Chengye Zou

Finite-time synchronization control of discrete neuronal networks with piecewise nonlinear characteristics is a critical and challenging issue in complex network dynamic research. This paper investigates the finite-time synchronization problem of discrete neuronal networks constructed by the Rulkov map, and proposes a novel double-power finite-time sliding mode control (SMC) strategy. Different from single-power SMC schemes with inherent performance trade-offs, the designed control law integrates super-power and sub-power terms organically. The super-power term accelerates the convergence of sliding variables when system errors are large, while the sub-power term guarantees precise finite-time convergence of system states near the equilibrium point, effectively solving the contradiction between transient response speed and steady-state synchronization accuracy in traditional SMC. A terminal sliding surface matching the discrete network dynamic characteristics is established for each neuron node, and the control law is derived based on system error dynamics with a coupling compensation term added to adapt to the dynamic adjustment of large-scale network coupling relationships. Based on Lyapunov stability theory and discrete finite-time stability theorems, the finite-time convergence of both the reaching phase and sliding phase of the control system is strictly proved, and the explicit upper bound of network synchronization time is deduced, which quantitatively reveals the correlation between synchronization efficiency and system initial states as well as control parameters. Numerical simulations on an 80-node randomly connected network demonstrate the effectiveness of the proposed scheme in achieving rapid error convergence and accurate synchronization, and a systematic parameter study reveals the dominant influence of the low-power gain and the high-power exponent on the synchronization time.

This paper proposes a molecular multilayer learning system that utilizes a novel activation function to fit and predict multi-input nonlinear functional relationships.

In the field of biomedical science, blood cell detection in microscopic images is crucial for aiding physicians in diagnosing blood-related diseases and plays a pivotal role in advancing medicine toward more precise and efficient treatment directions. Addressing the time-consuming and error-prone issues of traditional manual detection methods, as well as the challenge existing blood cell detection technologies face in meeting both high accuracy and real-time requirements, this study proposes a lightweight blood cell detection model based on YOLOv8n, named GPMB-YOLO. This model utilizes advanced lightweight strategies and PGhostC2f design, effectively reducing model complexity and enhancing detection speed. The integration of the simple parameter-free attention mechanism (SimAM) significantly enhances the model’s feature extraction ability. Furthermore, we have designed a multidimensional attention-enhanced bidirectional feature pyramid network structure, MCA-BiFPN, optimizing the effect of multi-scale feature fusion. And use genetic algorithms for hyperparameter optimization, further improving detection accuracy. Experimental results validate the effectiveness of the GPMB-YOLO model, which realized a 3.2% increase in mean Average Precision (mAP) compared to the baseline YOLOv8n model and a marked reduction in model complexity. Furthermore, we have developed a blood cell detection system and deployed the model for application. This study serves as a valuable reference for the efficient detection of blood cells in medical images.

As a research hotspot in the field of information processing, DNA computing exhibits several important underlying characteristics—from parallel computing and low energy consumption to high-performance storage capabilities—thereby enabling its wide application in nanomachines, molecular encryption, biological detection, medical diagnosis, etc. Based on DNA computing, the most rapidly developed field focuses on DNA molecular logic-gates computing. In particular, the recent advances in enzyme-based DNA logic gates has emerged as ideal materials for constructing DNA logic gates. In this review, we explore protein enzymes that can manipulate DNA, especially, nicking enzymes and polymerases with high efficiency and specificity, which are widely used in constructing DNA logic gates, as well as ribozyme that can construct DNA logic gates following various mechanism with distinct biomaterials. Accordingly, the review highlights the characteristics and applications of various types of DNAzyme-based logic gates models, considering their future developments in information, biomedicine, chemistry, and computers.

The finite time synchronization diffusion of uncertain networks with the switching topology is investigated in this paper. Spatiotemporal chaotic systems as nodes to construct the network, and the topology of network can jump from one construction to another to meet the actual demand. It is not like ordinary synchronization of network, our method can realize synchronization diffusion originated from boundary conditions, and the diffusion step can be determined by Taylor expand. The parameters are identified through proposed scheme against parameter uncertainties of system in practice. Numerical simulations are utilized to demonstrate the correctness and effectiveness of the finite time synchronization diffusion criteria. In the section of analysis and discussion, we have analyzed the impact of synchronization diffusion step and controller on the synchronization diffusion time.

Lorenz system is depicted by chemical reaction equations of an ideal formal chemical reaction network, and a series of reversible reactions are added into chemical reaction network in order to construct a cluster of hyper-Lorenz system. DNA as a universal substrate for chemical dynamics can approximate arbitrary dynamical characteristics of ideal formal chemical reaction network through auxiliary DNA strands and displacement reactions. Based on Lyapunov's stableness theory, a novel synchronization strategy is proposed. A 6-dimensional hyper-Lorenz system is taken as examples for simulation and shows that DNA strands displacement reactions can implement the synchronization of ideal formal chemical reaction networks. Numerical simulations indicate that synchronization based on DNA strand displacement is robust to the detection of DNA strand concentration, control of reaction rate, and noise.