.

Climate changes at larger scales have influenced dispersal and range shifts of many taxa in East Asia. The fascicularis species group of macaques is composed of four species and is widely distributed in Southeast and East Asia. However, its phylogeography and demographic histories are currently poorly understood. Herein, we assembled autosomal, mitogenome, and Y-chromosome data for 106 individuals, and combined them with 174 mtDNA dloop haplotypes of this species group, with particular focus on the demographic histories and dispersal routes of Macaca fuscata, M. cyclopis, and M. mulatta. The results showed: (1) three monophyletic clades for M. fuscata, M. cyclopis, and M. mulatta based on the multiple genomics analyses; (2) the disparate demographic trajectories of the three species after their split ∼1.0 Ma revealed that M. cyclopis and M. fuscata were derived from an ancestral M. mulatta population; (3) the speciation time of M. cyclopis was later than that of M. fuscata, and their divergence time occurred at the beginning of "Ryukyu Coral Sea Stage" (1.0-0.2 Ma) when the East China Sea land bridge was completely submerged by the sea level rose; and (4) the three parallel rivers (Nujiang, Lancangjiang, and Jinshajiang) of Southwestern China divided M. mulatta into Indian and Chinese genetic populations ∼200 kya. These results shed light on understanding not only the evolutionary history of the fascicularis species group but also the formation mechanism of faunal diversity in East Asia during the Pleistocene.

Bipartite synchronization of multilayer signed networks under aperiodic intermittent-based adaptive dynamic event-triggered control.

This article addresses the exponential bipartite synchronization (EBS) of multilayer signed networks with time-varying coupling (MSNs) under aperiodic intermittent-based adaptive dynamic event-triggered control (AAIDETC). Firstly, to increase the elasticity, a novel AAIDETC strategy is presented, whose superiority is that the control gains and the triggering parameters can vary with the evolution of the considered networks. Meanwhile, concerning the aperiodic intermittent control, a new definition of average control ratio (ACR) is put forward, which is more rigorous compared with the relevant results. Then, by the method of ACR, graph theory and Lyapunov approach, the simpler synchronization criterion is gained, which avoids the topology structure of MSNs. Moreover, the EBS issues of Chua's circuits and neural networks established on MSNs are studied, which are two practical applications of our theoretical results. Finally, corresponding numerical simulations are presented to verify the availability of the obtained results.

Stability Analysis of a Delayed Rumor Propagation Model with Nonlinear Incidence Incorporating Impulsive Vaccination.

The presence of information asymmetry can hinder the public's ability to make well-informed decisions, resulting in unwarranted suspicion and the widespread dissemination of rumors. Therefore, it is crucial to provide individuals with consistent and dependable scientific education. Regular popular science education is considered a periodic impulsive intervention to mitigate the impact of information asymmetry and promote a more informed and discerning public. Drawing on these findings, this paper proposes a susceptible-hesitant-infected-refuting-recovered (SHIDR) rumor-spreading model to explain the spread of rumors. The model incorporates elements such as time delay, nonlinear incidence, and refuting individuals. Firstly, by applying the comparison theorem of an impulsive differential equation, we calculate two thresholds for rumor propagation. Additionally, we analyze the conditions of global attractiveness of the rumor-free periodic solution. Furthermore, we consider the condition for the rumor's permanence. Finally, numerical simulations are conducted to validate the accuracy of our findings. The results suggest that increasing the proportion of impulsive vaccination, reducing the impulsive period, or prolonging the delay time can effectively suppress rumors.

Methodology for Testing Key Parameters of Array-Level Small-Area Hafnium-Based Ferroelectric Capacitors Using Time-to-Digital Converter and Capacitance Calibration Circuits.

