In this article, a periodic self-triggered impulsive (PSTI) control scheme is proposed to achieve synchronization of neural networks (NNs). Two kinds of impulsive gains with constant and random values are considered, and the corresponding synchronization criteria are obtained based on tools from impulsive control, event-driven control theory, and stability analysis. The designed triggering protocol is simpler, easier to implement, and more flexible compared with some previously reported algorithms as the protocol combines the advantages of the periodic sampling and event-driven control. In addition, the chaotic synchronization of NNs via the presented PSTI sampling is further applied to encrypt images. Several examples are also utilized to illustrate the validity of the presented synchronization algorithm of NNs based on PSTI control and its potential applications in image processing.

A novel periodically forced Filippov Holling II prey-predator model by applying threshold policy control (TPC) and integrated pest management (IPM) strategies is proposed, and the periodic forcing is described as a Fourier series with different forcing terms. Our work is aim to address how periodic forcing affects dynamic behaviors of the proposed system and to reasonably realize pest control. Based on the above considerations, the relations between sliding region and sliding periodic solution of the Filippov system are analyzed. Then, the existence conditions of sliding periodic solution and its global stability are addressed. Further, numerical investigations related to bifurcation analysis of crucial parameters including the amplitude of forcing terms, economic threshold ( $ET$ ) and the frequency of forcing terms are discussed. More importantly, the real biological implications are also addressed. Our results show that the globally stable sliding periodic solution could always lie in the sliding region, which indicates that the density of pest populations cannot reach and exceed the economic injury level (EIL), namely, the pest control is successful. Moreover, the results reveal that various complex dynamics of the Filippov system contains multiple attractors coexistence, switching transients and chaotic phenomena. In addition, the attraction basins of the coexisting attractors reveal that these two populations could be monitored and tracked carefully for the successful pest control, which depends on their initial density.

Insects such as locusts and some moths can transform from a solitarious phase when they remain in loose populations and a gregarious phase, when they may swarm. Therefore, the key to effective management of outbreaks of species such as the desert locust Schistocercagregaria is early detection of when they are in the threshold state between the two phases, followed by timely control of their hopper stages before they fledge because the control of flying adult swarms is costly and often ineffective. Definitions of gregarization thresholds should assist preventive control measures and avoid treatment of areas that might not lead to gregarization. In order to better understand the effects of the threshold density which represents the gregarization threshold on the outbreak of a locust population, we developed a model of a discrete switching system. The proposed model allows us to address: (1) How frequently switching occurs from solitarious to gregarious phases and vice versa; (2) When do stable switching transients occur, the existence of which indicate that solutions with larger amplitudes can switch to a stable attractor with a value less than the switching threshold density?; and (3) How does random perturbation influence the switching pattern? Our results show that both subsystems have refuge equilibrium points, outbreak equilibrium points and bistable equilibria. Further, the outbreak equilibrium points and bistable equilibria can coexist for a wide range of parameters and can switch from one to another. This type of switching is sensitive to the intrinsic growth rate and the initial values of the locust population, and may result in locust population outbreaks and phase switching once a small perturbation occurs. Moreover, the simulation results indicate that the switching transient patterns become identical after some generations, suggesting that the evolving process of the perturbation system is not related to the initial value after some fixed number of generations for the same stochastic processes. However, the switching frequency and outbreak patterns can be significantly affected by the intensity of noise and the intrinsic growth rate of the locust population.

The switched discrete host-parasitoid model with Beverton-Holt growth concerning integrated pest management has been proposed, and the switches are guided by the economic threshold (ET). The integrated pest management (IPM) tactics are applied to prevent the economic injury if the density of host population exceeds the ET, and the IPM tactics are called off once the density of host population descends below ET. To begin with, the regular and virtual equilibria of switched system has been discussed by two or three parameter-bifurcation diagrams, which reveal the regions of different types of equilibria. Besides, numerical bifurcation analyses about inherent growth rates show that the switched discrete system may have complicated dynamics behavior including chaos and the coexistence of multiple attractors. Finally, numerical bifurcation analyses about killing rates indicate that the system comply with the Volterra principle, and initial values of both host and parasitoid populations affect the host outbreaks times.

The switching discrete prey‐predator model concerning integrated pest management has been proposed, and the switches are guided by the economic threshold (ET). To begin with, the regular and virtual equilibria of switching system have been discussed and the key parameter bifurcation diagrams for the existence of equilibria have been proposed, which reveal the three different regions of equilibria. Besides, numerical bifurcation analyses show that the switching discrete system may have complicated dynamics behavior including chaos and the coexistence of multiple attractors. Finally, the effects of key parameters on the switching frequencies and switching times are discussed and the sensitivity analysis of varying parameter values for mean switching times has also been given. The results proved that economic threshold (ET) and the growth rate (α) were the key parameters for pest control.

In ecology, the impact of predators goes beyond killing prey, the mere presence of predators reduces the ability of prey to reproduce. In this study, we extend the predator-prey model with fear effect by introducing the state-dependent control with a nonlinear action threshold depending on the combination of the density of prey and its changing rate. We initially defined the Poincaré map of the proposed model and studied its fundamental properties. Utilizing the properties of the Poincaré map, periodic solution of the model is further investigated, including the existence and stability of the order-1 periodic solution and the existence of the order-k ($ k \ge 2 $) periodic solutions. In addition, the influence of the fear effect on the system's dynamics is explored through numerical simulations. The action threshold used in this paper is more consistent with the actual growth of the population than in earlier linear threshold studies, and the results show that the control objectives are better achieved using the action threshold strategy. The analytical approach used in this study provided several novel methods for analyzing the complex dynamics that rely on state-dependent impulsive.