Hafnium-based ferroelectric memories are a promising approach to enhancing integrated circuit performance, offering advantages such as miniaturization, compatibility with CMOS technology, fast read and write speeds, non-volatility, and low power consumption. However, FeRAM (Ferroelectric Random Access Memory) still faces challenges related to endurance and retention susceptibility to process variations. Hence, testing and obtaining the core parameters of ferroelectric capacitors continuously is essential to investigate these phenomena and explore the potential solution. The traditional method for measuring ferroelectric capacitors has limitations in timing generation capability, introduces parasitic capacitance, and lacks accuracy for small-area capacitors. In this study, we analyzed the working principle of ferroelectric capacitors and designed a method to detect the remnant polarization, saturation polarization, and imprint offset of ferroelectric capacitors. Further, we further proposed a circuit implementation method. The proposed test circuit conquers these limitations and enables high-precision testing of ferroelectric capacitors, contributing to developing hafnium-based ferroelectric memories. The circuit includes a flip-readout circuit, a capacitance calibration circuit, and a voltage-to-time converter and time-to-digital converter (VTC&TDC) readout circuit. According to simulation results, the capacitance calibration circuit reduces the deviation of the capacitance by 84%, and the accuracy of the readout circuit is 5.91 bits, with a readout time of 150 ns and a power consumption of 1 mW. This circuit enables low-cost acquisition of array-level small-area ferroelectric capacitance data, which can guide subsequent device optimization and circuit design.

Adaptive control-based synchronization of discrete-time fractional-order fuzzy neural networks with time-varying delays.

This paper is concerned with complete synchronization for discrete-time fractional-order fuzzy neural networks (DFFNNs) with time-varying delays. First, three original equalities and two Caputo σ-difference inequalities are established based on theory of discrete-time fractional Calculus. Next, a novel discrete-time adaptive controller with time-varying delay is designed, by virtue of 1-norm Lyapunov function and newly established lemmas herein as well as inequality techniques and contradiction method, some judgement conditions are derived to guarantee complete synchronization for the explored DFFNNs. Benefitting from discrete-time adaptive control strategy and our analysis method, the conservatism of the derived synchronization criteria is reduced. Ultimately, the effectiveness of our theoretical results and secure communication scheme are demonstrated through two numerical examples.

Prediction of drug-protein interaction based on dual channel neural networks with attention mechanism.

The precise identification of drug-protein inter action (DPI) can significantly speed up the drug discovery process. Bioassay methods are time-consuming and expensive to screen for each pair of drug proteins. Machine-learning-based methods cannot accurately predict a large number of DPIs. Compared with traditional computing methods, deep learning methods need less domain knowledge and have strong data learning ability. In this study, we construct a DPI prediction model based on dual channel neural networks with an efficient path attention mechanism, called DCA-DPI. The drug molecular graph and protein sequence are used as the data input of the model, and the residual graph neural network and the residual convolution network are used to learn the feature representation of the drug and protein, respectively, to obtain the feature vector of the drug and the hidden vector of protein. To get a more accurate protein feature vector, the weighted sum of the hidden vector of protein is applied using the neural attention mechanism. In the end, drug and protein vectors are concatenated and input into the full connection layer for classification. In order to evaluate the performance of DCA-DPI, three widely used public data, Human, C.elegans and DUD-E, are used in the experiment. The evaluation metrics values in the experiment are superior to other relevant methods. Experiments show that our model is efficient for DPI prediction.

Adaptive pinning cluster synchronization of a stochastic reaction-diffusion complex network.

This work aims to achieve cluster synchronization of a complex network by some pinning control strategies. Firstly, the network not only is affected by the reaction-diffusion and the directed coupling phenomena, but also is disturbed by the stochastic noise and Markovian switching. Secondly, switched constant gain pinning, centralized and decentralized adaptive pinning are proposed respectively to realize the cluster synchronization of the considered network. In these adaptive pinning controllers, the control gain and coupling strength can been adjusted automatically while only a part of the nodes are controlled. Thirdly, the target state of cluster synchronization is taken as the average state related to the directed topology of all nodes in the same cluster, and does not need to be given separately as an isolated node. Finally, to verify the theoretical results, some simulations of directed coupled reaction-diffusion neural networks with stochastic noise and Markovian switching are given.

Distributed Optimization for Resource Allocation Problem with Dynamic Event-Triggered Strategy.

This study aims to unravel the resource allocation problem (RAP) by using a consensus-based distributed optimization algorithm under dynamic event-triggered (DET) strategies. Firstly, based on the multi-agent consensus approach, a novel one-to-all DET strategy is presented to solve the RAP. Secondly, the proposed one-to-all DET strategy is extended to a one-to-one DET strategy, where each agent transmits its state asynchronously to its neighbors. Furthermore, it is proven that the proposed two types of DET strategies do not have Zeno behavior. Finally, numerical simulations are provided to validate and illustrate the effectiveness of the theoretical results.

Synchronization of discrete-time fractional fuzzy neural networks with delays via quantized control.

In this paper, synchronization issue of discrete-time fractional fuzzy neural networks (DFFNNs) with delays is solved via quantized control, and is applied in image encryption. Firstly, a novel fractional-order h-difference inequality which makes Lyapunov method more flexible and practical is strictly proved based on the properties of convex functions and theory of discrete fractional calculus. Secondly, by using compression mapping theorem and mathematical induction, we obtain two sufficient conditions to ensure the existence and uniqueness of solutions for DFFNNs. Whereafter, we design a suitable quantized controller, which not only saves channel resources but also reduces control costs. By utilizing our inequality and some analytical techniques, several conservative synchronization criteria for DFFNNs are acquired. Finally, two examples are arranged to illustrate the validity and practicability of our results.

Synchronization Analysis of Discrete-Time Fractional-Order Quaternion-Valued Uncertain Neural Networks.

This article studies synchronization issues for a class of discrete-time fractional-order quaternion-valued uncertain neural networks (DFQUNNs) using nonseparation method. First, based on the theory of discrete-time fractional calculus and quaternion properties, two equalities on the nabla Laplace transform and nabla sum are strictly proved, whereafter three Caputo difference inequalities are rigorously demonstrated. Next, based on our established inequalities and equalities, some simple and verifiable quasi-synchronization criteria are derived under the quaternion-valued nonlinear controller, and complete synchronization is achieved using quaternion-valued adaptive controller. Finally, numerical simulations are presented to substantiate the validity of derived results.

Optimizing a Solution Heat Treatment by Increasing the Cooling Rate of Directional Solidification for Ni-Based Superalloys.

The solution heat treatment (SHT) of the third generation of single crystal (SC) Ni-based superalloys required up to 45 h and was expensive. In this study, SHT based on liquid metal cooling (LMC) was optimized to greatly reduce processing time. The experimental and simulation results showed that residual segregation was evidently reduced, e.g., from 2.12 to 1.22 for the most heavily segregated Re. This led to a 16.7% increase in creep life, more uniform microstructures, and a decrease in solidification and homogenization porosity by a factor of 3.4. Structural refinement, approximately 0.32 times, served as the underlying mechanism for this optimization, which reduced diffusion distance and increased homogenization efficiency during SHT.

Quasi-projective and complete synchronization of discrete-time fractional-order delayed neural networks.

This paper presents new theoretical results on quasi-projective synchronization (Q-PS) and complete synchronization (CS) of one kind of discrete-time fractional-order delayed neural networks (DFDNNs). At first, three new fractional difference inequalities for exploring the upper bound of quasi-synchronization error and adaptive synchronization are established by dint of Laplace transform and properties of discrete Mittag-Leffler function, which vastly expand a number of available results. Furthermore, two controllers are designed including nonlinear controller and adaptive controller. And on the basis of Lyapunov method, the aforementioned inequalities and properties of fractional-order difference operators, some sufficient synchronization criteria of DFDNNs are derived. Because of the above controllers, synchronization criteria in this paper are less conservative. At last, numerical examples are carried out to illustrate the usefulness of theoretical upshots.

Dynamical Analysis of Hyper-ILSR Rumor Propagation Model with Saturation Incidence Rate.

With the development of the Internet, it is more convenient for people to obtain information, which also facilitates the spread of rumors. It is imperative to study the mechanisms of rumor transmission to control the spread of rumors. The process of rumor propagation is often affected by the interaction of multiple nodes. To reflect higher-order interactions in rumor-spreading, hypergraph theories are introduced in a Hyper-ILSR (Hyper-Ignorant-Lurker-Spreader-Recover) rumor-spreading model with saturation incidence rate in this study. Firstly, the definition of hypergraph and hyperdegree is introduced to explain the construction of the model. Secondly, the existence of the threshold and equilibrium of the Hyper-ILSR model is revealed by discussing the model, which is used to judge the final state of rumor propagation. Next, the stability of equilibrium is studied by Lyapunov functions. Moreover, optimal control is put forward to suppress rumor propagation. Finally, the differences between the Hyper-ILSR model and the general ILSR model are shown in numerical simulations.

Molecular phylogenetics and diversity of the Himalayan shrew ( Soriculus nigrescens Gray, 1842) (Eulipotyphla, Soricidae) in Southwest China.

The Himalayan shrew, Soriculus nigrescens Gray, 1842, belongs to the monotypic genus Soriculus, which is distributed mainly in the Himalayan region. Previous authors have studied its classification based on morphological and molecular data. However, no comprehensive study of the diversity and phylogeny of this species has been performed. In this study, we investigated the molecular phylogeny, genetic diversity, and species divergence of S. nigrescens based on one mitochondrial gene and three nuclear genes. A total of 124 samples from 27 sites in Southwest China were analyzed. Our molecular phylogenetic analyses and species divergence reveal non-monophyly of Soriculus, potentially representing two genera and three clades. Populations from Yunnan (Clade YN) represent the subspecies S. n. minors and should recover the full species status. Populations from Himalayas (Clade A) represent the species S. nigrescens, while populations from southeastern Nyenchen Tanglha Mountains and southern Himalayas (Clade B) represent a new cryptic and unnamed species. Species delimitation analyses and deep genetic distance analysis clearly support the species status of these three evolving clades. The putative new genus and cryptic species should be studied and identified in the future using a more extensive sampling combined with a comprehensive morphological and phylogenetic analysis.

Prescribed Performance Back-Stepping Tracking Control for a Class of High-Order Nonlinear Systems via a Disturbance Observer.

Due to the widespread presence of disturbances in practical engineering and widespread applications of high-order systems, this paper first pays attention to a class of high-order strict-feedback nonlinear systems subject to bounded disturbance and investigates the prescribed performance tracking control and anti-disturbance control problems. A novel composite control protocol using the technique of a disturbance observer-prescribed performance control-is designed using the back-stepping method. The disturbance observer is introduced for estimating and compensating for unknown disturbances in each step, and the prescribed performance specifications guarantee both transient and steady-state performance of the tracking error to improve the control performance and result in better disturbance rejection. Moreover, the technique of adding a power integrator is modified to tackle controller design problems for the high-order systems. The Lyapunov function method is utilized for rigorous stability analysis. It is revealed that while the control performance completely remains in the prescribed bound, all states in the closed-loop system are input-to-state stable, and the tracking error and the disturbances estimating error asymptotically converge to zero simultaneously. Then, the feasibility and effectiveness of the proposed control protocol are verified by a simulation result.

Distributed Fixed/Preassigned-Time Optimization Based on Piecewise Power-Law Design.

The problem of fixed-time (FXT) and preassigned-time (PAT) optimization is concerned in this article based on multiagent systems (MASs) and power-law algorithms. Under the framework of strong convexity of the cost functions, two types of piecewise algorithms are proposed, which ensure that the FXT optimization can be solved either by first achieving the FXT consensus or by first achieving local optimization. Correspondingly, the PAT optimization problem is also considered by designing several piecewise protocols, where the finished time of optimization can be arbitrary prescribed according to actual demands. Furthermore, these piecewise power-law algorithms on the weighted undirected graphs are generalized to the weighted digraphs. Finally, by providing two numerical examples, the presented algorithms are further verified.